Stem issue 9 vol3 3 by iasir journals

ISSN (PRINT): 2328-3491 ISSN (ONLINE): 2328-3580 ISSN (CD-ROM): 2328-3629

Issue 9, Volume 1, 2 & 3 December-2014 to February-2015

American International Journal of Research in Science, Technology, Engineering & Mathematics

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

STEM International Scientific Online Media and Publishing House Head Office: 148, Summit Drive, Byron, Georgia-31008, United States. Offices Overseas: Germany, Australia, India, Netherlands, Canada. Website: www.iasir.net, E-mail (s): iasir.journals@iasir.net, iasir.journals@gmail.com, aijrstem@gmail.com

PREFACE We are delighted to welcome you to the ninth issue of the American International Journal of Research in Science, Technology, Engineering & Mathematics (AIJRSTEM). In recent years, advances in science, technology, engineering, and mathematics have radically expanded the data available to researchers and professionals in a wide variety of domains. This unique combination of theory with data has the potential to have broad impact on educational research and practice. AIJRSTEM is publishing high-quality, peer-reviewed papers covering topics such as Computer and computational sciences, Physics, Chemistry, Mathematics, Applied

mathematics,

Biochemistry,

Robotics,

Statistics,

Electrical

Electronics

engineering, Mechanical & Industrial engineering, Civil Engineering, Aerospace engineering, Chemical engineering, Astrophysics, Nanotechnology, Acoustical engineering, Atmospheric sciences, Biological sciences, Education and Human Resources, Environmental research and education, Geosciences, Social, Behavioral and Economic sciences, Geospatial technology, Cyber security, Transportation, Energy and Power, Healthcare, Hospitality, Medical and dental sciences, Marine sciences, Renewable sources of energy, Green technologies, Theory and models and other closely related fields in the discipline of Science, Technology, Engineering & Mathematics. The editorial board of AIJRSTEM is composed of members of the Teachers & Researchers community who have expertise in the fields of Science,

Technology,

Engineering

Mathematics

order

develop

and

implement widespread expansion of highâ&#x20AC;?quality common standards and assessments. These fields are the pillars of growth in our modern society and have a wider impact on our daily lives with infinite opportunities in a global marketplace. In order to best serve our community, this Journal is available online as well as in hard-copy form. Because of the rapid advances in underlying technologies and the interdisciplinary nature of the field, we believe it is important to provide quality research articles promptly and to the widest possible audience.

We are happy that this Journal has continued to grow and develop. We have made every effort to evaluate and process submissions for reviews, and address queries from authors and the general public promptly. The Journal has strived to reflect the most recent and finest researchers in the field of emerging technologies especially related to science, technology, engineering & mathematics. This Journal is completely refereed and indexed with major databases like: IndexCopernicus, Computer Science Directory, GetCITED, DOAJ, SSRN, TGDScholar, WorldWideScience, CiteSeerX, CRCnetBASE, Google Scholar, Microsoft Academic

Search,

INSPEC,

ProQuest,

ArnetMiner,

Base,

ChemXSeer,

citebase,

OpenJ-Gate, eLibrary, SafetyLit, SSRN, VADLO, OpenGrey, EBSCO, ProQuest, UlrichWeb, ISSUU, SPIE Digital Library, arXiv, ERIC, EasyBib, Infotopia, WorldCat, .docstoc JURN, Mendeley,

ResearchGate,

cogprints,

OCLC,

iSEEK,

Scribd,

LOCKSS,

CASSI,

E-PrintNetwork, intute, and some other databases.

We are grateful to all of the individuals and agencies whose work and support made the Journal's success possible. We want to thank the executive board and core committee members of the AIJRSTEM for entrusting us with the important job. We are thankful to the members of the AIJRSTEM editorial board who have contributed energy and time to the Journal with their steadfast support, constructive advice, as well as reviews of submissions. We are deeply indebted to the numerous anonymous reviewers who have contributed expertly evaluations of the submissions to help maintain the quality of the Journal. For this ninth issue, we received 149 research papers and out of which only 57 research papers are published in three volumes as per the reviewersâ&#x20AC;&#x2122; recommendations. We have highest respect to all the authors who have submitted articles to the Journal for their intellectual energy and creativity, and for their dedication to the field of science, technology, engineering & mathematics.

This issue of the AIJRSTEM has attracted a large number of authors and researchers across worldwide and would provide an effective platform to all the intellectuals of different streams to put forth their suggestions and ideas which might prove beneficial for the accelerated pace of development of emerging technologies in science, technology, engineering & mathematics and may open new area for research and development. We hope you will enjoy this ninth issue of the American International Journal of Research in Science, Technology, Engineering & Mathematics and are looking forward to hearing your feedback and receiving your contributions.

(Administrative Chief)

(Managing Director)

(Editorial Head)

--------------------------------------------------------------------------------------------------------------------------The American International Journal of Research in Science, Technology, Engineering & Mathematics (AIJRSTEM), ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 (December-2014 to February-2015, Issue 9, Volume 1, 2 & 3). ---------------------------------------------------------------------------------------------------------------------------

BOARD MEMBERS

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EDITOR IN CHIEF Prof. (Dr.) Waressara Weerawat, Director of Logistics Innovation Center, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Divya Sethi, GM Conferencing & VSAT Solutions, Enterprise Services, Bharti Airtel, Gurgaon, India. CHIEF EDITOR (TECHNICAL) Prof. (Dr.) Atul K. Raturi, Head School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Laucala campus, Suva, Fiji Islands. Prof. (Dr.) Hadi Suwastio, College of Applied Science, Department of Information Technology, The Sultanate of Oman and Director of IETI-Research Institute-Bandung, Indonesia. Dr. Nitin Jindal, Vice President, Max Coreth, North America Gas & Power Trading, New York, United States. CHIEF EDITOR (GENERAL) Prof. (Dr.) Thanakorn Naenna, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London. ADVISORY BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Fabrizio Gerli, Department of Management, Ca' Foscari University of Venice, Italy. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Vit Vozenilek, Department of Geoinformatics, Palacky University, Olomouc, Czech Republic. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Praneel Chand, Ph.D., M.IEEEC/O School of Engineering & Physics Faculty of Science & Technology The University of the South Pacific (USP) Laucala Campus, Private Mail Bag, Suva, Fiji. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain.

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Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Dr. Cathryn J. Peoples, Faculty of Computing and Engineering, School of Computing and Information Engineering, University of Ulster, Coleraine, Northern Ireland, United Kingdom. Prof. (Dr.) Pavel Lafata, Department of Telecommunication Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 166 27, Czech Republic. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.) Anis Zarrad, Department of Computer Science and Information System, Prince Sultan University, Riyadh, Saudi Arabia. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India. Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Md. Rizwan Beg, Professor & Head, Dean, Faculty of Computer Applications, Deptt. of Computer Sc. & Engg. & Information Technology, Integral University Kursi Road, Dasauli, Lucknow, India. Prof. (Dr.) Vishnu Narayan Mishra, Assistant Professor of Mathematics, Sardar Vallabhbhai National Institute of Technology, Ichchhanath Mahadev Road, Surat, Surat-395007, Gujarat, India. Dr. Jia Hu, Member Research Staff, Philips Research North America, New York Area, NY. Prof. Shashikant Shantilal Patil SVKM, MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Bindhya Chal Yadav, Assistant Professor in Botany, Govt. Post Graduate College, Fatehabad, Agra, Uttar Pradesh, India. REVIEW BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Joel Saltz, Emory University, Atlanta, Georgia, United States. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain. Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London.

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Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Pravin G. Ingole, Senior Researcher, Greenhouse Gas Research Center, Korea Institute of Energy Research (KIER), 152 Gajeong-ro, Yuseong-gu, Daejeon 305-343, KOREA Prof. (Dr.) Dilum Bandara, Dept. Computer Science & Engineering, University of Moratuwa, Sri Lanka. Prof. (Dr.) Faudziah Ahmad, School of Computing, UUM College of Arts and Sciences, University Utara Malaysia, 06010 UUM Sintok, Kedah Darulaman Prof. (Dr.) G. Manoj Someswar, Principal, Dept. of CSE at Anwar-ul-uloom College of Engineering & Technology, Yennepally, Vikarabad, RR District., A.P., India. Prof. (Dr.) Abdelghni Lakehal, Applied Mathematics, Rue 10 no 6 cite des fonctionnaires dokkarat 30010 Fes Marocco. Dr. Kamal Kulshreshtha, Associate Professor & Head, Deptt. of Computer Sc. & Applications, Modi Institute of Management & Technology, Kota-324 009, Rajasthan, India. Prof. (Dr.) Anukrati Sharma, Associate Professor, Faculty of Commerce and Management, University of Kota, Kota, Rajasthan, India. Prof. (Dr.) S. Natarajan, Department of Electronics and Communication Engineering, SSM College of Engineering, NH 47, Salem Main Road, Komarapalayam, Namakkal District, Tamilnadu 638183, India. Prof. (Dr.) J. Sadhik Basha, Department of Mechanical Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia Prof. (Dr.) G. SAVITHRI, Department of Sericulture, S.P. Mahila Visvavidyalayam, Tirupati517502, Andhra Pradesh, India. Prof. (Dr.) Shweta jain, Tolani College of Commerce, Andheri, Mumbai. 400001, India Prof. (Dr.) Abdullah M. Abdul-Jabbar, Department of Mathematics, College of Science, University of Salahaddin-Erbil, Kurdistan Region, Iraq. Prof. (Dr.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, Tirupati517502, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Manjulatha, Dept of Biochemistry,School of Life Sciences,University of Hyderabad,Gachibowli, Hyderabad, India. Prof. (Dr.) Upasani Dhananjay Eknath Advisor & Chief Coordinator, ALUMNI Association, Sinhgad Institute of Technology & Science, Narhe, Pune- 411 041, India. Prof. (Dr.) Sudhindra Bhat, Professor & Finance Area Chair, School of Business, Alliance University Bangalore-562106. Prof. Prasenjit Chatterjee , Dept. of Mechanical Engineering, MCKV Institute of Engineering West Bengal, India. Prof. Rajesh Murukesan, Deptt. of Automobile Engineering, Rajalakshmi Engineering college, Chennai, India. Prof. (Dr.) Parmil Kumar, Department of Statistics, University of Jammu, Jammu, India Prof. (Dr.) M.N. Shesha Prakash, Vice Principal, Professor & Head of Civil Engineering, Vidya Vikas Institute of Engineering and Technology, Alanahally, Mysore-570 028 Prof. (Dr.) Piyush Singhal, Mechanical Engineering Deptt., GLA University, India. Prof. M. Mahbubur Rahman, School of Engineering & Information Technology, Murdoch University, Perth Western Australia 6150, Australia. Prof. Nawaraj Chaulagain, Department of Religion, Illinois Wesleyan University, Bloomington, IL. Prof. Hassan Jafari, Faculty of Maritime Economics & Management, Khoramshahr University of Marine Science and Technology, khoramshahr, Khuzestan province, Iran Prof. (Dr.) Kantipudi MVV Prasad , Dept of EC, School of Engg, R.K.University,Kast urbhadham, Tramba, Rajkot-360020, India. Prof. (Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, ( Women's University), Tirupati-517502, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications, National Institute of Technical Teachers' Training and Research, Bhopal M.P. India Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. P.R.SivaSankar, Head, Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P. India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science( AIES), Amity University, Noida, India. Prof. Manoj Chouhan, Deptt. of Information Technology, SVITS Indore, India.

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Prof. Yupal S Shukla, V M Patel College of Management Studies, Ganpat University, KhervaMehsana, India. Prof. (Dr.) Amit Kohli, Head of the Department, Department of Mechanical Engineering, D.A.V.Institute of Engg. and Technology, Kabir Nagar, Jalandhar, Punjab(India) Prof. (Dr.) Kumar Irayya Maddani, and Head of the Department of Physics in SDM College of Engineering and Technology, Dhavalagiri, Dharwad, State: Karnataka (INDIA). Prof. (Dr.) Shafi Phaniband, SDM College of Engineering and Technology, Dharwad, INDIA. Prof. M H Annaiah, Head, Department of Automobile Engineering, Acharya Institute of Technology, Soladevana Halli, Bangalore -560107, India. Prof. (Dr.) Shriram K V, Faculty Computer Science and Engineering, Amrita Vishwa Vidhyapeetham University, Coimbatore, India. Prof. (Dr.) Sohail Ayub, Department of Civil Engineering, Z.H College of Engineering & Technology, Aligarh Muslim University, Aligarh. 202002 UP-India Prof. (Dr.) Santosh Kumar Behera, Department of Education, Sidho-Kanho-Birsha University, Purulia, West Bengal, India. Prof. (Dr.) Urmila Shrawankar, Department of Computer Science & Engineering, G H Raisoni College of Engineering, Nagpur (MS), India. Prof. Anbu Kumar. S, Deptt. of Civil Engg., Delhi Technological University (Formerly Delhi College of Engineering) Delhi, India. Prof. (Dr.) Meenakshi Sood, Vegetable Science, College of Horticulture, Mysore, University of Horticultural Sciences, Bagalkot, Karnataka (India) Prof. (Dr.) Prof. R. R. Patil, Director School Of Earth Science, Solapur University, Solapur, India. Prof. (Dr.) Manoj Khandelwal, Dept. of Mining Engg, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur-313 001 (Rajasthan), India Prof. (Dr.) Kishor Chandra Satpathy, Librarian, National Institute of Technology, Silchar-788010, Assam, India. Prof. (Dr.) Juhana Jaafar, Gas Engineering Department, Faculty of Petroleum and Renewable Energy Engineering (FPREE), Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor. Prof. (Dr.) Rita Khare, Assistant Professor in chemistry, Govt. Women,s College, Gardanibagh, Patna, Bihar, India. Prof. (Dr.) Raviraj Kusanur, Dept of Chemistry, R V College of Engineering, Bangalore-59, India. Prof. (Dr.) Hameem Shanavas .I, M.V.J College of Engineering, Bangalore, India. Prof. (Dr.) Sandhya Mehrotra, Department of Biological Sciences, Birla Institute of Technology and Sciences, Pilani, Rajasthan, India. Prof. (Dr.) Dr. Ravindra Jilte, Head of the Department, Department of Mechanical Engineering,VCET, Thane-401202, India. Prof. (Dr.) Sanjay Kumar, JKL University, Ajmer Road, Jaipur Prof. (Dr.) Pushp Lata Faculty of English and Communication, Department of Humanities and Languages, Nucleus Member, Publications and Media Relations Unit Editor, BITScan, BITS, PilaniIndia Prof. Arun Agarwal, Faculty of ECE Dept., ITER College, Siksha 'O' Anusandhan University Bhubaneswar, Odisha, India Prof. (Dr.) Pratima Tripathi, Department of Biosciences, SSSIHL, Anantapur Campus Anantapur515001 (A.P.) India. Prof. (Dr.) Sudip Das, Department of Biotechnology, Haldia Institute of Technology, I.C.A.R.E. Complex, H.I.T. Campus, P.O. Hit, Haldia; Dist: Puba Medinipur, West Bengal, India. Prof. (Dr.) ABHIJIT MITRA , Associate Professor and former Head, Department of Marine Science, University of Calcutta , India. Prof. (Dr.) N.Ramu , Associate Professor , Department of Commerce, Annamalai University, AnnamalaiNadar-608 002, Chidambaram, Tamil Nadu , India. Prof. (Dr.) Saber Mohamed Abd-Allah, Assistant Professor of Theriogenology , Faculty of Veterinary Medicine , Beni-Suef University , Egypt. Prof. (Dr.) Ramel D. Tomaquin, Dean, College of Arts and Sciences Surigao Del Sur State University (SDSSU), Tandag City Surigao Del Sur, Philippines. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) R.K.Tiwari, Professor, S.O.S. in Physics, Jiwaji University, Gwalior, M.P.-474011, India. Prof. (Dr.) Sandeep Gupta, Department of Computer Science & Engineering, Noida Institute of Engineering and Technology, Gr.Noida, India. Prof. (Dr.) Mohammad Akram, Jazan University, Kingdom of Saudi Arabia.

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Prof. (Dr.) Sanjay Sharma, Dept. of Mathematics, BIT, Durg(C.G.), India. Prof. (Dr.) Manas R. Panigrahi, Department of Physics, School of Applied Sciences, KIIT University, Bhubaneswar, India. Prof. (Dr.) P.Kiran Sree, Dept of CSE, Jawaharlal Nehru Technological University, India Prof. (Dr.) Suvroma Gupta, Department of Biotechnology in Haldia Institute of Technology, Haldia, West Bengal, India. Prof. (Dr.) SREEKANTH. K. J., Department of Mechanical Engineering at Mar Baselios College of Engineering & Technology, University of Kerala, Trivandrum, Kerala, India Prof. Bhubneshwar Sharma, Department of Electronics and Communication Engineering, Eternal University (H.P), India. Prof. Love Kumar, Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), India. Prof. S.KANNAN, Department of History, Annamalai University, Annamalainagar- 608002, Tamil Nadu, India. Prof. (Dr.) Hasrinah Hasbullah, Faculty of Petroleum & Renewable Energy Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia. Prof. Rajesh Duvvuru, Dept. of Computer Sc. & Engg., N.I.T. Jamshedpur, Jharkhand, India. Prof. (Dr.) Bhargavi H. Goswami, Department of MCA, Sunshine Group of Institutes, Nr. Rangoli Park, Kalawad Road, Rajkot, Gujarat, India. Prof. (Dr.) Essam H. Houssein, Computer Science Department, Faculty of Computers & Informatics, Benha University, Benha 13518, Qalyubia Governorate, Egypt. Arash Shaghaghi, University College London, University of London, Great Britain. Prof. Rajesh Duvvuru, Dept. of Computer Sc. & Engg., N.I.T. Jamshedpur, Jharkhand, India. Prof. (Dr.) Anand Kumar, Head, Department of MCA, M.S. Engineering College, Navarathna Agrahara, Sadahalli Post, Bangalore, PIN 562110, Karnataka, INDIA. Prof. (Dr.) Venkata Raghavendra Miriampally, Electrical and Computer Engineering Dept, Adama Science & Technology University, Adama, Ethiopia. Prof. (Dr.) Jatinderkumar R. Saini, Director (I.T.), GTU's Ankleshwar-Bharuch Innovation Sankul &Director I/C & Associate Professor, Narmada College of Computer Application, Zadeshwar, Bharuch, Gujarat, India. Prof. Jaswinder Singh, Mechanical Engineering Department, University Institute Of Engineering & Technology, Panjab University SSG Regional Centre, Hoshiarpur, Punjab, India- 146001. Prof. (Dr.) S.Kadhiravan, Head i/c, Department of Psychology, Periyar University, Salem- 636 011,Tamil Nadu, India. Prof. (Dr.) Mohammad Israr, Principal, Balaji Engineering College,Junagadh, Gujarat-362014, India. Prof. (Dr.) VENKATESWARLU B., Director of MCA in Sreenivasa Institute of Technology and Management Studies (SITAMS), Chittoor. Prof. (Dr.) Deepak Paliwal, Faculty of Sociology, Uttarakhand Open University, Haldwani-Nainital Prof. (Dr.) Dr. Anil K Dwivedi, Faculty of Pollution & Environmental Assay Research Laboratory (PEARL), Department of Botany,DDU Gorakhpur University,Gorakhpur-273009, India. Prof. R. Ravikumar, Department of Agricultural and Rural Management, TamilNadu Agricultural University, Coimbatore-641003,Tamil Nadu, India. Prof. (Dr.) R.Raman, Professor of Agronomy, Faculty of Agriculture, Annamalai university, Annamalai Nagar 608 002Tamil Nadu, India. Prof. (Dr.) Ahmed Khalafallah, Coordinator of the CM Degree Program, Department of Architectural and Manufacturing Sciences, Ogden College of Sciences and Engineering Western Kentucky University 1906 College Heights Blvd Bowling Green, KY 42103-1066 Prof. (Dr.) Asmita Das , Delhi Technological University (Formerly Delhi College of Engineering), Shahbad, Daulatpur, Delhi 110042, India. Prof. (Dr.)Aniruddha Bhattacharjya, Assistant Professor (Senior Grade), CSE Department, Amrita School of Engineering , Amrita Vishwa VidyaPeetham (University), Kasavanahalli, Carmelaram P.O., Bangalore 560035, Karnataka, India Prof. (Dr.) S. Rama Krishna Pisipaty, Prof & Geoarchaeologist, Head of the Department of Sanskrit & Indian Culture, SCSVMV University, Enathur, Kanchipuram 631561, India Prof. (Dr.) Shubhasheesh Bhattacharya, Professor & HOD(HR), Symbiosis Institute of International Business (SIIB), Hinjewadi, Phase-I, Pune- 411 057 Prof. (Dr.) Vijay Kothari, Institute of Science, Nirma University, S-G Highway, Ahmedabad 382481, India. Prof. (Dr.) Raja Sekhar Mamillapalli, Department of Civil Engineering at Sir Padampat Singhania University, Udaipur, India.

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Prof. (Dr.)B. M. Kunar, Department of Mining Engineering, Indian School of Mines, Dhanbad 826004, Jharkhand, India. Prof. (Dr.) Prabir Sarkar, Assistant Professor, School of Mechanical, Materials and Energy Engineering, Room 307, Academic Block, Indian Institute of Technology, Ropar, Nangal Road, Rupnagar 140001, Punjab, India. Prof. (Dr.) K.Srinivasmoorthy, Associate Professor, Department of Earth Sciences, School of Physical,Chemical and Applied Sciences, Pondicherry university, R.Venkataraman Nagar, Kalapet, Puducherry 605014, India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science (AIES), Amity University, Noida, India. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.)P.Raviraj, Professor & Head, Dept. of CSE, Kalaignar Karunanidhi, Institute of Technology, Coimbatore 641402,Tamilnadu,India. Prof. (Dr.) Damodar Reddy Edla, Department of Computer Science & Engineering, Indian School of Mines, Dhanbad, Jharkhand 826004, India. Prof. (Dr.) T.C. Manjunath, Principal in HKBK College of Engg., Bangalore, Karnataka, India. Prof. (Dr.) Pankaj Bhambri, I.T. Deptt., Guru Nanak Dev Engineering College, Ludhiana 141006, Punjab, India . Prof. Shashikant Shantilal Patil SVKM, MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Shambhu Nath Choudhary, Department of Physics, T.M. Bhagalpur University, Bhagalpur 81200, Bihar, India. Prof. (Dr.) Venkateshwarlu Sonnati, Professor & Head of EEED, Department of EEE, Sreenidhi Institute of Science & Technology, Ghatkesar, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) Saurabh Dalela, Department of Pure & Applied Physics, University of Kota, KOTA 324010, Rajasthan, India. Prof. S. Arman Hashemi Monfared, Department of Civil Eng, University of Sistan & Baluchestan, Daneshgah St.,Zahedan, IRAN, P.C. 98155-987 Prof. (Dr.) R.S.Chanda, Dept. of Jute & Fibre Tech., University of Calcutta, Kolkata 700019, West Bengal, India. Prof. V.S.VAKULA, Department of Electrical and Electronics Engineering, JNTUK, University College of Engg., Vizianagaram5 35003, Andhra Pradesh, India. Prof. (Dr.) Nehal Gitesh Chitaliya, Sardar Vallabhbhai Patel Institute of Technology, Vasad 388 306, Gujarat, India. Prof. (Dr.) D.R. Prajapati, Department of Mechanical Engineering, PEC University of Technology,Chandigarh 160012, India. Dr. A. SENTHIL KUMAR, Postdoctoral Researcher, Centre for Energy and Electrical Power, Electrical Engineering Department, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa. Prof. (Dr.)Vijay Harishchandra Mankar, Department of Electronics & Telecommunication Engineering, Govt. Polytechnic, Mangalwari Bazar, Besa Road, Nagpur- 440027, India. Prof. Varun.G.Menon, Department Of C.S.E, S.C.M.S School of Engineering, Karukutty, Ernakulam, Kerala 683544, India. Prof. (Dr.) U C Srivastava, Department of Physics, Amity Institute of Applied Sciences, Amity University, Noida, U.P-203301.India. Prof. (Dr.) Surendra Yadav, Professor and Head (Computer Science & Engineering Department), Maharashi Arvind College of Engineering and Research Centre (MACERC), Jaipur, Rajasthan, India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences & Humanities Dehradun Institute of Technology, (D.I.T. School of Engineering), 48 A K.P-3 Gr. Noida (U.P.) 201308 Prof. Naveen Jain, Dept. of Electrical Engineering, College of Technology and Engineering, Udaipur-313 001, India. Prof. Veera Jyothi.B, CBIT ,Hyderabad, Andhra Pradesh, India. Prof. Aritra Ghosh, Global Institute of Management and Technology, Krishnagar, Nadia, W.B. India Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Sirhind Mandi Gobindgarh, Punajb, India. Prof. (Dr.) Varala Ravi, Head, Department of Chemistry, IIIT Basar Campus, Rajiv Gandhi University of Knowledge Technologies, Mudhole, Adilabad, Andhra Pradesh- 504 107, India Prof. (Dr.) Ravikumar C Baratakke, faculty of Biology,Govt. College, Saundatti - 591 126, India.

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Prof. (Dr.) NALIN BHARTI, School of Humanities and Social Science, Indian Institute of Technology Patna, India. Prof. (Dr.) Shivanand S.Gornale, Head, Department of Studies in Computer Science, Government College (Autonomous), Mandya, Mandya-571 401-Karanataka Prof. (Dr.) Naveen.P.Badiger, Dept.Of Chemistry, S.D.M.College of Engg. & Technology, Dharwad-580002, Karnataka State, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Tauqeer Ahmad Usmani, Faculty of IT, Salalah College of Technology, Salalah, Sultanate of Oman, Prof. (Dr.) Naresh Kr. Vats, Chairman, Department of Law, BGC Trust University Bangladesh Prof. (Dr.) Papita Das (Saha), Department of Environmental Science, University of Calcutta, Kolkata, India Prof. (Dr.) Rekha Govindan , Dept of Biotechnology, Aarupadai Veedu Institute of technology , Vinayaka Missions University , Paiyanoor , Kanchipuram Dt, Tamilnadu , India Prof. (Dr.) Lawrence Abraham Gojeh, Department of Information Science, Jimma University, P.o.Box 378, Jimma, Ethiopia Prof. (Dr.) M.N. Kalasad, Department of Physics, SDM College of Engineering & Technology, Dharwad, Karnataka, India Prof. Rab Nawaz Lodhi, Department of Management Sciences, COMSATS Institute of Information Technology Sahiwal Prof. (Dr.) Masoud Hajarian, Department of Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, General Campus, Evin, Tehran 19839,Iran Prof. (Dr.) Chandra Kala Singh, Associate professor, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India Prof. (Dr.) J.Babu, Professor & Dean of research, St.Joseph's College of Engineering & Technology, Choondacherry, Palai,Kerala. Prof. (Dr.) Pradip Kumar Roy, Department of Applied Mechanics, Birla Institute of Technology (BIT) Mesra, Ranchi-835215, Jharkhand, India. Prof. (Dr.) P. Sanjeevi kumar, School of Electrical Engineering (SELECT), Vandalur Kelambakkam Road, VIT University, Chennai, India. Prof. (Dr.) Debasis Patnaik, BITS-Pilani, Goa Campus, India. Prof. (Dr.) SANDEEP BANSAL, Associate Professor, Department of Commerce, I.G.N. College, Haryana, India. Dr. Radhakrishnan S V S, Department of Pharmacognosy, Faser Hall, The University of Mississippi Oxford, MS-38655, USA Prof. (Dr.) Megha Mittal, Faculty of Chemistry, Manav Rachna College of Engineering, Faridabad (HR), 121001, India. Prof. (Dr.) Mihaela Simionescu (BRATU), BUCHAREST, District no. 6, Romania, member of the Romanian Society of Econometrics, Romanian Regional Science Association and General Association of Economists from Romania Prof. (Dr.) Atmani Hassan, Director Regional of Organization Entraide Nationale Prof. (Dr.) Deepshikha Gupta, Dept. of Chemistry, Amity Institute of Applied Sciences,Amity University, Sec.125, Noida, India Prof. (Dr.) Muhammad Kamruzzaman, Deaprtment of Infectious Diseases, The University of Sydney, Westmead Hospital, Westmead, NSW-2145. Prof. (Dr.) Meghshyam K. Patil , Assistant Professor & Head, Department of Chemistry,Dr. Babasaheb Ambedkar Marathwada University,Sub-Campus, Osmanabad- 413 501, Maharashtra, INDIA Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Sudarson Jena, Dept. of Information Technology, GITAM University, Hyderabad, India Prof. (Dr.) Jai Prakash Jaiswal, Department of Mathematics, Maulana Azad National Institute of Technology Bhopal-India Prof. (Dr.) S.Amutha, Dept. of Educational Technology, Bharathidasan University, Tiruchirappalli620 023, Tamil Nadu-India Prof. (Dr.) R. HEMA KRISHNA, Environmental chemistry, University of Toronto, Canada. Prof. (Dr.) B.Swaminathan, Dept. of Agrl.Economics, Tamil Nadu Agricultural University, India.

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Prof. (Dr.) Meghshyam K. Patil, Assistant Professor & Head, Department of Chemistry, Dr. Babasaheb Ambedkar Marathwada University, Sub-Campus, Osmanabad- 413 501, Maharashtra, INDIA Prof. (Dr.) K. Ramesh, Department of Chemistry, C .B . I. T, Gandipet, Hyderabad-500075 Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences &Humanities, JIMS Technical campus,(I.P. University,New Delhi), 48/4 ,K.P.-3,Gr.Noida (U.P.) Prof. (Dr.) G.V.S.R.Anjaneyulu, CHAIRMAN - P.G. BOS in Statistics & Deputy Coordinator UGC DRS-I Project, Executive Member ISPS-2013, Department of Statistics, Acharya Nagarjuna University, Nagarjuna Nagar-522510, Guntur, Andhra Pradesh, India Prof. (Dr.) Sribas Goswami, Department of Sociology, Serampore College, Serampore 712201, West Bengal, India. Prof. (Dr.) Sunanda Sharma, Department of Veterinary Obstetrics Y Gynecology, College of Veterinary & Animal Science,Rajasthan University of Veterinary & Animal Sciences,Bikaner334001, India. Prof. (Dr.) S.K. Tiwari, Department of Zoology, D.D.U. Gorakhpur University, Gorakhpur-273009 U.P., India. Prof. (Dr.) Praveena Kuruva, Materials Research Centre, Indian Institute of Science, Bangalore560012, INDIA Prof. (Dr.) Rajesh Kumar, Department Of Applied Physics , Bhilai Institute Of Technology, Durg (C.G.) 491001 Prof. (Dr.) Y.P.Singh, (Director), Somany (PG) Institute of Technology and Management, Garhi Bolni Road, Delhi-Jaipur Highway No. 8, Beside 3 km from City Rewari, Rewari-123401, India. Prof. (Dr.) MIR IQBAL FAHEEM, VICE PRINCIPAL &HEAD- Department of Civil Engineering & Professor of Civil Engineering, Deccan College of Engineering & Technology, Dar-us-Salam, Aghapura, Hyderabad (AP) 500 036. Prof. (Dr.) Jitendra Gupta, Regional Head, Co-ordinator(U.P. State Representative)& Asstt. Prof., (Pharmaceutics), Institute of Pharmaceutical Research, GLA University, Mathura. Prof. (Dr.) N. Sakthivel, Scientist - C,Research Extension Center,Central Silk Board, Government of India, Inam Karisal Kulam (Post), Srivilliputtur - 626 125,Tamil Nadu, India. Prof. (Dr.) Omprakash Srivastav, Centre of Advanced Study, Department of History, Aligarh Muslim University, Aligarh-202 001, INDIA. Prof. (Dr.) K.V.L.N.Acharyulu, Associate Professor, Department of Mathematics, Bapatla Engineering college, Bapatla-522101, INDIA. Prof. (Dr.) Fateh Mebarek-Oudina, Assoc. Prof., Sciences Faculty,20 aout 1955-Skikda University, B.P 26 Route El-Hadaiek, 21000,Skikda, Algeria. NagaLaxmi M. Raman, Project Support Officer, Amity International Centre for Postharvest, Technology & Cold Chain Management, Amity University Campus, Sector-125, Expressway, Noida Prof. (Dr.) V.SIVASANKAR, Associate Professor, Department Of Chemistry, Thiagarajar College Of Engineering (Autonomous), Madurai 625015, Tamil Nadu, India (Dr.) Ramkrishna Singh Solanki, School of Studies in Statistics, Vikram University, Ujjain, India Prof. (Dr.) M.A.Rabbani, Professor/Computer Applications, School of Computer, Information and Mathematical Sciences, B.S.Abdur Rahman University, Chennai, India Prof. (Dr.) P.P.Satya Paul Kumar, Associate Professor, Physical Education & Sports Sciences, University College of Physical Education & Sports, Sciences, Acharya Nagarjuna University, Guntur. Prof. (Dr.) Fazal Shirazi, PostDoctoral Fellow, Infectious Disease, MD Anderson Cancer Center, Houston, Texas, USA Prof. (Dr.) Omprakash Srivastav, Department of Museology, Aligarh Muslim University, Aligarh202 001, INDIA. Prof. (Dr.) Mandeep Singh walia, A.P. E.C.E., Panjab University SSG Regional Centre Hoshiarpur, Una Road, V.P.O. Allahabad, Bajwara, Hoshiarpur Prof. (Dr.) Ho Soon Min, Senior Lecturer, Faculty of Applied Sciences, INTI International University, Persiaran Perdana BBN, Putra Nilai, 71800 Nilai, Negeri Sembilan, Malaysia Prof. (Dr.) L.Ganesamoorthy, Assistant Professor in Commerce, Annamalai University, Annamalai Nagar-608002, Chidambaram, Tamilnadu, India. Prof. (Dr.) Vuda Sreenivasarao, Professor, School of Computing and Electrical Engineering, Bahir Dar University, Bahirdar,Ethiopia Prof. (Dr.) Umesh Sharma, Professor & HOD Applied Sciences & Humanities, Eshan college of Engineering, Mathura, India. Prof. (Dr.) K. John Singh, School of Information Technology and Engineering, VIT University, Vellore, Tamil Nadu, India. Prof. (Dr.) Sita Ram Pal (Asst.Prof.), Dept. of Special Education, Dr.BAOU, Ahmedabad, India.

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Prof. Vishal S.Rana, H.O.D, Department of Business Administration, S.S.B.T'S College of Engineering & Technology, Bambhori,Jalgaon (M.S), India. Prof. (Dr.) Chandrakant Badgaiyan, Department of Mechatronics and Engineering, Chhattisgarh. Dr. (Mrs.) Shubhrata Gupta, Prof. (Electrical), NIT Raipur, India. Prof. (Dr.) Usha Rani. Nelakuditi, Assoc. Prof., ECE Deptt., Vignan’s Engineering College, Vignan University, India. Prof. (Dr.) S. Swathi, Asst. Professor, Department of Information Technology, Vardhaman college of Engineering(Autonomous) , Shamshabad, R.R District, India. Prof. (Dr.) Raja Chakraverty, M Pharm (Pharmacology), BCPSR, Durgapur, West Bengal, India Prof. (Dr.) P. Sanjeevi Kumar, Electrical & Electronics Engineering, National Institute of Technology (NIT-Puducherry), An Institute of National Importance under MHRD (Govt. of India), Karaikal- 609 605, India. Prof. (Dr.) Amitava Ghosh, Professor & Principal, Bengal College of Pharmaceutical Sciences and Research, B.R.B. Sarani, Bidhannagar, Durgapur, West Bengal- 713212. Prof. (Dr.) Om Kumar Harsh, Group Director, Amritsar College of Engineering and Technology, Amritsar 143001 (Punjab), India. Prof. (Dr.) Mansoor Maitah, Department of International Relations, Faculty of Economics and Management, Czech University of Life Sciences Prague, 165 21 Praha 6 Suchdol, Czech Republic. Prof. (Dr.) Zahid Mahmood, Department of Management Sciences (Graduate Studies), Bahria University, Naval Complex, Sector, E-9, Islamabad, Pakistan. Prof. (Dr.) N. Sandeep, Faculty Division of Fluid Dynamics, VIT University, Vellore-632 014. Mr. Jiban Shrestha, Scientist (Plant Breeding and Genetics), Nepal Agricultural Research Council, National Maize Research Program, Rampur, Chitwan, Nepal. Prof. (Dr.) Rakhi Garg, Banaras Hindu University, Varanasi, Uttar Pradesh, India. Prof. (Dr.) Ramakant Pandey. Dept. of Biochemistry. Patna University Patna (Bihar)-India. Prof. (Dr.) Nalah Augustine Bala, Behavioural Health Unit, Psychology Department, Nasarawa State University, Keffi, P.M.B. 1022 Keffi, Nasarawa State, Nigeria. Prof. (Dr.) Mehdi Babaei, Department of Engineering, Faculty of Civil Engineering, University of Zanjan, Iran. Prof. (Dr.) A. SENTHIL KUMAR., Professor/EEE, VELAMMAL ENGINEERING COLLEGE, CHENNAI Prof. (Dr.) Gudikandhula Narasimha Rao, Dept. of Computer Sc. & Engg., KKR & KSR Inst Of Tech & Sciences, Guntur, Andhra Pradesh, India. Prof. (Dr.) Dhanesh singh, Department of Chemistry, K.G. Arts & Science College, Raigarh (C.G.) India. Prof. (Dr.) Syed Umar , Dept. of Electronics and Computer Engineering, KL University, Guntur, A.P., India. Prof. (Dr.) Rachna Goswami, Faculty in Bio-Science Department, IIIT Nuzvid (RGUKT), DistrictKrishna , Andhra Pradesh - 521201 Prof. (Dr.) Ahsas Goyal, FSRHCP, Founder & Vice president of Society of Researchers and Health Care Professionals Prof. (Dr.) Gagan Singh, School of Management Studies and Commerce, Department of Commerce, Uttarakhand Open University, Haldwani-Nainital, Uttarakhand (UK)-263139 (India) Prof. (Dr.) Solomon A. O. Iyekekpolor, Mathematics and Statistics, Federal University, WukariNigeria. Prof. (Dr.) S. Saiganesh, Faculty of Marketing, Dayananda Sagar Business School, Bangalore, India. Dr. K.C.Sivabalan, Field Enumerator and Data Analyst, Asian Vegetable Research Centre, The World Vegetable Centre, Taiwan Prof. (Dr.) Amit Kumar Mishra, Department of Environmntal Science and Energy Research, Weizmann Institute of Science, Rehovot, Israel Prof. (Dr.) Manisha N. Paliwal, Sinhgad Institute of Management, Vadgaon (Bk), Pune, India Prof. (Dr.) M. S. HIREMATH, Principal, K.L.ESOCIETY’S SCHOOL, ATHANI, India Prof. Manoj Dhawan, Department of Information Technology, Shri Vaishnav Institute of Technology & Science, Indore, (M. P.), India Prof. (Dr.) V.R.Naik, Professor & Head of Department, Mechancal Engineering , Textile & Engineering Institute, Ichalkaranji (Dist. Kolhapur), Maharashatra, India Prof. (Dr.) Jyotindra C. Prajapati,Head, Department of Mathematical Sciences, Faculty of Applied Sciences, Charotar University of Science and Technology, Changa Anand -388421, Gujarat, India Prof. (Dr.) Sarbjit Singh, Head, Department of Industrial & Production Engineering, Dr BR Ambedkar National Institute of Technology, Jalandhar, Punjab,India

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Prof. (Dr.) Professor Braja Gopal Bag, Department of Chemistry and Chemical Technology, Vidyasagar University, West Midnapore Prof. (Dr.) Ashok Kumar Chandra, Department of Management, Bhilai Institute of Technology, Bhilai House, Durg (C.G.) Prof. (Dr.) Amit Kumar, Assistant Professor, School of Chemistry, Shoolini University, Solan, Himachal Pradesh, India Prof. (Dr.) L. Suresh Kumar, Mechanical Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India. Scientist Sheeraz Saleem Bhat, Lac Production Division, Indian Institute of Natural Resins and Gums, Namkum, Ranchi, Jharkhand Prof. C.Divya , Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli - 627012, Tamilnadu , India Prof. T.D.Subash, Infant Jesus College Of Engineering and Technology, Thoothukudi Tamilnadu, India Prof. (Dr.) Vinay Nassa, Prof. E.C.E Deptt., Dronacharya.Engg. College, Gurgaon India. Prof. Sunny Narayan, university of Roma Tre, Italy. Prof. (Dr.) Sanjoy Deb, Dept. of ECE, BIT Sathy, Sathyamangalam, Tamilnadu-638401, India. Prof. (Dr.) Reena Gupta, Institute of Pharmaceutical Research, GLA University, Mathura-India Prof. (Dr.) P.R.SivaSankar, Head Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P., India Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. (Dr.) Praveen Kumar Rai, Department of Geography, Faculty of Science, Banaras Hindu University, Varanasi-221005, U.P. India Prof. (Dr.) Christine Jeyaseelan, Dept of Chemistry, Amity Institute of Applied Sciences, Amity University, Noida, India Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications , National Institute of Technical Teachers' Training and Research, Bhopal M.P. India Prof. (Dr.) K.V.N.R.Sai Krishna, H O D in Computer Science, S.V.R.M.College,(Autonomous), Nagaram, Guntur(DT), Andhra Pradesh, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Asim Kumar Sen, Principal , ST.Francis Institute of Technology (Engineering College) under University of Mumbai , MT. Poinsur, S.V.P Road, Borivali (W), Mumbai, 400103, India, Prof. (Dr.) Rahmathulla Noufal.E, Civil Engineering Department, Govt.Engg.College-Kozhikode Prof. (Dr.) N.Rajesh, Department of Agronomy, TamilNadu Agricultural University -Coimbatore, TamilNadu, India Prof. (Dr.) Har Mohan Rai, Professor, Electronics and Communication Engineering, N.I.T. Kurukshetra 136131,India Prof. (Dr.) Eng. Sutasn Thipprakmas from King Mongkut, University of Technology Thonburi, Thailand Prof. (Dr.) Kantipudi MVV Prasad, EC Department, RK University, Rajkot. Prof. (Dr.) Jitendra Gupta,Faculty of Pharmaceutics, Institute of Pharmaceutical Research, GLA University, Mathura. Prof. (Dr.) Swapnali Borah, HOD, Dept of Family Resource Management, College of Home Science, Central Agricultural University, Tura, Meghalaya, India Prof. (Dr.) N.Nazar Khan, Professor in Chemistry, BTK Institute of Technology, Dwarahat-263653 (Almora), Uttarakhand-India Prof. (Dr.) Rajiv Sharma, Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai (TN) - 600 036, India. Prof. (Dr.) Aparna Sarkar, PH.D. Physiology, AIPT, Amity University , F 1 Block, LGF, Sector125,Noida-201303, UP, India. Prof. (Dr.) Manpreet Singh, Professor and Head, Department of Computer Engineering, Maharishi Markandeshwar University, Mullana, Haryana, India. Prof. (Dr.) Sukumar Senthilkumar, Senior Researcher, Advanced Education Center of Jeonbuk for Electronics and Information Technology, Chon Buk National University, Chon Buk, 561-756, SOUTH KOREA. . Prof. (Dr.) Hari Singh Dhillon, Assistant Professor, Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), INDIA. . Prof. (Dr.) Poonkuzhali, G., Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, INDIA. .

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Prof. (Dr.) Bharath K N, Assistant Professor, Dept. of Mechanical Engineering, GM Institute of Technology, PB Road, Davangere 577006, Karnataka, India. Prof. (Dr.) F.Alipanahi, Assistant Professor, Islamic Azad University, Zanjan Branch, Atemadeyeh, Moalem Street, Zanjan IRAN. Prof. Yogesh Rathore, Assistant Professor, Dept. of Computer Science & Engineering, RITEE, Raipur, India Prof. (Dr.) Ratneshwer, Department of Computer Science (MMV),Banaras Hindu University Varanasi-221005, India. Prof. Pramod Kumar Pandey, Assistant Professor, Department Electronics & Instrumentation Engineering, ITM University, Gwalior, M.P., India. Prof. (Dr.)Sudarson Jena, Associate Professor, Dept.of IT, GITAM University, Hyderabad, India Prof. (Dr.) Binod Kumar, PhD(CS), M.Phil(CS), MIEEE,MIAENG, Dean & Professor( MCA), Jayawant Technical Campus(JSPM's), Pune, India. Prof. (Dr.) Mohan Singh Mehata, (JSPS fellow), Assistant Professor, Department of Applied Physics, Delhi Technological University, Delhi Prof. Ajay Kumar Agarwal, Asstt. Prof., Deptt. of Mech. Engg., Royal Institute of Management & Technology, Sonipat (Haryana), India. Prof. (Dr.) Siddharth Sharma, University School of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Satish Chandra Dixit, Department of Chemistry, D.B.S.College, Govind Nagar,Kanpur208006, India. Prof. (Dr.) Ajay Solkhe, Department of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Neeraj Sharma, Asst. Prof. Dept. of Chemistry, GLA University, Mathura, India. Prof. (Dr.) Basant Lal, Department of Chemistry, G.L.A. University, Mathura, India. Prof. (Dr.) T Venkat Narayana Rao, C.S.E, Guru Nanak Engineering College, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) Rajanarender Reddy Pingili, S.R. International Institute of Technology, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) V.S.Vairale, Department of Computer Engineering, All India Shri Shivaji Memorial Society College of Engineering, Kennedy Road, Pune-411 001, Maharashtra, India. Prof. (Dr.) Vasavi Bande, Department of Computer Science & Engineering, Netaji Institute of Engineering and Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Hardeep Anand, Department of Chemistry, Kurukshetra University Kurukshetra, Haryana, India. Prof. Aasheesh shukla, Asst Professor, Dept. of EC, GLA University, Mathura, India. Prof. S.P.Anandaraj., CSE Dept, SREC, Warangal, India. Prof. (Dr.) Chitranjan Agrawal, Department of Mechanical Engineering, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur- 313001, Rajasthan, India. Prof. (Dr.) Rangnath Aher, Principal, New Arts, Commerce and Science College, Parner, DistAhmednagar, M.S. India. Prof. (Dr.) Chandan Kumar Panda, Department of Agricultural Extension, College of Agriculture, Tripura, Lembucherra-799210 Prof. (Dr.) Latika Kharb, IP Faculty (MCA Deptt), Jagan Institute of Management Studies (JIMS), Sector-5, Rohini, Delhi, India. Raj Mohan Raja Muthiah, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts. Prof. (Dr.) Chhanda Chatterjee, Dept of Philosophy, Balurghat College, West Bengal, India. Prof. (Dr.) Mihir Kumar Shome , H.O.D of Mathematics, Management and Humanities, National Institute of Technology, Arunachal Pradesh, India Prof. (Dr.) Muthukumar .Subramanyam, Registrar (I/C), Faculty, Computer Science and Engineering, National Institute of Technology, Puducherry, India. Prof. (Dr.) Vinay Saxena, Department of Mathematics, Kisan Postgraduate College, Bahraich â€“ 271801 UP, India. Satya Rishi Takyar, Senior ISO Consultant, New Delhi, India. Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Mandi Gobindgarh (PB) Prof. (Dr.) Harish Kumar, Department of Sports Science, Punjabi University, Patiala, Punjab, India. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India.

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Prof. (Dr.) Manish Gupta, Department of Mechanical Engineering, GJU, Haryana, India. Prof. Mridul Chawla, Department of Elect. and Comm. Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. Seema Chawla, Department of Bio-medical Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. (Dr.) Atul M. Gosai, Department of Computer Science, Saurashtra University, Rajkot, Gujarat, India. Prof. (Dr.) Ajit Kr. Bansal, Department of Management, Shoolini University, H.P., India. Prof. (Dr.) Sunil Vasistha, Mody Institute of Tecnology and Science, Sikar, Rajasthan, India. Prof. Vivekta Singh, GNIT Girls Institute of Technology, Greater Noida, India. Prof. Ajay Loura, Assistant Professor at Thapar University, Patiala, India. Prof. Sushil Sharma, Department of Computer Science and Applications, Govt. P. G. College, Ambala Cantt., Haryana, India. Prof. Sube Singh, Assistant Professor, Department of Computer Engineering, Govt. Polytechnic, Narnaul, Haryana, India. Prof. Himanshu Arora, Delhi Institute of Technology and Management, New Delhi, India. Dr. Sabina Amporful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Pawan K. Monga, Jindal Institute of Medical Sciences, Hisar, Haryana, India. Dr. Sam Ampoful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Nagender Sangra, Director of Sangra Technologies, Chandigarh, India. Vipin Gujral, CPA, New Jersey, USA. Sarfo Baffour, University of Ghana, Ghana. Monique Vincon, Hype Softwaretechnik GmbH, Bonn, Germany. Natasha Sigmund, Atlanta, USA. Marta Trochimowicz, Rhein-Zeitung, Koblenz, Germany. Kamalesh Desai, Atlanta, USA. Vijay Attri, Software Developer Google, San Jose, California, USA. Neeraj Khillan, Wipro Technologies, Boston, USA. Ruchir Sachdeva, Software Engineer at Infosys, Pune, Maharashtra, India. Anadi Charan, Senior Software Consultant at Capgemini, Mumbai, Maharashtra. Pawan Monga, Senior Product Manager, LG Electronics India Pvt. Ltd., New Delhi, India. Sunil Kumar, Senior Information Developer, Honeywell Technology Solutions, Inc., Bangalore, India. Bharat Gambhir, Technical Architect, Tata Consultancy Services (TCS), Noida, India. Vinay Chopra, Team Leader, Access Infotech Pvt Ltd. Chandigarh, India. Sumit Sharma, Team Lead, American Express, New Delhi, India. Vivek Gautam, Senior Software Engineer, Wipro, Noida, India. Anirudh Trehan, Nagarro Software Gurgaon, Haryana, India. Manjot Singh, Senior Software Engineer, HCL Technologies Delhi, India. Rajat Adlakha, Senior Software Engineer, Tech Mahindra Ltd, Mumbai, Maharashtra, India. Mohit Bhayana, Senior Software Engineer, Nagarro Software Pvt. Gurgaon, Haryana, India. Dheeraj Sardana, Tech. Head, Nagarro Software, Gurgaon, Haryana, India. Naresh Setia, Senior Software Engineer, Infogain, Noida, India. Raj Agarwal Megh, Idhasoft Limited, Pune, Maharashtra, India. Shrikant Bhardwaj, Senior Software Engineer, Mphasis an HP Company, Pune, Maharashtra, India. Vikas Chawla, Technical Lead, Xavient Software Solutions, Noida, India. Kapoor Singh, Sr. Executive at IBM, Gurgaon, Haryana, India. Ashwani Rohilla, Senior SAP Consultant at TCS, Mumbai, India. Anuj Chhabra, Sr. Software Engineer, McKinsey & Company, Faridabad, Haryana, India. Jaspreet Singh, Business Analyst at HCL Technologies, Gurgaon, Haryana, India.

TOPICS OF INTEREST Topics of interest include, but are not limited to, the following:  Computer and computational sciences  Physics  Chemistry  Mathematics  Actuarial sciences  Applied mathematics  Biochemistry, Bioinformatics  Robotics  Computer engineering  Statistics  Electrical engineering & Electronics  Mechanical engineering  Industrial engineering  Information sciences  Civil Engineering  Aerospace engineering  Chemical engineering  Sports sciences  Military sciences  Astrophysics & Astronomy  Optics  Nanotechnology  Nuclear physics  Operations research  Neurobiology & Biomechanics  Acoustical engineering  Geographic information systems  Atmospheric sciences  Educational/Instructional technology  Biological sciences  Education and Human resource  Extreme engineering applications  Environmental research and education  Geosciences  Social, Behavioral and Economic sciences  Advanced manufacturing technology  Automotive & Construction  Geospatial technology  Cyber security  Transportation  Energy and Power  Healthcare & Hospitality  Medical and dental sciences  Pesticides  Marine and thermal sciences  Pollution  Renewable sources of energy  Industrial pollution control  Hazardous and e-waste management  Green technologies  Artificial/computational intelligence  Theory and models

TABLE OF CONTENTS American International Journal of Research in Science, Technology, Engineering & Mathematics ISSN(Print): 2328-3491, ISSN(Online): 2328-3580, ISSN(CD-ROM): 2328-3629 (December-2014 to February-2015, Issue 9, Volume 1, 2 & 3) Issue 9, Volume 1 Paper Code

Paper Title

Page No.

AIJRSTEM 15-106

TBSC Compensator: Application and Simulation Results for Starting and Voltage Sag Mitigation of Induction Motor Swapnil Dadaso Patil, Dr. Anwar Mubarak Mulla, Dr. Dadgonda Rajgonda Patil

01-11

AIJRSTEM 15-109

On Volterra Type Integral Inequalities in Metric Spaces Hristo Kiskinov, Stepan Kostadinov, Lozanka Trenkova, Andrey Zahariev

12-19

AIJRSTEM 15-113

Review of Public Safety in Some Non Governmental Medical Radiation Facilities in Bangladesh M.Haider, S. Shill and QMR Nizam

20-24

AIJRSTEM 15-115

Reactions of MoOCl4 & MoO2Cl2 with Imidazole, Pyrazole, Acetamide, Succinimide, Benzamide & 2-Thiazoline-2-thiol Gursharan Singh, Seema Sharma, Vikas Mangla, Mamta Goyal, Kavita Singla, Deepika Rani

25-33

AIJRSTEM 15-118

Experimental Investigation on Plain Circular and 2:1 Rectangular Jets at Low Speed Dr. A. Arokkiaswamy

34-38

AIJRSTEM 15-126

Fingerprint Based Gender Classification for Biometric Security: A State-Of-The-Art Technique S. S. Gornale

39-49

AIJRSTEM 15-133

Energy Consumption in Wireless Network Control System Data Transmission K.Sanakkiyan, R.Kannan

50-55

AIJRSTEM 15-134

IMAGE RECONSTRUCTION USING SPARSE BASED MODEL Reetumala Thakre, Prof. Rajendra Singh, Dr. Soni Changlani

56-61

AIJRSTEM 15-135

Automatic Bangladeshi Vehicle Number Plate Recognition System using Neural Network Mohammad Badrul Alam Miah, Sharmin Akter and Chitra Bonik

62-66

AIJRSTEM 15-136

Navigation System for Multi-Floor Indoor Positioning during Urban Combat Operations using Geo-Magnetic Module N.Kanagapriya, N.Solaiyammal

67-70

AIJRSTEM 15-137

Survey on cloud computing technology, application, usage and its stack terminology Santvana Singh, Sarla Singh, Sumit Dubey

71-73

AIJRSTEM 15-139

Future monthly, seasonal and annual rainfall trend prediction for Tarai region of Uttarakhand Arvind Singh Tomar, Praveen Vikram Singh and Om Prakash Kumar

74-76

AIJRSTEM 15-140

Synthesis and Structural Studies of some complexes of Ni(II) and Co(III) with Hexamethylenedibiguanide Bina Rani, R.K. Prasad, and Divya Singh

77-80

AIJRSTEM 15-142

Experimental/Computational Results for Shielding Neutron & Photon Radiations C .E. Okon, I. P. Etim

81-89

AIJRSTEM 15-149

Intense Geomagnetic Storms and their Association with Solar Wind Plasma and Interplanetary Parameters Mukesh Kanwal Tripathi, R.S. Gupta, M.P. Yadav

90-94

AIJRSTEM 15-150

Analysis of modulation technique in free space optics system Vasundhara Shukla, Durgesh Shukla, Jayant Shukla, Richi Nigam

95-98

AIJRSTEM 15-152

Comparison: Introduction to Mobile Ad-Hoc Network and Routing Implementation Meenakshi Mishra, Niketan Mishra, Soni Changlani

99-101

Issue 9, Volume 2 Paper Code

Paper Title

Page No.

AIJRSTEM 15-153

Building scalable geo-replicated storage back ends for web application Rajkumar M. Chauhan

102-106

AIJRSTEM 15-156

Short term Size Deviation of Multivoltine, Multi x Bi and Bivoltine Raw Silk Niharendu Bikash Kar, Mrinal Kanti Majumdar, S. Nirmalkumar

107-112

AIJRSTEM 15-157

Synthesis of Well-defined Dihydroxyl End-Functional Polystyrene using Trifunctional Initiator via Atom Transfer Radical Polymerization Samiul Islam Chowdhury, Tariqul Hasan

113-118

AIJRSTEM 15-158

Identification of Data Quality Issues in Homogeneous Environment Sapna Pujara, Dr. Kanwal Garg

119-123

AIJRSTEM 15-160

Variablity of Rainfall in Bangalore city - Mapping of hourly variation of heavy rainfall event Roopa Bhandiwad

124-139

AIJRSTEM 15-161

Intuitionistic Z-Numbers G Velammal, M Shahila Bhanu

140-142

AIJRSTEM 15-162

Interchangeability of Motor-Cycle Parts for Maintenance Purposes Joseph W. and Chindapi N.

143-149

AIJRSTEM 15-163

Signal Processing for Music Analysis Poonam Priyadarshini, Soubhik Chakraborty

150-153

AIJRSTEM 15-164

REVIEW ON MODELING OF RESISTANCE SPOT WELDING PROCESS Boriwal Lokesh, Sarviya R. M. & Mahapatra M. M

154-159

AIJRSTEM 15-165

Dynamic Query Search Over Graph Stream Ms.Vaishali G. Powar and Mr.Pradeep C. Bhaskar

160-164

AIJRSTEM 15-171

A review on Optimization of material removal rate in micro-drilling process Ranadhir R Landge, Dr. Atul B Borade

165-169

AIJRSTEM 15-178

Application of Various Multi Criteria Decision Making Methods for the Assessment of Different Waste Water Treatment Systems Case Study: JSPM Hadapsar Campus, Pune Poonam K. Wakchaure, Dr. Sameer S. Shastri

170-175

AIJRSTEM 15-179

An Experimental Study of Thermal Performance of Concentric Annular Heat Pipe Nishtha Vijra, Tejinder Paul Singh

176-182

AIJRSTEM 15-183

Stability: Abstract Roadmap of Software Security Kavita Sahu, Raj Shree

183-186

AIJRSTEM 15-185

Image Filtering and Registration by using Hybrid Area and Feature Based Method Kiran P. Dange, Neha P. Athavale

187-193

AIJRSTEM 15-186

An Investigation on the Potential of Biogas Production from Elephant Grass and Guinea Grass Sindra L Summoogum-Utchanah and Doolar Poorneema

194-197

AIJRSTEM 15-187

Discursive Institutionalism: Analysis of the Forest Policy Review Process of Ghana Yaw Amo Sarpong, Daniel K. B. Inkoom, Ibrahim Latif Apaassongo

198-205

Issue 9, Volume 3 Paper Code

Paper Title

Page No.

AIJRSTEM 15-188

Survey on Clustering Techniques of Data Mining Namrata S Gupta, Bijendra S.Agrawal, Rajkumar M. Chauhan

206-211

AIJRSTEM 15-189

Effect of Spacer Length from Vinyl Group of Vinyl-bromoester Initiator on Atom Transfer Radical Polymerization of Styrene Samiul Islam Chowdhury and Tariqul Hasan

212-215

AIJRSTEM 15-190

The Influences of Surface Area on the Efficiency of a New Design of Solar Collector Suitable for Basrah City 30.50 N

216-220

Kawther K. Mankhi; Noori H.N. Al-Hashimi; Jassim M. Al-Asadi AIJRSTEM 15-191

Inertial effects on hydrodynamic convection in a passive mushy layer Prof. Dr.P.K.Srimani, Mr. R.Parthasarathi

221-229

AIJRSTEM 15-192

Initial Thermal Hydraulic Design of a 1000MWe Water Reactor with an Improved Thermal Efficiency C .E. Okon, D. E. Oku

230-238

AIJRSTEM 15-195

A hybrid approach to measure design improvement factor of website Prafulla Bafna, Hema Gaikwad

239-241

AIJRSTEM 15-197

EXCITATION OF DOUBLY-CHARGED YTTRIUM IONS IN ELECTRON-ATOM COLLISIONS Yu. M. Smirnov

242-245

AIJRSTEM 15-199

Simulated Results for Neutron Radiations Shielding Using Monte Carlo C .E. Okon, I. O. Akpan

246-251

AIJRSTEM 15-200

A Review on Various Error Detection and Correction Methods Used in Communication Varinder Singh, Narinder Sharma

252-257

AIJRSTEM 15-205

Dual Layer Image Scrambling Method Using Improved Arnold Transform Gyan Vardhan Artist, Dr. Mahesh Kumar Porwal

258-264

AIJRSTEM 15-209

An Open type Mixed Quadrature Rule using Fejer and Gaussian Quadrature Rules Dwiti Krushna Behera, Ajit Kumar Sethi & Rajani Ballav Dash

265-268

AIJRSTEM 15-215

Land holding effect on energy inputs for soybean production in Malwa plateau of Madhya Pradesh Dilip Jat, R.K. Naik, N.K.Khandelwal, Bharat Patel and Prateek Shrivastava

269-274

AIJRSTEM 15-219

Comparative Study of Dynamic Performance of Multi-Area Interconnected Power Systems with EHVAC/HVDC Links Ram Naresh Mishra, Dr. Prabhat Kumar

275-283

AIJRSTEM 15-223

Behavior of a Discrete SIR Epidemic Model A. George Maria Selvam, R. Janagaraj and D. Jerald Praveen

284-287

AIJRSTEM 15-227

Effect of additives on the structural and magnetic properties of electrodeposited NiMn thin film M.Rajeswari, S.Ganesan

288-292

AIJRSTEM 15-232

Does the knowledge and skill acquired during simulator training gets applied on the job by the seafarers- An empirical study Surender Kumar, Dr. Neeraj Anand, Dr. DK Punia, Dr. BK Saxena

293-297

AIJRSTEM 15-235

An Investigation on Causes and Preventive Measures of Trespass Accidents and Suicides in Katpadi Railway Jurisdiction of Tamil Nadu, India Dr. N. Sundaram, Mr. M. Sriram

298-301

AIJRSTEM 15-239

ACTIVE LEARNING METHODS IN HIGHER EDUCATION Badri Meparishvili, Lela Turmanidze, Gulnara Janelidze

302-305

AIJRSTEM 15-240

Appari’s Design – Get Extra Energy From Existing Resources Sidramappa Shivashankar Dharane, Archita Vijaykumar Malge, Savita Gururaj Malage

306-308

AIJRSTEM 15-242

Heat Transfer and Turbulent Nanofluid Flow over a Double Forward-Facing Step Mohammed Saad Kamel

309-316

AIJRSTEM 15-243

Experimental Simulation Analysis of Current Density Distribution by Kirchhoff’s laws in a resistor network cell model S. R. Rajkumar, Dr.M. Alagar, Dr.S. Somasekaran, S.R. Ravisankar

317-322

AIJRSTEM 15-245

Measuring infrastructure sustainability with the use of eco efficient performance criteria Saroop s and Allopi d

323-327

AIJRSTEM 15-246

Glauber Modeling in Heavy Ion Collisions Abhilasha Saini, Dr. Sudhir Bhardwaj

328-331

American International Journal of Research in Science, Technology, Engineering & Mathematics

Available online at http://www.iasir.net

ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 AIJRSTEM is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research)

Survey on Clustering Techniques of Data Mining 1

Namrata S Gupta, 2Bijendra S.Agrawal, 3Rajkumar M. Chauhan Asst. Prof. Smt. BK Mehta IT Centre (BCA College), Palanpur, Gujarat, INDIA 2 Principal, CCMS, Vadu, Gujarat, INDIA 3 Foreman Instructor I.T.I. Amirgadh Ex. Asst. Professor BCA College Palanpur, Gujarat, INDIA 1

Abstract: Data mining refers to extracting useful information from vast amounts of data. It is the process of discovering interesting knowledge from large amounts of data stored either in databases, data warehouses, or other information repositories. An important technique in data analysis and data mining applications is Clustering.It divides data into groups of similar objects. Each group, called cluster, consists of objects that are similar between themselves and dissimilar to objects of other groups.Data mining has two types of tasks: Predictive and the descriptive. There are different types of clustering algorithms such as hierarchical, partitioning, grid, density based, model based, and constraint based algorithms. Hierarchical clustering is the connectivity based clustering. Partitioning is the centred based clustering; the value of kmean is set. Density based clusters are defined as area of higher density then the remaining of the data set. Grid based clustering is the fastest processing time that typically depends on the size of the grid instead of the data. Model based clustering hypothesizes for each cluster and find the best fit of data to the given model. Constraint based clustering is performed by incorporation of user or application oriented constraints. In this survey paper, a review of different types of clustering techniques in data mining is done. Keywords: Data mining, Clustering, Types of Clustering, Classification. I. Background Bill Palace defines data mining (sometimes called data or knowledge discovery) as "the process of analyzing data from different perspectives and summarizing it into useful information--information that can be used to increase revenue, cuts costs, or both. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases." Data mining is one of the best ways to illustrate the difference between data and information: data mining transforms data into information. Data mining consists of extract, transform, and load transaction data onto the data warehouse system, Store and manage the data in a multidimensional database system, Provide data access to business analysts and information technology professionals, Analyze the data by application software, Present the data in a useful format, such as a graph or table. Data mining is a multi-step process. It requires accessing and preparing data for a data mining algorithm, mining the data, analyzing results and taking appropriate action. The accessed data can be stored in one or more operational databases, a data warehouse or a flat file. Data Mining is a four step: Assemble data, Apply data mining tools on datasets, Interpretation and evaluation of result, Result application.

Figure 1: Steps of Data Mining Process A. Data Mining Approaches In data mining the data is mined using two learning approaches i.e. supervised learning or unsupervised clustering. Supervised Learning In this training data includes both the input and the desired results. These methods are fast and accurate. The correct results are known and are given in inputs to the model during the

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Namrata S Gupta et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(3), December 2014-February 2015, pp. 206-211

learning process. Supervised models are neural network, Multilayer Perception, Decision trees. Unsupervised Learning The model is not provided with the correct results during the training. It can be used to cluster the input data in classes on the basis of their statistical properties only. Unsupervised models are different types of clustering, distances and normalization, k-means, self organizing maps. B. Data Mining Tasks Data mining tasks are generally divided into two major categories: Predictive task The goal of this task is to predict the value of one particular attribute, based on values of other attributes. The attributes that is used for making the prediction is named as independent variable. The other value which is to be predicted is known as the Target or dependent value. Descriptive task The purpose of this task is surmise underlying relations in data .In descriptive task of data mining, values are independent in nature and it frequently require post-processing to validate results. Data mining involves the anomaly detection, association rule learning, classification, regression, summarization and clustering. In this paper, clustering analysis is done. II. Introduction Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups. Cluster Analysis, an automatic process to find similar objects from a database. It is a fundamental operation in data mining. Clustering algorithms are used extensively not only to organize and categorize data, but are also useful for data compression and model construction. A good clustering algorithm is able to identity clusters irrespective of their shapes. Other requirements of clustering algorithms are scalability, ability to deal with noisy data, insensitivity to the order of input records, etc. A. Requirements of Clustering in Data Mining Here are the typical requirements of clustering in data mining:  Scalability - We need highly scalable clustering algorithms to deal with large databases.  Ability to deal with different kind of attributes - Algorithms should be capable to be applied on any kind of data such as interval based (numerical) data, categorical, binary data.  Discovery of clusters with attribute shape - The clustering algorithm should be capable of detect cluster of arbitrary shape. They should not be bounded to only distance measures that tend to find spherical cluster of small size.  High dimensionality - The clustering algorithm should not only be able to handle low- dimensional data but also the high dimensional space.  Ability to deal with noisy data - Databases contain noisy, missing or erroneous data. Some algorithms are sensitive to such data and may lead to poor quality clusters.  Interpretability - The clustering results should be interpretable, comprehensible and usable. From a machine learning perspective clusters correspond to hidden patterns, the search for clusters is unsupervised learning, and the resulting system represents a data concept. From a practical perspective clustering plays an outstanding role in data mining applications such as scientific data exploration, information retrieval and text mining, spatial database applications, Web analysis, CRM, marketing, medical diagnostics, computational biology, and many others. Presenting data by fewer clusters necessarily loses certain fine details (loss in data compression), but achieves simplification. It represents many data objects by few clusters, and hence, it models data by its clusters. Clustering is often one of the first steps in data mining analysis. It identifies groups of related records that can be used as a starting point for exploring further relationships. Clustering is a data mining (machine learning) technique used to place data elements into related groups without advance knowledge of the group definitions. Clustering techniques fall into a group of undirected data mining tools. The goal of undirected data mining is to discover structure in the data as a whole. In general, there are two types of attributes associated with input data in clustering algorithms, i.e., numerical attributes, and categorical attributes. Numerical attributes are those with a finite or infinite number of ordered values, such as the height of a person or the x-coordinate of a point on a 2D domain. On the other hand, categorical attributes are those with finite unordered values, such as the occupation or the blood type of a person. Many different clustering techniques have been defined in order to solve the problem from different perspective, i.e. partition based clustering, density based clustering, hierarchical methods and grid-based methods etc. IV. Classification of Clustering Clustering is the main task of Data Mining. And it is done by the number of algorithms. The most commonly used algorithms in Clustering are Hierarchical, Partitioning, Density based, Grid based, Model Based and Constraint based algorithms. A. Hierarchical Algorithms Hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. It is the connectivity based clustering algorithms. The hierarchical algorithms build clusters gradually. Hierarchical

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clustering generally fall into two types: In hierarchical clustering, in single step, the data are not partitioned into a particular cluster. It takes a series of partitions, which may run from a single cluster containing all objects to „n‟ clusters each containing a single object. Hierarchical Clustering is subdivided into agglomerative methods, which proceed by series of fusions of the „n‟ objects into groups, and divisive methods, which separate „n‟ objects successively into finer groupings.

A.1 Advantages of hierarchical clustering 1. Embedded flexibility regarding the level of granularity. 2. Ease of handling any forms of similarity or distance. 3. Applicability to any attributes type. 4.1.2 Disadvantages of hierarchical clustering 1. Vagueness of termination criteria. 2. Most hierarchal algorithm do not revisit once constructed clusters with the purpose of improvement. B. Partitioning Algorithms Partitioning algorithms divide data into several subsets. The reason of dividing the data into several subsets is that checking all possible subset systems is computationally not feasible; there are certain greedy heuristics schemes are used in the form of iterative optimization. Specifically, this means different relocation schemes that iteratively reassign points between the k clusters. Relocation algorithms gradually improve clusters.

Figure 2: Partitioned Clustering There are many methods of partitioning clustering; they are k-mean, Bisecting K Means Method, Medoids Method, PAM (Partitioning Around Medoids), CLARA (Clustering LARge Applications) and the Probabilistic Clustering. We are discussing the k-mean algorithm as: In k-means algorithm, a cluster is represented by its centroid, which is a mean (average pt.) of points within a cluster. This works efficiently only with numerical attributes. And it can be negatively affected by a single outlier. The k-means algorithm is the most popular clustering tool that is used in scientific and industrial applications. It is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. The basic algorithm is very simple

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1. Select K points as initial centroids. 2. Repeat. 3. Form K clusters by assigning each point to its closest centroid. 4. Re compute the centroid of each cluster until centroid does not change. The k-means algorithm has the following important properties: 1. It is efficient in processing large data sets. 2. It often terminates at a local optimum. 3. It works only on numeric values. 4. The clusters have convex shapes. C. Density-Based Clustering In density-based clustering, clusters are defined as areas of higher density than the remaining of the data set. Objects in these sparse areas - that are required to separate clusters - are usually considered to be noise and border points. There are two major approaches for density-based methods. The first approach pins density to a training data point and is reviewed in the sub-section Density-Based Connectivity. In this clustering technique density and connectivity both measured in terms of local distribution of nearest neighbours. So defined densityconnectivity is a symmetric relation and all the points reachable from core objects can be factorized into maximal connected components serving as clusters. Representative algorithms include DBSCAN, GDBSCAN, OPTICS, and DBCLASD. The second approach pins density to a point in the attribute space and is explained in the sub-section Density Functions. In this, density function is used to compute the density. Overall density is modelled as the sum of the density functions of all objects. Clusters are determined by density attractors, where density attractors are local maxima of the overall density function. The influence function can be an arbitrary one. It includes the algorithm DENCLUE. Density Based Spatial Clustering of Applications Noise DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm is a density based clustering algorithm. In this algorithm the regions grow with sufficiently high density are known as clusters. The Eps and the Minpts are the two parameters of the DBSCAN. The basic idea of DBSCAN algorithm is that for each object of a cluster, the neighbourhood of a given radius ( Eps ) has to contain at least a minimum number of objects ( MinPts ).The clustering quality of DBSCAN algorithm strongly depend on the parameters does not depend upon the database. The parameters are set by users which will consider in the computational of clusters. The users have to select the parameters properly to get the better results the reason is that the same database with different parameters; the algorithm can produce different results. However, DBSCAN algorithm uses global parameters, which are not suitable for discovering clusters with different densities, without considering different possible density, only using a given possible density of any clusters, when the densities of clusters are totally separated. D. Grid Based Algorithms Grid-based clustering where the data space is quantized into finite number of cells which form the grid structure and perform clustering on the grids. Grid based clustering maps the infinite number of data records in data streams to finite numbers of grids. Grid based clustering is the fastest processing time that typically depends on the size of the grid instead of the data. The grid based methods use the single uniform grid mesh to partition the entire problem domain into cells and the data objects located within a cell are represented by the cell using a set of statistical attributes from the objects. These algorithms have a fast processing time, because they go through the data set once to compute the statistical values for the grids and the performance of clustering depends only on the size of the grids which is usually much less than the data objects. The grid-based clustering algorithms are STING, Wave Cluster, and CLIQUE. All these methods use a uniform grid mesh to cover the whole problem. For the problems with highly irregular data distributions, the resolution of the grid mesh must be too fine to obtain a good clustering quality. A finer mesh can result in the mesh size close to or even exceed the size of the data objects, which can significant increase the computation load for clustering. Adaptive Mesh Refinement Adaptive Mesh Refinement (AMR) is a type of multi resolution algorithm. This algorithm achieves high resolution in localized regions of dynamic, multidimensional numerical simulations. This is successfully applied to model large scale scientific applications in a range of disciplines, such as computational fluid dynamics, astrophysics, meteorological simulations, structural dynamics, magnetic, and thermal dynamics. Basically, it can place very high resolution grids precisely where the high computational cost requires. The adaptability of the algorithm allows simulating multi resolution that are out of reach with methods using a global uniform fine grid. The AMR clustering algorithm firstly creates different resolution grids based on the density. After that grids comprise a hierarchy tree that represents the problem domain as nested structured grids of increasing resolution. The algorithm considers each leaf as the center of an individual cluster and recursively assigns the membership for the data objects located in the parent nodes until the root node is reached. The AMR clustering algorithm can detect the nested clusters at different levels of resolutions by using the hierarchical tree. As the AMR algorithm is grid density based algorithm so it also shares the common characteristics of all gridbased methods. AMR algorithm has a fast processing time. It has the ability to separate from the noise. The

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order of input data is insensitive. AMR is a technique that starts with a coarse uniform grid covering the entire computational volume and automatically refines certain regions by adding finer sub grids. From the connected parent grid cells, the new child grids are created whose attributes, density for instance, exceed given thresholds.

Figure 3: A 2-dimensional AMR example with 2 levels of refinement. A finer resolution mesh is applied each time a sub grid is created. Advantage  The major advantage of this method is fast processing time.  It is dependent only on the number of cells in each dimension in the quantized space. E. Model-Based Methods In this method a model is hypothesize for each cluster and find the best fit of data to the given model. This method locates the clusters by clustering the density function. This reflects spatial distribution of the data points. This method also serve a way of automatically determining number of clusters based on standard statistics, taking outlier or noise into account. It therefore yield robust clustering methods. F. Constraint-Based Method In this method the clustering is performed by incorporation of user or application oriented constraints. The constraint refers to the user expectation or the properties of desired clustering results. The constraint gives us the interactive way of communication with the clustering process. The constraint can be specified by the user or the application requirement. V. Conclusions The overall goal of the data mining process is to extract information from a large data set and transform it into an understandable form for further use. Clustering is important in data analysis and data mining applications. It is the task of grouping a set of objects so that objects in the same group are more similar to each other than to those in other groups (clusters). Clustering can be done by the different no. of algorithms such as hierarchical, partitioning, grid and density based algorithms. Hierarchical clustering is the connectivity based clustering. Partitioning is the centroid based clustering; the value of k-mean is set. Density based clusters are defined as area of higher density then the remaining of the data set. Grid based clustering is the fastest processing time that typically depends on the size of the grid instead of the data. The grid based methods use the single uniform grid mesh to partition the entire problem domain into cells. References [1] [2] [3]

[4] [5] [6]

[7]

Pavel Berkhin, “A Survey of Clustering Data Mining Techniques”, pp.25-71, 2002. Wei-keng Liao, Ying Liu, Alok Choudhary, “A Grid-based Clustering Algorithm using Adaptive Mesh Refinement”, Appears in the 7th Workshop on Mining Scientific and Engineering Datasets, pp.1-9, 2004. Cheng-Ru Lin, Chen, Ming-Syan Syan , “Combining Partitional and Hierarchical Algorithms for Robust and Efficient Data Clustering with Cohesion Self-Merging” IEEE Transactions On Knowledge And Data Engineering, Vol. 17, No. 2,pp.145-159, 2005. Oded Maimon, Lior Rokach, “DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK”, Springer Science+Business Media.Inc, pp.321-352, 2005. Pradeep Rai, Shubha Singh” A Survey of Clustering Techniques” International Journal of Computer Applications, October 2010. Zheng Hua, Wang Zhenxing, Zhang Liancheng, Wang Qian, “Clustering Algorithm Based on Characteristics of Density Distribution” Advanced Computer Control (ICACC), 2010 2nd International Conference on National Digital Switching System Engineering & Technological R&D Center, vol2”, pp.431-435, 2010. MR ILANGO, Dr V MOHAN, “A Survey of Grid Based Clustering Algorithms”, International Journal of Engineering Science and Technology, pp.3441-3446, 2010.

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[9] [10] 11. 12.

13. 14.

Amineh Amini, Teh Ying Wah,, Mahmoud Reza Saybani, Saeed Reza Aghabozorgi Sahaf Yazdi, “A Study of Density-Grid based Clustering Algorithms on Data Streams”,IEEE 8th International Conference on Fuzzy Systems and Knowledge Discovery, vol.3, pp.1652-1656, 2011. Guohua Lei, Xiang Yu, et.all, “An Incremental Clustering Algorithm Based on Grid”,IEEE 8th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp.1099-1103, 2011. Anoop Kumar Jain, Prof. Satyam Maheswari “Survey of Recent Clustering Techniques in Data Mining”, International Journal of Computer Science and Management Research, pp.72-78, 2012. M.Vijayalakshmi, M.Renuka Devi, “A Survey of Different Issue of Different clustering Algorithms Used in Large Data sets” , International Journal of Advanced Research in Computer Science and Software Engineering, pp.305-307, 2012. Ritu Sharma, M. Afshar Alam, Anita Rani , “K-Means Clustering in Spatial Data Mining using Weka Interface” , International Conference on Advances in Communication and Computing Technologies (ICACACT Proceedings published by International Journal of Computer Applications® (IJCA), pp. 26-30, 2012. Pragati Shrivastava, Hitesh Gupta. “A Review of Density-Based clustering in Spatial Data”, International Journal of Advanced Computer Research (ISSN (print), pp.2249-7277, September-2012. Gholamreza Esfandani, Mohsen Sayyadi, Amin Namadchian, “GDCLU: a new Grid-Density based CLUstring algorithm”, IEEE 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, pp.102-107, 2012.

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Available online at http://www.iasir.net

Effect of Spacer Length from Vinyl Group of Vinyl-bromoester Initiator on Atom Transfer Radical Polymerization of Styrene Samiul Islam Chowdhury* and Tariqul Hasan *Department of Chemistry, Bangladesh University of Textiles, Dhaka-1208, BANGLADESH Department of Chemistry, University of Rajshahi, Rajshahi-6205, BANGLADESH Abstract: Two vinyl α-bromresters with shorter methylene spacer allyl bromopropionate (ABP) and longer methylene spacer undecenyl bromopropionate (UBP) were used as initiator for styrene polymerization by Cu-bipyridene mediated atom transfer radical polymerization. In both cases, the yield and the molecular weight of polymers were increased with increasing the ratio of styrene and initiator (St/In). The molecular weight of the polymer obtained by UBP system was higher than the polymer obtained by ABP system. It was assumed that the free radical formed from longer spacer containing UBP system is more stable than the free radical formed from the shorter spacer containing ABP system. The structure of the polystyrene obtained from both catalysts system was characterized by 1H-NMR analysis. I. Introduction Atom Transfer Radical Polymerization (ATRP), a controlled system reported by Matyjaszewski et. al. [1, 2] has been widely used for synthesis of end-functional, telethelic, block, graft and various polymers/copolymers with precisely controlled architecture [3,4]. The ATRP process, in which a free radical generates by the transfer of a halogen (typically bromine) from a dormant initiator or polymeric chain to a transition metal. This free radical then adds of monomer to yield polymer. R-X

Mtn

Kact R

- Y / Ligand

X - Mtn+1- Y / Ligand

Kdeact Kp monomer

Kt termination

Scheme 1: Mechanism of metal complex-mediated ATRP Although various type of initiators have been reported for ATRP, α-haloesters have been successfully employed for well-controlled ATRP to synthesis of various functional polymer or copolymers.[2,5-9] Structural adjustment of the α-haloesters initiator provides a handle to fine-tune the rate of initiation in the ATRP system. For instance, α-haloisobutyrates produce initiating radicals faster than the corresponding α-halopropionates due to better stabilization of the generated radicals after the halogen abstraction step. The polymerization of methacrylates with slow initiation was found to report using α-halopropionates.[10,11] Matyjaszewski et. al. reported the ATRP of styrene with vinyl chloroacetate and allyl chloroacetate as initiator using CuBr-bipyridine catalyst system.[5] The former was found as better initiator for styrene polymerization. The initiation and propagation rate of the polymerization depend on the structure of initiator as well as the generation and stability of the free radical. Although several papers have been reported to clarify the effect of the structure of the initiator on ATRP, [12, 13] it is an importance topic to investigate the efficiency of new initiators for ATRP. In this work, undecenyl bromopropionate (UBP) and allyl bromopropionate (ABP) initiators were synthesized and applied as initiators on ATRP of styrene under different conditions and the effect of –CH2– spacer between CH2=CH– and ester groups of the initiators was investigated. II. Experimental Materials: Styrene was purchased from Aldrich and it was purified by passing through an alumina column to remove stabilizer and then stirred with CaH2 for 8 h and filtered. Finally it was stored in a Schelnk flask at 0 °C under nitrogen prior to use. Copper (I) bromide was purified by recrystalization in methanol and washed with ether. Bipyridine from Fluka, 2-bromopropenyl bromide, allyl alcohol and 10-undecen-1-ol were purchased from Aldrich and used without further purification. Triethylamine was distilled over CaH2. All solvents were purified by distillation followed by refluxed with sodium and benzophenone. Polymerization procedure: Polymerization was carried out in a 50 mL Schelnk type reactor equipped with magnetic stirrer in nitrogen atmosphere. The reactor was charged with prescribed amount of CuBr and bipyridine. Three freeze-pump-thaw cycles were performed, and the tubes were sealed under vacuum with rubber septum. A required amount of degassed styrene and initiator were added with syringe. The reactor was

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placed in an oil bath at the desired temperature and the reaction mixture was stirred for certain time. At timed intervals, the polymerization was stopped by added methanol followed by cooling the reactor into ice-water and the polymer was precipitated in methanol by stirring over night. The polymers obtained were filtered, adequately washed with methanol, and dried under vacuum at 60 C for 6 h. Analytical methods: Molecular weight (Mn) and molecular weight distribution (Mw/Mn) of polymer were measured by Toyo soda HLC-802; column, GMH6 ×2 + G4000H8, cluent, CDCl 3 as solvent and calibrated by polystyrene standards. 1H and 13C NMR spectra of polymers were recorded at room temperature on a JEOL GX 500 spectrometer operated at 125.65 MHz in pulse Fourier Transform mode with chloroform-d as solvent. The peak of chloroform in chloroform-d (7.26 ppm for 1H and 74.47 ppm for 13C) was used as internal reference. Synthesis of Initiators: Allyl-2-bromopropionate (ABP): A 5 g (8.6 mmol) of allyl alcohol and 5 mL (36 mmol) of triethylamine were dissolved in 80 mL of THF. The solution was cooled in an ice-water bath. To this solution was added drop-wise 1 mL (9 mmol) of 2-bromopropionyl bromide in 20 mL of THF. The mixture was stirred for another 2 h at room temperature. Triethylamine hydrogen bromide salt was filtered out. THF in the filtrate was removed under vacuum at room temperature. The residual was dissolved in CHCl 3 and washed with 50 mL of water three times. The aqueous parts were combined and shaken with 50 mL of fresh CHCl3. The total CHCl3 solution was then dried over anhydrous CaCl2 for overnight. After filtering off the drying agent, CHCl 3 was distilled out under vacuum. A brown liquid was obtained. A further distillation under high vacuum gave a colorless liquid; yield 3.5g (70 %). 1 H NMR (CDCl3): 5.92 ppm (m, 1H, CH2=CH-); 5.45 ppm (dd, 2H, =CH2); 4.71 ppm (d, 2H, -O-CH2CH-); 4.4 ppm (q, 1H, CH3CH(Br)-OC(O)-); 1.80 ppm (d, 3H –CH(Br)-CH3), 13 C NMR (CDCl3): 167.21 ppm (-O-C(O)-); 128.72 ppm (CH2=CH-); 116.25 ppm (CH2=CH-); 63.70 ppm (O-CH2CH-); 37.31 ppm (CH3CH(Br)-); 19.07 ppm (CH3CH(Br)-). Undecen-2-Bromopropionate (UBP): A 5.0 g (30 mmol) of 10-undecen-1-ol and 5 mL (36 mmol) of triethylamine were dissolved in 80 mL of THF. This solution was cooled in an ice-water bath. To this solution was added drop-wise 3.20 mL (30 mmol) of 2-bromopropenyl bromide in 20 mL of THF. The mixture was stirred for 2 h at room temperature. The reaction mixture was worked-up according to the same method used for ABP synthesis. Finally, a colorless liquid was obtained; yield 7.60 g (82 %). 1 H NMR (CDCl3): 5.72 ppm (m, 1H, H-b); 4.85 ppm (dd, 2H, H-a); 4.41 ppm (q, 1H, H-l); 4.1 ppm (t, 2H, Hk); 2.00 ppm ( q, 2H, H-c); 1.80 ppm (d, 3H, H-m), 1.55 ppm (t, 2H, H-j); 1.2 – 1.4 ppm (m, 14H, H-d-i). 13 C NMR (CDCl3): 167.46 ppm (C-l); 136.34 ppm (C-b); 111.47 ppm (C-a); 63.30 ppm (C-k); 37.48 ppm (Cm); 31.10 ppm (C-c); 25.71 - 26.73 ppm (C-d-h, j); 23.05 ppm (C-i); 18.97 ppm (C-n). III. Results and Discussion The initiators ABP and UBP were synthesized from the reaction between 2-bromopropenyl bromide and allyl alcohol or 10-undecene-1-ol, respectively, in the presence of triethylamine. The both initiators ABP and UBP were characterized by 1H NMR and 13C NMR analysis. In the 1H NMR spectrum and 13C NMR spectrum of the initiators, the signals correspond to all protons and carbons were assigned clearly. Styrene was polymerized by ATRP at 110 ºC initiated by ABP and UBP at three different ratio of styrene and initiator (St/In) in conjunction with cupper (I) bromide and bipyridine as a catalyst under nitrogen atmosphere. The results of the polymerization are listed in Table 1. The ratio of styrene and initiator significantly affects the results of the polymerization. In both cases, the yield of polymer was increased with increasing the ratio St/In. The yield of polymer was higher for UBP than that of ABP system at similar polymerization conditions. The molecular weight of the polymers obtained was measured with gel permeation chromatography (GPC) and the GPC curves obtained polymers are displayed in Figure 1. In the both catalysts system, the polymers obtained with high molecular weight which is increased with increasing St/In ratio. The molecular weight of the polymer obtained by UBP system was higher than that of the polymer obtained by ABP system. The UBP system showed better catalytic efficiency that could be explained as; the free radical (ii) formed in UBP system is more stable because it does not form cyclic structure due to longer –CH2– spacer between carbon free radical and CH2=CH– group. On the other hand, the free radical (i) formed in ABP system might have a possibility to form a six member cyclic structure which causes slow initiation during polymerization. Table 1: Polymerization of styrene using ABP or UBP as initiator with CuBr/Bipyridine.a entry

initiator

yield (g) 0.82

Mw/Mn b

ABP

St/initiator (mmol) 800

400

0.68

7479 5017

1.52

2 3

200

0.31

3096

1.30

UBP

800

1.22

400

0.96

18864 9759

1.47

5 6

200

0.61

4863

1.50

1.38

1.50

Polymerization conditions: CuBr = 0.08 mmol, BiPy = 0.24 mmol, temperature = 110 ºC, time = 2h. cNumber average molecular weight and molecular weight distribution were measured by GPC analysis using polystyrene standard.

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C 2.5

3.5

4.5

2.5

5.5

3.5

4.5

5.5

Log M

Figure 1: GPC curves of ABP (A, B & C) and UBP (D, E & F) Br O

Posibility for cylization

O O ABP

O i

Br O

No cyclization

UBP

O ii

Scheme 2: Cyclization possibility H NMR spectra of polystyrene and ABP were compared in Figure 3. In the 1H NMR spectrum of polystyrene, the presence of a double doublet at 5.4 and 5.9 ppm assignable to vinyl protons and a signal at 4.5 ppm assignable to CH proton Îą- to Br indicate that the polymerization was initiated with ABP. A broad signal at 7.26 ppm was assigned to aromatic protons (meta- and para-position) of styrene unit labeled as l and m, and at 6.75 ppm for ortho-proton labeled as n. The signals observed at 1.50 and 2.00 ppm were assigned for the CH 2 and CH protons of main chain of polystyrene labeled as h, j, g and i, respectively. 1

e b

Br e CH3

O c

A 7.5

6.5

5.5 a

c b

m, n

4.5

O d

3.5

h CH2

fCH 3

l m

2.5 i CH

1.5

j k CH2 CH n-1

0.5

n h, j

k g, i

4 B

7.5

6.5

5.5

4.5 ppm

3.5

2.5

1.5

0.5

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Figure 2: NMR of ABP and ABP end functional Polystyrene The 1H NMR spectra of the UBP initiator and the polystyrene obtained by UBP initiator were compared in Figure 4. In the 1H NMR spectrum of the polystyrene (Figure 3B), the signals observed at 5.6 and 6.3 ppm assignable to H2C=CH- protons (H-a and H-b), at 4.7 ppm for CH proton of α to Br (H-r) and at 4.0 ppm for H-l and H-k. These assignments indicate the presence of undecenyl group in the polymer. Two broad signals observed at 7.28 ppm assignable to aromatic protons H-t and H-u (meta- and para-position) and at 6.75 ppm for ortho-proton H-s of styrene unit. The signals observed at 1.50 and 2.00 ppm assignable to CH 2 and CH protons (H-o and H-p) of main chain of polystyrene. b

m CH3

d-i

O m

l k

j A

7.5 a

6.5 g

c d

5.5

4.5

i h

3.5

2.5

o CH2

m CH3

p CH

1.5 q r CH2 CH n-1

0.5

s t

t, u

u o, q

l, k r

5.5

n, p 4.5

3.5 B

7.5

6.5

5.5

4.5

3.5

2.5

1.5

0.5

ppm

Figure 3: NMR of UBP and UBP end functional Polystyrene IV. Conclusion Allyl bromopropionate (ABP) and undecenyl bromopropionate (UBP) were used as initiator for styrene polymerization by Cu-bipyridene mediated atom transfer radical polymerization. The yield and the molecular weight of the polymer obtained by UBP system was higher than the polymer obtained by ABP system. The free radical formed from longer spacer containing UBP system is assumed to be more stable than the free radical formed from the shorter spacer containing ABP system. The CH2=CH- end-functional polystyrenes were obtained from both catalysts system. V. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

Wang, J. S.; Matyjaszewski, K., Controlled/"living" radical polymerization. atom transfer radical polymerization in the presence of transition-metal complexes, J. Am. Chem. Soc. 1995, 117, 5614-5615. Matyjaszewski, K.; Xia, Atom Transfer Radical Polymerization, J. Chem. Rev. 2001, 101, 2921-2990. Coessens, V.; Pintauer, T.; Matyjaszewski, K. Functional polymers by atom transfer radical polymerization, Prog. Polym. Sci. 2001, 26, 337. Kamigaito, M.; Ando, T.; Sawamoto, M. Metal-Catalyzed Living Radical Polymerization, Chem. Rev. 2001, 101, 3689 Matyjaszewski, K.; Coessens, V.; Nakagawa, Y.; Xia, J.; Qiu, J.; Gaynor, S.; Coca, S.; Jasieczek, C. ACS Symp. Ser. 1998, 704, Sarbu, T.L.K.-Y.; Ell, J.; Siegwart, D.J.; Spanswick, J.; Matyjaszewski, K. Polystyrene with Designed Molecular Weight Distribution by Atom Transfer Radical Coupling, Macromolecules 2004, 37, 3120 Yurteri, S.; Cianga, I.; Yagci, Y. Synthesis and Characterization of α,ω-Telechelic Polymers by Atom Transfer Radical Polymerization and Coupling Processes, Macromol. Chem. Phys. 2003, 204, 1771–1783. Otazaghine, B.; David, G.; Boutevin, B.; Robin, J.J.; Matyjaszewski, K. Synthesis of Telechelic Oligomers via Atom Transfer Radical Polymerization, 1, Macromol. Chem. Phys. 2004, 205, 154 Matyjaszewski, K. Atom Transfer Radical Polymerization (ATRP): Current Status and Future Perspectives, Macromolecules 2012, 45, 4015 Haddleton, D. M.; Waterson, C.; Derrick, P. Monohydroxy terminally functionalised poly(methyl methacrylate) from atom transfer radical polymerization, J. Chem. Commun. 1997, 683. Coessens, V.; Matyjaszewski, K. Synthesis of polymers with hydroxyl end groups by atom transfer radical polymerization, Macromol. Rapid. Commun. 1999, 20, 127. Paul A. Gurra, Martin F. Millsb, Greg G. Qiaoa,, David H. Solomona, Initiator efficiency in ATRP: the tosyl chloride/CuBr/PMDETA system, Polymer, Volume 46, Issue 7, 10 March 2005, Pages 2097–2104. Michael R. Tomlinson , Kirill Efimenko , and Jan Genzer, Study of Kinetics and Macroinitiator Efficiency in Surface-Initiated

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The Influences of Surface Area on the Efficiency of a New Design of Solar Collector Suitable for Basrah City 30.50 N Kawther K. Mankhi; Noori H.N. Al-Hashimi; Jassim M. Al-Asadi Department of Physics, College of Education for pure science, University of Basra, Basra, IRAQ Abstract: In this work we try to acquire benefit of the climate behavior in Basra city 30.5 0N south of Iraq to design a new system of solar water heating. Two differences solar collectors are designs which are almost symmetrical in everything, with one main exception that is the dissimilarity in green house of the collectors. The dimension of the green house of first collector is (112 cm, 62 cm, 12 cm), whereas the dimension of the second green house is (112 cm, 27 cm, 12 cm). All data is recorded, during daylight hours between (8:00Am-13:00Pm). Experimental data from these solar collectors have been analysis. The result shows that the first collector is more efficiencies than the second collector, but the second collector are more suitable for our purpose. Keywords: Optical efficiency; ICSSWH; Thermal performance; Technological development I. Introduction The device which is used to transform solar energy to heat is referred to as solar collector or SC. Depending on the temperatures gained by them, SC can be divided into low, middle and high temperature systems. Midtemperature systems are applicable for refrigeration systems and industrial processes. Numerous theoretical and experimental research of solar collectors for the mid-temperature conversion of solar radiation into heat via a liquid water as a working fluid have been conducted see for example [1-5]. Flat plate solar collectors form a subset of devices that are sometime used to convert solar energy into heat and, as such, can be deployed for duties such as domestic water heating. The traditional flat plate solar collector consists of number of important functional components fabricated as a ‘sandwich’. The integral collector/storage solar water heater (ICSSWH) is quite possibly the most well-known and simplest solar water heating system. It's developed from early systems often known as the "bread box" system; it was originally produced in the 1970's but is still in use now. It is simple, efficient and cheap to build. You simply paint a tank black, put it in a big crate, and insulate it all around except one side that needs to be covered by glass or plastic. To be viable economically, the system has evolved to incorporate new and novel methods of maximizing solar radiation collection whilst minimizing thermal loss. All it takes is a tank, insulation and sun. The water is collected, stored and warmed all in one container. Advances in ICS vessel design have included glazing system, methods of insulation, reflector configurations, use of evacuation, internal and external baffles and phase change materials.[6]. To understand the basic theory of the solar collector one should refers to the works of Hottel and Woertz [7-9], Bando[10], Hatfield [11], Zomorodian [12]. Fechner [13]. Minnerly [14], Bohn[15] and Duffie [16]. The advantages to the integral collector/storage system are low cost, no pumps or controls Simple, and Long-lasting. The large size of the tank helps to avoid freezing problems often seen in thermo-siphon units. The disadvantages are water doesn't get really hot, heat loss from the collector, and discontinuity of the optimal use of the hot water produced. In this paper we try to build an integral collector solar heater suitable for Basra city 30.5N south of Iraq. Our model has been tested in winter days where the outside temperatures fall below 50 C and it seem working wall. To generalized our model, a theoretical analysis has been carried out which allow us to predict the amount of hot water needed ant its temperature. The theoretical model shows a good agreement with our experimental data. II. Experiments Requirement We made some factors be fixed during all steps of this work, as follows: 1. Place of study: the experimental setup and carried out on the roof of a building of the department of physics, college of education for pure science at Basrah University. Basrah city located in southern of Iraq (latitude 30 ° 33 '56.55 "N, longitude 47 ° 45' 5.86" E). 2. Duration of record data: The data is recorded, during daylight hours between (8:00Am-2:00Pm). 3. Cold water tank: it made of galvanized iron material with (90 liter) capacity. 4. Inclination angle of the collectors: The angle of inclination of the collectors are 450 .

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5. The direction of the collectors: All the collectors are installed 450 south–easts, in order to receive a large amount of solar radiation. 6. Fitting pipe: The pipes diameter is (1.25cm). The following materials were used in the manufacture of solar collectors: 1. Iron galvanized materials which are available in the local market to fabricate the tube. This is the main component in all models of solar collectors used in this work as well as for the manufacture of cold water tank. 2. Wooden staves used to make the rectangles "greenhouse" that reserved all components of the solar collector inside it. 3. Glass plates with a thickness (4 mm) used to make the cover of the front side of greenhouse; the glasses are manufactured in Iraq and available in local markets. 4. Rubber silicon which is inorganic polymer compound used to fix the glass plates tightly so as to avoid the air circulation to and out of the greenhouse. 5. Sheets of copper used as a good reflective material to reflect the solar radiation. 6. Pieces of glass wool thickness (7 cm), a thermally insulating material to minimize heat losses out of the greenhouse. 7. Black stain used to paint the main tubes. 8. Transparent plastic tube in different lengths is used for draining and supply water. 9. Water taps to control the amount of the input and output water of the solar collector . III. Theory To allow the solar radiation transmitted through the glazing propagates onto the absorber plate. The absorber plate must absorb an optimum amount of solar radiation whilst minimizing thermal re-radiation. Typically, the absorber is painted with black. Based on this experiment set up the general conservation equation of this model can be written as : +S (1) This equation is one dimensional unsteady heat equation with source term S, ρ is the density , c is the specific heat, and k stand for conductivity. For convenient we shall assume the terms ρc and k are constant. According to the above set up this equation seem to be one way coordinates in term of spatial and time coordinates, one can obtained the solution by marching in time from a given initial distribution of the temperature. The discretization equation can be derived by integrating equation (1) over a convenient control volume and over the time interval from t to t + ∆t thus: (2) where the order of integrations are chosen according to the nature of the term. By employing the CrankNicolson scheme, the result is (3) For first approximation assume , , (4) The solution shown in Eq. (3) and the linearization used in this solution "the nonlinearity in this set of equations results from the quadratic temperature terms" allowed this problem to formulated as a linear system of algebraic equations, shown in vector form in Eq. (5); the solution to each iteration of this linear system therefore became the solution to a matrix inversion problem: T = M-1C (5) Here M is a square matrix, which contains all the coefficients of the temperature dependencies that result from the energy balance equations. The boundary conditions for the solution domain were specified temperatures at the solar collector inlets and out let only. The initial condition for the temperature field was that all nodes were specified to be a known, and uniform, temperature. . IV. Result and Discussion In this work we design laboratory models of two sympathetic of solar collectors shown in figure (1). The common properties of these collectors are: 1) They consist both of collector tube made from iron with dimension of 80 cm in length and 5 cm in diameter. 2) The total amount of water inside the tube is 1570 cm3 3) The tube is closed tightly from both ends. 4) The collector tube is fitted 5 cm away from the upper and lower basses.

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5) The inlet and outlet pipes are fitted in opposite side of the green house with 25 cm in length and 1,25 cm in diameters. The inlet cold water enters the collector from bottom and exit from top of the collector. 6) A copper plate was used as reflector material for solar radiation. The main differences of the our two models are 1) The dimensions of the green house of the first model are (62 cm, 12 cm, and 112 cm) as shown in figure (1a), whereas the dimensions of the second model are (27 cm, 12 cm, and 112 cm) figure (1b). 2) The dimensions of the reflector of the first model are (50 cm, 95.5 cm), whereas the dimensions of the reflector plate for the second model is (25 cm, 95.5 cm). In order to preserve the thermal energy from losses from the hot space inside the green house to the cold weather outside through its sides and its lower base, we have situate an glass wool layer as a thermal insulation material inside the house specifically on its sides and lower base; The insulation was used adjacent to the black paint and the reflector material to enriched the collector efficiency . The real setup of our two laboratories model is shown in figures (2). Table (1) shows the recorded data on Sunday (8-12-2013), the weather that day was totally clear, the recorded data started at 8:00 am and end at 13:00 pm. It was found that model no. (1) is more effective than model No.(2). The highest temperatures were recorded at 13:00 pm by model No. (1) is 89.50, and by model No.(2) is 800. On Monday (9-12-2013) the weather was totally cloudy and is a chance to test our two models. The performance of our two models was dropped dramatically as shown in table (2). The highest temperature recorded is 290 for the collector No. (1) at 13:00 pm, and the highest temperature recorded for collector No.(2), was 260. The temperatures shown in table (1) and table (2) are as follow: Tair represents the air temperatures outside the collectors; T cw represents the cold water temperatures; T1 and T2 represent the temperature of collectors No.(1) and No.(2) respectively; Î&#x201D;T 1-cw and Î&#x201D;T2-cw represent the gain in temperatures for the two collectors respectively. Figure (3a) shows the temperatures as a function of daylight hours for totally clear day, whereas figure (3b) plotted for totally cloud day. Figures (4) show the amount of gain in temperature for the two collectors aimed at totally clear and totally cloud day. To explain why collector no.(1) is more effective than collector no.(2), one can be argued that the relatively large space of greenhouse means that there is more air molecules in space of model no.(1); and when the air molecules increase, the amount of radiation absorbed by these molecules increases too, and the amounts of radiation reflected by these molecules are increase also in the space of greenhouse. As a conclusion one can recommend model no.(2) as a heat water suppliers for the domestic use although that model no.(1) is more efficient than model no.(2) because the fitted space of model no.(2) about half that for model no.(1). And for multiple component used of model no.(2) one can supply more hot water by using this model for same fitting space.

. Figure (1) Schematic diagram of Model No. (1a)

Figure (1) Schematic diagramof Model No. (1b)

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Figure (1) Real of Model No. (2a)

Table (1) Temperatures at Sunday on (8/12/2013) the weather are clear.

Figure (1) Real of Model No. (2b)

Table (2) Temperatures at Monday on (9/12/2013) the weather are Totally Cloudy.

Figure (3a) Temperatures at Sunday on (8/12/2013) Figure (3b) Temperatures at Monday on (9/12/2013) The Sky Are Totally Clear. The Sky Are Totally Cloudy

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Figure (4a) The Gain in Temperatures at Sunday on (8/12/2013) The Sky Are Totally Clear. V. [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16]

Figure (4b) The Gain in Temperatures at Monday on (9/12/2013) The Sky Are Totally Cloudy. References

Stefanović, V., Bojić, M.: "The model of Solar receiver for middle temperature conversion of solar radiation in heat"; Thermal Science, Vol.10, (2006), No. 4, pp. 177-187. Tchinda R., Kaptouom E., Njomo D., "Study of the C.P.C. collector"; thermal behavior, Energy Conversion and. Management Vol. 39, (1998), No. 13, pp. 1395-1406. Norton B., Kothdiwala A. F. and Eames P. C., "Effect of Inclination on the Performance of CPC Solar Energy Collectors", Renewable Energy, Vol.5, (1994), Part I, pp. 357-367. Akhtar N., Mullick S.C., "Computation of glass-cover temperatures and top heat loss coefficient of flat-plate solar collectors with double glazing", Energy 32 (2007) 1067–1074. Orel Z. C, Gunde M.K., Hutchins M.G., "Spectrally selective solar absorbers in different non-black colors", Solar Energy Materials and Solar Cells 85 (2005) 41–50. Smyth M. , Eames P.C. , Norton B.; "Integrated collector storage solar water heaters"; Volume 10 (2006), Issue 6, PP 503–538 Mankhi K. ; Al-Hashimi N. H.N.; Al-Asadi J. M.; "New Laboratories Design of Solar Collectors Suitable for Basrah City 30.50 N"; IOSR Journal of Engineering (IOSRJEN); Vol. 04, (2014) Issue 12, PP 15-19 Badran, A.A., Al-Hallaq, A.A., Eyal S. I.A., Odat, M.Z., "A solar still augmented with a flat-plate collector", Desalinisation, vol. 172, (2005) ; pp. 227-234. Mills D.R., Morrison G.L., "Compact linear Fresnel reflector solar thermal power plants", Solar Energy 68 (2000) 263–283. Bando T., Nishimura M., Kuraishi M., Kasnga T., and Hasatani M.. "optical depth on outdoor performance of volume heat trap type solar collector"; Heat Transfer. Japanese Research, V 15 (1986); PP 57- 71. Hatfield, D. W.,and n. K. Edwards.. "Effects of wall radiation and conduction on the stability of a fluid in a finite slot heated from below". lnt. J. Heat Mass Transfer 25 (1982): PP 1363-1376. Zomorodian, A. & Barati, M.; "Efficient solar air heater with perforated absorber for crop drying"; J. Agr. Sci. Tech, 12 ((2010), PP 569-577. Fechner, H. & Bucek, O. "Investigations on several series produced collectors"; Renewable Energy, V 28 (1998); PP 293-302. Minnerly, B.V.V., Klein, S.A., Beckman, W.A; "A rating procedure for solar domestic hot water systems based on ASHRAE-95 test results". Solar Energy, Vol. 47(1991), No. 6, pp. 405-411. Bohn, M. S.; "Direct absorption receiver"; Proc, Solar Thermal Research Program Annual Conference SER I/CP-25 1-2680, DE85002938, (1985); pp, 89-96 Duffie, J.A., Beckman, W.A.; "Solar Engineering of Thermal Processes"; (1991) New York: John Wiley & Sons.

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Inertial Effects on Hydrodynamic Convection in a Passive Mushy Layer Prof. Dr. P.K.Srimani R & D Director (DSI), Former Chairman, B.U. Bangalore, Karnataka, INDIA Mr. R.Parthasarathi Professor and HOD, Dept. Mathematics. Jain University-CMS, Bangalore, Karnataka, INDIA Abstract: This paper deals with the analytical investigation of convection in a passive mushy layer under the influence of inertial effects, formed during the solidification of a binary alloy melt. The mush-solid interface is at a eutectic temperature while the mush-liquid interface is impervious. Analytical solution for the basic state, zeroth and first-order systems are determined by a perturbation technique through normal mode approach. Higher - order solutions are obtained by applying the solvability conditions. The profiles of total Rayleigh number, vertical velocity, temperature and local solid - fraction are presented for the case of constant permeability. It is found that the inertial effect has a stabilising influence in the convection in a mushy layer, which in turn facilitates the suppressions of chimney formation which has catastrophic effects on the internal structure and the quality of the solid formed. The present analytical results are extremely sensitive to the far-field temperature and are in excellent agreement with the available numerical results in the limiting cases. Keywords: Inertial effects, Mushy layer, Chimney, Passive layer, Dendrites, far field temperature. I.Introduction During the solidification process of aqueous solutions, metallic alloys and magmas the interface between the liquid and the solid becomes dendritic due to the morphological instability. Thus a mushy region consisting of a partially solidified melt and the fluid, will be formed and at the transition point, convection prevails. The same phenomenon will be observed even in the case of pure materials when they are cooled to a Meta stable state which is much below its melting temperature. The interesting phenomenon that occurs within the solidifying melt is the formation of chimneys which are narrow dendritic-free cylindrical regions or narrow cylindrical region of zero solid fractions and are very much similar to the imperfections called ‘freckles’ that appear in the casting of metallic alloys[1][2]. During the evolution of the solidification process three stages viz., finger, plume and chimney convections are observed[3][4]. The formation of chimneys during the solidification of a binary or a multicomponent alloy constitutes the following evolution procedure. Actually, during the solidification process the solidification front or the interface between the solid and the liquid becomes highly dendritic due to the morphological instability. As a consequence there will be a formation of region called ‘ mushy layer’ consisting of a partially solidified melt, the dendritic structure of which is quite complex[5]. Then the system becomes unstable due to the density gradient that results from the rejected materials and there will be a transition to convection. Further, as a result of the interaction of the thermal fields and the generated convective motions, chimneys which are responsible for the imperfections in the resulting solid will be formed [1][6][7][8][9]. Especially in metallurgy, dynamics of sea and geophysics, the mechanism and the process of formation of chimneys which spoil the quality, physical properties and the internal structure of the resulting solid, are important study areas[10][11][12]. In the past three decades the study pertaining to the development of different convective models and analysis for the case of convection in mushy layers has attracted researchers [13][8]. The works connected with the formulation of the governing equations in the study of convection in mush layers, the development of mathematical models and the solution procedure are available[14][15] . Linear and weakly nonlinear convective instability in a mushy layer has been studied by quite a number of researchers under different types of assumptions and approximations[15][6][16][17]18]19][20]. Quite a number of works on convective flow in a mushy layer is available. A detailed review on convection in mushy layers is given by[6][21]. Recently [22][23][24] have applied weakly nonlinear evolution approach to study two-

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dimensional convective motions in a mushy layer with impermeable solidification front under different situations.Finally[25] have studied numerically the effects of inertia on convection in a mushy layer with constant permeability. The main objectives behind these studies are to study the history of the solidification process which is responsible for the formation of the pure solid and to suppress the formation of freckles which have catastrophic effects on the internal structure of the resulting solid. A thorough survey of the literature pertaining to the subject reveals that no analytical work is available for the case of convection in a mushy layer with and without constraints. Therefore the present analytical study is made to study the effects of inertia on convection in a mushy layer under near eutectic temperature, large far-field temperature and latent heat. The boundaries are assumed to be impervious so that the Darcy’s equation is valid. Marginal stability curves and the profiles of the total vertical velocity, temperature and local solid fraction are presented for the experimental values of the governing parameters. II. Mathematical formulation The physical configuration consists of a horizontal mushy layer formed during the solidification of a binary alloy as shown in fig 1. The process of uniform cooling from below of the system results in the upward advancement of the solid – mush interface with a constant solidification speed V0 .In other words, the mushy layer is sandwiched between the solid and the liquid regions. The study is carried out in a moving frame of reference.

Figure 1. The schematic diagram of the physical system Following are the assumptions made for the study: i. The top and the bottom boundaries of the mushy layerare assumed to be isothermal, non-deformable and impermeable to the fluid flow, so that the mushy layer is kept dynamically isolated from the other components of the system [17]. ii. The solidification front is moving upwards with a velocity V0 relative to the solid formed and the solid dendrites within the mushy layer. This makes the basic state to be steady. iii. The temperature T and the composition C of the liquid in the mushy layer are required to satisfy a linear liquidus relationship T = T0(C0) +  (C – C0), where  is a constant. The liquid is assumed to be Newtonian with a linearized equation of state = 0[1 + (C- C0)] where is the density of the liquid andρ0is a reference density, = *– α *  , α*and *are constant exponent coefficients for heat and solute respectively. iv. First, following [17] we study in the limit in which the thickness of the mushy layer is much less than the diffusion length scale by letting 1. v. However that a key implication of the near-eutectic approximations C= O( δ-1 ) is that the solid fraction is small and hence the permeability is uniform to the lowest order. As a consequence, we follow[17] and expand the permeability in terms of the small solid fraction : K( Φ ) = 1+ K1( Φ ) + K 2( Φ 2 ) + K3( Φ 3 )+----------Where on physical grounds, we demand that K 1, K 2 K3,….etc arenon-negative. Under the above assumptions and approximations the governing equations of the system are Conservation of momentum, Conservation of mass, Conservation of heat and solute :

1 q  q+ (∂t+ ) q = -∇p – ( 1  1 

 k

(1)

∇. q = 0 (2) + ( q .∇) T = k ∇2 T +



lh 

+ ( q .∇) C =(C – C0)

(3 (4)

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where = 1-

 : Local solid fraction,  – Local liquid fraction, P – Dynamic pressure, =

function of the local solid fraction ,  :Dynamic viscosity, t, T, k,

lh , 

, permeability is a

are time, temperature, thermal

diffusivity, specific heat, latent heat/unit mass. ,Cs: Composition of the solid phase ,C0: Composition of the liquid phase , ,

densities , g = (0,0,g) acceleration due to gravity.,

: The reference permeability., q =

u i + vj +w k ,d: the mushy layer thickness, q is the Darcy velocity vector and (u,v,w) are the horizontal and vertical components of q i, j, k : unit vectors along the x, y and z axes. , The boundary conditions are: T = Te, w =0 on z =0, T = T0, w = 0, Φ =0 on z = d. (5) Here T0 is the temperature at the mush-liquid interface (z = d) and Ts and Cs are the eutectic temperature and concentration at the mush-solid interface (z =0). The dimensionless equations using the scales mentioned below are: Kq +

1

[(∂t − ∂z) + (

 q.



) q = -∇ P – R.Ɵ. kˆ

(∂t − ∂z)(θ−SΦ)+ ( q .∇) θ = ∇ θ,

 (∂t − ∂z) ((1 − Φ) θ + C Φ)+ ( q ·∇ )θ =0, ∇. q = 0 In this paper the case of constant permeability i.e., K =1 is studied. The scales used for the non- dimensionalisation of the governing equations are   q = V0 .. q *(velocity) (x,y,z) = t=

(6) (7) (8) (9)

(x*,y*,z*) (length)

t* (time)

p*(pressure)

K= (10) The dimensionless parameters appearing in the problem are: R=

(Rayleigh number)

is the volume expansion coefficient of the combined heat and solute[6]. S= : Stefan Number –

C= I*=

: Concentration Ratio : Inertia parameter

where = T0 – Te, = C0 – Ce, (0) is the reference of T : Time variable Φ : Local solid fraction : Kinematic viscosity V 0 : Velocity of the solidification front

θ=

(11)

(The non-dimensional temperature or composition)

The dimensionless boundary conditions are: The non-dimensional boundary conditions at the upper boundary z = d correspond to an impermeable (rigid) flat boundary with zero solid fraction (i.e., =0).Thus we have θ = -1, w = 0 @ z = 0 θ = 0, = 0, w = 0 @ z = δ (12) where δ = is the growth peclet number and also the dimensionless thickness of the mushy layer

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III. Method of solution The method of solution constitutes two stages viz., basic state analysis and linear stability analysis. In the case of variable permeability K is a function porosity or the local solid fraction and is expressed as K(x,y,z,t) = which is similar to the Kozney - Carman relation[6]. The case of constant permeability corresponds to the decoupling of permeability and porosity. In that case ) = [1- (x,y,z,t)] n , n = 0 (13) As discussed earlier, the physical configuration is such that it consists of a mushy layer. Te is the eutectic temperature at which the lower mush – solid interface is maintained in which T∞ is the temperature of the liquid far above the mushy layer. Further T 0(C0) is the liquidus temperature of the alloy such that T ∞>T0(C0) and the mushy layer is assumed to be in a state of thermodynamic equilibrium so that T = T0(C0) + (C – C0) (14) In this work the attention is focused on the prediction of inertial effects on convection in a mushy layer with constant permeability IV. Basic state analysis  The basic state corresponds to the steady motionless state in which q = 0 and = 0. Thus we have the following set of equations : Conservation of solute: (1 – b) D θb+ D b (C - θb) = 0 (15) Conservation of heat : D 2 θb+ D θb– S D b = 0 (16) Conservation of momentum – k.θb= 0 (17) The boundary conditions are θb = -1 @ z = 0 θb = θ∞, b = 0 @ z =δ (18) where θ∞ is the far-field temperature Here we take b= δ b0 , θb= θb0 (19) Substituting (19) in (15) and (16) we get (1 - δ b0) D θb0 + δ D b0 ( C - θb0) = 0 (20) D 2 θb0 + D θb0 – δ S.D b0 = 0 (21) Collecting the terms of O (δ0) and O(δ) from (20) and (21) and solving the differential equations by using (18), we get the basic state solutions as θb0 = (- 1 + e θ∞) - e θ∞. (22a) Or θb0 - 1 + e θ∞ - e θ∞.(1+z) θb = θb0 = - 1 + e.θ∞ z Finally, we can write θb = C1 + C2 Z and = C3 + C4 Z (22b) ∞ where C1 = -1, C2 = , C4 = ∞ C3 = V. Linear stability analysis As discussed earlier the analysis consists of two stages viz., the basic state analysis and the linear stability analysis. For this purpose, we consider the expansion of the dependent variable Ɵ and Φ as w(x,y,z,t) = 0 + wˆ (x,y,z,t), wˆ = (w00+ I* w01) θ(x,y,z,t) = θb + ˆ (x,y,z,t), ˆ = (θ00+ I* θ01) Φ(x,y,z,t) = Φb + R= R00 + I* R01

ˆ

ˆ =(Φ00+ I* Φ01) (x,y,z,t) , 

wˆ , ˆ

ˆ

(23)

Where perturbation parameter with << 1 and and are the perturbed quantities, which are expanded in terms of a small parameter I*. Here k is the horizontal component of the wave number α and σ is the growth rate of the disturbance. Further W00, W01, θ00, θ01, Φ00, Φ01 are purely functions of z. The basic state analysis has been performed in sec 4. Next from equations (6) to (9) and (23), the localized perturbed system is given by

 q +∇ pˆ

+ R ˆkˆ +



[(∂t - ∂z )] q = 0

(24)

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(∂t - ∂z -∇ ) ˆ - S(∂t - ∂z ) ˆ +

wˆ Dθb = 0

(∂t - ∂z) [(θb - C) Φ - (1 - Φb ) ˆ ]-

(25)

wˆ Dθb = 0

(26)

Together with the boundary conditions (27) to (28) The upper boundary i.e., mush-liquid interface is assumed to be flat and impermeable with zero solid fraction and whose temperature is equivalent to the liquidus temperature of the mushy layer as discussed earlier. Further the continuity of heat flux at the boundary is also assumed in order to facilitate the solution process. Mathematically, these are expressed as ˆ = ˆ = 0 @ z = 0 w

wˆ = ˆ

(27)

= ˆ =0 @ z = δ

(28)

All the quantities have their predefined meanings (In all the future expressions, the ‘caps’ are dropped for the sake of simplicity). Next, we eliminate the pressure in (24),by applying curl twice and then consider the z – component of the equation for further analysis. In fact, applying the transformation [

] - ∇1 2(kth)

(29)

on the momentum equation (in the component form) is same as that of applying curl twice and consider the zcomponent of the result. Now by using the following result, the resulting equations are obtained: [ ∂t - ∂z] qˆ ) =-

∇x∇x( where D =

[(∂t - ∂z ) ∇2 w +

(∂t - ∂z)Dw]

(29a)

.We write the resulting perturbed system as

∇2 w + R ∇12 θ +

(∂t - ∂z)Dw + (∂t - ∂z )∇2w ] =0

[

(∂t - ∂z+ ∇2) θ – S((∂t - ∂z) - (1-

)θ]+D

(30)

w=0

(31)

Now by using the expansion (23) through the normal mode approach for the physical variables, we write the system of order (I*) 0 as (D2 – α2) W00 – R00 α2θ00 = 0 (32) (D2 +D – α2 - σ)θ00 –S (D – σ) 00 – Dθb W00 = 0 (33) (D – σ) [(θb – C) 00 – θ00 ] + Dθb W00 = 0 (34) The above system can be expressed as L α00 = 0 where α00 = [w00, θ00,

(D 2 –  2 ) -D  b D b

00]

, T denotes the transpose and L is the linear operator given by

R 00  2  D – 2  

 D –   b - C   D b

–S



0 D – 

  D –  1  b   Db

(35) By letting Ɵ00 = -Sin z, The solutions for the system (32) to (34) are given by 00 = A1* Sin z + A2* Cos z +b1(z) 00 = A1Sin z + A2Cos z +b2(z) where b1(z) = A2*Z and b2(z) = A2 (2Z – 1) A1* = , A2* =

(36)

A1 =

( A2*(C+1))

A2 =

( + 1)(C+1)

For the marginal stability σ = 0 and by using the above results in (32), the expression for R00 is obtained: α

R00 = (37) α whereα is the wave number. Now in order to compute R = R00 + I* R01,we consider from (6) to (9) and (23), the system of order I* as

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(D2–α2)W01 – R00 α2θ01 = R01 α2θ00 2

[

(-D)Dw00 + (-D)∇2w00 ]

(38 )

(D +D – α - σ) θ01 –S( D – σ) 01 – Dθb W01 = 0 (39) (D – σ) [(θb – C) 01 – θ01] + D θb W01 = 0 (40) The above system in the matrix form is given by Tα01 = f(α00) (41) where α01 = [w01, θ01, 01 ] T By using the results of the zeroth- order system and the solvability condition, the inhomogeneous equation (38) is solved for R01 in which the inertial effects appear. Thus we have R01 =

[(1 + P2)(A12 + A22) - P2 A1A2 ] +

[

] (42)

where C4 = C2A1*,P1 = (1 – C3),P2 = The other quantities have their predefined meanings. The critical value αc corresponding to R00 is and R0C = 2A1. Marginal stability curves for R = R00+ I* R01 are presented in fig.2 for the experimental values of the parameters S= 3.2, C= 9, θ∞ = 0.6, 0.7, 0.8 and I* = 0.0, 0.05, 0.1.etc respectively. The results are in excellent agreement with the numerical results of [25]. In order to study the effects of inertia on the vertical component of velocity w, temperature θ and the solid fraction , the first order system O(I*) is solved by using the results of the basic state and the zeroth order system, with the computed value of R01 by solving the respective differential equations along with the boundary conditions. The following results are obtained: = C10Sin z + C11Cos z + b3(z) = C12Sin z + C13Cos z + b4(z) 01 = C15Sin z + C16Cos z + b5(z) 01 where

(43) (44) (45)

C5 = -α R01α

C6 = -[ C7 = -( C8 = -( C9 =

(SA2)

](SA1)

α )C5- (C+2)C6 α )C6 + (C+2)C5

C10 =

α α

C11 =

C12 = [ C13 = [

α α

C15 = C16 = b3(Z) = (2Z – 1)C11, b4(Z) = (2Z – 1)C13 b5= C16(Z) By using the above results the values of w = w00 + I* w01, = 00 + I* 01 =

+ I*

(46) (47) (48)

are computed and the profiles are presented in figs.(2-5) and table 1for the specified values of the parameters S, θ∞, C and I*. The graphs clearly indicate (i) the stabilizing effect of the inertial terms on the convection in a mushy layer with constant permeability. (ii) the decrease in the non-uniformity in the w – profile (vertical velocity) for the increase in I * and w is maximum at the middle of the layer and

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(iii) the increase in the non-uniformity in the profiles of the temperature as well as the solid fraction of the mushy layer for the increase in I * VI. Results and discussion As discussed earlier, the solution procedure consists of two stages. In the first stage, the basic state solutions is obtained by considering the steady motionless state and the corresponding solutions are functions of z only. These solutions are required for studying the linear stability analysis. In the second stage the perturbed system for zeroth and first order are considered along with the boundary conditions. To start with the solutions W00, θ00 and Φ00 are found by solving the linear differential equations and then finally R00and the critical wave number are determined. From the higher order system which is an inhomogeneous system, R01 is computed by applying the solvability conditions. (i) By setting σ =0, marginal stability results are determined analytically for the experimental values of the parameters[26], Stefan number S concentration ratio C, far field temperature and the expansion parameter I*. After computing R00 the analytical expression for R01 is derived. From the zeroth order system, the expressions for W00, θ00 and Φ00 are found analytically by using the basic state solutions. Then the Rayleigh number and the wave numbers are determined. The critical values of these numbers are computed. In the solution process, the far field temperature is considered by the condition D θb = @ z = δ (the top boundary). (ii) From the first order system R01 is determined by performing suitable operations on the first order system of equations, since it is an inhomogeneous system. In fact the effect of inertia appears only in the first order system as a part of the inhomogeneous term. We then compute total R = R00 +I* R01and present the graphs in figs (2 – 5) and table 1 for various specified values of the parameters S=3.2, C=9.0 and = 0.6, 0.7, 0.8 respectively. For the computation of total R the values of I* are taken as 0.0, 0.05 and 0.1 respectively. The results show that the marginal stability curves are extremely sensitive to the far-field temperature and the Concentration ratio. Also as increases R decreases there by indicating the destabilizing nature of The presence of inertia term increases the value of R as expected. Thus the chimney formation could be suppressed. (iii) The solutions for W01, θ01, Φ01are obtained by solving the first order system of equations O(I*). The profiles of W= W00 + I* W01, θ =θ00 + I* θ01, Φ = Φ00+I* Φ01 are drawn for I* =0.0, 0.05, 0.1 respectively.

TOTAL R

30 1.0

28 0.8

I* = .5

I* = .3 I* =.1

-----> z

26 0.6

0.4

0.2

0.0

1.7 2.5 3.1416 4 5 5.5

Fig.2 Total R vs α for I* = 0.5, 0.3 , 0.1

------> TOTAL W

Fig. 3 Total W vs Z for I* = 0.05, 0.1 , 0

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Fig. 4 Total Ɵ vs Z for I* = 0.05, 0.1 , 0

Fig. 5 Total Φ vs Z for I* = 0.05

TOTAL W = W00 + I* W01 I* = 0 I * =0.05 I* = .1 0.1 0.384936766 0.632774898 0.137098633 0.2 1.3026524 1.825030792 0.780274008 0.3 2.520699251 3.304705401 1.736693102 0.4 3.777231021 4.771601769 2.782860273 0.5 4.80663422 5.926773768 3.686494671 0.6 5.365528389 6.501789165 4.229267612 0.7 5.256589985 6.285005762 4.228174208 0.8 4.347867875 5.142288502 3.553447247 0.9 2.585699687 3.030139606 2.141259768 1 -3.53115E-05 -4.35405E-05 -2.70825E-05 Table 1: The effect of I* on w From fig.3,it is observed that the effect of increase in the inertia parameter I*, is to reduce the vertical component of the velocity W. Further, the non-uniformity of the velocity profile decreases as I* increases. Figs (3 - 5) indicate that the increase in the inertial effects increases the value of the temperature and solid fraction in the mushy zone. Accordingly the increase in the inertial effects increases the amount of non-uniformity in the temperature and the local solid fraction in the mushy layer by facilitating the suppression of chimney formation during the solidification process. The following are the important conclusions drawn from the present investigation: (i) Very sparse literature exists in the case of hydrodynamic convection in a mushy layer under different types of constraints. No analytical work is available with regard to mushy layer convection in the presence and absence of magnetic field. Therefore the present work is undertaken and the mathematical model is presented along with the necessary assumptions. (ii) The method of analysis constitutes the application of modified perturbation technique. The determination of the Rayleigh number is done by setting σ = 0 and the results are in excellent agreement with the available numerical results in the limiting cases although, the analytical procedure is quite tedious, the results are accurate and well justified. Z

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(iii)

The method is found to be very elegant and effective in predicting the effect of the inertial terms on the convection in a mushy layer. The main objective of all research workers is to provide a suitable mathematical model which can predict the conditions under which the formation of freckles could be avoided during the solidification process of a binary alloy. This in turn facilitates the non-formation of chimneys that is formed due to the morphological instabilities at the interface of the mushy layer and the solid phase. Through the analytical approach it is shown that the complete solution of the basic zeroth and the first order systems could be accurately determined. This has not been undertaken in any of the available literature and hence the present work is first of its kind. Finally it is concluded that the introduction of the inertial effects suppresses the formation of freckles and increases the amount of non-uniformity present in the temperature and solid fraction of the mush- layer. Therefore through the proper choice of the governing parameters it is possible to have a complete control over the formation of chimneys during the solidification process of a binary alloy.

[1]

S.M.Copley, A.F.Giamei, S.M.Johnson, and M.F.Hornbecker, The origin of freckles in uni-directionally solidified castings, netall.mater.Trans.1, 1970, 2193-2204. J.R.Sarazin, and A.Hellawell, Channel formation in Pb-Sn, Pb-Sb, and Pb-Sn-Sb alloy ingots and comparison with the system NH4CL –H20. Metall.Trans.19A, 1988, 1861-1871. C.F.Chen, and F.Chen, Experimental study of directional solidification of aqueous ammonium chloride solution, J. Fluid Mech.227, 1991, 567-586. S.Tait, C.Jaupart, Compositional convection in a reactive crystalline mush and melt differentiation, J.Geophys. Res. 97, 1992, 6735 – 6756. W.W.Mullins, and R.F.Sekerka, Stabiliy of a planar interface during solidification of a binary alloy, J.Appl.Phys.35,1964,444451. M.G.Worster, Instabilities of the liquid and the mushy regions during solidification of alloys, J.Fluid Mech.237, 1992, 649-669. T.P. Schulze, and M.G .Worster, A numerical investigation of steady convection in mushy layers during the directional solidification of binary alloys, J.Fluid Mech. 356,1998,199-220. T.P. Schulze, and M.G .Worster, Week convection, liquid inclusions and the formation of chimneys in mushy layers, J.Fluid Mech. 388,1999,197-215. C. A Chung, and M.G.Worster, Steady-state chimneys in a mushy layer, J.Fluid Mech. 455, 2002, 387-411. J.S. Wettlaufer, M.G.Worster, H.E..Huppert, The evolution of sea ice : solute trapping and brine-channel formation, J.Fluid Mech.344,1997,291-316. H.E.Huppert, and M.G. Worster, Dynamic solidification of a binary melt, Nature 314, 1985,703-707. M. G. Worster, Solidification of fluids. In Perspectives in fluid dynamics,(ed.G.K.Batchelor,H.K Moffatt, and M. G. Worster),2000,393-446, Camb.Univ.Press. C.Beckermann, and C.Y.Wang, Multiface/Scale modelling of alloy solidification, Ann.Rev.Heat Transfer, 6, 1995,115-198. R.N.Hills, D.E.Loper, and P.H.Roberts, A thermodynamically consistent model of a mushy zone,Q.J.Mech.Appl.Maths, 36,1983,505-539. A.C.Fowler, The formation of freckles in binary alloys, IMA J.Appl.Math. 35, 1985 159-174. C. A Chung, and F.Chen, Onset of plume convection in mushy layers, J.Fluid Mech, 408, 2000, 53-82. G.Amberg, and G.M.Homsy, Nonlinear analysis of buoyant convection in binary Solidification with application to channel formation, J. Fluid Mech.252, 1993,79-98. D.M.Anderson, and M.G. Worster, A new oscillatory instability in a mushy layer during the solidification of binary alloys, J. Fluid Mech.307, 1996, 245-267. D.M.Anderson, and M.G. Worster, Weekly nonlinear analysis of convection in mushy layers during the solidification of binary alloys, J. Fluid Mech.302, 1995,307-331., S.M.Roper, S.H.Davis, and P.W.Voorhees, An analysis of convection in a mushy layer with a deformable permeable interface, J.Fluid Mech. 596,2008,333-352. M. G. Worster, Convection in mushy layers,Annu.Rev.Fluid Mech. 29,1997,91-122. D.N. Riahi, On nonlinear convection in mushy layers, Part 2. Mixed oscillatory and stationary modes of convection, J.Fluid Mech. 517,2004,71-102. D.N.Riahi, On nonlinear convection in mushy layers, Part 1. Oscillatory modes of convection, J.Fluid Mech. 467, 2012,331-359. D.N.Riahi, On three dimensional non-linear buoyant convection in ternary solidification, Transp.Porous Med.103, 2014, 249277. D.Bhatta, D.N.Riahi, and M.S.Muddumallappa, Inertial effect on convective flow in a passive mushy layer, J. Appl.Math. & Informatics, 30, 2012,499 – 510. C.F.Chen, Experimental study of convection in a mushy layer during directional solidification, J. Fluid Mech.293, 1995,81-98.

References [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26]

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American International Journal of Research in Science, Technology, Engineering & Mathematics

Available online at http://www.iasir.net

Initial Thermal Hydraulic Design of a 1000MWe Water Reactor with an Improved Thermal Efficiency C. E. Okon*1, D. E. Oku*2 School of Physics & Astronomy, University of Manchester, Manchester, UNITED KINGDOM. *2 Department of Physics, University of Calabar-NIGERIA,

Abstract: This design considers the thermal hydraulic processes involved in the transfer of power from the core to the secondary system of a PWR nuclear plant. In practice a net plant efficiency of around 32% 36% would be typical for a water-cooled reactor. I choose to operate with a much higher temperature (330oC) to see its effect on the reactor performance. This result to a higher thermal efficiency (about 45%) and core power (about 2218MWth). Safety margins and conditions have been considered and all thermodynamical parameters and heat transfer processes involved has been calculated accurately. Analytical approach of the general heat conduction equation in cylindrical coordinate is employed to solve the temperature distribution and heat flux and the calculated temperature distribution follows the expected decay pattern. The axial and radial profile is shown. I. Introduction The quest for nuclear technology lies on its importance in the generation of electricity. Although many reactors were built primarily to produce plutonium for nuclear weapons and for scientific research, they continue to be developed as power generators. They became economically viable in the 1960’s and today fission reactors are a major source of energy in many countries[6]. Most modern reactor designs capitalized on the good qualities of light water (H2O) as a moderator. Its excellent slowing-down properties and relatively low diffusion and slowing-down lengths (compared with carbon) enables the size of the core to be reduced considerably to one which is, typically, about 3m in height and diameter. However, enriched fuel is used to offset the reduction in thermal utilization factor f, due to greater tendency of water to absorb neutrons. In order to maintain its effectiveness a coolant, water must be prevented from boiling as far as reasonably possible and it is subjected to a very high pressure (about 150-160bar) to raise its boiling point. It is circulated through external heat exchangers, where steam for turbines is generated. Such a reactor using water as both moderator and coolant became my choice of reactor design. It uses enriched uranium dioxide fuel and zircaloy cladding. This reactor is cheaper to build and operate. Material specification for my chosen design can be found in Appendix. II. Aim & Objective The aim of this design is aim at improving upon the thermal efficiency of a Pressurized Water Reactor (PWR). The reason for my choice is because PWR is the only commercial nuclear power plant currently in operation in Africa, located in South Africa. Also the first commercial nuclear power plant expected to be built in my country “Nigeria” is likely to be PWR. This reactor is easy to build and manage, less expensive and there is much availability of the cooling source in my country. Besides all this attractive features, there is need to improve upon its thermal efficiency, because high thermal efficiency implies a lower cooling-water requirement. III. Methodology A. Power Conversion This design considers a steam power plant with two turbine operating on an ideal reheat-regenerative Rankine cycle with two open feedwater heater, one close feedwater heater and one reheater. It is assumed that the heat enters the turbine at 7MPa and 330oC and it is condensed in the condenser at 0.01MPa. The figure below shows the layout of the power conversion system. In order to calculate the thermal efficiency and the core power, certain assumptions were made  300oC and 330oC inlet and outlet temperatures respectively. By reviewing various articles and publications majority of PWR power plant operates at about 280oC and 330oC. The reason for my choice is to examine the effect on the reactor efficiency and performance.

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 Condenser pressure is chosen to be 0.01MPa because any value lower than this will result to air leakage into the condenser and moisture content of steam in the turbine [2].  The steam exiting the low turbine is assumed to have a quality of about 90% to avoid excessive wear of the turbine blades [2]

Also, the following thermodynamic analysis was carried out in order to ascertain the thermal efficiency and the thermal power output. The figure below shows the ideal circle for the layout above.  The enthalpy at various steps is calculated using the steam table.  The pressure at both turbines is calculated. The closed FWH and reheat loops are splitted after the high pressure (HP) turbine rather than bleeding off steam from the high pressure turbine. This enables a simpler and less costly turbine.  The two Open Feedwater Heater pressure is gotten by finding the bleeding saturated temperature using this relation T5 = T1 + 2/3(T7 – T1) and T3 = T1 + 1/3(T7-T1) and relating this temperature to pressure via the steam tables.  The operating pressure of the HP turbine and the Close Feedwater Heater is found by assuming that entropy S13 ≈ S16 and T13 ≈ T11  Between point 6 and 10, the diagram account for temperature change due to mixing. At this point the energy balance for the mixing is introduced in order to get the enthalpy h9. Hence T9<T8 Therefore, the efficiency from the thermodynamic analysis is found to be 45%. This percentage value has contributed a significant improvement on the current PWR operating all over the world offering between 32% 36% thermal efficiency.

B. Nominal and Hot Channel Description During Core Thermal Design, there are some safety factors that need to be considered in order to guide against the temperature of the fuel exceeding its theoretical set limit. Those safety factors are called the hot channel factors. In practice, however, local deviations in behavior from strict adherence to that predicted by theoretical correlations take place. These local deviations (aside from those due to the fuel acting as a sink and the moderator as the source of thermal neutrons are caused by factors, such as the presence of partially inserted control rods depressing thermal neutrons in their vicinity with consequence large peaking elsewhere, nonhomogeneity in the moderator (e.g., when in solid form or when boiling takes place) or fuel and the presence of other structural homogeneities [7].

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Also, the presence of a good, infinite reflector causes peaking of the thermal neutron flux at the ends of the reactor core and consequent high heat generation and high fuel temperatures there. The safety factor for flux distribution usually takes into account the above deviation as well as the deviation of the overall flux distribution from the core average. C. Power Peaking Factor (PPF) In this design, the most realistic PPF was found by looking at historical data cited from 1999 World Nuclear Industry Handbook. The average data of 2.25 for six different reactors is used [1]. D. Material Specification of the Design The material specifications for fuel, cladding and coolant is the same as those use for PWR reactors currently in operation although some values varies, they still operate within the safe margin and initial conditions. The system pressure is 155bar, Core Inlet Temperature is 300 oC, Core Outlet Temperature is 330oC and the bulk coolant temperature is 315oC. Other parameters for this design can be found in the Appendix. D.1 Fuel Centerline Temperature The theoretical temperature limit for this design is 2000 oC. The design specification is carried out in such a way that it cannot exceed this temperature margin. All initial and hot-spot factors are being considered in this design so that this safe margin is not exceeded. If the centerline temperature exceeds this set limit, the fuel will melt as a result of boiling. D.2 Rod Length The maximum rod length that could be use for this design is 6.6337m (if the length is greater than this set value, the surface heat flux will increase beyond the critical point resulting to boiling crisis). The minimum rod length is 1.70m (any value less than this will result to greater heat generation in the core above the theoretical set value of 2000oC) which could also cause the failure of the cladding material. My chosen rod length for this design is 3.5m. The main reason why I choose 3.5m wasn’t because most of the conventional reactors in operation were using this value, rather it is because I got an exact (not approximate) value for the total fuel length (189,189m). Using the rod with length 4m could also give an approximate value (216,216) but this could be more expensive to build considering the total numbers of rods involve. So low cost must also be put into consideration for any design proposed. IV. Result & Discussions A. Mass Flow Rate Now using the exit and inlet temperatures, 300 oC and 330oC respectively, the calculated core power of Q=2200MWth and the specific heat cP=6296.8 [J/kgK] we find:

If the mass rate of flow of the coolant and the coefficient of heat transfer are the same in all fuel channels, irrespective of their position in the core, the maximum fuel temperature of the fuel elements, Tm, vary accordingly. The fuel elements near the center of the core may have temperatures in excess of the safe limit, while those near the core boundaries operate much below this limiting temperature. For this design, my calculated and desired value for the coolant channel flow rate is 0.21720kg/s. B. Temperature Profile An analytical approach of the general heat conduction differential equation in cylindrical coordinate is employed to solve the temperature distribution and heat flux for the fuel element. In steady state operation of a reactor, the temperature distribution is determined by thermal balance between heat generated in the core and heat transferred to the coolant, since the neutron flux level is limitless. Provided that there is an adequate heat removal system and initial conditions are acknowledge, the temperature in the core will not exceed the specific safety limits, and damage to fuel pins and other reactor materials would be prevented. Calculated values for the temperature profiles for this design can be found in Table 1C of the appendix. Table 1A shows the DNBR profile for each node. While Table 1B shows the surface temperature for each node by taking into account the Power Peaking Factor (PPF) at each axial step. It can be seen from the table that the maximum clad surface temperature is ≈ 426oC using this equation

Plots showing the Axial temperature profile, Radial temperature profile, DNBR profile and Power Peaking Factor can be seen in Fig. 1A”1”, 1A”2”, Fig. 1B and Fig. 1C in the Appendix. C. Thermal Limits/Limiting Powers C.1 Variation Of Mass Flow Rate  Effect on CHF and Fuel Rod Surface Temperature In a nuclear reactor system the critical heat flux is the heat flux at which a boiling crisis occurs that causes an abrupt rise of the fuel rod surface temperature and, subsequently, a failure of the cladding material [8].

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It is well known that CHF is dependent on the geometrical conditions as well as the thermal-hydraulic conditions. Examples of operating conditions for water cooled reactors can be found from the table below. Table A: Examples of Operating Conditions of Different Reactors Pressure P (MPa) Avg. Mass Flux G (Mg/m2s) Avg. Outlet Steam Quality X, (-) Fuel Rod Diameter d (mm) Pitch of Diameter Ratio, (-)

PWR ≈15.7 ≈4.0 ≈-0.15 ≈9.5 ≈1.3

BWR ≈7.2 ≈3.0 ≈0.15 ≈12.3 ≈1.3

PHWR ≈10.5 ≈5.0 ≈0.0 ≈13.1 ≈1.15

WER ≈15.7 ≈4.0 ≈-0.15 ≈0.1 ≈1.4

Table A, gives just one example from each group. It should be kept in mind that for the same reactor group, e.g. PWR, the operating conditions may differ from one design to another and vary over a wide range except for the operating pressure. The CHF was calculated using the W-3 correlation developed by Tong [8]. According to my results in Table 3A and 3B (variation of mass flow rate) in the Appendix, it is seen that the CHF and Tmax is affected by channel flow rate. As you decrease the flow rate, there is a corresponding decrease in the HTC and G, which in turns causes an increase in CHF and Tmax. As the mass flow rate is decreased further below 0.0028tons/hr, the CHF remains massively high. This effect causes a rise in the fuel centerline temperature above the theoretical limiting value of 2000oC. At this point, boiling crisis occur which could result to a failure of the cladding material. The graph of Mdot Vs CHF, Mdot Vs Tmax, CHF Vs Tmax are plotted to explain this phenomenon, those graphs can be found in the Fig.3B 1, 2&3 of the Appendix.  Effect On Mass Flux G (Kg/M2s) For different designs of PWR’s the operating pressures only ranges from 15.0MPa to 16.0MPa. The mass flux changes more significantly. In a conventional PWR the mass flux is about 4Mg/m^2s, whereas in a highly conversional PWR a much higher mass flux (6Mg/m^2s) is required (Oldekop et al. 1982). By increasing the mass flow rate above 0.34tons/hr, it is observed that the mass flux exceed its maximum operating value of 4Mg/m2s( Limit) as stated above for a conventional PWR. Further increase above 0.57786tons/hr will result to an increasing value of mass flux beyond the limiting value (6Mg/m 2s) for the validity of the W-3 Correlation equation. The graph of Mdot Vs HTC, Mdot Vs G, are plotted to explain this phenomenon. See Fig.3A”2” and Fig. 3A”5” of Appendix.  Effect on DNBR Further increase above 12.593tons/hr could results to the melting of the core because the MDNBR limit of 1.3 would have been exceeded. The graph of Mdot Vs MDNBR is plotted to explain this phenomenon. See Fig.3A”6”of Appendix. The design of water cooled reactor requires a sufficient safety margin with regard to the critical heat flux, mass flux and DNBR in order to avoid boiling crisis and high void fraction in sub-channels. D. Variation of the Inlet Temperature The enthalpy of the coolant changes directly with the inlet temperature. To investigate further how inlet temperature affects the reactor design, it was necessary to vary the inlet temperature from 290oC to 299oC. In carrying out this analysis, there was no significant effect on factors which would have been considered to depend on the inlet conditions. Those factors are; the fuel centerline temperature, the mass flow rate and the heat transfer coefficient. Rather it was observed that the quality has an influence on the variation. As the inlet temperature is decreased below 300oC the quality also increases significantly. Different plots to show its effect are shown in Fig. 4a, 4b, 4c, 4d, and 4e of the Appendix. E. Variation of Rod Length In order to give justification to my desire rod length of 3.5m, I did some calculations of different parameters for different rod lengths to see the most realistic rod length value. From my calculated analysis, the following conclusions were drawn;  LONG ROD: increase in rod length result to increase in quality with a corresponding increase in the total number of rods and the heat generated in the core is minimized. Departure from nuclear boiling (DNB) occurs when the rod is increased above the maximum set value.  SHORT ROD: a shorter rod length result to a lesser number of rods. But at very shorter value below 2m, the centerline temperature increases above the theoretical set value of 2000oC. Therefore, the most realistic rod length for this design is 3.5m with quality of 10%. Different plots for the above explanation can be seen in Fig. 2A, 2B, 2C, and 2D. Table 2 of the Appendix shows the calculated parameters for different length values. F. Coolant Systems F.1 Circuit Pressure Drop (Assume a homogeneous model) Pumping-power requirements are determine by the pressure drop in the cooling system and the rate of flow of coolant. In homogenous model, the two phases are assumed to flow as a single phase possessing mean flow properties and a suitable single-phase friction factor is developed to represent the two phase flow [4]. Using the

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data for Pressure Drop Calculation in Appendix 1, it is observed that the inlet enthalpy is less than the saturation enthalpy and thus at the inlet we are dealing with a single phase flow, therefore, the exit enthalpy is gotten from the thermodynamics equation below. Assume power of 64000W.

From the above result, it is seen that the exit enthalpy is greater than the saturation enthalpy and thus somewhere in the pipe the flow changes from single phase to two phase. We know that the single flow part (or the region where h<hf) is some fraction of the total enthalpy, multiplying this fraction by the total length of the pipe will tell us the length of the pipe in single phase flow Lsp.

The pressure drop required to overcome friction for the turbulent flow of coolant in the channel between the fuel rods is given by [4]. ;

Where,

is the Single Phase Pressure drop,

(=1.3) is the correction for fluid flow in rod bundles [1] and

the friction factor for single phase flow is given as; For two phase calculation, the pressure drop equation is expressed as;

where;

is the friction multiplier factor and the exit specific volume is given as Hence, from my analysis the Total Pressure Drop “

” across the core is given as;

F.2 Pumping Power Many reactor parameters depend upon temperature. Reactor temperature, however, is usually a function of the operating power of the reactor, and changes in power level may lead to changes in the criticality of the system [1]. The pumping work required by the circulating coolant to overcome pressure losses through a complete loop (reactor, piping, heat exchangers, etc) with allowance for pump efficiency is gotten by

Where



is the efficiency (assumed 80%),

is the core mass-flow rate and average

flow density of the coolant respectively. With no allowances for efficiency, the pumping power required to circulate the coolant through the core is 2.86E+5W. G. Heat Removal (Steam Generator) A typical 1000MWe plant steam generator, with an overall height of about 20.7m is considered in this design. The heated primary system coolant from the reactor vessel passes through the inverted U-shaped tubes and saturated steam at 7MPa. The following parameters is used to calculate the overall heat transfer coefficient, mean temperature difference and number of tubes used. Hot Leg Temperature Thot = 330oC Cold Leg Temperature Tcold = 300oC Pinch Point DTsat = 20oC Sat. Temp from Sec. Tsat. @ 70bar = 285.8oC

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Kt = 30W/m.K Tube inner diameter D1 = 0.019m Tube outer diameter D2 = 0.0222m Linear Heat Flux q’’ = 2.64E4W/m Core Power Q = 2.22E9W Therefore, the overall heat transfer coefficient is gotten from the heat transfer equation;

Where

Number of tubes used according to AP1000 design equation is given as

Steam generators in Nuclear Plants are made up of between 3000-16000 tubes. H. Safety Strategy The fundamental process which underlies the operation of a reactor is, of course, the fission chain reaction, and the central problem of the reactor designer is to provide a system in which a self-sustained chain reaction can occur with complete safety. At the same time, the reactor must be capable of fulfilling the function for which it is designed, i.e., the production of power, isotopes, etc. A reactor will become supercritical or subcritical if its properties are changed in such a way that its multiplication factor becomes different from unity. For instance, if a coolant channel becomes clogged, some of the fuel rods, denied proper cooling, may increase in temperature to a point where they melt. In this design, the following safety strategies where considered;  In super critical region, the heat transfer property of water decline (reduces). Therefore, operating in or near this region should be avoided in normal and accident scenarios.  Since my operating pressure is 155bar and the critical saturation pressure 221bar, there is a safety margin of 66bar. Similarly, my operating temperature is 330 oC with the corresponding saturation temperature of 345oC and the critical saturation temperature is 374.15 oC, there is safety margin of 29.19oC.  The core average exit coolant temperature should not be lower than that desired for good thermal efficiency of the plant. That is, the exit temperature of the coolant should be equal in all channels. Also, during the adjustment of the coolant mass flow rate to a desired value, the flow rate should be made proportional to the heat generated in the adjacent fuel element (the inlet temperature should be assumed uniform in this process) V. Conclusion Nuclear reactors are potentially dangerous devices, and for this reason they must be designed with care. The accidental release of the accumulated fission products from even a small power reactor can lead to a disaster of major proportions. Even if the fission products are prevented from escaping to the surrounding community by a suitable containment vessel, repairs of the reactor can be a lengthy and expensive undertaking. References [1] [2] [3] [4] [5] [6] [7] [8]

Nuclear Engineering International, 1999 World Nuclear Industry Handbook, Nick Fielder, 1999. Y. A. Cengel and M. A. Boles, Thermodynamics an Engineering Approach, McGraw Hill, 1994. M. El-Wakil, Nuclear Power Engineering, McGraw-Hill, 1962. Glassione and Sesonske, Nuclear Reactor Engineering, Van Nostrand Reinhold Company, 1981. J. Lamarsh and A. Baratta, Introduction to Nuclear Engineering, Prentice Hall Inc., 2001. J. Lilley, Nuclear Physics Principles and Applications, John Wiley and Sons, Ltd, 2001. N. Todreas and M. Kazimi, Nuclear Systems 1 Thermal Hydraulic Fundamentals, Thermisphere Publishing Corporation, 1990. R. H. S. Winterton, Thermal Design of Nuclear Reactors, Wheaton & Co. Ltd., Exeter, 1981.

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A hybrid approach to measure design improvement factor of website Prafulla Bafna1, Hema Gaikwad2 Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International University (SIU), Atur Centre, Gokhale Cross Road, Model Colony, Pune – 411 016, Maharashtra, India 1,*2

Abstract: Software Engineering is the major activity of Information Technology. We can engineer the various types of end user applications such as games, spread sheets, websites with the help of Software Development Life Cycle. Different types of principles to asses such as Usability, efficiency, reliability, cost, risk, economic value are applied to above mentioned applications. Usability is the quality measure of a user's interaction with a product. When this product is website its termed as web usability. It is the quality attribute that assesses how easy user interfaces. It contributes to improve the design process. Web usability is an approach to make website easy to use for an end-user, without the requirement that any specialized training be undertaken. Website design is the key issue, because how the end user experiences the design is the key to acceptance. There are several usability principles mentioned in the literature but all these principles depend upon the application domain. We propose a two-step hybrid approach. In the first step according to performance of clusters output web usability principles are selected and in the second step Function Point matrix is applied to optimal web usability principles selected in the first step. It grades optimal usability principles and suggest improvement factor for each principle. Keywords: web usability, Function Point matrix, improvement factor I. INTRODUCTION With advances in Internet technology, website design had become critical issue .The main objective of website is to achieve goals set by domain. User can be frustrated and may give up if more efforts are spent by him/her to accomplish a particular task.[14] Confusing website might affect the productivity. Though Usability principles are application specific [9], software engineering suggests many domain independent quantification techniques. Function point metrics is one of them and the most accurate and effective metrics it is also used for studying software productivity, usability, quality, costs, risks, and economic value software applications[10].We have studied 50 websites using a questionnaire. It consists of web usability questions according to the standard guidelines given in a literature. Usability count is calculated and top 50 websites with respect to its usability count are considered for further study[1]. Clustering based technique for different combination of various web usability principles is used according to expert’s knowledge. Cluster evaluation techniques are used to select optimal web usability principles [7]. Now we apply a well-known Function point matrix to optimal web usability principles. It will result into grading of each principle and in turn it will suggest the measurement for modification of website design. Data flow Diagram [11] is the first step to calculate measuring parameters. These Measuring parameters are used to calculate Function Point .We calculated measuring parameters [11] for entire application based on Data flow Diagram [10]. We applied all 14 rating scale questions to each web usability principle [11]. Thus each principle is scaled. Then we converted above retrieved measure into the scale of 0 and 1. It represents the improvement factor of each principle. . A value near to zero represents poor interface design and needs improvement by a specific scale. This approach selects optimal principles with their scale. It saves a lot of time, cost, and efforts for updating website design. II. Background Usability process involves design with its evaluation and follows basic steps as requirement analysis, conceptual design, prototype, production, launch and maintenance [2]. Home page should be created in such a way that it will create positive first impression of a website. The important issues while designing websites are navigation, graphics, screen based control, page layout, etc.[3]. Benefits of planning usability into the website are Higher satisfaction leads to productivity, Completion with success to acquire brand loyalty, A higher rate of repeat users to progress in competition . The challenging task lies in extracting useful information from a large collection of data either from a data warehouse or from a database [3,8]. Generally collected data contains irrelevant or redundant attributes. Classification and clustering do not give accurate result if there are interdependent attributes. Correct feature selection is a fundamental data preprocessing step in data mining. Feature Mine algorithm contains sequence mining and classification algorithms which efficiently handles very large data sets with thousands of items and millions of records[4]. Edie Rasmussen states Cluster analysis is a

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technique which assigns items to groups based on a calculation of the degree of association between items and groups. Cluster analysis can be used for hierarchical algorithm. Nested data set is produced in which pairs of items or clusters are connected successively. However, the hierarchical methods are better information retrieval. The commonly used hierarchical methods, such as single link, complete link, group average link, and Ward's method, have high space and time requirements. In order to cluster the large data sets with high dimensionality there is need to have a better algorithm Examples are the minimal spanning tree algorithms for the single link method, the Voorhees algorithm for group average link, and the reciprocal nearest neighbor algorithm for Ward's method. Edie listed steps of clustering including Selecting of the attributes on which items are to be clustered , selecting appropriate clustering method, Creating the clusters or cluster hierarchies ,interpreting clusters and validating the results etc.[5]. They have focused on feature selection algorithms for classification (knowing class label ) and clustering (unsupervised feature selection) where data is unlabeled. Feature selection algorithms designed with different evaluation criteria broadly fall into three categories: the filter model, the wrapper model and the hybrid model. The filter model relies on general characteristics of the data to evaluate and select feature subsets without involving any mining algorithm [6]. This paper states that Function point metrics are the most accurate and effective metrics it is also used for studying software productivity, usability, quality, costs, risks, and economic value and in near future function point metrics can easily become a universal metric used for all software applications [11]. A. Felfernig, A. Salbrechter states that Function point matrix is very versatile it is not only used for checking usability but it is also used to measure other attributes such as errors per FP, defect per FP, $ per FP, page of documentation per FP and FP per person month [12]. The paper stated that function points is an important tool to measure the probable errors at the all the development stages. However, errors found may be more if development process is not matured, thus it an indication to improve the process[13]. III. Process Usability count is the measure that quantifies web usability. It is calculated based of questionnaire. It includes questions depending on standard guidelines of usability. To select optimal web usability principles, the first step is to study the websites which follows standard rules. So we have 50 websites having topmost usability count [14] [1]. Clustering approach is for selection of web usability principles. Cluster evaluation parameters with expertâ&#x20AC;&#x2122;s knowledge are used for selecting different website principles [7]. To grade these optimal principles and to suggest improvement factor, we propose a hybrid approach, using function point matrix. It includes measuring parameters such as no. of user inputs, no. of user outputs, no. of user inquiries, no. of external interfaces and no. of database files. For getting measurement parameters, Data Flow Diagram is drawn for the complete application. Count Total is calculated using weighting factor table that is table 1 [13]. The Fi is complexity adjustment factor and the value Fi varies for optimal web usability principles. After rating all fourteen questions [10]we get the value of Fi. The Function point FP represents quantitative value for each principle. It is converted within the scale of 0 and 1. Zero represents the poor design. The value near to zero represents the degree to which website should be modified for that particular principle. Measuring Parameter Number of user inputs Number of user outputs Number of user inquiries Number of files Number of external interfaces COUNT TOTAL

Count 5 6 2 3 1

simple 3 4 3 7 5

average 4 5 4 10 7

complex 6 7 6 15 10

= = = = =

15 30 6 45 5 101

Table 1. Weighting Factor The formula for computing FP is as follows: FP=count total*[0.65+0.01*â&#x2C6;&#x2018;Fi (i= 1 to 14)] FP=101*[0.65+0.01*14] FP=79.79 FP=80 FP=0.8 ( range between 0 to 1) Principle Applications W1 W2 W3 W4 W5 W6 W7 W8 W9 W10

0.6 0.8 0.1 0.9 0.3 0.6 0.2 0.4 0.9 0.1

0.7 0.5 0.5 0.3 0.3 0.7 0.5 0.8 0.2 0.1

0.2 0.4 0.6 0.6 0.9 0.5 0.9 0.4 0.9 0.3

0.8 0.3 0.1 0.8 0.8 0.9 0.6 0.1 0.7 0.4

0.4 0.9 0.6 0.9 0.2 0.2 0.4 0.7 0.6 0.6

0.2 0.6 0.8 0.2 0.3 0.1 0.2 0.4 0.4 0.5

0.1 0.6 0.5 0.1 0.5 0.7 0.7 0.9 0.7 0.4

Table 2 Function Point mapping between Applications and various Principles

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Table 2 shows the representative 10 applications and 07 principles amongst 50 and 35 respectively. Above mentioned matrix indicates mapping between Applications and various Principles. Pi: Principles (i=1 to 7) and Wj: Applications (Websites) (j=1 to 10) The data indicates the converted value of FP within the range of 0 and 1 for specific website and principle correspondingly.

Function Point Mapping 25

FP count

20 15 Column C

10 5 0 0

Applications(Web sites)

Graph 1 Graph1: Function Point Mapping represents total FP count for 50 websites IV. Conclusion The overall quality of a website is actually sum of many quality attributes, important one of which is usability. Focusing usability as a quality goal, there are serval usability principles explored in literature. Though These principles are domain dependent, above hybrid approach grades each principle using Function point matrix and suggests the factor of improvement .As optimal principles are being measured it saves time, cost, efforts for updating website design. References [1] [2] [3] [4]. [5]. [6]. [7]. [8]. [9] [10]. [11]. [12].

[13] [14]

Prafulla bafna,Shailaja Shirwaikar ,Human Computer Interaction-paradigms,process,practices,ACVIT,09 Nielsen, Iterative User Interface Design, IEEE Computer Vol. 26, No. 11 (November 1993), Alan Dix, ‘Human computer interaction’, prentice-Hall, Microsoft Corporation, 2004 Lesh, N. MERL, Zaki, M.J.,Scalable feature mining for sequential data, Intelligent Systems and their Applications, IEEE (Volume:15 , Issue: 2 ) ,2000 CHAPTER 16: CLUSTERING ALGORITHMS, Edie Rasmussen, University of Pittsburgh Huan Liu and Lei Yu, Toward Integrating Feature Selection Algorithms for Classification and Clustering, IEEE,2004 Prafulla Bafna1 , Pravin Metkewar2* and Shailaja Shirwaikar3#,,Novel Clustering approach for Feature Selection , American International Journal of Research in Science, Technology, Engineering & Mathematics,2014 Prafulla Bafna et al ,COMPARATIVE ANALYSIS OF APRIORI AND IMPROVED APRIORI ALGORITHM, International Journal of Emerging Technologies in Computational ,and Applied Sciences (IJETCAS),2014 Tomayess Issa et al,Applying Usability and HCI Principles in developing marketing websites,IJIC andIMA,ISSN 2150-7988 Volume 4 (2012) pp. 076-082 Roger S. Pressman “Software Engineering- A Practitioner’s approach” Capers Jones, VP and CTO Namcook Analytics LLC “Function Points as a Universal Software Metric” A. Felfernig, A. Salbrechter, Department of Business Informatics and Application Systems University Klagenfurt Universitätsstraße 65-67 A-9020 Klagenfurt, Austria , “APPLYING FUNCTION POINT ANALYSISTO EFFORT ESTIMATION IN CONFIGURATOR DEVELOPMENT” Neelam Bawane nee’ Singhal, and C. V. Srikrishna, World Academy of Science, Engineering and Technology Vol:2 2008-0627, “A Case Study to Assess the Validity of Function Points” Prafulla Bharat Bafna, A novel clustering approach to select optimal usability principles for educational websites, International Journal of Software and Web Sciences, Issue 11, December-2014 - February-2015

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ISSN (Print): 2328-3777, ISSN (Online): 2328-3785, ISSN (CD-ROM): 2328-3793 AIJRFANS is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research)

EXCITATION OF DOUBLY-CHARGED YTTRIUM IONS IN ELECTRON-ATOM COLLISIONS Yu. M. Smirnov National Research University «MPEI» Krasnokazarmennaia str., 14, 111250 Moscow RUSSIA Abstract: Excitation of a doubly-charged yttrium ion in e–Y collisions has been studied using the method of extended crossing beams. Forty-one YIII excitation cross-sections have been measured at exciting electron energy of 50 eV. Three optical excitation functions (OEFs) have been recorded in the electron energy range of 0…200 eV. Keywords: inelastic collisions, excitation, cross-sections, yttrium ion, energy levels, spectral lines I. Introduction Excitation with simultaneous double ionization is an elementary process that has so far received little attention both by theoretical and experimental researchers. However, studies of such processes are of much interest not only for fundamental science but for today’s practical problems. The threshold energy of excitation with simultaneous double ionization is in tens of electron volts range; the chance for electrons with such energies to occur in regular gas-discharge plasma is hardly probable. However, the recent decades have seen increased use of electron-beam plasma devices that may use primary electrons with velocities ranging from only slightly suprathermal to relativistic. The range of cascade electron velocities is similarly broad. Electron velocities are also large in magnetron sputtering and in plasma chemical reactors. However, only limited information is available on properties of collisions occurring in beam plasma specifically. In particular, as far as inelastic collisions of electrons with metal atoms are concerned, the process of excitation accompanied with double ionization has so far been studied for two subjects only: yttrium [1] and ytterbium [2]. In addition, the work cited in [1] has been performed in suboptimal conditions and its results call for extension and refinement. A doubly-charged yttrium ion is isoelectronic with a rubidium atom. However, in contrast with rubidium atom’s ground term of 4s24p65s 2S, the doubly-charged yttrium atom has 4s24p64d 2D as its ground term while the term 4s24p65s 2S is by 7467 cm-1 above the ground level 4s24p64d 2D3/2. Nevertheless, in all other aspects the energy structure of YIII electron shell is similar to that of alkali metal atoms where all states resulting from excitation of a single valence electron belong to the doublet term system. The most extensive study of YIII spectrum and energy levels is provided in [3], with a total of 113 spectral lines identified and classified within a wavelength range stretching from vacuum UV (λ = 64.3 nm) to near IR (λ = 911.6 nm). 50 energy levels belonging to ns (n = 5…9), np (n = 5…9), nd (n = 4…8), nf (n = 4…10), ng (n = 5…8), nh (n = 6…8) sequences have been located and interpreted. Our work is focused on studying collisions of electrons with yttrium atoms resulting in doublycharged yttrium ions in excited states as follows: Y + e  Y++* + e + e′ + e′′ , (1) where e and e are the incident and scattered electrons, respectively; e′ and e′′ are electrons knocked out yttrium atom as a result of its ionization. Asterisk denote excited particle. The experiment was carried out using the method of extended crossing beams described in detail in several papers [4]–[6]. Therefore, in the present paper, we will only note experiment conditions relevant to the yttrium experiment directly. II. Main Experimental Conditions ITM-2 brand of metallic yttrium with total impurities content of 0.18% (mainly comprised by Ta, Mo, Cu, Gd, Tb) was evaporated to produce atomic beam by heating the metal surface with electronic beam to 1870 K. Atom concentration within intersection zone of atomic and electronic beams was 4.3 × 10 9 cm-3. Electron beam current densities inside the entire range of electron energies of 0…200 eV stood below 1.0 mA/cm 2. The real spectral resolution of the optical system was about 0.1 nm within the short-wavelength part of the spectrum (λ < 600 nm) but was successfully reduced to 0.05 nm in individual cases. It degraded to 0.2 nm for wavelengths λ > 600 nm as monochromator diffraction grating had to be replaced (a resolution of about 0.1 nm was still achieved for some lines in this part of the spectrum). Unlike the case in [1], spectral lines emitted by helium atoms were used as reference for scaling absolute cross-section values. For these spectral lines, cross-section values at electron energy of 50 eV have been determined with outstandingly low error in [7]. The ground term of yttrium atom, 4d5s2 a2D, has a J-splitting ΔE = 530.5 cm-1; for the ground level, J = 3/2, and for a higher-lying level, J = 5/2.

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With evaporation temperature indicated above the concentration ratio is n(5/2)/n(3/2) = 1.00; therefore, the effect of Boltzmann exponent is precisely compensated for by the relationship between statistical weights of these levels. Higher-lying excited levels are populated to a negligible extent, as the nearest of these, 5s25p z2P°1/2, is separated from the ground state by a gap greater than 10000 cm-1. Therefore, when process (1) is accomplished to our experimental setup, we only considered two levels of the ground state of yttrium atoms as initial states. Inasmuch that excitation cross-sections of YIII spectral lines are, on average, much smaller than those of YI, relative values of excitation cross-sections of YIII have been measured with a margin of error ranging between 8% and 15%. Absolute excitation cross-section values have been determined with a margin of error within 23…30%. III. Results and Discussion The optical emission spectrum resulting from bombardment of yttrium atoms with electrons at the energy of 50 eV has been recorded within a spectral range of 190 to 850 nm. At this energy, YI, YII and YIII spectra are excited simultaneously. Of the 41 YIII excitation cross-sections measured in our work, 9 are related to blends which, with only a single exception, also belong to the YIII spectrum. These blends occur inevitably owing to the available spectral resolution of our equipment, as the splitting of ng2G and nh2H terms is quite narrow (0.1 cm-1 and less). The results of measurements supplemented with necessary spectroscopic data are summarized in Tables I, II. Table I lists spectral lines for which optical excitation functions have been recorded in the exciting electron energy range of 0…200 eV. This table includes wavelengths λ, transitions, quantum numbers of total electron shell momenta for the lower Jlow and upper Jup levels, the energies of the lower Elow and upper Eup levels (relative to the YIII ground level), excitation cross-sections at exciting electron energy of 50 eV Q50 and in the OEF maximum Qmax, and the location of OEF maximum E(Qmax). Numbers in the OEF column correspond to curve numbers in Fig. 1. Table II contains similar data on lines for which reliable recording of OEF has proved impossible; the difference with Table I is that the last three columns have been omitted. Table I Excitation Cross-Sections of Two-Charged Yttrium Ion (with OEFs Recorded)  (nm) 232.731 236.723 241.464 281.704 294.601 403.960 404.011

Transition

Jlow–Jup

4d2D–5p2P 4d2D–5p2P 4d2D–5p2P 5s2S–5p2P 5s2S–5p2P 4f2F–5g2G 4f2F–5g2G

3/2–3/2 5/2–3/2 3/2–1/2 1/2–3/2 1/2–1/2 7/2–9/2 5/2–7/2

Elow (cm-1) 0 724 0 7467 7467 101088 101091

Eup (cm-1) 42954 42954 41401 42954 41401 125836 125836

Q50 (10-18 cm2) 1.01 4.81 3.23 5.93 2.89

 1.41

Qmax (10-18 cm2) 1.65 7.87 4.25 9.71 3.80 2.83

E(Qmax) (eV) 75 75 65 75 65 75

OEF 2 2 1 2 1 3

Information on wavelengths, transitions, J values and energy levels has been provided per [3]. It should be noted that [3] indicates a transition 5s2S1/2–5p2P°5/2 for λ = 294.601 nm, however the only valid J values for term 5p2P° are 1/2 and 3/2. Based on transition energy, the upper level 5p2P°1/2 should be indicated here. It should also be noted that, among above-mentioned blends associated with relatively narrowly split terms ng2G and nh2H, there is a close pair of YIII lines, λ = 403.960 and 404.011 nm, that is superimposed spectrally with an YI line, λ = 403.983 nm. Absolute values of excitation cross-sections turned out to be numerically close for these YI and YIII lines, even though their threshold excitation energies differ considerably (by more than 31 eV). This has provided an opportunity for separating YI and YIII graphically, as the above-mentioned YI line participate in branching and shares a common upper level with another line, λ = 412.831 nm, for which an OEF has also been recorded. And besides, a line pair was discussed in paper [1], comprised by an YII line of λ = 241.393 nm Table II Excitation Cross-Sections of Two-Charged Yttrium Ion (without OEFs Recorded)  (nm) 206.058 206.898 212.798 219.116 220.076 220.603 226.141 226.157 228.434 231.992 271.030 271.053 280.327 286.767 291.341 291.856 297.042 301.393

Transition

Jlow–Jup

5d2D–6f2F 5d2D–6f2F 5p2P–5d2D 5p2P–5d2D 5p2P–5d2D 5p2P–6s2S 4f2F–7g2G 4f2F–7g2G 5p2P–6s2S 6p2P–8d2D 4f2F–6g2G 4f2F–6g2G 5d2D–7p2P 6p2P–7d2D 6p2P–7d2D 6p2P–8s2S 6p2P–8s2S 4f2F–7d2D

3/2–5/2 5/2–7/2 1/2–3/2 3/2–5/2 3/2–3/2 1/2–1/2 7/2–9/2 5/2–7/2 3/2–1/2 3/2–5/2 7/2–9/2 5/2–7/2 3/2–1/2 1/2–3/2 3/2–5/2 1/2–1/2 3/2–1/2 7/2–5/2

Elow (cm-1) 88379 88578 41401 42954 42954 41401 101088 101091 42954 99943 101088 101091 88379 99345 99943 99345 99943 101088

Eup (cm-1) 136894 136895 88379 88578 88379 86717 145294 145294 86717 143035 137973 137973 124041 134206 134257 133599 133599 134257

Q50 (10-18 cm2) 0.85 1.48 3.02 4.68 0.72 0.87

 0.61

1.37 0.23

 0.92

0.17 0.22 0.39 0.25 0.53 0.28

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Yu. M. Smirnov, American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(3), December 2014February 2015, pp. 242-245 301.885 412.161 473.762



510.288 512.040 523.810 526.358 538.364 556.281 556.727 557.224 559.548 560.208 725.458



755.871 786.453 791.671 798.941 799.143 817.141

4f2F–7d2D 5g2G–8h2H 5f2F–7g2G 5f2F–7g2G 6p2P–6d2D 5g2G–7h2H 6p2P–6d2D 6p2P–6d2D 6p2P–7s2S 6p2P–7s2S 6d2D–6f2F 4f2F–6d2D 6d2D–6f2F 4f2F–6d2D 5f2F–6g2G 5f2F–6g2G 6s2S–6p2P 5d2D–4f2F 6s2S–6p2P 5d2D–4f2F 5d2D–4f2F 5g2G–6h2H

5/2–3/2 7/2,9/2–9/2,11/2 5/2–7/2 7/2–9/2 1/2–3/2 7/2,9/2–9/2,11/2 3/2–5/2 3/2–3/2 1/2–1/2 3/2–1/2 3/2–5/2 7/2–5/2 5/2–7/2 5/2–3/2 5/2–7/2 7/2–9/2 1/2–3/2 3/2–5/2 1/2–1/2 5/2–5/2 5/2–7/2 7/2,9/2–9/2,11/2

101091 125836 123192 123193 99345 125836 99943 99943 99345 99943 118936 101088 119029 101091 123192 123193 86717 88379 86717 88578 88578 125836

134206 150091 145294 145294 118936 145360 119029 118936 117915 117915 136894 119029 136895 118936 137973 137973 99943 101091 99345 101091 101088 138070

0.19 0.32

 0.28

0.37 0.44 0.54 0.063 0.41 0.73 0.49 0.57 0.78 0.39

 0.41

0.47 0.58 0.47

 0.91

0.59

and an YIII line of λ = 241.464 nm; this pair was not resolved in [1]. Now we have been able to resolve this pair instrumentally by optimizing recording conditions despite atom concentration being just one-third of that in [1] at an equal electron beam current density. Fig. 2 shows a state diagram for a doubly-charged yttrium ion with transitions investigated. J-splitting of terms above ΔE = 100 cm-1 in the case of YIII only has place for np2P° terms. For known terms nd2D (n = 4…8), it stays within 30…100 cm-1, decreasing to 1 cm-1 or less for all other terms. Therefore, for the sake of visual simplicity, Fig. 2 shows all terms without J-splitting; naturally, any transitions between terms correspond to multiplets, but not to individual lines. All state properties with the exception of principal quantum number are indicated below the horizontal axis; principal quantum number values are indicated next to levels on the chart. All YIII levels known from [3] correspond to excitation of the only valence electron, while the closed shell 4p6 is not affected by excitation. Hence, all YIII terms being discussed are displaced doublets. Figure 1 Optical Excitation Functions of YIII.

Resonant YIII transitions, as well as transitions to the low-lying 5s2S1/2 metastable level are almost exclusively located within the vacuum UV area of spectrum and are beyond the possibilities of our equipment. The only exception is provided by five primary (series head) transitions in 5s2S1/2–np2P° and 4d2D–np2P° series located within 232…295 nm wavelength range and corresponding to n = 5. Excitation cross-sections measured by us have been the highest for these five transitions, their values being within the range Q50 = (1.0…5.9) × 10-18 cm2. OEFs recorded have been identified for them. Transitions ending in 5p2P° levels are, too, almost exclusively located in the vacuum UV range, the exception being head multiplets 5p2P°–6s2S1/2 and 5p2P°–5d2D. Notably, 5d2D–5f2F° transitions are absent while 5d2D–4f2F° and 5d2D–6f2F° transitions have been recorded and their excitation cross-sections are far from small: Q50 ~ 10-18 cm2. However, intensities for transitions from 5f2F°5/2,7/2 levels have been estimated in [3] to be much less than intensities for transitions from 4,6f2F°5/2,7/2 levels. Moreover, in the conditions of our experiment, both transitions from 5f2F°5/2,7/2 levels have been blended with lines of yttrium atom and singly-charged yttrium ion. In paper [3] also notes irregular behavior of a fine structure in nf configurations.

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Figure 2 State Diagram of Two-Charged Yttrium Ion with Transition Studied.

As indicated in the previous section, initial states for YIII excitation correspond to two levels of yttrium atom’s ground term, 4d5s2 2D3/2,5/2; they are populated identically in the conditions of our experiment. When the process (1) is put in place, two outer electrons are removed while the remaining valence electron transfers to either of excited states of a doubly-charged yttrium ion. Considering that the ground state of an yttrium atom is even and removal of any pair of outer electrons (5s2 or 4d5s) does not alter parity, the excitation of the remaining valence electron into odd states of YIII is more likely. Thus, excitation cross-sections of np2F°, nf2F°, nh2H° levels should be on average greater than those of ns2S, nd2D, ng2G levels. The data from Table I generally agree with this assumption, however information on YIII transitions in VUV range would be necessary to confirm such a conclusion. The same is true for both transitions from levels with energy E < 100000 cm-1 and transitions from high-lying levels with E > 130000 cm-1. It would be possible to clarify the matter if additional data on transition probabilities or branching factors were available, however the data on YIII is quite scarce at present. Only recently were Aki values reported for ten transitions between low-lying levels of doubly-charged yttrium ion and compared with previous researchers’ data in [8]. All these results have been obtained computationally, relying on various theoretical schemes. No experimental data whatsoever is available on transition probabilities or branching factors for YIII. Somewhat earlier, theoretical evaluations of radiative lifetimes of energy levels for ScIII and YIII doubly-charged ions have been presented in [9] but these belong to the lowest-lying metastable levels of the two ions. The circumstances cited made impossible more detailed analysis of our results. IV. Conclusion The excitation of YIII spectral lines in the wavelength range of 190 to 850 nm by collisions of electrons with yttrium atoms has been studied in detail. Forty-one excitation cross-sections have been measured at electron energy of 50 eV. Even though our results cover transitions from the majority of known YIII energy levels, a similar experiment in vacuum UV range would still be relevant. References [1] [2] [3] [4] [5] [6] [7] [8] [9]

A. N. Kuchenev and Yu. M. Smirnov, Izvestiya Vuzov. Fizika. (University News. Physics, in Rus.), №8 (1982) 90–92. Yu. M. Smirnov, J. Appl. Phys. (in Rus.), 63 (1996) 535–542. G. L. Epstein and J. Reader, JOSA, 65 (1975) 310–314. Yu. M. Smirnov, in: Physics of Electron and Atom Collisions (in Rus.), Ed. V. V. Afrosimov, Leningrad, USSR: PhTI Acad. Sci. USSR, 1985. A. N. Kuchenev, Ye. A. Samsonova, and Yu. M. Smirnov, Avtometriya (in Rus.), №5 (1990) 109–113. Yu. M. Smirnov, J. Phys. II France, 4 (1994) 23–35. B. Van Zyl, G. H. Dunn, G. Chamberlain, and D. W. O. Heddle, Phys. Rev. A, 22 (1980) 1916–1929. Tian-yi Zhang and Neng-wu Zheng, Chin. J. Chem. Phys., 22 (2009) 246 –254. B. K. Sahoo, H. S. Nataraj, B. P. Das, R. K. Chaudhuri, and D. Mukherdjee, J. Phys. B: At. Mol. Opt. Phys., 41 (2008) 055702 (6pp).

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Simulated Results for Neutron Radiations Shielding Using Monte Carlo C .E. Okon*1, I. O. Akpan2 School of Physics & Astronomy, University of Manchester, Manchester, UNITED KINGDOM. *2 Department of Physics, University of Calabar, NIGERIA. *1

Abstract: This report demonstrates the results of the experiment that was carried out in the Radiation Laboratory in University of Liverpool, United Kingdom. The radiation source used for the Neutron experiment was Americium Beryllium (Am/Be) put in water tank. Materials used as moderators were 2cm by 100cm by 100cm Polythene and 1cm by 100cm by 100cm. Cadmium sheet was also used to filter out thermal neutrons with energies of 0.025eV or less. The cadmium sheet was placed by the tank so that it is backed by adequate material for attenuating the capture neutrons. The detector used for the neutron experiment was 3He-RSP41640203 Detector (100cm long x 5cm diameter). I. Introduction Nuclear radiation such as gamma and neutron radiations are hazardous to human beings due to their ionizing effect in body tissue[3]. Gamma radiation and neutrons have high penetrating power and sources of these radiations must be shielded[6]. Although it present a serious shielding problem in any situation where it is produced in large scale. It is very difficult to shield high-speed neutrons because absorption cross sections are much lower at higher energies[1]. In reactor physics, it is important when constructing a nuclear reactor to provide adequate shielding so that neutrons and gamma radiation originating in the reactor core are prevented from escaping into the reactorâ&#x20AC;&#x2122;s surroundings where people are working [2]. The same problem arises to a smaller extent with radioactive isotopes which are used on a large scale in science and engineering. Although the size and strength of the radioactive sources in this aspect is very much less than the core of a nuclear reactor, it is nevertheless necessary to reduce the radiation escaping from such a source to an acceptably low level. II. Aim & Objective The aim of this research was to establish the relationship between the source strength and shield thickness. Monte Carlo simulations using MCNP was also employed for the analysis. III. Methodology The radiation source used for the Neutron experiment was Americium Beryllium (Am/Be) put in water tank because water slows down an appreciable fraction of the moderately fast neutrons. Water is the best neutron shield material, although it is a poor absorber of gamma radiation[5]. Materials used as moderators were 2cm by 100cm by 100cm Polythene and 1cm by 100cm by 100cm Borated Polythene. Cadmium sheet was also used to filter out thermal neutrons with energies of 0.025eV or less. Cadmium is more effective than boron for absorbing thermal neutrons, whereas boron is more effective for absorbing epithermal neutrons (energy range 0.1eV to 10eV)[4]. Since cadmium captures slow neutrons so readily, and it also has a fairly high mass number, it was used as a shielding material. The cadmium sheet was placed by the tank so that it is backed by adequate material for attenuating the capture neutrons. There is a drawback associated with cadmium sheet, having a high resonance peak for the capture of neutrons in a limited energy range (below about 1eV). Although most of the neutrons within this range are captured, a large proportion of those with higher energies still penetrate the shield[8]. The detector used for the neutron experiment was 3He-RSP41640203 Detector (100cm long x 5cm diameter). IV. Result & Discussions A. Polythene before the Detector (MCNP-Simulations) As the polythene is added, neutrons are thermalized and, therefore more are detected. As the moderator thickness is increased further, it causes the neutrons to lose too much of their energy. Some neutrons would be captured or would scatter off its path to the detector which causes the drop in the detected number of neutrons as a function of moderator thickness. The results also show that, the incident high, the detected high and the total

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incident all decrease with moderator thickness; whereas the incident thermal, detected thermal and total detected all increase parabolically with a maximum value at about 8cm moderator thickness. See Data: Appendix “A” Table 1 & 2

B. Polythene after the Detector (MCNP-Simulations) The moderator thickness ratio increases as polythene is added behind the detector, but not as greatly as when the polythene is between the source and the detector. This is because, when the fast neutrons which passes the detector gets to the moderator, it is thermalized and reflected back through the scattering process which leads to the thermalization of the fast neutrons, thus the polythene moderated behind the detector acts both as a moderator and a reflector. As the thickness increases, the number of neutrons detected approaches a constant value because at this point the moderator might have reached its effective moderation thickness. The graphs below also indicate that, the incident thermal, detected thermal and detected total all increase exponentially with moderator thickness; whereas the moderation effect of the polythene material is not really effective as much of the high neutrons are being detected in the detector as compared to that observed with the polythene before the detector. See Data: Appendix “A” Table 3 & 4

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C. Borated Polythene before the Detector (MCNP-Simulations) More effective shield can sometimes be obtained by adding borated polythene. The addition of borated polythene results in a lower capture neutron does than that provided by pure polythene [7]. In the experiment setup, it shows that with borated polythene, the moderation effect of polythene is removed. The number of neutrons detected greatly decreases as the thickness of the borated polythene is increased. This is a neutron shield. Cadmium and Boron-10 both have high neutron cross-section as well as serve as good neutron absorbers. Hence, these materials greatly shield the neutrons from reaching the detector. The thermal neutrons which escaped from being captured in the cadmium is easily captured in the borated polythene and the fast neutron which cadmium is ineffective in capturing is thermalized by the hydrogen atom in the borated polythene and some are captured further thereby reducing the number of observed neutrons. See Data: Appendix “A” Table 5 & 6

D. Borated Polythene after the Detector (MCNP-Simulations) The case shown in Fig.8 shows the reverse case of when the Borated Polythene is before the detector. In this instance, more neutrons are being detected as a result of backscattering effect and fewer neutrons are being absorbed compared to the case where the material was before the detector. See Data: Appendix “A” Table 7 & 8

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E. Normalized Efficiencies for Polythene and Boratedpolythene From the graphs below it is observed that the efficiency before the detector is higher than that after the detector. See Data: Appendix “A” Table 9 & 10

References [1] [2] [3] [4] [5] [6] [7] [8]

APPENDIX A Table 1:

MCNP SIMULATION POLYTHENE BEFORE DETECTOR Incident Energy Flux Thickness (cm)

Thermal

Detected Energy Flux

High

Thermal

High

Total Energy Flux Incident

Detected

Efficiency

0.00E+00

5.72E-06

0.00E+00

1.76E-10

1.24E-05

3.60E-10

0.00

1.50E-07

5.10E-06

2.34E-08

1.61E-10

1.25E-05

2.97E-08

0.24

6.78E-07

4.34E-06

1.16E-07

1.40E-10

1.18E-05

1.29E-07

1.10

1.04E-06

3.57E-06

1.85E-07

1.16E-10

1.02E-05

1.98E-07

1.93

1.10E-06

2.86E-06

1.99E-07

9.30E-11

8.50E-06

2.10E-07

2.47

1.00E-06

2.25E-06

1.82E-07

7.33E-11

6.83E-06

1.91E-07

2.79

8.38E-07

1.76E-06

1.53E-07

5.74E-11

5.39E-06

1.60E-07

2.97

6.81E-07

1.37E-06

1.24E-07

4.48E-11

4.22E-06

1.30E-07

3.08

MCNP SIMULATION POLYTHENE BEFORE DETECTOR NORMALIZED VALUES

Table 2:

Normalized Incident Energy Flux Thickness (cm)

Thermal

High

Normalized Detected Energy Flux Thermal

High

Normalized Total Energy Flux Incident

Detected

Efficiency

0.00

1.00

0.00

1.00

0.00

0.17

0.14

0.89

0.12

0.92

1.00

0.14

14.14

0.61

0.76

0.59

0.79

0.94

0.61

65.19

0.94

0.62

0.93

0.66

0.82

0.94

114.42

1.00

0.50

1.00

0.53

0.68

1.00

146.88

0.91

0.39

0.91

0.42

0.55

0.91

165.68

0.76

0.31

0.77

0.33

0.43

0.76

176.51

0.62

0.24

0.62

0.25

0.34

0.62

182.71

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Table 3:

MCNP SIMULATION POLYTENE AFTER DETECTOR Incident Energy Flux Detected Energy Flux Total Energy Flux Thickness (cm) 0 2 4 6 8 10 12 14

Thermal

High

0.00E+00 1.03E-08 5.08E-08 8.88E-08 1.13E-07 1.29E-07 1.41E-07 1.52E-07

Thermal

5.72E-06 5.76E-06 5.79E-06 5.81E-06 5.82E-06 5.83E-06 5.84E-06 5.85E-06

0.00E+00 1.62E-09 8.69E-09 1.56E-08 2.01E-08 2.30E-08 2.52E-08 2.72E-08

High 1.76E-10 1.77E-10 1.78E-10 1.79E-10 1.79E-10 1.80E-10 1.80E-10 1.80E-10

Incident 1.24E-05 1.26E-05 1.27E-05 1.28E-05 1.29E-05 1.29E-05 1.30E-05 1.30E-05

Detected

Efficiency

3.60E-10 2.38E-09 1.00E-08 1.73E-08 2.19E-08 2.49E-08 2.72E-08 2.94E-08

0.00 0.02 0.08 0.13 0.17 0.19 0.21 0.23

MCNP SIMULATION POLYTHENE AFTER DETECTOR NORMALIZED VALUES

Table 4:

Normalized Incident Energy Flux Thickness (cm) 0 2 4 6 8 10 12 14 Table 5:

Thermal 0.000 0.067 0.334 0.583 0.743 0.847 0.925 1.000

High

Normalized Detected Energy Flux Thermal

0.979 0.986 0.991 0.993 0.995 0.997 0.999 1.000

High

0.000 0.059 0.319 0.574 0.739 0.845 0.925 1.000

0.975 0.983 0.989 0.992 0.994 0.996 0.998 1.000

Normalized Total Energy Flux Incident

Detected

0.954 0.965 0.977 0.984 0.989 0.993 0.997 1.000

0.012 0.081 0.341 0.587 0.745 0.848 0.926 1.000

Efficiency 1.29 8.40 34.92 59.66 75.33 85.43 92.92 100.00

MCNP SIMULATION BORATEDPOLYTHENE BEFORE DETECTOR Incident Energy Flux Detected Energy Flux Total Energy Flux Thickness (cm)

Thermal

High

Thermal

High

Incident

Detected

Efficiency

0 2 3 4 5

0.00E+00 1.31E-12 4.37E-12 9.00E-12 6.37E-12

4.93E-06 4.15E-06 3.80E-06 3.48E-06 3.19E-06

0.00E+00 9.84E-14 3.03E-13 6.15E-13 4.61E-13

6.62E-11 5.57E-11 5.11E-11 4.68E-11 4.28E-11

1.24E-05 1.18E-05 1.12E-05 1.06E-05 9.93E-06

3.60E-10 6.05E-10 6.53E-10 6.58E-10 6.36E-10

0.003 0.005 0.006 0.006 0.006

5.37E-12

2.91E-06

3.49E-13

3.92E-11

9.23E-06

6.03E-10

0.007

MCNP SIMULATION BORATEDPOLYTHENE BEFORE DETECTOR NORMALIZED VALUES Normalized Incident Normalized Detected Normalized Total Energy Energy Flux Energy Flux Flux

Table 6:

Thickness (cm) 0 2 3 4 5 6

Table 7:

Thermal 0.00 0.15 0.49 1.00 0.71 0.60

High

Thermal

1.00 0.84 0.77 0.71 0.65 0.59

0.00 0.16 0.49 1.00 0.75 0.57

High 1.00 0.84 0.77 0.71 0.65 0.59

Incident

Detected

1.00 0.95 0.90 0.85 0.80 0.74

0.55 0.92 0.99 1.00 0.97 0.92

Efficiency 54.74 97.22 109.67 117.20 120.96 123.36

MCNP SIMULATION BORATEDPOLYTENE AFTER DETECTOR Incident Energy Flux Detected Energy Flux Total Energy Flux Thickness (cm) 0 2 3 4 5 6

Thermal 0.00E+00 4.50E-12 7.01E-12 7.69E-12 7.73E-12 7.80E-12

High 4.93E-06 5.76E-06 5.78E-06 5.80E-06 5.81E-06 5.82E-06

Thermal 0.00E+00 2.73E-13 4.00E-13 4.91E-13 5.47E-13 5.71E-13

High 6.62E-11 1.77E-10 1.78E-10 1.79E-10 1.79E-10 1.80E-10

Incident 1.24E-05 1.25E-05 1.26E-05 1.26E-05 1.26E-05 1.27E-05

Detected 3.60E-10 3.78E-10 3.86E-10 3.93E-10 3.98E-10 4.01E-10

Efficiency 0.0029 0.0030 0.0031 0.0031 0.0031 0.0032

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MCNP SIMULATION BORATEDPOLYTHENE AFTER DETECTOR NORMALIZED VALUES Normalized Incident Normalized Detected Normalized Total Energy Energy Flux Energy Flux Flux

Table 8:

Thickness (cm) 0 2 3 4 5 6

Thermal 0.00 0.58 0.90 0.99 0.99 1.00

High

Thermal

0.85 0.99 0.99 1.00 1.00 1.00

Table 9:

0.00 0.48 0.70 0.86 0.96 1.00

High 0.37 0.99 0.99 0.99 1.00 1.00

Incident 0.98 0.99 0.99 1.00 1.00 1.00

Detected 0.90 0.94 0.96 0.98 0.99 1.00

Efficiency 91.44 95.18 96.96 98.30 99.22 100.00

Table 10: NORMALIZED EFFICIENCY VALUES USING POLYTHENE Thickness (cm) 0 2 4 6 8 10 12 14

Before Detector 0.17 14.14 65.19 114.42 146.88 165.68 176.51 182.71

After Detector 1.29 8.40 34.92 59.66 75.33 85.43 92.92 100.00

NORMALIZED EFFICIENCY VALUES USING BORATEDPOLYTHENE Thickness (cm) 0 2 3 4 5 6

Before Detector 54.74 97.22 109.67 117.20 120.96 123.36

After Detector 91.44 95.18 96.96 98.30 99.22 100.00

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A Review on Various Error Detection and Correction Methods Used in Communication 1

Varinder Singh, 2 Narinder Sharma Student, Dept. of ECE, Amritsar College of Engineering and Technology, Amritsar-143001, Punjab, INDIA 2 HOD, Dept. of EEE, Amritsar College of Engineering and Technology, Amritsar-143001, Punjab, INDIA

Abstract: In todayâ&#x20AC;&#x2122;s world of wireless communication, the basic need of any communication system is to transmit and receive the error free data through any noisy channel. Due to the advancement in the data transmission the sources of noise and interference has also increased. Many efforts have been made by engineers to meet the demand for more reliable and efficient techniques for error detection and correction in the received data. To detect and correct the errors in the data transmission various techniques are used . This review paper delivers numerous error detection and correction techniques being used since last few decades. Keywords: Error detection and correction (EDAC), Triple Modular Redundancy (TMR), Cyclic Redundancy Check (CRC), Parity check method, Horizontal Vertical Diagonal(HVD), Hamming method. I. INTRODUCTION EDAC methods are used to find that the data is error free or is not corrupted, either by noisy channel, by hardware failure or during read-write operation in the memory segment. Various error detection methods exist in the communication system. One method currently utilized to produce reliable memory is the use of Error Correction Codes (ECC) to encode data before it is stored in the memory. Error correction codes take a set of information bits at the producer of the information and create a set of redundant bits based on the information bits. These redundant bits are sent or stored with the original set of information bits. The consumer of the information then uses the redundant bits to determine if any errors have occurred in transmission or storage. In the case of memory, the redundant bits are calculated and stored along with the original bits and then when they are read from the memory they are examined to determine if any errors have occurred between the time the information was stored and the time it was retrieved. The most common error detecting and correcting scheme being employed are parity bit, CRC, HVD and Hamming codes. All these methods are implemented on the second layer of OSI model at Data link layer. The upper layers work on some generalized view of network architecture and are not aware of actual hardware data processing. Therefore, the upper layers require error-free transmission between two systems. Almost every application did not work if it receiver data with errors. Applications like voice and video may not get that much affected and may still function well with some error.Data-link layer uses some error control mechanism to ensure that data bit streams are transmitted with certain level of accuracy. But to recognize how errors can be controlled, it is important to know what types of errors may occur. A. Hardware Redundancy Vs Software EDAC In order to protect semiconductor memories, software EDAC or redundancy can be used. Redundancy can either be hardware redundancy that is provided by extra components or time redundancy that is provided by extra execution time or by different moment of storage or can be a combination of both the hardware and time redundancies. To allow redundancy to detect permanent faults, the repeated computations are performed differently. TMR (Triple Modular Redundancy) is a suitable technique for SRAM-based FPGAs because of its full hardware redundancy property in the combinational and sequential logic. One solution for the protection of memories is use of hardware redundancy techniques, but they are too costly. When hardware redundancy is not possible, we have to go for software solutions. By using software Error Detection and Correction, transient faults in the combinational logic will never be stored in the storage cells, and bit flips in the storage cells will never occur or will be immediately corrected. For applications where read and write operations are done in blocks of words, such as secondary storage systems made of solid-state memories (RAM discs), software-implemented EDAC could be a better choice than hardware EDAC, because it can be used with a simple memory system and it provides the flexibility of implementing more complex coding schemes. With software EDAC, the data that is

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read from main memory may be erroneous, if the error occurs after the last scrub operation and before the time of reading. In other words, single-bit errors may cause failures. In contrast, hardware EDAC checks all the data that is read from memory, and corrects single-bit errors. Therefore, hardware EDAC provides improved reliability and when feasible should be the first choice for protecting the main memory. When hardware EDAC is not available or affordable, software EDAC can be used as a low cost solution for enhancing the reliability of systems. For cases where data is read and written in blocks of words rather than individual words, software EDAC may be a better choice than hardware EDAC. The paper is organized as, the related work that has been done previously is given in section 3, conclusion is given in section 5 and in the end of the paper, references are included in the section 6. II. TYPES OF ERRORS There may be three types of errors: 1. Single bit error: In this a frame consist of only one bit corrupted anywhere thoughout.

2. Multiple bit errors: In this a frame is received with more than one bits in corrupted state.

3. Burst bit errors: In this a frame contains more than one consecutive bits corrupted or more than one bit flips.

III. LITERATURE SURVEY Various error detection and correction methods are being used to maintain good level of reliability, to protect memory cells using protection codes. The method used in [3], is based on the hardware and time redundancy, although this technique reduces the number of input and output pins of the combinational logic; it requires additional encoding/decoding circuitry. The reliability issue can be solved, but the hardware redundancy schemes like duplication or triple modular redundancies are expensive. In [5], the encoder and the decoder can use any error detection and correction code. But the data is only coded in write operations, and decoded in read operations. So, the gathering of upsets is likely to occur and it depends on the reading and writing application request frequency. In order to avoid this accumulation of upsets, it is necessary to use an extra logic which is able to constantly detect and correct upsets in all coded data. The EDAC method given in [11] is again based on TMR, so increases the density as it is a hardware redundancy method. The method given in [4], perform memory error correction code which reduces power consumption in single-error correcting and double error-detecting checker circuits. This method can be employed to solve the non linear power optimization problem but it involves tedious computation of H- matrix. The method in [6], is called HVD, provides eminent detection coverage rate that can correct up to three upsets in a data array. It make use of parity codes in four directions in a data part to satisfy the reliability of memories and it can detect and correct the errors in the actual data bits. If the parity bits are itself erroneous, then those errors are detected by generating the parity bits for parities that is syndrome bits, but this is a complicated process. An easy way to find the errors in parity bits is presented in this paper. For this, we can take data bits and parity bits as a whole word. These words can be viewed as an m x n array. The hamming code will be used for the error detection and correction for this whole codeword containing both the data bits and the parity bits throughout the length of an array. After detecting the error, it can discover whether it is a data bit or a parity bit. The method used in [9], it shows that All of multiple error bit flips can be detected and 3-bit errors can be corrected, based on the experimental results. But it can correct only three bit error in a 8Ă&#x2014;8 matrix. The method used in[8], it shows that it can detect and correct up to 4 bits. With this method a large combination of multiple faults can be corrected which depend upon the length of the coded word array. IV. VARIOUS METHODS FOR EDAC A. Type of Error Control The information of data is transfer from one hop to another hop. In TCP/IP model, the physical layer and the final layer of TCP/IP model transforms the data into stream of bits and transfers them into a signal toward the receiver device. Meanwhile those bits flow from one hop to another, they are exposing to channels interference, for example electrical interference or thermal noise that subject to unpredictable change. These channel interferences can change the shape of the transmitted signal leading into errors in the signal. There are two kinds of error single-bit error and burst error.

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B. Parity Check The most common method for detecting bits error with asynchronous character and character- oriented synchronous transmission is parity bit method. There are two types of parity check schemes: even and odd parity checks. With the even parity check, the redundant bit is chosen so that an even number of bits are set to one in the transmitted bit string of N+r bits, where r is the bit that used to be the even parity check and N is the bit that is transmitted by the transmitter of the network. The receiver re-computes the parity of each received bits from the transmitter and discard the strings with the invalid parity. The parity scheme is always used if 7-bits character is exchanged. If there are 7-bits that are transmitted by the transmitter and parity check are used to detect the error and often the eighth bit is the parity bit. C. Cyclic Redundancy Check (CRC) The second method in error detection in data link layer is cyclic redundancy check. As the parity check which is based on the submission of the binary the cyclic redundancy check is based on the binary division. In CRC, rather than adding bits to attain a desired parity, a series of redundant bits, called the CRC remainder, is attached to the end of a data unit so that the resulting data unit becomes exactly divisible by a second. On the receiver side, the incoming binary data bits are divided by the same number to be compared on the transmitter side. Implies that, if the remainder of the division is identical to the value that added to CRC when the data was transmitted, the data will be accepted, otherwise the unmatched reminder produced on the destination after the CRC is indicates the data unit has been damage during the transmission of data. The redundancy bits used by CRC are derived by dividing the data unit by a predetermined divisor; the remainder is the CRC. To be valid, a CRC must satisfy two conditions: It must have exactly one less bit than the divisor and appending it to the end of the data string must make the resulting bit sequence exactly divisible by the divisor. Operations of CRC described by the figure shown below:

Fig 1. CRC Operation

Fig 2. CRC in sender side

Fig 3. CRC in receiver side

D. HVD Method Another method used for error detection and correction is called as HVD code. As the parity bits are applied at the three directions i.e row, column, forward slash and backward slash diagonals on a data part. Moreover, two horizontal (H), Vertical (V), parity bits, the diagonal (D), parity in both the directions can be used as shown in figures below a, b, c, d respectively:

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a. Horizontal

c. Forward slash

b. Vertical

d. Backward slash

Fig 4. Parity bits position To improve the ability of detection an additional parity bit is calculated on the basis of manipulated parity bits in all dimensions. In HVD code implementation H , V, D and Dâ&#x20AC;&#x2122; represents the number of errors in the horizontal, vertical, forward slash and backward slash lines resp. Whereas H1,V1, F1 and B1 represents the position of first error in the horizontal , vertical and in both the diagonal parity lines resp.

Fig 5. Two dimensional coded array This method is based on 2-dimensional parity. Parities are calculated in all the directions that are horizontal, vertical, forward slash and backward slash. The 8x8 matrix is shown in figure 5 where the symbol h, v, f and b denotes the parity bits in horizontal, vertical, forward slash and backward slash lines respectively and subscripts represents the position of parity. D. Hamming Method The small size of the transistors or capacitors, along with cosmic ray effects, produces instant errors in stored information in large, solid RAM chips, especially that those are influential. These errors can be detected and

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corrected by using error detecting and correcting codes in the RAM’s. The frequent error detection scheme is the parity bit. The parity bit is generated and then stored in the memory along with the data word. The parity of the word is checked after reading the word from memory. The word is accepted if the parity of the bits read out is correct. If the parity of the bits read is incorrect, an error is detected, but it cannot be corrected. An errorcorrecting code uses multiple parity check bits that are stored with the data word in memory. Each check bit is a parity bit for a group of bits in the data word. When the word is read from memory, the parity of each group, including the check bit, is calculated. If the parity is correct for all groups, it signifies that no detectable error has occurred. If one or more of the newly generated parity values is incorrect, a unique pattern called a syndrome results that may be able to identify which bit is in error. A single error occurs when a bit changes in value from 1 to 0 or from 0 to 1 while stored or if it erroneously changes during a write or read operation. If the specific bit in error is identified, then the error can be corrected by complementing the erroneous bit. Hamming Codes The most common types of error-correcting codes used in RAM are based on the codes devised by R. W. Hamming. In the Hamming code, k parity bits are added to an n-bit data word, forming a new word of n _ k bits. The bit positions are numbered in pattern from 1 to n _ k. Those positions numbered with powers of two are reserved for the parity bits. The remaining bits are the data bits. The code can be used with words of any length. Before giving the general characteristics of the Hamming code, we will illustrate its operation with a data word of eight bits. Consider, for example, the 8-bit data word 11000100. We include four parity bits with this word and arrange the 12 bits as follows: The 4 parity bits P1 through P8 are in positions 1, 2, 4, and 8, respectively. The 8 bits of the data word are in the remaining positions. Each parity bit is calculated as follows: P1 _ XOR of bits (3, 5, 7, 9, 11) _ P2 _ XOR of bits (3, 6, 7, 10, 11) _ P4 _ XOR of bits (5, 6, 7, 12) _ P8 _ XOR of bits (9, 10, 11, 12) _ Recall that the exclusive-OR operation performs the odd function. It is equal to 1 for an odd number of 1’s among the variables and to 0 for an even number of 1’s. Thus, each parity bit is set so that the total number of 1’s in the checked positions, including the parity bit, is always even. The 8-bit data word is written into the memory together with the 4 parity bits as a 12-bit composite word. Substituting the 4 parity bits in their proper positions, we obtain the 12-bit composite word written into memory: When the 12 bits are read from memory, they are checked again for errors. The parity of the word is checked over the same groups of bits, including their parity bits. The four check bits are evaluated as follows: C1 _ XOR of bits (1, 3, 5, 7, 9, 11) C2 _ XOR of bits (2, 3, 6, 7, 10,11) C4 _ XOR of bits (4, 5, 6, 7, 12) C8 _ XOR of bits (8, 9, 10, 11, 12) Bit position 1 2 3 4 5 6 7 8 9 10 11 12 P1 P2 1 P4 1 0 0 P8 0 1 0 0 Bit position 1 2 3 4 5 6 7 8 9 10 11 12 001110010100 1⊕1⊕0⊕0⊕0 = 0 1⊕0⊕0⊕1⊕0 = 0 1⊕0⊕0⊕0 = 1 0⊕1⊕0⊕0 = 1 A 0 check bit designates an even parity over the checked bits, and a 1 designates an odd parity. Since the bits were written with even parity, the result, C _ C8C4C2C1 _0000, indicates that no error has occurred. However, if, the 4-bit binary number formed by the check bits gives the position of the erroneous bit if only a single bit is in error. V. CONCLUSION This paper presents different techniques which are used for detection and correction of single bit error and burst errors with improvement in efficiency and reliability of data transmission. All above mentioned techniques can detect and correct errors in data bits along with the parity bits without any extra calculations. It is observed that the Hamming method can correct and detect more number of errors as compared to other techniques mentioned above. Any other correction code can be used with hamming method to further increase the number of error detection and correction which results in enhancing code rate and reducing bit overhead. REFERENCES [1] [2]

Anlei Wang, Naima Kaabouch, “FPGA Based Design of a Novel Enhanced Error Detection and Correction Technique, IEEE, Vol. 3, Issue No. 5, March 2008, pp 25-29. Behrouz A. Forouzan “Data Communication and networking” 2nd edit. Tata McGraw Hill.

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[7] [8] [9]

[10] [11]

Fernanda Lima, Luigi Carro, Ricardo Reis “Designing Fault Tolerant Systems into SRAM-based FPGAs” Anaheim,Vol. 3, June 2003,pp 312-318. Heesung Lee, Joonkyung Sung, and Euntai Kim, “Reducing Power in Error Correcting Code using Genetic Algorithm”, World Academy of Science, Engineering and Technology 25 2007 M. Imran , Z. Al-Ars, G. N. Gaydadjiev, “Improving Soft Error Correction Capability of 4-D Parity Codes”, IEEE transaction, Vol. 4, Issue 2, January 2009, pp 233-238. M.KISHANI, H. R. ZARANDI, H. PEDRAM, A TAJARY, M. RAJI, B. GHAVAMI, “HORIZONTAL-VERTICAL DIAGONAL E RROR DETECTING AND C ORRECTING C ODE TO PROTECT AGAINST WITH SOFT E RRORS” SPRINGER SCIENCE , VOL. 15, ISSUE NO . 3-4, M AY 2011, PP 289-310. NARINDER PAL SINGH, SUKHJIT SINGH, VIKRANT SHARMA, AMANDEEP SEHMBY, “RAM ERROR DETECTION AND CORRECTION USING HVD IMPLEMENTATION” EUROPEAN SCIENTIFIC JOURNAL,VOL. 9, ISSUE NO.33, NOVEMBER 2013,PP 424-435. S. Sharma, Vijay Kumar, “ An HVD Based Error Detection and Correction of Soft Errors in Semiconductor Memories Used for Space Application”, International conference on devices, circuits and systems (ICDCS), March 2012, pp. 563-56. Shubham Fadnavis, “An HVD Based Error Detection And Correction Code In HDLC Protocol Used For Communication”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, Issue No.6, June 2013, pp 2349-2353. T. Kasarni , A Kitai and S. Lin "On the Undetected Error Probability for Shortened Hamming Codes", IEEE Transaction Communication , Vol.33, Issue No. 2, 1985, pp 570 -574. Y. Bentoutou, “Program Memories Error Detection and Correction On- Board Earth Observation Satellites”, World Academy of sciece, Engineering and Technology 66 2010.

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Available online at http://www.iasir.net

Dual Layer Image Scrambling Method Using Improved Arnold Transform 1

Gyan Vardhan Artist, 2Dr. Mahesh Kumar Porwal M.Tech, Digital Communication, 2 Professor, ECE Department Shrinathji Institute of Tech & Engg, Nathdwara, Rajsamand, Rajasthan, INDIA 1

Abstract: In this paper various version of image scrambling based on Arnold transform are discussed and new method, which is an extension of improved Arnold Transform is proposed, and then compared those method statistically and by figures, experimental result clearly revels that proposed method gives better result then previous versions of Arnold Transform Keywords: Arnold transform, block location scrambling, multi area scrambling, improved arnold transform I. Introduction With the rapid development of computer network and multimedia technology, security problem of digital images has been highlighted, therefore image encryption technology has become vast topic of research [9]. Image encryption converts true image into meaningless image [4]. There are many algorithms about image scrambling such as orthogonal latin square, affine transform, magic square, baker transformation, Fibonacci transformation and so on. These methods have different visual effects but they have certain limitations like there parameters are small hence ability of encryption is small which use for simple data encryption [7]. Arnold transform is widely used in image scrambling since it is periodic in nature. It is mainly encryption and decryption tool. It disturbs image auto correlation and creates chaotic random image, which makes it impossible to get original image without using improved Arnold transform. Arnold transform is named after ‘VLADIMIR ARNOLD’ and he demonstrate its effect on cat map, hence it is also known as “ARNOLD CAT MAP” [5]. It is basically a tool of changing one matrix into another matrix. It is very simple and easy to implement. It is point to point transform which shuffles each pixel value in an image. II.

Arnold Transform

A. Traditional Arnold transform [5] Arnold transform is given by : (1) where x ,y = original image co-ordinate x’, y’ = transform image co-ordinate demerits of above conventional Arnold transform is that the all four transform parameters or coefficients are fixed in nature so if somehow any one can identify that conventional Arnold transform is used to scramble the image, by using fixed value of those coefficient , he can easily descramble the image. B. Block location scrambling algorithm of digital image based on Arnold transform [9] It performs similar Operation to Arnold transform except its matrix coefficients are different from traditional Arnold transform. The transform algorithm for above proposed algorithm is given below: (2) QI DAONG-XU has proved that for a matrix

when elements satisfying the criteria that ad-bc = 1 [9]. Its

transformation coefficients can be used as scrambling transformation. Demerits of above algorithm is that out of four matrix coefficients only two coefficient are unknown so we have a limited choice to choose different matrix coefficients i.e. or simply 2. Secondly its first matrix coefficients i.e. are still fixed to one or unity.

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C. Improved Arnold Transform or IAT [5]: This transform is proposed in 2010 by MA DING & FAN JING .Transform equation for IAT is given as: (3) (4) (5)

K=max A=

(6)

Where Eq. 3 = matrix for Improved Arnold Transform or IAT Eq. 6 = transformation matrix coefficient, which can be any Matrix or could be conventional matrix

When transform matrix choose different coefficients, K is having maximum value from transform matrix coefficient, which ensures that transformed parameters are to be not negative. And hence we get unique inverse matrix, when this matrix is nonsingular in nature that means the determinant of that matrix is non-zero which is shown below: (7) D. Advantage of improved Arnold transform over above variants of Arnold Transform: (1) In improved Arnold transform, various sets of matrix coefficients can be used while conventional Arnold matrix uses fix set of matrix coefficients. (2) In IAT all four matrix coefficients are different and we have a lot of choice to choose them but in Block location scrambling algorithm first parameter ( ) is fixed to unity and we have only 2 choice to select rest three coefficients. (3) Scrambling factor that used to calculate difference among true real image and transformed image should be as high as possible so if we somehow able to increase scrambling factor then it is difficult for the attacker to get original content of an image. That is what we did in this proposed algorithm. E. Proposed extension of improved Arnold transform : Above concept is based on IAT, in which scrambling ratio is improved significantly by dividing an image into either four or sixteen sub-images. Then apply improved Arnold transform on each sub-image. By doing so size of an original image is changed.. When an image is divided into (1) Four sub-images = size of each sub-image is 128*128 (2) Sixteen sub-images = size of each sub-image is 64*64 The greatest advantage of proposed algorithm is that different sub-images can be sent at different frequency and via different route to its destination. At the destination side true image can be reconstructed by knowing: (a) Exact transform matrix coefficients, which was used to scramble an image. (b) Correct size of an image (c) Frequency for particular size of an image because frequency depends on size of an image. Frequency for size as shown in table below [5]: Size of an image

100

125

128

256

512

Image frequency

150

250

192

384

(d) Meaningless sub-images must be put at proper place so that random meaningless image can form meaning full content of an image. So it is very difficult for the attacker to get original image because: (1) Instead of sending complete image, we are sending sub-images, which can be sent at same or different frequency. (2) Sub-images can be sent either in same order or in different order so it looks like a random image until subimages are rearranged in a proper way. Hence by doing so security of improved Arnold transform is greatly increased since scrambling ratio is increased.

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III. Calculation for scrambling ratio Steps to be used to calculate scrambling ratio [5]: (1) Divide the whole image into block size of 4*4. (2) Compute mean and variance for each block. (3) Compute image mean square signal to noise or SNR ratio. (4) Put above calculate value in the scrambling ratio formula. The formula to calculate mean, variance and SNR are shown below: (8) D=

(9)

SNR=

(10)

By Defining the image scrambling ratio, which is given below: (11) Where D = variance before scrambling of an image D’= variance after scrambling of an image I = true image and I’ = scrambled image N = order of an Image Ratio of image variance is increased when difference between each pixel gray value and mean is increased. The frequency at which an image cannot be reconstructed, at those frequency value of SNR is decreased and viceversa is also true. Because from SNR eq. it is clear that SNR will decreased when denominator is increased that means difference between original image and scrambled image is increased and hence scrambling ratio is increased. While the frequency at which image is reconstructed properly, value of SNR is increased i.e. difference between true original image and scrambled image is decreased, since image is restored properly, this is the only and unique frequency at which image is recovered. IV. Simulation result The comparison of above discussed algorithm in tabular form and in the form of an image. A. For cameraman.tif image: frequency

IAT

IAT 4 sub-image

16 sub-image

2.4842

2.6088

2.7713

2.7788

2.8769

2.9079

3.0199

3.0298

100

2.8514

3.0296

2.9926

3.0709

192

2.0777

1.2209

1.2207

(1) Original Image

(5) 4 sub-images at f = 5

(2) Recovered from AT

(3) Recovered from IAT

(4 ) true & 4 sub-images

(6) recover image at f =5

(7) 4 sub-images at f = 50

(8) recover image at f = 5

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(9) sub-images at f = 100

(10) recover image at f= 100

(11) sub-images at f=192

(12) recovery at f = 192

(14) recovery at f=5

(15) 16 sub-images at f=50

(16) recovery at f = 50

(18) recovery at f = 100

(19) 16 sub-images at f = 192

(20) recovery at f = 192

(13)16 sub-image at f = 5

(17) 16 sub-images at f = 100

for Lena.jpg image frequency f 5 50 100 192

IAT

2.1107 2.3396 2.255 0

2.1651 2.3539 2.274 0.6277

IAT 4 sub-image 2.1891 2.3815 2.2974 0.62

16 sub-images 2.2192 2.3832 2.3971 0.6277

(21) original image

(22) recovery from AT

(23) recover from IAT

(24) true & 4 sub images

(25) 4 sub-images at f = 5

(26) recovery at f = 5

(27) sub-images at f = 50

(28) recovery at f = 50

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(29) sub-images at f = 100

(30) recovery at f =100

(31) 4 sub-images at f = 192

(32) recovery at f = 192

(33) 16 sub-images at f = 5

(34) recovery at f = 5

(35) 16 sub-images at f = 50

(36) recovery at f =50

(37) sub-images at f = 100

(38) recovery at f = 100

(39) sub-images at f = 192

(40) recovery at f = 192

for pout image frequency

IAT

0.972

0.9889

4 sub-images 1.0065

16 sub-images 1.043

0.9942

1.027

1.975

1.9903

100 192

1.0361 0

1.227 0.4998

1.9002 0.7665

1.9303 0.7655

IAT

(41) true image

(42) recover from IAT

(43) 4 sub-images at f = 5

(44) recovery at f = 5

(45) 4 sub-images at f = 50

(46) recovery at f = 50

(47) 4 sub-images at f = 100

(48) recovery at f = 100

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(49) 4 sub-images at f = 192

(50) recovery at f = 192

(51) 16 sub-images at f = 5

(52) recovery at f =5

(53) 16 sub-images at f = 50

(54) recovery at f = 50

(55) 16 sub-images at f = 100

(57) 16 sub-images at f = 192

(58) recovery at f = 192

V. Remarks (1) Image no. 2 and 22 give result from AT (Arnold Transform). (2) Image no. 3, 23, and 42 give result from IAT (Improved Arnold Transform). (3) Except image no. 2, 3, 22, 23, 42 give result from proposed AT (Arnold Transform). (4) In this paper (a) f = frequency of Arnold Transform (b) AT = Arnold Transform (c) IAT = Improved Arnold Transform VI. Conclusion A new image scrambling concept is proposed in my paper. To encrypt an image with the help of image scrambling method, security of an image is improved by even better encryption method. That’s what is done in this paper by using multi area scrambling concept [1] by choosing various transform coefficients, which creates dilemma for the attacker & hence leads to difficulty in deciphering the image since we are not using unique transform coefficients [1]. Statistical results and image shows that extended proposed algorithm is more efficient & hence can be used as digital image information hiding tool i.e. for watermarks. For different attacks it also shows excellent robust effect which does not affect original quality of an image hence can also be used in medical image processing. Hence above proposed method is extensively used because of its simple mathematical structure. References [1] [2] [3] [4] [5] [6] [7]

Min , Ting Liang2, Yu-Jie . “Arnold Transform Based Image Scrambling Method”. 3rd International conference on Multimedia Technology (ICMT 2013). Atlantis press pp. 1309-1316 Veena V K, Jyothish Lal G, Vishnu Prabhu S, Sachin Kumar S, Soman K P. “A Robust Watermarking method based on Compressed Sensing and Arnold scrambling” 2012 IEEE . pp. 105-108. Jingbing Li, Mengxing Huang, Huaiqiang Zhang, Chunhua Dong, Yong Bai. “The Medical Images Watermarking Using DWT and Arnold”. IEEE 2012, pp. 27-31. Zhenjun Tang and Xianquan Zhang. “Secure Image Encryption without Size Limitation Using Arnold Transform and Random Strategies”. JOURNAL OF MULTIMEDIA, VOL. 6, NO. 2, APRIL 2011 Academy Publisher. pp. 202-206. Ma Ding, Fan Jing, “Digital Image Encryption Algorithm Based on Improved Arnold Transform”. International Forum on Information Technology and Applications 2010 IEEE . pp. 174-176. Mingju Chen, Xingbo sun. “A Digital Image Watermarking of Self Recovery Base On the SPIHT Algorithm”. 2 nd International Conference on Signal processing System(ICSPS) 2010 IEEE. pp. 621-624. Lingling Wu, Weitao Deng, Jianwei Zhang, Dongyan He. “Arnold Transformation Algorithm and Anti-Arnold Transformation Algorithm” . The 1st International Conference on Information Science and Engineering (ICISE) 2009 IEEE . pp. 1164-1167

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Zhang Yanqun, Wang Qianping. “A New Scrambling Method Based on Arnold and Fermat Number Transformation”. International Conference on Environmental Science and Information Application Technology 2009 IEEE. pp. 624-628. Zhenwei Shang, Honge Ren, Jian Zhang. “A Block Location Scrambling Algorithm of Digital Image Based on Arnold Transformation” .the 9th International Conference for Young Computer Scientists 2008 IEEE. pp. 2942-2947. Chaokun Wang, Jianmin Wang, Ming Zhou, Guisheng Chen . “ATBaM: An Arnold Transform Based Method onWatermarking Relational Data” . International Conference on Multimedia and Ubiquitous Engineering 2008 IEEE. Pp. 263-270.

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An Open type Mixed Quadrature Rule using Fejer and Gaussian Quadrature Rules Dwiti Krushna Behera, Ajit Kumar Sethi & Rajani Ballav Dash Department of Mathematics, Ravenshaw University, Cuttack-754209, Odisha(INDIA) Abstract: A mixed quadrature rule of higher precision for approximate evaluation of real definite integrals has been constructed. The analytical convergence of the rule has been studied. The relative efficiencies of the proposed mixed quadrature rules have been compared with the help of suitable test integrals. The error bound has been determined asymptotically. Keywords: Fejer’s second quadrature rule, mixed quadrature rule. 2000 Mathematics Subject Classification: 65D30, 65D32

I. Introduction Apart from two types of basic quadrature rules such as: 1. Newton-Cotes type of quadrature rules 2. Gaussian type of quadrature rules, other effective rules such as: Clenshaw-Curtis and Fejer’s type of quadrature rules are available in literature. Newton-Cote type of quadrature rules are based on approximation of the integrand by using simple interpolation such as: Lagrange’s interpolation, where as Gaussian quadrature namely Gauss-Legendre quadrature is based on the approximation of the integrals by orthogonal polynomials such as: Legendre polynomials. Again both Clenshaw-Curtis and Fejer’s type of quadrature rules are based on the approximation of the integrands by orthogonal polynomials such as: Chebyshev polynomials. Mostly Newton-Cotes rules are of closed type. Though open type Newton-Cotes rules are available, the Gaussian quadrature rules, itself is open, more powerful and efficient. This is because point Newton-Cotes rules has precision or according as is odd or even. Where as point Gaussian rule has precission On the other hand Fejer’s quadrature rule is an open type rule. It has been tested that it is better than NewtonCotes rule of same precision. This inspires us to form an open type mixed quadrature rule blending two open type given rules such as: Gauss-Legendre and Fejer’s type of quadrature rules. The formulation of mixed quadrature rules was first coined by R. N. Das and G. Pradhan[1]. Many author’s [1-5, 14] have produced different mixed quadrature rules. These mixed quadrature rules are mostly closed type rules. In mixed quadrature rule, linear/ convex combination of two quadrature rules or more rules of equal precisions is taken to produce a new type of quadrature rule of higher precision. Though in literature we find methods such as Konord extension method[ 7, 10, 11] an d Richardson’s extrapolation method[12 ] for precision enhancement, the mixed quadrature method is very simple and easy to compute as no additional evaluation of function is required while integrating the integral by this rule. Due to above facts, In this paper, we get motivation for successfully forming an open type mixed rule of precision seven taking linear combination of Fejer’s second and Gaussian 3-point quadratue rules, each of precision five. The mixed quadrature rule so found has been tested and compared with its constituent rules by computing numerically five tested integrals. The results have been tabulated in Table-4.1. II. Construction of the Mixed Quadrature Rules of Precision Seven Expressing the integrand in terms of Chebyshev polynomials one can derive Fejer’s second quadrature rule[9] as

point

Where Taking

one can write 5-point Fejer’s second rule as .

(2.1)

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We have well known the Gaussian Legendre 3-point rule: (2.2) Each of the rules (2.1) and (2.2) is of precision five. Let and respectively. Then

denote the errors in approximating the integrals

by the rules (2.1) and (2.2)

(2.3) (2.4) Using Maclurin’s expansion of functions in equations (2.1) and (2.2) we have (2.5) and (2.6) Now multiplying the equations (2.3) and (2.4) equations, we have

and

respectively, and then adding the resulting

(2.7) Where and

Hence

(2.8)

This is the desired mixed quadrature rule of precision seven for the approximate evaluation of The truncation error associated to this rule is given by or

(2.9)

III. Error Analysis An asymptotic error estimate and an error bound of the rule are given in theorems respectively. Theorem Let be a sufficiently differentiable function in the closed interval associated with the rule is given by

and . Then the error

Proof: From eq. Where and Hence So, Theorem

The bound for the truncation error

is given by ท

Where Proof: We have

ท

ท ท

. ท

ท

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ท

Hence ท ท

= From this we have

ท

ท ท

ท

ท ท

ท

Where . This shows that the error bound as ท ท are unknown points in . Also, it gives the error in approximation will be minimize if the points ท ท are very close to each other. Corollary The error bounds for the truncation error is given by . Proof: From we have ท ท ท ท Choosing ท

Where ท

we have

IV. Numerical Verifications Comparison of the mixed quadrature rule with 5-point Fejer’s second rule and Gauss-Legendre 3-point rule in approximation of some real definite integrals. Table-4.1 Integrals

Exact value

Approximate value

Error Approximated

0.5278870147

0.5267202238

0.5222262547

0.528095924

1.1667909

5.66076

2.089093

1.933421496

1.933412684

1.933390469

1.933419484

0.599294656

3.1027

2.012

1.09260498

1.092562943

1.092434788

1.092602237

4.2037

1.70192

2.743

3.701158418

3.696798227

3.684143231

3.700672204

4.360191

0.017015187

4.86214

35.88047234

35.87568054

35.86068652

35.88027053

4.7915

0.01978582

2.6704

V. Conclusions From the above table, we observed that the new type of mixed quadrature rule formed in this paper gives better approximation as compared to the constituent rules such as: 5-point Fejer’s second rule and Gauss-Legendre 3-point rule . Hence, we conclude that the mixed quadrature rule is more preferred than constituent basic rules. References [1] [2] [3]

[4] [5] [6] [7] [8] [9] [10]

R. N. Das and G.Pradhan, “A mixed quadrature rule for approximate evaluation of real definite integrals”, Int. J. Math Edu. Sci. & Technology, 1996, Vol.27, No.2, 279-283. R. N. Das and G.Pradhan, “A mixed quadrature rule for numerical integration of analytic functions”, Bulletin, Cal. Math Society, 89, 37-42. R. B. Dash and D. Das, “A mixed quadrature rule by blending Clenshaw-Curtis and Gauss-Legendre quadrature rules for approximation of real definite integrals in adaptive environment”, Proceedings of the International Multi Conference of Engineers and Computer Scientists”, 2011, Hong Kong, Vol.I, 202-205. R. B. Dash and D. Das, “Identification of some Clenshaw-Curtis quadrature rules as mixed quadrature of Fejer and NewtonCote type of rules”, Int. J. of Mathematical Sciences and Applications, 2011, Vol.1, No.3, 1493-1496. R. B. Dash and D. Das, “A mixed quadrature rule by blending Clenshaw-Curtis and Lobatto quadrature rules for Approximation of real definite Integrals in adaptive environment”, J. Comp. & Math. Sci., 2012, Vol.3(2), 207-215. Kendall E. Atkinson, “An Introduction to numerical Analysis”, 2nd ed.(John Wiley). Konrod A., “Nodes and Weights of Quadrature Formulas”, Consultants Bureau, 1965, New York. J. Stoer and R.Bulirsch, “Introduction to Numerical Analysis”, 3rd ed.(Springer International Edition). Philip J. Davis and Philip Rabinowitz, “Methods of Numerical Integration”, 2nd ed.(Academic Press). Patterson T., “Algorithm 468: Algorithm for numerical integration over a finite interval”, Comm. ACM 16, 1973, 694-699.

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Piessens R. E. deDoncker-Kapenga, C. Uberhuber and D. Kahaner, “A Subroutine Package for Automatic Integration”, QUADPACK, Springer-Verlag, 1983, New York. Ralson A., “A First Course in Numerical Analysis”, McGraw-Hill, 1965, New York. W. Gautschi, “Numerical quadrature in the presence of a singularity,” SIAM J. Numer. Anal., 1967, Vol.4, No.3, 357-362. P. K.Mohanty, M. K. Hota & S. R. Jena, “A Comparative Study of Mixed Quadrature rule with the Compound Quadrature rules”, American International Journal of Research in Science, Technology, Engineering & Mathematics”, 2014, Vol.7(1), 4552.

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Land holding effect on energy inputs for soybean production in Malwa plateau of Madhya Pradesh Dilip Jat, R.K. Naik*, N.K.Khandelwal, Bharat Patel and Prateek Shrivastava Department of Farm Machinery and Power Engineering, College of Agricultural Engineering, JNKVV, Jabalpur, INDIA. Abstract: The aim of this study were to determine the input–output energy used in soybean production to investigate the efficiency of energy consumption of soybean production under different farmer’s category. Data used in this study were collected randomly from 128 farmers of Malwa plateau by using a face to face questionnaire method. The farmers were classified into four categories of marginal (less than 1 ha), small (1 to 2 ha), medium (2 to 4 ha) and large farmers (more than 4 ha). The results indicated that total energy consumed by large farmers was found to be 10385 MJ/ha followed by medium farmers (9317 MJ/ha), small (8094 MJ/ha) and marginal (7749 MJ/ha). The highest yield value (1564 kg/ha) was obtained from large farmers followed by medium (1443 kg/ha), small (1307 kg/ha) and marginal farmers (1278 kg/ha). The results also revealed that energy use efficiency (4.28) was found for marginal farmers and decreased as the farm size increased. Marginal farmers were more successful in energy use efficiency, indicating a better management of energy and input consumptions. It was concluded that energy management at farm level could be improved to give more efficient and use of energy. Keywords: energy; input-output; land holdings; soybean

Introduction

Soybean (Glycine max L.) is an important oil seed crop. The total area under soybean cultivation in India is10.69 million hectare and total production is 12.67 million tonnes with average productivity of 1185 kg/ha. The area, production and productivity of soybean in the states of Madhya Pradesh are 5.81 million hectare, 6.68 million tonnes and 1050 kg/ha, respectively [1]. Madhya Pradesh is as major soybean producing state contributing 54.35 per cent in area 52.72 per cent in production to soybean cultivation of all over India. Energy consumption in agriculture for developing countries has been increasing rapidly due to recent economic growth and development [2]. However, increased input use in agricultural production may not bring maximum profits due to increasing production costs [3]. Energy, economics, and the environment are mutually dependent [4]. The productivity and profitability of agriculture depend upon energy use. The amount of energy used depends on the mechanization level, quantity of active agricultural work and cultivable land [5, 6]. Energy demand in agriculture can be divided on the basis of source of energy i.e. direct and indirect energy. Direct energy inputs include those quantities that are consumed during the crop production period such as human, animal, fuel wood, agricultural waste, petrol, diesel and electricity; whereas, indirect energy use refers to the energy embodied in all the input factors that are to be consumed in a production system and it includes machinery, fertilizers, herbicides, pesticides and material for plant propagation [7]. It has been estimated that fuel and fertilizers, in particular nitrogen, amount more than 60% of all the consumables embodied energy [8, 9]. The energy input-output analysis is usually made to evaluate the efficiency and environmental impacts of production systems. This analysis will determine how efficiently the energy is used. Calculating energy input in agricultural production is more difficult in comparison to the industry sector due to the high number of factors affecting agricultural production [10]. The study on effect of farm size on energy use and input costs for cotton production was conducted in Turkey and it was found that large farms were more successful in energy productivity, energy use efficiency and economic performance. They were also concluded that energy management at farm level could be improved to give more efficient and economic use of energy [11]. Another study [12] concluded that the dry apricot production in different farm sizes in terms of energy use efficiency and economic analysis. They reported that, both the total energy input and output energy in apricot production decreased when farm size increased; while, the energy use efficiency and energy productivity increased when farm size increased. [13] investigated the energy consumption in small, medium and large farms of tomato production; they concluded that large farms were more successful in terms of energy use and economic performance. The effect of farm size on energy ratio for wheat production and concluded that better energy efficiency and productivity were found on the large farms [14]. In addition, Technical efficiency (weighted output energy to weighted input energy ratio) is another way to explain the efficiency of farms [15]. The energy efficiency and cost analysis of canola production in different farm sizes. The results revealed that total energy input for canola

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production increased from small farms to large farms; while, the highest yield value was obtained from medium farms [16]. Besides these, still there limited study has been found on effect of size of land holdings on energy use in soybean productions was done. Therefore, the main objective of this study was to compare the energy use and energy indices for soybean production under different farmer’s categories in Malwa plateau of India. It also identifies operations where energy savings could be realized by changing applied practices in order to increase the energy use efficiency and propose improvements to reduce energy consumption for soybean production. II. Materials and Methods A preliminary survey was conducted in different villages to investigate the pattern of energy utilization for soybean production during 2011-12 of Malwa Plateau Zone. The soils of the zone are too deep having slight to moderate erosion and the major crops are Soybean, sorghum and maize for Kharif (May–October) season. For the analysis of energy use in different farmers category, the selected farmers were classified into four groups of marginal (less than 1 ha), small (1 to 2 ha), medium (2 to 4 ha) and large (more than 4 ha). A stratified random sampling procedure was adopted to find the sample size [17].

n

( N h S h ) 2

(1)

( N 2 D 2   N h Sh2 )

Where n is the required sample size; N is the number of total holdings in the target population; Nh is the number of the population in the h stratification; Sh is the standard deviation in the h stratification, Sh2 is the variance in the h stratification, D2 is equal to d2/z2; d is the precision, where 5% is permissible error and z is the reliability coefficient (1.96, which represents 95% reliability). Thus, calculated sample size in this study was 128. The source wise energy inputs used for soybean production including human power, animal power, diesel fuel, electricity, seeds, farm yard manure, fertilizers, chemicals and machinery were determined per hectare. In order to determine output and input energy, multiplying the physical quantities of output and input with their energy conversion factors gave the energy equivalents in MJ per hectare unit. Energy output arises mainly from the product and by-products. Energy output from main products is calculated by multiplying production and their corresponding energy equivalent. In calculation of energy output from by-product of the soybean, its straw weight was assumed to be equal to the weight of grain. The energy values were calculated by transforming data using energy equivalents shown in Table 1. The energetic efficiency of the agricultural systems was calculated by the relation between energy inputs and output. Based on the energy equivalents of inputs and outputs, the indices of energy use efficiency, energy productivity, specific energy and net energy were calculated using the following equations [14] Energy ratio = (Energy output) / (Energy input) Specific energy = (Energy input (MJ/ha)) / (Grain output (kg/ha)) Energy productivity = (Grain output (kg/ha)) / (Energy input (MJ/ha)) Net energy = Energy output - Energy input Energy use efficiency is defined as the ratio between the caloric heat of the output products and the total sequestered energy in the production factors. Energy productivity is the amount of a product obtained per unit of input energy. Energy output and net energy are crucial parameters when the availability of arable land is the limiting factor for plant production [18]. Table I Energy equivalents of inputs and output in soybean production Energy sources Inputs Human Man Woman Animal-pair (Body weight 350–450 kg) Machinery Diesel Petrol Fertilizers Nitrogen (N) Phosphorus (P2O5) Potassium (K2O) Farm Yard Manure Electricity Chemicals Superior (need dilution at the time of application) Inferior (not need dilution) Output Soybean Straw

Units

Equivalent energy (MJ/unit)

Reference

h h h

1.96 1.57 10.10

[19,20] [19,20,21] [19,20,21]

h l l

64.8 56.31 48.20

[21,22] [19,20,22] [19]

kg kg kg kg kWh

60.60 11.1 6.7 0.3 11.93

[19,23] [19,23] [19,23] [20,24] [21,24]

l kg

120 10

[21,22] [21,22]

kg kg

14.7 12.5

[6,19,21] [19]

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III. Results and Discussion A. Operation wise energy consumption Table 2 represents the amount of input and output for soybean production in different farmers category. From the study it was observed that the operation wise energy requirement varied from 4181 to 4961 MJ/ha with mean value of 4575 MJ/ha. The minimum energy consumption was observed for marginal farmers (4181 MJ/ha) compared to small farmers (4380 MJ/ha), medium farmers (4775 MJ/ha) and large farmers (4961 MJ/ha) respectively.The higher energy expenditure by large farmers was mainly due to the use of larger machinery and tractor to performed different operations on their farms. Similar trend was observed for source wise energy consumed among different categories of farmers. Figure 1 Operation wise and source wise energy use pattern for soybean production

Fig.1 shows that energy required in tillage was maximum (43.21%) of total energy followed by harvesting & threshing (24.48%), sowing (18.71%), transportation (7.37%), weeding (3.06%), spraying (2.75%) and fertilizer application (0.42%). Higher energy use in tillage by large farmers was mainly due to the more number of operations required to prepare the field during summer season after harvesting of Rabi crops and due to the use of high horse power tractors. In weeding operation, energy used by the marginal and small farmers is high as compared to medium and large farmers. It was due to the manual weeding and use of bullock power for intercultural operation by marginal and small farmers. Weeding by medium and large farmers was manual as well as chemical resulting in more energy use in spraying by these categories of farmers. In the operation of fertilizer application energy consumed by marginal farmers is more and energy consumption is inversely proportional to farmers category. It is due the increased use of seed cum fertilizer drill by medium and large farmers, with use of seed cum fertilizer drill the operation of sowing and fertilizer application was combined in one operation. Table II Operation wise and source wise input and output of soybean production Operations/Sources Operation wise energy (MJ/ha) Tillage Sowing Weeding Fertilizer application Spraying Harvesting & Threshing Transportation Total Source wise energy (MJ/ha) Human Animal Diesel Electricity Seeds FYM Fertilizer Chemicals Machinery Total energy Yield (kg/ha)

Farmers category MSF

Weighted average

1775 792 175 25 87 1013 314 4181

1891 821 181 27 74 1065 321 4380

2037 894 110 14 158 1184 378 4775

2203 918 93 11 183 1219 334 4961

1977 856 140 19 126 1120 337 4575

297 28 2837 176 1323 309 2374 117 288 7749 1278

305 32 3089 156 1296 371 2393 126 326 8094 1307

221 0 3861 74 1441 246 2912 189 373 9317 1443

195 0 4574 49 1463 233 3177 273 421 10385 1564

255 15 3590 114 1381 290 2714 176 352 8887 1398

B. Source wise energy consumption Source wise energy requirements for raising soybean crop under different farmerâ&#x20AC;&#x2122;s category in Malwa plateau are given in Table 2. It shows that the average energy input from different source was 8887 MJ/ha. The variation among the total energy input on the different categories of farmers was 7749-10385 MJ/ha. The total energy consumed by large farmers (10385 MJ/ha) was found to be higher followed by medium farmers (9317 MJ/ha), small (8094 MJ/ha) and marginal (7749 MJ/ha) respectively. Energy use per hectare was 34 % higher by large farmers and decreasing when the farm size group increased.

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Fig. 1 shows that the maximum energy input was through diesel 3590 MJ/ha (40.40%). The diesel energy was mainly used for operating tractor and combine harvester. Diesel energy is followed by fertilizer 2714 MJ/ha (30.54%), seeds 1381 MJ/ha (15.54%), machinery 352 MJ/ha (3.96%), FYM 290 MJ/ha (3.26%), human 255 MJ/ha (2.87%), chemical 176 MJ/ha (1.98%), electricity 114 MJ/ha (1.28%) and animal 15 MJ/ha (0.17%). The yield of soybean was in the range of 1279-1565 kg/ha with mean value of 1398 kg/ha. It was observed that when the land holding groups was increased from marginal to large farmers there was a decrease in the energy input value for human, animal, electricity and FYM while there was an increase for diesel, electricity, seeds, fertilizer, chemicals and machinery energy. Machinery energy consumed on farms of large farmers was around 46 % higher than marginal farmers and corresponding 22% increase in yield was observed. An increase in energy was mainly due to increased use of tractor driven implements, threshers and combine. It contributed to achieving higher productivity through timely completion of operations. Diesel and fertilizer energy was the biggest energy input for soybean production and account for more than 70 % of total input energy. With respect to the improving of energy efficiency, the diesel and fertilizer seemed to be the most significant categories for energy management. C. Relationship between energy inputs and soybean yield Fig 2 presents the effect of tillage energy, diesel energy, fertilizer energy and total input energy on yield of soybean. It is clear from the figure that the yield of soybean increases with input energy. The use of energy in tillage, diesel energy, fertilizer energy and total input energy by large farmers increased up to 2203 MJ/ha, 4574 MJ/ha, 3177 MJ/ha and 10385 MJ/ha respectively, corresponding increase in yield of soybean 1564 kg/ha was observed. Considering input energy as predictor and yield as response following linear equation was derived: Tillage energy, ŷ = 0.7472 x + 28.899, (R2 = 0.7845) Diesel energy, ŷ = 0.2892 x+360.31, (R2=0.7202) Fertilizer energy, ŷ = 0.4566 x+159.24, (R2=0.6023) Total input energy, ŷ = 0.1484 x+80.225, (R2=0.7866) Fig.2 further reveals that observed data are within the yield limit and has not reached to maximum limit, hence in the plateau there is a great scope to enhance yield by increasing additional input energy. Figure 2 Relationship between energy inputs and soybean yield

D. Effect of different size of land holdings on energy indices The energy indicators for soybean production under different farmer’s category are tabulated in Table 3. The results revealed that, soybean production in marginal farms showed the highest energy ratio as 4.49, while, energy ratio in large farms was the lowest as 4.01. An average of the energy ratio was determined as 4.28 and decreased as the size of land holdings increased and gave parallel results for energy productivity ranging from 0.150 kg/MJ on large farms to 0.165 kg/MJ on marginal farms. Specific energy in marginal farms was the lowest (6.06 MJ/kg)

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and it was highest in large farms (6.64 MJ/kg). With increase farm size the specific energy also increased. Moreover the large farms had the highest net energy (32156 MJ/ha).The total input energy use in the farms by marginal farmers and large farmers were 7749 MJ/ha and 10385 MJ/ha, the corresponding yield were 1278 kg/ha and 1564 kg/ha, respectively. According to the research results, energy was used more efficiently by the marginal farmers because they supervise their farms by themselves and manage all operation practices efficiently. The large farmers depend on labour to manage and supervision of farms. The increases in yield in large farms were due to additional physical inputs. Table III Operation energy indices for different farmerâ&#x20AC;&#x2122;s category for soybean cultivation Indices

Unit

Farmers category SF MSF

Weighted average

Direct energy

MJ/ha

3338

3582

4156

4818

3974

Indirect energy

MJ/ha

4411

4512

5161

5567

4913

Renewable energy

MJ/ha

1957

2004

1908

1891

1941

Non-renewable energy

MJ/ha

5792

6090

7409

8494

6946

Commercial energy Non-commercial energy energy ratio Specific energy Energy productivity Net energy

MJ/ha MJ/ha MJ/kg Kg/MJ MJ/ha

7115 634 4.49 6.06 0.165 27013

7386 708 4.39 6.19 0.161 27456

8850 467 4.21 6.45 0.155 29932

9957 428 4.01 6.64 0.15 32156

8327 560 4.28 6.34 0.158 29139

Figure 3 Percentage of different forms of energy for soybean cultivation

The distribution of inputs used in the production of soybean according to the direct, indirect, renewable, nonrenewable, commercial and non-commercial energy forms for all of farmers groups are also given in Table 3. The results revealed that, in all of the farmers groups, the indirect energy was greater than that of direct energy. The share of direct input energy was 44.72% in the total energy compared to 55.28% for the indirect energy. The contribution of non-renewable energy forms was higher than that of renewable energy consumption. The research results shows that on average the renewable form of energy input were 21.84% compared to 78.16% for nonrenewable energy. Commercial energy was much higher than that of non-commercial energy because diesel and fertilizer is account for more than 70% of source wise energy use. The commercial energy input was 93.70% compared to 6.30% for the non-commercial energy, (Fig. 3), that indicate that the current energy use pattern among the different size of farms is based on commercial energy for soybean production. Moreover, the ratio of direct and indirect energy resources were nearly the same, while, the renewable, non-renewable, commercial and non-commercial energies were fairly different from each other. IV. Conclusions This research was undertaken to evaluate the present energy use pattern for the most growing soybean crop of Malwa plateau of Madhya Pradesh. The data used in this research for the soybean production were collected from 128 farmers of Malwa plateau, where soybean is cultivated as one of the major crop. Soybean production consumed a total of 8887 MJ/ha energy, which is mainly from commercial sources. Energy use per hectare was increased when the farm size group increased, and it was 34% higher on large farms as compare to marginal farms. Diesel and fertilizer energy was the biggest energy input for soybean production and account for more than 70 % of total energy. The operational energy use varies between 4181 MJ/ha to 4961 MJ/ha from marginal to large farmers with mean value of 4575 MJ/ha. Among various field operations, seedbed preparation was observed to be maximum energy-consuming operation for soybean production under the different farmer's category. Seed bed preparation, sowing, harvesting and threshing were the main operations for energy consumption. Among the different categories of farmers, it was observed that better outputâ&#x20AC;&#x201C;input energy ratio and energy productivity were found on the marginal farms. According to the result, marginal farmers were more successful in energy utilization.

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[1] [2] [3] [4]

[5] [6] [7] [8]

[9]

[10]

[11] [12] [13] [14]

[15]

[16]

[17] [18] [19]

[20] [21] [22] [23] [24]

References SOPA, 2014. Available from: http://www.sopa.org/crop%20report%202014.pdf. J. Iwaro and A. Mwasha, “Implications of building energy standard for sustainable energy efficient design in buildings,” Int. J. Energy Environ 1 (2010), no. 5, 745-756. G. Erdal, K. Esengün, H. Erdal and O. Gündüz, “Energy use and economical analysis of sugar beet production in tokat province of turkey,” Energy 32 (2007), no. 1, 35-41. D. Pimentel, M. Herdendorf, S. Eisenfeld, L. Olander, M. Carroquino, C. Corson, J. McDade, Y. Chung, W. Cannon and J. Roberts, “Achieving a secure energy future: Environmental and economic issues,” Ecological Economics 9 (1994), no. 3, 201-219. M. Alam, M. Alam and K. Islam, “Energy flow in agriculture: Bangladesh,” American Journal of Environmental Sciences 1 (2005), no. 3, 213. B. Ozkan, H. Akcaoz and C. Fert, “Energy input–output analysis in turkish agriculture,” Renewable energy 29 (2004), no. 1, 39-51. D. Pimentel, Handbook of energy utilization in agriculture, CRC Press, Inc., 1980. K.-J. Hülsbergen, B. Feil, S. Biermann, G.-W. Rathke, W.-D. Kalk and W. Diepenbrock, “A method of energy balancing in crop production and its application in a long-term fertilizer trial,” Agriculture, Ecosystems & Environment 86 (2001), no. 3, 303-321. N. Nassi o Di Nasso, S. Bosco, C. Di Bene, A. Coli, M. Mazzoncini and E. Bonari, “Energy efficiency in long-term mediterranean cropping systems with different management intensities,” Energy 36 (2011), no. 4, 1924-1930. O. Yaldiz, H. Ozturk, Y. Zeren and A. Bascetincelik, “Energy usage in production of field crops in turkey,” 5th International Congress on Mechanisation and Energy Use in Agriculture. Turkey: Kusadasi, 1993, p.^pp. 11-14. I. Yilmaz, H. Akcaoz and B. Ozkan, “An analysis of energy use and input costs for cotton production in turkey,” Renewable Energy 30 (2005), no. 2, 145-155. K. Esengun, O. Gündüz and G. Erdal, “Input–output energy analysis in dry apricot production of turkey,” Energy Conversion and Management 48 (2007), no. 2, 592-598. B. Cetin and A. Vardar, “An economic analysis of energy requirements and input costs for tomato production in turkey,” Renewable Energy 33 (2008), no. 3, 428-433. S. Shahin, A. Jafari, H. Mobli, S. Rafiee and M. Karimi, “Effect of farm size on energy ratio for wheat production: A case study from ardabil province of iran,” American-Eurasian Journal Agricultural and Environment Science 3 (2008), no. 4, 604-608. N. S. Chauhan, P. K. Mohapatra and K. P. Pandey, “Improving energy productivity in paddy production through benchmarking—an application of data envelopment analysis,” Energy Conversion and Management 47 (2006), no. 9, 1063-1085. S. Mousavi-Avval, S. Rafiee, A. Jafari and A. Mohammadi, “Energy efficiency and cost analysis of canola production in different farm sizes,” International Journal of Energy and Environment 2 (2011), no. 5, 845-852. T. Yamane, Elementary sampling theory, Prentice-Hall, Englewood CliKs, NJ, USA, 1967. A. Tabatabaeefar, H. Emamzadeh, M. G. Varnamkhasti, R. Rahimizadeh and M. Karimi, “Comparison of energy of tillage systems in wheat production,” Energy 34 (2009), no. 1, 41-45. B. Panesar and A. Bhatnagar, Energy norms for inputs and outputs of agricultural sector, energy management and conservation in agricultural production and food processing, Ludhiana: USG Publishers and Distributors (1994), 5-16. H. Singh, D. Mishra and N. Nahar, “Energy use pattern in production agriculture of a typical village in arid zone, india––part i,” Energy Conversion and Management 43 (2002), no. 16, 2275-2286. D. De, R. Singh and H. Chandra, “Technological impact on energy consumption in rainfed soybean cultivation in madhya pradesh,” Applied Energy 70 (2001), no. 3, 193-213. J. Singh, On farm energy use pattern in different cropping systems in haryana, india, Master of Science, Germany, International Institute of Management, University of Flensburg (2002). J. Mittal, Research manual on energy requirements in agricultural sector, All India Co-ordinated Research in Agricultural Sector, 1988. S. Singh and J. Mittal, Energy in production agriculture, Mittal Publications, 1992.

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Comparative Study of Dynamic Performance of Multi-Area Interconnected Power Systems with EHVAC/HVDC Links Ram Naresh Mishra1, Dr. Prabhat Kumar2 Assistant Professor, Department of Electrical Engineering, G.L.A. University, Mathura, Uttar Pradesh, INDIA. 2 Ex-Professor & Chairman, Department of Electrical Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh, INDIA. 1

Abstract: This paper presents comparative study of dynamic performance of 3-area interconnected HydroThermal power systems when subjected to small step load perturbations. For the present study, power system model-1 consists of one area with reheat thermal power plants and other two areas with hydro power plants having identical capacity and power system model-2 consists of two areas with reheat thermal power plants and other area with hydro power plants having identical capacity . The system interconnection is considered namely (I) EHVAC transmission link only (II) EHVAC in parallel with HVDC transmission link. The dynamic model of incremental power flow through HVDC transmission link is derived based on frequency deviation at both rectifier and inverter ends. Moreover, the HVDC link is considered to be operating in constant current control mode. To carry out the investigations, optimal AGC regulators are designed using proportional-plus-integral control strategy and implemented on the system under consideration in the wake of 1% step load perturbation in thermal/hydro area. The system responses have been simulated in Mat lab. Responses of deviation in frequencies, deviation in tie line powers ac as well as dc and integral of area control errors have been plotted for three areas for both power system models. Thus, on the basis of these responses, the comparative study of dynamic performance of the systems has been studied. Besides this, to study the closed loop system stability, the closed loop system eigen values are computed. Keywords: Interconnected power systems; System dynamic performance; EHVAC//HVDC Transmission link; Optimal AGC regulator.

I. Introduction The normal operation of interconnected power systems requires that each area maintains the load and generation balance. The change in frequency and voltage from their nominal values, when there is any mismatch in real and reactive power generations and demands. Generally, the power systems are frequently subjected to varying load demands. For an efficient and successful power system operation in the wake of area load changes and abnormal conditions, such as outages of generation, leading mismatches have to be corrected via supplementary control. Automatic Generation Control (AGC) of interconnected power systems is defined as the regulation of power output of generators within a prescribed area, in response to change in system frequency, tie-line loading, or the relation of these to each other, so as to maintain scheduled system frequency and/or established interchange with other areas within predetermined limits. Thus optimal AGC is a very important issue in the operation of power systems to supply sufficient & reliable electric power with good quality. The good quality of power means consistency of frequency, voltage and level of reliability. In real practice power system components are nonlinear. Therefore linearization around a nominal operating point is usually performed to get a linear system model, which is used in the controllers design process. The number of control engineers Fosha, Elgerd[1], Calovic[2], Carpentier, M.L.Kothari, J. Nanda & Prabhat Kumar[5] have presented their pioneer work on optimal AGC regulator design using modern optimal control theory. The main objectives of this piece of work are as under: (a) To design an optimal AGC controllers for an interconnected 3-area Hydro-Thermal power system model1&2 with full state vector feedback control strategy in the wake of 1% step load disturbance in thermal / hydro area incorporating EHVAC/HVDC inter-ties and study(comparative) the systemâ&#x20AC;&#x2122;s dynamic performance. (b) To study the closed loop system stability, the closed loop system eigen values have been computed for the power systems model-1&2 with EHVAC/HVDC inter-ties.

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II. Power System Model: Power system model-1 consists of one area with reheat thermal power plants and other two areas with hydro power plants having identical capacity and power system model-2 consists of two areas with reheat thermal power plants and other area with hydro power plants having identical capacity . The system interconnection is considered namely (I) EHVAC transmission link only (II) EHVAC in parallel with HVDC transmission link.

Fig. 5: BLOCK DIAGRAM OF 1-THERMAL & 2-HYDRO AREA INTERCONNECTED HYDROTHERMAL POWER SYSTEMS

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Fig. 6: BLOCK DIAGRAM OF 2-THERMAL & 1-HYDRO AREA INTERCONNECTED HYDROTHERMAL POWER SYSTEMS III. OPTIMAL AGC REGULATOR DESIGN WITH FULL STATE VECTOR FEED-BACK [4] An s-area interconnected power system described by a completely controllable and observable linear timeinvariant state space representation is considered for the present work. The differential equations of the system in state variable form can be written as 

X  A X + B U + Fd Pd Y=CX Where: X , U, Pd and Y are the state, control, disturbance and output vectors respectively. A, B, C and Fd are the matrices of compatible dimensions. Problem may be stated as find the control U, so as to minimize the performance index 

 2 1

XT Q X + U T R U

 dt

(1.1) (1.2)

(1.3)



Where, Q – a positive semi-definite symmetric state cost weighting matrix. R – a positive definite symmetric control cost weighting matrix. In the application of optimal control theory, the term Fd Pd in eqn (1.1) is eliminated by redefining the states and controls in terms of their steady-state values occurring after the disturbance. Eqn (1.1) can be rewritten as; 

X

A X + B U ; X (0) = Xo Where, X (0) = Xo is the initial condition. With a full state vector feedback control problem, a control law is stated in the form

(1.4)

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U* = -K* X (1.5) Hence, in order to design optimal regulator so as to minimize the performance index (1.3), a Matrix- Riccatti (MR) equation given by the following equation is to be solved (The inbuilt LQR command has been used): AT P + PA – P B R-1BT P + Q = 0 (1.6) By solving this equation, we get positive definite symmetric matrix P such that the optimal control law is calculated as U* = - R-1 BT P X (1.7) Hence, the desired optimal feedback gain matrix will be K* = R-1 BT P (1.8) State Variable Model The state space representation is described by Equations (1.1) and (1.2). The structures of state, control, and disturbance vectors for all case studies are given as follows. Power System Model-1 Case Study b1 :( EHVAC Transmission Link) State vector: [XI] = [∆f1, ∆Pg1,∆PR1,∆Xg1,∆f2,∆Pg2, ∆PR2,∆Xg2,∆f3,∆Pg3,∆Xg3, ∆Xgh3, ∆Ptie1, ∆Ptie2, ∆Ptie3, IACE1, IACE2, IACE3] T Control vector: [UI] = [U1 U2 U3] T = [∆Pc1, ∆Pc2, ∆Pc3] T Distribution vector: [PdI] = [∆Pd1, ∆Pd2, ∆Pd3] T Case Study b2 :( Parallel EHVAC/HVDC Transmission Link) State vector: [XII] = [XI ∆Pdc1 ∆Pdc2 ∆Pdc3] T Control vector: [UII] = [UI] Distribution vector: [PdII] = [PdI] Power System Model-2 Case Study c1:( EHVAC Transmission Link) State vector: [XIII] = [∆f1,∆Pg1,∆PR1,∆Xg1,∆f2,∆Pg2,∆Xg2,∆Xgh2,∆f3,∆Pg3,∆Xg3,∆Xgh3 , ∆Ptie1, ∆Ptie2, ∆Ptie3, IACE1, IACE2, IACE3] T Control vector: [UIII] = [U1 U2 U3] T = [∆Pc1, ∆Pc2, ∆Pc3] T Distribution vector: [PdIII] = [∆Pd1, ∆Pd2, ∆Pd3] T Case Study c2: :( Parallel EHVAC/HVDC Transmission Link) State vector: [XIV] = [XIII ∆Pdc1 ∆Pdc2 ∆Pdc3] T Control vector: [UIV] = [UIII] Distribution vector: [Pd IV] = [Pd III] IV. SIMULATION RESULTS The system responses have been simulated in Mat lab. In the present study, Optimal AGC regulators based on full state vector feedback control strategy are designed. The closed loop system eigen values are computed to investigate the systems stability and the same are presented in Table. TABLE: Optimal Closed-loop system eigen values. Power System Model-1 CASE STUDY (b1): -41.4388 -41.4555 -0.3327 ± 3.2146i

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-0.3188 ± 3.1051i -3.5272 -3.1568 -2.6479 -1.9087 -1.2534 ± 0.7453i -0.4609 -0.3314 -0.1955 ± 0.0717i -0.1150 CASE STUDY (b2): -57.2633 -57.2813 -2.2648 ± 9.6325i -2.2798 ± 9.5847i -3.6623 -5.0000 -2.7957 -2.4751 ± 0.5362i -1.1923 ± 0.6144i -0.5093 -0.4576 -0.4861 -0.2638 -0.1636 ± 0.0687i -0.1149 Power System Model-2 CASE STUDY (c1): -57.2634 -3.6275 -0.2716 ± 3.1270i -0.2986 ± 3.0821i -2.8832 -2.6398 -1.4447 -0.9643 ± 0.5989i -0.4835 -0.1893 ± 0.0736i -0.1471 ± 0.0844i -0.1152 CASE STUDY (c2): -41.4382 -2.2693 ± 9.6093i -2.2819 ± 9.5587i -3.5931 -5.0000 -2.8122 -1.9946 ± 0.3631i -1.0121 ± 0.6075i -0.5147 -0.4991 -0.4529 -0.2105 ± 0.0618i -0.1530 ± 0.0902i -0.1150

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FIG: 9 (a & b) FIG: 7(a & b)

FIG: 10 (a & b)

FIG: 8(a & b)

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FIG: 11(a & b)

FIG: 13 (a & b)

FIG: 12 (a & b) Fig (7) shows that response plots of deviation in frequencies in area -1, 2 and 3 subjected to 1% step load disturbance in thermal/hydro area, with higher number of oscillations and large settling time. The magnitude of overshoot and settling time is high in case of THH. It is inferred that TTH have better dynamic performance in all aspects of system responses in all areas subjected to 1% step load disturbance either in thermal or hydro area. Fig (8) shows that response plots of deviation in tie line powers in area -1, 2 and 3 subjected to 1% step load 7 of oscillations, large settling time. The settling time is disturbance in thermal/hydro area with higher number high in case of THH. It is inferred that TTH have better dynamic performance in all aspects of system responses excluding area-2 subjected to 1% step load disturbance either in thermal or hydro area. Fig (9) shows that response plots of integral of area control errors in area -1, 2 and 3 subjected to 1% step load disturbance in thermal/hydro area with higher number of oscillations, large settling time. The magnitude of overshoot and settling time is high in case of integral of area control error for THH. It is inferred that TTH have better dynamic performance in all aspects of system responses in all areas subjected to 1% step load disturbance either in thermal or hydro area. Fig (10) shows that response plots of deviation in frequencies in area -1, 2 and 3. This trend of response is exhibited for 1% step load disturbance in thermal/hydro area. The magnitude of overshoot and settling time is high in case of THH. It is inferred that TTH have better dynamic performance in all aspects of system responses in all areas subjected to 1% step load disturbance either in thermal or hydro area. Fig (11) shows that response plots of deviation in ac tie line powerâ&#x20AC;&#x2122;s in area -1,2 and 3 subjected to 1% step load disturbance in thermal/hydro area with higher magnitude of overshoot, large settling time and higher value

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of steady state error even after 15 s of time. It is clear that TTH have better dynamic performance in all aspects of system responses excluding area-2 subjected to 1% step load disturbance either in thermal or hydro area. Fig (12) shows that response plots of deviation in dc tie line powers in area –1, 2 and 3 subjected to 1% step load disturbance in thermal/hydro area with higher magnitude of overshoot, large settling time and higher value of steady state error even after 15 s of time. It is clear that, in case of responses of delta Pdc1 (LDTA), delta Pdc2 (either in LDTA or LDHA) for THH have better dynamic performance in all aspects. In case of response of delta Pdc1 for TTH have better dynamic response subjected to 1% step load disturbance in hydro area. But the response of delta Pdc3 is nearly same for both cases (i.e. THH &TTH) subjected to 1% step load disturbance either in thermal or hydro area. Fig (13) shows that response plots of integral of area control errors in area -1, 2 and 3 subjected to 1% step load disturbance in thermal/hydro area with higher number of oscillations, large settling time and steady error exits even after 15 s of time, The magnitude of overshoot is high in case of integral of area control error for hydro area. The response of IACE1 is better for TTH .But the response of IACE3 is nearly same for both cases (i.e. THH &TTH) subjected to 1% step load disturbance either in thermal or hydro area. V. CONCLUSION The paper presents comparative study of dynamic performance of 3-area interconnected hydro-thermal power systems when subjected to 1% step load disturbance in thermal/hydro area. It is clear that TTH (power system model-2) have better dynamic performance in all aspects of system responses excluding deviation in ac tie line power in area-2 subjected to 1% step load disturbance in thermal/hydro area. In case of responses of delta Pdc1 (LDTA), delta Pdc2 (either in LDTA or LDHA)8 for THH (power system model-1) has better dynamic performance in all aspects. In case of response of delta Pdc1 for TTH (power system model-2) have better dynamic response subjected to 1% step load disturbance in hydro area. But the response of delta Pdc3 is nearly same for both cases (i.e. THH &TTH) subjected to 1% step load disturbance either in thermal or hydro area. The response of IACE1 is better for TTH .But the response of IACE3 is nearly same for both cases (i.e. THH &TTH) subjected to 1% step load disturbance either in thermal or hydro area. In case of IACE2, for TTH has reduction in overshoot as compared to THH case. However, small value of steady state error exists. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16]

FOSHA C.E., and ELGERD 0.I.,”The megawatt-frequency control problem: a new approach via optimal control theory”, IEEE Trans., PAS-89, 1970, pp.563-577. IEEE Committee Report,” Dynamic models for steam and hydro turbines in power system Studies”, IEEE Trans., PAS-92, 1973, pp. 1904- 1915. M Calovic, “linear regulator design for a load and frequency control”, IEEE transactions, PAS (91), Nov/Dec1972, pp. 22712285. M.L. Kothari, Prof. J. Nanda, “Application of optimal control Strategy to Automatic generation control of a hydrothermal system”, IEE proceedings, Vol. 135, Pt. D, No. 4, July 1988, pp.268-274. Prabhat Kumar, K E Hole’ and R P Aggarwal, “Design of suboptimal AGC Regulator for Hydro-Thermal Power System”. IE (I) Journal. pt EL6, June 1983 ,pp 304-309. Ibraheem, P Kumar and S Ahmad, “Dynamic Performance Enhancement of Hydro-Power Systems with Asynchronous Tielines”. IE (I) Journal Vol 85, June 2004 pp. 23-34. O. I. Elgerd and C. Fosha, “Optimum megawatt frequency control of multi-area electric energy systems,” IEEE Trans. Power App. Syst., vol. PAS-89, no. 4, Apr. 1970, pp. 556–563. T. E. Bechert and N. Chen, “Area automatic generation control by multi-pass dynamic programming,” IEEE Trans. Power App. Syst., vol. PAS-96, no. 5, Sep./Oct. 1977, pp. 1460–1468. D. Das, J. Nanda, M. L. Kothari, and D. P. Kothari, “Automatic generation control of hydrothermal system with new area control error considering generation rate constraint,” Elect. Mach. Power Syst., vol. 18, no. 6, Nov. /Dec. 1990. pp. 461–471. Ibraheem, Prabhat Kumar, and Dwarka P. Kothari, “Recent Philosophies of Automatic Generation Control Strategies in Power Systems”, IEEE transactions on power systems, vol. 20, no. 1, February 2005, pp.346-357. IEEE PES Working Group, Hydraulic turbine and turbine control models for system dynamic,” IEEE Trans. Power Syst., vol. PWRS-7, no. 1, Feb. 1992, pp. 167–174, IEEE Committee Report, “Standard definitions of terms for automatic generation control on electric power systems”, IEEE Trans. Power App. Syst., vol. PAS-89, Jul./Aug. 1970.pp.7-11. E.V. Bohn, S.M. Miniesy, “Optimum load-frequency continuous control with unknown Deterministic power demand”, IEEE committee report, 1971, pp.1910-1915. Ibraheem & Prabhat Kumar, “Study of Dynamic Performance of Power Systems with Asynchronous Tie-lines considering Parameter Uncertainties”, IE (I) Journal vol83, June 2004, pp.35-42. Ibraheem & Prabhat Kumar, “Current Status of the Indian Power Systems and Dynamic Performance Enhancement of Hydro Power Systems with Asynchronous Tie Lines”, Electric Power Components &Systems 2003, pp.605-626. H. L. Zeynelgil, A. Demiroren, and N. S. Sengor, “The application of ANN technique to automatic generation control for multiarea power system,” Elect. Power Energy Syst., vol. 24, no. 5, Jun.2002,pp. 345–354.

NOTATIONS: i =Subscript referring to area (i=1,2,3) ∆Xgi= Incremental change in governor valve position of ith area ∆Pci= Incremental change in speed changer position of ith area ∆Pgi= Incremental change in power generation of ith area ∆Pdi= Incremental change in load demand of ith area (p.u. MW/Hz)

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Ram Naresh Mishra et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(3), December 2014-February 2015, pp. 275-283 ∆Fi= Incremental change in frequency of ith area ∆Ptiei =Incremental change in tie-line power flow of ith area (MW) ∆Pdci= Incremental change in DC link power flow of ith area ∆Pri= Incremental change in reheat turbine output of ith area fo=Nominal system frequency (Hz) Hi =Per unit inertia constant of ith area (sec) Di= Load frequency constant of ith area (p.u. MW/Hz) Ri= Speed regulation parameter of ith area (Hz/p.u. MW) Bi= Frequency bias constant of ith area (p.u. MW/Hz) Kgi= Speed governor gain of ith area Tgi =Speed governor time constant of ith area (sec) Kri= Reheat turbine gain Tri =Reheat turbine time constant (sec) Kdc =DC-Link gain Tdc= DC-Link time constant (sec) Pri= Rated power output of ith area Δi= Power angle of ith area Pmax= Maximum rated power T12= Synchronizing coefficient of AC link a12= Area size ratio coefficient A =System matrix B=Control matrix C= Output matrix fd= Disturbance matrix X= State vector Y= Output vector U =Control vector Pd= Disturbance vector J= Performance index value Q= Positive semi-definite symmetric state cost weighting matrix R= Positive definite symmetric control cost weighting matrix P= Positive definite symmetric matrix T1,T2,T3= Time constants representing hydro governor Tw= Water inertia time constant K =Feedback gain matrix I= Identity matrix Z =Closed loop system matrix S =Symmetric cost matrix MR= Matrix Riccatti ACE= Area Control Error IACEi= Integral Area Control Error of ith area. AGC= Automatic Generation Control LFC= Load Frequency Control LQR= Linear Quadratic Regulator Hz= Hertz MW =Mega Watt α= Rectifier Firing Angle EHVAC= Extra High Voltage Alternating Current HVDC =High Voltage Direct Current PI= Proportional Integral Control THH= One Thermal & two hydro Power Systems TTH =Two Thermal &one hydro Power Systems LDTA= Load disturbance in thermal area LDHA=Load disturbance in hydro area

APPENDIX A Numerical data: Power System Model-1: For Reheat Thermal Plants; Pr1 =Pr2= 2000 MW; H1 = H2=5 Sec; D1 =D2= 0.00833 p.u. MW/Hz; M1=M2=0.167pu MW/Hz; R1 =R2= 2.4 Hz p.u.MW; B1 =B2= 0.425 p.u.MW/Hz; Tg1 =Tg2= 0.08 Sec; Tt1 =Tt2= 0.3 sec; a12 = -1; ∆Pd1 = 0.01; ∆Pd2 = 0.00; Kr1 =Kr2= 0.5; Tr1 =Tr2= 10 Sec;For Hydro plant Pr3=2000 MW; H3=5 Sec; D3=.00833 p.u.MW/HZ; M3=0.167pu MW/Hz; R3 =2.4 Hz p.u.MW;B3=0.425p.u.MW/Hz;T1=0.513Sec;T2=5 Sec; T3=48.7 Sec; Tw=1.0 Sec; ∆Pd3= 0.00;For AC& DC Link Pmax = 200 MW (10%of Rated Power);2*pi*T12=2*pi*T23=2*pi*T31=0.545a = δ1- δ2 = δ2- δ3 =δ3δ1=30°;Kdc1=Kdc2=Kdc3=1.0;Tdc1=Tdc2=Tdc3=0.2Sec;Power System Model-2:For Reheat Thermal Plant Pr1 = 2000 MW; H1 =5 sec; D1 = 0.00833 p.u. MW/Hz; M1=0.167pu MW/Hz; R1 = 2.4 Hz p.u.MW; B1 = 0.425 p.u.MW/Hz; Tg1 = 0.08 Sec; Tt1 = 0.3 sec; a12 = -1; ∆Pd1 = 0.01; Kr1 = 0.5; Tr1 = 10 Sec;For Hydro plant Pr2=Pr3=2000 MW; H2=H3=5 sec;D2=D3=.00833p.u.MW/HZ;M2=M3=0.167puMW/Hz;R2=R3=2.4Hzp.u.MW;B2=B3=0.425p.u.MW/Hz;T11=T12=0.513Sec;T21=T22 =5Sec;T31=T32=48.7Sec;Tw1=Tw2=1.0Sec; ∆Pd2 = ∆Pd3=0.00;For AC& DC Link Pmax = 200 MW (10% of Rated Power);2*pi*T12=2*pi*T23=2*pi*T31= 0.545 puMW,a = δ1- δ2 = δ2- δ3 =δ3-δ1=30°;Kdc1 = Kdc2=Kdc3=1.0; Tdc1=Tdc2=Tdc3 = 0.2 Sec;

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Behavior of a Discrete SIR Epidemic Model A. George Maria Selvam1, R. Janagaraj2 and D. Jerald Praveen3. Sacred Heart College, Tirupattur - 635 601, Vellore, Tamil Nadu, INDIA. 2 Government College of Engineering, Bargur - 635 104, Vellore, Tamil Nadu, INDIA. 1, 3

Abstract: In this paper, we propose a discrete-time SIR epidemic model described by difference equations. The basic reproductive number of a discrete SIR epidemic model is computed and the dynamical behavior of the model is studied. The stability the disease free equilibrium and the endemic equilibrium are demonstrated. Numerical simulations are performed to illustrate the theoretical results. Keywords: Epidemic Model, difference equations, stability. I. Introduction Infections and infectious diseases are a great burden on many societies, including the countries. An epidemic may be described as a sudden outbreak of a disease that infects a substantial portion of the population in a region before it disappears. In the nineteenth century, recurrent waves of cholera killed millions in India. The influenza epidemic of 1918-1919 killed at least 20 million people overall, more than half a million in the United States. Epidemics of infectious diseases have been documented throughout history. Mathematical models provide an explicit framework within which to develop and communicate an understanding of infectious disease transmission dynamics. The earliest account of mathematical modeling of spread of disease was carried out in 1766 by Daniel Bernoulli. The SIR model is a simple model, due to Kermack and McKendrick, of an epidemic of an infectious disease in a population, [3]. In the theoretical studies of epidemic dynamical models, there are two kinds of mathematical models: the continuous-time models described by differential equations, and the discrete-time models described by difference equations. Analysis of steady states of the model and the stability for the epidemic model is of a great importance as it can help our society and direct us to determine and forecast the development trend of infection. The solution results can be used to describe the spread characteristics of infectious diseases, predict the status of the infection and evaluate the efficiency of the control strategies. II. Formulation of Model The host population is divided into three epidemiological classes: the susceptibles , the infectives , and the removed/recovered. Few authors studied discrete epidemic models [2, 4, 6] where as there exists a vast literature on continuous epidemic models [1, 5]. In this section, we analyze the following discrete SIR epidemic model.

where and the initial conditions are The parameters have the following meaning: is the birth rate, is the death rate, is the average number of contacts per infective per day, is the recovery rate, and is the death rate of infectives caused by the disease. The system (1) always has a disease-free equilibrium and an endemic equilibrium , where is called the disease free equilibrium since and III.

classes are empty.

Dynamic Behavior of the Model and Numerical Simulations

This section deals with the stability of equilibria. By mathematical analysis, we derive a threshold value and prove that the values of determine the dynamics of system. An important technique for analyzing nonlinear systems qualitatively is the analysis of the behavior of the solutions near equilibrium points using linearization. For the discrete time model, stability of the equilibrium solution requires the dominant eigenvalue to have magnitude less than one. For the system described by equations (1), this reduces to requiring all roots of the following equation to lie in the unit circle [6]. The local stability analysis of the model can be carried out by computing the Jacobian matrix corresponding to each equilibrium point. We first determine the stability of the system. The Jacobian matrix of system (1) is

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A. Disease Free Equilibrium. At the disease-free equilibrium, the matrix of the linearization is given by

The eigen values of the matrix

are

and

. The basic reproductive

number, , is fundamental in the study of epidemiological models. Here the basic reproductive number , where The epidemic spreads when and dies out when . If , the disease-free equilibrium is stable. Example 1. We choose the parameter the initial condition (S, I, R) = (0.85, 0.15, 0.0). Here

and so the equilibrium point is

globally stable, see fig - 1.

Figure 1. Time plot and Phase diagram for the system (1) with Thus the disease free equilibrium of (1) is asymptotically stable when . In the following figure, the effect of the parameter on the disease dynamics (infection) is demonstrated.

Figure 2. Variation of

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Example 2. Choose the parameter condition (S, I, R) = (0.85, 0.15, 0.0). Here

so the equilibrium point

and the initial is globally stable, see fig -

Figure 3. Time plot and Phase diagram for the system (1) with 3.2. Endemic Equilibrium. The linearization matrix of (1) at the positive equilibrium

The eigen values of the matrix where If

are and the endemic equilibrium

is,

, is stable.

Example 3. Choose the parameter and the initial condition (S, I, R) = (0.5, 0.6, 0.4). Here so the equilibrium point is asymptotically stable, see fig - 4. When , the average number of a new infection by an infected individual is more than one. Hence the disease may keep persistent in the population.

Figure 4. Time plot and Phase diagram for the system (1) with The discrete SIR model considered in this paper is simple, but it exhibits rich and complicated dynamical behavior. The analytical findings are confirmed with numerical simulations. IV. References [1]. [2].

Leah Edelstein-Keshet, Mathematical Models in Biology, SIAM, Random House, New York, 2005. Hu et al.: Dynamical analysis and chaos control of a discrete SIS epidemic model. Advances in Difference Equations 2014, 2014:58

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[4]. [5]. [6]. [7].

Kermack, W. O. McKendrick, A. G. (1927). "A Contribution to the Mathematical Theory of Epidemics". Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 115 (772): 700. doi:10.1098/rspa.1927.0118. JSTOR 94815. edit Ma et al.: Global stability of the endemic equilibrium of a discrete SIR epidemic model. Advances in Difference Equations 2013 2013:42. J.D.Murray, Mathematical Biology I: An Introduction, 3-e, Springer International Edition, 2004. M.Reni Sagayaraj, A. George Maria Selvam, R.Janagaraj, J.Kanimozhi, Analysis of a Discrete SIR Epidemic Model, International Journal Of Informative Futuristic Research, Volume -1 Issue -10, June 2014, Page No.:86-91. Saber Elaydi, An Introduction to Difference Equations, Third Edition, Springer International Edition, First Indian Reprint, 2008.

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Effect of additives on the structural and magnetic properties of electrodeposited NiMn thin film M.Rajeswari, S.Ganesan Department of Physics, Govt. College of Technology, Coimbatore-641 013, INDIA Abstract: Nanocrystalline Ni-Mn thin films were deposited by electrodeposition method on copper substrate with different additives such as saccharin and urea at 50°C and 70°C temperatures. The structural and surface properties of Ni-Mn thin films were studied by using X-ray Diffractometer (XRD) and Scanning Electron Microscopy (SEM). Elemental compositions of the films were measured by means of Energy Dispersive X-ray Spectroscopy (EDAX). Magnetic properties of the thin films were studied with the aid of Vibrating Sample Magnetometer (VSM). Hardness of the films was measured by using Vickers Hardness Number (VHN). Micro hardness testing was carried out to determine the relation between these mechanical properties with crystallite size. The deposits of Ni-Mn thin films were found to be shiny, smooth, nanocrystalline and good adherence to the substrate. The deposits were found to have face centered cubic (FCC) structure. NiMn thin films electrodeposited with saccharin as an additive found to have higher magnetization with lower coercivity and are suitable for MEMS devices. Key words: NiMn thin film; magnetic property; mechanical property; MEMS application; I. Introduction Soft magnetic films with a high magnetic moment are used in variety of applications, such as magnetic recording systems, high frequency plasma inductors and modern non-volatile magnetic memory [1]. Permalloy (NiFe) is the best known thin film alloy in MEMS applications [2], because of their higher saturation flux density, lower coercivity, higher saturation magnetization and lower magnetostriction. The stress free thin film alloys with enhanced magnetic properties were very much used in magnetic recording heads and MEMS [3]. The well known stress reducing agents [4], [5] for nickel based electro deposition are sulfur containing organic additives (saccharin, thiourea, etc). The electrodeposited film exhibit grain sizes less than 100 nm. In most of the investigations, Ni-Mn alloys were electrodeposited from sulphate and sulfamate baths and very few from chloride bath. Fathi et al reported that the percentage of Mn content presents in the film increases with increasing current density. Moreover the effectiveness of cathode has enhanced by chloride ions and films were deposited even at low voltages due to the high conductivity of chloride bath [6], [7]. The Ni-Mn alloy is an intellectual combination to investigate further in their abundant inimitable properties viz, the contradictory type of magnetic alignment in their fundamental state is one among them. The ferromagnetic nickel and paramagnetic manganese come together to produce Ni-Mn alloys with attractive magnetic properties [8]. Babanov et al reported that Ni75Mn25 shows paramagnetic behavior at room temperature and Ni 80Mn20 shows ferromagnetic behavior [9]. By keeping this context in mind, in the current investigation Ni-Mn alloy thin films were electrodeposited from chloride bath by means of glycine used as an additive. As literature reveals that the effects of bath temperature for the eletrodeposition of NiMn thin film have not been reported so far. This makes the interest to study the effect of bath temperature. This paper reports the preparation of NiMn thin films by electro deposition method. In order to enhance magnetic properties of the film, additives like saccharin and urea were added and the effects of additives and the effect of temperature on the structural, magnetic and mechanical properties of the NiMn films in chloride glycine bath were studied and are reported here. II. Experimental Part A. Electrodeposition of NiMn thin films NiMn thin film was electrodeposited on copper substrate in chloride-glycine bath at 50˚C and 70˚C temperature with the additives such as saccharin (2gl-1) and urea (2gl-1). The chemical compositions of the electroplating bath are 25gl -1 of Nickel chloride and Manganous chloride, 20 gl-1 of glycine and 10 gl-1 of ammonium chloride. A copper substrate of size (2 x 6 cm) as cathode and pure nickel of same size as anode were used for electrodeposition of NiMn thin films. An adhesive tape was used to mask off all the substrate except the area on which the deposition of films was desired. All the reagent grade chemicals were dissolved in double distilled water. Copper electrodes were degreased and slightly activated with 5% sulphuric acid and then rinsed with distilled water just before deposition. The pH of Solution was adjusted to 4.5 by adding few drops of HCl solution. The films were galvanostically deposited on copper substrate by applying a constant current of

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6mA/cm2 for a period of 60 minutes at 50˚C and 70˚C bath temperature. Electro deposited NiMn thin films were decomposed when the bath temperature increased to 80˚C. Hence the temperature was optimized to 70˚ C for these NiMn thin films. B. Characterization of NiMn alloy thin films The chemical composition of the film was determined by using the EDAX analyzer attached in (JEOL 6390 model) Scanning Electron Microscope (SEM). Surface morphological studies were carried out with Scanning Electron micrographs. The structural analysis of the films was carried out using a computer controlled Shimadzu X-ray diffractometer employing Cu Kα radiation. The scanning was carried out using θ-2θ scan coupling mode, the rating begins with 30 Kv, 20 mA. Magnetic properties (Coercivity, Magnetization, and retentivity) were studied using Vibrating Sample Magnetometer (VSM).Hardness of the as deposited and annealed film was measured by Vickers Hardness Test (VHN) and thickness of the film was measured by digital micrometer. III. Results and Discussion A. Composition of the electro deposited NiMn thin films The electrodeposited NiMn alloy films were smooth, uniform, adherent. The composition of the NiMn film from chloride-glycine bath and annealed film was obtained from the EDAX analysis and the weight percentages of the electro deposited films are tabulated as shown in Table 1. EDAX result showed that when the temperature was increased then the Mn percent was reduced accordingly. Table 1. Results of EDAX analysis S.No 1 2

Name of the Additive Saccharin 2 g/l Urea 2 g/l

Temperature ˚C 50 70 50

Ni Wt% 99.7 99.83 99.55

Mn Wt% 0.3 0.17 0.45

99.59

0.41

B. Morphology of the deposits The surface morphology of the NiMn thin films with the saccharin and urea at different temperature are investigated by scanning electron microscopy (SEM). The SEM images of electrodeposited NiMn thin films from chloride-glycine bath are shown in Figure 1. Figure 1. SEM images of Electro deposited NiMn thin film with saccharin as an additive (a) deposited at 50˚C, at 70˚C bath temperature and urea as an additive (c) deposited at 50˚C, (d) at 70˚C bath temperature.

(a)

(b)

(c)

(d)

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The films obtained at different temperature do not have micro cracks. The film was uniform and bright. The grain sizes were visible and very clear. At 50˚C having smaller crystallites and granular. This is due to uniform crystal orientation during electrodeposition. Hence the film has low stress. Film coated at 50˚C is having ball like structure and 70˚C is found to have pinholes in the surface for the bath contains saccharin. Film coated at 50˚C is having cauliflower structure and for 70˚C film found to have flower like structure for the bath contains urea as an additive. C. Structural and mechanic properties of NiMn thin films. Electrodeposited NiMn film from chloride-glycine bath at 50˚ C and 70˚ C was subjected to XRD studies and it is shown in Figure 2. Mechanical properties of the film were measured by Vickers hardness tester. The crystalline size of NiMn alloy films and other structural and mechanical are tabulated as shown in Table 2. Figure 2. XRD pattern of Electro deposited NiMn thin film with saccharin as an additive (a) deposited at 50˚C, at 70˚C bath temperature and urea as an additive (c) deposited at 50˚C, (d) at 70˚C bath temperature.

The data obtained from the XRD pattern compared with the standard JCPDS data and were found to have FCC structure. The presence of sharp peaks in XRD patterns of film reveals that the films are crystalline in nature. The peaks corresponding to (111), (200), (220), (311) and (222) reflections were observed in deposited films. It almost matches with JCPDS for nickel with the slight shift in the peak position due to the low percent of Mn in the film. Remaining peaks are corresponding to Cu substrate. Crystallite size increases due to the increment of temperature in the bath which contains saccharin and urea as an additive. Stress present in the film decreases with the reduction of Mn% [10]. Film stress has been reduced much in the film coated at 70°C with saccharin as an additive. Adhesion of the film (deposited at temperature 50˚ C and 70˚ C) with the substrate is tested by bend and scratch test. It showed that as deposited film having good adhesion with the substrate. Hardness of the film was examined using a Vickers hardness tester by the diamond intender method. The results are tabulated and shown in Table 2.

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Table 2. Effect of saccharin (2gl-1) on the structural and mechanical properties on NiMn thin film electrodeposited from chloride glycine bath at 6 mA cm-2 current density for 60 min Additive

Temperature °C

Crystalline size

Internal strain

Stress

Dislocation denstity

Lattice parameter

Vickers Hardness

(nm)

X10-3

(MPa)

24.85

1.457

276.83

2.05

3.581

380

77.46

0.467

088.80

2.84

3.587

542

22.09

1.639

311.30

6.24

3.627

377

32.3

1.121

212.96

9.58

3.522

252

(VHN)

Saccharin

Urea

The results show that the hardness increases with increment of bath temperature for saccharin bath and decreases for the urea bath. Highest hardness value of 542 is obtained at 70˚ C for the bath contains saccharin. D. Magnetic Properties of electrodeposited NiMn thin films. The hysteresis loop parameters, saturation magnetization (Ms), Coercivity (Hc), retentivity (Mr), magnetic flux density (Bs) of the films were evaluated by using VSM. The magnetic properties of the i n thin films deposited at 50 C and at 70 C have been observed from VSM are tabulated as shown in Table 3. Table 3. Effect of saccharin (2gl-1) and Urea (2gl-1) with temperature on the thickness and magnetic properties of Ni-Mn thin film from chloride-glycine bath. Additive

Saccharin

Urea

Current density (mAcm-2)

Temperature °C

Thickness (µm)

Coercivity

Magnetic saturation

Remanence

(Oe)

(emu g-1)

Squarness

Flux density Tesla

8.163

1.8

493.28

58.09

12.94

0.223

2.1

417.05

57.83

20.73

0.358

8.128

1.9

430.82

44.38

17.55

0.395

6.235

2.3

485.2

34.13

12.17

0.357

4.797

The crystalline nature of the material determines the magnetic properties of the materials. The saturation magnetization and coercivity are important parameters that determine the magnetic properties of soft magnetic materials [11], [12]. The soft magnetic properties are strongly dependent on the microstructure of the thin films. The microstructure contribution to magnetization arises from morphology properties such as magnetic anisotropy, magnetostriction and coercivity [13]. When the bath temperature increases grain size of the film also increases [14] and due to that saturation magnetization decreases [15]. Very low magnetization of 34.13emu g-1 is obtained for the film coated at 70 C with the urea as an additive and high magnetization with the reduced coercivity has been obtained for the film deposited at 70 C with the saccharin as an additive and it exhibits good magnetic properties than the other film. Thickness of the film found to be increased with the increment of temperature. Many factors contribute to the development of stress in electro deposits including film composition, natures of the substrate surface, bath composition, bath temperature, current density, and deposit thickness etc., The high initial intrinsic stress in the film is associated with lattice mismatch and with the grain size of the underlying substrate. But at high bath temperatures, the electro deposited film has low stress. This is due to uniform crystal orientation during electro deposition. IV. Conclusion Nano crystalline NiMn electrodeposited coating was deposited on Cu substrate. The effects of temperature and additives on structural and magnetic properties of the films were systematically studied. The results show that, 1. Due to the increment of temperature, the crystalline size increases. 2. The coercivity of the film was decreased from 493.28Oe to 417.05Oe. Saturation magnetization of the film was fond to be decreases, retentivity, suareness and flux density were found to be decreased as temperature of the bath (contains saccharin) increases. Hardness of the annealed film increased from 380 VHN to 542 VHN.

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3. 4.

Stress of the film has been reduced much in the film deposited at 70°C with saccharin as an additive. This shows that the soft magnetic properties of NiMn thin films are greatly enhanced by increasing bath temperature to 70˚ C with saccharin which can be used for MEMS applications.

[1]

Chechenin, E.V. Khomenko, and J.Th.M. de Hosson, “ FCC/BCC Competition and enhancement of saturation magnetization in nanocrystalline Co-Ni-Fe film” JETP Letters, Vol 85, No.4, p. 212-215, (2007). M.A.ISLAM and M.MONIRUZZAMAN, “Anomolous electrodeposition of Fe-Ni alloy coating from simple and complex baths and its magnetic property” IIUM Engineering Journal, Vol 10, No.2, (2009). T.M.Selvakumari, P.Muthukumar, S.Ganesan, “Enhanced performance of nanostructured FePtP alloy film for microelectro mechanical system applications” Digest J. of Nanomaterials and Biostructures,Vol 5, No4, p.903-907, (2010). N.Sulztanu, Fbrinza, “Electrodeposited i-Fe-S films with high resistivity for magnetic recording devices” J. of Optoelectronics and Advanced Materials,Vol 6, No-2, p.641-645 (2004). S.H.Goods, J.J.Kelly and .Y.C. Yang, “Electrodeposited ickel-Manganese: an alloy for microsystem applications”, Microsysytem Technologies, No.10,p. 498-505 (2004). R. Fathi, S. Sanjabi, “Electrodeposition of nanostructured Ni(1-x)Mnx alloys films from chloride bath”, C. Appl. Phys. 12, p. 89 (2012). R. Fathi, S. Sanjabi, . Bayat, “Synthesis and characterization of i-Mn alloy nanowires via electrodeposition in AAO template”, ater. Lett. 66, p.346 (2012). C. B. Zimm, . B. Stearns, “ agnetization of layered n-Ni and Mn-Co thin films”, J. agn. agn. ater. 50, p.223 (1985). Yu. A. Babanov, V. P. Pilyugin, T. Miyanaga, A. M. Patselov, E. G. Chernyshev, A. V. Ryazhkin, and T. Ogasavara, “Nanocrystalline Ni– n Solid Solutions: ew aterials with Competing Exchange Interaction”, Journal of Surface Investigation. X-ray, Synchrotron and Neutron Techniques, 1, 3, p.359 (2007). B.Stephenson, Jr., Cincinnati and R Edward , “Electrodeposition of ickel anganese alloy”, US patent, April 5 (1996). K.Sundaram, V.Dhanasekaran, T.Mahalingam, “ Structural and magnetic properties of high magnetic moment electroplated Co iFe thin films” Springer Verlag Ionics Vol 17:835-842 DOI 10.1007/s11581-011- 0580-0 (2011). Xiang Shen, Haiteng Li , HaiHua Li, Jianghua Nie, “Effect of deposit conditions on magnetic parameters of electroless CoFeB films” J. Mater Sci: Mater Electron, Vol 20, p. 272-275, (2009). M.Watanable, T.Nakayama, K. Watanable,, T.Hirayama and A.T.Onomura, “ icrosturcture and magnetic properties of high coercive Fe-Pt alloy thin films” Materials Transactions, JIM, Vol 37, No.3, p.489-493, (1996). A.M. Rashidi and A. Amadeh, “The effect of saccharin addition and bath temperature on the grain size of nanocrystalline nickel coatings”Surface & Coatings Technology 204, p.353-358 (2009), doi:10.1016/j.surfcoat.2009.07.036. A. Stephen, T. Nagarajan and .V. Ananth, “Magnetization behaviour of electrodeposited Ni– n alloys” aterials Science and Engineering B55 (1998) 184–186.

References [2] [3] [4] [5] [6] [7] [8] [9]

[10] [11] [12] [13] [14] [15]

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Does the knowledge and skill acquired during simulator training gets applied on the job by the seafarers- An empirical study 1

Surender Kumar, 2Dr. Neeraj Anand, 3Dr. DK Punia, 4Dr. BK Saxena 1 Research Scholar,2,3 Professor 1,2,3 University of Petroleum and Energy Studies, Dehradun, Uttarakhand, INDIA 4 Principal, Tolani Maritime Institute, Pune, Maharashtra, INDIA Abstract: Training is an integral part of organisational development process. A training imparted is expected to have the desired results. The results depend upon various factors. After a successful training, it is also important to evaluate if the acquired knowledge and skills were applied on the job by the trainees. The study is aimed at identifying the favourable and unfavourable perceptions of the trainees for all the factors of the knowledge acquired during the training imparted, being used on the job. The results show that the knowledge and skill acquired during the simulator training is being used on the job by the seafarers. Keywords: Training transfer, transfer of knowledge, training evaluation maritime training, simulator training, I. Introduction A simulator, in the simplest way, may be defined as a machine with a similar set of controls designed to provide a realistic imitation of the operation of a ship, vehicle, aircraft, or other equipment. Simulation is the imitation of the operation of a real-world process or system over time. The act of simulating something first requires that a model be developed; this model represents the key characteristics or behaviours of the selected physical or abstract system or process. The model represents the system itself, whereas the simulation represents the operation of the system over time. There are three attributes that every simulation has. If all three attributes exist, then you can legitimately call something a simulation. However, if even one attribute is missing, then it’s not a simulation. Here are the three attributes required for every simulation; A simulation: a) Imitates something real, but b) It is not real, and c) It may be altered by its users (hence instructor plays an important role) Training Evaluation: Training evaluation is considered a critical component of analysing, designing, developing, and implementing an effective training programme. To understand whether the training programme had the desired results or not, the training programme needs to be evaluated. Even if the participants leave the training room looking happy and they also give high scores on an evaluation or feedback sheets, it may not necessarily mean that the course participants learned or if they can apply what they learned to their job. Of course, it may be taken that course participants enjoyed themselves for the time spent in the training session with some of old friends or shipmates. Only a systematic, targeted approach to training evaluation will help you answer the question, did participants learn? There are many models and different ways to evaluate training. The Four-Level Model approach is most often used to evaluate training and development programs (Kirkpatrick, 1994). It focuses on four levels of training outcomes: reactions, learning, behaviour, and results. The major question guiding this kind of evaluation is, “What impact did the training have on participants in terms of their reactions, learning, behaviour, and organizational results?” II. Sample size Sample Size: There were 2850 students/officers trained at the four training centres chosen for research. Out of these 2850 students 1922 were trained using simulators.

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Hence N= 1922. A 95% confidence level is deemed acceptable and thus statistically z = 2. The proportion of responses that would be relevant to the survey is p. If p is 0,5 , a new formula is derived as illustrated in the equation below; Mathematically derived Yamane formula;

If another value were to be used for p, the denominator in the formula in equation above would increase and a smaller response size would then be required. p = 0,5 therefore offers the biggest possible response rate and confidence and risk levels can be maintained. Placing information in mathematical formulae above at a 95% confidence level and an error limit of 5% results in: n = ____1922_____ 1 + 1922 (0,05)2 = 331 responses III. The Research Methodology Kirkpatrick’s model level three i.e. Behavior (The Transfer of Training)has been used for this study to evaluate if the knowldge and skills acquired during the training are being utilised on the job. Kirkpatick gave the following guidelines for evaluating Behavior (The Transfer of Training): 1. A systematic appraisal should be made of on-the-job performance. 2. The trainees who attended the simulator based trainingg were sent a questionnaire to get the feedback on whether they were able to apply the knowlege and skill acquired on the job. 3. A statistical analysis was carried out using SPSS version 20 and a one sample t-test was carried out. Survey method has been used for measuring the The transfer of training imparted by using maritime simulators. The course participants were observed before, during and after training. The questionnaire for the purpose was prepared based upon and adapted from Kirkpatrick’s model. The questionnaire was served to the specialists in the field for their views. The questionnaire was tested for internal consistency using Cronbach’s Alpha Test. The final questionnaires were used to collect the data for the study. The data collection was done from three training centres. One sample t-test: A one sample t-test is a type of Univariate analysis. It is used whenever the variable is on Interval scale or Ratio scale. For this study, all the factors of knowledge acquired after training being used on the job, are on interval scale. A hypothesis test uses sample data to test a hypothesis about the population from which the sample was taken. One sample t-test using SPSS is one of many procedures available for hypothesis testing. Testing a hypothesis means making inferences about one or more populations when sample data are available. The following tools are utilised for this research: • Charts and tables for diagrammatic representation • Microsoft: Excel, power point and word • Cronbach's Alpha test • One sample t-test Hypothesis: H a: There is a significant difference in the perceptions of the trainees for all the factors of the knowledge acquired being used on the job. (Ha: ≠ 3). H 0: There is no significant difference in the perceptions of the trainees for all the factors of the knowledge acquired being used on the job. (H0: = ). Analysis; The paired t-test was used to analyse the scores of the repsondents before and fater training. For teesting the hypotheses, one sample t-test was utilised. The results of paired t-test are given as below; A one-sample t-test was run to determine whether the scores as calculated using Kirkpatrick’s model and SPSS, were different from the hypothesized score of 3. The scores were assumed to be normally distributed.

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Out of the total twelve questions from the questionnaire, it was decided to choose the most relevant to indicate the knowledge and skills acquired during training is being used by the seafarers on the job. A total of eight questions were picked up and analysed by formulating sub-hypothesis. H1 0: I did not have the opportunity to use the knowledge and/or skills presented in this course. H1 a: I have had the opportunity to use the knowledge and/or skills presented in this course H2 0: I did not use the knowledge and/or skills presented in this course, to good extent. H2 a: I used the knowledge and/or skills presented in this course, to good extent. H3 0: There is no increase in my confidence using knowledge and skills as a result of this course. H3 a: There is an increase in my confidence using knowledge and skills as a result of this course. H4 0: I did not have a good access to the necessary resources to apply the knowledge and/or skills on your job. H4 a: I had good access to the necessary resources to apply the knowledge and/or skills on your job. H5 0: As a result of this course, my performance on the course objectives has not changed for good. H5 a: As a result of this course, my performance on the course objectives has changed for good. H6 0: I did not receive help, through coaching and/or feedback, with applying the knowledge and/or skills on the job. H6 a: I received help, through coaching and/or feedback, with applying the knowledge and/or skills on the job. H7 0: As a result of this course, my overall job performance has not improved. H7 a: As a result of this course, my overall job performance has improved. H8 0: The simulator training did not help me do my job better. H8 a: The simulator training helped me do my job better. The results are analysed using the table below; Factors Sub Hypothesis H1 0 : oppor to use k s=3 Change in behaviour H1 a: oppor to use k s ≠3 H2 0 :

act use k s =3

p-Value .001

Inference(α=0.025) H10 – Rejected H1a – Accepted (p < α)

.001

H20 – Rejected H2a – Accepted (p < α)

.001

H30 – Rejected H3a – Accepted (p < α)

.001

H40 – Rejected H4a – Accepted (p < α)

.001

H50 – Rejected H5a – Accepted (p < α)

.001

H60 – Rejected H6a – Accepted (p < α)

.001

H70 – Rejected H7a – Accepted (p < α)

.001

H80 – Rejected H8a – Accepted (p < α)

H2 a: act use k s ≠3 H3 0 :

confi in k s

H3 a: confi in k s ≠3 H4 0 :

resource in k s=3

H4 a: resource in k s ≠3 H5 0 :

perfo change=3

H5 a:perfo change ≠3 H6 0 :

coach f b=3

H6 a: coach f b ≠3 H7 0 :

overall perfo=3

H7 a: overall perfo ≠3 H8 0 : sim- job better=3 H8 a: sim- job better ≠3

There was a statistically significant difference between means (p < .05) and, therefore, we can reject the null hypothesis and accept the alternative hypothesis. These results suggest that the knowledge acquired during training is being used by the seafarers on the job.

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Perception Table Factors Change in behaviour To what extent did you use the knowledge and/or skills prior to attending this course?

Mean

Inference/Decision

3.34

Favourable perception by the respondents.

To what extent have you had the opportunity to use the knowledge and/or skills presented in this course? To what extent have you actually used the knowledge and/or skills presented in this course, after completing the course? To what extent has your confidence in using the knowledge and/or skills increased as a result of this course? To what extent have you had access to the necessary resources to apply the knowledge and/or skills on your job? As a result of this course, my performance on the course objectives has changed by. To what extent have you received help, through coaching and/or feedback, with applying the knowledge and/or skills on the job? As a result of this course, my overall job performance has changed by % I feel that the simulator training helped me do my job better.

4.04

Favourable perception by the respondents.

4.17

Favourable perception by the respondents.

4.33

Favourable perception by the respondents.

3.99

Favourable perception by the respondents.

4.01

Favourable perception by the respondents.

3.99

Favourable perception by the respondents.

4.16

Favourable perception by the respondents.

4.54

Favourable perception by the respondents.

There was a statistically significant difference between means (p < .05) and, therefore, we can reject the null hypothesis and accept the alternative hypothesis. IV. Conclusions Most respondents agreed that the simulator training improved their overall performance, had access to the necessary resources to apply the knowledge and/or skills on job; there is an increase in confidence using knowledge and skills. They also agreed that the simulator training helped them to do their job better. The null hypotheses (Main hypotheses and sub hypotheses) were rejected and alternate hypotheses were accepted. These results suggest that the knowledge acquired during training is being used by the seafarers on the job. V. Future Works In near future the training effectiveness of other maritime training courses being offered by other training centres may be carried out similarly, using the research methodology as discussed above. VI. Acknowledgements The authors would like to thank the management, staff and the students of the training centres where this study was carried out. Without the frank inputs from the students attending the simulator based training, the study wouldn’t have been successful. References [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8].

[9]. [10]. [11].

Tracey, J. B., Tannenbaum, S. I., & Kavanagh, M. J. (1995). Applying trained skills on the job: The importance of the work environment. Journal of applied psychology, 80(2), 239. Cheng, E. W. (2000). Test of the MBA knowledge and skills transfer. International Journal of Human Resource Management, 11(4), 837-852. Minbaeva, D., Pedersen, T., Björkman, I., Fey, C. F., & Park, H. J. (2003). MNC knowledge transfer, subsidiary absorptive capacity, and HRM. Journal of international business studies, 34(6), 586-599. Baldwin, T. T., & Ford, J. K. (1988). Transfer of training: A review and directions for future research. Personnel psychology, 41(1), 63-105. Cheng, E. W., & Ho, D. C. (2001). A review of transfer of training studies in the past decade. Personnel review, 30(1), 102-118. Olivero, G., Bane, K. D., & Kopelman, R. E. (1997). Executive coaching as a transfer of training tool: Effects on productivity in a public agency. Public personnel management, 26(4), 461-469. Seafarer’s Training, Certification and Watch keeping Code (STCW Code) London: IMO IMO/MSC circular 645 dated 06 June, 1994 (Kvitrud, A. (2011, January). Collisions between platforms and ships in Norway in the period 2001-2010. In ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering (pp. 637641). American Society of Mechanical Engineers. Nadler, J., Thompson, L., & Boven, L. V. (2003). Learning negotiation skills: Four models of knowledge creation and transfer. Management Science, 49(4), 529-540. Kirkpatrick, D. L. (2009). Implementing the Four Levels: A Practical Guide for Effective Evaluation of Training Programs: Easyread Large Edition. ReadHowYouWant. com. Kirkpatrick, D. (2007). The Four Levels of Evaluation: Measurement and Evaluation (Vol. 701). American Society for Training and Development.

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Burke, L. A., & Hutchins, H. M. (2007). Training transfer: An integrative literature review. Human resource development review, 6(3), 263-296. Barnett, ML “The role of simulators and the qualifications of instructors and assessors under the STCW Convention” Marine Simulation and Ship Manoeuvrability (Marsim 1996) Copenhagen September 1996. Asariotis, R., Benamara, H., Finkenbrink, H., Hoffmann, J., Lavelle, J., Misovicova, M & Youssef, F. (2011). Review of Maritime Transport, 2011(No. UNCTAD/RMT/2011). Swanson, R. A. (2007). Analysis for improving performance: Tools for diagnosing organizations and documenting workplace expertise. Berrett-Koehler Publishers. Are Equipment Simulators Effective When Used For Technology-Based Training? P. Gibbings K. McDougall Project on Improvement of Local Administration in Cambodia: Manual on Training Evaluation Board, M. (1992). Shiphandling Simulation:: Application to Waterway Design. National Academies Press. Orlansky, J., Dahlman, C. J., Hammon, C. P., Metzko, J., & Taylor, H. L. (1994). The Value of Simulation for Training (No. IDA-P-2982). INSTITUTE FOR DEFENSE ANALYSES ALEXANDRIA VA. Board, M. (1996). Simulated Voyages:: Using Simulation Technology to Train and License Mariners. National Academies Press. Harrald, J. R., Mazzuchi, T. A., Spahn, J., Van Dorp, R., Merrick, J., Shrestha, S., & Grabowski, M. (1998). Using system simulation to model the impact of human error in a maritime system. Safety Science, 30(1), 235-247. Sandaruwan, D. (2011). A Ship Simulation System for Maritime Education. ICTer, 3(2). Barnett, M., Gatfield, D., & Habberley, J. (2002). Shipboard Crisis Management: A Case Study. In Proceedings of the Human Factors in Ship Design and Operation Conference.. RINA. Kluj, S. (2005). A diagnostic simulator applied to engineering training. Global J. of Engng. Educ, 9(2). Gatfield, D., & IEng, A. (2006). Using simulation to determine a framework for the objective assessment of competence in maritime crisis management. INTERNATIONAL SIMULATION AND GAMING YEARBOOK-NEW SERIES-, 14, 44. avidovitch, L., Parush, A., & Shtub, A. (2006). Simulation‐based Learning in Engineering Education: Performance and Transfer in Learning Project Management. Journal of Engineering Education, 95(4), 289-299. http://www.kongsberg.com http://www.transas.com www.nautinst.org www.istd.co.in www.astd.org www.maib.gov.uk

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An Investigation on Causes and Preventive Measures of Trespass Accidents and Suicides in Katpadi Railway Jurisdiction of Tamil Nadu, India Dr. N. Sundaram1, Mr. M. Sriram2 Professor of Commerce , Research Scholar, Commerce 2, School of Social Sciences and Languages1, 2 VIT University1, 2 Vellore – 632 014, Tamil Nadu1, 2 INDIA1, 2 1

Abstract: This study investigates the causes of passengers’ fatality in railways by means of trespass accidents and suicides and to elicit the measures to prevent the death occurrences in Katpadi railway jurisdiction. This study consists of 385 respondents, who are the travelers in Katpadi jurisdiction which has 20 railway stations including Katpadi junction. Under probability sampling, survey method and stratified random sampling is used as the technique for collecting sample in the study area. It is found that 59 people died in railway accidents in the year 2014. It was also found that 30.9 % use foot board on train travel and 34.8 % of the respondents get down from the train before it stops in the railway station and 51.43 % from 385 respondents replied financial problem as the major reason for conduct of suicide and 49.87 % respondents said that family issues provoke the aggrieved person to commit suicide. It is recommended to create barricade like fences, cameras, anti-trespass panels and warning and prohibitive sign boards to prevent trespass. Proper counseling, discussion of problems with family and friends, avoiding the usage of alcohol and drugs, playing mind games like chess and doing meditation and yoga will diverting the mind form suicide intent. Keywords: Causes and preventive measures; Katpadi railway jurisdiction; suicides; trespass accidents I. Introduction Railway transport is considering the safest and comfortable transport with cost effective to all the people for more than one and half centuries, it is the fourth largest railway network system in the world next to United States, China and Russia. Despite of its notable service, it faces many difficulties to protect the people from railway collision and suicide. The national transportation safety board of India reported on December 01, 2014 that, the number of deaths in railways due to trespass accidents and suicides from January, 2014 till October, 2014 were 18, 735. The reasons and causes that influence the trespass and suicide deaths are still not properly sorted out by the railways, though there are models developed by researchers to a particular region, which do not suit to regions dissimilar to the zone where such models are implemented. This investigation elucidates the causes and preventive measures of railway trespass accidents and suicides in Katpadi railway jurisdiction of Tamil Nadu, India. II.

Review of Literature

Many researchers throughout the world assign study to find out the occurrence of accidents, which results in formation of models to find out the cause of incident [1]. The literature on railway trespassing and pedestrians accident remains scarce when compared to the domain of railway suicides [2]. Researches were undergone on prevention of railway trespass accidents [3], which came out with models to provide a built-in view of different aim and steps in the process of trespass accidents and the related preventative measures for such accidents as well. Reference [4] said that many investigations undergone for incident and accident found the human element as major contributing factor. They also quoted that seventy percent of the accidents occur due to faulty activities of humans. Humans, especially residents near railway tracks are frequently crossing railways without suicidal intention, rather carelessness and unconsciousness, which leads to accident. People use railways for other purposes such as taking a walk in the tracks, consuming liquors, seeking risk by playing on the tracks, or even committing criminal activities such as metal theft and fare evasion. The Designing of protection from trespass and suicidal accidents must be in a way which should make the attempters to not proceed further and the content of awareness should not pave the way to seek risk. Another possible cause for accident is unintentional trespassing. It is a serious loss of human control [5], due to unconsciousness and carelessness, the human will not be aware of happenings in the environment due to lack of consciousness or concentration to any factor.

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Several researchers cited that the railway officials face problems with trespass fallacy [6]. Especially Railway Police are taking crucial steps to prevent deaths and injuries occurring in rail tracks. The inspector of police in the study area said that the people nowadays are clever is mishandling the rules and regulations of any departments. College students travelling in trains use doorway travel even if there is space inside the train. Passengers who are hanging themselves outside the train getting hit on the overhead equipment mast and lead to major injury or even death as well. Reference [7] said that some people choose unsafe travel by sitting near the door. Subsequent to trespass accident, there is another major type of accident which an individual intentionally plan and commit death called as suicide, which is a kind of psychological accident. It was mentioned by the researchers that, in Indian railways, till now there is no organized and incorporated view of preventive steps or measures that support decision about prevention of suicides [8]. Literatures revealed the types and factors pertained to cause of suicides attempts. Especially on railway suicides, the efficient measures of railways so far taken were proposed with evidences like developing awareness from the suicidal problem outside psychological care [9], having proper fence to protect people particularly in densely populated areas [10]. There are a few researches which argues that 80 % and more suicidal attempters have made serious attempt but hindered and living their life [11]. As far as researches undergone, it was found that there is no any standardized system, steps and events that supports decision and reasoning integrated to measure the prevention of suicide under railway undertakings. The models for suicide prevention were proposed by reference [12], which is so-called suicidal model that describes the analysis and prevention of suicide. This model has a process that starts from thoughts to suicide and ends with suicidal decision and act out. The model consists of four steps associated to measures such as influence the perceive attractiveness and rail traffic availability as a mean, influence the accessibility and potential of accident and influence the consequence of accidents. The above literatures supported the researcher to find out the gap to be filled through current investigation, which paved the way to define the research objective. III.

Objective of the study

The core objective of this study is to find out the causes of passengers fatality in railways by means of trespass accidents and suicides and to elicit the measures to prevent the death occurrences in the study area. IV.

Research Methodology

The population of this study is the travelers in Katpadi jurisdiction of Tamil Nadu, India, which has 20 railway stations including Katpadi junction. The population is considered as infinite because it is not possible to trace the number of people travel in Katpadi jurisdiction daily. Under probability sampling, survey method and stratified random sampling is used as the technique for collecting sample in the study area. The sample size is 385, which was determined using the formula derived by Krejcie and Morgan (1970). This study use descriptive research design to describe the characteristics of the respondents pertains to accidental deaths. Statistical tools such as cross tabulation and Chi â&#x20AC;&#x201C; Square test are used to analyze data. Primary and secondary data are used in this study; primary data is collected through hybrids of questionnaire, interview and schedule and secondary data is collected from: (a) Printed sources: Government reports, books, newspapers and magazines, (b) Electronic sources: e - journals, e - books, e â&#x20AC;&#x201C; papers and websites. V.

Results and Discussions

A. Demographic profile This study took core factors to include in demographic profile such as age, gender and occupation, which found that 66.2 % (255 from 385 respondents) of the respondents are male and 33.8 % are female. Many female are hesitant to answer the questions though the importance of the study was explained and hence the number of male is higher. This study contains maximum number of respondents (29.7 %; 114 from 385 respondents) under the age group of 26-35 and 23.6 % of respondents under the age group of 19-25. The respondents under the age group of 50 and above are 20.5 %, age groups 36-50 contain 18.4 % and 7.8 % respondents are below 18 years of age. The government and private employed people are found the maximum number of respondents (68.2 %) among 220 respondents, and self employed are 31.8 %. The remaining 109 respondents were found as students and 56 respondents as unemployed, who does not fall into the employment category. The unemployed people include job seekers and retired. B. Railway death tolls in the study area From this study, it is found that 59 people died in railway accidents in the year 2014, out of which, 39 trespassers died due to trespass accident, 10 passengers due to natural death, five passengers slipped/ fallen down from the running train, four trespassers due to suicide and one passenger got electrocuted. Katpadi junction is recorded with maximum railway deaths in the year 2014 (14 out of 59) in the katpadi jurisdiction. It is also found that more death tolls are recorded in the year 2014, where the death toll is 44 in the year 2012 and 58 in the year 2013 respectively.

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Trespass accidents are found as maximum cause death in railways and hence the factors influencing such cause are studied. C. Factors influencing trespass accident To identify the reasons for trespass accident, the influencing factors are studied by the researcher. Table I portrays the factors influencing trespass accident considered for the study. Table I Factors influencing trespass accident Yes No f % f % Existence of rail track near residence 129 33.5 256 66.5 Respondents come across Level Crossing gate to reach railway station 175 45.5 210 54.5 Carrying heavy luggage in the train 86 22.3 299 77.7 Usage of footboard on train travel 119 30.9 266 69.1 Respondents getting down from running train 134 34.8 251 65.2 Source: Primary data; f â&#x20AC;&#x201C; frequency, % - percent; Note: frequency of each factor is out of 385 respondents Factors influencing trespass accidents

From the Table I, it is found that 33.5 % from 385 respondents resides near railways, 45.5 % come across level crossing gate to reach the railway station, 22.3 % carry heavy luggage in the train, 30.9 % use foot board on train travel and 34.8 % of the respondents get down from the train before it stops in the railway station. These results build a trauma, which educe the negligence of the people, who are not following the rules framed by the railways to safeguard the travellers. This may lead to trespass accident. Or slip/ fall down. The above factors were analysed using Pearsonâ&#x20AC;&#x2122;s chi-square test to know the association with the age. The result is tabulated in Table II. Table II Chi-Square test for Factors influencing trespass accident and age group Age group cross tabulated and analysed with these factors using Pearson ChiSquare Existence of rail track near residence Respondents come across Level Crossing gate to reach railway station Carrying heavy luggage in the train Usage of footboard on train travel Respondents getting down from running train Source: Primary data compilation

Value

Asymp. Sig. (2-sided)

71.428 11.370 22.919 47.251 19.273

4 4 4 4 4

0.000 0.023 0.000 0.000 0.001

In the Table II, all the above factors are less than p value (p = 0.05), hence the null hypothesis is rejected and it is inferred that there is an association with factors influencing trespass accident and age group of the respondents. D. Possibilities to avert trespass behaviour As far as trespass accident is concerned, the prevention or aversion of trespass behaviour in the study area consists of six measures such as (i) campaigns to raise awareness and prevent trespass, (ii) installation of warning and prohibitive signs, (iii) placing posters, (iv) providing education about accidental deaths, (v) installing anti-trespass panels and (vi) installing fences near residential areas to restrict people from rail track access. E. Reasons for conduct of suicide Suicide is found as one of the major unidentified phenomenon. From this study, the reasons for suicide cited by the respondents are financial problem, family issues, dowry, continuous failures in life, unbearable emotional or physical pain and love failure. More than half of the respondents (51.43 % from 385 respondents) replied financial problem as the major reason for conduct of suicide and 49.87 % respondents said that family issues provoke the aggrieved person to commit suicide. There are other reasons such as severe depression, bipolar disorder (person's mood to swing from feeling very high and happy to feeling very low), psychotic (inner voices often command self-destruction for unintelligible reasons), Schizophrenia (seeing or hearing things that are not real), borderline personality disorder (unstable emotions, disturbed thinking patterns, impulsive behaviour and intense but unstable relationships with other people), anorexia nervosa (eating disorder), homeless, people who feel guilty for committing mistake. These terms are referred by a psychiatrist, who is one among the respondents. F. Possibilities suggested by the respondents to prevent suicide The respondents are asked to suggest the possibilities of preventing suicide. As far as psychological factors are concerned, it is not possible for researchers to find a model to prevent suicides [13] because, it is not viable to predict the suicide planned by a person because the act of suicide is sudden and it happens in less than a second. This study evokes the possibilities to prevent suicides, which found that counselling is responded by 63.1 % of the respondents and rank as one, followed by discussion with friends and family members by 49.6 % and 44.7 %, avoiding drugs and alcohol as forth rank with 40.3 % and other suggestions replied by 2.1 % such as playing mind games like chess, diverting the mind by doing meditation and yoga.

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Suicide is found as one of the major unidentified phenomenon. From this study, the reasons for suicide cited by the respondents are financial problem, family issues, dowry, continuous failures in life, unbearable emotional or physical pain and love failure. Hoardings with images of deceased by means of suicide and trespass accidents should be displayed at the platform entrances of the railway stations which will educate and alert the people not to attempt suicide or use rail track. It is recommended to install warning and prohibitive sign boards in 14 spots to control trespass behaviour. The places that require installations are Katpadi Railway Junction main entrance, east end, west end and in Sevur railway gate, Thiruvalam railway station, Mukundarayapuram railway station, Walajah Road railway station east and west end, Thalangai railway station east end and west end, Sholinghur Road railway station east and west end, Anwarthikhanpet railway station and four sign boards towards Vellore Cantonment railway station. VI.

Conclusion

The railway undertakings should take severe action on trespass behaviour by strengthening fine and imprisonment, so that the death which occurs due to trespassing and suicides can be drastically controlled [14]. It is also the duty of the stakeholders such as police, media, public and private institutions and voluntary organizations should support railway undertakings to protect the passengers and provide them a safe and sophisticated travel. VII. Scope for further research This study is limited to 20 railway stations including Katpadi junction. This study can be further extended to other railway jurisdictions across the country, which will be useful for the people, who are concerned about safety and awareness of trespass accidents and suicidal deaths. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]

Katsakiori, Panagiota, George Sakellaropoulos, and Emmanuel Manatakis. "Towards an evaluation of accident investigation methods in terms of their alignment with accident causation models." Safety Science 47.7 (2009): 1007-1015. Silla, Anne, and Juha Luoma. "Opinions on railway trespassing of people living close to a railway line." Safety science 50.1 (2012): 62-67. Lobb, Brenda. "Trespassing on the tracks: a review of railway pedestrian safety research." Journal of safety research 37.4 (2006): 359-365. Rao, Dr Bh Venkateswara, and Dr J. Durga Prasad. "Indian railways: Trends in accidents and safety measures." International Journal of Logistics & Supply Chain Management Perspectives 2.4 (2014): 709-714. Beskow, J., J. Thorson, and M. Öström. "National suicide prevention programme and railway suicide." Social science & medicine 38.3 (1994): 447-451. Rao, Bh. "passenger business of Indian railways: need for turnaround measures." International Journal of Logistics & Supply Chain Management Perspectives 2.1 (2013): 89-91. Rao, Venkateswara. "Financial performance of Indian railways." International Journal of Applied Financial Management Perspectives 2.2 (2013): 338-342. Burkhardt, Jean-Marie, et al. "A model of suicide and trespassing processes to support the analysis and decision related to preventing railway suicides and trespassing accidents at railways." Transport Research Arena (2014): 14-17. Emmerson, Brett, and Chris Cantor. "Train suicides in Brisbane, Australia, 1980–1986." Crisis: The Journal of Crisis Intervention and Suicide Prevention (1993). Clarke, Ronald V., and Barry Poyner. "Preventing suicide on the London Underground." Social science & medicine 38.3 (1994): 443-446. Thomas, Lauren J., Daniel JA Rhind, and Katie J. Robinson. "Rail passenger perceptions of risk and safety and priorities for improvement." Cognition, Technology & Work 8.1 (2006): 67-75. Rådbo, Helena, Inge Svedung, and Ragnar Andersson. "Suicide prevention in railway systems: Application of a barrier approach." Safety science 46.5 (2008): 729-737. Evans, Andrew W. "The economics of railway safety." Research in transportation economics 43.1 (2013): 137-147. Bhandari, Rajendra Kumar. "Guide to Safety." Disaster Education and Management. Springer India, 2014. 271-324.

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ACTIVE LEARNING METHODS IN HIGHER EDUCATION 1

Badri Meparishvili, 2Lela Turmanidze, 3Gulnara Janelidze 1,3 Georgian Technical University, 77 Kostava st., 0175 Tbilisi, Georgia 2 Batumi Shota Rustaveli State University, 35 Ninoshvili str.6010 Batumi, Georgia

Abstract: Dynamism of modern world, especially the problems of the labore market determine the necessity of widely implantation of active learning methods in Higher Education System. This paper presents a new approach of the active methods of learning process in the different universities based on the computer business games, designed for undergraduate and graduate strategic management courses. The actuality of this approach is induced by simulation's complexity and dynamism of the market economic. Under the notion of the computer business games is understood the Virtual Business Game (VBG), a webbased an on-line multi-player business simulation aimed at education of higher-school students in economics, politics, and media studies. VBG allows students to manage a virtual company operating either in the industrial production, or in the different economic objects. In Georgian Technical University have been elaborated some business games based on the simulation of real-world business environment as the decision-making process in industrial enterprise management system, in the marketing firms, and also in the purely economic segment as stock exchange, commodity exchange and banking system. Each of these games has different strategy and rules, algorithms and scenarios. The students learn to manage and optimize their company owned through the distribution of human, material and financial resources. Benefits from these business games is making the practical skills in virtual simulative conditions. In the context of the given approach, the Georgian Technical University is ready to take an active part in the implementations process of the presented computer business games at the universities and in the elaboration models, algorithms and software of similar systems, as an active laboratory method of learning process in the different universities. Keywords: Active learning methods, Computer business games, Business simulation I. Background Economics education studies recommend the adoption of more active and collaborative learning methodologies [1]. More is needed to be done in the classroom to excite students about economics education. Simulations supplement the standard lecture. The word game means different things to different people. "Business is a game," proclaimed IBM founder Thomas J. Watson. In reality any human behavior, particularly everyday decision-making or business actions involve the components of games. The solving the educational problems according to Bolonia declaration and Prague Communique the reforms in the system of Higher education is closely related with distance learning, which in parts means by the active learning methods implementation [2]. The idea of using computer games to support training and learning objectives is more than several ten yearss old [3]. Recent works have explored the potentialities of economical strategy games and simulations in formal education and their alleged advantages over classical e-learning and edutainment tools, e.g. [4]. Learning via computer games as one of yet for the past some years, a movement has been afoot to examine how digital games work as pedagogical devices. There are oodles of games that are now available in electronic format [5]. While many of these are distributed commercially, many others are available for free play on the Web, and some can be downloaded at no cost. Some electronic games are merely computerized versions of games that existed long before computers. Others only exist in a computer format. Computer networks have made possible games that allow many thousands of players to be participating simultaneously. The computerized animation and interaction in these games bring a dimension to games. II. Introduction Gaming is, in short, a vast and complex world. We define active learning games as â&#x20AC;&#x153;A pedagogical technique that uses playful in-class activities designed to actively engage students with key concepts, the faculty and each otherâ&#x20AC;?. The play of the game involves making a variety of decisions, such as distribution of resources, pricing or expenses variation, and so on. Players are actively engages in receiving and paying out money in buying and

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selling, and keeping track of their money or resources. A player gradually learns effective strategies useful in becoming a better player. Each player plays as an individual, competing against other individuals playing the game [6]. Next, we present an overview of active learning games â&#x20AC;&#x201C; those that are meant to be conducted during classroom lectures â&#x20AC;&#x201C; as a specific pedagogical technique under the umbrella of active learning [7]. Generally, business games have many goals of application. Three of the important ideas that can be summarized as follows: - Studies or acquisition of knowledge by the games in education; - Attestation of staff; - Researches. Why do we believe that active learning games are effective? We report empirical evidence of the success of this method at Georgian Technical University, where a number of different active learning techniques have been used in the studies. This paper presents some examples of such teaching games, which can serve as learning objects, from which both students and educational staff can learn (and, increasingly, are learning). And gaming is a rich world, intersecting with campuses at multiple levels. III. Overview of Virtual Business Games Business simulations have many facets. They combine at least three concepts: simulation, games, and contests. There are at least four elements found in all business simulations or games: The Scenario, Roles, An Accounting System, Algorithms [8]. In Georgian Technical University have been elaborated a series of business games based on the real-world business environments simulation as the decision-making process in divers domain as: the industrial enterprise management system; the project planning; the inventory management; the marketing firms, and also the purely economic segment as stock exchange, commodity exchange and banking system. Each of these games has different strategy and rules, algorithms and scenarios. The students learn to manage and optimize their company owned through the distribution of human, material and financial resources. Benefits from these business games is making the practical skills in virtual simulative conditions. A number of activities in above games have been proposed and are being used by various corresponding experts. Generally, every game consists of two parts: initial planning and current management. Generalized algorithm for any variant of games is shown in Figure 1. start

Explain Rules Initialization

Current management

Initial Planning

Randomized Changes of Parameters

RNG

DecisionMaking Current evaluation

Yes

Next Round? No Summarize Game Final Results

End

Figure 1: Generalized algorithm

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The games proceed in rounds. An event that takes place in one round can be considered as players’ actions or results from the economical and social model or consequences from previous rounds. For approximating the simulation to the reality and for perturbing the gaming situation the contingency is drawing via the RNG. It is clear, that it requires certain decision to be taken by the players, which should be evaluated at the end of each round. IV. Brief review Every game is a multi-player network game. For each game we provide the name under which it is known, the primary learning objective of the game, a brief summary of the activities that are conducted, the estimated duration as well as the name and institution of the instructor who invented or has practiced this game recently. “Industrial enterprise management” Essence: This game gives some experience of dynamic distribution of hypothetical resources in manufacturing for every round of game. Modeling method : based on J.Foresters model. Economic instruments: 1.Technologies; 2. Resources; 3. Manpower resources. Perturbations: Sales opportunities. Roles: Manager, chief of enterprise. “Planning and scheduling large-scale projects” Essence: This game helps understand how scheduling the tasks and dynamic managing them depending on randomized perturbations. Modeling method : P.E.R.T Economic instruments: 1. Duration of the tasks; 2. Resources; 3. Gross value. Perturbations: Scheduling variances. Roles: Project manager. “Inventory management” Essence: This game helps understand how ordering of supplies depending on market demand and selling. Modeling method : 1.Risk management because of the deficit; 2.Risk management because of the over-indulgence; Economic instruments: Pricing management; Emergency request for goods. Perturbations: Sales opportunities. Roles: Supplier, provider. “Free Market” Essence: This game simulates a market which is free from government intervention (i.e. no regulation, no subsidization, no single monetary system and no governmental monopolies). Modeling method : Games Theory, marketing theory. Economic instruments: Free (flexible) relations with customers. Perturbations: Market conditions, damages. Roles: Employer, owner of a firm, entrepreneur, businessman. ”Stock exchange” Essence: Virtual Stock Exchange allows to practice buying and selling stocks using purpose of gaining experience with stock trading. Modeling method : Games Theory Economic instruments: Stock-exchange deal, price regulation, sell by auction Perturbations: Market conditions, business climate. Roles: Exchange dealer, stockbroker, customer.

imaginary money for

“Commodity exchange” Essence: Virtual Stock Exchange allows to practice buying and selling comodities using imaginary money for purpose of gaining experience with stock trading. Modeling method : Games Theory

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Economic instruments: Stock-exchange deal, price regulation, sell by auction. Perturbations: Market condition, business climate. Roles: Exchange dealer, stockbroker, customer. “Banking system” Essence: This game helps understand how virtual carrying economic policies of state bank against commercial banks to attain social goals. Modeling method : Games Theory, banking management theory. Economic instruments: Financial reserves, bank-rates, lending, deposits, circulating notes. Perturbations: Inflation, economic crisis or depression. Roles: Bank manager. V. Support systems Technically, the VBG is a client-server application; the students can play the game via the Internet. Databases are managed by MySQL. The server part comprises PHP scripts generating the game interface. The client part or application and the economical simulation are written in Java.

Server Proceeding Manager

Economic Simulation

RNG

Interface generator

Client

Figure 2: Architecture of VBG VI. Conclusions This paper discusses active learning via computer games as an important pedagogical technique in support of traditional education. We have briefly applied to serious games, which are a multi-player round-based strategy game aimed at education of Higher-school students in economics and managements studies. This series of various genres business games can forming a unified wide profile meta-game for universities. In this context of the given approach, the Georgian Technical University is ready to take an active part in the implementations process of the presented computer business games at the universities and in the elaboration models, algorithms and software of similar systems, as an active laboratory method of learning process in the different universities. References [1] [2] [3] [4] [5] [6 ] [7 ] [8 ]

Greenlaw, S.A. (1999). Using groupware to enhance teaching and learning in undergraduate economics, Journal of Economic Education, 30(winter), 33-42. Ediberidze, A., Meparishvili, B. (2008), Several approaches to flexible Higher education system in Georgia. EUNIS International Conference “Visions for IT in Higher Education”, Denmark.114-115pp. Simkins, S.P.(1999), Promoting active-student learning using the World Wide Web in economics courses, Journal of Economic Education, 30(Summer), 278-91. Dodge B. (2003). "Active Learning on the Web", San Diego State University, URL: http://edweb.sdsu.edu/people/bdodge/active/ActiveLearningk-12.html. Sandford, R., Ulicsak, M., Facer, K., Rudd, T. (2007), Teaching with Games. Using commercial off-the-shelf computer games in formal education, Futurelab, Bristol, UK, www.futurelab.org.uk/download/pdfs/research/TWG_report.pdf. Allgood, S., Bosshardt, W., Van der Klaauw, W., and Watts, M. (2004). What Students Remember and Say about College Economics Years Later.American Economic Review, 94(2), 259-65. Lean, J., Moizer, M., Towler, C. A.(2006), Active Learning in Higher Education, Journal of Simulation and games, 7(3), 227242. Dobbins, C. L., Boehlje, M., Erickson, S., and Taylor, R. (1995). Using Games to Teach Farm and Agribusiness Management, Review of Agricultural Economics, 17(3), 247-255.

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Appari’s Design – Get Extra Energy From Existing Resources Sidramappa Shivashankar Dharane Assistant Professor, Department of Civil Engineering, SVERI’S College of Engineering Pandharpur, Maharashtra, India. Archita Vijaykumar Malge Assistant Professor, Department of Mathematics, SVERI’S College of Engineering Pandharpur, Maharashtra, India. Savita Gururaj Malage 195, East Mangalwar Peth, Solapur. Maharashtra, India. Abstract: Appari’s design is simple and applicable to get the extra energy from the existing recourses. The article focuses how to get the extra energy from wind and sun. Also it focuses the design and applicability of these energy sources. Key words: Appari, solar energy, solar dryer, solar water heater, wind energy, design of fans. I. Introduction Appari’s design is simple and applicable to get the extra energy from the existing recourses. The article focuses how to get the extra energy from wind and sun. Also it focuses the design and applicability of these energy sources. It consists of how to make the solar panels and solar dryers effective to get the extra energy from the existing resources. Also it focuses how to get the extra energy just by changing the angle of blades of the wind mills. A. APPARI’S Solar Panel APPARI’s solar panel is a solar panel manufactured with well known techniques along with one additional detachable or inbuilt/embedded panel consisting of number of convex lens which increases the light and heat. The Appari’s solar panel is more effective than the present solar panels as it gives more heat and light from the sun. Design 1. 2. 3.

4. 5.

7. 8. 9.

The use of existing solar panels is made more effective by using the convex lens. The layer of convex lens may be fitted separately in one panel in detachable system and can be manufactured in the embedded system. This additional panel fitted with convex lens can be used along with the existing solar panels to increase the heat and light and there by the total amount of electricity/energy produced will be more than the present system. The convex lens fitted in the panels can be placed above the glass covering of the exiting solar panels. Depending on the capacities of the convex lens which are used in detachable system, the gap distance between the top glass cover of the existing solar panel and the layer of convex lens can be maintained properly so that it will generate the maximum heat and light on the glass covering of the solar panel. The same principle of using the convex lens to increase the more heat and light can also be used in the embedded system of the solar panels. In this system the layer of convex lens fixed in the toughened glass may be directly used above the solar cells glass covering or directly above the solar cells by keeping the proper gap between the layers of convex lens and the glass covering or the solar cells such that it will generate the maximum heat and the light on the solar cells and thereby generates the maximum solar energy. The gap distance between the layers of convex lens fitted in the panels and the top glass covering of the solar panel will depend upon the capacities of the convex lens used. The number and the capacity of the convex lens should be adjusted in such a way that each solar cell will get maximum heat and light from it. The solar panels may be of any shape which will give more effect of heat and light to generate the more energy. e.g. inverted U shape with flanges on both the sides.

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B. 1. 2.

Solar Dryers The principle of generating and increasing more heat and light by using convex lens can also be used for solar dryers. Solar dryers consisting of simple container to store the grains, fruits to dry. The system in such solar dryer is made up of any shape with glass covering/ roofing material. Above the roofing glass material the detachable panel consisting of convex lens can be also used to generate the more heat and light thereby the heat generated in the solar dryer will be more than the heat outside the dryer and even more than the existing solar dryers. The embedded system of roof of dryer can also be simply manufactured by attaching the various capacities of convex lens in roofing glass which will produce the more heat for drying the grains and fruits inside the solar dryer.

C. Solar water heater Appariâ&#x20AC;&#x2122;s solar water heater is simple in construction and applicable for low cost housing. It consists of one over head small water tank and the two layers of corrugated G. I. Sheets which are completely water tight. And in which the cold water from the over head water tank can be automatically collected by gravity action. The G. I. Sheets can be covered by one layer of convex lens to heat the G. I. Sheets. The convex lens may be fitted in the welded wire mesh in detachable system or it can be inbuilt in the toughened glass. The capacities of the convex lens may be different as par the need. Depending on the capacities of the convex lens the gap distance between the top layer of G.I. sheet and the layer of convex lens may be adjusted so that it will produce the maximum heat in the G. I. Sheet and thereby heats the water stored inside the G.I.sheets. The heated water will be collected in the same overhead water tank. The overhead water tank can be covered with insulating material so that the heat from the tank cannot be dissipated. The outlet connection (above the inlet connection to feed the water in space provided in G. I. Sheets) from the overhead water tank can be given for the house. Design 1. The two layers of G.I. Sheets can be perfectly welded by keeping the some gap in between them to collect and store the water from the overhead water tank. 2. The connection of inlet water from the over head water tank can be given to the space provided in between the two layers of G. I. Sheets. 3. The water in between the two layers of G. I sheets can be heated by the radiation/ heat generated from the sun. 4. The effectiveness to generate the more heat from the sun can be enhanced by providing one detachable or inbuilt layer of convex lens which will be placed above the top surface of the G. I . sheet. 5. The gap distance between the top surfaces of G. I sheet and the layer of convex lens can be adjusted by keeping in view the capacities of the convex lens fitted in the layer. 6. The heated water can be collected automatically in the same water tank. 7. The small capacities over head water tank can be separately used for this purpose and can be filled with the water as per the need and availability of hot water. Even one float can also be used to maintain the particular water level in this water tank. In case of this water tank feeds the water from the another over head water tank. In such case the level of the two water tanks can be properly maintained i. e. The water tank used for heating the water can be kept below the water tank which feeds the water for this water tank. 8. The same two layers of G. I . Sheets along with convex lens can also be used for roof covering. The width and the length of this can be used as per the need of roof covering, quantity of water to be heated. 9. The bottom surface of the G. I. sheets can be painted with black colour or thermal insulating material, so that the rooms from inside will not get heated and the heat from the G.I. sheets will not be dissipated. 10. This system provides economical for low cost housing along with facility of sound and thermal insulation property to the rooms. 11. This roof designed in such way that it also provides the resistance to the earthquake forces as the water pressure force acts exactly opposite to the earthquake forces and thereby it auto balances the earthquake force. 12. The care should be take while fixing such G.I sheets for roofing, the G. I. sheets should be welded and not to be bolted by J bolts to facility of the water tightness and to make it stable against the wind forces. D. Appariâ&#x20AC;&#x2122;s design of fan In residential and public buildings and other purposes the various types of fans are in use to get the wind and to maintain the natural temperature to the lowest possible. If the blades of the fans are flat which do not have any bend then the blades of the fan will simply rotate and will not give any wind. So the blades are to be bent to get

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the wind. If this bent is made 900 towards the free end of the blade then we will get maximum wind even for small capacity of the motor also. Just by changing the shape of blades of the fan we will get maximum wind, so there is saving in the cost of capacity of motor and thereby saving in the electricity, which leads to improve the national economy. This type of fan is also suitable for wind mill to generate the maximum wind energy. Design of ceiling fan/ table fan, blades of wind mill. (a) 1. The inside total width of the blades may be kept equal to the one third perimeter of the drum/ inside Perimeter available for the blades for three blade, one fourth for four blades, one half for two blades and so on. 2. The inside width of each blade is equal. 3. The total width of blades at outside end is equal to one third perimeter available at outside of the blade for three blades , one forth for four blades, one half for two blades and so on. 4. The outside width of each blade is equal. 5. The angle of bent of each blade at the inside is equal to zero degrees. 6. The angle of bent shall be gradually increased from zero degrees to ninety degrees from inside to outside. (b) The material used for the blades may be any metal, plastic, etc. (c) The thickness of the blades should be kept minimum for economy, but in any case the thickness should not be less than the thickness required for plane rotation which should avoid the vibration of the blades and thereby the vibration of the fan, noise etc. (d) The thickness of the blades may vary according to the material used. (e) The length of the blades may vary according to the requirement and the capacity of the motor (f) Such blades if we use for the wind mill will prove to produce the maximum energy from the wind.

1. 2.

3. 4. 5. 6. 7. 8. 9. 10.

II. Salient Features and Conclusions Use of existing solar panels for more energy simply by attaching one additional panel consisting of number of convex lens will produce the more heat and light. The embedded solar panels consisting of number of convex lens fitted in a toughened glass along with the present system of solar panels can also be manufactured for generating more heat and light thereby generates more energy. Use of existing solar dryers along with one additional detachable panel consisting of number of convex lens can produce more heat and light for drying the grains and fruits. The embedded solar dryers consisting of layer of convex lens fitted in a toughened glass can also be manufactured to produce more heat and light to dry the grains and fruits. Solar water heater is applicable for low cost housing. Solar water heater can be constructed on self help basis as the principle is very simple. The provision of such solar heater as a roof of the structure makes the structure earthquake resistant. The capacity of water heater can be adjusted/ designed as per the requirement. The provision of such solar heater provides Sound and thermal insulation property to the building. The blades designed having bent zero to ninety degrees from inside to outside of the fans will generate the maximum energy from the wind. Also for the same capacity of motor it produces the maximum wind and saves the electricity. References

[1] [2]

[3]

[4]

[5]

[6]

[7]

S. S. Dharane, “Behaviour of ferrocement slab under gradual and cyclic loading”, M. E. Dissertation submitted to Shivaji University, Kolhapur (India). Sidramappadharane & Architamalge, “Experimental Performance of Flexural Behavior of Ferrocement Slab Under Cyclic Loading”, “International Journal of Civil Engineering and Technology (IJCIET)”, ISSN 0976–6308(Print), ISSN 0976– 6316(Online), Volume 5, Issue 3, (2014), pp. 77-82. Dharane Sidramappa Shivashaankar and Patil Raobahdur Yashwant, “Design and Practical Limitations in Earthquake Resistant Structures and Feedback”, “International Journal of Civil Engineering and Technology (IJCIET)”, ISSN 0976–6308(Print), ISSN 0976–6316(Online), Volume 5, Issue 6, (2014), pp. 89 - 93. Dharane Sidramappa Shivashaankar and Patil Raobahdur Yashwant, “Earthquake Resistant High Rise Buildings –New Concept”, “International Journal of Advanced Research in Engineering & Technology (IJARET)”, ISSN 0976-6480(Print), ISSN 09766499(Online), Volume 5, Issue 6, (2014), pp. 121 - 124. Sidramappa Shivashankar Dharane, Madhkar Ambadas Sul and Patil Raobahdur Yashwant, “Earthquake Resistant RCC and Ferrocement Cicular Columns With Main Spiral Reinforcement”, “International Journal of Civil Engineering and Technology (IJCIET)”, ISSN 0976–6308(Print), ISSN 0976–6316(Online), Volume 5, Issue 9, (2014), pp. 100 - 102. Dharane Sidramappa Shivashankar, “Ferrocement Beams and Columns With X Shaped Shear Reinforcement And Stirrups”, “International Journal of Civil Engineering and Technology (IJCIET)”, ISSN 0976–6308(Print), ISSN 0976–6316(Online), Volume 5, Issue 7, (2014), pp. 172 – 175. Sidramappa Shivashankar Dharane, Archita Vijaykumar Malge, “Appari’s Design of Fan”, “International Journal of Innovations in Engineering and Technology (IJIET)”, ISSN 2319-1058, Vol.4. Issue 1- June 2014. Pp. 131-132.

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Heat Transfer and Turbulent Nanofluid Flow over a Double ForwardFacing Step Mohammed Saad Kamel Department of Mechanical Techniques/ Al- Nassiriyah Technical Institute Southern Technical University Dhi â&#x20AC;&#x201C;Qar/ Al-Nassiriyah/Baghdad street IRAQ Abstract: Heat transfer and turbulent nanofluids flow over a double forward-facing step were investigated numerically. The finite volume method was used to solve the continuity, momentum, and energy equations using the k- model. Four cases, corresponding to different nanofluids at constant step height, were investigated for Reynolds numbers ranging from 30,000 to 80,000. The bottom of the wall was heated at constant temperature 333K. Whereas the top wall was insulated. The results show that the surface Nusselt number increased with the Reynolds number. The maximum Nusselt number was observed for water/ ZnO nanofluids, with a Reynolds number of 50,000.The behavior of the Nusselt number was similar for all cases at a given Reynolds number and temperature. The results indicate also, that the pressure drop increased with increasing Reynolds number for all cases and the pressure drop in water/ CuO was higher that other at Re 80000. FLUENT 6.3.26 software was employed to run this simulation. Keywords: Nanofluids; Heat transfer; Double forward-facing step; Nusselt number. I.

Introduction

The purpose of this study is to investigate two-dimensional double forward-facing step flows, and the results of numerical computations for different nanofluids types, and Reynolds numbers at constant bottom wall temperature are presented herein. Numerous studies have been performed on single forward- and backward facing steps; however, the literature on double forward and backward-facing steps is very limited, and the physical basis of flow separation and vortex creation remains unclear. Fluid flow over a backward- or forwardfacing step generates recirculation zones and subsequent reattachment regions, due to sudden contraction or expansion in flow passages. Many practical engineering applications, such as the cooling of electronic devices, open channels, power generating equipment, heat exchangers, combustion chambers, and building aerodynamics, involve separating flows [1]. The first attempts to study heat transfer and fluid flow over forward- or backward-facing steps were made in the 1950's. Later, researchers were able to analyze complex flows in three dimensions due to the development of CFD software. Seban et al. [2] and Seban [3] pioneered the study of fluid flow over backward- and forward-facing steps froma heat transfer perspective. The authors discovered that the maximum heat transfer coefficients occur at the reattachment point and decrease toward the outlet. The effect of stream turbulence on the heat transfer rate in the reattachment region on the bottom surface of a backward-facing step was demonstrated by Mabuchi et al. [4]. Improvements in device capabilities have allowed researchers to measure reattachment points and heat transfer characteristics; Mori et al. [5] used a thermal Tuft probe, Kawamura et al. [6, 7] obtained the temporal and spatial parameters of heat transfer in the reattachment region using a new heat flux probe, and Oyakawa et al. [8, 9] employed jet discharge. The hydrodynamic characteristics of gas flows past a rib and a downward step in feature separation flow regions were studied by Terekhov et al. [10]. An early study on turbulent heat transfer and airflow over a double forward-facing step was reported by Yilmaz and Oztop [11]. The top of the wall and steps were insulated, and the bottom of the wall was heated. The authors used k- model and found that the step ratio affected the heat transfer and flow more strongly than the length ratio. Later, Oztop et al. [12] presented a numerical study of heat transfer and turbulent airflow over a double forward-facing step with an obstacle. The bottom of the wall and steps were heated, and the top of the wall was insulated. The results showed that the obstacle aspect ratio (Ar) affected the heat transfer, with the maximum Nusselt number corresponding to Ar = 1.

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More recently, the majority of studies have been utilizing nanofluid because of its higher thermal conductivity compared to normal fluid [13]. Abu Nada [14] is a pioneer in research on laminar nanofluid flow over a backward-facing step with Cu, Ag, Al2O3, CuO, and TiO2 nanofluid, volume fractions between 0.05 and 0.2 and Reynolds numbers ranging from 200 to 600. An investigation of findings signifies that the Nusselt number increased with the volume fraction and Reynolds number. Later, Kherbeet et al. [15] presented a numerical investigation of heat transfer and laminar nanofluid flow over a micro-scale backward-facing step. The Reynolds numbers ranged from 0.01 to 0.5, nanoparticle types comprised Al2O3, CuO, SiO2, and ZnO, and the expansion ratio was 2. An increasing Reynolds number and volume fraction seemed to lead to an increasing Nusselt number; the highest Nusselt number value was obtained with SiO2. The objective of this paper is to contribute new data regarding water and nanofluids flow over double forwardfacing steps to improve the design of heat exchangers. II.

Model description

A. Physical Model A schematic diagram of the double forward-facing step and the flow shape employed in this study is presented in Figure 1.The bottoms of the wall and the steps were heated to a given temperature (T h), while the top of the wall was adiabatic. The first and second step heights (h1, h2) was fixed at 20mm. The entrance width (H) was 100 mm. The Reynolds number was varied from 30,000 to 80,000 and calculated based on entrance width (H), and The temperature of heated wall was 313.

Figure 1: Schematic diagram of physical model. B. Governing equation The two-dimensional instantaneous governing equation of mass, momentum and energy equations for study incompressible in fully developed flow can be written in conservation form expressed in Cartesian coordinates as follows [19]:-

u i 0 xi

 ( u i u j ) x j

u j u p    [ ( i  )  u iu j ] xi x j x j xi

u i T  k T  ( ) x j x j cp x j The Reynolds stress tensor

 uiu j

= t (

 u iu j

(1) (2)

(3) can be determined according to the Boussinesq assumption as

ui u j 2  )   ij k x j xi 3

(4)

-Where μt is the turbulent eddy viscosity and is estimated by the (k-ε) two equations turbulent model.

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μt= cμρk2/ε

(5)

The differential equation of k and ε are given as

( u j k ) x j ( u j  ) x j



 k  [(   t ) ]  Gk   x j  k x j



    2 [(   t ) ]  C 1G  C 2  x j   x j k k

-Where Gk   uiuj (

u j xi

(6)

(7)

) is the turbulent production term.

The remaining coefficients that appeared in the above equation are as quoted by[19] : Cμ=0.09 ,Cε1=1.44 , Cε2=1.92 , σk=1 and σε=1.3 The Reynolds number is computed based on inlet channel height (H). .

(8) (9)

Where (L) is the length of the heated wall. C.

Numerical Procedure and grid dependence

Simulations were carried out using FLUENT 6.3.26 The gambit 2.3.16 was used for meshing, and the k-  standard model in Fluent was used to analyze the water and nanofluids flow and heat transfer over the double forward-facing step in the turbulent region. Grid independence was verified by increasing the grid size step wise, which yielded similar results. Independent verification was performed for Re=30,000 and water used as working fluid the initial grid sizes of (6522, 13765, 22647, 34780, 89228, 129438 and 178972). The difference in the Nusselt number relative to that of the selected grids was less than 0.5% and grid number 6 was adopted as shown Figure 2.

Figure 2: Grid dependence test III.

Thermo-physical properties of nanofluids

Thermophysical properties of the base fluid and the nanoparticles used in this study shown in table.1. The effective properties of nanofluids are defined as follow: Density: (10)

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Heat capacity:

…….(11) The Eqs. (6) and (7) were introduced by [15]. The thermal conductivity: –

(12)

This was introduced by [16]. Where (n) is a shape factor and equal to (3) for spherical nanoparticles. The viscosity: The effective viscosity can be obtained by using the following mean empirical correlation [17]. (13) Where: ……… (14) Where: M is the molecular weight of base fluid, N is the Avogadro number = 6.022*1023 mol -1, ρbf is the mass density of the base fluid calculated at temperature T0=300 K. the table 1. Show the thermo-physical properties of nanoparticles and working fluids. IV.

Results and Discussion

4.1 Effect of the Reynolds number The effects of the Reynolds number on the local Nusselt number for the turbulent ranges are presented in Figs.3 (A, B and C). With the increase of Reynolds number, the Nusselt number increased in the turbulent ranges for all nanofluids. Results indicate that since higher Reynolds number leads to higher velocity and temperature gradients at heated wall, consequently the local nusselt number is increased by increasing Reynolds number. 4.2 Effect of different types of nanoparticles Different types of nanoparticles such as Al2O3, CuO and ZnO and pure water as a base fluid are used. The Nusselt number for different nanofluids and different values of Reynolds number are shown in Fig.3 (A, B, C and D). It can be clearly seen that ZnO nanofluid has the highest average Nusselt number, followed by CuO, Al2O3 respectively. This is because ZnO has the lowest thermal conductivity than other nanofluids, but higher than water also, the heat capacity for ZnO is small compared with other nanofluids that means the heat transfer in particles move quickly and that let to get high temperature in it. 4.3 Pressure drop The pressure drop variation with axial distance for water/ Al2O3 at different Reynolds numbers and different nanofluids at Re 80000 is presented in Fig.4 and Fig.5. According to the results, the pressure drop intensified as the Reynolds number increased for water flow and also, the water/ CuO nanofluids has higher drop than other nanofluids at constant volume fraction. Generally, the highest pressure drop occurred at the downstream inlet region due to recirculation flow which caused the improvement of heat transfer. V.

Conclusion

Turbulent nanofluids forced convection and heat transfer over a double forward-facing step was studied. Four cases, corresponding to three different nanofluids and base fluid, were investigated at different Reynolds numbers

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and constant temperature. Recirculation zone increased the separation length at the same Reynolds number and temperature, which increases the Nusselt number. The results show that increasing the Reynolds number and temperature increased the Nusselt number for all cases. The enhancement of the Nusselt number occurred at the water/ ZnO nanofluid at 50000. Reynolds number compard with other nanofluids. In addition, the obtained results indicate an increase in the pressure drop with increasing Reynolds number for all cases.

surface nusselt number

24000

water Re 30000

21000 18000

water/Al2O3 Re 30000

15000 12000

water/Cuo Re 30000

9000 6000 3000

water/ ZnO Re 30000

0 0

0.5

1.5

position X in (m)

(A) 30000 water Re 50000

surface nusselt number

27000 24000 21000

water/ Al2o3 Re 50000

18000 15000

water/ Cuo Re 50000

12000 9000 6000

water/ ZnO Re 50000

3000 0 0

0.5

1.5

position X in (m)

surface nusselt number

(B) 39000 36000 33000 30000 27000 24000 21000 18000 15000 12000 9000 6000 3000 0

water Re 80000 water/ Al2o3 Re 80000 water/ Cuo Re 80000 water/ ZnO Re 80000

0.5

1.5

position X in (m)

(C)

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average nussult number

28000 26000 24000 22000 20000 18000 16000 14000 12000 10000 8000 6000 4000 2000 0 30000

water

water/Al2O3

water/CuO

water/ZnO

40000

50000

60000

70000

80000

90000

Reynolds number

(D) Figure 3. (A) surface nusselt number at Re 30000, (B) surface nusselt number at Re 50000, (C) surface nusselt number at Re 80000 and (D) Average nusselt number at different Reynolds number. 50000

water Re 80000

static pressure (pa)

45000 40000

water/ Al2O3 Re 80000

35000 30000 25000

water/ CuO Re 80000

20000 15000

water/ ZnO Re 80000

10000 5000 0 0

0.2

0.4

0.6

0.8

1.2

1.4

1.6

1.8

position X in (m)

pressure drop (pa)

Figure 4: Pressure drop with different Nanofluids at Re 80000. 45000 40000 35000 30000 25000 20000 15000 10000 5000 0

water/ Al2O3 Re 30000

water/ Al2O3 Re 50000

water/ Al2O3 Re 80000

0.2

0.4

0.6

0.8

1.2

1.4

1.6

1.8

position X in (m)

Figure 5: Pressure drop with different Reynolds number for (Al2O3-water)

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Table 1. Thermophysical properties of the base fluid and the nanoparticles used in this study [16, 17, 18] property (kg.m-3) Cp (J/kg.k) K (w/m.k) (N.s/m2)

water 998.2 4182 0.6 0.001003

Al2O3 3970 765 40 -

VI. Cp NuL Nuav P Pr Re Tw T u x, y L h1 h2 H a b c M

CuO 6500 535.6 20 -

ZnO 5600 495.2 13 -

Nomenclature

specific heat capacity (J kg-1 K-1) surface Nusselt number average Nusselt number Pressure (Pa) Prandtl number Reynolds number Heated Wall temperature (K) Temperature (K) velocity component (m s-1) spatial coordination (m) Length of the heated downstream wall (m) First Step height (m) Second Step height (m) Height of inlet channel (m) Length of bottom wall before the first step (m) Length of bottom wall after the first step (m) Length of bottom wall after the second step (m) molecular weight of basefluid Particles diameter (nm) Basefluid diameter (nm) Avogadro number

Greek symbols K µ ρ ρbf

thermal conductivity (W m-1 K-1) dynamic viscosity (Pa s) density (kg m-3) Density of basefluid (kg m-3) Volume friction (%)

nf p bf

nanofluid Nano particles basefluid

Subscripts

VII. [1]

[2] [3] [4]

[5]

[6]

[7] [8]

References

Hussein Togun, Ahmed Jassim Shkarah, S. N. Kazi,1 and A. Badarudin. "CFD Simulation of Heat Transfer and Turbulent Fluid Flow over a Double Forward-Facing Step", Hindawi Publishing Corporation " Mathematical Problems in Engineering" Volume 2013, Article ID 895374, 10 pages. R. A. Seban, A. Emery, and A. Levy, “Heat transfer to separated and reattached subsonic turbulent flows obtained downstream of a surface step,” International Journal of Aerospace Sciences, vol. 2, pp. 809–814, 1959. R. A. Seban, “The effect of suction and injection on the heat transfer and flow in a turbulent separated air flow,” Journal of Heat Transfer, vol. 88, no. 3, pp. 276–282, 1966. I. Mabuchi, T. Murata, and M. Kumada, “Effect of free-stream turbulence on heat transfer characteristics in the reattachment region on the bottom surface of a backward-facing step (for different angles of separation),” Transactions of the Japan Society of Mechanical Engineers B, vol. 52, no. 479, pp. 2619–2625, 1986. Y. Mori, Y. Uchida, and K. Sakai, “A study of the time and spatial micro structure of heat transfer performance near the reattaching point of separated flows,” Transactions of the Japan Society of Mechanical Engineers B, vol. 52,no. 481, pp. 3353 –3361, 1986. T. Kawamura, A. Yamamori, J. Mimatsu, and M. Kumada, “Time and spatial characteristics of heat transfer at the reattachment region of a two-dimensional backward-facing step, ” in Proceedings of the ASME-JSME Thermal Engineering Joint Conference, vol. 3, pp. 197–204, 1991. T. Kawamura, S. Tanaka, I. Mabuchi, and M. Kumada," Temporal and spatial characteristics of heat transfer at the reattachment region of a backward-facing step,” Experimental Heat Transfer, vol. 1, no. 4, pp. 299–313, 1987. K. Oyakawa, T. Taira, and E. Yamazato, “Studies of heat transfer control by jet discharge at reattachment region downstream of a backward-facing step,” Transactions of the Japan Society of Mechanical Engineers B, vol. 60, no. 569, pp. 248–254, 1994.

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K. Oyakawa, T. Saitoh, I. Teruya, and I.Mabuchi, “Heat transfer enhancement using slat at reattachment region downstream of backward-facing step,” Transactions of the Japan Society of Mechanical Engineers B, vol. 61, no. 592, pp. 4426–4431, 1995. V. I. Terekhov, N. I. Yarygina, and R. F. Zhdanov, “Heat transfer in turbulent separated flows in the presence of high freestream turbulence,” International Journal of Heat and Mass Transfer, vol. 46, no. 23, pp. 4535–4551, 2003. I. Yilmaz and H. F. Oztop, “Turbulence forced convection heat transfer over double forward facing step flow,”International Communications in Heat and Mass Transfer, vol. 33, no. 4, pp. 508–517, 2006. H. F. Oztop, K. S.Mushatet, and ˙I. Yılmaz, “Analysis of turbulent flow and heat transfer over a double forward facing step with obstacles,” International Communications in Heat and Mass Transfer, vol. 39, no. 9, pp. 1395–1403, 2012. M. Hassan, R. Sadri, G. Ahmadi, M. Dahari, S. Kazi, M.R. Safaei, E. Sadeghinezhad, Numerical study of entropy generation in a flowing nanofluid used in micro- and minichannels, Entropy 15 (2013) 144–155. E. Abu-Nada, Application of nanofluids for heat transfer enhancement of separated flows encountered in a backward facing step, Int. J. Heat Fluid Flow 29 (2008) 242–249. A.S. Kherbeet, H.A. Mohammed, B.H. Salman, The effect of nanofluids flow on mixed convection heat transfer over microscale backward-facing step, Int. J. Heat Mass Transfer 55 (2012) 5870–5881. R. S. Vajjha and D. K. Das, “Experimental determination of thermal conductivity of three nanofluids and development of new correlations,” International Journal of Heat and Mass Transfer, vol. 52, no. 21-22, pp. 4675–4682, 2009. R. S.Vajjha, D. K. Das, andD.P.Kulkarni, “Development of new correlations for convective heat transfer and friction factor in turbulent regime for nanofluids,” International Journal of Heat and Mass Transfer, vol. 53, no. 21-22, pp. 4607–4618, 2010. J. H. Lienhard and J. Lienhard, A Heat Transfer Textbook, Phlogiston Press, Cambridge, Mass, USA, 2000. Ferzigen, J. H. and Peric, M. "Computational methods for fluid dynamic" .2th edition, Springer. Berlin , (1999).

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Experimental Simulation Analysis of Current Density Distribution by Kirchhoff’s laws in a resistor network cell model S. R. Rajkumar *@, Dr.M. Alagar a, Dr.S. Somasekaran b, S.R. Ravisankar c Corresponding author, Assistant Professor, Department of Physics, Rajapalayam Rajus’ College, Madurai Kamaraj University,Rajapalayam-626117,TamilNadu,India. a Head & Associate Professor, DST – FIST Sponsored Center for Research and Post Graduate Department of Physics, Ayya Nadar Janaki Ammal College (Autonomous), Madurai Kamaraj University, Sivakasi -626124, TamilNadu, India. b Head & Associate Professor, Department of Physics, Rajapalayam Rajus’ College, Madurai Kamaraj University, Rajapalayam-626117, TamilNadu, India. c Scientific Officer, Production Section, Department of Atomic Energy (Govt of India), Heavy Water Plant, Tuticorin-628007, TamilNadu, India.

Abstract: In this process, variations in individual current distribution are due to a combined effect of spatially and temporally varied condition in the cell and usual preparations carried out during the cell operation. This process presents a resistor network model of an industrial copper reduction cell with equivalent circuits representing path resistance at individual anodes at the same time the practical cell structure. The model acquires time-varying resistance as the inputs and determines the corresponding timevarying anode current distribution. Unlike usual move towards that treat the system as a network of only anodes connected in parallel, the proposed model also considers the structure of an industrial reduction cell. The simulation results include current distribution during anode change and noise level change due to slot disappearance. This model and the simulation studies are the original measure in the direction of cell monitoring based on anode current measurements. Keywords: current density, copper reduction cell, resistor network, electrodeposition, anode current distribution, direction of cell monitoring.

I. Introduction In this process Copper is produced via electrochemical reactions in electrolytic reduction of Copper in a molten bath of copper sulphate. The most favourable process efficiency of the progression can be achieved when the cell is in a balanced state. By its nature, this process is a semi-batch practice with a distributed nature. Electrical energy is required to break the Copper-oxygen bond in the thermodynamically stable Copper dissolved minimally in the electrolyte. A stream of constant direct current is applied 1-4 to the cell connected in series at varying Adm2 ‘s Each cell contains anodes, where each anode is supported by an anode rod which is attached to an anode beam. Thus anode is consumable during the process. The normal life of anodes varies depending on the anode size and operating conditions. This means that the anodes need to be replaced at the end of the trials. Anode resistance path in this work is classified as the individual path of the parallel network starting from the anode beam to the cathode. Each path consists of components as follows:  Anode resistance.  Sulphate bath resistance, as oxide is produced during the reduction process and forms a layer underneath the anodes, contributing to the path resistance. Since the implementation of slotted anodes, the resistance caused by oxide layer has been greatly reduced. However, the resistance will become significant once the slots are consumed during the operation 5-7.  Electrolyte resistance and  Impedance arisen due to reaction at both anode and cathode. A. Anode as a resistor network By measuring line current, deposition weight and cell voltage performance monitoring of measurements reflect the overall cell performance, but they are not able to address spatial variations occurring inside the cell. Recently, the measurement of individual anode current has attracted a lot of attention8-13. This measurement scheme has been used in anode current signal analysis, thermal model and process fault diagnosis. Seeing as the anode current signal is affected by both local cell condition and the signals on the other cathode, the distributed nature of the process can be represented by the measurement. There is a limited number of works which

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consider the anodes as a resistor network, for example, considers the cell with anode connecting in parallel for detecting anode spikes. However, the anode in copper reduction cell connected through anode and crossover with ammeter and voltmeter. Thus, the anode paths should be considered as a resistor network with anode, crossovers that connect them. B. Equivalent Kirchhoff’s Circuit Network (Fig.1.1). illustrated an example of equivalent circuit network. “Anode” represents the anode resistance path from anode to cathode “Anode beam” is attached to anode rod, which supports anode, “Crossover” is the bridge that joins between the anode beams and “Riser” is where the direct current is introduced from a current source1419 . All the anode resistance paths are connected through the liquid molten copper (metal panel) at the bottom of the cell. The direct current is providing for stable current source which is then divided into four streams through the risers. The current coming from the risers is delivered to all the anodes through the anode beams and the crossovers. The current on the anode paths is then accumulated in the metal panel at the bottom of the cell. To solve the circuit network, Kirchhoff’s circuit laws are used. The Kirchhoff’s current law states that the algebraic sum of currents in a network of resistors meeting at a point is zero, while the Kirchhoff’s voltage law states that the sum of all voltage drops around a closed loop in an electrical circuit is zero.  Therefore, joints (Fig.1.2) in the equivalent circuit can be treated with Kirchhoff’s current law  Whereas the loops (Fig.1.3.) are dealt with Kirchhoff’s voltage law. The equation can be written as Where and are Kirchhoff-related matrices is a function of anode path resistance and is a vector of current distribution in the cell. C. Estimation of Modeling of electrolyte resistance Assume that the anode path resistance should be provided as the initial condition. In this model, the components of path resistance are considered as follows: • Anode resistance is decreasing, according to the consumption rate of copper anode. It is believed that the anodes are consumed evenly from the bottom so that the cross-sectional area (5.0 cm × 2.5 cm) of the anode is always the same. The rate of resistance change can be calculated by Faraday’s equation.

Where is the resistivity of copper, is the anode current, is the molar mass of the copper, is the Faraday’s constant, is the valency of the copper, is the anode density and is the cross-sectional area of the anode. • Sulphate bath resistance, as oxide is produced during the reduction process and forms a layer underneath the anodes, contributing to the path resistance. Since the implementation of slotted anodes, the resistance caused by oxide layer has been greatly reduced. However, the resistance will become significant once the slots are consumed during the operation; • Other resistance components on the anode path, including electrolyte resistance and impedance arise due to reaction at both anode and cathode are assumed to be constant during normal operation. • During anode setting with respect to the various point position of cathode, the cell environment is disturbed, affecting all path resistance. In addition, as the temperature of a newly set anode is much lower than that of the electrolyte bath, the bath will “stagnant” around the newly set anode. The resistance change during these periods.

Fig.1.1. An Equivalent Copper reduction cell model

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Fig. 1.2. A joint in network

Fig. 1.3. A loop in network

D. Common Features of resistor network The path resistance is varying due to anode consumption and bubble generation, thus the resulting current distribution will be time-dependent. This model calculates the individual anode current distribution of the resistor network based on the anode path resistance20.The model structure is shown in (Fig.1.4). In “Anode Resistance” block, the resistance of copper anode is maintained for every set of individual anode current distribution results. In “Sulphate bath resistance” block, the model determines the resistance of the Sulphate bath based on the slot height, which is dependent on previous current distribution. The “Resistor Network” takes the varying path resistance as the input and determines the current distribution in different parts of the cell. Thus, the model is able to predict the path lines of individual anode current21. It also provides information on anode beams, crossovers and risers, which contain potential information for cell performance characterization. E. Simulation of Anode locations The resistance values and parameters used in the simulation are obtained and derived from various literatures. The relative ratio of the resistance components in an anode resistance path is from22, the resistance due to bubble layer formation before and after the slot disappears can be found in23. And the percentage changes of path resistance during anode settings as well as the exponential current rise after the setting are derived from24. The anode design of the model is shown in (Fig.1.5).

Fig. 1.4. Block diagram for the resistor network model

Fig. 1.5. Relative anode location in simulation [A1-Anode 1]

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The idea of the following simulation is to show the model is capable of capturing the dynamics in anode resistance and sulphate bath layer resistance. The starting path resistance and slot height are selected accordingly. The results also demonstrate that it is important to include anode beams and risers in the resistor network as they generate relation between different anode paths and the potential of use riser current as a cell performance display. If not at all the simulations start with a set of anodes with a random combination of anode ages. F. Anode Setting Anodes in this process need to be relocated as they are consumed during the reduction. (Fig.1.6). shows the response of individual anode currents during an anode setting. As Anode 1 was replaced at about an hour after the simulation has started. The current on that anode drops to zero, followed by an exponential current rise during the stagnant layer melts. At the time when the current of Anode 1 is reduced to zero, current increases in the other anodes are observed. These currents decreased when the new anode starts to represent current exponentially. These variations are due to the relative position of the anode that was recently set. It must be distinguished that only the anodes opposite, adjacent to and away from the recently set anode are shown in (Fig.1.7). which represent current represent of anodes at different position. The correlations of anode related processes from G.C.Barber24 are used in this model to simulate the impact of a recently set anode on anode current distribution, which might vary between different cell technologies. The correlations can be calculated each time an anode is replace and estimated so that the results can be close to reality. G. Route of Anode Current In (Fig.1.8). represented all the anodes in the cell are placed in the cell at the same time, which are focus to exponential current rise. If the anode paths are considered as resistors merely connected in parallel, all the anodes will expose the same behavior. Conversely, which is showing that even if the differences are small, there are deviations of current curves from each other due to the interactions between anode paths and anode beams, risers and crossovers. This is a case in point of illustrating the significance of including cell structure into the model. H. Riser Current At the same time as the response of the riser currents to anode setting at different locations varies significantly. Every abrupt alteration in riser currents corresponds to one anode setting event in the simulation occurring at different parts of the cell, the behavior of riser currents is characterized in (Fig.1.9). This shows the potential to use riser currents to detect changes in anode currents. A1 A2 A3 A11 A12

12000 11000 10000 9000

Current (mA)

8000 7000 6000 5000 4000 3000 2000 1000 0 0

Time (sec)

Fig.1.6. Anode current response during anode setting

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Fig. 1.7. Anode setting at different position during Anode current response A1 A2 A3 A11 A12

1.018 1.017

Current (Amp)

1.016 1.015 1.014 1.013 1.012 1.011 0

Time (sec)

Fig. 1.8. Anode current distribution under same condition

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Riser Riser Riser Riser

5.8

1 2 3 4

5.6

Current (Amp)

5.4

5.2

5.0

4.8

4.6

4.4 0

Time (min)

Fig.1.9. Riser current showing a number of Anode settings at different position II. Conclusion This work process represents an equivalent resistor network model for current distribution analysis. It takes the time-varying resistance of the anode paths as the input and determines the time-varying current distribution at different parts of the cell. The model not only considers resistance of the anode paths, but also includes resistance of the anode beams, the crossovers and the risers. The model is capable of capturing the dynamics change of anode and bath resistance as well as the interactions between anode paths and anode beams, risers and crossovers. As explained by the simulation results, it is important to include cell structure in the resistor network to account for the interactions. It also provides predictions to analyze currents on anode beams, risers and crossovers to obtain more information about the system. The progress of this model can be regarded as the first step towards cell performance monitoring using individual anode current measurement. References [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8]. [9]. [10]. [11]. [12]. [13]. [14]. [15]. [16]. [17]. [18]. [19]. [20]. [21]. [22]. [23]. [24].

Shanthi, C., Barathan, S., RajasrisenJaiswal, Arunachalam, R.M., & Mohan, S. “The effect of pulse parameters in electrodeposition of silver alloy.” Materials letters, 62(2008), 4519–4521. K. Schladitz, S. Peters, D. Reinel-Bitzer and A. Wiegmann, J. Ohser, Comput.Mater.Sci. 38 (2006) 56. J. Nam and M. Kaviany, Int. J. Heat Mass Transfer 46 (2003) 4595. P.C. Andricacos "Damascene Copper Electroplating for Chip Interconnections.”, Interface., Vol-7,(1998)23. A.M Lafront, B. Veilleux and E.Ghali: Electrochemical polarization studies of nodulation phenomenon during copper electrolysis, Electrometallurgy (2001) 389-400. J. Keniry and E. Shaidulin, “Anode signal analysis: the next generation in reduction cell control”. Proceedings of TMS Light Metals, New Orleans, LA(2008)287-292. A.M Lafront, B. Veilleux and E.Ghali:, “Galvanostatic and microscopic studies ofmodulation during copper electrolysis,” Journal of Applied Electrochemistry, 2002, 32(3), 329-337. Z.Kang-gen, L.Ging-Gang and Z.Qi-Xiu, “Electrodeposition of copper by IMPC method”, Journal of Central South University of Technology, 2000, 7(4), 186-190. R. Hvidsten and K. Rye, “Practical application of the continuous measurement of individual anode current in Hall-Héroult cells”, Proceedings of TMS Light Metals, New Orleans, LA(2008)329-331. P. W.Peacock and J.Robertson, Phys.Rev.Lett.92(2004)057601. J. LaPlante, Metal Finishing, 103(2005)18. M. Valetas, M. Verite, A. Bessaudou, F. Cosset and J.C. Vareille, Computational Materials Science, 33(2005)163. K. N. Tu, “Recent advances on electromigration in every-large-scale integration of interconnects”, Journal of Applied Physics, v. 94, No. 9(2003)5451-5473. Y. Liu, L. Liang, S. Irving and T. Luk, “3D modeling of electromigration combined with thermal mechanical effect for IC device and package”, Microelectronics Reliability, v. 48(2008)811-824. R.N.Bhattacharya, et al., “Electroless Deposition of Copper Thin film,”Electrochemical and solidstate letter., Vol-2, (1999).222 - 223. K.G.Mishra, et al., “Kinetics and mechanism of Electroless deposition of copper.,” Journal of electrochem society.,Vol-143(1996),510-516. Hwang J. and Lai J., “The effect of temperature on limiting current density and mass transfer in electrodialysis”, Journal of Chemical Technology & Biotechnology, 2007, 37(2), 123-132. S. Mohan & V.Raj, “The effect of additives on the pulsed electrodeposition of copper.” Transactions of Institute of metal finishing, 83, (2005).194-198. Y.B. Park and I.S. Jeon, “Effects of mechanical stress at no current stressed area on electromigration reliability of multilevel interconnects”. Microelectron. Eng.71(2004)76. L. K. Bieniasz, Electrochemistry Communications 3(2001)149. R. D. Braatz, M. L. Tyler, M. Morari, F. R. Pranckh and L. Sartor, “Identification and cross-directional control of coating processes,” AIChE J.,vol. 38(1992)1329–1339. W. Haupin, “Interpreting the components of cell voltage” Proceedings of TMS Light Metals, Warrendale, PA(1998)531 – 537 G. Bearne, D. Gadd and S. Lix. “The impact of slots on reduction cell individual anode current variation”. Proceedings of TMS Light Metals, Orlando, FL(2007)305-310. G.C.Barber. “The Impact of Anode-Related Process Dynamics on Cell Behavior During Aluminum Electrolysis”. Ph.D. Thesis. The University of Auckland, New Zealand(1992).

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Measuring infrastructure sustainability with the use of eco efficient performance criteria 1

Saroop S and Allopi D2 Royal Haskoning DHV, P O Box 55, Pinetown, 3600, The Netherlands 2 Department of Civil Engineering and Surveying, Durban University of Technology, P O Box 1334, Durban, South Africa 1

Abstract: Civil engineering projects can have significant site-specific and cumulative impacts on our ecological and social systems if not correctly planned, designed and implemented. As we face significant planetary issues such as global warming, it is clear that the engineering profession has a significant part to play in affecting the future of our planet. This paper aims to demonstrate the importance of eco efficient criteria on infrastructure projects. The use of the proposed criteria would ensure a sustainable design for township infrastructure services through the consideration of scare resources, ecological sensitivity in the design and planning of infrastructure projects. This paper also gives an overview of the proposed Green Township Infrastructure Design toolkit and promotes the use of green practices on infrastructure services design, that are environmentally sound, placing fewer burdens on the environment. This paper also compares two case studies that utilises green infrastructure criteria to rate its eco efficiency. Keywords: Infrastructure design, Eco-efficiency, Sustainable development, Green infrastructure I. Introduction In order to stay competitive and to meet upcoming stricter environmental regulations and customer requirements, designers have a key role in designing civil infrastructure so that it is environmentally sustainable. Relatively few designers have as yet explored the transformative potential of ecological design and have preferred to remain apolitical and unconcerned with the distributional impacts of design as they affect the health of humans and ecosystems [5]. By utilising improved environmentally friendly-seeking design solutions, this study aims to introduce environmentally friendly design decisions prior to the infrastructure design approval process. This increases overall competitiveness by bringing a whole new class of productive solutions to problems while at the same time adding a fresh perspective to the traditional infrastructure design process. This paper describes a case study that uses this green infrastructure approach, as opposed to traditional methods of design as well as the benefits of using an eco-approach to infrastructure design. II. Climate Change and Sustainability The need to make development sustainable is based on the sound evidence showing that we are using up critical resources and ecological carrying capacity faster than they are being renewed, replaced or replenished [2]. It has been established that municipalities are not delivering infrastructure service in a sustainable manner. Most of the challenges are due to planning, implementation and monitoring systems failure. Further assessments revealed that municipalities do not comply with basic principles for sustainable service delivery [1]. Climate change is introducing many uncertainties into the management and planning of township infrastructure projects. In the area of sustainability, there is an urgent need to apply technologies and methods that deliver better and more sustainable performance in a way that is cost effective Engineers will have to be at the forefront of developments finding ways to maximise water capture, ensuring conservation of the resource from supply through to distribution, and the issues of innovation, technology and design. Sustainability and adaptive and mitigative approaches to climate change, in the design of infrastructure are therefore important steering elements [2]. Making the wrong choices now will cause the future generations to live in a changed climate, depleted resources and without the green space and biodiversity.

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III. The need to implement green technology on civil engineering infrastructure projects In the area of sustainability, there is an urgent need to apply technologies and methods that deliver better and more sustainable performance in a way that is cost effective. Sustainability, adaptive and mitigative approaches to climate change, in the design of infrastructure are therefore important steering elements [2]. Environmentally sustainable design on township infrastructure services entails the use of ecologically sensitive innovative design solutions, integrating a consideration of resources, the environment, maintenance and recyclable materials, from the early design stages of a project. Infrastructure elements such as roads, water, sewage and stormwater can result in loss of critical ecosystems and biodiversity. There is a need to create an eco-sensitive infrastructure design rating system that encourages and promotes the use of “softer” design solutions. Due to the ecologically sensitive nature of civil engineering infrastructure, there is a need for a green rating tool to evaluate the performance of infrastructure. The proposed research assesses the environmental impacts of infrastructure design decisions on development. Diligent attention to greener infrastructure solutions from the very earliest phases of a project will help guarantee that quality design environmental solutions are "built in" from the beginning. IV. Infrastructure eco efficiency criteria The role of criteria for sustainable green infrastructure are tools which can be used in the conceptualization, implementation, and monitoring of progress in township infrastructure projects. The Criteria define the essential components of the proposed Green infrastructure toolkit against which sustainability may be assessed. Thus, collectively, the criteria provide an implicit, generally agreed-upon global definition for the concept of eco efficient infrastructure solutions. Each criterion relates to a key element of sustainability. Through the measurement and monitoring of these indicators, the overall effects of the proposed Green infrastructure toolkit, can be assessed and evaluated, and action can be adjusted to meet stated aims and objectives more effectively The eco efficient criteria that characterize sustainable criteria of Green township infrastructure are listed in Table 1. The criteria, namely Economy, Functional Efficiency, Safety and Environmental Quality were derived from goals that were set out for layout planning and related services for residential township developments [3]. The proposed Eco Efficient Infrastructure Criteria namely Efficient Layout Planning ensures that infrastructure is placed in environmentally responsible ways. The Resources criteria encourage an efficient utilisation of materials/ resources. Environmental Quality mitigates environmental impacts of infrastructure. Functional Efficiency ensures that infrastructure is designed optimally. Future Maintenance maximizes the opportunities for integrating capital and operation of infrastructure. Economy maximizes the opportunities for integrated cost effective adoption of green infrastructure options. Safety minimises the environmental impact of infrastructure by incorporating safety into the design. Social sustainability of infrastructure promotes the use of social resources, encourages public participation and the placement of infrastructure in the most convenient manner Table 1: The Eco Efficient infrastructure performance criteria Eco-efficient infrastructure Sustainable criteria 1. Efficient Layout planning 2. Resources 3. Environment quality 4. Functional efficiency 5. Future maintenance 6. Economy 7. Safety 8. Social

Measure

Placement of infrastructure in environmentally responsible, efficient ways, conserve land. Encourages the efficient utilisation of materials/ resources, selection of environmentally friendly materials. Design features that mitigate environmental impacts of infrastructure, by reducing effects of pollutants Design of infrastructure that maximizes functional efficiency of infrastructure. Maximizes the opportunities for integrating capital and operation of infrastructure, ensuring reliability of level of service Maximizes the opportunities for integrated cost effective adoption of green infrastructure options. Minimizes the environmental impact of infrastructure by incorporating safety into the design. Ensuring social sustainability of infrastructure promoting convenience, social resources and public participation.

The Infrastructure eco efficient criteria used in the proposed Green Township Infrastructure Design Toolkit were developed to:  Determine the means by which eco- environmental efficiency can be assessed, monitored, quantified and verified at any stage of the project, to ensure a value-added, quality driven, green approach to infrastructure design;  Provide a basis for the consultants and clients to work together on creating and evaluating sustainable infrastructure solutions, thereby ensuring comprehensive infrastructure planning with maximum stakeholder involvement;

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

Achieve the required balance of sustainability, expenditure, value for money and quality, between the various elements of the project; V. Weighting of Eco Efficient Criteria

Weighting of the Infrastructure eco efficient criteria allows the design team to target or prioritise certain infrastructure environmental sustainable performance categories over the various elements of the project. The weighting of the various categories is carried out at this early stage, before the design is developed, to avoid redesign later in the process. Weighting the infrastructure eco efficient criteria enables the project to be tailored to the client’s project requirements and specifications, at the earliest stages of the development process. A lot of flexibility exists in the green township design rating system, so that designers can benefit by focusing on specific categories applicable to each design situation. VI. The Green Township Infrastructure Rating System for infrastructure projects This paper proposes a rating system that enforces environmentally sustainable design on township infrastructure services by integrating a consideration of resources, the environment, ecologically sensitive innovative design, maintenance and recyclable materials, from the early design stages of a project. The Green Township Infrastructure Design Toolkit, as illustrated in Figure 1, uses the concept of eco-efficiency and would allow the designer to evaluate design options, enabling him/her to choose the one likely to yield the best performance with the least environmental impact, based on proven technology. This toolkit is intended to encourage developers to consider green methods and practices in the earliest stages of project planning, by assessing a number of recommended green practices and its environmental impacts on infrastructure services design, placing fewer burdens on the environment. The various Green Report Forms, enables the client to select a combination of alternatives and evaluate a number of possible design options – with their environmental implications – at each stage of the design process . During the briefing and preliminary design stage, (1 and 2), the client and engineer have a joint responsibility of deciding just how green the project should be, or alternatively of deciding what environmental quality of services can be provided. During the detailed stages (3), the engineer has the responsibility of designing, while maximising the green value of the project. Stage 4 gives the designers an opportunity to add environmental value at the construction stage, by analysing eco-friendly construction material.

Figure 1 The Green Township Infrastructure Design Toolkit.

VII. A case study to compare the usefulness of the eco efficient criteria The two residential development case studies were compared to each in order to test the usefulness of the rating in searching for green solutions. Each element is categorised, prioritised and rated into the various eco efficient criteria. Case study 1used conventional infrastructure and was chosen to assess how the model rates conventional infrastructure.

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The second case study was a low income development that aimed to have a restricted damage to the environment, by using a combination of green solutions and conventional infrastructure The results indicated that Case Study 2 achieved a green rating of 66 and performed satisfactory to moderate scores across all dimensions of sustainability, being able to maintain a balance between the needs of society and the preservation of the environment. Case Study 1 on the other hand demonstrated a significantly different performance, achieving a green rating of 18 and receiving low scores for almost all components, due to the lack of environmental interventions. This therefore offers a useful contrast to the situation in that Case study 1 indicates the results when only conventional designs are used, compared to simple, inexpensive green interventions that can be used, as shown in Table 2 and illustrated in Figure 2. Table 2: Comparative assessment between Case Study 1 and Case Study 2

LAYOUT EFFICIENCY FUNCTIONAL EFFICIENCY ENVIRONMENTAL QUALITY

WATER

SEWER

STORM WATER

ROADS

SEWER

WATER

OVERALL PROJECT

Case Study 2 STORM WATER

PERFORMANCE CATEGORIES

ROADS

OVERALL PROJECT

Case Study 1

ECONOMY

FUTURE MAINTENANCE

SAFETY

SOCIAL

100

RESOURCES

CONSTRUCTION

SCORING

Figure 2: Comparative assessment between Case Study 1 and Case Study 2 LAYOUT EFFICIENCY

100 CONSTRUCTION

FUNCTIONAL EFFICIENCY

50 25

RESOURCES

ENVIRONMENTAL QUALITY

SOCIAL

ECONOMY

SAFETY

Case Study 2

FUTURE MAINTENANCE CASE STUDY 1 Case Study 4

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VIII.Advantages of using the eco approach to infrastructure design Resource benefits:  Recycling of used products  Conservation of natural resources  Recharged ground water flow for streams, conserving water supplies. Environmental benefits:  Enhance and protect ecosystems and biodiversity  Increased vegetation, improve air quality by filtering many airborne pollutants  Minimized impervious surfaces reducing soil erosion  Reduced concentrations of pollutants Economic benefits:  Reduced Infrastructure Costs by water collection, storage, treatment and distribution  More efficient use of existing infrastructure  Reduced operating costs  Enhanced asset value and profits  Optimized life-cycle economic performance Health and community benefits:  Improved air, thermal, and acoustic environments  Community safety  Convenience of users  Enhanced occupant comfort and health  Minimized strain on local infrastructure  Contributed to overall quality of life. VI. CONCLUSIONS Sustainability criteria focus on scarce resources and prioritize areas; and to improve accountability linking project level work to the achievement of strategic objectives. Improvement in the awareness of eco-efficiency concepts is urgently needed among policy-makers, planners and decision-makers. However, the criteria applicable to, and measures for developing eco-efficient and sustainable infrastructure are yet to be fully identified [4]. Green techniques provide adaptation benefits for a wide array of circumstances, by conserving and reusing water, promoting groundwater recharge, and reducing surface water discharges that could reduce to flooding. A new paradigm for infrastructure design is required in order to ensure environmental sustainability on infrastructure projects. VII. [1] [2] [3] [4] [5]

References

Department: Provincial and Local Government (DPLG), Guidelines: Sustainable Municipal Infrastructure Provision and Service Delivery. 2007. http://www.dplg.gov.za/subwebsites/mig/docs/Municipal Infra Policy 2 April2007.doc FIDIC, FIDIC State of the World Infrastructure Report 2009, 2009. National Housing Board, Guidelines for the provision of engineering services and amenities in residential township developments, 1995. United Nations Economic and Social Commission for Asia and the Pacific. 2006. Sustainable Infrastructure in Asia. http://www.unescap.org/esd/environment/mced/singg/documents/Sustainable Infrastructure in Asia.pdf Van Wyk, L., EcoBuilding: Towards an Appropriate Architectonic Expression, In Green Building Handbook for South Africa, 2009. http://researchspace.csir.co.za/dspace/bitstream/10204/3262/1/vanWyk1_2009.pdf

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American International Journal of Research in Science, Technology, Engineering & Mathematics

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Glauber Modeling in Heavy Ion Collisions Abhilasha Saini1, Dr. Sudhir Bhardwaj2 Research scholar, Department of Physics, Gyan Vihar University, Jaipur & Lecturer, Atharva College of Engg., Mumbai, India 2 Assistant Professor, Govt. College of Engineering & Technology, Bikaner, India 1

Abstract: With increasing search of critical point at RHIC fluctuations in flow, Glauber model provides a baseline over which effects of fluctuations start gaining importance. Glauber model is used to have a quantitative idea of the geometrical configuration of nuclei, when nuclei collide at ultra relativistic energies, and treats the collision as a sequence of nucleon-nucleon collision. This method is able to calculate the number of participating nucleons and the number of binary collisions analytically, for a given impact parameter and centre of mass energy. To compare the results to real experimental data, the nuclear density profile and inelastic nucleon-nucleon cross-section is given as an input to the model. Glauber model comes into two variants-The Optical Glauber model, which assumes smooth matter density described by Fermi distribution function, and The Monte Carlo Glauber model which analyses the quantities mentioned, by considering individual nucleons as distributed stochastically event by event. This analytical study of the flow mechanism of heavy ion collisions with the help of Glauber model is highly significant for the study of high energy collisions. This work will also help scientific society for the search of parameters yet not explored at high energy. Keywords: Heavy Ion collisions, Quark Gluon Plasma, Glauber Modeling, fluctuations, Global variables. I. Introduction The Glauber model was developed by Roy Glauber (Nobel Prize winner in Physics, 2005) for the analytical study of high energy collusions. Glauber model is used to calculate the geometric parameters in the initial state of heavy ion collisions. This model provides a quantitative idea of geometrical configuration of nuclei, when they collide at high energies. It is based on the assumption that baryon-baryon interaction cross-section is constant throughout and nuclei move along the direction of collision in a straight line path. It helps to determine the number of participating nucleons, initial eccentricity, the number of binary collisions among the nucleons for the two nuclei, colliding with fixed energy and impact parameter, and obey the nuclear density distribution. This model falls into two main classesThe Optical Glauber model is one in which a smooth matter density is assumed, described by Fermi distribution function in the radial direction and uniform over solid angle. The Monte Carlo based model considers that individual nucleons are stochastically distributed event by event and the collision properties are calculated by averaging over multiple events. Both the model provide mostly similar results for number of participating nucleons and impact parameter, but give different results in the quantities, where event by event fluctuations are significant. This is a semi classical model and to compare its results with the experimental data, few model inputs are required like nuclear density profile of colliding nuclei and the energy dependence of inelastic nucleon-nucleon cross-section. Few model inputs for calculations: Nuclear charge density: The nucleon density inside the nucleus in Wood-Saxon form (1) Where

R: Nuclear radius. a : Diffuseness parameter, which measures how fast the nuclear density falls off at the nuclear surface and can be calculated by normalization condition. For a spherical nucleus. (2) Where A is the mass number Following formula [5] can express nuclear radius R in terms of A as-

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(3) From electron scattering experiment more realistic form of

(r) is given as(4)

Where c1, c2 & c3 are the parameters which has different values for different nuclei. Sometimes foe deformed nuclei (nuclei which are not symmetric) a deformed Wood-Saxon profile is used [6] (r) = (5) Where Râ&#x20AC;&#x2122; =R

(6)

Here indicates the spherical harmonics, the polar angle with respect to the symmetry axis is . Inelastic nucleon-nucleon cross-section: The model is based on the assumption of nucleons inelastically and on an average the number of charged particle produced in each collision remains same. Also the static cross-section is assumed to be independent of nuclear environment and same as that of single p+p collision and interchangeability of proton and neutron is inherent to the model. The experimentally measured values of inelastic nucleon-nucleon cross-section is used as input. This provides for the only non-trivial dependence of the Glauber calculations on the beam energy [7][8]. i. Optical Glauber Model Semi classical concepts are used to get the dependence of the number of participating nucleons and the number of binary nucleon collisions, on the impact parameter, for a given nuclear density profile and inelastic nucleonnucleon cross-section. When the target and projectile heavy ions collide with small impact parameter it is called central collision and when takes place with large impact parameter, termed as peripheral collisions. Consider two heavy ions, target (A) and projectile (B) colliding at relativistic speed with an impact parameter (b) as shown in Fig. 1.

Fig. 1 Schematic representation of the optical Glauber model geometry, with transverse (a) and longitudinal (b) views. Let two flux tubes are there at a displacement s and (s-b) from the centre of target and projectile nuclei respectively. Then the probability per unit transverse area of a nucleon being located in the flux tube can be given by the nucleus thickness function as(7) Where

is the probability of finding the nucleon at the point (s,

) per unit volume in projectile

(A) or target (B) nucleus, normalized to unity. Thus the joint probability per unit area of finding nucleons in the respective overlapping region is defined as thickness function & given as(8) The number of inelastic nucleon-nucleon collision is given as[3](9) The probability of n elastic collisions at an impact parameter b and when summed over all probabilities, we get,

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(10) The number of participants in nucleus A is proportional to the nuclear profile function , at transverse position s, weighted by the sum over the probability for a nucleon-nucleon collision at transverse position (b-s) in nucleus B. So the number of participants at a given impact parameter b (11) Hence the number of participants and the number of binary collisions decrease with increasing impact parameter. From geometric consideration it is estimated as â&#x20AC;&#x201C; (12) Here NColl: Number of binary collisions. NPart: Number of participating nucleons. When the value of x is calculated theoretically and experimentally the values are matched well. To calculate the integration required for the calculation of and Glauber Model, trapezoidal integration and Monte Carlo integration [10] methods are used. These quantities can be measured directly by experiments, so this method helps to connect the theoretical calculations and experimentally measured observables. But it is failed to locate the nucleons at specific coordinates. ii. Monte Carlo based Model In this model individual nucleons are considered to be randomly distributed event by event and collision quantities are calculated by averaging over multiple events.(Event means collision of two nuclei.) The following steps are used to relate the number of participating nucleons and the number of binary collisions with the impact parameter (b),of the nucleon-nucleon collision. 1. The impact parameter is randomly selected from the distribution, when N is the number of events and (b) is impact parameter(13) 2. The nucleons are distributed in accordance with the given nuclear density distribution in nucleus, for a given impact parameter. The radial part is where r is the radial distance of nucleon from the centre of nucleus. The polar part is weighted by , where is the polar angle of nucleon have range (0, ), the azimuthal part is from (0, 2 ). So the elementary volume in spherical polar coordinates system is given as 4 d . 3. The centres of two nuclei are shifted to (-b/2, 0, 0) and (b/2, 0, 0) respectively. 4. Two nucleons from different nuclei collide if transverse distance (d) between them is (14) 5. For each event the total number of binary collisions is calculated by the sum of individual number of collisions, and the total number of participating nucleons is the number of nucleons that interact only once. Here the calculations are done in two steps; first the position of nucleons are determined stochastically, and the nucleons are assumed to be moving in straight line along the beam axis for collisions(such that nucleons are participating and spectators.)In quantum mechanical picture the position of each nucleon is determined according to a probability density function, and for that a minimum inter-nucleon separation is required between the centers of nucleons. The nuclear charge density is usually estimated by a Fermi distribution function with parameters like nucleon density, the nuclear radius and the skin depth. The inelastic nucleon-nucleon cross-section which is a function of collision energy is extracted from p+p collision. The nucleon from two nuclei is assumed to collide if relative transverse distance is less than ball-diameter (D). And D is expressed as D = Where

is total inelastic nucleon-nucleon cross-section.

II. Summary & Conclusions This model is very much helpful in understanding the geometrical configuration of nuclei quantitatively, when they collide at high energy considering the collision as a sequence of nucleon-nucleon collision. For a given impact parameter and a given value of centre of mass energy, the number of participating nucleons and the number of binary collisions can be estimated theoretically by this model.

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The nuclear density profile and inelastic nucleon-nucleon cross-section are given as an input to the model for comparing it to the experimental data. It is assumed that the static cross-section is same as that for p+p collision and does not depend on nuclear environment. In Optical Model the number of participating nucleons and binary collisions are analytically derived but in Monte Carlo Model it is counted. For the application, this model is mapped to the number of charged particles produced by defining centrality classes, and it is seen that for Au+Au collision at RHIC For 0.5 and energy range 7 GeV to 200 GeV, the charged particle multiplicity is obtained as explained by this simple geometrical model. In non-central collision the reaction volume is elliptic in shape just after the collision, and the pressure gradient decreases from the centre to the ends. The initial spatial anisotropy is characterized by eccentricity and translates to the momentum anisotropy of produced particles. Experimentally the momentum anisotropy i.e. is proportional to eccentricity. So with the help of this model by calculating eccentricity the anisotropy in momentum space can be established theoretically. Glauber model suggests that these system carry very large angular momentum, and from the conservation of momentum this must be transferred the initial angular momentum of Quark-Gluon Plasma. This uncompensated angular momentum may affect the initial longitudinal flow velocity. But still a sound theoretical analysis which includes the effect of large initial angular momentum is required. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Ultra-relativistic Heavy Ion Collision by Romana Vogt, Elsevier, 2007. Introduction to High Energy Heavy Ion Collisions by C. Y. Wong, World Scientific, Singapore, 1994 R.J. Glauber, W.E. Brittin, L.C. Dunham (Eds.), Lectures in Theoretical Physics, Vol. 1, Interscience, New York, 1959, p. 315. P. Shukla, arXiv: nucl-th/0112039v1. M. L. Miller et al, arXiv: nucl-ex/0701025v1. WA98 Collaboration, arXiv: nucl-ex/0008004v2 R. Haque et al, Phys. Rev. C 85 034905 (2012) S. S. Alder et al, Phys. Rev. Lett. 91 241803 (2003) J. Adams et al, Phys. Lett. B 637 (2006) 161. K. Varga, S.C. Pieper, Y. Suzuki, R.B. Wiringa, Phys. Rev. C 66 (2002) 034611

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