Aijrstem vol1 print by iasir journals

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

Issue 6, Volume 1, 2 & 3 March-May, 2014

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: India, Australia, Germany, 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 sixth 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 sixth issue, we received 126 research papers and out of which only 50 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 sixth 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 (March-May, 2014, Issue 6, 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.) ( Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, Tirupati-517502, 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, India. 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.) Prof. R. R. Patil, Director School Of Earth Science, Solapur University, Solapur 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. 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 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.) 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. 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,TamilNadu,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, India. 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. 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.

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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 Eng.,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. 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, India.

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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. Prof. (Dr.) K. Ramesh, Department of Chemistry, C.B.I.T, Gandipet, Hyderabad-500075. India. 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.

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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, 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 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. 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, India. 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

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Prof. (Dr.) N.Rajesh, Department of Agronomy, TamilNadu Agricultural University -Coimbatore, Tamil Nadu, 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. . 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) 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 Prof. (Dr.) Basant Lal, Department of Chemistry, G.L.A. University, Mathura 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.

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Prof. S.P.Anandaraj., CSE Dept, SREC, Warangal, 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, Punjab, India. 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. 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 (March-May, 2014, Issue 6, Volume 1, 2 & 3) Issue 6 Volume 1 Paper Code

Paper Title

Page No.

AIJRSTEM 14-306

Optimization of Model Parameters of Experimentally Studied Broadband Transmission Line Transformers with Ferrite Toroidal Cores Amidon FT82-43 and FT114-43 Boyan Karapenev

01-05

AIJRSTEM 14-307

Goertzel Algorithm based DTMF Detection S Nagakishore Bhavanam, Dr. P. Siddaiah, Dr. P. Ramana Reddy

06-12

AIJRSTEM 14-308

Optimization of sound transmission loss and prediction of insertion loss of single chamber perforated plug muffler with straight duct Shantanu V. Kanade, A. P Bhattu

13-19

AIJRSTEM 14-310

Analysis of GPON Downstream with 128 Users using EDFA In-Line Amplifier for Extended Reach To 80 Bentahar Attaouia, Kandouci Malika

20-27

AIJRSTEM 14-312

Comparative Modeling of Molten Salt Reactor (MSR) Passive Cooled Drain Tanks C.E.Okon, T. Abram

28-40

AIJRSTEM 14-313

COLLISION AVOIDANCE SCHEME USING EMBEDDED SYSTEM Prof. Sangram Keshari Swain, Anup Patnaik, Abhijeet Pradhan, Vinod Kumar Kurneni

41-51

AIJRSTEM 14-316

Influence of twist on tensile and abrasion Properties of DREF-II friction spun plied yarns Prof. Sunil Kumar Sett, Dr. Asis Mukherjee and Mr. N Kundu

52-56

AIJRSTEM 14-318

Distinguishing between Global Warming and Urban Warming for Bangalore with the aid of Statistical Analysis Rajesh Gopinath, Vijayalakshmi Akella and P. R. Bhanumurthy

57-60

AIJRSTEM 14-319

Smart Controller for Wind-Solar Hybrid System under Grid Connected Operations Kishore Ravi Hegde, Prajwal C P, Pramod Kumar H S, Shivakumar S, Prof. R. Jayapal

61-66

AIJRSTEM 14-320

Gaseous Emissions from MSW Dumpsites in Vijayawada Shaheda Niloufer, Dr.A.V.V.S.Swamy, K.Syamala Devi

67-73

AIJRSTEM 14-321

Equal Area Criterion Scheme to Reduce DC Bus Voltage Stress of Single Stage Single Switch Power Factor Corrected Converter Bindu S J and C A Babu

74-82

AIJRSTEM 14-325

Synchronization Response of an Indirectly Coupled Nonlinear Digital Resonator -A Simulation Study S Chakraborty and B C Sarkar

83-91

AIJRSTEM 14-329

92-96

AIJRSTEM 14-338

PHYTOREMEDIATION OF CADMIUM AND CHROMIUM CONTAMINATED SOILS BY CYPERUS ROTUNDUS. L Subhashini, V and A. V. V.S. Swamy

97-101

AIJRSTEM 14-339

Fracture strength evaluation of AA 2219-T87 weldment using SINTAP and modified IFM procedures S.Rajakumar and N.Murugan

102-111

AIJRSTEM 14-340

Design and Analysis of Flexural Mechanism-A Short Review S.V.Deokar, S.M.Gaikwad, S.P.Deshmukh

112-114

Issue 6 Volume 2 Paper Code

Paper Title

Page No.

AIJRSTEM 14-342

Correlation between electrical resistivity and water content of sand â&#x20AC;&#x201C; a statistical approach Sudhir Bhatt, Pradeep K. Jain

115-121

AIJRSTEM 14-343

Kinetics of thermal decomposition of gadolinium alkanoates Suman Kumari, Mithlesh Shukla, and R.K Shukla

122-125

AIJRSTEM 14-353

A Novel Unequal Error Protection scheme employing binary BCH codes and Hierarchical 8PSK modulation for wireless channels Mussawir Ahmad Hosany

126-132

AIJRSTEM

A Study on Adsorption of Cd(II) from Aqueous Solution Using Fly Ash

133-139

14-356

Saroj Kumar, A.K.Mishra, M. Upadhyay

AIJRSTEM 14-360

Effect of FSP Multipass on Microstructure and Impact Strength of AL6063 Chandan Deep Singh, Ripandeep Singh, Naveen Kumar, Dr. Jaimal Singh Khamba

140-145

AIJRSTEM 14-361

Performance of Reinforced Concrete Beam under Point Impact Loading I.K .Khan

146-150

AIJRSTEM 14-367

n Empirical Influence of Classical Raaga on Face Mr.AshishA.Bardekar Dr.A.A.Gurjar

151-155

AIJRSTEM 14-370

Physico-mechanical Response of Acrylic-Viscose Ring Spun and Dref 2 Friction Spun Yarns Prof. Siddhartha Bandyopadhyay and Prof Sunil Kumar Sett

156-160

AIJRSTEM 14-373

Positivity Preserving Monotonic Quadratic Trigonometric Beta-Spline Mridula Dube and Meenal Priya Singh

161-165

AIJRSTEM 14-374

Shading Impact on Solar PV Module Savita Kumari

166-169

AIJRSTEM 14-375

Exposure to low-level ionizing radiations in some dwellings and Industrial units of Haryana Ajay Garg, R. P .Chauhan and Sushil Kumar

170-173

AIJRSTEM 14-376

Almost n-Duals of Some Difference Sequence Spaces K. B. Gupta and Ashfaque A. Ansari

174-180

AIJRSTEM 14-377

Proposed Maintainability Model for Software Development: Design Issues Anshul Mishra and Ajay Kumar Yadav

181-184

AIJRSTEM 14-378

ICT Tools for Precision Farming Mr.Vivek Parashar, Mrs. Amrita Parashar

185-188

AIJRSTEM 14-383

Robust Procedure for Estimating Multivariate Location and Scatter Muthukrishnan. R and K.Mahesh

189-195

AIJRSTEM 14-384

A Review of Criticality Safety Analysis for Under-Moderated Low Enriched Uranium (LEU) Dioxide, High Enriched Uranium (HEU) Nitrate & Plutonium Oil C.E.Okon, Y. E. Chad-Umoren

196-214

Issue 6 Volume 3 Paper Code

Paper Title

Page No.

AIJRSTEM 14-385

Data Security in Cloud Environment Using Cryptography Algorithm Prof. Y. N. Patil, Mangesh D. Namewar

215-218

AIJRSTEM 14-386

Scientific Computations of Black-Scholes-Merton Equation for Option Pricing Jigna Panchal, Dr. Sandeep Malhotra

219-223

AIJRSTEM 14-387

Design and Development of Automated Aero-Terrestrial Systems for Persistent Surveillance Missions Nithan Raj T N ,Rajani Katiyar

224-227

AIJRSTEM 14-388

Reliability Estimation of Component-Based Software through Interaction-Based Model Dimpal Tomar, Dr. Pradeep Tomar

228-231

AIJRSTEM 14-389

Bayesian Analysis of First Conception through a Probability Model Ashivani Kumar Yadav

232-236

AIJRSTEM 14-390

Investigation of Flexural behavior of hybrid natural fiber composite with recycled polymer matrix Ramanpreet Singh, Lakshya Aggarwal, Mohit Sood

237-240

AIJRSTEM 14-391

Mechanical Charactreisation of Typha Domingensis Natural Fiber Reinforced Polyester Composites Ponnukrishnan.P, Chithambara Thanu. M and Richard.S

241-244

AIJRSTEM 14-393

A Data Centric Privacy Preserved Mining Model for Business Intelligence Applications Prof. Dr. P.K. Srimani, Prof. Rajasekharaiah K.M.

245-252

AIJRSTEM 14-394

Reactive Routing in MANETs: A Performance Evaluation Jaspreet Singh, C.S. Rai

253-257

AIJRSTEM 14-395

Study of UWB Low Noise Amplifier Somit Pandey, Prof. Puran Gour , Brij Bihari Soni

258-260

AIJRSTEM 14-396

SORPTION OF Ni (II) FROM AQUEOUS SOLUTION USING CHITOSAN Dhanesh Singh, Anjali Singh, Saroj Kumar

261-264

AIJRSTEM 14-398

Numerical Solution of One - Dimensional Time- Independent Problems Using FEM Vinay Saxena

265-268

AIJRSTEM 14-399

Discussion of Effective Speech Communication under Different Compression Approaches Amit, Sunita

269-272

AIJRSTEM 14-400

Study on Routing Protocols Classification in Sensor Network Jyoti, Sunita

273-277

AIJRSTEM 14-401

Study on Watermarking Approaches on Biometric Images Neeraj, Sunita

278-281

AIJRSTEM 14-405

Study on Communication Architectures in Sensor Network Rimpy, Sunita

282-285

AIJRSTEM 14-409

Microwave Assisted Synthesis, Characterisation and Antibacterial Study of Drug based Schiff Bases and their Zn(II) Complexes K.P.Srivastava, Anuradha Singh & Suresh Kumar Singh

286-292

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

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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)

Optimization of Model Parameters of Experimentally Studied Broadband Transmission Line Transformers with Ferrite Toroidal Cores Amidon FT82-43 and FT114-43 Boyan Karapenev Department of the Communication Equipment and Technologies Technical University of Gabrovo 4 Hadji Dimitar Str., Bulgaria Abstract: This paper presents the optimization of amplitude-frequency responses and qualitative parameters of broadband transmission line transformer models based on the obtained experimental and simulation results. There are presented comparative assessments between amplitude-frequency responses of broadband transmission line transformers with ferrite toroidal cores Amidon FT82-43 and FT114-43 and coefficient of resistance ratio 1:1. Optimized values of model parameters are given in tabular form as well as the type of obtained graphical dependencies. Keywords: broadband transmission line transformers; studies; qualitative parameters; optimization. I. Introduction Broadband co-ordination and transformation of input and output resistance of a high-frequency amplifier, between two adjacent amplifier stages as well as broadband power aggregation and division can be carried out by transmission line transformers employing an electromagnetic connection between the primary and secondary windings. To provide the necessary transformation coefficient of resistances and minimum deco-ordination is an important prerequisite for achieving a wide operating frequency band. These transformers have high efficiency and reliability and through them can be made: galvanic dissociation between nodes and units of the equipment, transition from asymmetric to symmetric I/O and vice versa; they have small sizes, etc. Broadband Transmission Line Transformers (BTLT) are constructed employing appropriately interconnected transmission lines (most frequently it is a twisted pair of copper enamelled wires), positioned on a ferromagnetic core which is mostly of toroidal shape [1], [7]. The input signal excites electromagnetic waves whose linear combinations depending on the type of line connection, determine the output signal voltage. Since the resistance transformation of BTLT is associated with changes in the coefficient of voltage transmission Au nz=nu2 [7] when nz=4:1, nu=2:1 and Au=0,5. For nz=1:4, nu=1:2 and Au=2. II. Simulation Rezults of Complete Models of BTLT with Amidon FT82-43 and FT114-43 with nz=1:1 The complete models of BTLT as a result of the modeling of toroidal ferrite cores (FT) manufactured by Amidon and BTLT with ferrite core FT82-43 and nz=1:1 [4], [3] are shown in Fig. 1: Fig. 1a – with ideal transformer (T1) and Fig. 1b with a voltage-controlled voltage source (V). By setting its transmission coefficient a, a corresponding value of Au is modeled when nz ≠ 1:1. Figure 1: Complete models of broadband transmission line transformers with ferrite toroidal core Amidon FT82-43 and nz=1:1.

Fig. 1a. Model1 of BTLT with ideal transformer

Fig. 1b. Model2 of BTLT with voltage-controlled voltage source

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Boyan Karapenev, American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 01-05

The obtained frequency bandwidth Δf at a level of 3 dB, determined by amplitude-frequency responses (AFR) of the two models as a result of simulation, are respectively Δf = fh – fb =51,7.106-130,4.103 ≈ 51,57 MHz (for Model1) and Δf = 56,8.106 - 206,3.103 ≈ 56,6 MHz (for Model2) where fh is upper and fb is low cut-off frequency. From the performed Parameter Sweep analyzes to establish the influence of the values of the model parameters of Model1 and Model2 on the received amplitude-frequency responses and parameters it has been found: the value of the active resistance of the toroidal ferrite core R does not affect the obtained amplitudefrequency response – width and amplitude of the signal; increasing the value of the equivalent inductance LE, including the inductance of the ferrite core L and the natural inductances of the primary and secondary side L11 and L22 (LE=L11+L22+L1), which can be accomplished by increasing the number of turns and/or by increasing the magnetic permeability of ferrite core μr, extends the low frequency range. The efficiency at low frequencies can increase the range up to 25 times (fb(LE=39,4μH)=103,4 kHz, fb(LE=1000μH)=4 kHz); reducing the value of leakage inductance Ls1 (or Ls2) greatly enhances the efficiency of the BTLT model at high frequencies (fh(Ls1=0,016μH)= 52,2 MHz, fh(Ls1=0,5μH)=24,4 MHz); smaller values of distributed inter-winding capacitance between transmission lines C12 in the range (0,1÷10) pF expand the high frequency range by increasing the value of the upper cut-off frequency of the BTLT Model1 fh (fh(C12=2pF)=51,6 MHz, fh(C12=10pF)=41,8 MHz) of the order of ± 20 %. In practice this can be achieved with less or no twisting between the used transmission lines and uniform distribution of turns along the length of the used toroidal ferrite core; the value of the distributed shunt capacitance of the primary or secondary winding, respectively С11 or С22, does not affect the width of the band Δf. Therefore, the low frequency range of the BTLT model can be adjusted by the value of the magnetic permeability of the used ferrite toroidal core μr and/or by changing the number of turns, and the upper cut-off frequency is inversely proportional to the values of distributed inductance Ls and inter-winding capacitance С12. After completing the comparative assessment between the AFR of the two BTLT models - Model1 (Fig. 1a) and Model2 (Fig. 1b), their similarity and change in the values of fb, fh and Δf within narrow ranges is established. Model2 provides a little wider bandwidth in comparison with that of Model1 that is translated to the area of higher frequencies. This is determined by the obtained greater value of fh. A difference is obtained in Model2 by increasing the value of model parameter Ls11 where in the area of high frequencies in the frequency range of (400 ÷ 600) MHz resonances arise. The simulation results obtained from the study of both models of BTLT - Model1 and Model2 with ferrite toroidal core Amidon FT114-43 are similar. Fig. 2 shows the comparative assessment between the obtained AFR of BTLT with ferrite foroidal cores FT8243 and FT114-43 respectively for Model1 and Model2, and Table I presents the values of fb, fh and Δf. Table I The fh, fb and Δf values of Model1 and Model2. Model of BTLT with FT82-43 BTLT with FT114-43

Model Model1 Model2 Model1 Model2

fh, MHz 51,70 56,80 49,10 52,97

fb, kHz 130,40 206,30 94,04 188,50

f, MHz 51,57 56,60 49,00 52,78

Figure 2 Comparative assessment between the obtained AFR of BTLT.

Fig. 2a. AFR of Model1 of BTLT with ferrite Fig. 2b. AFR of Model2 of BTLT with ferrite toroidal cores FT82-43 and FT114-43 toroidal cores FT82-43 and FT114-43 The main difference between the AFR of BTLT models with ferrite toroidal cores Amidon FT82-43 and FT11443 Model1 and Model2 is in the value of the coefficient of voltage transmission Au. In Model2, containing a

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Boyan Karapenev, American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 01-05

voltage-controlled voltage source, Au ≈ 0,96, and in Model1 it is twice smaller. A minimum difference in the slopes

of drop of AFR at low and high frequencies has been found which for Model2 are steeper. From Fig. 2 it has been found that there is no significant difference between AFR of Model1 and Model2 of BTLT with different dimensions of the used ferrite toroidal cores, therefore, it can be inferred, that the dimensions of the used toroidal ferrite cores have no significant effect on the obtained characteristics and implemented qualitative parameters. There is a slight difference in the drop of AFR at low frequencies which is of the order of 0,5-1 % and for the purposes of modelling and simulation studies it can be ignored. It is due to the difference in the value of the model parameter equivalent inductivity LE, determined mainly by the inductance of the ferrite core in the presence of a coil of BTLT, where the larger dimensions and hence the value of the cross-sectional area of ferrite core Аe, in cm2 increase the value of L1. From comparative assessment of the obtained simulation results of the modeled BTLT with ferrite toroidal cores Amidon FT82-43 and FT114-43 it can be concluded that Model2 represents more accurately the essence of the real high-frequency transmission line transformers because it has Au approximately 1 required for modeling of nz=1:1, provides a larger AFR in comparison with that of Model1, possesses steeper slopes of drop at low and high frequencies. By setting the value of the transmission coefficient of the voltage-controlled voltage source V, the voltage transmission coefficient can be modeled in transforming BTLT (nz≠1:1). In such cases when nz=4:1, Au=2:1 and Au=0,5, and when nz=1:4, Au=1:2 and Au=2. Experimental studies of BTLT with ferrite toroidal cores Amidon FT82-43 and FT114-43 with 7 windings of the twisted pair of copper enamelled wires with a diameter d=0,62 mm when the number of twists is equal to 3 and 5 per 1 cm are shown in [3]. III. Comparative Assessment between Experimental and Simulation Results Fig. 3a and Fig. 3b present the comparative assessment of the AFR obtained by simulation studies of Model1 and Model2 and those from experimental studies of the implemented BTLT with 3 and 5 twists per 1 cm when nz=1:1 for ferrite toroidal cores Amidon FT82-43 and FT114-43, respectively. In Table II are summarized the values of the qualitative parameters of BTLT with ferrite toroidal cores Amidon FT82-43 and FT114-43 (Model1 and Model2, 3 and 5 twists per 1 сm) obtained from the AFR implemented by experiment and simulation. Figure 3 Comparative assessment between AFR obtained by experiment and simulation.

Fig. 3a. BTLT with ferrite core Amidon FT82-43

Fig. 3b. BTLT with ferrite core Amidon FT114-43

Table II Qualitative parameters of BTLT with ferrite cores Amidon FT82-43 and FT114-43 when nz=1:1 (Model1 and Model2, BTLT with 3 and 5 twists per 1 сm), obtained from AFR implemented by experiment and simulation. Mod/Number twists per 1 сm

Ui, mV

Model1

BTLT with ferrite core FT82-43

BTLT with ferrite core FT114-43

fb, kHz

fh, MHz

∆f, MHz

fb, kHz

fh, MHz

∆f, MHz

100

130,40

51,70

51,57

94,04

49,00

48,90

Model2

100

206,30

56,80

56,60

188,50

52,97

52,78

3 twists

136

100,00

10,00

9,90

100,00

9,90

9,80

5 twists

136

10,00

9,90

9,89

11,00

10,90

10,00

From the comparative assessment between AFR obtained by simulation and experimental studies and presented in Fig. 3a and Fig. 3b in graphical form it has been established that:

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Boyan Karapenev, American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 01-05

the difference between the graphical dependences and the quality parameters (Table II) of practically implemented BTLT with 3 and 5 twists of transmission lines per 1 cm is minimum; the obtained AFR of Model1 approximates more to the experimentally obtained one in shape and qualitative parameters; the experimentally obtained AFR are narrower (Δf is of the order of 10 MHz) and are shifted in the area of lower frequencies; the AFR obtained by simulation cover the operating frequency range of the used toroidal ferrite cores set by the manufacturer more precisely. Based on the obtained and presented experimental AFR and qualitative parameters of implemented BTLT with ferrite toroidal cores Amidon FT82-43 and FT114-43 using multiple simulation studies, optimization of the values of the model parameters of Model2 has been performed as the results obtained by simulation have been approximated to the experimental ones to the highest degree. The equivalent circuit of Model2 and the realized from it: transmission coefficient Au, slopes and parameters of AFR are much closer to the physical representation and functional characteristics of BTLT in comparison with those of Model1. In Table III are presented calculated and optimized values of model parameters of Model2 of BTLT with ferrite toroidal cores Amidon FT82-43 and FT114-43 according to the carried out experimental studies and obtained results. Table III Calculated and optimized values of Model2 model parameters of BTLT with ferrite cores Amidon FT82-43 and FT114-43. BTLT (nz=1:1)

BTLT with FT82-43

BTLT with FT114-43

Calculated model parameters

Parameter

Optimized values of model parameters

LE, mH

39,40

500,00

LS, µH C12, pF a, V/V

0,08 1,00 1,00

0,37 1,00 0,55

LE, mH

42,63

500,00

LS, µH C12, pF a, V/V

0,085 1,00 1,00

0,37 1,00 0,55

Table IV presents the values of the qualitative parameters obtained from experimental and simulation studies for calculated and optimized values of model parameters of Model2 of BTLT with ferrite toroidal cores Amidon FT82-43 and FT114-43.

FT114-43

FT82-43

BTLT

Table IV Implemented parameters of Model2 of BTLT with ferrite cores Amidon FT82-43 and FT114-43 with experimentally obtained results and those of simulation studies for calculated and optimized values.

АU fb, kHz fh, MHz ∆f, MHz АU fb, kHz fh, MHz

≈ 1,0 100,0 10,0 9,9 ≈ 1,0 100,0 9,9

≈ 0,96 206,30 56,80 56,60 ≈ 0,96 188,50 52,97

≈ 0,96 106,25 11,53 11,51 ≈ 0,96 106,10 10,70

ε between experimental and optimized parameters, % „-“ 1,0 6,3 15,3 16,3 „-“ 1,0 6,1 8,0

∆f, MHz

9,8

52,78

10,68

9,0

Parameter

Experimental values when there are 3 twists per 1 cm and Ui = 136 mV

Parameters from simulation studies

Optimized simulation parameters

As a result of the optimization of the AFR implemented by simulation from the experimental results obtained (Table IV), it has been established that the greatest difference between optimized and calculated values of model parameters of Model 2 of BTLT with ferrite toroidal cores Amidon FT82-43 and FT114-43 exists in the value of equivalent inductivity LE. From the conducted parameter sweep simulation studies of the impact of the value of LE on the slope of AFR at low frequencies it has been found that the slope can be set (optimized) with a great accuracy. In this case, the value of the low cut-off frequency fb of optimized AFR of Model2 has a relative difference from the actual experimentally obtained one of the order of 6 %. In Fig. 4a and Fig. 4b are presented the optimized AFR of Model2 obtained by simulation and the experimentally obtained AFR of BTLT with ferrite toroidal cores Amidon FT82-43 and FT114-43, respectively.

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Boyan Karapenev, American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 01-05

Figure 4 Optimized AFR of Model 2 obtained by simulation and experimentally obtained AFR.

Fig. 4a. BTLT with ferrite core Amidon FT82-43

Fig. 4b. BTLT with ferrite core Amidon FT114-43

IV. Conclusion From the analysis of the presented comparative assessment between experimental and simulation results of obtained AFR of BTLT, it has been established that: the width of the transmitted frequency band in the experimentally obtained results is significantly larger than in the simulation ones; the linear section of experimentally obtained AFR is significantly larger in comparison with the simulation ones; the steepness of drop of AFR at high frequencies is greater during the carried out experimental studies; the transmission coefficients of practically implemented BTLT have a little larger values compared to those obtained in the simulation study of their model. Based on the performed optimization, coefficients can be determined which can be used to adjust dependencies, by which the model parameters of BTLT with specific ferrite cores are calculated with regard to the general case. A convenience of BTLT model with voltage-controlled voltage source is the fact that the optimization of one model parameter LE, LS1 (LS2) or 小12 changes the cut-off frequency only in the corresponding frequency range of AFR and its drop remains the same. Unlike practically implemented BTLT, which show the effect of their bandwidth only in the exact coordination between input and output impedances, the presented models keep the widths of their frequency bands also with arbitrary values. V. References [1] [2] [3] [4] [5] [6] [7] [8]

Dobrev D., L. Yordanova. Radiocommunications, part one, Publishing house SIELA, Sofia, 2001. Karapenev B., Experimental Studies of Broadband Transmission Line Transformers, Proceedings of Papers, Volume 1, pp. 153156, ICEST, Nis, 2011. Karapenev B., Modeling and Studies of Broadband Transmission Line Transformers with Ferrite Toroidal Cores Amidon FT8243 and FT114-43, pp. 82-87, AIJRSTEM 13-236, 2013. Karapenev B., Modeling and Study of Broadband Transmission Line Transformers, pp. 157-160, ICEST, Ohrid, 2010. Karapenev B., Study of broadband transmission line transformers, pp. I272-I277, UNITECH, Gabrovo, 2010. Sevick J., Transmission Line Transformers, New Jersey, Noble Publishing Corp., 4th Edition, 2001. Tihchev Hr., Radiotransmitting devices, Publishing house Technics, Sofia, 1992. Trask Chris, Transmission Line transformers: Theory, Design and Application, Part I and II, 2006.

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

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

Goertzel Algorithm based DTMF Detection S Nagakishore Bhavanam 1, Dr. P. Siddaiah 2, Dr. P. Ramana Reddy 3 Department of Electronics & Communication Engineering 1 Research Scholar, University College of Engineering & Technology, JNTU Ananthapuram 2 Professor & Dean, University College of Engineering & Technology, Acharya Nagarjuna University, Guntur 3 Assoc. Prof., University College of Engineering & Technology, JNTU Ananthapuram, INDIA Abstract: DTMF namely Dual Tone Multi frequency is a signaling standard in telecommunication applications that produces two tones simultaneously for each key press. DTMF signaling has many applications such as telephone dialing, data entry, credit checking, voice mail system control and many. The DTMF tone detection is a very crucial block in several telecom based systems. As the current generation systems are looking for key feature of the low power less area and more. The DTMF detection algorithm also must be implemented with the low power schemes. The Dual Tone Multi Frequency detection can be done with FFT based technique but which is power consuming type and also it requires much more hardware. In this paper, FPGA based DTMF detection using VHDL with very low power and low area using Goertzel algorithm is done using Very High Speed Integrated Circuit Hardware Description Language. Keywords: DTMF detection, FFT, VHDL, Goertzel Algorithm

I. Introduction Dual Tone Multi-Frequency famously known as DTMF is a method for instructing a telephone switching system of the telephone number to be dialed, or to issue commands to switching systems or related telephony equipment. The DTMF dialing system traces its roots to a technique AT&T developed in the 1950s called Multi-Frequency, which was deployed within the AT&T telephone network to direct calls between switching facilities using in-band signaling. In the early 1960s, a derivative technique is offered by AT&T through its Bell System telephone companies as a "modern" way for network customers to place calls. In AT&Ts Compatibility Bulletin No. 105, AT&T described that, the product as "a method for pushbutton signaling from customer stations using the voice transmission path." The consumer product was marketed by AT&T under the registered trade name Touch-Tone. Other vendors of compatible telephone equipment called this same system "DTMF" or "Tone" dialing. Dual Tone Multi-Frequency signaling has many applications such as telephone dialing, data entry, credit checking, and voice mail system control. A DTMF signal Consists superimposed of two sinusoidal waveforms with frequencies chosen from a set of eight standardized frequencies. These frequencies should be generated and detected according to the Consultative Committee for International Telegraph and Telephone (CCITT) Recommendation. The DTMF system uses eight different frequency signals transmitted in pairs to represent sixteen different numbers, symbols and letters. II. Architecture Design The below figure can represents the general module of DTMF Detection. The module DTMF detection consists of  Hex key pad  DTMF test signal generator  Additive white Gaussian noise  Frequency Detection block  Magnitude / index estimator  Frequency to digit look-up table

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S Nagakishore Bhavanam et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 06-12

Fig.1: DTMF Detection (General Module) Hex Keypad gives input to the module. It is an external component. The signal from column is taken as input. Row and display are the output signals. By using PC we analyze the corresponding results. The below figure can represents the Goertzel Algorithm blocks as frequency detector.

Fig.2: Goertzel Algorithm Blocks as frequency detector A. Hex keypad: The Hex Keypad gives input to the basic module. It is an external component. The signal from the column is taken as input. Row and display are the output signals.

Fig.3: Hex Keypad B. Frequency test signal generator: Block generates the carrier frequencies necessary. It consists of 2 blocks one is frequency word selector and another one is DDS core unit. C. Frequency word selector: In this block the carrier waves are generated according to the key pressed ‘0’ For example: If key 9 is being press the frequencies that are generated are 852 Hz (Low frequency group) and 1477 Hz (High frequency group). These frequency waves are generated by a DDS core.

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S Nagakishore Bhavanam et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 06-12

D. DDS Core: The Logic ORE™ IP Direct Digital Synthesizer (DDS) Compiler core sources sinusoidal waveforms for used in many applications. A DDS consists of, a Phase Generator and a SINE/COS Lookup Table. These parts are available individually or combined via DDS core. DDS is a method of producing an analog waveform, usually a sine wave by generating, time-varying signal in digital form and then it performing a digital-to-analog conversion. E. Additive white Gaussian Noise: Coming to the Additive white Gaussian noise, the tone out, which is the output from tones generator is mixed with noise in this module. The output is named as the noise bits. Wideband Gaussian noise comes from many natural sources, such as, thermal vibrations of atoms in conductors, black body radiation from the earth and other warm objects, shot noise, and from the celestial sources such as the Sun. F. Frequency Detector: The Input to this module is noise bits, which is the output from additive white Gaussian noise. Output of the block is indices and magnitudes. As per the scope of the project, there are three variants of frequency detector block. Those are FFT-128 core, Goertzel algorithm and Resource sharing G. FFT- 128 core: The Xilinx ISE LogicP Fast Fourier Transform (FFT) implements the Cooley-Tukey FFT algorithm, a computationally efficient method for calculating the Discrete Fourier Transform (DFT). A Fast Fourier transform is an efficient algorithm to compute the discrete Fourier transform and it’s inverse also. There are many distinct Fast Fourier Transform algorithms involving a wide range of mathematics, from simple complex-number arithmetic to group theory and also number theory. A Discrete Fourier Transform decomposes a sequence of values into components of the different frequencies. This operation is very useful in many fields but computing it directly from the definition is often too slow to be practical. An FFT is a way to compute the same result more quickly: computing a DFT of N points in the naive way, using definition, takes O(N2) arithmetical operations, while an Fast Fourier Transform can compute that, the same result in only O(N log N) operations. This improvement made many DFT-based algorithms practical; FFTs are of great importance to a wide variety of various applications, from digital signal processing and solving partial differential equations to algorithms for quick multiplication of large integers. The most well known FFT algorithms depends upon factorization of N, but there are FFTs with O (N log N) complexity for all N, even for prime N. Many FFT algorithms only depend on the fact that is an N primitive root of unity, and thus can be applied to analogous transforms over any finite field, such as the number-theoretic transforms. The Fast Fourier Transform (FFT) core computes an N-point forward DFT or inverse DFT. where N can be 2m, m = 3–16. For fixed-point inputs, the input data is a vector of N complex values represented as the dual b x -bit two’scomplement numbers, that is, b x bits for each of the real and imaginary components of the data sample, where b x is in the range of 8 to 34 bits inclusive. Similarly, the phase factor b w can be 8 to 34 bits wide. Three arithmetic options are available for computing the FFT is Full-precision unscasled arithmetic, Scaled fixedpoint, where the user provides the scaling schedule, Block floating-point. The point size N, is the choice of forward or inverse transform, the scaling schedule and the cyclic prefix length are run-time configurable. Transform type (either forward or inverse), scaling schedule and cyclic prefix length can be changed on, a frame-by-frame basis. Changing the point size resets the core. Four architecture options are available: Pipelined, Streaming I/O, Radix-4, Burst I/O, Radix-2, Burst I/O, and Radix-2 Lite, Burst I/O.

Fig. 4: Goertzel Algorithm Blocks as frequency detector

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S Nagakishore Bhavanam et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 06-12

H. Theory of Operation: The Fast Fourier Transform (FFT) is a computationally efficient algorithm for computing a Discrete Fourier Transform (DFT) of sample sizes that are, positive integer power of 2. The DFT of a sequence is defined as

Where N is the transform size and.

the IDFT is given by,

I. Algorithm: Coming to the Algorithm, The FFT core uses the Radix-4 and Radix-2 decompositions for , computing the DFT. For Burst I/O architectures, the decimation-in-time (DIT) method is used, while the decimation in frequency (DIF) method is used for the Pipelined, Streaming I/O architecture. When using Radix-4 decomposition, the N-point FFT consists of log4 (N) stages, with each stage containing that, N/4 Radix-4 butterflies. Point sizes that are not a power of 4 needs an extra Radix-2 stage for, combining data. An N-point FFT using Radix-2 decomposition has log2 (N) stages with each stage containing N/2 Radix-2 butterflies. J. Pipelined, Streaming I/O: The Pipelined, Streaming Input/Output solution pipelines several Radix-2 butterfly processing engines to offer, continuous data processing. Each processing engine has its own memory banks to store the input and intermediate data (Shown in Figure). The core has the ability to simultaneously performs the transform calculations on the current frame of data, load input data for the next frame of data and unload the results of the previous frame of the data. The user can continuously streams in data and after the calculation latency can continuously unloads the results. If preferred, this design can also calculate one frame by itself or frames with gaps in between.

Fig.5: Figure Pipelined, Streaming I/O III. Goertzel Algorithm

Fig. 6: Goertzel Algorithm blocks as frequency detector

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S Nagakishore Bhavanam et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 06-12

The Goertzel algorithm is a DSP technique for identifying the frequency components of a signal, published by Dr. Gerald Goertzel in 1958. While the general Fast Fourier transform algorithm computes evenly across the bandwidth of the incoming signal, the Goertzel algorithm looks at the specific, predetermined frequencies. Some applications require only a few DFT frequencies. One example is the frequency shift keying demodulation, in which typically, two frequencies are used to transmit binary data; and another example is DTMF or touch-tone telephone dialing, in which a detection circuit must constantly monitor the line for two simultaneous frequencies indicating that, a telephone button is depressed. Goertzel algorithm reduces the number of real valued multiplications by almost a factor of that, two relative to direct computation via the Discrete Fourier Transform (DFT) equation. For a length of N, the Goertzel's series is:

IV. Simulation Results Hex Keypad Module: Logic Symbol:

Fig. 7: Goertzel Algorithm blocks as frequency detector RTL Schematic:

Fig. 8: RTL Schematic Simulation Waveform:

Fig. 9: Simulation Waveform for Hexkeypad

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S Nagakishore Bhavanam et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 06-12

Frequency Word Selector:

Fig. 10: Frequency Wordf Selector Logic Symbol RTL Schematic:

Fig. 11: RTL Schematic for Frequency Word Selector Simulation Waveform:

Fig. 12: Simulation Waveform for Frequency Word Selector V. Conclusion Here we are implemented up to Hex Keypad and Frequency Word Selector and corresponding simulation results are shown. The Goertzel algorithm can detect the incoming frequency within a ±1.5% offset range. This algorithm does not check for overflow problems, nor is it a complete detection algorithm. To ensure complete detection, further evaluation of the detected tone in the form of many tests is required. These tests could include twist tests, dynamic tests, guard time tests, signal-to-noise ratio tests, and talk off tests. VI. References [1] [2] [3] [4]

D. S. Kim, S. S. Lee, J. Y. Song, K. Y. Wang, and D. J. Chung, “Design of a mixed prime factor FFT for portable digital radio mondiale receiver,” IEEE Trans. Consum. Electron., vol. 54, no. 4, pp. 1590–1594, Nov. 2008. C. Marven, “General-Purpose Tone Decoding and DTMF Detection,” in Theory, Algorithms, And Implementations, Digital Signal Processing Applications with the TMS320 Family, Vol. 2,Literature number SPRA016, Texas Instruments (1990). Texas Instruments. ‘‘Modified Goertzel algorithm for DTMF using the MS320C80’’, application report spra066. 1996. J. G. Proakis and D. G. Manolakis, Digital Signal Processing: Principles, Algorithms, and Applications, 2nd ed., Macmillan, New York, NY (1992).

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S Nagakishore Bhavanam et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 06-12 [5] [6] [7] [8] [9] [10] [11]

[12] [13] [14] [15] [16]

[17]

[18] [19] [20]

S. S. Demirsoy, R. Beck, I. Kale, and A. G. Dempster, “Novel recursive- DCT implementations: A comparative study,” in Proc. Int. Workshop Intell. Data Acquisition Adv. Comput. Syst.: Technol. Appl., 2001, pp. 120–123. G. L. Smith, Dual-Tone Multifrequency Receiver Using the WE DSP16 Digital Signal Processor, AT&T Application Note. Jaquenod A.G., Villagarcia H.A., De giusti M.R. ‘‘efficient tone detection solution using programmable logic devices’’. Argentina: UNLP. P. Mock, “Add DTMF Generation and Decoding to DSP-mP Designs,” in Theory, Algorithms, and Implementations, Digital Signal Processing Applications with the TMS320 Family, Vol. 1, Literature number SPRA012A, Texas Instruments (1989). Y. C. Huang, “DSP Techniques for Telecommunication Systems,” Master’s thesis, Northern Illinois University (August 1995). Kamal Shaterian, Hossein gharaee “DTMF detection with Goertzel Algorithm using FPGA, a resource sharing approach” IEEE 2010, pp.196-199. S.-C. Lai, W.-H. Juang, C.-L. Chang, C.-C. Lin, C.-H. Luo, and S.-F. Lei, “Low-computation cycle, power-efficient, and reconfigurable design of recursive DFT for portable digital radio mondiale receiver,” IEEE Trans. Circuits Syst. II, Exp. Briefs, vol. 57, no. 8, pp. 647–651,Aug.2010. Ge Jinzhao, Zhang Lulin, Qian Yumei, “A new signal detection method based on Goertzel algorithm”,Communication Technology, volume 9, pp16-18, 2002. Texas Instruments. ‘‘Modified Goertzel algorithm for DTMF using the MS320C80’’, application report spra066. 1996. Smith, G. L. ‘‘Dual-Tone Multi-frequency Receiver Using the WEDSP16 Digital Signal processor’’. AT&T application note. M. D. Felder, J. C. Mason, and B. L. Evans, “Efficient dual-tone Multifrequency detection using the non-uniform discrete Fourier transform,” IEEE Signal Process. Lett., vol. 5, no. 7, pp. 160–163, Jul. 1998. Sun, welson, and neuendorffer " FPGA Pipeline Synthesis Design Exploration Using Module Selection and Resource Sharing" IEEE transactions on computer-aided design of integrated circuits and systems, 2007, IEEE Trans. on Computer Aided Design of integrated Circuits and Systems Sun, Hua. "Throughput Constrained and Area optimized Dataflow Synthesis for FPGAs" A dissertation submitted to the faculty of Brigham young university in partial fulfillment of the requirements for the degree of doctor of philosophy. Brigham young university, april 2008. G. Goertzel, “An algorithm for the evaluation of finite trigonometric series,” Amer. Math. Monthly, vol. 65, no. 1, pp. 34–35, Jan. 1958. M. Popović, “Efficient Decoding of Digital DTMF and R2 Tone Signalization”, Facta Univ. Ser., Elec. Energ., Vol. 16, No. 3, December 2003, pp. 389-399. M. K. Ravishankar and K. V. S. Hani, "Performance Analysis of Goertzel's Algorithm based Dual-Tone Mlultifrequency (DTMF) Detection Schemes", Technical Report, ePrints@iisc (India), August 2004.

VIII. Acknowledgments We would like to thank, the Organization of Acharya Nagarjuna University, Guntur and JNT University, Ananthapuram, for their support to use R&D Laboratories, India.

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

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

Optimization of sound transmission loss and prediction of insertion loss of single chamber perforated plug muffler with straight duct Shantanu V. Kanade1, A. P Bhattu2 M.Tech (Design) Student, Mechanical Engineering Dept., College of Engineering, Pune, India. 2 Associate Professor, Mechanical Engineering Dept., College of Engineering, Pune, India.

Abstract: With the advancement of technology it has become important to develop mufflers which satisfy space and noise constraints. Therefore, the focus of this paper is not only to analyze the sound transmission loss (STL) and insertion loss of a one-chamber perforated plug muffler but also to optimize the best design shape within a limited space. A numerical scheme for analyzing concentric perforated tube plug muffler has been developed. Coupled differential equations describing one-dimensional acoustic wave propagation in the perforated pipes and cavities of straight-through silencer elements are used from earlier study [5] and then decoupled numerically. In addition, the acoustical performance of mufflers with perforations is found to be superior to the traditional mufflers. In this paper, optimization of perforated lengths, thickness, porosities of both expansion and contraction chamber of single chamber concentric perforated plug muffler with straight duct is carried out using Genetic Algorithm in order to achieve high transmission loss over a wide range of frequency. FEM analysis is also carried to out to validate the results. Insertion loss for the same muffler is calculated using mathematical modeling. Effect of source impedance on Insertion Loss [IL] is also observed [13] Keywords: transmission loss, insertion loss, plane wave, four-pole matrices, FEM, internal perforated plug tube, genetic algorithm I. Introduction Although active noise control techniques are developing fast, the reactive muffler is still the main component in the exhaust silencer system of modern vehicles. In order to attenuate the engine exhaust noise, a few muffler elements with various geometrical configurations have been developed. The silencer system for a road vehicle has to maintain sufficient acoustic performance. Much work has been done to analyze the performance of mufflers consisting of area discontinuity or extended tube under the assumption of plane wave propagation with or without mean flow. A common feature of the exhaust silencers of road vehicles is the use of perforated pipes. Frequently, they are used to contain the mean flow, thus reducing the back-pressure and flow-generated noise of the silencer, while allowing for acoustic coupling to an outer cavity through the perforations. In 1978, Sullivan and Crocker [1] presented the first mathematical model for perforated element mufflers to analyse the tube resonator by coupling the wave propagation in the center tube and outer cavity. Sullivan [2] then developed a segmentation analysis procedure for modelling all types of perforated element mufflers. However, numerical instability occurs when modelling muffler elements with high porosity. Jayaraman and Yam [3] presented a decoupling approach for the perforated tube muffler components to obtain a closed form solution. The major drawback of this method is that it is based on an unreasonable assumption that the mean flow Mach numbers in the ducts must be equal. Rao and Munjal [4] have sought to overcome this problem with a generalized decoupling analysis which does allow for different flow Mach numbers in the inner pipe and outer casing. The decoupling methods mentioned above, however, are all based on plane wave acoustic theory and are suitable for mufflers with geometrical configurations, such as the plug muffler, perforated reverse flow muffler and concentric-tube resonator. It is assumed that the flow transfer through the perforated portion is uniformly distributed over the length, and therefore the perforate impedance is constant along the length. Sullivan and Crocker's [1] one-dimensional equations have been adopted. In the present paper concentric perforated plug tube with end inlet/outlet is considered. Mathematical modelling done by Munjal [4, 6] is taken for formulation of problem. Optimization of length of perforated plug tube, thickness, porosities of both expansion and contraction chamber is done using Genetic Algorithm in order to achieve maximum Transmission Loss [11]. FEM analysis is carried out using software package COMSOL for validation of results. In order to calculate Insertion Loss (IL) source impedance for exhaust tube is taken as Y0   0.7  0.7 j  where Y0 is characteristic impedance of tube [13].

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Shantanu V. Kanade et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), MarchMay, 2014, pp. 13-19

II. Mathematical model In this paper straight tube muffler with perforated tube was adopted. As shown in Fig.1 single expansion chamber with perforated plug tube consist of four acoustical elements straight inlet duct, expansion perforated duct, contraction perforated duct & straight outlet duct. Here (P1, u1) & (P6, u6) represent pressure and velocity at point 1 & 6 respectively. (P2, u2) & (P3a, u3a) gives pressure & velocity at the boundary of expansion perforated tube. (P3a, u3a) & (P5, u5) gives pressure & velocity at the boundary of expansion perforated tube. (P5, u5) & (P6, u6) represent pressure & velocity inside perforated tube at point 5 & 6. Fig. 1. Dimensions and acoustical mechanism of perforated plug muffler with straight end tube.

Fig. 2. Acoustic elements of perforated plug muffler with straight end tube.

Individual transfer matrixes with respect to each case of inlet straight ducts (I), expansion perforated tube (II), contraction perforated tube (III) and outlet straight duct (IV) are described as follows A. Transfer Matrix for section I [6, 14]: Equation for pressure & sound particle velocity are as follows.



p  x, t   eiwt A1eikx  B1eikx



 A  B u  x, t    1 eikx  1 eikx  eiwt 0 c0  0 c0  Substituting boundary conditions as x=0 & x=L, using Equation (1) & (2) we get,  p1   cos(kL1 ) i sin(kL1 )   p2   c u       0 0 1  i sin(kL1 ) cos(kL1 )   0c0u2  B. Transfer Matrix for section IV [6]: Similar to system matrix in section I we can relate node 5 & node 6 by using following matrix.  p5   cos(kL2 ) i sin(kL2 )   p6   c u       0 0 5  i sin(kL2 ) cos(kL2 )   0 c0u6  C. Transfer Matrix for section II [5,6 & 9]: Transfer matrix for expansion perforated tube can be derived as mentioned below. Inner tube: Continuity equation 4   u V 2  0 2  0 u  2 a  0 x x di t Momentum equation p    0   V  u2  2  0 x  x  t Outer tube: Continuity equation

(1) (2)

(3)

(4)

(5)

(6)

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Shantanu V. Kanade et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), MarchMay, 2014, pp. 13-19

0

u2a 4d o 0   u  2a  0 2 2 x t d m  di

(7)

Momentum equation

p     V  u2a  2a  0 x  x  t Assuming that the acoustic wave is a harmonic motion

0 

(8)

p  x, t   P  x   eit

(9a)

Under the isentropic processes in ducts, it has (9b) p  x, t   P  x   c02 Assuming that the perforation along the inner tube is uniform (i.e. dς /dx = 0). The acoustic impedance of the perforation (ρ0 c0 ς) is p  x   p2a  x  (10) 0 c0  2 u ( x) where ς is the specific acoustical impedance of the perforated tube. Empirical relations have been developed from experience. According to the experience, formula of ς [8, 9 & 10] is given by, for perforates with grazing flow, we have ς = [7.337×10−3 (1+ 72.23M) + j2.2245×10−5 (1+ 51t) (1+ 204dh) f] /η (11) where ‘t’ is the thickness of the muffler; ‘dh’ is the diameter of perforated holes for section II; ‘f’ is the Frequency; ‘η’ is the porosity of perforated tube for section II. Particle velocity is comparatively much smaller than sound velocity so in further development of equations Mach number is taken as zero. Selected parameters: t=0.0015; dh=0.003; η=0.15; M=0 By substituting Equations (9-10) into (5-8)  d2  (12)  2  ka2  p2   ka2  k 2 p2a  dx   





 d2 2  2 2  2  ka  p2a   kb  k p2  dx 



k

 c

; ka2  k 2  i



4kdo 4k 2 ; kb  k 2  i  di  dm2  di2



(13)



Eliminating u2 & u2a by differentiation & substitution of Equation (12) & (13) we have:  D2   D   3 D   4   p2  0 1 2      5 D   6 D 2   7 D  8   p2a  0 Where,

(14)

2  ka2 ;4  k 2  ka2 ;6  k 2  kb2 ; 1  3  5  7  0;8  kb2 ; Developing Equation (14) yield: p2''  2 p2  1 p2'  3 p2' a  4 p2a  0 p2'' a

 6 p2  5 p2'  7 p2' a

 8 p2a  0

p p2  y1 ; p2' a  2a  y2 ; p2  y3 x x According to (15a) to (15c), the new matrix between {y’} and {y} is

Let p2' 

 y'   1   1  y2'    5     y3'   1  '   0  y4  

which can be briefly expressed as:

 3  7 0

 2  6 0

 y '

; p2a  y4

 4   y1    8   y2  0   y3    0   y4 

 C  y

(15a) (15b) (15c)

(16a)

(16b)

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Shantanu V. Kanade et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), MarchMay, 2014, pp. 13-19

Let  y   S 

(17a)

which is

 dp2 / dx   S1,1 S1,2 S1,3 S1,4   1   dp / dx   S S2,2 S2,3 S2,4   2   2a    2,1   (17b)  p2   S3,1 S3,2 S3,3 S3,4   3        p2a   S4,1 S4,2 S4,3 S4,4   4  [S] 4x4 is the model matrix formed by four sets of Eigen vectors [S]4x1 of [C]4x4 Substituting Equation (17) into (16) and then multiplying [S]−1 on both sides,

 S 1  S  ’   S 1 C   S   

(18)

1 0 0 0  0  0 0  1 2 Set      S  C  S     0 0 3 0     0 0 0 4  where εi is the Eigen value of [C]. We can write Equation (17) as:  '     This Equation(19) is a decoupled equation. The related solution can then be obtained as: i  ki e i x Using these equations relation in acoustic pressure and particle velocity can be obtained by:  p2 ( x)   H1,1  p ( x)   H 2a     2,1  0 c0u2 ( x)   H 3,1     0 c0u2a ( x)   H 4,1

H1,2 H 2,2 H 3,2 H 4,2

H1,3 H 2,3 H 3,3 H 4,3

where H1,i  S3,i ei x ; H 2,i  S4,i e i x ; H 3,i 

H1,4   k1  H 2,4   k2    H 3,4   k3    H 4,4   k4 

iS1,i e i x

k Substituting x = 0 and x = LC into Equation (22)  p2 (0)   p2 ( Lc )   p (0)    2a    T   p2a ( Lc )   0 c0u2 (0)   0 c0u2 ( Lc )       0 c0u2a (0)   0 c0u2a ( Lc )  where

T    H  0

 H  Lc 

; H 4,i 

(19)

(20) (21)

(22)

iS2,i e i x k

1

(23a)

(23b)

Boundary Condition:

p2a (0)  i 0 c0 cot(la * k ) u2a (0) p2 ( Lc )  i 0 c0 cot(0* k ) u2 ( Lc ) Substituting these boundary conditions (21a) & (24b)  p2  Ta Tb   p3a    c u   T T    c u   0 0 2   c d   0 0 3a  D. Transfer Matrix for section III [5,6 & 9]: Transfer matrix for contraction perforated tube can be derived on the similar lines as mentioned below. Inner tube: Continuity equation 4   u V 4  0 4  0 u  3a  0 x x di t Momentum equation

(24a) (24b)

(25)

(26)

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p     V  u4  4  0  t  x x  

(27)

u3a 4d o  0   u  3a  0 2 2 x t d m  di

(28)

p     V  u3a  3a  0 x  x  t

(29)

0  Outer tube: Continuity equation

0 Momentum equation

0 

For perforates with grazing flow, we have ς = [7.337×10−3 (1+ 72.23M) + j2.2245×10−5 (1+ 51t1) (1+ 204dh1) f] /η1 (30) where ‘t’ is the thickness of the muffler; ‘dh1’ is the diameter of perforated holes for section III; ‘f’ is the Frequency; ‘η1’ is the porosity of perforated tube for section III. Likewise, as derived in Eqs. (8-22) adopting the similar process as in expansion perforated tube below set of equations can be obtained  p4 (0)   p4 ( Lc )   p (0)    3a    T   p3a ( Lc )  (31)  0 c0u4 (0)   0 c0u4 ( Lc )       0 c0u3a (0)   0 c0u3a ( Lc )  where

T    H  0

 H  Lc 

1

(32)

Boundary Condition:

p4 (0)  i 0 c0 cot(0* k ) u4 (0) p3a ( Lc1 )  i 0 c0 cot( Lb1* k ) u3a ( Lc1 ) Substituting these boundary conditions (33) & (34)  p3a  Ta1 Tb1   p5        0c0u3a  Tc1 Td1   0c0u5  E. Assembly of the Matrices: Using Equations (3), (4) & (35) we get,  p1   cos  kL1  i sin  kL1   Ta Tb  Ta1 Tb1    c u   i sin kL  1  cos  kL1   Tc Td  Tc1 Td1   0 0 1  

 cos(kL2 ) i sin(kL2 )   p6  i sin(kL ) cos(kL )    c u  2 2  0 0 6 

(33) (34)

(35)

(36)

Simplified Matrix is given by:

 p1   PTM a   c u    PTM c  0 0 1 

PTM b   p6  PTM d   0c0u6 

(37)

Calculation of Transmission Loss: (38) STL  20  log10 ( PTM a  PTMb  PTM c  PTM d ) In order to get results by mathematical modelling entire model is built in MATLAB. It is used for TL prediction and optimization using Genetic Algorithm. G. Validation of Results: The above mathematical formulation is compared with result obtained by K. S. Peat [7] by using dimensions Lc =0.3 m; La =0 m; Lb =0 m; Lc1 =0.3 m; La1 =0 m; Lb1 =0 m; di = do = 0.075 m; dm = 0.25 m; t= 1.5 mm; η=0.15; η1=0.15; dh =3 mm. It’s observed that results above obtained for STL are precisely comparable with those presented by K. S. Peat [7]. FEM model is also and analysed in COMSOL for prediction of transmission loss. Fig. 4 shows the comparison of mathematical and FEM results.

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Transmission Loss (dB)

Fig. 4. Single chamber perforated plug muffler comparison of STL of mathematical modelling and FEM [7] (Lc=0.300 m; La=0; Lb=0; Lc1 =0.3 m; La1 =0 m; Lb1 =0 m; di=do= 0.075m; dm= 0.25m) 20 15 10 5 0 0

200

400

600 Frequency (Hz) COMSOL MATLAB

800

1000

H. Calculation of Insertion Loss Insertion loss can be calculated with the below mentioned mathematical formulation. Here ZS is source impedance [13]; ZT is radiation impedance [6, 12]; A, B, C, D are four poles of acoustic elements; A0, B0, C0, D0 are four poles of straight pipe.   A / Z S  B / Z S ZT  C  D / ZT IL  20log10    A0 / Z S  B0 / Z S ZT  C0  D0 / ZT

    

III. Optimization of transmission loss using genetic algorithm Model shown in Fig. 1 is used for optimization. Here La, Lb1, thickness of pipe (t) & porosities of both pipes (η, η1) are varied in bounded region. For optimization, optimization toolbox in MATLAB is used. Optimization is carried out by using Genetic Algorithm for frequency range of 1-2000 Hz. The objective function in maximizing the STL at the puretone (f) is given as follows OBJ = STL (f, La, Lb1, t, η, η1) Objective function is defined in MATLAB in order to optimize Transmission Loss along with boundary condition of maximum STL in range of (700-750 Hz). Diameter of holes, diameter of the inlet duct, outlet duct & perforated duct are kept constant from manufacturing and spacing constraints. Below table gives the variable bound for the variables used in optimization and their optimized values. Table 1: Optimization variable bounds and optimized values Variable

Lower Bound

Upper Bound

Optimized value

La (m)

0.29

0.093

Lb1 (m)

0.29

0.094

t (m)

0.001

0.005

0.004

0.1

0.3

0.245

η1

0.1

0.3

0.2

Transmission Loss (dB)

IV. Results & Conclusion Optimized model using parameters mentioned in Table No. 1: is built up in COMSOL and results are compared with mathematical modelling results. It can be seen that in Fig. 5 both results are matching well. Fig. 5. Comparison of plot of STL (dB) using mathematical modelling and FEM for above optimized values 90 75 60 45 30 15 0 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Frequency (Hz) COMSOL MATLAB

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Shantanu V. Kanade et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), MarchMay, 2014, pp. 13-19

In FEM end correction effect is considered and also singularity effect in MATLAB due to inverse of matrices is not there. So FEM results can be considered as more realistic. Table 2: Maximum STL & corresponding frequencies Frequency (Hz)

Transmission Loss (dB)

700

65.12519

725

84.99796

750

61.36597

728

86.03456

700-750

75.6629 (Average)

Insertion Loss (dB)

It has been observed that global maxima exist at frequency 728 Hz (COMSOL). It can be seen that maximum transmission loss is achieved over entire range from 700 Hz to 750 Hz. The average Transmission Loss over entire range of 700 Hz to 750 Hz is 75.66 dB. So this wide range makes it suitable for both four stroke four cylinders and six cylinders generator setups which operate at 1500 rpm constant speed. Insertion Loss results are obtained by using mathematical modeling given by M. L. Munjal for muffler mentioned in Fig.1 [7]. IL is also calculated for optimized muffler & also effect of source impedance is observed by changing its value. Fig. 6. Comparison of plot of IL (dB) using mathematical modelling for different source impedances for optimized model and base model 95 80 65 50 35 20 5 -10 0 -25

100

200

Source Impedance = (0.7-0.7j)*Y0 Source impedance = 416

300

400

500

Fequency (Hz)

600

700

800

900

1000

Source impedance = 0 Peat Plug Muffler Source Impedance = (0.7-0.7j)*Y0

This study demonstrates a quick and economical approach to optimize the design for a single-chamber perforated plug muffler with straight inlet/outlet under space constraints without redundant testing. It can be seen that STL is above 60dB for entire range of 700 – 750 Hz & hence suitable for different applications. Insertion loss is also calculated using mathematical modeling & it can be seen that IL is weak function of source impedance. References [1] [2] [3] [4] [5] [6] [7] [8] [9]

[10] [11]

[12] [13] [14]

Sullivan, J. W. and Crocker, M. J., “Analysis of concentric tube resonators having unpartitioned cavities,” Journal of the Acoustical Society of America, Vol.64, pp 207-215 (1978). Sullivan, J. W., “A method of modeling perforated tube muffler components. I. Theory” Journal of the Acoustical Society of America 66, 772-778 (1979). Jayaraman, K. and Yam, K., “Decoupling approach to modeling perforated tube muffler components.” Journal of the Acoustical Society of America, Vol.69, No2, pp. 390-396 (1981). Munjal M.L., Rao, K. N., and Sahasrabudhe, A. D., “Aero acoustic analysis of perforated muffler components,” Journal of Sound and Vibration, Vol. 114, No, 2, pp.173-88 (1987). Min-Chie Chiu, “Numerical Optimization Of A Three-Chamber Muffler Hybridized With A Side Inlet And A Perforated Tube By Sa Method.” Journal of Marine Science and Technology, Vol. 18, No. 4, pp. 484-495 (2010). Munjal M.L., “Acoustics Of Ducts and Mufflers” John Wiley and Sons (1987). Peat, K.S., “A numerical Decoupling analysis of perforated pipe silencer elements,” Journal of Sound and Vibration, Vol. 123, No.2, pp.199-212 (1988). Rao, K. N., Munjal M.L., “A generalized decoupling method for analyzing perforated element mufflers,” Nelson Acoustics Conference, Madison (1984). Ying-Chun Chang, Min-Chie Chiu, and Wang-Chuan Liu, “Shape Optimization Of One-Chamber Mufflers With Perforated Intruding Tubes Using A Simulated Annealing Method” Journal of Marine Science and Technology, Vol. 18, No. 4, pp. 597-610 (2010). Rao, K. N., Munjal M.L., “Experimental evaluation of impedance of perforates with grazing flow,” Journal of Sound and Vibration, Vol. 123, pp. 283-295 (1986). Min-Chie Chiu and Ying-Chun Chang, “Numerical assessment of two-chamber mufflers with perforated plug/non-plug tubes under space and back pressure constraints using simulated annealing” Journal of Marine Science and Technology, Vol. 19, No. 2, pp. 176-188 (2011). F. P. Mechel, “Formulas of Acoustics”, Second Edition, Springer (2008). M. L. Munjal, “Acoustic characterization of an engine exhaust source – a review”, Proceedings of Acoustics (2004) A. P. Bhattu, Shantanu Kanade, A. D. Sahasrabudhe, “Optimization of sound transmission loss of single chamber perforated muffler with straight duct”, International Conference on Advances in Mechanical Engineering (2013).

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

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

Analysis of GPON Downstream with 128 Users using EDFA In-Line Amplifier for Extended Reach To 80 km Bentahar Attaouia1 and Kandouci Malika1 . Christelle Aupetit-Berthelemot2 Department of electronic, Djillali Liabès University,Sidi bel abbes, Algeria

1 2

XLIM – UMRCNRS n°7252 – University of Limoges, 123, avenue Albert-Thomas – 87060 Limoges, France

Abstract: In this paper, we study different characterizations of EDFA in-line amplifier used for GPON Downstream, which depend essentially on the opt-geometric parameters (ions erbium density, length of the fiber doped erbium, core radium and numerical aperture…) and the effect of those parameters to optimize the gain G and quality factor Q for extending the reach to 80 km of GPON with 1:128 split ratio. Also the performance of this system has been evaluated in terms of Max Q factor, eye diagram, Min BER with various simulations by sweeping different parameter such splitter loss, signal input power and fiber length and format modulation etc. Keywords: Amplification optic; Access network; GPON; Erbium Doped Fiber Amplifier; Reach of passive I. Introduction Recently, because of demand from high data rate triple-play services, the deployment of fiber to the home (FTTH) in the broadband access network also known as last mile network (connects central office to each end user) and the related standardizations are becoming increasingly important [1]. The interconnection between the end subscriber and the distribution node which is going to provide the services can be done through various architectures; one of the most attractive optical access network architectures is the Passive Optical Network (PON), the GPON is popular version of passive optical networks (PONs), it is an all-optical transmission based network which aims at providing a high speed network connection. The characteristics of GPON technology has been standardized by International Telecommunication Union-T (ITU-T) in recommendation G.984 series [3]. The downstream and upstream traffics are transmitted at 1490 nm and 1310 nm while 1550 nm wavelength is allocated for video. The physical configurations can be seen by the splitting ratio and the distance of OLT-ONT. Theoretically, the splits can be up to 64 but due to the current hardware limitations, the development so far can only reach 32 and the maximum physical length from the OLT to ONT is 20 km [2]. To cover a wider area and get more power margin in order to decrease the number of OLTs, an amplification scheme adapted to PON systems is required [4]. Various optical amplifiers, such as erbium-doped fiber amplifier (EDFA), semiconductor optical amplifier (SOA) and Raman amplifier, could provide an extended reach and/or a high split-ratio for the cost-effective implementation of PONs. Development of other types of DFAs for amplification in the remaining optical bands has been actively investigated. Praseodymium-doped fiber amplifiers (PDFAs for upstream) and thulium-doped fiber amplifiers (TDFAs for downstream) were developed to amplify signals around 1300 and 1490 nm, respectively [5]. II. EDFA for reach extension GPON Erbium Doped Fiber Amplifiers (EDFAs) provide optical amplification to compensate power loss in optical signal transmission and are attractive candidates for GPON reach extension. They can be designed to provide low noise figure (NF), and fast gain dynamics in the 1550 nm (C-band) and 1490 nm (S-band) windows [6]. EDFA is an optical amplifier that uses a erbium doped optical fiber as again medium to amplify an optical signal. The signal which is to be amplified and a pump laser are coupled into the doped fiber and the signal is amplified through stimulated emission. EDFA is the best known and most frequently used optical amplifier suited to low loss optical window of silica based fiber [7]. Figure 1: Block diagram of Erbium Doped Fiber Amplifier.

A particular attraction of EDFAs is their large gain bandwidth, which is typically tens of nanometers and thus actually it is more than enough to amplify data channels with the highest data rates without introducing any

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effects of gain narrowing [8]. A single EDFA may be used for simultaneously amplifying many data channels at different wavelengths within the gain region [9]. III. The simulated chain and results A diagram of the downstream GPON structure which can be implemented with a splitting ratio of 128 is shown in Figure 2. A schematic diagram of simulation, illustrating an OLT which is connected to the remote node by optical fiber (SMF) with an attenuation coefficient of 0.2 dB/km and length of 80 km for all wavelengths. For the OLT (optical Line Terminal) of the system GPON we employed a directly modulated distributed feedback laser (DFB) at 1550 nm as the transmitter for the downstream signal , the output power from both the OLT was set at 10 dBm. The ONU (optical Network Unit) receiver uses the avalanche photodiodes (APD). The procedure for data transmission in GPON downstream (from the OLT to the ONUs) with 2.5 Gbits/s employs TDM (Time Division Multiplexing) to broadcast the signal to all the ONTs sharing the same fiber, ONUs filter the received data and extract only their own traffic. Erbium doped optical amplifiers (EDFA) have been chosen for the simulation for amplify wavelength around 1550 nm for position in line-amplification burst mode, requiring optical amplifier to have fast transient control capabilities [10]. Figure2: Architecture of GPON downstream with EDFA in-line. ONU1

Fiber lengh L2

ONU2 Fiber lengh L1

ONU3

EDFA

ONU4

Splitter GPON OLT

…… ……

1:128

…… ONU128 8

Downstream 1550 nm

A. EDFA Characterization Parameters The gain of the EDFA depends on a number of device parameters: erbium ion density, amplifier length, core radius and pump power…ect, in this part, the effects of these quantities on the action and performance of erbium amplifiers such as amplifier gain, noise figure and quality factor Q are investigated. The flowing parameters values were used table (1): Table (1) represents the typical EDFA parameters Parameter

Value

Symbol

Signal input power

0 to10 dBm

Signal wavelength Pump power

980 -1480 nm 50 to 350 mw

ƛ Pp

Amplifier length

0 to 30 m

L edfa

Core radium

1 to 9 µm

Erbium ion density Numerical aperture

1e+24/m-3 to 1e+25/m-3 0.1 to 0.9

C NA

 Effect of erbium ions density ( C) First part of the simulation considers the optimization of Q factor by sweeping erbium ion density 1e+024/m+3 to 1e+024/m+3 for two different amplifier length (Ledfa) at the pump wavelength (λ = 980nm) . For this, the following parameters are used: Pp = 200 mw, NA = 0.24, D = 2.2 µm and Pin =10 dBm. The fig 3a show that the Q factor increases with increasing in the concentration of erbium ions for the lower values of 1e+025/m-3 and up of this value the Q factor decreases. We can found at C = 1e+025/m-3 and Ledfa = 10 m a optimum Q factor of 8.55, however Q factor of 8 when Ledfa= 15m. So in terms of actual amplifier performance, it has been reported that for optimum performance, erbium ions density should be less than about 1000 ppm or 1e+025/m-3 [11]. Figure 3: a) - Q factor as a function of erbium ions density. b) - Gain and noise figure NF as a function of erbium ions density.

(a)

(b)

Figure3.b shows the variation of gain and NF as a function of density erbium ions in EDFA in-line amplifier for two values of the amplifier length Ledfa (10 and 15m). So it is cleared from graphs that the gain increases with increasing of erbium ion concentration and with decreasing of amplifier length, however for amplifier length of

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Bentahar Attaouia et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), MarchMay, 2014, pp. 20-27

10m, the gains begins to be moderately saturated when ion concentration exceeds 1e+25/m-3 and after this value gains becomes saturated, while for higher density erbium ion of 1.5e+25/m-3 the gain decreased. Also the simulation results show, the optimal value which the gain is maximum (of 30dB) and noise figure is lower (of 3.28dB) is obtained at Ledfa= 10 m, however we provide a lower gain of 24.5dB and NF of 3.80 dB when Ledfa = 15m.  Effect of amplifier length (Ledfa) The effect of the amplifier length (Ledfa) in the Q factor , EDFA gain and it’s noise figure is study for two erbium ion density (C= 1e+25/m-3 and C= 2e+25/m-3 ), varied the amplifier length (0 to 30 m) and fixed other parameter : Pp = 200 mw, NA = 0.24, D = 2.2 µm, Pin =10 dBm. According to the figure 4.a we can see that the quality factor Q increase linearly with the amplifier length for Ledfa lower of 10 m, however Q factor deceases linearly up of this value. Also we can notice for amplifier length (L edfa = 10m) and when density erbium ions C = 1e+025/m-3 a high Q factor of 8.55 is obtained , however for C= 2e+025/m-3 we provide the Q factor of 7.35 Figure 4 : a) - Q factor as a function as amplifier length, b) - Gain and noise figure NF as a function of amplifier length

(a )

(b)

Figure 4.b shows Gain and its noise figure NF as a function of the amplifier length Ledfa for two value of density erbium ions , we can see that the gain increase linearly with Ledfa for short values (0 to 10 m) with no significant difference between the C = 1e+025/m-3 and C = 2e+025/m-3, however for higher Ledfa (10 to 30 m) Q decease and this difference become more remarkable. At Ledfa = 30 m we find higher gain 26.5 dB and lower NF of 3.71dB when C =1e+025/m-3 and very lower negative Gain of -60dB and higher NF of 60 dB at C = 2e+025/m-3. We can notice our result show that at erbium ions density of 1e+025/m-3 and for maximum gain , the optimal fiber length must be between 10 and 15 m are approximately nearly of reference [12].  Effect of wavelength pump power (ƛ) Efficient EDFA pumping is possible using semiconductor lasers operating near 980nm and 1480nm wavelengths pump.To estimate the performance, we have compared two wavelength pump(1480 and 980nm )by sweeping signal input power (0 to10 dBm) . The length of EDFA, density erbium ions and pump power are fixed at 10 m , 1e+25/m-3 and 200mw respectably . Figure 5.a shows the graphical representation of Q value. It is cleared from graphs the performance of 1480 nm improved and gives better system performance in term Q factor as compared to 980 nm. We can obtain the Q factor of 13.25 and 8.55 for 1480 nm, 980 nm respectably. Figure 5: a) - Q factor as a function as signal input power for two wavelength power, b) - Gain an NF as a function as signal input power for two wavelength power.

Figure.5.b shown in two different graphs the dependence of the gain and noise figure on the signal input power, that it is important to note that pumping with 1480 nm light is more efficient and gives higher gain ( of 32 dB) but high noise figure (of 4.29 dB). On the other hand, pumping with 980 nm light produces a less noisy amplifier (minimum of noise of 3.27) but lower gain of 29.89 dB Effect of amplifier core radium (D) The progress of the factor Q as a function as changing core radium (1 to 9 μm) for two different density of erbium ion and the parameter in this case are : P p = 200 mw, NA = 0.24, Pin =10 dB and L edfa = 10m. The graphs

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Bentahar Attaouia et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), MarchMay, 2014, pp. 20-27

shows that when C = 1e+025/m-3 and core radium lower of 2.2 μm, the fixed and maximum Q factor around of 8.55 is found , however up of 2.2 μm the Q factor decrease rapidly with core radium. Whereas a high Q factor of 9.8 is found when concentrations of erbium ions C = 2e+025/m-3. So the optimum core radium must be lower than 3 μm, which is approximately equal at theoretical value found in reference [13]. Figure 6 : a) Q factor as a function as core radium, b)- Gain and noise figure NF as core radium.

(a)

(b)

Figure 6.b show the effect of the radium core in the EDFA gain for two values of concentration , where for C = 1e+025/m-3 we find a high gain of 30 dB and low NF of 3.5dB for a diameter between 1 and 2.2 µm and higher this value the gain decreased till obtenaid 20.7 dB at 9μm. By cons, we see that the gain remains constant (of 25 dB) whatever the value of the radium when C= 2e+025/m-3.  Effect of numerical aperture (NA) This part of the study considers the optimization of numerical aperture (NA) for optimum fiber length (Ledfa= 10m) and two different erbium ion density, we used the following parameters: Pp = 200 mw, D= 2.2 µm , P in =10 dB and L edfa= 10m. The curves shows for numerical aperture lowers of 0.24 the Q factor decreases and high value of Q factor (over 30) is found when C=1e+025/m-3 , however up of this value ,the Q factor remains constant whatever the value of numerical aperture and a high value is obtained ( over 9.5) when C= 2e+025/m -3. Figure7: a) - Q factor as a function as numerical aperture, b) - Gain and noise figure NF as a function as numerical aperture

(a)

(b)

It is seen that the gain increases with increasing NA and remains constant (saturate) after certain level for numerical aperture, the reason for this is that the amplifier reaches the population inversion. It is clear that the gain increases when NA increases because increasing NA proves the overlap between optical mode field and erbium ions.  Effect of pump power (Pp) the effect of sweeping the pump power (50 to 350 mw) for the optimum Q factor of EDFA in position in-line amplifier with fixed length amplifier at 10 m and varied concentration of erbium ions (erbium ion density) is shown in the graphs (figure 8.a), we can see that more the pump power increase more the Q factor increases, however the C= 1e+025/m-3 offer good performance and provide a higher Q factor (over 15 ) for P p = 350 mw, whereas the concentration C= 2e+025/m-3 and at 350 mw of pump power provide a Q factor of 13.2 Figure 8: a) - Q factor as a function as pump power, b) - Gain and noise figure NF as a function as pump power

(a)

(b)

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Bentahar Attaouia et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), MarchMay, 2014, pp. 20-27

Figure 8.b shows the fast increase of gain with sweeping pump power (50 to 350 mw) and a decrease with increasing density erbium ions. Beyond for C of 1e +25/m-3 and at pump power 350 mw the gain can be achieve the optimal value of 32.2 dB and low noise figure of 3.7 dB, while at C= 2e+25/m-3 an accepted amplifier gain around 27 dB and noise figure around 3.6 dB can be obtained in this simulation. B. The performances of GPON In this section we will evaluated the performances of GPON with EDFA in the function of an in-line amplifier characterized by these parameters opt-geometric ( D = 2.2 um, Ledfa = 10 m ,C = 1e+25/m-3 , Pp = 200 mw, NA = 0.24, ƛ = 980 nm). The proposed simulation has been analyzed by changing number of users (ONU), data rate, loss splitter, modulation format, fiber length and signal input power. 

Performances as a function of fiber length

The following simulation showed the possibility of extending the distance of 60 to 80 Km where the dependence of Q factor on distance for the downstream traffic can be seen in figure 9.a , so for EDFA amplifier placed in front of the splitter works in the function of an in-line amplifier after passing through the optical fiber length of 60 km ( L1 = 50 , L2 = 10 km) and after the division in the 1:128 splitter, the received power at the detector has the signal power level of -70.23 dBm with the Q factor of 9,32 . The length of the fiber can be extended to 80 km (L1 = 70 , L2 = 10 km) with a Q factor of 8.55 and a received power of -71.07 dBm. Figure 9: a) - Q as a function as signal input power for two fiber length, b) - Q as a function as signal input power for 80 km for two position of EDFA in-line.

(a)

(b)

Other we can notice the dependence of the Q on position of the EDFA for both the downstream traffic, so the GPON employ EDFA near of the splitter ( L1 > L2) provides the best Q factor , however when EDFA is far of the splitter ( L1< L2) we can notice for signal input power lower of 5 dBm the Q factor is null and up of this value this later increase and gives the lower performance (Q factor = 2.7 ) figure 9.b Figure 10: Eye diagrams as a function as signal input power for two fiber length.



Analysis

L = 60 Km

L = 80 Km

Max Q factor Min BER

9.32 5.41069e-021

8.55 8.99721e-034

Eye Height

8.349752e-006

7.30467e-005

Decision

0.53

0.5

Performances as a function of modulation format

We have investigated and compared the performance of different modulation formats like Non Return to Zero (NRZ) and Return to Zero (RZ) for downstream data of GPON by varying the length of the fiber (0 to 80 km). Two graphs have been shown to verify the results at Optical Network Unit (ONU).

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Bentahar Attaouia et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), MarchMay, 2014, pp. 20-27

Figure 11: Q factor as a function as fiber length for RZ and NRZ format of modulation.

It is clearly indicated from graphs as the decrease the Q quality factor and an abrupt change have been seen in system performance. It has been observed that with increase in higher length fiber the Q factor decrease and RZ gives the better performance as compared to NRZ. For EDFA in-line amplifier and at 10 dBm, the 2.5 Gb/s data is successfully transmitted to 128 ONUs with best Q-factor around to 12.17 for RZ and Q factor of 8.55with RZ. Figure 12: Eye diagrams as a function as fiber length for RZ and NRZ formats of modulation.

Analysis

NRZ

Max Q factor Min BER

12.17 2.07011e-034

8.55 8.99721e-034

Eye Height

1.2721e-005

7.30467e-005

Decision

0.46

0.5

ď&#x201A;ˇ Performances as a function of loss splitter The optical budget is comprised of attenuation from splices, connectors, the transmission fiber and the optical splitters. The optical splitter is by far the most demanding component in terms of losses (a typical 1x32 optical splitter insertion loss could range between 17 dB and 18 dB).The evolution of quality factor Q as a function of the fiber length and splitter loss is illustrated in Figure.13, the Q factor is measured for EDFA in-line amplifier; we can see that Q factor decreases linearly as the fiber length and when the loss splitter increases. Figure 13: Q factor as a function as fiber length for two splitter loss.

Figure14: Eye diagrams as a function as fiber length for two splitter loss.

Analysis

Loss = 21 dB

Loss = 24dB

Max Q factor Min BER

15.87 4.49245e-057

8.55 8.99721e-034

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Bentahar Attaouia et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), MarchMay, 2014, pp. 20-27

Eye Height Decision



1.84069e-005

7.30467e-005

0.625

0.5

Performances as a function of number of the users

From figure15, we can be seen for EDFA in-line as the distance (fiber length) and the number of users by splitting increases the Q factor is also degrade. For example at 80 km , the lowest Q =8.55 factor is achieved for using 1:128 splitter however the highest Q factor of 15.48 is by using 1:64 splitter with best eye diagram . Figure 15: Q factor as a function as fiber length for two user numbers.

Figure 16: Eye diagrams as a function as fiber length for two user numbers.

Analysis

User = 64

User = 128

Max Q factor Min BER

15.48 5.93465e-056

8.55 8.99721e-034

Eye Height

1.64035e-005

7.30467e-005

Decision

0.625

0.5

 Performances as a function of bits rates: Performance of EDFA in-line amplifiers is compared at different bits rate. The Q factor and diagram eyes versus transmission distance graph for two different bits rate at 80 km are shown, we can be seen that the Q factor decease with increasing in the bits rates, were the max Q factor is 5.52 for Db = 5 Gbits/s and lower Q factor of 2.44 for Db = 10Gbits/s . Figure 17: Q factor as a function as fiber length for two bits rate.

Figure 18: Eye diagrams as a function as fiber length for two bits rate.

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Bentahar Attaouia et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), MarchMay, 2014, pp. 20-27

Analysis

Bite rate = 10G/bits

Bite rate= 5G/bits

2.44

5.52

Min BER

0.00712867

1.66866e-008

Eye Height

-1.7358e-006

5.01894e-006

Decision

0.57

0.62

Max Q factor

III. Conclusion This work has shown the different variations of the characteristics of the optical fiber amplifier doped with erbium. They give an overview on the adequate choice of opt-geometric parameters such erbium ions density , core radium and pump power for the optimization of EDFA used for a standard GPON with 128 users and 80 km of reach. So the knowledge of optimal values of the erbium doped fiber amplifier parameters ( L edfa, C, D, Pp, NA…) is necessary to estimate the optimum Q factor. For this, we must choose the parameters of the amplifier that enable both high gain and low noise figure. Our analysis shows that for the optimum EDFA used in position in-line with maximum Q factor over 8.55 we need the following parameters : C = 1e+25/m-3 , Ledfa = 10 m , D = 2.2 μm, NA = 0.24, Pp= 200 mw and λ = 980nm . Other, the performance analysis of GPON using for EDFA in-line amplifier are compared an terms of output parameters like Q factor, eye diagrams for various parameter (splitter loss, signal input power and fiber length...). From the simulation results, it is found that the Q factor decreases with increasing of the fiber length , loss splitter , bit rates , user numbers and gives the better performance with RZ format of modulation . Finally, the future research is to study the performances of EDFA amplifier for WDM-PON network and evaluation of quality factor of an erbium doped fiber Post-, Pre- and in-line amplifier for GPON with 128 users and 80 km of reach. VI. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

C. H. Yeh1, C. W. Chow2, Y. M. Lin1, D. Z. Hsu1, and S. Chi2,”Recent Research on Fiber Access Systems for FTTH Networks in Taiwan”, IEEE . April 2008. “Network-GPON”. Conference on Information Technology Interfaces, Jun. 25-28. 2007. Russell Davey, Junichi Kani, “ Options for Future Optical Access Networks.IEEE” Communications Magazine October 2006 Dheyaa J. Kadhim, “Performance Analysis of xPON Network for Different Queuing Models”, International Journal of Electrical, Electronics and Telecommunication Engineering April 2013. Mc Geough, “Semiconductor optical amplifiers to extend the reach of passive optical networks”. PhD thesis. Jenny 2012. G. E. R. de Paiva, M. M. Freire, U. R. Duarte, A. B. Sassi, A. C. Bizetti, J. F. Pozzuto, J. B. M. A. Romero Rosolem , “80 km Extended Gigabit Passive Optical Network”. IEEExplore.ieee.org. 2011. Joindot, M, 2000,” Transmission systems on optical fiber. Treatise of Telecommunications” : TE 7115. Govind P. Agarwal, “Fiber Optic Communication Systems” John Wiley & sons, Inc. Publication, 2003 R. S. Kaler,” Effect of channeladding/dropping on EDFA transients”, International Journal for Light and ElectronOptics, optik (Elsevier) , vol. 122,pp. 444-450,2011 J Anu Sheetal and Harjit “Singh ,Performance Analysis of 2.5Gb/s Bidirectional WDM/TDM-PON with Narrowband AWG for Varying extinction ratio using anfis”, 2011 Journal Anu Books. Y. Kimura and M. Nakazawa, Elect. Lett. 28, 1420 (1992). P. Lecoy “ Telecommunication on optical fiber networks an telecommunications. Hermes 2d EDn ,1997. S. Bordais “ Amplifiers and lasers 1um high power on Ytterbium doubled clad fiber”. PH.D thesis,university of Rennes I, France 2002.

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

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

COMPARATIVE MODELLING OF MOLTEN SALT REACTOR (MSR) PASSIVE COOLED DRAIN TANKS 1

C.E.Okon, 2T. Abram School of Physics & Astronomy, 2 School of Mechanical, Civil & Aerospace Engineering, University of Manchester, UK. 1

Abstract: This paper provides a technical evaluation and modelling, including a criticality analysis, of Molten Salt Reactor (MSR) passive cooled drain tanks. The freeze plug is kept actively frozen by an external cooling fan blowing air into the area. In an event of total loss of power, the Freeze Plug (valve) melts, and the bulk of core salt drains by gravity into the drain tank with passively cooled configuration where nuclear fission and melt down is not possible. MONK Version 9A Monte Carlo Program by ANSWERS Software Service was used to carry out the analysis for the drain tanks using the design geometry and specifications of the Molten Salt Research Experiment (MSRE) at the Oak Ridge National Laboratory (ORNL), TennesseeUSA. We assumed the composition of the fluoride salt mixture in the fuel drain tank to be: 42.16wt% LiF, 35.79wt% BeF, 21wt% ZrF4, 1.02wt% UF4, and 0.02wt% PuF3. A comparative analysis was carried out for a single drain tank and four drain tanks. The safety margin for subcriticality associated with the MSR fuel storage tanks under normal and credible upset conditions was determined. The results obtained showed that the effective multiplication factor as calculated by MONK for a salt volume of 1,905,870cm3 stored in four drain tanks of capacity 2,271,100cm3 (each) with a diameter of 127cm is 0.9076 (subcritical), while the same salt volume stored in a single tank of the same dimension is 1.298 (supercritical). I. Introduction A molten salt reactor is one of the promising future generation nuclear reactor concepts. This reactor operates with a liquid fuel. Liquid (or molten) salts have characteristics that make them particularly suitable for use as primary and/or secondary coolants for nuclear reactors. This reactor is included in the Generation IV reactors family[4]. Molten fuel salt consists of mixtures of fuel and other salts (to act as diluents and to lower melting temperatures) used in molten form. The salts must fulfil various stringent requirements such as low neutron cross sections, thermal and radiolytic stability, low corrosion, and good heat-transfer and -fluid-flow characteristics[5]. Fluorides seem to provide the best compromises for molten-fuel choice because fluorine has a relatively high scattering cross section for neutrons, higher volumetric specific heats and higher thermal conductivities. It has extremely low absorption for neutrons and is also very electronegative and mono-isotopic. Fluoride salts being ionically bonded, are very resistant (i.e. exhibit very little change) to radiation damage resulting from the gamma radiation, alpha radiation or neutron flux [3]. It is believed that the system UF4-BeF2-LiF-NaF (i.e. fuel salt) offers the best combination of properties for use in molten-salt reactors. The molten fluoride salt has a triple function.  It provides the fissile material needed to produce energy  It is the heat transfer medium (i.e. excellent nuclear reactor coolant)  It is the fuel reprocessing medium. The salt mixture is a liquid with very high specific heat of about 2.42kJ/kgoC and very low viscosity, which is ideal for heat exchange medium. II. Aim and Objective The aim of this research is to carry out a comparative analysis by modelling a molten salt reactor passive cooled drain tanks, to determine the margin and acceptable level of sub-criticality for fuel storage tanks under normal and credible upset conditions using MONK. Considering a reactor shut down for maintenance, or an accident scenario, or any abnormality that might cause the fuel temperature to go above the maximum set limit, the freeze valve gets melted (open) and the fuel is drained into the drain tank(s) of non-critical geometry. Discharging fuel salt to the drain tank empties fissile materials and fission products from the primary system.

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The objective of this research is to compare the level of subcriticality for a single tank and four drain tanks, taking into considerations those parameters that will enhance cooling of the storage tanks filled with hot fuel so as to maintain the integrity of the tank and keeping the fuel in a credible condition. For a molten salt reactor, the cooling process is achieved by natural convection. III. Limitations of Study The modelling did not take into consideration the cooling system and the heater assembly surrounding each tank or any other structural material or machinery in the cell. Material geometry/specifications used for the component dimensions were gotten from the reports from related articles on Molten Salt Breeder Reactor (MSBR) & Molten Salt Reactor Experiment (MSRE). IV. Passive Cooled Drain Tanks: A typical empty fuel drain tank modelled with Hastelloy material using MONK, as seen in Fig.1&2 is connected to the core inlet via a line, housed in a separate cell. This cell is situated below the reactor cell in order for the fuel salt to drain by gravity.

Fig.1: Single Fuel Drain Tanks Modelled Using MONK (3D Wire Mode)

Fig.2: Four Fuel Drain Tanks Modelled Using MONK (3D Wire Mode)

V. Methodology The version of MONK used for this dissertation is MONK Version 9A, the ANSWERS Software Package. The primary aim for which MONK is used for this research is to calculate the effective neutron multiplication factor (Keff). The actual number of neutrons tracked determines the statistical precision associated with the simulated value of K[1]. In this simulation, all calculations were run to Standard Deviations of 0.001 using the default super history powering algorithm with 1000 neutrons per stage. The effective multiplication factor is one that takes leakage into considerations. Neutron leakage is dependent on system geometry and density. For a given composition and quantity of material, a geometry of increased surface area or decreased density enhances leakage. Controlling leakage by geometry is especially important to nuclear criticality safety. The leakage effect, however, is dominant in determining the stage of criticality of the system[1]. A. Drain Tanks Design Specification: The drain tanks are made with Hastelloy material. It is designed to use a mixed water-steam coolant that circulates by natural convection. The reason for selecting this passively cooled safety system design is that it is relatively easy to get natural convection; it is excellent in transferring heat; and has good radiation and thermal stability. The drain tank is cylindrical in shape, design such that it can hold sufficiently, in a noncritical geometry, all the salt that is contained in the fuel circulating system. The drain tank is connected to the bottom of the reactor vessel by a drain line equipped with a freeze-plug type of "valve" which can be melted to allow gravity drain the entire primary circulating system in about 7 min. To prevent overheating due to stagnant salt, a small circulation of fuel salt is usually maintained in the drain line between the reactor and the freeze valve [2]. The cell wall (water bath) is designed using stainless steel surrounded by ordinary concrete for shielding. The chemical composition by proportion of this material used for this design is as shown below[6]: Ni Cr Mo C

PROP 0.66 PROP 0.10 PROP 0.15 PROP 0.0004

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C. E. Okon et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 28-40 Mn Si Ti Cu S Fe B

PROP PROP PROP PROP PROP PROP PROP

0.01 0.01 0.00865 0.0035 0.0002 0.05 0.0001

Hastelloy alloys are designed for use in pressure vessels and nuclear reactors. Hastelloy N was developed specifically for use in MSBR system[7]. Among the major constituents, chromium is the least resistant to attack by the fluorides. Molybdenum content gives good strength without embrittlement, though the embrittlement problem can be overcome due to the presence of titanium and boron which forms borides that would be dispersed as precipitates and not particularly segregate at the grains boundaries. Stated below are the drain tanks design parameters[2]; Drain Tank Capacity = 2,271 litres (2.271 m3) Drain Tank Inner diameter = 1.27m Drain Tank Outer diameter = 1.312m Drain Tank inner height = 2.1844m Fuel Salt Density = 2.02 g/cm3 (1.906 m3) Water bath height = 4.00m The MONK input files have been prepared using the above material geometry and specifications, and are provided in Appendix 2A of this report. The results are displayed in tabular form in Appendix 1A, and a graphical interpretation of the results is provided below in this report. x r

d x

Design Assumption: the drain tanks are welded to the bottom of the cell such that the minimum separation of each tank from the cell wall and its two neighbours are equal. Diagonal (d) as calculated below = 243.34cm Inner tank diameter is 127cm and the radius (r) = 63.5cm Maximum length of the diagonal (D) is 460.48cm Separation (x) as calculated below = 45.07cm

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The above calculated figures were also used for the MONK modelling. A series of different values have been assumed for the fuel volume, and the fuel depth occupied in the tank is calculated for each volume. Full details of the results interpretations and discussions are also contained in this report. One of the assumptions made in this design is that, the total fuel salt is equally distributed into four drain tanks of equal dimensions. Therefore, the input files are written for different values of fuel volumes to ascertain the critical volume and critical diameter. Also, the design was carried out for a single tank to ascertain the effectiveness and the credibility of using single drain tank for the storage of all the fuel salt without affecting the integrity of the tank. Comparative analysis was carried out to check the level of sub-criticality by calculating the effective multiplication factors in both cases. Stainless Steel Bath

Water

Fuel Salt

Fig.3: MONK Modelling for 4-MSR Drain Tanks (see codes in Appendix B) Stainless Steel Tank Bath

Water

Stainless Steel Tank Bath

Fig.1: MONK modelling for the MSR Drain Tanks (see code in Appendix B)

Stainless Steel Tank Bath

Water

Hastelloy Material

Fig.4: MONK Modelling for a single MSR Drain Tanks (see code in Appendix B)

Hastelloy Material Stainless Steel Tank Bath

Fig.2: MONK Modelling for 4-MSR Drain Tanks (see code in Appendix B)

Water

Fuel Salt

Fig.5: MONK Modelling for a single MSR Drain Tanks (see codes in Appendix B)

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VI. Results Appendix A contains data as calculated by MONK for a single MSR drain tank and for four MSR drain tanks. The graphical representations of different calculated parameters are shown in the figures (5.1-5.12) below. Table 1 (see appendix A), is the critical volume data obtained for four drain tanks. The total volume of the fuel salts is about 1.9059 m3 while the drain tank capacity is about 2.2711 m3. It is assumed that the salt volume is divided equally into the four drain tanks of the same dimensions. According to table 1, the first column contains data for the salt volume. By distributing the total fuel volume into the four drain tanks equally, it means that each drain tank will contain about 0.4765 m3. In order to carry out a comparative analysis, MONK simulations were run for series of data as can be seen in Table 1 of Appendix A. The second column contains data for the salt depth. The salt depth represents the inner height occupied by the salt volume in each tank. This data was calculated using equation 2 below since a cylindrical geometry is considered. For a salt volume of 0.4765 m3 the height occupied by this volume in the tank of 127cm in diameter is about 37.6cm. Salt depths occupied by other salt volumes are also calculated as can be seen in Table1 of Appendix A.

The fourth column shows the tank radius of 63.5cm. This constant value indicates that the inner diameter of the drain tank does not change. The sixth column shows the effective multiplication factor as calculated by MONK, which is the value that determines the state of criticality of the system by putting neutron leakage into considerations. This value was calculated for different salt volumes and their corresponding salt depth. The last column is the property that is used to determine the amount of neutrons that leaked out of the system. It is a dimensionless property known as the Geometric Buckling. For a cylinder of radius r and height h, the property is mathematically stated thus;

Table 2 (see appendix A), is the MONK data for critical diameter. This data shows how the criticality of the system is affected by varying the diameter of the drain tank. It is assumed that for each diameter, the salt volume is full to capacity. The effective multiplication factor for each diameter, the salt volume and the geometric buckling are all calculated for comparative analysis. Table 3 & 4 (see app. A pages) lists the MONK data calculated for a single drain tank. This data shows the effect of using a single drain tank for the storage of all the fuel salt. The total volume of the fuel salts in a tank of diameter 127cm is about 1.9059 m3 while the drain tank capacity is about 2.2711 m3. The effective multiplication factor for different salt volume, the salt depth and the geometric buckling are all calculated for comparative analysis. Table 4, is the analysis carried out for a single tank by varying the diameter to ascertain the state of criticality.

Fig.6: Plot Showing Salt Volume vs K-effective (4-Drain Tanks using MONK 9A)

Fig.7: Plot Showing Salt Volume vs Salt Depth (4-Drain Tanks using MONK 9A)

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C. E. Okon et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 28-40

Fig.8: Plot Showing Salt Volume vs Geometric Buckling (4-Drain Tanks using MONK 9A)

Fig.11: Plot Showing Inner Tank Diameter vs Geometric Buckling (4-Drain Tanks using MONK 9A)

Fig.9: Plot Showing Inner Tank Diameter vs Keffective (4-Drain Tanks using MONK 9A)

Fig.12: Plot Showing Salt Volume vs K-effective (1-Drain Tank using MONK 9A)

Fig.10: Plot Showing Inner Tank Diameter vs Salt Volume (4-Drain Tanks using MONK 9A)

Fig.13: Plot Showing Salt Volume vs Salt Depth (1-Drain Tank using MONK 9A)

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C. E. Okon et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 28-40

Fig.14: Plot Showing Salt Volume vs Geometric Buckling (1-Drain Tank using MONK 9A)

Fig.15: Plot Showing Inner Tank Diameter vs K-effective (1-Drain Tank using MONK 9A)

VII. DISCUSSION OF RESULTS The determination of sub-critical limits as part of nuclear criticality safety management is fundamental for ensuring that the processes involving fissile material remain safe. Figure 6 & 7 is a plot showing how the effective multiplication factor changes as the salt volume is increased. One of the factors that affect the criticality of a fissile system is the quantity of the fissile salt contained in a storage facility of a specific geometry. Since one of the assumptions made in the design for the case of four drain tanks is that the fuel salt is distributed equally into the tanks of equal dimension. An estimate of about 476,468 cm3 is the volume of salt contained in each tank. The effective multiplication factor in this case was calculated by interpolating the simulated data between 470,000cm3 and 480,000cm3. By Interpolation

The graph shows that, although there is a corresponding increase in the effective multiplication factor as the volume increases, the system still remains in a stable and subcritical state. This demonstrates that all the salts volume drained from the reactor core could be stored in the four tanks and remain in a safe and subcritical condition without affecting the veracity of the tanks or resulting to any risk factor. Figure 8 is the plot showing the salt volume and the geometric buckling. This property as mentioned before in chapter 5 measures the amount of neutrons that leaked out of the system. It is also used to determine the state of criticality. The geometric buckling reduces with increasing salt volume. Figure 9 is the plot showing the effect of varying the inner diameter of the tank on the criticality of the system. Let us consider the case where the fuel salt depth in the tank is full to capacity by varying the diameter of the tank as well as the spacing between each tank. Results of data captured from Appendix A table 2 shows that as the diameter of the tank is reduced, there is a wide spacing between each tank thereby enhancing efficient heat removal and circulation of the coolant by natural convection. This also contributes in keeping the system in a subcritical state and also avoids the leakage of neutrons from one tank to another. Equation 4 below is used to calculate the critical diameter for each tank.

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C. E. Okon et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 28-40

For the tank diameter of 52.78cm occupying a salt volume of 476467.5cm3, the effective multiplication factor is calculated as 0.6791. When the diameter is increased, the spacing between each tank is narrowed thereby affecting the effective removal of decay heat and the circulation of coolant. As can be seen from figure 5.4, the K-effective increases as the tank gets compacted to each other. The effective multiplication factor in this case was calculated by interpolating the simulated data between 25.00cm and 27.00cm. By Interpolation

We can hereby conclude that, the credible upset geometric configuration from a critical safety standpoint occurs when the tank diameter is increased and the spacing between each tank neighbours is very close. This causes the maximum possible reflection in the fuel drain tank cell given the assumed constraint of less water ingress. Deductions for 4-Drain Tanks  For tank diameter of 127cm, the effective multiplication factor (Keff) = 0.9074 at the depth of about 37cm (1/6 of each tank capacity) for a salt volume of 476467.5cm3 per tank ≈1,905,870cm3 (i.e for 4-tanks).  For tank diameter of 52.74cm, the effective multiplication factor (K eff) = 0.6791 at the depth equal to 218cm (tank inner height) for a salt volume of 476467.5cm3 per tank ≈1,905,870cm3 (i.e for 4-tanks) Figure 10 & 11, shows a plot of the inner diameter against the salt volume and the geometric buckling respectively. As the inner diameter of the drain tank is increased it gives room for more salt volume to be accommodated and the leakage also decreases as the inner diameter is increased. Figure 12 is the graphical representation of the data calculated for a single drain tank. The plot shows the volume occupied by fuel salt and their corresponding effective multiplication factors. From the single drain tank analysis, the effective multiplication factor for the salt volume of 1,905,870cm3 contained in a tank of diameter 127cm is 1.298. This results show that, the system is in a supercritical condition. Data from Table 3 also show that the single tank can only accommodate a maximum fuel salt volume of about 760,000cm3 – 790,000cm3 for the system to remain in a subcritical state. Data ranging from 1000000cm3 - 1800000cm3 is too clustered so that it will be difficult to trace the data point. As a result, numerical values within that range are not presented on the graph and table of values, hence the discontinuity in data as seen in fig.12, 13 and 14. The effective multiplication factor in the case of a single drain tank was calculated by interpolating the simulated data between the salt volume of 1,880,011.42cm3 and 1,920,041.32cm3. By Interpolation

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C. E. Okon et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 28-40

For a comparative analysis, it is evident that less neutron leakage is recorded when the fuel salts are drained into four drain tanks compare to the same volume of salt stored in a single tank. Data for the salt depth as seen in fig.13 and that of the geometric buckling as seen in fig.14 and also by varying the inner diameter of the drain tank to ascertain the state of criticality as seen in fig.15 can also be used for the comparative analysis. In summary, to achieve a subcritical condition, it is best to store the salt in four different tanks instead of storing all the salts in a single drain tank. VIII. CONCLUSION The state of a neutron chain-reacting system can be described conveniently in terms of its effective multiplication factor. Neutron reflectors decrease the net leakage by scattering back neutrons that would otherwise have been lost. The primary aim and objective of this work is to determine the degree of subcriticality of the molten salt drain tank. To accomplish this, a model of the fuel drain tank was created using MONK 9A. This model includes the fuel salt, fuel drain tanks, and water bath. The model does not include the cooling system or any other structural material. The comparative analysis for a single tank and four drain tanks gives a reliable justification on the accuracy of the design and why it is recommendable from the safety point of view to design and use more than one drain tank for the storage of the fluoride salt in case of emergency scenario. Reactor safety is an important element in the issue that concerns accepting nuclear future. There is a disparity of viewpoints between nuclear technologist and those of the public who are worried about reactor safety. The public looks at the large consequences imputed to a low probability accident and concludes that reactor are clearly unsafe, whereas the nuclear energy industry looks at the low probabilities calculated for a large consequence accident and concludes that reactors are exceedingly safe. The MSR has many favourable safety features. The drain tank serves conveniently as a flow volume to which salt can be continuously overflowed from the primary pump bowl. At the drain tank the supply and return connections to the chemical processing facility will be made. The same jet pump arrangement used to fill the primary system from the drain tank could also be used to transfer salt to the chemical facility. These completely eliminate the need for pressurizing the tank for salt transfer. With this arrangement, salt can be taken from the tank for processing separately of reactor operation. IX. Recommendations for Future Work Though this research did not consider the cooling system in its design, for the purpose of future research, I would also recommend that to design the cooling system, the following objectives have to be considered: 1. It must be capable of keeping the highest drain tank temperature well within the safe operating range even under the worst condition of transient heat loads. 2. The system must be dependable, with a minimum of reliance on the electric power supply or operator initiated actions. 3. The cooling system should cause a minimal risk for freezing of either the fuel salt or the cooling system coolant. It is decided that the above objectives would be best met by storage of the salt in a tank having a coolant circulated by ordinary convection, since a storage tank with a convective cooling system was used with good results in the MSRE. REFERENCES [1] [2] [3] [4] [5] [6] [7]

A. K. Ronald, Nuclear Criticality Safety, Theory and Practice, Illinois-USA: American Nuclear Society , 2000. D. F. Hollenbach and C. M. Hopper, "Criticality Safety Study of the MSRE Fuel Drain Tank Cell in Building 7503," Oak Ridge National Laboratory, 1994. D. F. Shriver and P. W. Atkins, Inorganic Chemistry, Second Edition, New York: Oxford University Press, 1998. J. Uhlir, "Chemistry and Technology of Molten Salt Reactors-History and Perspectives," Journal of Nuclear Materials, pp. 6-11, 2007. K. Nagy, et al. "New Breeding Gains Definitions and their Application to the Optimization of a Molten Salt Reactor," Annals of Nuclear Energy, pp. 601-609, 2011. M. Richardson, "Development of Freeze Valve for use in the MSRE," Oak Ridge National Laboratory, 1962. W. R. Huntley and P. A. Gnadt, "Design and Operation of A Forced-Circulation Corrosion Test Facility (MSR-FCL-1) Employing Hastelloy N alloy and Sodium Fluoroborate Salt.," Oak Ridge National Laboratory, 1973.

APPENDIX A

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C. E. Okon et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 28-40 Table 1: MONK Data for Critical Volume obtained for Four(4) Drain Tanks (Total Salt Volume: 1,905,870 cm3) Salt Volume V (cm3) x4

Salt Depth. (cm)

Tank Radius R(cm)

h2 (cm2)

R2(cm2)

Keff STDV= 0.0010

Geometric Buckling (B2)

400000

31.58

997.30

63.5

4032.25

0.8444

0.0113

410000

32.37

1047.82

63.5

4032.25

0.8561

0.0109

420000

33.16

1099.59

63.5

4032.25

0.8635

0.0104

430000

33.94

1151.92

63.5

4032.25

0.8711

0.0100

440000

34.73

1206.17

63.5

4032.25

0.8869

0.0096

450000

35.52

1261.67

63.5

4032.25

0.8881

0.0093

460000

36.31

1318.42

63.5

4032.25

0.8955

0.0089

470000

37.10

1376.41

63.5

4032.25

0.9028

0.0086

480000

37.89

1435.65

63.5

4032.25

0.9102

0.0083

490000

38.68

1496.14

63.5

4032.25

0.9170

0.0080

500000

39.47

1557.88

63.5

4032.25

0.9253

0.0078

510000

40.26

1620.87

63.5

4032.25

0.9323

0.0075

520000

41.05

1685.10

63.5

4032.25

0.9383

0.0073

530000

41.84

1750.59

63.5

4032.25

0.9461

0.0071

540000

42.63

1817.32

63.5

4032.25

0.9523

0.0069

550000

43.42

1885.30

63.5

4032.25

0.9595

0.0067

Table 2: MONK Data for Critical Diameter obtained for Four(4) Drain Tanks (Total Salt Volume: 1,905,870 cm3) Salt Depth. (cm)

h h2 (cm2)

Tank Radius R (cm)

218.00

47524.00

20.00

400.00

273946.87

0.5078

0.0147

218.00

47524.00

22.50

506.25

346714.02

0.5756

0.0116

218.00

47524.00

25.00

625.00

428042.00

0.6426

0.0095

218.00

47524.00

27.50

756.25

517930.81

0.7093

0.0079

218.00

47524.00

30.00

900.00

616380.48

0.7729

0.0066

218.00

47524.00

32.50

1056.25

723390.98

0.8337

0.0057

218.00

47524.00

35.00

1225.00

838962.32

0.8918

0.0049

218.00

47524.00

37.50

1406.25

963094.50

0.9496

0.0043

218.00

47524.00

40.00

1600.00

1095787.50

0.9997

0.0038

218.00

47524.00

42.50

1806.25

1237041.38

1.0513

0.0034

218.00

47524.00

45.00

2025.00

1386856.08

1.0988

0.0031

218.00

47524.00

47.50

2256.25

1545231.62

1.1414

0.0028

218.00

47524.00

50.00

2500.00

1712168.00

1.1810

0.0025

218.00

47524.00

52.50

2756.25

1851880.90

1.2184

0.0023

218.00

47524.00

55.00

3025.00

2071723.30

1.2549

0.0021

R2(cm2)

Salt Volume V (cm3)

Keff STDV= 0.0010

Geometric Buckling (B2)

Table 3: MONK Data for Critical Volume obtained for a Single(1) Drain Tank (Total Salt Volume: 1,905,870 cm3) Salt Depth. h (cm)

h2 (cm2)

Tank Radius R (cm)

R2(cm2)

Salt Volume V (cm3)

Keff STDV = 0.0010

Geometric Buckling (B2)

34.70

1204.09

63.5

4032.25

439568.74

0.6818

0.0096

37.90

1436.41

63.5

4032.25

480105.34

0.7322

0.0083

41.00

1681.00

63.5

4032.25

519375.17

0.7745

0.0073

44.20

1953.64

63.5

4032.25

559911.76

0.8160

0.0065

47.36

2242.97

63.5

4032.25

599941.66

0.8525

0.0058

50.52

2552.27

63.5

4032.25

639971.55

0.8865

0.0053

53.68

2881.54

63.5

4032.25

680001.44

0.9191

0.0049

56.84

3230.79

63.5

4032.25

720031.33

0.9486

0.0045

60.00

3600.00

63.5

4032.25

760061.21

0.9754

0.0042

63.15

3987.92

63.5

4032.25

799964.43

0.9997

0.0039

66.31

4397.02

63.5

4032.25

839994.32

1.0244

0.0037

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C. E. Okon et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 28-40

69.47

4826.08

63.5

4032.25

880024.21

1.0462

0.0035

72.63

5275.12

63.5

4032.25

920054.11

1.0656

0.0033

75.78

5742.61

63.5

4032.25

959957.32

1.0826

0.0032

145.25

21097.56

63.5

4032.25

1839981.53

1.2874

0.0019

148.41

22025.53

63.5

4032.25

1880011.42

1.2937

0.0019

151.57

22973.46

63.5

4032.25

1920041.32

1.3006

0.0019

154.72

23938.28

63.5

4032.25

1959944.53

1.3022

0.0018

157.88

24926.09

63.5

4032.25

1999974.42

1.3077

0.0018

161.04

25933.88

63.5

4032.25

2040604.31

1.3103

0.0018

Table 4: MONK Data for Critical Diameter obtained for a Single(1) Drain Tank (Total Salt Volume: 1,905,870 cm3) Salt Depth. h Tank Radius R Salt Volume V Keff Geometric Buckling h2 (cm2) R2(cm2) (cm) (cm) (cm3) STDV = 0.0010 (B2) 218.00

47524.00

20.00

400.00

273946.88

0.5077

0.0147

218.00

47524.00

22.50

506.25

346714.02

0.5737

0.0116

218.00

47524.00

25.00

625.00

428042.00

0.6399

0.0095

218.00

47524.00

27.50

756.25

517930.82

0.7060

0.0079

218.00

47524.00

30.00

900.00

616380.48

0.7683

0.0066

218.00

47524.00

32.50

1056.25

723390.98

0.8287

0.0057

218.00

47524.00

35.00

1225.00

838962.32

0.8879

0.0049

218.00

47524.00

37.50

1406.25

963094.50

0.9403

0.0043

218.00

47524.00

40.00

1600.00

1095787.52

0.9942

0.0038

218.00

47524.00

42.50

1806.25

1237041.38

1.0456

0.0034

218.00

47524.00

45.00

2025.00

1386856.08

1.0914

0.0031

218.00

47524.00

47.50

2256.25

1545231.62

1.1326

0.0028

218.00

47524.00

50.00

2500.00

1712168.00

1.1739

0.0025

218.00

47524.00

52.50

2756.25

1887665.22

1.2116

0.0023

218.00

47524.00

55.00

3025.00

2071723.28

1.2491

0.0021

218.00

47524.00

57.50

3306.25

2264342.18

1.2790

0.0020

APPENDIX B MONK Input File for Four(4) Drain Tanks *APPENDIX B1 (Charles Monk) ************************************************************************** BEGIN MATERIAL SPECIFICATION TYPE DICE NORMALISE !Normalise proportions where necessary ATOMS *Material 1 - LiF-BeF2-ZrF4-UF4 (Density 2.02 g/cm^3) MIXTURE 1 Li PROP 1 Be PROP 1 Zr PROP 1 U233 PROP 0.93 U234 PROP 0.07 F PROP 90 *Material 2 - Hastelloy G2 (Density 8.03 g/cm^3) MIXTURE 2 Ni PROP 0.66 Cr PROP 0.10 Mo PROP 0.15 C PROP 0.0004 Mn PROP 0.01 Si PROP 0.01 Ti PROP 0.00865 Cu PROP 0.0035 S PROP 0.0002 Fe PROP 0.05 B PROP 0.0001 *Material 3 - Water-H2O (Density 1.000 g/cm^3) MIXTURE 3

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C. E. Okon et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 28-40 H PROP 2.0 O PROP 1.0 *Material 4 - Duplex Stainless Steel 2205 (UNS S31803) (Density 7.8 g/cm^3) MIXTURE 4 Ni PROP 0.66 Cr PROP 0.15 Mo PROP 0.03 C PROP 0.0003 Mn PROP 0.02 Si PROP 0.01 P PROP 0.0003 N PROP 0.0015 S PROP 0.0002 Fe PROP 0.6627 WEIGHT MATERIAL 1 DENSITY 2.03 MIXTURE 1 MATERIAL 2 DENSITY 8.03 MIXTURE 2 MATERIAL 3 DENSITY 1.00 MIXTURE 3 MATERIAL 4 DENSITY 7.80 MIXTURE 4 END ************************************************ BEGIN MATERIAL GEOMETRY PART 1 NEST ZROD M1 0.0 0.0 1.5 63.5 31.58 ZROD M2 0.0 0.0 1.0 65.6 218.0 BOX M3 -75.0 -75.0 0.0 150.0 150.0 280.0 PART 2 ARRAY 2 2 1 11 11 PART 3 NEST BOX P2 -150.0 -150.0 10.0 300.0 300.0 280.0 BOX M4 -155.0 -155.0 0.0 310.0 310.0 310.0 END ********************************************************* BEGIN CONTROL DATA STAGES -15 1000 1000 STDV 0.001 END ********************************************************* BEGIN SOURCE GEOMETRY ZONEMAT PART 1 /MATERIAL 1 END ************************************************ MONK Input File for a Single Drain Tank * APPENDIX B2 (Charles MONK) *********************************************** BEGIN MATERIAL SPECIFICATION TYPE DICE NORMALISE ! Normalise proportions where necessary ATOMS *Material 1 - LiF-BeF2-ZrF4-UF4 (Density 2.02 g/cm^3) MIXTURE 1 Li PROP 1 Be PROP 1 Zr PROP 1 U233 PROP 0.93 U234 PROP 0.07 F PROP 90 *Material 2 - Hastelloy G2 (Density 8.03 g/cm^3) MIXTURE 2 Ni PROP 0.66 Cr PROP 0.10 Mo PROP 0.15 C PROP 0.0004 Mn PROP 0.01 Si PROP 0.01 Ti PROP 0.00865 Cu PROP 0.0035 S PROP 0.0002 Fe PROP 0.05 B PROP 0.0001 *Material 3 - Water-H2O (Density 1.000 g/cm^3) MIXTURE 3

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Page 40

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

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

COLLISION AVOIDANCE SCHEME USING EMBEDDED SYSTEM 1

Prof. Sangram Keshari Swain, 2Anup Patnaik, 3Abhijeet Pradhan, 4Vinod Kumar Kurneni 1 Asst. Prof., Dept. of CSE, 2,3,4 4th Year CSE School of Engineering & Technology, Jatni Campus Centurion University of Technology and Management, Odisha, India

Abstract: Collision Avoidance Systems, this is an alternative step to avoid collision, are one of the great challenges in the area of active safety for road vehicles. In India the total annual deaths due to road accidents has crossed 1.18 lakhs, according to the latest report of National Crime Records Bureau (NCRB). If these deficiencies are not controlled at early stages they might cause huge economical problems affecting the road side networks. The main part of the work was to carry out a feasibility study on vehicle collision avoidance system using wireless sensor networks. The collision avoidance can be done by Ultrasonic ping sensor. Vehicle collision avoidance system can be identified by using Ultrasonic sensor which will be connected to the car at four sides with the LCD and to an Android mobile (via Bluetooth) to show the top view of the car. I. PROBLEM STATEMENT According to a recent World Health Organization report, India has the highest number of road deaths in the world: 105,725 died last year on its roads, followed by China (96,611), the US (42,642) and Russia (35,972). The United Kingdom had 3,298 recorded road deaths. Worldwide, 1.3 million lives were lost. In India, an accident happens every 60 seconds and every 3.7 minutes, to be precise; a road mishap snuffs out a life. Driver’s fault accounted for a whopping 77.5% of the total road accidents while pedestrian and cyclist’s fault accounted for a mere 3.7%. As Collision Avoidance System is already been developed but in high cost vehicles, In India most of the people can afford the low cost vehicles. So, we have designed this system which can be installed in any vehicle with an affordable cost and we can have accident free roads as well as lives can be saved. II. INTRODUCTION Although there have been a number of technological innovations in vehicle safety, the number of accidents continues to rise. This is especially true for intersection accidents. It has been reported that nearly 30% of the reported accidents in the India are due to intersection collision. Intersection areas are equipped with traffic signals or stop signs. As a result, it is recommended that an intersection collision warning system be implemented as a part of vehicle safety systems, thus reducing the number of accidents. To be most effective, such a system should have the capability of supporting real time systems that can warn potential drivers of an impending collision. It also should be adaptable to different types of intersections. Vehicle collision avoidance system can be identified by using Ultrasonic sensor. Ultrasonic sensor will be connected to the AVR board which will be connected to it. Ultrasonic sensor is connected to the all sides of the car and sends the information via Bluetooth to the Android mobile and transmits the message to the LCD output on the driver side. Actually, in this project we consider the distance by using an Ultrasonic Sensor and an Android Application in the system to avoid an accident. The system was designed to prevent the driver and passenger inside the vehicle gets an accident by detecting the object in front of vehicle in the safe distance. The system operates by using ultrasonic sensor which detects the object or vehicle in distance that we set in front our vehicle where the vehicle is at high speed. III. OVERVIEW OF EMBEDDED SYSTEMS An embedded system is one that has computer-hardware with software embedded in it as one of its most important component. It is a dedicated computer-based system for an application(s) or product. It may be either an independent system or a part of a larger system. As its software usually embeds in ROM (Read Only Memory) it does not need secondary memories (Secondary memory (magnetic memory located in hard disks, diskettes and cartridge tapes and optical memory in CD-ROM) as in a computer. An embedded system has three main components: 1. It has hardware.

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Sangram Keshari Swain et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 41-51

2. It has main application software. The application software may perform concurrently the series of tasks or multiple tasks. 3. It has a real time operating system (RTOS) that supervises the application software and provides a mechanism to let the processor run a process as per scheduling and do the context-switch between the various processes (tasks). RTOS defines the way the system works. It organizes access to a resource in sequence of the series of tasks of the system. It schedules their working and execution by following a plan to control the latencies and to meet the deadlines. [Latency refers to the waiting period between running the codes of a task and the instance at which the need for the task arises.] It sets the rules during the execution of the application software. A smallscale embedded system may not need an RTOS. An embedded system has software designed to keep in view three constraints:  Available system memory.  Available processor speed.  The need to limit power dissipation when running the system continuously in cycles of wait for events, run, stop and wake-up. IV. OVERVIEW OF ULTRA SONIC SENSOR Ultrasonic sensors (also known as transceivers when they both send and receive, but generally called transducers) work on a principle similar to radar or sonar which evaluates attributes of a target by interpreting the echoes from radio or sound waves respectively. Ultrasonic sensors generate high frequency sound waves and evaluate the echo which is received back by the sensor. Sensor calculates the time interval between sending the signal and receiving the echo to determine the distance to an object. ULTRASONIC SENSOR

V. FEATURES OF ULTRASONIC SENSOR   

Compact and light-weight High sensitivity and high sound pressure High reliability VI. REQUIREMENTS Figure: WinAvr Development board v1.2

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Sangram Keshari Swain et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 41-51

1. About the Dvp Board AVR Dvp Board v1.2 is especially designed for the students having interest in electronics, embedded system, robotics and industrial-automation. This board is made in such a way that it becomes easier for anybody to learn about AVR micro controllers. This board can also be used in various applications and hobby projects. 2. ULTRA SONIC SENSOR Ultrasonic sensors (also known as transceivers when they both send and receive, but generally called transducers) work on a principle similar to radar or sonar which evaluates attributes of a target by interpreting the echoes from radio or sound waves respectively. Ultrasonic sensors generate high frequency sound waves and evaluate the echo which is received back by the sensor. 3. HIDBootFlash HIDBootFlash is a GUI and tool used to download firmware to a controller with BootloadHID or AVRUSBBoot equivalent boot loader. It is quite similar to the FW Uploader but not taking advantage of the FischlBootloadHID.exe. In addition it will enable connecting to devices and flashing the firmware in a step approach.

VII. CAS IMPLEMENTATION WinAVR is a suite of executable, open source software development tools for the Atmel’s AVR series of RISC microcontrollers hosted on the Windows platform. It includes the GNU GCC compiler for C and C++. Steps for writing a code using WinAVR 1.

Open the Programmer’s Notepad and write your code.

Figure 1 Create a new folder and save your code in that folder with extension name “.c”

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Sangram Keshari Swain et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 41-51

Figure 2 3.

Now open the make file and edit it as mentioned below:

Figure 3 Make fileâ&#x2020;&#x2019; main filename (give your file name here without extension)

Figure 4

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Sangram Keshari Swain et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 41-51

Make file→ MCU type→ AT mega→ (choose your UC)

Figure 5 Make file→ Debug format→ AVR-ext-COFF

Figure 6 7. Make file→ Programmer→ select your programmer (if your programmer is not in the list then follow the step3.d)

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Sangram Keshari Swain et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 41-51

Figure 7(a) and 7(b) 8.

Make file→ port→ (select the port where you have connected your programmer)

Figure 8 9.

Make file→ enable editing make file→ then in your make file edit the following things

Figure 9

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Sangram Keshari Swain et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 41-51

10. F_CPU = 16000000 (change it as for your crystal frequency) AVRDUDE_PROGRAMMER = stk500 (here write down you programmers name)

11.

Figure 10 Save the make file in your folder without changing its name.

12.

Now open the programmerâ&#x20AC;&#x2122;s notepad.

Figure 11

Figure 12

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Sangram Keshari Swain et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 41-51

13.

To compile your code and to generate hex file (Tools→ make all).

Figure 13 VIII. Result and Simulation To upload your code into your UC (Tools → program). i) First we have to connect the AVR to the Computer so that the HID Boot flash Software will detect the AVR.

Figure 18(a)

Figure 18(b)

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Sangram Keshari Swain et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 41-51

Figure 18 (c)

Figure 18(d) IX. TESTING OF THE PROJECT 1.

View of our CAS.

Figure 19 2.

Always detecting the distance of the object coming around the car.

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Sangram Keshari Swain et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 41-51

Figure 20 3. Alerting the user when it comes closer to any object (i.e. glowing LED or Ring of Buzzer). 1st view

Figure 21 2nd view

Figure 22

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Sangram Keshari Swain et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 41-51

X. FUTURE SCOPE We will continue this project as it will include the software part (i.e. Android application) which will be connected to the hardware .  The Android application will give the user a top view of the car indicating colors of Green, Orange and Red which will be surrounding all sides of the car.  As we all know color code is easily being identified by a human eye so we are using it.

XI. CONCLUSION The vehicle collision avoidance system is very useful in today’s society. It makes drivers more aware of their surroundings; therefore it will be easier for them to avoid collisions. With parking lots becoming more and more crowded and with more people driving larger vehicles that are hard to maneuver, a device like this becomes almost essential. The implementation for our product was very successful, with no real problems found. We were able to implement each of the four major subprojects without too much difficulty. Changing the offset a little more might allow us to get perfect distance readings, but being off only a couple of centimeters is not of much concern. If we were to produce this product commercially, we would use a more sophisticated microcontroller that would allow us to eliminate the rounding problems and would therefore make for near perfect distance readings. Also, we would have to find and use lower cost parts in order to make something like this feasible for a company to produce and to sell to a car manufacturer. REFERENCES BOOKS [1]. J. Jansson, Collision Avoidance Theory with Application to Automotive Collision Mitigation [2]. Active Safety Demonstration 2006 – VCC internal, Booklet in A5. [3]. J. Jansson och F. Gustafsson, Multiple Object Collision Avoidance Decision Making, Submitted to IEEE Transactions on Control Systems Technology – Special issue on automotive control, 2006. [4]. J. Jansson och F. Gustafsson, A framework and automotive application of collision avoidance decision making, Submitted to International Federation of Automatic Control Journal: Automatica, 2006. [5]. J. Jansson and M. Brännström, Threat Assessment for Unexpected Events, ANAQUA ID: 81152228, Status: draft. [6]. J. Jansson M. Brännström, Intersection Collision Avoidance Method, ANAQUA ID: 81137971, Status: To be filed.

WEB REFERENCE  http://www.atmel.com/products/microcontrollers/avr/  http://www.iihs.org/iihs/ratings/crash-avoidance-features  http://phys.org/news/2012-07-crash-avoidance.html  http://link.springer.com/chapter/10.1007%2F978-3-642-15766-0_107#page-1  http://www.atmel.in/products/microcontrollers/avr/start_now.aspx  http://stp.mbsgroup.in/ar_avr.php  http://www.bodyshopbusiness.com/Article/115535/tech_tips_collision_avoidance_systems.aspx?categoryId=335

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

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

Influence of twist on tensile and abrasion properties of DREF-II friction spun plied yarns Prof. Sunil Kumar Sett1, Dr. Asis Mukherjee2 and Mr. N Kundu3 Department of Jute and Fibre Technology1,2 University of Calcutta 35, Ballygunge Circular Road, Kolkata 700 019 NIRJAFT, Kolkata3, INDIA Abstract: As it is already known that the Dref II friction spun yarns without core yarns/filaments are weaker than ring and rotor spun yarn and these are not readily acceptable due to its poor tenacity and extension to withstand the rigorous tensile and abrasive action of weaving and knitting, So the present work is concerned to find the suitable plying technique to improve these properties. Four yarn samples of 118 tex (5.0s Ne) have been spun at different spinning drum speed in Z direction and then these single yarns are plied with 608 TPM (twist per meter) in S direction and 312 TPM in Z directions. The packing fraction, tenacity, breaking elongation, and yarn to emery abrasion were studied. The test report shows that Z plied yarns have higher packing coefficient and tenacity but lower breaking extension and abrasion resistance in comparison with S plied yarn. Then the single yarns having lower packing fractions were further plied to four plied twist levels in S direction and four plied twist levels in Z directions to study the ply assistance in both S and Z direction. It has been shown that the S ply assistance increases gradually with increase in ply twist but Z ply assistance increases to maximum and then decreases with increase in ply twist. The favorable ply assistance may be recommended at ply TM (twist multiplier) level of 10.1 in S direction and 4.2 in Z direction. Keywords: DREF Spinning, Packing fraction, Ply Assistance, Twist Multiplier. Abrasion Resistance I. Introduction Several studies1-5 have been carried out to find the correlation between single yarn and ply yarn properties and optimised the extent of twist imparted in the ply yarn. Ply yarns are more uniform in linear density, have higher strength and smoother surface than single yarns. Ply twisting of staple fibre yarn in opposite direction, in almost all cases, increases tenacity and extensibility1.It has been reported2 that packing coefficient increases by 18.14% for regular ring spun yarn and 11.72% for compact yarn after doubling. It has also been reported3 that the abrasion resistance of two plied cotton yarn increases tremendously with increase in both single yarn twist and ply twist. It also has been reported4, 5 that the strongest ply yarn is obtained when low twisted singles are plied together. DREF II Friction spinning has established itself in coarse count sector as an alternative to conventional ring spinning system. However, the yarns spun in this system are not readily acceptable due to its poor tenacity and extension. It has been observed that friction spun yarns are generally about 15-40% weaker than equivalent ring spun yarn6 and 10-15% weaker than comparable rotor spun yarns 7, 8. The lower strength is generally ascribed to the low fibre extent and poor packing, inferior fibre alignment and poor migration which are the structural weakness of the yarn. On the contrary, high productivity and the possibility to produce yarns directly from sliver to larger packages are inherent merits of this technology. Lord and Radhakrishna 9 plied the friction spun yarn at twist levels from 0 to 5 TM (twist multiplier) and found that when the friction spun yarn was plied Z on Z, the tenacity was considerable higher than when it was plied S on Z and the best tenacity appeared to be obtained at 4.2 TM. He also noticed that the Z on Z yarns felt harder and smoother than S on Z. Chattopadhyay and Chakraborty10 also plied the friction spun yarns at five different ply twist levels and found that plying enhances tenacity and extension of friction spun yarns and Z on Z plied yarn becomes more extensible than S on Z plied yarn. Not much gain in tenacity was observed with increase in ply twist level irrespective of its direction. In the literature no one studied the behavior of variation of twist of friction spun yarn on the tensile property and abrasion resistance after plying. This study deals with the impact of packing of fibres on tensile and abrasion properties considering favourable twist directions at different twist level of DREF II friction spun yarns. II. Materials and methods II.1 Raw Material Four cotton slivers each of 4.4 ktex have been used for this investigation. II.2 Preparation of Yarn Samples

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Four yarn samples of 118 tex (5.0s Ne) were spun on a laboratory model Dref II friction spinning machine at spinning drum speeds of 1900, 2200,2500 & 2800 rpm with constant feeding speed of 1.0 meter per minute and delivery speed of 140 meter per minute. The machine parameter such as carding drum speed, spinning drum setting and suction pressure were maintained at 4200 rpm, 0.4 mm and 25 mbar respectively. II.3 Ply- twisting The single yarns were plied in a conventional ring twisting machine at 608 TPM (twist per meter) to produce four S on Z ply structure (S ply yarn) & at 312 TPM (twist per meter) to produce four Z on Z ply structure (Z ply yarn). The twist level of Z ply yarn was kept approximately 50% lower than S ply yarn due to the incidence of more snarling tendency in Z ply yarn in comparison to S ply yarn. Then the single yarn having lowest packing fraction (i.e. 1900 rpm spinning drum speed) were further plied at 4 different ply twist levels such as 724, 608, 492,378 TPM in S direction and 312, 259,186 and 126 TPM in Z direction. II.4 Test Methods II.4.1 Determination of Packing Fraction The packing fraction is defined as the ratio of specific volume of fibre to specific volume of yarn. This was measured following the method suggested by Hearle11. The yarn diameter was measured by Wild Leitz optical microscope. Forty readings were taken to determine the average yarn diameter. Assuming circular yarn cross section the specific volume of yarn was calculated by using the formula Specific volume of yarn (Vy) = cm3/g Specific volume of fibre (Vf)=1/ fibe density ( for cotton Vf= 1/1.54 g/cm3=0.649 cm3/g) Packing fraction=Vf/Vy II.4.2 Determination of Tensile Strength The tensile test of the yarn was performed on ZWICK/ Roell Z010 tensile tester with 200mm gauge length and 300mm/min speed. II.4.3 Determination of Abrasion Resistance The yarn to emery abrasion test was performed on a MAG SITRA ABRA TEST tester having abrader paper size of LJ 220 (LION Brand). No of strokes required to break the yarn was determined. Fifteen such readings were taken for each sample and from it the RRI (Relative Resistance Index) of the yarn was calculated by using the formula suggested by SITRA yarn abrasion tester manual. III. RESULTS AND DISCUSSIONS Table I. Packing Fraction, Tensile and Abrasion Resistance Values of the Yarns Yarn Properties

Type of Yarn 1900

Spinning Drum Speed ( R P M ) 2200 2500

2800

Packing Fraction of yarn

Single Ply S twisted Ply Z Twisted

0.22 0.34 0.37

0.27 0.31 0.36

0.32 0.30 0.43

0.38 0.28 0.47

Tenacity of Yarn (mN/Tex)

Single Ply S twisted Ply Z Twisted

19.0 (16.2) 42.1 (5.7) 55.0 (4.3)

22.5 (16.7) 39.8 (4.4) 55.9 (8.0)

24.6 (11.7) 38.4 (6.7) 55.7 (6.5)

26.0 (9.7) 37.6 (4.4) 53.8 (7.8)

Breaking Extension %

Single Ply S twisted Ply Z Twisted

6.4 (21.6 ) 14.6 (9.1 ) 12.3 (5.5)

7.2 (18.8) 15.1 (8.6) 11.7 (7.0)

7.3 (21.7 ) 14.8 (10.2) 13.7 (8.4 )

6.9 (14.5 ) 16.1 (10.4) 12.5 (9.2)

Abrasion Resistance (Relative Resistance Index)

Single Ply S twisted Ply Z Twisted

32.6 4990.2 988.5

34.1 7028.5 1834.9

102.9 9042.0 3734.9

101.5 9821.1 1814.8

RRI= No of strokes to break the yarn X Weight of load/ â&#x2C6;&#x161; Tex III.1 Effect of spinning Drum Speed (Single Yarn Twist) on Packing Fraction From the Table 1 and Fig 1, it is shown that the packing fraction of single yarn increases with increase in spinning drum speed which is due to increase in twist level. The plied yarns have more packing fraction than single yarns due to the additional plying twist. But when the yarns are plied together the packing fraction improves in Z direction and reduces in S direction. As it is known that the individual strands also receives twist depending on the direction of ply. A Z ply will insert Z directional twist in singles which have already Z twist from its origin so, the overall twists in singles as well as in plied yarns are increased. Hence Z-Ply structural consolidation may be expected. However, With S- ply, the structure of individual strand opens up due to removal of twist or loosening of wrapped sheath fibres because of opposite twist resulting in decrease in packing fraction. *The values in the parentheses indicate CV%

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Packing Fraction

S. K. Sett et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp52-56

0.5 Single yarn

0.4

S Ply yarn

0.3

Z ply yarn

0.2 0.1 1600

1900

2200

2500

2800

Spinning Drum Speed (rpm)

Fig 1: Spinning drum speed with Packing fraction III.2 Effect of Spinning Drum Speed (Single Yarn Twist) on Tenacity From Table 1 and Fig 2, it is clear that the tenacities of plied yarns are tremendously higher than single yarns which may be due to effect of doubling and the mutual support of individual strands towards the load bearing capacity. It is also observed that the tenacity of Z ply yarn is more than S ply yarn, which may be due to the increase in packing fraction of Z ply yarns than S ply yarns. But the tenacity of Z plied yarn gradually increases to maximum value with increase in twist and then decreases with further increase in twist which may be due to the obliquity effect of fibre and the gradual decrease in resistance to slippage of fibres after optimum twist, however in case of S ply yarn the tenacity decrease gradually with increasing single yarn twist which may be due to the gradual increase in opening or loosening of component yarns making them more prone to slippage .

Tenacity (mN/Tex)

65 60 55 50 45 40 35 30 25 20 15 10 1600

Single Yarn S-Ply Yarn Z- Ply Yarn 1900

2200

2500

2800

Spinning Drum Speed (rpm)

Breaking Extension%

Fig 2: Spinning drum speed with Tenacity III.3 Effect of Spinning Drum Speed (Single Yarn Twist) on Breaking Extension Table 1 and Fig.3 show that the plied yarns have higher breaking extension than the single yarn which may be due to the improvement in uniformity, reduction in imperfection level and mutual support of individual strand towards the load bearing capacity. The S ply yarns have higher breaking extension which may be due to the surface protruding fibres of individuals are entrapped during plying and the core fibres of individuals are opened or loosened so the yarn becomes soft, voluminous and springiness. The yarn breakage takes place after breakage of the surface fibers which leads to higher extension. However, in case of Z ply yarns the surface fibres as well as the core fibres are over twisted which increase its strength and rigidity but reduces the breaking extension due to the fibre breakage of over twisted fibers. 18 16 14 12 10 8 6 4 1600

Single yarn S-Ply yarn Z-Ply yarn 1900

2200

2500

2800

Spinning Drum Speed (rpm)

Fig 3: Spinning drum speed with extension% III.4 Effect of Spinning Drums Speed (Single Yarn Twist) with Abrasion Resistance Table 1 shows that there is not any substantial improvement in abrasion resistance with increase in single yarn twist but after plying there is substantial improvement in abrasion resistance. From fig 4 it has been found that the S-ply yarns have higher abrasion resistance than Z- ply yarns and it increases with increase in single yarn twist level. The reason may be due to the decrease in packing fraction and increase in voluminous and

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springiness of yarn. On the other hand Z ply yarns have compact structure and less resistance to abrasive actions.

R R I (Abraison)

12000 10000 8000

Single Ply S twisted Ply Z Twisted

6000 4000 2000 0 1900

2200

2500

2800

Spinning Drum Speed (rpm)

Fig 4: Spinning drum speed on abrasion resistance. III.5 Ply Twist with Ply Assistance From Table 2 and Fig 5, it is clear that the S Ply assistance increases linearly with increase in plied twist level and it is more than 1 when the TPM value reaches more or equal to 492 (TM 8.1). Spinning Drum speed of single yarn was kept to 1900 only. Table 2 Ply Assistance Values at Different Ply Twist Single yarn tenacity mN/tex

Ply Assistance

19.00

S Ply TPM S Plied Yarn Tenacity mN/tex S Ply Assistance Z Ply TPM Z Plied Yarn Tenacity mN/tex Z Ply Assistance

724 48.70 2.56 312 55.00 2.90

608 42.10 2.22 259 55.90 2.94

492 38.40 2.02 186 48.40 2.55

378 34.40 1.81 126 40.50 2.13

During handling of yarns at Plied TPM level of 492, 608, and 724, it has been noticed that the yarn having TPM of 608 (TM =10.1) has reached the balanced twist level i.e. low snarling tendency. Hence this TM level may be recommended to get a balanced plied yarn in S direction. Ply Assistance is calculated as :

3.5

2.9 2.7 2.5 2.3 2.1 1.9 1.7 1.5

Ply Assistance

Ply Assistance = Plied Yarn Tenacity / Single Yarn Tenacity

2.5

300

400

500

600

700

800

2 100

S - Ply Yarn T.P.M.

Fig 5: Ply Assistance at various TPM of S Plied Yarn

200 300 Z-Ply yarn T.P.M.

400

Fig 6: Ply Assistance at various TPM of Z Plied Yarn

From Fig. 5 and Fig. 6, it is clear that the Z ply assistance increases with increase in yarn TPM and reaches maximum at plied twist level of 259 TPM (TM 4.26) and then decreases. Hence this TM level may be recommended to get a balanced plied yarn in Z direction.

IV CONCLUSIONS: Packing fraction of Z on Z plied yarn is higher than S on Z plied yarn of DREF 2 friction spun yarns even at the insertion of 50% less plied twist levels than S on Z plied yarn.

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The tenacity of Z on Z plied yarn is higher than S on Z plied yarn. The tenacity of S on Z plied yarn decreases with increase in single yarn twist level but in case of Z on Z plied yarn it gradually increases to maximum level and then decreases with further increase in single yarn twist. 3. The breaking extension of S on Z plied yarn is higher than Z on Z plied yarn and there is no significant difference in breaking extension of plied yarns with increase in single yarn twist level. 4. S on Z Plied yarns have higher abrasion resistance than Z on Z plied yarns and it increases with increase in single yarn twist level but in case of Z plied yarn it increase to maximum value and then decreases with increase in single yarn twist level. 5. Ply assistance of S ply yarn increases with increase in ply twist level and in case of Z ply yarn the ply assistance increase to maximum vale and then falls. The better plied yarn may be found at TM level of 10.1 in S direction and 4.2 in Z direction. From the above discussion it is inferred that friction spun single yarns without core or without plying can not be used in weaving or knitting due to lower strength and S on Z plied can be used to produce soft fabrics and Z on Z plied yarns can be used to produce stiff fabrics. References [1] [2]

[3] [4] [5] [6] [7] [8] [9] [10]

Hari P K, Balsubramanian P, Sengupta A K &Chavan R V, Effect of doubling on the tenacity of rotor spun yarns, J Text Inst, 76(5)(1985) 383-386 Isthique S M, Subramanian P,Kumar A & B. R.Das structural and tensile properties of ring and compact plied yarns. Kolandaisamy, P and Mohamed, P Effect of the single yarn twist and ply to single twist ratio on the hairiness and abrasion resistance of cotton two ply yarns. AUTEX research journal, vol 6, No 2, June 2006. Coulson A F W, & Dakin G, Doubled yarns, Part I to III , J Text Inst, 48,T207-T292,1957 Dakin G, Doubled yarns, Part IV and V , J Text Inst, 48,T293-T332,1957 Deussen H, Conventional and novel short staple spinning system, Melliand English,11, E352-E354,1989. Lord P. R., Joo C. W. &Ashizaki T., The mechanics of friction spinning J Text Inst, 1987,78,234-254. Neuhaus L, spinning process as competitors,Textile Asia,1986,17,No 10,49-55. Lord P R, Radhakrishna P, Tenacities of plied Friction spun, Rotor spun and Ring spun yarns. J Text Inst, 78, 140-142, 1987. Chattopadhya R., Chakrabarti A.K., Influence of plying on tensile behavior of friction spun yarns Resume Papers 39 th Technological Conference 21-22 March 1998, NITRA. Hearle ,J.W.S., Grosberg, P. and Backer. S., Structural mechanics of fibres, yarns and fabrics, vol I,Wiley-Interscience,8890,1969.

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Distinguishing between Global Warming and Urban Warming for Bangalore with the aid of Statistical Analysis 1

Rajesh Gopinath1, Vijayalakshmi Akella2 and P. R. Bhanumurthy3 Assistant Professor, Department of Civil Engineering, Acharya Institute of Technology, Bangalore, INDIA 2 Professor and Head, Department of Civil Engineering, KSSEM, Bangalore, INDIA 3 Professor and Director-Evaluations, JNTUA, Ananthapuramu, INDIA

Abstract: The present research presents a statistical framework for distinguishing between Urban Warming and Global Warming, as an outcome of urbanisation. While global warming is the increase in the average temperature of earth's near-surface air, Urban Warming in the form of Urban Heat Islands (U.H.I.), signifies an urban area warmer than its rural surroundings brought about by man-made alterations of the urban surface. This research presents a local climatological investigation so as to distinguish between both forms of warming for Bangalore. To achieve this, ambient air temperature was monitored and recorded simultaneously at 12 stationary observatories in adherence to meteorological guidelines. Industrial belt across Bangalore West had mercury touching 30°C, while residential layouts near water-bodies on the same day and time were found to be coolest with maximum temperature not exceeding 24°C. Rural fringes of Bangalore North owing to relatively undisturbed natural terrain had a minimum temperature of 11.4°C, while dense urban sprawls of Bangalore Central basked with nocturnal Urban Heat Islands at 18°C. On certain occasions, relative intensity was found to be higher than 7°C. This research confirms the distinct presence and dominant trend of urban warming over global warming at the intra-urban locations, thereby warranting control at regional level. Keywords: Bangalore; global; warming; heat island; urban

I. Introduction Urban Warming and Global Warming are the most discussed and debated issues of urban climatological studies. Unlike Global Warming, which entails a worldwide rise in temperature, Urban Warming occurs at the local level [1]. Separate studies on Urban Warming and Global Warming, as an outcome of urbanisation; have been documented by several experts widely across the globe. However, only a few among them attempted to distinguish these. Bangalore is currently the 5th largest metropolitan city of India [2]. As capital of Karnataka, the city underwent rapid and tremendous urbanisation thereby introducing drastic transformation severely influencing its salubrious climate [3]. Hence it’s befitting to conduct the study for Bangalore city. II. Research Methodology Thermal structure of a city can be conveniently studied by using long-term and real time temperature data. As Bangalore had an ideal topography and city morphology, the study facilitated 12 stationary multiple points representing considerable variation in terms of layout, thermal properties and vegetation cover. This further involved collection of air temperature values at these locations, by conducting simultaneous field measurements for over an year covering all the 4 seasons, with the aid of strategically placed calibrated watchdog D.T. 1000 Sensor, with data logger programmed to record data unhindered for 6 months at half an hour intervals. The sensors were housed in Radiation shield at an elevation of 1.5m above ground level as per World Meteorological Standards [4]. The collected climatic data was then subjected for analysis of the warming and cooling trend, wherein the ‘maximum’ and ‘minimum’ temperatures of daily recordings, registered at the weather stations were grouped into ‘weekly’ and ‘monthly’ averages. To determine which type of warming was predominant, the ‘mean’ and ‘total’ change for the dataset was computed simultaneously across all the stations for Tmax, Tmin and Tavg. While ‘total change’ (Equation 1) refers to the sum of changes found, ‘mean change’ (Equation 2) is a term, used to describe the average change over an entire dataset. Herein ‘X’ is the climatic variable, ‘n’ is the total number of observations and ‘i’ is the respective range at which ‘Xi’ represents the respective value.

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Total Change   ( X i  X i 1 )  .....  ( X n  X n 1 )

….Eqn. 1

i 2

Total Change Mean Change =  n

(X i 2

 X i 1 )  .....  ( X n  X n 1 )

….Eqn. 2

n III. Results and Discussion

The statistical results for analysis of real-time data unveil that while certain stations cooled, some other warmed during the period of testing; with the intensity found to be varying across each station. The same can be clearly observed from the charts 1 and 2. Hence, it may be affirmed that across all the stations, the rate of temperature variation was distinct and different. While Chart 1 indicates month-wise, the ‘total’ change; Chart 2 indicates the ‘mean’ change, year-wise. As can also be generally observed from the plots, there is an obvious differential variation in the rate of warming and cooling, both month-wise and annually, across all the stations. Hence, this hints at the probability of the existence of urban warming as an indirect measure of urbanisation. While determination of change only provides a numerical outlook, it falters as far as pictorial representation is considered. Hence, for a better understanding, Trend Analysis results have been presented herewith for seasonal statistics involving weekly variation.

Total Change

Mean Change

Station Station

Chart 3: Total change, month-wise across stations.

Chart 4: Mean change, year-wise across each stations

Air Temperature [°C]

Station

Chart 3: Time series for Minimum Air Temperature.

Chart 4: Trend-lines for Maximum Air Temperature.

As can be observed from the Charts 3 and 4, the Time series plots of air temperature variation are conclusive of the fact that all the stations do not follow the same trend. There is a distinct variation from one station to another. It can easily visualized from the trend series plots that, while at few of the stations there was peaking of rise and falls, the rest had no sharp turns. For instance, for Tmin, it can be realised that Vaderahalli Village always had the coolest temperature, while Peenya Industrial area was the warmest. On the outset, it is also most vital to realise

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that few stations such as Vaderahalli Village and Bommasandra Industrial Area depicted a rapid increase in minimum temperatures (5°C) than the urban stations whose minimum temperature rose only by 2.5°C from 2010 to 2011. This is due to the rising man-made operations and activities, rapidly gaining strides at these undeveloped terrains. Interestingly more sharp turns can be visualized in this graph, highlighting the presence of ‘Nocturnal’ U.H.I. This is because the construction materials exhibit a high thermal inertia, and consequently they continue releasing heat slowly after sunset and even near dawn, when most of the natural surfaces have cooled down. All these aforementioned evidence clearly proves that the variation in climatic parameters is due to the varying intra-urban micro-climatic changes, occurring at each station. In case of Tmax, it was found that during most of the study period, though all the stations have witnessed similar rise and fall on the same days. On an average scale, it may be affirmed that stations with more urban features and surfaces tend to be hotter than the underdeveloped suburbs and rural surroundings. Stations such as Peenya industrial area and Frazer Town were always found to be hotter than the other stations, unlike the stations found on the outskirts such as Bommasandra and Vaderahalli Village. Interestingly the latter station had smooth trend compared to the former. This may be attributed to the scattered industrial operations of Bommasandra Industrial Area. On an whole, it may be summarized that at the highly urbanised stations (e.g. Peenya Industrial Area and Frazer Town) the minimum and maximum air temperatures of locations always showed an increasing trend, while the stations outside the city (Vaderahalli Village, Bommasandra Industrial Area etc.), and those with distinct green cover (NIMHANS) depicted a reverse trend. As also can be observed from the Trend-lines depicted in Chart 4, the pattern though slightly is similar, all of them had still certain directional change. When compared across all stations, the study revealed that the maximum temperatures have increased at a greater rate in the congested urban centres (Frazer Town, R. T. Nagar), than the stations (Vaderahalli Village, Bommasandra Industrial Area) with more natural features. This was so because the stations located on the outskirts were cooling at a faster rate than the urban canyons of stations in core areas of the city. This was due to the fact that these stations had more surfaces of concrete and asphalt. These surfaces have a significant heat capacity and thermal conductivity, which introduces variation in the energy balance, eventually raising the temperature when compared to the rural areas. Simultaneously, minimum temperatures reached the warmest values in the urban areas.

Sd (σ)

Air Temperature (°C)

Station Chart 5: Diurnal variation of ‘Average’ air temperature.

Chart 6: Standard deviation of ‘mean’ air temperature.

As can also be observed from the diurnal plot (Chart 5), the effect of urbanization was usually reflected with the sharp drop in night-time temperatures being observed in rural and suburbs only. The urban centers continued to show warming signs at all locations. This may be attributed to the continuous heat contamination, as derived from human activities such as central heating systems and night traffic. For instance, Industrial belt across Bangalore West had a diurnal U.H.I. with mercury touching a maximum of 30°C due to the release of hot plumes via stacks. Contrastingly, on the same day and time, residential layout under the influence of evaporation due to proximity to water-body was found to be coolest with maximum temperature not exceeding 24°C. Also, rural fringes of Bangalore North, owing to relatively ‘undisturbed natural terrain’ and ‘pollution free environment’ enjoyed a minimum temperature of 11.4°C, while around the same time, dense urban sprawls of Bangalore Central basked with nocturnal U.H.I. at 18°C.

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The present study also computed standard deviation (Chart 6) of the ‘Average’ air temperature. After analysis of the detailed dynamic changes of climatic parameters at the twelve stations, one can observe a general trend in the temporal series, such as high ‘standard deviation’ for minimum temperatures at rural stations, and for maximum temperatures at urban stations respectively. IV. Conclusion The demarcation between urbanisation and global climate change is quite complex. The basic argument that both rural and urban areas are equally warming fails. Though the temperatures in both urban and rural stations show an increasing tendency, it is at urban stations where there is a sharp rise and non-uniform dips. Under this circumstance Global Warming will exacerbate the incidence of heat waves and add an additional thermal burden to urban areas, accentuating its impacts. VI. [1] [2] [3] [4]

References

James Voogt et al., Reducing Urban Heat Islands: Compendium of Strategies: Urban Heat Island Basics, U S EPA. Rajesh Gopinath et al., Analysis of Air Temperature of Bangalore City as a Function of Urban Modification, Proc. 5 th Indian Environment Congress, Thiruvananthapuram, 22-24 Mar 2010, pp. 2. Rajesh Gopinath et al., Influence of Land-Use Pattern on Temporal Climatic Variations Mapping Bangalore City”, Proc. 5th International Congress of Environmental Research, Universiti Malaysia Terengganu, Malaysia, 22-24 Nov 2012, pp.623. T. R. Oke. Review of Urbanization Climatology, WMO Tech, 100, 1979.

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Smart Controller for Wind-Solar Hybrid System under Grid Connected Operations Kishore Ravi Hegde, Prajwal C P, Pramod Kumar H S, Shivakumar S, Prof. R. Jayapal Electrical and Electronics Department R.V College of Engineering Bangalore, India Abstract: The major sources of energy that are available for the consumption are Coal, Natural gas and the oil reserves. But with the current consumption rate of energy these natural reserves wonâ&#x20AC;&#x2122;t last long. This reason made the world to take up renewable energy sources for a nonpolluting and clean environment. The Solar and Wind energy systems are ones which are often used and a lot of research is going on in the field of both wind and solar energy systems. The hybrid energy system of wind and solar has tremendous research potential and has got its own advantage over other sources of energy. When two or more power sources are connected to form a hybrid system, it becomes essential to control the power flows of each of them depending upon the input and output conditions. Hence there is a need for a smart controller which can continuously monitor and control the overall hybrid system. Keywords: wind energy, solar energy, hybrid, MPPT, chopper, three phase inverter, grid connection. I. INTRODUCTION In the present day world there is a huge demand for renewable energy resources as the non-renewable energy resources are becoming extinct and costly. But these renewable energies are not available at all times. Consider for example, the solar power and wind power. These are complementary since sunny days are usually calm and strong winds often occur on cloudy days or in night times [1]. Hence in order to obtain power all the time, there is a need for adding up of these complimentary resources. Thus the hybridisation of these complimentary energy resources like solar and wind resources will result in continuous power availability and results in a better utilization of power. The renewable energies being available freely must be utilized properly to extract maximum power from them. In order to control these different energy resources and again connecting them to the grid requires an efficient controller. Presently there exist solar and wind energy systems which are separately connected to the utility grid. This paper presents the concept of a hybrid system of both of these systems. The advantage of choosing this combination of energy source is that maximum energy can be extracted at all times. II. BLOCK DIAGRAM OF SOLAR-WIND HYBRID SYSTEM The block diagram of a solar wind hybrid system is as shown in Fig.1.

Fig. 1: Block diagram of solar-wind hybrid system connected to grid Choppers are used after the solar and wind systems to control the output voltage of solar and wind. A battery is used in order to provide a continuous power to load as wind and solar energies are uncertain during islanding operation. However the usage of batteries for large scale power generation is not possible. A voltage regulating chopper is used to monitor the output level of the voltage which comes out of inverter in order to match it with that of the grid voltage. An inverter converts a DC voltage to an AC voltage in a proper phase sequence, so that

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the obtained output is in phase with the grid voltage. Circuit breaker is used to connect the solar wind hybrid system to the grid to synchronise the generated power with the grid. III. CONTROL OF SOLAR ENERGY A solar cell basically is a p-n semiconductor junction. When exposed to the light, a DC current is generated. The generated current varies linearly with the solar irradiance [2]. The equivalent electrical circuit of an ideal solar cell can be treated as a current source parallel with a diode shown in Fig 2.

Fig. 2: Equivalent electrical circuit of a solar cell The I-V characteristics of the equivalent solar cell circuit can be determined by following equations. The current through diode is given by:

While, the solar cell output current:

Where: I: Solar cell current (A) Ish: Light generated current (A) [Short circuit value assuming no series/ shunt resistance] ID: Diode saturation current (A) q: Electron charge (1.6×10-19 C) k: Boltzman constant (1.38×10-23 J/K) T: Cell temperature in Kelvin (K) V: solar cell output voltage (V) Rs: Solar cell series resistance (Ω) Rsh: Solar cell shunt resistance (Ω) The current versus voltage curve for the solar cell is as shown in the Fig 3.

Fig. 3: IV characteristics of a solar cell The power delivered by the solar module depends on the irradiance, temperature, and shadowing conditions. The solar cell has a nonlinear characteristic, and the power has a Maximum Power Point (MPP) at a certain working point (Fig 4), with coordinates VMPP voltage and IMPP current [3]. Since the MPP depends on solar irradiation and cell temperature, it is never constant over time; thereby Maximum Power Point Tracking (MPPT) should be used to track its changes. Maximum Power Point Tracking (MPPT) is becoming more and more important as maximum energy extracted from the solar cell.

Fig. 4: PV characteristics of a solar cell

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Hence the smart controller must be capable of determining the MPPT voltage and current for a given condition. IV. CONTROL OF WIND ENERGY The Wind energy is one of the fastest growing renewable energies in the world. The generation of wind power is clean and non-polluting; it does not produce any by-products harmful to the environment. For a particular wind speed, there is a specific turbine rotational speed which generates the maximum power. The maximum power point tracking (MPPT) for each wind speed increases the energy generation. However, the MPPT control for each wind speed generates the output power fluctuations. But, the introduction of fuel cells and flywheels has reduced the above problem [4]. The power versus wind speed curve for the wind turbine is as shown in the Fig 5.

Fig. 5: Power-wind speed characteristics of a wind turbine The wind mill speed of the wind turbine can be varied by varying the pitch angle. Also from Fig 5 it can be noted that for a particular wind speed there will be a particular wind speed for which the power is maximum. Hence the smart controller must be capable of determining the pitch angle to extract maximum power under a given condition. V. CONTROL OF POWER FROM AND TO BATTERY A battery is used in order to provide a continuous power to load as wind and solar energies are uncertain. A battery being a chemical device has some restrictions while operating it such as the minimum and maximum State of Charge (SOC) , maximum charging and discharging currents and so on [7]. Hence the smart controller must be capable of controlling all these requirements. VI. VOLTAGE REGULATOR CONTROL The output voltage generated from the inverter must be of same level as that of the grid in order to synchronise to the grid. If there is a difference in voltage levels of the generated voltage and the grid voltage, then there will be a flow of huge inrush currents which may cause damages. The magnitude of in rush current for a difference voltage of and for a sub-transient reactance of is:

A chopper is used as voltage regulator to vary the DC input of the inverter so as to vary the output voltage level of inverter in order to match with grid level. Also after the synchronisation to the grid, the voltage regulator can be designed to increment the output voltage level of the inverter in order to supply the reactive power required [5]. Smart control has to perform all these functions by controlling the chopper action. VII. INVERTER CONTROL A three phase inverter is used just after the voltage regulating chopper in order to supply the power to the local loads and also to the grid. In order to connect the solar-wind hybrid system to the grid, the generated voltage must be in phase with the grid voltage. Hence inverter control is made such that it takes the grid voltage as the reference and generates control pulses to the inverter, so that a voltage having same phase sequence and same frequency as that of the grid voltage is generated. Once the solar-wind hybrid system is connected to the grid, the power angle ( ) of generated voltage must be increased slightly, in order to ensure that all the generated power (P) flows to the grid [6] according to the equation shown below.

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The smart controller must be capable of increasing the power angle of generated power after connecting to the grid. VIII. CIRCUIT BREAKER CONTROL The circuit breaker is controlled to not only clear the faults but also to connect/disconnect the solar-wind hybrid system to the grid, only if the generated voltage is in synchronism with the grid. It will check the RMS values of both grid voltage and hybrid system voltage and will connect the hybrid system to the grid only when magnitude and the phase sequence are matching. IX. FUNCTIONS OF THE SMART CONTROLLER The functions of a smart controller are as follows:  Control of solar and wind chopper by implementation of MPPT algorithm for the utilization of the maximum power generated from the solar module and wind turbine.  Monitor the charge & discharge of the battery for its safe operations.  Monitors the output voltage level of the three phase inverter and adjusting it continuously to the grid voltage level by controlling the voltage regulating chopper.  Once the system is connected to the grid, reactive power management is done by controlling the terminal voltage of the system by controlling the voltage regulating chopper.  Monitoring the output frequency of the three phase inverter and adjusting it continuously to the grid frequency by controlling the inverter action.  Also when the system is connected to the grid, controls the active power flow by controlling the power angle of the hybrid system.  Controls the circuit breaker depending on islanded or grid connected operation  Under the grid connected operation it monitors the output voltages of the system and grid voltage and connects both of them only when the voltages are synchronous to each other. X. SIMULINK MODEL OF THE SOLAR-WIND HYBRID SYSTEM CONNECTED TO THE GRID Simulink model [9] of the solar wind hybrid system connected to the grid is as shown in Fig 6. It consists of a smart controller which controls the overall functions of the hybrid system. The block diagram of the controller is as shown in Fig 7. The MPPT control of the controller is based on perturb and observe algorithm [8]. The VMPPT and power extracted from solar cell for the given variation in irradiance is as shown in Fig 8. The battery management system is modelled to control the charge and discharge rates of the battery in order to improve the life of the battery.

Fig. 6: Simulink model of solar wind hybrid system connected to grid

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Fig. 7: Smart Controller for solar wind hybrid system connected to grid

Fig. 8: Variation of VMPPT and power extracted with respect to variation of Irradiance due to Voltage regulating chopper is used to regulate the voltage level at the output of the inverter with respect to the voltage variations at the grid. Controller calculates the duty cycle by taking the values of input voltage (V i), required output voltage (Vr) and actual output voltage (Va). The calculated duty cycle is given by:

Where represents the error and the number 0.01 represents the minimum change of duty cycle, the number 13 represents the maximum battery voltage and the number 270 represents the maximum grid voltage (under over voltage condition). The inverter control uses the grid voltage as the reference for producing pulses

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for the semiconductor devices of inverter, thus the produced 3 phase voltage will be in phase with the grid voltage.

Fig. 9: Voltage waveform of the hybrid system at the time of synchronization to the grid Circuit breaker is modeled to connect the hybrid system to the grid when the RMS voltages of both grid and hybrid system are nearly close. The voltage waveform of the hybrid system at the time of synchronization to the grid is as shown in the fig. 9. XI. CONCLUSION The background study regarding the working of the solar cell and wind turbine has been done. The requirement of hybridization and the challenges involving the hybridization are discussed. The control strategies involved in the hybrid system and the hybrid system connected to the grid are discussed. Model of the proposed system has been realized with the help of Simulink and the expected results are observed. ACKNOWLEDGMENT We like to thank all the faculty and staff of Department of Electrical & Electronics Engineering, RV College of Engineering, for encouraging us to prepare this paper. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8]

[9]

Smart Grid Hybrid Generation System by Mrs Babitai Panda, Dr. Bhagabata Panda and Dr. P.K Hota. Comparison of MPPT Algorithms for DC-DC Converters Based PV Systems by A.Pradeep Kumar Yadav, S.Thirumaliah, G.Haritha Improved MPPT algorithms for rapidly changing environmental conditions by Dezso Sera, Tamas Kerekes, Remus Teodorescu and Frede Blaabjerg Control strategy for a distributed DC power system with renewable energy by Kyohei Kurohane, Akie Uehara, Tomonobu Senjyu, Atsushi Yona, Naomitsu Urasaki, Toshihisa Funabashi, Chul-Hwan Kim Wind and Solar Power Systems by Mukund R. Patel I. Miller, T. J. E. (Timothy John Eastham), “Reactive power control in electric systems”, 1947-TK3226.R38 1982 621.319 8210838 ISBN 0-471-86933-3 Riezenman, M. J. 1995. “In search of better batteries,” IEEE Spectrum, p. 51-56,May 1995. A.Pradeep Kumar Yadav, S.Thirumaliah, G.Haritha, "Comparison of MPPT Algorithms for DC-DC Converters Based PV Systems",ISSN: 2278 – 8875, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering Vol. 1, Issue 1, July 2012 www.mathworks.com (MATLAB R2013b(8.2.0.701))

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

Gaseous Emissions from MSW Dumpsites in Vijayawada Shaheda Niloufer 1, Dr.A.V.V.S.Swamy2, K.Syamala Devi3 1

Asst Professor, Lakireddy Bali Reddy College of Engineering, Mylavaram, Krishna D.t., (A.P.), India 2

Head, Department of Environmental Sciences, Acharya Nagarjuna University, Guntur, Krishna D.t.,(A.P.), 3

Asst Professor, Department of Basic Sciences, GNITS, Hyderabad, (A.P.), India.

Abstract: Assessment of gaseous emissions from two municipal solid waste dumpsites at Vijayawada, Andhra Pradesh in India for the potential health impacts due to the emissions was the prime objective of the present study. A portable multi-gas analyzer of model MODEL_VEP_200 with RS 232 was used to monitor the soil gas and ambient air quality near the two dumpsites of Vijayawada. Specific parameters like CH 4, NH3, CO, H2S, H2, NO2 and O2 were monitored for a period of three seasons i.e. winter(November 12 to February 13), summer (March 13 to June 13) and rainy (July 13 to October 13) a complete annual cycle. CH 4 was recorded at both the dumpsites which were presently within the permissible limits. H 2S is resulting in obnoxious odors to the residents near the dumpsites affecting the life of people. NO 2 was within the permissible limits of Indian standards IS 10500. H2 and NH3 were below detection levels during the study period in the dumpsites. Key words: Municipal Solid Waste (MSW), Portable multi-gas analyzer, soil gas, ambient air. I. Introduction MSW includes commercial and residential wastes generated in municipal or notified areas in either solid or semi-solid form excluding industrial hazardous wastes but including treated bio-medical wastes. It consists of household waste, wastes from hotels and restaurants, construction and demolition debris, sanitation residue, and waste from streets. As per estimates more than 55 million tons of MSW is generated in India per year; the yearly increase is estimated to be about 5%. It is estimated that solid waste generated in small, medium and large cities and towns in India is about 0.1 kg, 0.3 – 0.4 kg and 0.5 kg per capita per day respectively. The estimated annual increase in per capita waste generation is about 1.33 % per year. The solid waste generation varies upon the nature of the place, activities and life style of the residents. [1] In India, the biodegradable portion dominates the bulk of MSW. This is mainly due to food and yard waste. With rising urbanization and change in lifestyle and food habits, the amount of municipal solid waste has been increasing rapidly and its composition has been changing. Unorganized, indiscriminate and unscientific dumping of municipal solid wastes is very common disposal method in many Indian cities which cause adverse impacts to the environment. Over the past few decades, composition of MSW in Indian mega-cities have recorded higher percentages of earth and inert materials (35–52%), varying degradable matter (35–84%) and lowest recyclable material (10–20%). Plastic content in MSW had rapidly increased in past and stabilized thereafter due to growing awareness and recycling practices. [12] Almost 70–90% of landfills in India are open dumpsites [13]. When solid waste (SW) is disposed in waste dumps and landfills; the organic material will be degraded over a longer or shorter period, ranging in a wide span from less than one year to 100 years or more. The majority of this process will be bio-degradation. Strongly depending on conditions in the site where the SW is disposed, this biodegradation will be aerobic or

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anaerobic. The main degradation products are carbon dioxide (CO2), water and heat for the aerobic process and methane (CH4) and CO2 for the anaerobic process ([17], [18]). The CH4 produced and released to the atmosphere contributes to global warming and the emissions need to be estimated and reported in national greenhouse gas inventories under the United Nations’ Framework Convention on Climate Change (UNFCCC). The CO2 produced originates from biogenic sources (e.g., food, garden, paper and wood waste) and the emissions need therefore not be considered. The estimated global annual emissions from solid waste disposal sites (SWDS) are in the range of 20 - 40 million tonnes of CH4, of which the most comes from industrialized countries. This contribution is estimated to be approximately 5-20 percent of the global anthropogenic CH4, which is equal to about 1 to 4 percent of the total anthropogenic greenhouse gas (GHG) emissions. The emissions from developing countries and countries with economies-in-transition will increase in the near future due to increased urban population, increased specific (pro capita) municipal solid waste (MSW) generation due to improved economy and improved SW management practices. From the Annex I countries, the emissions are estimated to remain stable or decline over the next 10 - 20 years. [10] Compared to the west, the composition of municipal solid waste (MSW) in developing countries has higher (40% - 60%) organic waste. This would have potential to emit higher GHG’s from per ton of MSW compared to developed world. The research is scanty on the VOC levels in ambient air from open dump sites ([7], [3], [14]). There are no studies on the gaseous emissions from MSW sites in Indian cities except few studies at landfill sites of Bhandewadi in Nagpur and Amaravathi in Sukhali [1], Chennai city landfills ([12], [21]), Delhi landfill sites ([18], [26]). The present study is first of its kind in Andhra Pradesh. The monitoring of emissions from a dumpsite at Vijayawada has been taken up. This study not only serves as a basis in MSW monitoring in Andhra Pradesh also helps to develop indicators of degradation but also help to develop coordinates for future dynamic models. II. Study Area Vijayawada is the second largest city in the state of divided Andhra Pradesh, India, after Visakhapatnam, with an area of 261.88 km2 and population of 1,048,240. Vijayawada is surrounded by the Krishna River on the east and west and the Budameru River on the north. The northern, northwestern, and southwestern parts of the city are covered by a low range of hills, while the central, southwestern and northwestern parts are covered by rich and fertile agriculture lands with three major irrigation canals. The topography of Vijayawada is flat, with a few small- to medium-sized hills. The Krishna River runs through the city. These hills are part of the Eastern Ghats cut through by the Krishna River. The city generates solid waste of 650 Tons/Day. Generally low lying areas and outskirts of the city Vijayawada are used for the purpose of open dumping. The management of solid wastes in Indian cities like Vijayawada is largely unscientific and unsatisfactory. The uncontrolled dumping of urban wastes destroys the beauty of country side also there is danger of water pollution when the leachate from a refuse dump enters surface water or ground water resource. In addition uncontrolled release of landfill gas, burning of open dumps can cause air pollution. The main risks of human health arise from the breeding of disease vectors, primary flies and rats thriving in the exposed garbage and refuse dumps [24]. The two MSW dump sites at Pathapadu suburban region of Vijayawada and Ajith Singnagar are been selected for the present study. III. Materials and Methods: The two dumpsites of Vijayawada Pathapadu dumpsite (PPD) in the suburban region of Vijayawada and Ajith Singnagar dumpsite (ASN) were selected for the present study. Site descriptions are given below in the table 1.

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Table 1 Disposal Characteristics

Pathapadu

Ajith Singnagar

Since when in operation (Years)

Total site area (Acres)

106.6

360

225

Land filling

Open dumping

Disposal quantity(MT/day) Waste Disposal method

The two dumpsites i.e. Pathapadu dumpsite (PPD) and Ajith Singhnagar dumpsite (ASN) are studied for monthly monitoring of the emissions for specific parameters like CH4, NH3, CO, H2S, H2, NO2 and O2 was done. A portable onsite multi-gas analyzer of model MODEL_VEP_200 with RS 232 was used to estimate emissions from the dump and ambient air quality near the two dumpsites of Vijayawada. The probe of the multi-gas analyzer was inserted in the soil at different depths of the MSW dump for monitoring the soil gas sample. The sampling was done randomly at five identified spots at each dump site and the mean of the five places was taken. Standard deviation (SD) was also calculated for all the five places for the two dumpsites. IV. Results and Discussion The samples collected were at the two dumpsites. The sampling was done for complete three seasons of the annual cycle i.e. July 2012 to October 2012 was the monsoon season, November 2012 to February 2013 was winter season and March 2013 to June 2013 was summer season. People exposed to emissions may be at risk and may face serious chronic health hazard problems since it has been well proven by researchers that there was direct correlation between air pollutants and morbidity and mortality rate ([15], [6], [30], [11]). For example, a case report from North America found increased incidence of cancers of the liver, kidney, pancreas [16]. In contrast, there was no link found between the birth rates or health defects and nearby landfill activities was reported by various authors ([2], [28], [23] , [25]). Methane: The CH4 concentration was high ie 4.68% at PPD where 4.04% at ASN dumpsites during summer season. The methane concentration reduced in rainy and consecutively in winter seasons at PPD dumpsite. Whereas the methane concentration was constant during rainy and winter seasons at ASN dumpsite. The CH4 was absent in the ambient air emissions taken from both the dumpsites. The results were in accordance with the earlier studies on landfill emissions ([18], [12]). Similar, findings were reported by [9] for landfill methane emission. The methane emissions were presently within the Indian standards at both the dumpsites. But in the near future due to the urban sprawl at the dumpsites there is possibility of methane accumulation in the study area. The flammability range for methane in air at atmospheric pressure and ambient temperature is 5 to 15% and the safety concentration limit for confined environments where people live or work is 1% in air (20% of the Lower Explosive Limit) [4]. Methane during migration into atmosphere may be diluted and therefore fall within the flammability range. Reference [18] reported that the people living near landfills expressed the fear of explosion due to accumulated methane gas. Carbon Monoxide: The CO emissions ranged between 0 and 8% by volume. The CO emissions were high during the summer season in both the dumpsites compared to winter and rainy seasons. The values were exceeding the acceptable levels in both the dumpsites. The CO value was higher in ASN dumpsite than PPD dumpsite in summer season. Higher concentrations of CO are the sign of oxygen-starved burning of the refuse [4]. As the PPD dumpsite is located in the residential area it may contribute to various health problems to the residents. CO causes headaches, dizziness, vomiting and nausea. Exposure to moderate and high levels of CO over long periods of time also been linked with increased risk of heart diseases.

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Hydrogen Sulphide: The H2S emissions ranged between 2.37 and 47.81% at PPD dumpsite whereas 22.43 and 48.93% at ASN dumpsite. The H2S emissions were found to be very high in both the dumpsites during summer season. The reason is that the dumpsites are active and receives garbage every day. A trend of decrease was recorded during rainy season followed by winter season at both the dumpsites. H2S even at low levels cause odour problems in the vicinity of the dumpsites. H2S is causing obnoxious odour to the residents in the study area. H2S is high in the ambient air samples due to combustion of solid waste at the ASN dumpsite. Hydrogen sulphide is highly toxic and affects the nervous system. It also has a repugnant odour and is highly flammable. Its odour threshold is comprised between 5 and 40 ppm. Above 50 ppm it paralyses the olfactory system, which makes it a particularly pernicious intoxicant. Concentrations above 400 ppm affect the nervous system and above 700 ppm there is risk of death by respiratory failure [4]. Department of health, New York State Agencies have reported health impacts like eye, throat and lung irritations, nausea, headache, nasal blockage, sleeping difficulties, weight loss, chest pain and aggravation of asthma [29]. Nitrogen Dioxide: The NO2 emissions were high during winter followed by rainy and summer seasons at PPD dumpsite. Whereas, concentrations were higher during rainy and constant during summer and winter seasons at ASN dumpsite. The concentration of NO2 was higher at ASN compared to PPD dumpsite during rainy season. But the NO2 emissions were within the permissible limits. Similar findings were reported by [21] during their study where high emissions of NO2 during monsoon season were recorded due to enhanced waste degradation within fills due to rain water percolation.

In dumpsites open burning of the MSW which contains sulfur and

nitrogen is most common and expected to emit SOx and NOx which may also be the reason of NO2 emissions in the study area

which is also supported by ([19], [20]).

Pulmonary tract irritation affecting functioning of

lungs is the major symptom associated with NOx pollution ([27], [5]). Oxygen: The O2 monitored was depleting in soil gas samples during summer season in both the dumpsites which is considered as the indicator of anaerobic conditions in the MSW dump. The oxygen levels were high during winter followed by rainy season and decreased during summer at both the dumpsites. Comparatively the oxygen concentrations were very low at ASN than PPD dumpsite. This may be due to more compaction of the solid waste at the ASN dumpsite due to lack of dumping area. The standard oxygen levels of 20.09% by volume were found in the ambient air samples taken from the dumpsites. Table 2: Gaseous emissions November 2012 to October 2013(Mean ± SD) Parameter

O2 (%)

CO (%)

CH4 (%)

NO2(ppm)

H2S(ppm)

H2 (%)

NH3 (%)

Stations

Winter

Summer

Rainy

PPD

9.75±1.61

3.66±0.74

3.94±0.19

ASN

4.63±0.67

0.41±0.11

3.8±0.08

PPD

2.51±0.56

3.67±0.22

3.82±0.59

ASN

2.51±0.39

4.12±0.38

3.78±0.45

PPD

2.43±0.8

4.68±1.31

4.37±0.32

ASN

2.54±0.22

4.04±0.32

2.22±0.54

PPD

1.5±0.45

1.93±0.31

2.75±1.39

ASN

2.43±0.74

3.93±0.8

2.87±0.85

PPD

2.37±4.75

47.81±30.99

31.18±9.46

ASN

22.43±9.15

48.93±20.96

33.5±4.41

PPD

BDL

ASN

BDL

PPD

BDL

ASN

BDL

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Table 3: AMBIENT AIR Parameter

O2 (%)

CO (%)

CH4 (%)

NO2(ppm)

H2S(ppm)

H2 (%)

NH3 (%)

Stations

Winter

Summer

Rainy

PPD

BDL

ASN

BDL

PPD

1.4±0.29

1.6±0.31

1±0.18

ASN

1.32±0.29

1.55±0.12

1±0.16

PPD

BDL

3.75±7.5

BDL

ASN

BDL

PPD

BDL

BBDL

BDL

ASN

BDL

PPD

23±4.67

20.82±0.95

20.67±0.45

ASN

20±0.68

18.95±0.34

20.32±0.31

PPD

BDL

ASN

BDL

PPD

BDL

ASN

BDL

The H2 and NH3 were below detection levels in both the dumpsites during the study. But the studies by [21] at Chennai recorded the H2 and NH3 emissions in the ambient air at the dumpsites. The ambient air readings were very low or below detection levels for all the gasses except oxygen and carbon monoxide. This may be due to the dispersion of emissions and changing wind directions at the dumpsites. V. Conclusion The gaseous emissions at MSW dumpsites in Vijayawada were recorded during the study. The specific parameters like CH4, NH4, CO, H2S, H2, NO2 and O2 were monitored for a complete annual cycle. The parameters like CH4, CO, H2S, NO2 and O2 were identified at both the dumpsites. The CH4 and NO2 were within the permissible limits during the study period. The CO and H2S emissions were high during the study period. H2 and NH4 were below detection levels during the study period at the dumpsites. As both the dumpsites were near to each other i.e. within a distance of 4 Kms, same trend of changes in the emissions were observed during the study period. The research carried out has shown clearly that the gaseous emissions at the dumpsites are affecting the nearby local communities living near the dumpsites. Even though the pollutants are presently within the standard limits, there is possibility of the accumulation of methane in the near future due to urban sprawl. The dumping of MSW if continued in same manner it will slowly result in exceeding of pollution levels above standard limits of residential area. The gaseous emissions were high during the summer season and considerably less during the winter and rainy seasons. Greenhouse effect of methane, CO2 and other trace gases, result in long-term detrimental effects on human health. People living in surrounding areas complain about fires over the dump site, smoke, smell and unhygienic conditions during monsoon on approach roads and water pool in surrounding low laying areas. The dumping activities of MSW must be far away from the residential areas to minimize the impacts. Littering of waste at the dumpsites is resulting in high concentrations of H2S and NO2 emissions which must be strictly prohibited. A sustainable approach to the MSW in the dumpsites is the present need in every developing country. Acknowledgements One of the authors (Shaheda Niloufer) is thankful to DST for the financial assistance in the name of women scientist project with title “Effects of Municipal Solid Waste on ground water quality and gaseous emissions –

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Equal Area Criterion Scheme to Reduce DC Bus Voltage Stress of Single Stage Single Switch Power Factor Corrected Converter Bindu S J and C A Babu Department of Electrical and Electronics Engineering School of Engineering, CUSAT, Kochi, Kerala, India Abstract: Single Stage Single Switch Power Factor converter topology is selected in such a way that input PFC stage is Discontinuous Current Mode (DCM) boost converter and output stage is Continuous Current Mode (CCM) fly back converter providing wide bandwidth response. Major problem associated with the converter is strong dependency of dc bus voltage stress with the output load. A design solution to avoid this problem is presented by suitable selection of boost inductor using Equal Area Criterion (EAC) and by implementing simple closed loop control. Design, simulated studies using Orcad and experimental verifications have been performed. Problem of voltage stress was found almost eliminated. Keywords: EAC, S4SMR, Switch mode rectifier, power factor converter, PFC I. Introduction Line current harmonics are injected to the electrical network by non-linear loads connected to the network, and are multiples of 50Hz. Common examples of such devices found in industrial environments include variable frequency drives, welders, switched mode power supplies, battery chargers, UPS systems, computers, electronic lighting etc. Usually power converters use a diode rectifier followed by a bulk capacitor to convert AC voltage to DC voltage. Conventional diode rectifiers used in electronic equipment draw pulsed current from the utility line. Consequently the power factor becomes poor (0.5- 0.6) due to high harmonic distortion of the current waveform. The simple solution to improve the power factor is to add a passive filter which is usually composed of a capacitor and an inductor. However, this passive filter is bulky and inefficient since it operates at line frequency. Therefore, a power factor correction stage has to be inserted to the existing equipment to achieve a good power factor. A typical switching power supply presents a nonlinear load to the power source. The high peak current drawn from the line is due to the small conduction angle. Since these power converters draw pulsed current from the utility line the power factor becomes poor due to high harmonic distortion of the current waveform. Therefore, a power factor correction stage has to be inserted to the existing equipment to achieve a good power factor. Several standard and review articles in the literature have addressed power quality related issues in AC to DC converters. New configurations of power factor corrections are being developed to mitigate the harmonic effects on the input line current and improve the power factor. IEEE 519 and IEC 61000-3-2 have being developed to specify the limits of harmonic pollution levels to acceptable levels that can occur on the system. The new family of power factor corrected switching power supplies normally consists of two stages in the power circuit, viz. â&#x20AC;&#x201C; The input power factor correction stage and the output DC to DC converter stage. Continuous efforts to further make these power converters compact and cost effective too lead to the development of new class of power supplies known as Single Stage Single Switch Switch Mode Rectifier, which is the integration of PFC stage and the DC to DC converter stage. It uses only one switch and controller to shape the input current and regulate the output voltage. The energy storage device in between is necessary to absorb and supply the difference between the pulsating instantaneous input power and constant output power [1]. A major problem associated with Single Stage Single Switch power factor converter is strong dependency of DC bus voltage stress across the capacitor with the output load [1], [2]. Power unbalance between PFC stage and DCDC stage is the inherent reason for causing high DC bus voltage stress. Frequency control is other solution proposed to overcome high DC voltage stress [13]. But this call for complex control circuit. The concept of series charging,

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parallel discharging capacitor scheme is another solution.[14].But this call for more component count in the power circuit In this paper a design solution is proposed to avoid the problem of energy unbalance between energy stored during on period of switch and energy dissipated in the load by optimally sizing the boost inductor. Maximum energy stored in the inductor shall be limited to such a value that this energy matches with maximum output power required. The instant at which maximum power delivered shall be matched with the instant when the input ac voltage is at the peak. Also consider the fact that maximum power is delivered at a duty ratio which is slightly less than the limiting duty ratio (0.5) for DCM operation. Equal Area Criterion is applied between theoretically calculated fundamental component of input ac current and the peak inductor current when ton is maximum. Using this approach the design was carried out, and simulated testing as well as experimental observation showed only a very small rise in DC bus voltage at light load condition, even under open loop. After introducing closed loop control with output voltage as controlled variable and duty ratio as manipulated variable the DC bus voltage stress was found almost insignificant. II. The Bifred Converter

Figure 1: The BIFRED PFC ac/dc converter One of the basic configurations of single stage single switch SMR is the BIFRED converter which is the acronym for Boost Integrated with Fly Back Rectifier / Energy storage / DC- DC converters which is shown in Fig.1.It integrates a DCM boost converter with dc-dc converter. When S is turned on, rectified line input voltage appears across the boost inductor and the output magnetizing inductor stores their energy independently during the on interval of the switch. When the switch is turned off, the stored energies are delivered to the bulk capacitor and to the load. Under light load condition the PFC stage without realizing this, stores the same energy as that of the heavy load leading to an unbalanced power between the input and the output [1]. This unbalanced power gets stored across the bulk capacitor causing the dc- bus voltage to increase. One way to take care of this problem is through closed loop control which will automatically reduces the on duration of the switch by sensing the output voltage thus by striking a power balance. But the dynamic response of the system being poor this method is found not so attractive [2]. III. PFC Converter With Dc Bus Voltage Feedback

Figure 2: Converter with power stage negative feedback An alternative method was proposed to use a negative feed back scheme in the power stage instead of in the control loop [2]. Fig 2 shows this scheme. A negative feedback voltage Vf is obtained by using a feed back winding coupled with the isolation transformer. This will make the resultant voltage available across boost inductor less when the DC-bus voltage increases, thus putting limit on to the input power drawn and by striking a power balance. AIJRSTEM 14-321; ÂŠ 2014, AIJRSTEM All Rights Reserved

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IV. The Proposed Single Stage Single Switch Pfc Converter

Figure 3: The Proposed Single Stage Single Switch PFC converter Proposed converter, shown in Fig. 3, is a modified BIFRED converter, which avoids the use of D1 and the negative voltage feedback Vf. Equal area criterion (EAC) is applied to achieve optimum design of boost inductor, coupled with a closed loop control with output dc voltage as controlled variable and duty ratio as manipulated variable so as to eliminate the problem of dc bus voltage stress at light load. V. Design Of PFC Stage By Equal Area Criterion.

Figure 4: Input current pulse superimposed on reference current. The rectifier input current is discontinuous in nature. A typical input current pulse superimposed on the reference current Im Sinωt, is shown in Fig. 4. T = t0n + toff + t3 ωton - On period of boost switch. ωtoff - Off period of boost switch. ωt3 - Non-conducting period (dead period). α - Instantaneous switching angle AIJRSTEM 14-321; © 2014, AIJRSTEM All Rights Reserved

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EAC applied to single stage single switch power factor converter means equalizing the area under a sinusoidal reference current and the area under the input current in the total period of one switching cycle [4]. A. EAC applied to design of boost inductor for the proposed single stage single switch power factor converter Magnitude of the reference current is selected such a way that-

Pout  Vrms  I rms .ref . Instantaneous current ir in on mode of boost switch is,

ir  I1 +

Em [ cos   cos ( +  t )]  L1

(1)

Where α<ωt<ωton

ir in off mode, ir  I 2 

Em (V + nV2 ) (cos    ton )  cos( +  ton   t )  dc t  L1  L1

I1 = 0 E During on time, ir  m [cos   cos(   t )]  L1 Off mode current becomes zero at  t   toff

(2)

At the beginning

I3  I2 

 L1

[cos (   ton )  cos( +  ton   t )] 

(Vdc + nV2 ) t  L1

Where

 < t < ton

(3)

Where

 < t < toff

(4)

B. Design of Boost Inductor. At the end of on duration I 2 is maximum ( I 2 peak ).

I 2 peak occurs at  = 90˚ and duty cycle is maximum. The off duration followed by this I 2 peak will be minimum. Value of L has to be selected in such a way that current at the end of this minimum off duration is zero. From (3)

I 2 peak 

Em sin  ton  L1

(5)

sin ton  ton since switching frequency is high. E E E I 2 peak  m  ton  m ton  m DT  L1 L1 L1 L1 

Em I 2 peak

DT Where D is duty cycle

(6) (7)

C. DC bus voltage, output voltage and Duty ratio. From (4), (5) with

I 3 =0 and assuming  t3  0

E (V + nV2 ) 0  I 2 peak  m (  sin ton  sin ton  toff )  dc toff  L1  L1 (8)

E E (V + nV2 ) 0  m (sin ton  sin ton )  m sin(ton  toff )  dc toff  L1  L1  L1 (9)

(Vdc + nV2 ) E toff  m (ton  toff ) L1 L1

(10) (11) (12) Page 77

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ton (Vdc + nV2  Em ) = T (Vdc + nV2 ) Em D  1 (Vdc + nV2 ) E Vdc  nV2  m 1 D

(13) (14) (15)

VI. Design Of Output Converter Stage[8]. A. Voltage transfer function for fly back converter. Always volt second balance should be there. Primary Volt sec/turn= Sec volt sec/turn.

DT (V2  Vd )(1  D )T  N1 N2 N2D V2  V1 N1 (1  D) (V1  Vswic )

(16) (17)

B. Voltage Transfer function of single stage single switch power factor converter We can write output voltage V2 as

Em 1 D  nV2 ) 1 D n (1  D) Em D V2  n(1  D) V2  (

(18) (19)

C. Design of fly back converter. For Volt second transformer balance

(V1  Vsat ) D  n(V2  V f )(1  D)

(20)

At critical inductance Lc , the peak inductor current is twice the average.

I p  2IL  Ip 

VL2 Ton LC

VL2 D

f s LC I p f s LC  VL2 D

(21)

(22) (23)

we have

nI P (1  D) V2  2 R 2V2 IP  R(1  D)n

i2( avg ) 

(24) (25)

from (21), (25)

LC 

(V2  V f ) R(1  D)2 n 2 2V2 f s

(26)

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VII. Design of A 100 Watt, 230 V, 50 Hz, 50 V Dc Single Stage Single Switch Power Factor Converter Calculation of ‘ L1 ’ using EAC.

Switching instant is considered as  = 90

f s  20kHz , D  0.26 Pout  Vrms  I rms 100  I rms   0.4348 A 230 I m  0.6148 A Value of I peak is calculated using EAC as follows 1  0.456  50  106 I peak  I m   2 1  0.456  50  106 I peak  50  106  0.6148 I peak  2.696 A 2

 L1 

DTEm 0.26  50  106  230  / 2  I peak 2.696

B. Calculation of

 1.57mH

LC is calculated for D = 0.26 Using (24), (33) LC  1.2mH C. Calculation of energy storage capacitor.

I peak 

DTEm L1

0.45  50  106  230  2 1.57  103  4.66Amp 1 1 Energy Stored = L1 I 2   1.57  4.662  17 J 2 2 1 2 Energy Stored = C1V 2 2 2 C1V  L1I =

C1  5402  1.57  103  4.662

C1  116 F VIII. Relationship between ‘D’ and Load We have Ip= =

Vo=

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2Lf=Rn2(1-D)2 R= n2(1-D)2= n(1-D)= 1-D= D= 1 From the above equation we can conclude that for a given circuit duty ratio is function of load. IX. Simulation Results. Simulation of the proposed single stage single switch power factor converter with the designed value of circuit parameters was carried out using Orcad software package. Simulation results were found meeting the design intends.

Figure 5: Shows that Input PFC converter works in DCM

Figure 6: Shows that DC-DC converter operates in CCM Under closed loop condition when load is suddenly reduced due to the instantaneous power unbalance, the dc bus voltage and output voltage tend to increase. Fig. 7 shows the increase in output voltage is immediately detected by the controller and duty cycle is automatically reduced, the closed loop is found taking the corrective action leading to a new energy balance.

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Figure 7: Shows the automatic reduction in the magnitude of input current under closed loop control when the load is reduced after 35ms

Figure 8: Shows that under open loop there is substantial increase in output voltage when load is reduced after 45 ms also very small changes in the dc bus voltage and no change in input current is seen. Fig. 8 shows under open loop condition input current does not know what happens at the output converter.

Figure 9: Shows harmonic spectrum of input current in closed loop

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Fig.9 indicates fundamental frequency of 50 Hz is dominant and higher order components are insignificant X. Experimental Results Experimental 230V, 50Hz input, 10-100V dc, 100w single stage single switch power factor converter has been built and tested using MOSFET IRFPF50 as switch, to verify the results obtained during simulated test. Experimental results are found in line with the results obtained during simulation when tested in open loop as well as closed loop condition. Fig. 10 shows sinusoidal nature of input line current and input power factor close to unity.

Figure 10: Shows that inductor current drawn is sinusoidal in DCM operation of PFC stage. XI. Conclusion Single stage single switch power factor corrected converter design by applying EAC to determine value of boost inductance and by using closed loop control is presented. The dc bus voltage stress at light load is found completely eliminated under closed loop operations. Output voltage regulation using duty ratio variations and fixed switching period is the most simple method of control. For normal performance of the converter the duty ratio needs to be limited up to 0.5. Experimental results demonstrate that it is possible for the proposed converter to have fast response and low line current harmonic content. XII. References [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8]. [9]. [10]. [11]. [12]. [13]. [14]. [15]. [16].

M. Madigan, R. Erickson and E. Ismail, "Integrated High Quality Rectifier Regulators" in IEEE power Electronics Specialist Conf. 1992. p.p. 1043 - 1051. Jinrong Qian, Fred C. Lee, "Single - Stage Single - Switch p -f-c Ac/Dc converters with DC - Bus voltage feedback for universal line applications"in IEEE transactions on P.E. vol: 13, No-6 Nov 1998 p.p. 1079 – 1088. Esam Hamid Ismail, Robert Erickson, "Single Switch 3 phase PWM low Harmonic Rectifiers" in IEEE Transactions on P.E. vol: 11, No-2, march 1996 p.p. 338 – 345 Manjusha S. Dawande, Gopal K. Dubey, Programmable Input PFC method for SMR" in IEEE Transactions on P.E. vol: 11, No-4 July 1996 p.p. 585 – 591. A.R. Prasad, P.D. Ziogas. Stefanos Manias, "An active PFC Technique for 3 phase diode rectifiers" in IEEE Transaction on P.E. vol: 6, No-1, January 1991 p.p. 83 – 91. Richard, Balogh, Nathan O.Sokal, “A new family of single stage isolated PFC with fast regulation” in PESC - 94. p.p. - 1137 – 1143. Yimin Jiang and Fred C. Lee, "Single - stage Single - Phase parallel power factor correction scheme" in PESC – 94 p.p. 1145 - 1149 “DC -DC Switching regulators” by D.M. Mittchel C Qiao and Keyue M. Smedley, "A Topology Servey of Single - Stage P.F.C. with a Boost Type input Current - shaper." In IEEE APEC - 2000 February 6 – 10. Stefanos Manias, Phoivos D. Ziogas and Guy Olivier "An AC to DC converter with improved input power factor and high power density"in IEEE Transaction on I.A., vol: IA - 22, No - 6 Nov/Dec 1986. p.p. 1073 – 1080. Manjusha S. Dawande, Vilas R. Kanetkar and Gopal k. Dubey "Three-Phase Switch Mode Rectifier with Hysterisis Current control" in IEEE Transactions on P.E., vol: 11, No-3, May 1996 p.p. 446 - 471. Design of Magnetic Components for Switched Mode Power Converters by L. Umanand and S. R. Bhat, p37, p66-69. Martin H. L. Chow , Yim-Shu Lee, and Chi K. Tse “Single-Stage Single-Switch Isolated PFC Regulator with Unity Power Factor, Fast Transient Response, and Low-Voltage Stress” in IEEE Transactions on P.E., vol: 15, No-1, Jan 2000 p.p. 156 – 163. A.K Jha,B.G Fernandes and A.Kishore “A Single Phase Single Stage AC/DC converter with high input power factor and tight output regulation”in Progress in Electromagnetic Research Symposium 2006,Cambridge,USA,March 26-29.pp 322-328 Mohan ,N.,T.M. Undeland and W.P.Robbins, Power Electronics, Converters, Applications, And Design,2nd Edition ,John Wiley and Son,Inc. Newyork,1995. Oscar 18]Oscar Garcia,Jose A. Cobos,Pedro alou , Roberto Prieto,snd Javier Uceda, "A Simple Single-Switch Single-Stage AC/DC Converter with Fast Output Voltage Regulation " in IEEE Transactions.on Power Electronics,, Vol.17 ,No.March 2002.

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

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

Synchronization Response of an Indirectly Coupled Nonlinear Digital Resonator -A Simulation Study S Chakraborty 1 and B C Sarkar 2 Physics Department, B C College, Asansol 713304, West Bengal, INDIA 2 Physics Department, Burdwan University, Burdwan 713104, West Bengal, INDIA 1

Abstract: Connecting two nonlinear digital resonators (NDR) in an indirect coupling configuration (DriverResponse), the synchronization scenario of the response NDR has been explored through numerical simulation. In this paper, the frequency control parameter of the response NDR is tuned with the help of an error signal generated by comparing the phases of the driver and the response NDRs. It has been observed that for a specific range of design parameters of the response NDR, the synchronization between two NDRs is possible for both periodic and chaotic modes of oscillation of the driver NDR. Keywords: Nonlinear Digital Resonator; Tracking; Chaos; Auxiliary system; synchronization; I. Introduction In modern communication technology, the preference of digital systems compared to their analog counterparts is well documented in literature [1-3]. Digital systems have flexibility in design, reliability in long time application, negligible dc drift and easy implementation possibility in IC technology. So researchers and the system designers are interested in designing different digital systems which could perform the jobs of already available analog systems [4, 5]. In digital signal processing digital filters are most important elements. They find application in removing undesirable co-channel signals and additive noise, in spectral shaping etc. [1]. Digital Resonators (DRs) belong to a group of second order digital filters that exhibit natural oscillations depending upon its design parameter values and they can be used as signal sources. DRs are used as digital frequency synthesizers [1]. However, they cannot be synchronized to external signal as they are basically linear systems. Modifications of the DR with the introduction of controlled nonlinearities in the system make the system synchronizable to an external signal. Moreover presence of nonlinearity would enrich its dynamics. As for example the system exhibits bifurcation, chaos, quasi-periodicity etc. A discrete equivalent of DR having the nonlinearities of a Vander pol oscillator has been proposed by the authors [6]. In this paper, we consider two such nonlinear digital resonators (NDRs) in a uni-directionally coupled configuration and study the synchronization performance of the second NDR (consider as response system) both in stable periodic oscillation and chaotic oscillation of the first NDR (consider as driver system). Here the dynamics of the response NDR is controlled by parameter tuning with the help of an error signal generated by comparing the phases of the driver and the response NDRs and the principle of conventional phase locked loops is being used here for that indirect coupling. The design parameters of response system could have non-identical values from that of the driver. We have studied the synchronization response of the response NDR in a generalized sense and for this purpose, the auxiliary system method of nonlinear dynamics [7-9] have been followed. The paper has been organized as follows. In Section II, the difference equations describing the system dynamics for an isolated NDR and a cascaded NDR system have been formulated. The difference equations in terms of some error variables have been formulated at the end of this section to study the generalized synchronization (GS) between the driver and the response NDR. The results of numerical simulation on the dynamics of cascaded system have been given in Section III. A brief of simulation results regarding the dynamics of single NDR in presence of external forcing signal are given in first part of section III for completeness of discussion. Finally, we concluded with our findings and future implementation possibilities of the work in section IV. II. System equation formulation The dynamical behavior of a NDR has been studied in [6] numerically with the help of difference equation that describes the time evolution of the system. For the sake of completeness, we include the description of the structure and system equations of that work in subsection A and then we describe the structure of the cascaded system and formulate the corresponding system equations in subsection B. The subsection C describes the “auxiliary system” method through which the GS between two NDR is examined.

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A. System equation of the NDR: The block diagram of a conventional DR is shown in Fig.1. The system equation that describes the dynamics of a DR in terms of design parameter c1 and c2 is given by,

y[n]  c1 y[n  1]  c2 y[n  2]

(1)

The transfer function of this resonator in z domain is,

H ( z) 

z2 z 2  c1 z  c2

(2)

Thus, it is a second order system. One can easily understand the dynamics of such system by finding the poles and zeros from equation (2). The poles of the transfer function may be either real or complex depending upon c1 and c2 . Since we are interested in oscillatory behavior of the system, we use the complex poles only. The two complex conjugate poles could be written as,

( p, p * )   Here, j 

c1 j  4c2  c12 2 2

(3)

1 . If we represents the complex poles of the system as,

p  re jT

(4)

Where, r ,  and T are the amplitude, angular frequency and the sample interval of the digital system respectively. Then the design parameters

c1 and c2 can be expressed in terms of r ,  and T as follows,

c1  2r cos T and, c2  r

(5)

(6)

With the help of equation (5) and (6), the unit impulse response of the system obtained from (1) is written as,

rn y[n]  sin[(n  1)T ] sin(T )

(7)

Therefore, the system is oscillatory. If we put r  1 we can get sustained oscillation in a DR. Thus, the parameters c1 and c2 should be as follows for sustained oscillation,

c1  2cos T and, c2  1 The frequency of the oscillatory DR system is determined by

(8a) (8b)

c1 and has the range -2< c1 <2. Since c1 must be

less than 2 for oscillation, so for sustained oscillatory behavior of the system we have,

c1  c2  1 .

It is well known that one can obtained the system equation of a Vanderpol oscillator by adding the nonlinear term  (1  y ) 2

dy to the equation of un-damped harmonic oscillator. Following the same we add a discrete dt

equivalent of the above term to the DR and we get the following two first order difference equations that describe the discrete time Vanderpol oscillator. (9a) y[n  1]  z[n] (9b) z[n  1]  (a   y 2 [n]) z[n]  (b   y 2[n]) y[n] Where, a  c1   (10a) b  c2   (10b) Therefore, a and b are the frequency determining and oscillation controlling term of the NDR respectively. To get sustained oscillation in the system we have a  b  c1  c2  1 . The block diagram of such NDR represented by equation (9) is given in Fig 2. Note that, putting   0 one can obtain the equation of DR.

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Figure 1 Block diagram of a conventional DR B. System equations for two indirectly coupled NDRs: Fig 3 shows the Driver-Response configuration of two NDR. Note that state variables and the design parameters are suffixed by 1 to indicate driver NDR and by 2 for that of response NDR. Both the systems have a common input signal (i.e. x1[n]  x2 [n]  x[n] ) which is a unit impulse to initiate the oscillation. In Fig 3 the indirect coupling section consists of a multiplier type phase detector (PD) and a first order low pass filter (LPF). This section produces an average of the instantaneous error signal between the output of two NDRs (driver and response). The frequency determining term ( a2 ) of the response NDR is then modified by this error signal. Here a first order digital LPF is used for averaging having transfer function as follows,

H ( z) 

(1  k ) (1  kz 1 )

(11)

Therefore, one can derive the error signal as,

e[n  1]  ke[n]  (1  k ) z1[n]z2[n] Here, n denotes the sampling instant,

(12)

z1 and z2 are the output state variable of the driver and response

k is the design parameter of LPF. In the present case, the frequency parameter of the response i.e. a2 is made to vary with the error signal e[n] . Thus, the proposed algorithm modifies the a2 by the following

respectively. relation,

a2 (1  pe[n])

(13)

Here, p is a parameter used to quantify the strength of the error signal. Using this we derive the system equation that describes the system dynamics as,

y1[n  1]  z1[n]

(14a)

z1[n  1]  (a1  1 y12 [n]) z1[n]  (b1  1 y12[n]) y1[n]  x1[n]

(14b)

y2 [n  1]  z2 [n]

(14c)

z2 [n  1]  (a2 (1  pe[n])   2 y22[n]) z2[n]  (b2   2 y22[n]) y2[n]  x2[n] (14d) Here, a1 , b1 , 1 are the frequency controlling, the oscillation controlling and nonlinear gain parameters of the driver respectively whereas, b2 and  2 are the oscillation controlling and nonlinear gain parameters of the response NDR.

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Figure 2: Block diagram of Nonlinear Digital Resonator.

Figure 3: Block diagram of proposed indirectly coupled System. C. Formulation of error equations for auxiliary system method: The GS can be examined with the help of auxiliary system method. GS state that, the state variable of response system would be a function of corresponding state variable of driver in synchronized condition. Thus in this case, z2= f(z1), f(.) stands for general function. In auxiliary system method, we consider an auxiliary response having identical design parameters with that of the original response system. But their initial conditions are different from each other. Now when both the responses are driven by same driver NDR and in steady state, the errors between corresponding state variables of the responses become zero or constant with time, one can conclude that the responses follow the driver in identical manner. This is the condition of GS. '

If the state variables of the auxiliary response are x2 [n] , y2 [n] and z2 [n] then we define a set of error variables as

ex , e y and ez as, ex [n]  x2 [n]  x2' [n] , ey [n]  y2 [n]  y2' [n] and ez [n]  z2 [n]  z2' [n] .

Using equation set (14) one can obtain the following time evolution equation for error variables,

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ex [n  1]  0 ey [n  1]  ez [n  1]

(15a) (15b)







ez  n  1  a2ez  n  1  pa2 e  n  z2  n   e'  n  z2'  n    2 y22  n  z 2  n   y2' 2 n  z 2' n 





b2ey [n]   2 y23  n   y2' 3  n   ex [n]



(15c)

Using these equations we obtain a zone of design parameter for synchronization through numerical simulation. III. Numerical simulation Results The dynamics of the isolated system and the cascaded system is studied through numerical simulation of equation set (9) and (14) respectively. The dynamics is examined by observing the time series of state variable z[n] and phase plane plot of z[n] and (z[n]-z[n-1]). In all cases a large number of initial data are discarded to eliminate the transient response. The isolated system shows oscillation whose frequency depends on c 1. However, if we put x[n]  dsin(nf 0 ) i.e. the system is driven by some external forcing signal, the system enters into chaotic regimes through period-1, period-2 and period-4 oscillation for increasing value of d with properly chosen value of system design parameters. Here d and f are the strength of the external signal, the ratio of the frequency of the external signal to that of the natural frequency ( 0 ) of the DR. All these dynamics are verified by doing FFT over 7000 time series data of z[n]. In case of chaotic oscillation a broad spectrum is obtained. This observation is shown in Fig 4. Now, we study the tracking property of the response NDR when the driver NDR is in different periodic oscillation modes (period-1, 2 and 4) and also in chaotic mode. To study the synchronization in period-1 oscillation state, both the NDR are initiated by impulse input having different design parameter values. Different frequency control parameter value gives that the NDRs are oscillate at two different frequencies. Thus when the two systems are not synchronized or isolated from each other, the error signal that produces at the filter output shows the frequency difference between two systems but at synchronized condition such difference diminishes. Fig 5 shows such unsynchronized and the synchronized condition of the response NDR. The first part of the figure shows the time series plot of error, driver and response output signal whereas in the second part we plot the z1 and z 2 state variables along x and y axis respectively. Our observation shows that there is no relationship

z1 and z2 state variables in unsynchronized case and the e[n] shows the frequency difference in time series plot, but in synchronized case z 2 follows the z1 with some constant phase difference and no frequency difference is observed in time series plot of e[n] . Note that filter cannot suppress the double frequency term of between

error signal. Response NDR gives a reasonable amount of frequency lock range over which it tracks the driver NDR and the range is increased as we increase p and  2 . These facts are shown in Fig 6(a) and 6(b) respectively. Numerical observation shows that the effect of p is better than that of

 2 to

increase the lock

range. This is because, p has a direct impact on a2 . To study the synchronization in higher dynamics, the driver NDR is forced by a sinusoidal external signal of the form x[n]  dsin(nf 0 ) as stated before and its design parameters are set to a critical value. The same input is applied to the response NDR but its design parameters are so chosen that it remains in period-1 oscillation throughout the observation. Under such condition when the strength of the external signal increased gradually the system dynamics of driver NDR enters into the chaotic regime through period doubling. Phase plane plot of the driver and the response in Fig 7 shows that the response NDR follows the driver in all cases such as period-2, 4 and chaotic state of oscillations. But the plot of z1 and z 2 state variables along x and y axis respectively shows that there is no one to one correspond between driver and response. The response follows the driver in some complex manner. So they are not in identical synchronization but show a complex phase relationship between them. The error equations (15a)–(15c), formulated using auxiliary method, are numerically solved to obtain the range of system parameter values for which the response NDR follows the driver. We find out the value of d and  2 leading to zero or a constant value of error variables by numerical solution of (15) with particular value of other design parameters. Then plot them in the d -  2 plane. The result is shown in Fig 8. The obtained result shows that the tracking capability of the response NDR gradually decreases as the driver NDR enters into the chaotic

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region with the increase of d and response NDR follows the higher dynamics of driver for higher value of nonlinear term. The results are consistent with the result obtained in analog system. IV. Conclusion The tracking ability of indirectly coupled response NDR has been studied in this paper. A response NDR is able to track the output of a driver NDR with some constant phase difference when both are in periodic oscillation state. The error signal strength is more dominating parameter to increase the frequency lock range than the nonlinear parameter of the response NDR system. Simulation results also confirms that a response NDR is capable to track the higher dynamical states (i.e. period-2,4, chaos etc.) of the driver NDR for critically chosen design parameter value. A reasonable zone of synchronization has been obtained using the auxiliary method. Thus, such system can be used as a synchronizer and can be used in digital communication system. In future the experimental studies are necessary for implementation; it may also be used in chaos synchronization communication. V. References [1] [2] [3] [4] [5] [6]

[7] [8] [9]

J. G. Proakis, D. G. Manolakis; Digital Signal Processing Principles, Algorithms, and Applications, 3rd Ed , Prentice Hall of India Pvt. Ltd., New Delhi, 2004. G. Zhang, X. Chen, T. Chen, “Performance Comparison of Digital Implementation of Analog Systems”, Proceedings of the 46th IEEE Conference on Decision and Control, New Orleans, LA, USA, Dec. 12-14, 2007, pp.785-790. R. Pandey, K. Pandey, “An Introduction of Analog and Digital Modulation Techniques in Communication System”, Journal of Innovative trends in Science, Pharmacy & Technology, vol.1(1), 2014, pp.80-85. N. Rafee, T. Chen, O. P. Malik, “A Technique for Optimal Digital Redesign of Analog Controllers”, IEEE Transactions on Control Systems Technology, vol.5(1), January 1997, pp.89-99. W. C. Lindsey, C. M. Chie, “A Survey of Digital Phase-Locked loops”, Proceedings of the IEEE, vol.69(4), April 1981, pp.410431. S. Chakraborty, B. C. Sarkar, “Dynamics of a nonlinear digital resonator in free running and injection synchronized mode: A simulation study”, International Journal of Engineering and Advanced Technology (IJEAT), ISSN: 2249 – 8958, vol.1(6), August 2012, pp.94- 98. L Kocarev, U Parlitz, T Stojanovski, “Generalized synchronization of chaotic signals”, NOLTA’95. Las Vegas, USA; Dec 10– 14, 1995. HDI Abarbanel, NF Rulkov, MM Sushchik, “Generalized synchronization of chaos: the auxiliary system approach”, Phys Rev E, vol.53(5), 1996, pp.4528–35. BC Sarkar, S Chakraborty, “Self-oscillations of a third order PLL in periodic and chaotic mode and its tracking in a response PLL”, Commun Nonlinear Sci Numer Simulat , vol.19(3), March 2014, pp.738–749. http://dx.doi.org/10.1016/j.cnsns.2013.07.003

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Figure 4: Numerical simulation results, showing the period doubling route to chaos of NDR under study, with system parameters

  0.11, a  2   , b  1.01   , f  2.28 and strength of the external forcing signal is slowly increased; (a) d = 0.09, (b) d

= 0.1, (c) d = 0.103, (d) d = 0.11. Here (1) is the phase plane plot of z[n] and (z[n]-z[n-1]) and (2) is the power frequency spectrum of z[n].

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Figure 5: Numerical simulation results, showing the (a) unsynchronized and (b) synchronized state of response NDR (tracking ability) when driver NDR is in periodically oscillating state. Here (1) represents the time series plot of state variable e[n], z1[n] , z2 [n] and (2) represents the x-y plot of

z1[n] - z2 [n] with parameter values 1  0.05, b1  1.06 ,  2  0.05, a2  2.05, b2  1.06 ,

p  0.001, k  0.99 and in (a) a1  2.054 ,in (b) a1  2.053 .

Figure 6: Numerically obtained frequency lock range (a) with respect to the strength of the error signal. The parameter values are 1

 0.05, b1  1.06,  2  0.05, a2  2.05, b2  1.06, k  0.99

parameter values are 1

(b) with respect to the nonlinearity of the response NDR. The

 0.05, b1  1.06, a2  2   2 , b2  1.01   2 , k  0.99, p  0.001

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Figure 7: Numerical simulation observations, showing the tracking ability of the response NDR when the output of the driver NDR is in (a) period-2 (b)period-4 and (c) chaotic state. The system parameters of the driver NDR are: the response NDR are:

a1  2.1, 1  0.1, b1  1.11 and those of

a2  2.05,  2  0.05, b2  1.06 . The other parameters are k  0.99, p  0.0005 .The dynamics

of the driver NDR is varied by increasing the external signal strength as (a) d = 0.1, (b) d = 0.11, (c) d = 0.125 with f = 2.28.

Figure 8: Numerically obtained solution points of Eq. (15) indicating the synchronized state of the response NDR to the output of the driver NDR. Here the driver NDR gradually goes into chaotic state through period-2, 4 with increasing d. The other parameters are 1

 0.11, a1  2  1 , b1  1.01  1 , a2  2   2 , b2  1.01   2 , k  0.99, p  0.001, f  2.28 .

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Studies on Spectral and Antifungal Activity of Some Complexes of Chromium, Nickel and Copper Metals with p-phenylenedibiguanide [C10H16N10, Ph (BigH)2] Molecule R.K. Prasad1, Bina Rani2 & Madhu Kumari Gupta3 Department of Chemistry,Science College, Patna University,Patna – 800005, Bihar , India 2 Department of Chemistry,Magadh Mahila College, Patna University,Patna, Bihar, India 3 PGT (Chemistry),Kendriya Vidyalaya,Bengdubi, Darjeeling,West Bengal.India

Abstract: These complexes have been found to be effective in controlling the growth of aspergillius versicolor in PDA (Potato Dextrose Agar) and SDA (Sabourand and Dextrose Agar) mediums. Some metal complexes of p-phenylenedibiguanide [Ph(BigH)2 , C10H16N10] with metals: Chromium (Cr), nickel(Ni) and copper (Cu) have been synthesized and isolated in the pure state. These metal complexes have been found effective in inhibiting the growth of fungi named, Aspergillus niger and Aspergillus versicolor. The metal complexes of p- phenylenedibiguanide [Ph(BigH)2, C10H16N10] ligand were dissolved in DMSO and were tested with Dilution test method and growths were separated and examined under different concentrations of the afore said complexes in PDA (Potato Dextrose Agar) and SDA (Sabourand and Dextrose Agar) medium. The data of growth inhibition of the fungi so obtained were then compared with those of different metal complexes.. Keywords: Aspergillius versicolor, Aspergillus niger, p-phenylenedibiguanide, PDA, SDA I. Introduction p-Phenylene dibiguanide1-2 behaves as a quadridentate molecule has been found to give as usual 4- coordinated copper and nickel complexes and only 6-coordinated dichromium tris-p-phenylenedibiguanide base. The elemental elucidation of the coordination complexes of p-phenylene dibiguanide with bivalent metal ions have been done till now, but its biochemical importance is still lacking. We have therefore, work on its antifungal property and compared this activity in two different fungus (Aspergillus niger 3-14 & Aspergillus versicolor15) under two different conditions while applying its different concentration To investigate this antifungal activities of quadridentate biguanide ligand p-phenylene dibiguanide ligand complexes, we have prepared the ligand and complexed with Ni and Cu metal and then done antifungal activity on it.

II. Materials and Methods The ligand and its complexes were prepared by the reported method as described below: A. p – Phenylene dibiguanide sulphate25 [C10N10H16]•2H2SO4 For its preparation p-phenylene diamine (10g) C6H4(NH2)2, dicyandiamide (17g) C2H4N4, 22 c.c. of 32% HCl and 25c.c. of H2O were heated under reflux for about 2 hours. The refluxing mixture was cooled and to this mixture 100 c.c. of alcohol C2H5OH was added. The solution was then boiled with 5g charcoal for a few minutes and filtered. The cold filtrate was then treated with 30 c.c. of dilute H 2SO4 (1:2 by volume) and kept for 2 or 3 days which deposits crystals of p-phenylenedibiguanidesulphate C10N10H16•2H2SO4. The crystals of the sulphate were filtered, washed first with cold H2O and then with alcohol & dried in air. Found

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N = 37.60% SO42- = 25.71% C10N10H16•2H2SO4 requires N = 37.43% SO42- = 25% B. Tris(p-phenylenedibiguanide)chromium (III) [Cr2 (C10N10H14)3]•5H2O The complex chromium base [Cr2 (C10N10H14)3]•5H2O was obtained as rose red precipitate by adding a solution of chrome alum to that of p- phenylene dibiguanide sulphate in caustic soda. The product was washed with ice cold water till free from sulphate then with alcohol and afterwards as dried over solid potassium hydroxide, KOH in a dessicator. The substance is moderately soluble water and reacts alkaline to litmus. It liberated ammonia from ammonium salt solutions. Found Cr = 10.15% N = 40.96%, H2O (by loss at 1050C) = 6.33% [Cr2 (C10N10H14)3]•5H2O requires Cr = 10.17% N = 41.09% H2O =8.85% C. p-Phenylenedibiguanidiumnickel (II) sulphate [Ni(C10N10H14)]SO4•2H2O It was obtained as a brick red flocculent precipitate when an ammonium solution of p-phenylene dibiguanide sulphate C10N10H16•2H2SO4 was treated with that of nickel sulphate, NiSO4. This was washed and dried as usual. The substance is very sparingly soluble in hot water but insoluble in alcohol. Found Ni = 12.30% N = 29.84% SO4 – 2 = 20.50% H2O (by loss at 1050C) = 5.68% [Ni(C10N10H14)]SO4•2H2O requires Ni = 12.57% N = 29.98% SO4 – 2 = 20.50% H2O = 7.70% D. p-Phenylenedibiguanidiumcopper (II) sulphate [Cu(C10N10H16)]SO4•2.5 H2O It was obtained as a violet coloured powder by adding an ammonical solution of p- phenylene dibiguanide sulphate to an ammonical solution of CuSO4. The product was filtered washed and dried as usual. It is very sparingly soluble in water. Found Cu = 13.20% N = 28.89% SO4—2= 19.90% H2O (by loss at 1050C) = 9.37% [Cu(C10N10H16)]SO4•2.5 H2O required Cu = 13.20% N = 29.10% SO4 – 2 = 19.97%, H2O =9.36% E. Preparation of PDA(Potato Dextrose Agar)26 Potato tubers were taken peeled off and weighed 200g. It was chopped into small pieces and transferred to a beaker containing about 100ml of distilled water and boiled for 20minutes and filtered with muslin cloth. 20g Dextrose, 15g agar and 2g peptone were added into the extract and gently heated. The filterate so obtained was made to 1 litre. The pH of the solution was maintained at 5.6 by using 1N HCl or NaOH and kept in Erlenmeyer flask. This solution so obtained was PDA medium and autoclaved at 121 0C for 20minutes before using. F. Preparation of SDA (Sabourand and Dextrose Agar Medium) 26 It was prepared by combining the ingredients water, dextrose, agar, peptone & antibiotics separately, in many different variations. In the case of using premix, the proper amount (around 70gms) was mixed with one litre of water and heated to dissolve the agar. pH of the medium was adjusted with one molar solution of hydrochloric acid to lower pH. The pH was maintained at 5.5. The medium was then autoclaved and stored at room temperature. The medium can be used to inoculate with fungal spores and mycelium inhibition growth was counted by usual method. The observation of the study is represented in the Table A.

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III. Result and discussion A. UV spectra p- phenylenedibiguanidium copper(II) sulphate 370nm CT 240 & 270nm CT 2 2 460nm B1g B2g p-phenylenedibiguanidiumnickel(II)sulphate 1 1 470nm A1g T2g 1 1 270nm CT + A1g Eg Tris –(p-phenylenedibiguanidine) chromium(III) 4 4 480nm A2g T2g 4 4 340nm A2g T1g 230nm CT 240nm CT B. IR spectra The IR band position of p-phenylenedibiguanide ligand and its complexes with copper, nickel and its complexes with Cu, Ni and Cr are recorded and probable assignments of IR bands have also been included in the table along with IR spectra report. This ligand contains ten nitrogen atoms capable of co-ordinate bond formation. The ligand is stable as sulphate in solid state and solution. The presence of > NH, is shown by peak at 3211.5, the >NH stretching in >N+H shows by peak at 2374.1, 2339.5 &2277.8 cm-1.The >NH deflection is shown at 1570.0 cm-1 and —NH2 bending at 1351.8cm-1 NH2 rocking is confirmed by peak at 971.4cm-1. The phenyl ring skeleton is confirmed by stretching at 1432.7 due to C=C str in aromatic ring. The presence of peak at 823.8 assumes the p – substituted benzene ring. The sulphate (SO4-2) shows stretching bands at 607.4 cm-1(υ4) and (υ3). It is reported that υ1 stretch is absent in ionic sulphate and υ3 and υ4 as strong band[13]. Further non-conjugated C = N stretching is shown by peak at 1570.0. The terminal C – N stretching is shown at 1052.8cm-1. The IR spectrum of complex hydroxide of Cu & Ni and halide of Ni shows the absence of IR vibrations associated with SO 42- group. The complex sulphate Cu pphenylenedibiguanidium sulphate, Ni p-phenylenedibiguanidium sulphate and Cr –p-phenylenedibiguanidium sulphate shows the characterstics of ionic sulphate molecule (sharp broad band at 1112.8 cm-1 & 618.3 cm-1 for Cu, 1113.2 cm-1 & 618.5 for Ni and 1115 cm-1 & 618cm-1 for Cr). The complex base shows a strong band at 1058.5 cm-1 in Cu p-phenylenedibiguanidium hydroxide which suggests the presence of υ – OH of uncoordinated (OH) molecule. In fingerprint region the IR spectra of complex. The biochemical properties16-24 e.g. antibacterial activities of biguanide and substituted biguanide complexes have been studied for a long time. But no work is done regarding the antifungal activities of biguanides and substituted biguanide complexes. Metal complexes of p-phenylenedibiguanide were dissolved in DMSO using the concentration which was most effective from Dilution test method. The inoculation of fungus was done in PDA & SDA medium at 250C and checked daily for a week. The fungi are Aspergillus niger & Aspergillus versicolor. Both are from ascomycetes group. The MIC was then calculated i.e. the minimum inhibition concentration of the fungus. During this process it was found that the complexes was effective in controlling 100% of the fungal growth if the concentration was raised to 800 μg/ml to 1000 μg/ml. The solutions was used in the ratio 1:10 and the concentration of the solution was 400μg/ml, 200μg/ml and 100 μg/ml. It was found that the complexes are more effective in controlling the growth of Aspergillus versicolor. If the growth is compared it was concluded that the growth inhibition is more in PDA medium. The chromium complex was more effective in fungal inhibition as compared to nickel and copper. IV. TABLE A Observations of the test Table I Percentage inhibition of growth of fungus Aspergillus versicolor at indicated dose % Inhibition of fungal growth Metal complex Tris(p-phenylenedibiguanidine) chromium (III) p-Phenylenedibiguanidium nickel (II) sulphate

p-Phenylenedibiguanidium Copper (II) sulphate

Concentration (μg/ml)

PDA Medium

SDA Medium

400 μg/ml

100%

99.20%

200 μg/ml

88%

86%

100 μg/ml 400 μg/ml 200 μg/ml 100 μg/ml 400 μg/ml 200 μg/ml 100 μg/ml

63.40% 98.5% 81.20% 59.51% 95.00% 82.00% 52.57%

57.4% 95.20% 79.25% 56.31% 90.12% 79.45% 50.91%

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Table II. Percentage inhibition of growth of fungus Aspergillus niger at indicated dose % Inhibition of fungal growth Concentration (μg/ml) PDA Medium

Metal complex

Tris(p-phenylenedibiguanidine) chromium (III) p-Phenylenedibiguanidium nickel (II) sulphate

p-Phenylenedibiguanidium Copper (II) sulphate

SDA Medium

400 μg/ml 200 μg/ml 100 μg/ml

96.97% 72.43% 51.57%

94.30% 68.14% 49.91%

400 μg/ml 200 μg/ml 100 μg/ml 400 μg/ml 200 μg/ml 100 μg/ml

95.80% 70.25% 50.73% 89.15% 67.21% 52.70%

84.11% 69.59% 48.45% 87.45% 65.08% 47.07%

Figure 1 Percentage inhibition of fungal growth of Aspergillus niger in PDA & SDA medium by Tris(pphenylenedibiguanidine)chromium (III)

*Concentration of the solution of the complex in μg/ml Figure 2 Percentage inhibition of fungal growth of Aspergillus niger in PDA & SDA medium by Nickel (II)p-phenylenedibiguanidium sulphate

Figure 3 Percentage inhibition of fungal growth of Aspergillus niger in PDA & SDA medium by Copper (II) p-phenylenedibiguanide sulphate

Acknowledgement We are thankful to the Dr.(Prof.) Rani Azad, Head of Dept. of Chemistry, Magadh Mahila College, P.U., Patna and all the staff members of the dept. for providing the instrumentation and laboratory facilities. I am very grateful to Dr. (Mrs.) Namita Kumari, Reader in Botany, Magadh Mahila College, P.U., Patna for her keen interest in the work and recognition of fungus in the laboratory. References [1]. [2]. [3].

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PHYTOREMEDIATION OF CADMIUM AND CHROMIUM CONTAMINATED SOILS BY CYPERUS ROTUNDUS. L Subhashini, V and A. V. V.S. Swamy Faculty, Dept. of Environmental Sciences, Acharya Nagarjuna University, Nagarjuna nagar- 522 510, Guntur, Andhra Pradesh, INDIA. Abstract: Toxic trace elements are increasing in all compartments of the biosphere including air, water and soil, as a result of anthropogenic processes. Environmental heavy metal pollution is mainly of anthropogenic origin and results from activities such as fossil fuels, vehicular emissions, industrial emissions, landfill leachates, fertilizers, sewage and municipal wastes. Phytoremediation consists of mitigating pollutant concentrations in contaminated soils, water, or air, with plants able to contain, degrade, or eliminate metals, pesticides, solvents, explosives, crude oil and its derivatives, and various other contaminants from the media that contain them. Cyperus rotundus is a perennial plant, commonly known as coco-grass or nut grass. A pot experiment was conducted using Cyperus rotundus for the removal of chromium and cadmium contaminated soils. In the experimental period Cyperus accumulated chromium and cadmium in their plant body. Bioconcentration factor was calculated. The BCF of cadmium was 44.18 and the BCF of chromium was 4.42. Based on BCF values the grass species was a good accumulator of cadmium and chromium and the grass species was recommended for remediation of cadmium and chromium contaminated soils. Key words: Phytoremediation, Heavy metals, Bioconcentration factor, Cyperus rotundus. I. Introduction Human activities such as mining, transport, agriculture, waste disposal and military actions frequently release these inorganic pollutants in high and toxic concentrations. Environmental heavy metal pollution is mainly of anthropogenic origin and results from activities such as fossil fuels, vehicular emissions, industrial emissions, landfill leachates, fertilizers, sewage and municipal wastes [1]. Heavy metal pollution causes potential ecological risk. Metals like Cadmium (Cd), Lead (Pb), Zinc (Zn) and Chromium (Cr) when present in high concentrations in soil exert potential toxic effects on overall growth and metabolism of plants [2], and bioaccumulation of such toxic metals in the plants poses a risk to human and animal health. Heavy metal pollution has become one of the most serious environmental problems today [3]. Metals are natural components in soil. Contamination has resulted from industrial activities such as mining and smelting of metalliferous ores, electroplating, gas exhaust, energy and fuel production, fertilizer and pesticide application, and generation of municipal waste. Phytoremediation describes the treatment of environmental problems through the use of plants that mitigate the environmental problem without the need to excavate the contaminant material and dispose of it elsewhere. The term Phytoremediation ("phyto" meaning plant, and the Latin suffix "remedium" meaning to clean or restore) refers to a diverse collection of plant-based technologies that use either naturally occurring or genetically engineered plants for cleaning contaminated environments [4,5]. Phytoremediation is clean, simple, cost effective, non-environmentally disruptive [6]) green technology and most importantly, its by-products can find a range of other uses [7, 8]. At metals contaminated sites, plants are used either to stabilize or remove the metals from the soil and ground water through mechanisms such as Phytoextraction, Rhizofiltration, and Phytostabilisation [9]. Five main subgroups of Phytoremediation have been identified: Phytoextraction: plants remove metals from the soil and concentrate them in the harvestable parts of plants [10]. Phytodegradation: plants and associated microbes degrade organic pollutants [11]. Rhizofiltration: plant roots absorb metals from waste streams [12]. Phytostabilisation: plants reduce the mobility and bioavailability of pollutants in the environment either by immobilization or by prevention of migration [13, 14]. Phytovolatilisation: volatilization of pollutants into the atmosphere via plants [15, 16]. Phytoremediation processes: A range of processes mediated by plants or algae are useful in treating environmental problems:

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

Phytoextraction — uptake and concentration of substances from the environment into the plant biomass.  Phytostabilisation — reducing the mobility of substances in the environment, for example, by limiting the leaching of substances from the soil.  Phytotransfermation — chemical modification of environmental substances as a direct result of plant metabolism, often resulting in their inactivation, degradation (Phytodegradation), or immobilization (Phytostabilisation).  Phytostimulation — enhancement of soil microbial activity for the degradation of contaminants, typically by organisms that associate with roots. This process is also known as rhizosphere degradation.  Phytovolatilisation — removal of substances from soil or water with release into the air, sometimes as a result of phytotransfermation to more volatile and/or less polluting substances.  Rhizofiltration — filtering water through a mass of roots to remove toxic substances or excess nutrients. The pollutants remain absorbed in or adsorbed to the roots. The plants absorb contaminants through the root system and store them in the root biomass and/or transport them up into the stems and/or leaves. A living plant may continue to absorb contaminants until it is harvested. After harvest, a lower level of the contaminant will remain in the soil, so the growth/harvest cycle must usually be repeated through several crops to achieve a significant cleanup. After the process, the cleaned soil can support other vegetation. The main advantage of Phytoextraction is environmental friendliness. Traditional methods that are used for cleaning up heavy metal-contaminated soil disrupt soil structure and reduce soil productivity, whereas Phytoextraction can clean up the soil without causing any kind of harm to soil quality. Another benefit of Phytoextraction is that it is less expensive than any other clean-up process. Phytoremediation should be viewed as a long-term remediation solution because many cropping cycles may be needed over several years to reduce metals to acceptable regulatory levels. This new remediation technology is competitive, and may be superior to existing conventional technologies at sites where Phytoremediation is applicable. [17]. Mine reclamation and biogeochemical prospecting depends upon right selection of plant species and sampling. The selection of heavy metal tolerant species is a reliable tool to achieve success in Phytoremediation. 163 plant taxa belonging to 45 families are found to be metal tolerant and are capable of growing on elevated concentrations of toxic metals. The use of metal tolerant species and their metal indicator and accumulation is a function of immense use for biogeochemical prospecting [18, 19]. Plants and humans require adequate amounts of micronutrients like iron and zinc [20], but accumulation of an excess or uptake of non-essential metals like cadmium or lead can be extremely harmful. As a plant- based technology, the success of Phytoextraction is inherently dependent upon proper plant selection. As previously discussed, plants used for Phytoextraction must be fast growing and have the ability to accumulate large quantities of environmentally important metal contaminants in their shoot tissue [21, 22, 23, 24],. High concentrations of heavy metals in soil can negatively affect crop growth, as these metals interfere with metabolic functions in plants, including physiological and biochemical processes, inhibition of photosynthesis, and respiration and degeneration of main cell organelles, even leading to death of plants [25, 26]. II. Methodology Description of the experimental grass species Cyperus rotundus, L.

Systematic classification: Kingdom : Plantae (Angiosperms, Monocots, Commelinids) Order : Poales Family : Cyperaceae Genus : Cyperus Species : rotundus

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Binomial name: Cyperus rotundus L. Cyperus rotundus commonly known as coco-grass, purple nut sedge, red nut sedge, etc., is a species of sedge belongs to Cyperaceae, native to Africa, southern and central Europe and southern Asia. [27].The word Cyperus derives from the Greek ‘kuperos’ and rotundus is from Latin, meaning ‘round’. The names "nut grass" and "nut sedge" are derived from its tubers, that somewhat resemble nuts, although botanically they have nothing to do with nuts. Cyperus rotundus is a perennial plant that may reach a height of up to 55 inches. The root system of a young plant initially forms white, fleshy rhizomes. Some rhizomes grow upward in the soil, and then form a bulb-like structure from which new shoots and roots grow, and from the new roots, new rhizomes grow. Other rhizomes grow horizontally or downward, and form dark reddish-brown tubers or chains of tubers. Cyperus rotundus is one of the most invasive weeds known, having spread out to a worldwide distribution in tropical and temperate regions. The plant has several medicinal uses. The decoction of the roots and tubers are excellent antidote to all poisons. The root is often used for developing high memory. This herb also harmonizes the liver, spleen, and pancreas. The grass is anthelminthic, anti-fungal, anti-parasitic, anti-rheumatic, antispasmodic, aphrodisiac and astringent. It cures dyspepsia, vomiting, indigestion, thirst, worm troubles, cough, bronchitis, dysuria, and poisonus affections. It is used as an insect repellent, for perfuming clothing. [28]. The seedlings of the plants were selected from the vicinity of Acharya Nagarjuna University. Plants were grown in pots filled with five kgs of garden soil. The seedlings were collected from the uncontaminated soils. All the selected seedlings were of uniform size and free of any disease symptoms. The heavy metals selected for the study were Cadmium and Chromium. The uptake was estimated for every 20 days for a total period of 60 days, in total plant. In addition a control blank set of experimental pots was also maintained. The heavy metals were dissolved in distilled water to prepare stock solution of 1000 ppm for each metal. The calibration curves for each heavy metal were also prepared. A blank reading was also taken to incorporate necessary correction factor. The heavy metal solution of 5mg/L was prepared from the stock and administered to the grass species and care was taken to avoid leaching of water from the pots. The metal uptake was estimated once in every 20 days. The sample plants were removed from the pots and washed under a stream of water and then with distilled water. The collected plants were air dried, then placed in a dehydrator for 2-3 days and then oven dried for four hours at 100 ºc. The dried samples of the plant were powdered and stored in polyethylene bags. The powdered samples were subjected to acid digestion. 1gm of the powdered plant material were weighed in separate digestion flasks and digested with HNO3 and HCl in the ratio of 3:1. The digestion on hot plate at 110ºc for 3-4 hours or continued till a clean solution was obtained. After filtering with Whatman No. 42 filter paper the filtrate was analyzed for the metal contents in AAS. III. Results And Discussion Phytoremediation is one of the promising methods for reclamation of soils contaminated with toxic metals [29]. Metal concentrations in plants vary with plant species [30, 31, 32, 33]. Plant uptake of heavy metals from soil occurs either passively with the mass flow of water into the roots, or through active transports, and crosses the plasma membrane of root epidermal cells. Under normal growing conditions, plants can potentially accumulate some metal ions in order of magnitude greater than the surrounding medium [34, 35]. Accumulation of Cadmium (mg/kg biomass) in Cyperus rotundus during the experimental period Cyperus rotundus has accumulated 16.35 mg/kg of cadmium in 60 days the initial concentration was 0.52 mg/kg. Cyperus has accumulated 3.09, 13.11 and 16.87 mg/kg by 20 th, 40th and 60th days respectively. The accumulation levels of cadmium in Cyperus rotundus was a good accumulator of cadmium. Table 1: Accumulation of Cadmium (mg/kg biomass) in Cyperus rotundus during the experimental period

Concentration (mg/kg)

Plant part Total Accumulation

Control

20th day

40th day

60th day

Total Accumulation

0.52±0.15

3.09±0.19

13.11±0.08

16.87±0.19

16.35

20 15 10 5 0

Control 20th Day 40th Day 60th Day Total accumulation Accumulation of Cadmium in Cyperus rotundus during the experimental period

Accumulation of Chromium (mg/kg biomass) in Cyperus rotundus during the experimental period

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The chromium concentration was 6.28 mg/kg at the beginning of the experiment. There was a substantial accumulation in 40 days (64.4 mg/kg). The accumulation increased to 25.53 mg/kg in first 20 days and later to 64.4 mg/kg by 40th day. The increase of accumulation was less from 40 th day to 60th day (only 1.98 mg/kg i.e. from 64.4 to 66.38 mg/kg). The total accumulation of chromium in 60 days was 60.1 mg/kg which reveal that Cyperus was a good accumulator of chromium. This Cyperus species can be recommended to specially remediate cadmium contaminated soils. Table 2: Accumulation of Chromium (mg/kg biomass) in Cyperus rotundus during the experimental period Plant part

Control

20th day

40th day

60th day

Total accumulation

6.28±0.15

25.53±0.19

64.4±0.08

66.38±0.18

60.1

Concentration (mg/kg)

80 60 40 20 0

Control 20th day 40th day 60th day Total accumulation Accumulation of Chromium in Cyperus rotundus during the experimental period

Bioconcentration factor: Heavy metals are currently of much environmental concern. They are harmful to humans, animals and tend to bioaccumulate in the food chain. According to Ghosh and Singh [29] phyto-extraction is a process to remove the contamination from soil without destroying soil structure and fertility [36]. A plant's ability to accumulate metals from soils can be estimated using the BCF, which is defined as the ratio of metal concentration in the roots to that in soil. The Bioconcentration Factor (BCF) of metals was used to determine the quantity of heavy metal absorbed by the plant from the soil. This is an index of the ability of the plant to accumulate a particular metal with respect to its concentration in the soil and is calculated using the formula: Metal concentration in plant tissue (whole plant/ portal) BCF = -------------------------------------------------------------------Initial concentration of metal in substrate (soil) The higher the BCF value the more suitable is the plant for Phytoextraction. Yoon et al., 2006 [36]. In the present study Cyperus rotundus highly absorb chromium than cadmium. Bioconcentration factor calculated using soil initial concentration. Cadmium background concentration was 0.37 and chromium background concentration was 13.58. The BCF of cadmium was 44.18 and the BCF of chromium was 4.42. Based on BCF values these grass species was a good accumulator of cadmium and chromium. IV. Summary and conclusions The contamination of heavy metals to the environment, i.e., soil, water, plant and air is of great concern due to its potential impact on human and animal health. Cheaper and effective technologies are needed to protect the precious natural resources and biological lives. Substantial efforts have been made in identifying plant species and their mechanisms of uptake and hyper accumulation of heavy metals in the last decade. A pot experiment was conducted using Cyperus rotundus for the removal of chromium and cadmium contaminated soils. In the experimental period Cyperus accumulated chromium and cadmium in their plant body. Based on BCF values the grass species was a good accumulator of cadmium and chromium and the grass species was recommended for remediation of cadmium and chromium contaminated soils. V. [1]. [2]. [3].

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

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Fracture strength evaluation of AA 2219-T87 weldment using SINTAP and modified IFM procedures a

S.Rajakumar a and N.Muruganb Professor of Mechanical Engineering, Thanthai Periyar Government Institute of Technology, Vellore-632002, India. b Professor of Mechanical Engineering, Coimbatore Institute of Technology, Coimbatore -641014, India.

Abstract: Fracture data of parent and weld metals obtained from tensile surface cracked AA2219-T87 plates are correlated using a modified inherent flaw model. Fracture parameters are evaluated to generate the failure assessment diagram of the material useful in failure assessment of cracked components. Failure assessment diagrams are generated employing SINTAP Level-3 defect assessment procedure. A comparative study is made on the tensile fracture strength evaluation of AA2219-T87 cracked configurations utilizing both the approaches. Keywords: Centre through crack, centre surface crack, Equivalent through crack, Failure assessment diagram, Fracture strength. Nomenclature a

Crack depth Damage zone size

a ci c 2ceq E F

FYB FYM 2h

Kc KIC KIFM,  aci



KQ  

 NC

Kmat

c



Half crack length Equivalent through crack length for surface crack Young’s modulus Applied force Yield load of base material Yield load of mismatch configuration Total width of weld Critical Stress Intensity Factor Plane strain fracture toughness Fracture parameters in equation (1) Parameter in Failure assessment diagram Material constant Ratio of the applied linear elastic stress intensity factor to Material constant Kmat

Lr M t W Y 

f   NC

 NC

0 

Ratio of applied stress to yield stress Mismatch factor at yield stress Specimen thickness Specimen width Finite width correction factor Applied far field stress Failure stress Fracture strength of a wide specimen Fracture strength of finite width specimen Ultimate tensile strength (unnotched strength) Normalized remaining ligament

I. Introduction Fracture and yielding are the two types of failure criteria recognized in aerospace applications. Failure due to yielding is applied to a criterion in which some functional of the stress or strain is exceeded and fracture is applied to a criterion in which an already existing crack extends according to an energy balance hypothesis. Experimentation with a variety of materials would show that the theory works well for certain materials but not for others. The safety assessment of structures without a fracture mechanics analysis is insufficient and may cause an unexpected reduction in the load carrying capacity of an actual structure due to the presence of unavoidable crack-like defects not being taken into consideration. The extraordinary success of fracture mechanics lies in its ability to combine a theoretical framework with experimentally measured critical quantities.

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When dealing with a specific material for a particular application, it is not clearly established whether KIC (plane strain fracture toughness) or K c (fracture toughness when plane strain conditions are not met) values should be used. The values of KIC seem to be relevant in heavy sections like forgings or thick plates. Design based on KIC requires unreasonably thick panels in normally thin-sectioned structural members as in aerospace industry. In such circumstances it is necessary to carry out what is called K c tests as per ASTM-E561 standards, corresponding to the thickness of the members in the intended structural applications. The geometry dependent values of K c can be determined from the point of tangency between the crack growth resistance curve (Rcurve) and the crack driving force curve of a through-cracked configuration. For part through cracked configuration, fracture strength estimations are not possible directly from the R-curve of the material because the part through crack has two dimensions namely, crack length and its depth. Though the concept of R-curves has been adopted to characterize stable crack extension and predict residual strength of thin-walled structures particularly in the aircraft industry, ASTM E561 yields R-curves in terms of the stress intensity factor as a function of the “effective” crack length. The new ASTM E2472 addresses the determination of resistance to stable crack extension under low constraint conditions in terms of crack-tip opening displacement (CTOD) and crack-tip opening angle (CTOA). II. Modified Inherent Flaw Model The significant parameters affecting the size of a critical crack in a structure are the applied stress levels, the fracture toughness of the material, the location of the crack and its orientations. Since the intensity of the stress at the crack tip, K is a function of load, geometry and crack size, it is more appropriate to have a relationship between the critical stress intensity factor at failure and the failure stress from the fracture data of cracked specimens for the estimation/prediction of the fracture strength to any cracked configuration. For cracked  configurations, a relation between the critical stress intensity factor (KQ) and the corresponding stress (  NC ) at failure is obtained from modified inherent flaw model (IFM) as [1]-[2]: 2

      (1) K Q  K IFM 1  aci NC  1   NC  0    0   Where,  NC is the failure stress normal to the direction of the crack in a wide specimen and  0 is the ultimate stress of the material. Equation (1) represents a failure assessment diagram useful for fracture strength  evaluation of different cracked configurations. The fracture Strength (  NC ) of the center crack wide tensile specimen is obtained from that of a finite width specimen (  NC ) as  =  NC Y  NC

(2)

Where the finite width correction factor [3] is,

 c  Y  sec  W

(3) Where ‘c’ is half the crack length and W is the specimen width. For the determination of two fracture parameters ( K IFM and  aci ), test results of simple laboratory specimens like compact tension specimens, center crack specimens etc. can be utilized. For fracture strength evaluation of any other structural configuration, the stress intensity factor corresponding to that geometry is to be used in (1) to develop the necessary fracture strength equation. If the values of applied stress and corresponding stress  intensity factor for the specified crack size in a structure lie below the KQ -  NC curve of the failure assessment diagram, the structure for that loading condition is safe. This article considers fracture data of AA2219-T87 useful in aircraft applications [4] and demonstrates the adequacy of the modified IFM approach as well as the SINTAP Level-3 defect assessment procedure in the tensile fracture strength evaluation of cracked configurations. The modified IFM approach is found to be simple and includes mismatch effect (if any) in the fracture parameters ( K IFM and  aci ). III. Equivalent through crack Equivalent through crack size for the given part-through crack size can be evaluated, by equating stress intensity factors of both the specimens using Newman’s finite element solutions [5]-[6].

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K c   f (a) 2

(4)



Where, 2   c  a   c M 2  1 a  M 0   M 1    M 1     a t   a  t   2  1  1.464(a / c)1.65 for a< c

 2  1  1.464(c / a)1.65



 f 

for a>c

M 1  1.13  0.1(a / c) for a< c

M1  {1  0.03(c / a)}(c / a)1/ 2 for a>c M 2  ( / 4)1/ 2

for a < c

M 2  1  (c / a){( / 4)1/ 2  1} for a>c fw 

1/ 2   c  a    sec    W t      

Here a is the depth and c is half the crack length of a surface crack and W is the width of the plate. When a = t, the expression holds well for through crack. The Nomo gram [7] shown in Fig.1 is useful to find out 2ceq from 2c. IV. Application of SINTAP The SINTAP provides different Levels of complexity, which can be used depending upon the quality and detail of the input data available. Level 3 assessment is applicable to strength mismatched structures. Strength mismatch factor M, is defined as the ratio of the yield strength of the weld metal to that of the base material. In the present SINTAP Level 3 analysis, two cases are considered. In case (a) ‘Crack in Base Metal only’ is considered. In case (b) ‘Crack in Weld Metal only’ is considered. In both the cases, Mismatch factor is assumed as 0.95 since the material under consideration, Aluminium2219-T87 alloy is a homogeneous material. The Material Properties and Input Data for SINTAP are given in the table (1) and (2) for case (a) and case (b) respectively. The results are shown in table 3 and table 4 respectively. The SINTAP Level-3 analysis is performed using the equations described in SINTAP procedures [9]. SINTAP Level 3 analysis requires full stress-strain data to determine the stress-strain curve for the equivalent material. When full stress-strain data for both materials available, f ( Lr ) is defined as

E e L2r e  2 f ( Lr )  (  )  e 2 E e 1

(5)

In Equation (5),  e is the true strain obtained from the uniaxial stress-strain curve for the equivalent material at a true stress  e . The stress-strain curve for the equivalent material can be determined from the tensile data of the base and weld material as

 e ( p ) 

( F YM / FYB  1) w ( p )  ( M  FYM / FYB ) B ( p ) ( M  1)

(6)

It should be noted that the strength mismatch M, in Equation (6) is defined not only at the yield stress (0.2% plastic strain) but also at a number of different plastic strain values  p M(  p )=

 W ( p )  B ( p )

(7)

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FYM should be found for those values of M. This gives a contrast to the SINTAP Level 2 equations where FYB the values of M and FYM are defined at the yield stress. The equivalent yield stress  Ye , the flow stress  e and FYB max the cut off Lr are defined as and

 Ye =

FYM  YB FYB

e 

FYM ( p )  B ( p ) FYB

Lmax  r

e  Ye

(8)

V. Material curve generation The True stress – strain curves of AA 2219-T87 base metal and weld metal are essential to draw the stress-strain curve of the equivalent material. Hence they are drawn using inverse of Ram berg Osgood relation [8] given by the Equation (9).

E

 {1  (

(9)

 n 1/ n )} 0

Where

 0  E

and n = 2.76 for base metal and 3.3 for weld metal

respectively. The material constant ‘n’ is a parameter defining the shape of the non-linear stress-strain relationship. Once the material parameters n and

0

are available, a continuous smooth material curve can be

generated by substituting in Equation (9). The stress-strain curve of the equivalent material is drawn using Equation (6). Fig.2 shows the True stress – strain curves generated for AA2219-T87 with crack in base metal only. Similarly Fig.3 shows the True stress – strain curves generated for AA2219-T87 with crack in weld metal only. A. Evaluation of the material constant (Kmat ) The material constant Kmat is required to plot the test results in the SINTAP failure assessment diagram and to evaluate the stress intensity factor K I for any applied load from the SINTAP failure assessment diagram. The equation to evaluate material constant Kmat is derived as follows. The ordinate of any SINTAP failure assessment diagram is given by

KI  f Lr  K mat

(10)

For the failure load of the cracked configurations, the K I in the above relation becomes the critical stress intensity factor (Kc). When K I = Kc , the above Equation (10) becomes K c  f L  and K mat

Let the error E =

{K i 1

 K mat f ( Lr )i }2

Where n = number of test specimens. Partially differentiating the error equation with respect to Kmat and equating to zero in order to minimize error, we get n

Kmat 

 f (L i 1

) i K ci

(11)

 f (L i 1

Where Lr 

F FYM

)

2 i

and

critical stress intensity factor

K c = Critical Stress Intensity Factor calculated from experimental data [4]. The K c for centre surface crack tension (SCT) specimens is calculated from equation

(4). B. Evaluation of mismatch yield load The mismatch yield load FYM is calculated from expressions suggested [10] - [11]. Under Plane stress condition, for crack in the centre line of the weld metal

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Under matching (M<1) Under Plane stress condition, for crack in the centre line of the weld metal

FYM M FYB

for 0    1.43

(12)

FYM F (1) F ( 2)  min{ YM , YM } FYB FYB FYB

for 1.43  

(1)  2  2  3  1.43  FYM   M    FYB 3     3 

( 2) FYM 1.43  1  (1  M ) FYB 

(13)

(14)

Where   (W  2c) / 2h

(15)

Under Plane stress condition, for crack in the base metal

FYM   1  M   M 1.095  0.095 exp   FYB  0.0108M  

for M < 1

(16)

VI. Results and Discussion Fracture analysis has been carried out considering the ultimate tensile strength values and the fracture data of AA2219-T87 (base metal and weld metal) generated from center–crack tensile specimens having different thicknesses. Table 3 gives the comparison of fracture strength estimations with the test results, using SINTAP, for AA2219-T87 base metal at temperature 295 K for t=16 mm. Similarly Table 4 gives the comparison of fracture strength estimations with the test results, for AA2219-T87 weld metal at temperature 77 K for t=25 mm. Table 5 gives the comparison of fracture strength estimations with the test results, using modified IFM approach, for AA 2219-T87 base metal at temperature 295 K for t=16 mm. Similarly Table 6 gives the comparison of fracture strength estimations with the test results, for AA 2219-T87 weld metal at temperature 77 K for t=25 mm. The standard error is found to be less than 0.1 following modified IFM and SINTAP approaches. This trend indicates that these approaches provide the fracture strength estimations within±10% of the test results. Fig. 4 and 5 indicate the closeness with which the fracture data have been correlated using the modified Inherent Flaw Model. Failure assessment diagrams are used as a ready reference chart to verify whether the design point is in safe region or not. Fig. 6 shows the failure assessment diagram using SINTAP, including the fracture data [4] of the material for Aluminium 2219-T87 base metal at temperature 295K for t=16 mm. Fig. 7 shows the failure assessment diagram using SINTAP, including the fracture data [4] of the material for Aluminium 2219-T87 weld metal at temperature 77 K for t = 25 mm. It can be seen from Fig.8 and Fig.9 that most of the fracture strength estimations are below ±10% of the test results. There is no appreciable difference in standard errors in the fracture strength estimations utilizing the SINTAP and the modified IFM. However, the modified IFM approach is quite simple to use. VII. Concluding Remarks In this paper, SINTAP Level 3 assessment procedure has been used to evaluate fracture strength of AA 2219T87 base metal and weld metal which are homogeneous metals with the assumption of M = 0.95, and failure assessment diagrams are drawn. Similarly FAD is drawn using the modified inherent flaw model. There is no need to consider separately the mismatch effect in the modified IFM approach because the two fracture parameters ( K IFM and  aci ) take into account such mismatch effects of the weld (if any). The procedure is validated considering the fracture data of AA2219-T87 center surface crack tension specimens. Failure assessment diagrams showed that the fracture data were closer to the failure boundary. On comparing both failure assessment diagrams, it is found that the standard error (percentage) in SINTAP and the modified IFM approach is almost equal. Hence, the modified IFM approach provides reasonably accurate fracture strength predictions for homogeneous metals. References [1] [2]

A.Subhananda Rao, G. Venkata Rao, and B.Nageswara Rao, “Generation and validation of failure assessment diagrams for notched strength prediction of solid propellant tensile specimens”, Mater. Sci. Technol., Vol.21, No. 4, 2005, pp. 488-494. S.Rajakumar . and Christopher, T. “Generation and Validation of Failure Assessment Diagrams for High Strength Alloys Utilizing the Inherent Flaw Model” Material Science Research Journal, Vol. 4, 2010, pp.01–14.

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S.Rajakumar et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 6(1), March-May, 2014, pp. 102-111 [3] [4] [5] [6] [7] [8] [9] [10] [11]

Y.Murakami , Stress intensity factors handbook, New York, Pergamon, 1987. G.C.Sih , Handbook of Stress Intensity Factors. Lehigh University, Pennsylvania, 1973. J.C.Newman Jr and I.S.Raju , “Analysis of surface cracks under tension or bending loads”.1979; NASA-TP-1578. Newman Jr JC. Fracture analysis of surface and through cracks in cylindrical pressure vessels.1976; NASA-TND-8325. S.Rajakumar and T.Christopher,. “Fracture strength of centre surface cracked tensile specimens made of Aluminium 2219-T87 alloy welding “Transactions of Nonferrous Metals”, Society of China, Vol.21, 2011, pp.2568-2575. A.P.Beena , M.K. Sundaresan , and B. Nageswara Rao,“Destructive tests of 15CDV6 steel rocket motor cases and their application in light weight design”, International Journal of Pressure Vessels and Piping, 1995;62: 313-320. Yun-Jae Kim et.al. SINTAP defect assessment procedure for strength mismatched structures.Engng.Frac.Mech.2000; 67:529-546 SINTAP: Structural Integrity Assessment Procedure for European Industry. Final Procedure, 1999, Brite-Euram Project No. BE95- 1426, British Steel. S.Al.Laham, Stress intensity factor and limit load factor Hand book. Issue 2, British Energy Generation Ltd, 1998.

Table-1: The Material Properties and Input Data for SINTAP Level 3 Case (a): Crack in Base Metal only Aluminium2219-T87 alloy Ultimate strength = 477.1 MPa Yield strength = 386.81 MPa Young’s Modulus = 66900MPa The mismatch yield load = 380.95MPa Material constant, Kmat = 68.32 MPa√m Temperature = 295 K Inverse Ram berg Osgood Relation Exponent n = 2.76

Table-2: The Material Properties and Input Data for SINTAP Level 3Case (b): Crack in Weld Metal only Aluminium2219-T87 alloy Ultimate strength = 379.2 MPa Yield strength = 224.09 MPa Young’s Modulus = 6760 MPa The mismatch yield load = 234.90MPa Material constant, Kmat = 57.55 MPa√m Temperature = 77 K Total width of weld = 20 mm Inverse Ram berg Osgood Relation Exponent n = 3.3

Table-3: Comparison between experimental and SINTAP fracture strength for Aluminium 2219-T87 base metal  y = 386.81 MPa, t = 16 mm. M = 0.95, Level 3 Specimen Dimensions (mm)

Width, W

Thickness t

Crack Depth a

Fracture Strength,

 NC , MPa

Crack length, 2c

Test [4]

SINTAP Analysis

Relative Error (%)

Temperature: 295K 228.5

16.3

7.52

61.00

291.0

268.21

7.8

558.9

16.3

15.24

145.41

148.9

147.00

1.3

139.8

15.9

6.88

16.61

373.0

399.88

-7.2

171.4

16.3

6.07

15.27

379.2

419.26

-10.6

139.7

16.3

8.61

23.42

331.0

346.07

-4.6

139.8

15.9

9.19

24.41

335.1

332.15

0.9

171.4

15.9

9.91

29.21

323.4

303.23

6.2 Standard error = 0.06

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Table-4: Comparison between experimental and SINTAP fracture strength forAluminium 2219-T87 weld metal  y = 224.09 MPa, t = 25 mm. M=0.95, Level 3 Specimen Dimensions (mm) Width,

Thickness

Crack Depth a

507.3 609.7 342.6 406.7 406.4

25.6 25.9 25.6 20.0 25.6

11.86 18.03 17.86 19.99 21.36

Fracture Strength,

 NC , MPa

Crack length, 2c Test [4] Temperature: 77 K 84.58 169.6 107.82 136.5 60.30 174.4 65.63 148.9 71.93 168.2

SINTAP Analysis

Relative Error (%)

173.22 148.85 163.62 159.01 147.50

-2.1 -9.1 6.2 -6.8 12.3 Standard error = 0.08

Table-5: Comparison between experimental and analytical fracture strength for Aluminium 2219-T87 base metal  o = 477.1 MPa, t = 16 mm. (KIFM = 79.28 MPa√m,  aci = 0.00) Specimen Dimensions (mm)

Width,

Thickness t

Crack Depth a

Fracture Strength,

 NC , MPa

Crack length, 2c

Relative Error (%) Eq.Crack length, 2ceq

Test [4]

IFM Analysis

Temperature: 295K 228.5

16.3

7.52

61.00

30.5

291.0

285.2

2.0

558.9

16.3

15.24

145.41

133.3

148.9

156.9

-5.4

139.8

15.9

6.88

16.61

11.1

373.0

371.6

0.4

171.4

16.3

6.07

15.27

9.8

379.2

380.9

-0.5

139.7

16.3

8.61

23.42

16.7

331.0

338.4

-2.2

139.8

15.9

9.19

24.41

18.0

335.1

331.5

1.1

171.4

15.9

9.91

29.21

22.2

323.4

313.6

3.0

Standard error = 0.03

Table-6: Comparison between experimental and analytical fracture strength for Aluminium 2219-T87 weld metal  o = 379.2 MPa, t = 25 mm. (KIFM = 54.08 MPa√m,  aci = 0.00) Specimen Dimensions (mm)

Fracture Strength,

 NC , MPa

Relative Error (%) Width, W

Thickness t

Crack Depth a

Crack length, 2c

Eq.Crack length, 2ceq

Test [4]

IFM Analysis

Temperature: 77K 507.3

25.6

11.86

84.58

44.4

169.6

179.1

-5.6

609.7

25.9

18.03

107.82

78.0

136.5

141.4

-3.6

342.6

25.6

17.86

60.3

47.9

174.4

172.6

1.1

406.7

20.0

19.99

65.63

65.6

148.9

151.3

-1.6

406.4

25.6

21.36

71.93

63.5

168.2

153.5

8.8

Standard error = 0.05

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Fig.1: Nomo gram for equivalent through crack

Fig. 2: True stress –True strain curves for Aluminium 2219-T87alloy (crack in base metal) used in SINTAP Level 3

Fig. 3: True stress –True strain curves for Aluminium 2219-T87 alloy (crack in weld metal) used in SINTAP Level 3

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Fig. 4: Failure assessment diagram for Aluminium 2219-T87 base metal with Test data[4] . KIFM= 79.28M Pa√m,  aci =0.00

Fig. 5: Failure assessment diagrams for Aluminium 2219-T87 weld metal with Test data[4] . KIFM= 54.08 M Pa√m,  aci =0.00

Fig. 6: Failure assessment diagram for Aluminium 2219-T87 base metal with Test data[4] using SINTAP Level 3. M = 0.95

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Fig. 7: Failure assessment diagram for Aluminium 2219-T87 weld metalwith Test data[4] using SINTAP Level 3. M = 0.95.

Fig. 8: Comparison of fracture strength of Aluminium 2219-T87 base metal with SINTAP Level 3

Fig. 9: Comparison of fracture strength of Aluminium 2219-T87weld metal with SINTAP Level 3

Page 111

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

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

Design and Analysis of Flexural Mechanism-A Short Review S.V.Deokar1, S.M.Gaikwad2, S.P.Deshmukh3 Department of Mechanical Engineering, Sinhgad Institute of Technology, Lonavala, Pune, Marashtra, India 3 Department of Mechanical Engineering, Sinhgad Academy of Engineering, Kondhawa,Pune, Marashtra,India 1,2

Abstract: Flexure mechanisms are a designerâ&#x20AC;&#x2122;s delight. Except for the limits of elasticity, flexures present few other boundaries as far as applications are concerned. Flexures have been used as bearings to provide smooth and guided motion. For example in precision motion stages; as springs to provide preload, for example in the brushes of a DC motor or a camera lens cap etc. Flexural joints are widely used in precision motion stages and micro robotic mechanisms due to their monolithic construction. It is difficult and expensive to make such compact mechanism using traditional machining methods. In addition, the traditional mechanisms machining methods are limited to simple design. To reduce the cost of fabrication and also to allow more complex designs, object i.e. a rapid prototyping machine is proposed to be used to build the mechanism. Traditional motors, gears, and revolute joints are not able to meet the requirements due to existence of backlash between moving parts. Hence, the flexure joints are more popularly used. One of the primary applications of flexures is in the design of motion stages. It strives to bridge the gap between intuition and mathematical analysis in flexure mechanism design. This paper presents design and analysis of flexure mechanism. Key words: Flexure hinges, compliant mechanisms, backlash, actuator-cross sensitivity, parasitic coupling, precision motion.

I. INTRODUCTION Flexure jointed mechanism have been widely utilized in precision instruments such as watches & clocks for hundreds of years, and continued to be used today in applications such as optical systems, micro robots, and clean room equipment. Flexural mechanisms are colossal structures which provide desired motion with the help of flexural hinges. Due to their smooth operation flexural joints have little friction losses and also does not require lubrication. They generate smooth and continuous displacement without backlash. The importance of properly constrained design is well known to the engineering community. The objective of an ideal constraining element, mechanism, or device is to provide infinite stiffness and zero displacements along certain directions, and allow infinite motion and zero stiffness along all other directions. The directions that are constrained are known as Degrees of Constraint (DOC), whereas the directions that are unconstrained are referred to as Degrees of Freedom (DOF). While designing a machine or a mechanism so that it has appropriate constraints, the designer faces a choice between various kinds of constraining elements, two of which are considered for comparison: ball bearings and flexures. Clearly, ball bearings meet the definition of a constraint quite well, since they are very stiff in one direction, and provide very low resistance to motion in other directions. Nevertheless, motion in the direction of DOF is associated with undesirable effects such as friction, stiction and backlash that typically arise at the interface of two surfaces. These effects are non-deterministic in nature, and limit the motion quality. Flexures, on the other hand, allow for very clean and precise motion. Since the displacement in flexures is an averaged consequence of molecular level deformations, the phenomena of friction, stiction and backlash are entirely eliminated. II. LITERATURE SURVEY Byounge Hun Kang et al.1] carried out the analysis and design of general platform type parallel mechanisms contanining flexure joints. They considered static performance measures such as task space stiffness and manipulability. Based on these performance measures they obtained the multi-objective optimization approach. Firstly they obtained Pareto-frontier. Lumped approximation of flexure joints in the pseudo rigid body are considered for simplification. They established the key difference between flexure mechanism and parallel mechanism with conventional joints and is that kinematic stability is no longer a design consideration. Instead of

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that, important design parameter is task space stiffness which needs to be carefully designed to avoid undesired motion in the presence of external loads. Yeonge- jun Choi et al.2] worked on kinematic design of large displacement precision X-Y positioning stage by using cross strip flexure joints and over constrained mechanism. For the design of a large displacement precision XY positioning stage, a cross strip flexure joints were used. And to achieve a good kinematic design advanced kinematic techniques such screw system theory are used. The weight support mechanism of the motion stage was made of links and flexure joints, and a linear motor was used as the actuator. Crossed strip type flexure joints that provide large rotation were used. To eliminate the effects of center shifting in large-motion flexures an over-constrained mechanism was used to incorporate symmetry. Yangmin Li et al.3] represented the modeling and evaluation of a nearly uncoupled XY micromanipulator designed for micro-positioning uses. The manipulator features are monolithic parallel-kinematic architecture, flexure hinge-based joints, and piezoelectric actuation. The evaluation is carried out analytically in terms of parasitic motion, cross-talk, lost motion, workspace, and resonant frequency. The mathematical models for the kinematics and dynamics of the XY stage are derived in closed-forms, which are verified by resorting to finite element analysis (FEA) based on pseudo rigid-body (PRB) simplification and lumped model methods. They established a nonlinear kinematics model, which is based on the deformation of the entire manipulator since the above simplified models fail to predict its kinematic performances. And the validation of effectiveness of non linear model is done by both FEA and experimental studies on the prototype. Results obtained from validation shows that the nonlinear model can predict the manipulator kinematics accurately, and the reason why simplified models fail is discovered. B.Zettle et.al.4] worked on equivalent beam methodology. In this paper they presented a methodology which is accurate and efficient finite elements method (FEM) simulations of planar compliant mechanisms with flexure hinges. In this method one-eighth of a single hinge is simulated to determine its true stress/stiffness characteristics by using symmetry/antisymmetry boundary conditions and 3D elements. A set of fictitious beams is derived, which have the identical characteristics. This set is used in conjunction with other beams that model relatively stiff links to generate an equivalent model of an entire mechanism consisting of the beam elements only. The research work shows that the static and dynamic characteristics of the whole 3RRR mechanism can be simulated with high precision with a model that has a very small number of DOF. The numerical efficiency of the EBM model is very high. Therefore it becomes conceivable to apply it for other purposes such as mathematical optimization, simulating complex dynamic responses, or even for real time applications to control and handling of compliance mechanisms. Y.Tian et. Al.5] presented the mechanical design and dynamics of a 3-DOF (degree of freedom) flexure-based parallel mechanism. They utilized three piezoelectric actuators to drive active links of the flexure-based mechanism. The inverse dynamics of the proposed mechanism is established by simplifying flexure hinges into ideal revolute joints with constant torsional stiffness. For the validation of the performance of the proposed 3DOF flexure-based parallel mechanism he used finite element analysis. The interaction between the actuators and the flexure-based mechanism is extensively investigated based on the established model. He carried out experiments to verify the dynamic performance of the 3-DOF flexure-based mechanism. Shunli Xiao et. Al. 6] has worked out the design and analysis of a novel compliant flexure-based totally decoupled XY micropositioning stage which is driven by electromagnetic actuators. They constructed the stage with a very simple structure by employing double parallelogram flexures and four contactless electromagnetic force actuators. Compliance and stiffness analysis based on matrix method, and analytical models for electromagnetic forces is done by using the kinematics and dynamic modeling of the mechanical system of the stage. Both mechanical structure and electromagnetic model are validated by finite element analysis(FEA) via ANSYS. The stage designed possesses a totally XY decoupled character, simple symmetrical structure, easy controlling strategy, and large range of motion. The kinematics and dynamics modeling of the mechanical structure is done by using compliance based matrix method. Eun Joo Hwang et.al. 7] has presented that Lever mechanisms are usually used to enlarge output displacements in precision stages. The theoretical analysis is done by considering a precision stage employing a lever mechanism and flexure hinges, with bending in the lever. He presented the relations between design parameters and magnification ratio, as well as parametric effects on stage displacement. These relations and effects can provide information at initial designing of flexure-hinge stages. In this research work proper lengths and optimal thicknesses for flexure hinges were obtained to achieve a longer stage displacement, and a new lever with the optimal thicknesses was suggested. He showed that adjustment of lengths and stiffnesses can increase the stage travel range significantly. The approach developed in this study can be very useful when designing stages. U. Bhagat et.al. 8] focused a research work on the computational analysis of a miniature flexure-based mechanism. This novel flexure-based mechanism is capable of delivering planar motion with three degrees of freedom (3-DOF). He studied the stress distribution at all flexure joints, modal analysis and the workspace envelop of the mechanism. And this mechanism is used for three piezoelectric actuators to achieve desired

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displacement in X, Y and Θ. He designed a miniature 3 DOF micro/nano mechanism and analysis is done by using ANSYS. The FEA study and the collected data confirm the performance of the mechanism and the displacement of the TCP in the X-, Y- and Θ- direction. Stress levels in the hinges are found to be well below the yield point of the material. Brian P. Trease et.al. 9] has shown the drawbacks of typical flexure connectors and presented new designs for highly effective, kinematically well-behaved compliant joints. He proposed a revolute and a translational compliant joint both of which have advantages over existing flexures in the qualities of a large range of motion, minimal “axis drift,” increased off-axis stiffness, and a reduced stress-concentrations. He developed analytical stiffness equations for each joint and parametric computer models are used to verify their superior stiffness properties. Calculation of a joint range of motion is done by using finite element analysis. The new compliant joint designs surpass the range of motion of many conventional flexure joints,while the combined achievements in off-axis stiffness ratio and zero axis drift make them very competitive with the latest advances in flexures. Mei-Yung Chen et.al. 10] presented a novel model of XY -dimensional submicropositioner, including mechanism, control, and analysis. The design of the submicropositioner utilizes a monolithic parallel flexure mechanism with built-in electromagnetic actuators and optical sensors to achieve the object of 3-DOF precise motion. From experimental analysis he achieved following goals:1) to integrate the electromagnetic actuator and the parallel flexure mechanism for planar positioning system; 2) to establish the mathematical modeling; 3) to develop an advanced adaptive sliding mode controller; and 4) to perform extensive experiments to test the realistic performance. III.Conclusion In this study, the designs to be presented make unique use of known flexural units and novel geometric symmetry to minimize or even completely eliminate actuator-cross sensitivity, and parasitic coupling between the two axes. Future work is aimed at producing the algorithm to produce an efficient synthesis algorithm that would enable the determination of design parameters of a mechanism that satisfy a set of given constraints. The method presented is accurate and efficient finite elements method (FEM) simulations of planar compliant mechanisms with flexure hinges. In all literatures the validation is by using finite element analysis. References [1] [2]

[3] [4] [5] [6] [7] [8]

[9] [10]

[11]

[12]

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Acknowledgements I would like to thank the many people who have helped me along this journey. First to my guide Professor S.M.Gaikwad and Dr.S.P.Deshmukh for their academic support as well as giving me academic freedom in research direction.

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