Vol. 6 N.1 - Journal of Aerospace Technology and Management by Journal of Aerospace Technology and Management

Journal of aerospace technology and management

JOURNAL OF

AEROSPACE TECHNOLOGY AND MANAGEMENT Vol. 6 N. 1 Jan./Mar. 2014 ISSN 1984-9648 ISSN 2175-9146 (online)

www.jatm.com.br

V. 6, n. 1, Jan./mar., 2014

Journal of Aerospace Technology and Management

General Information Journal of Aerospace Technology and Management (JATM) is a techno-scientific publication serialized, published by Departamento de Ciência e Tecnologia Aeroespacial (DCTA) and aims to serve the international aerospace community. It contains articles that have been selected by an Editorial Committee composed of researchers and technologists from the scientific community. The journal is quarterly published, and its main objective is to provide an archival form of presenting scientific and technological research results related to the aerospace field, as well as promote an additional source of diffusion and interaction, providing public access to all of its contents, following the principle of making free access to research and generate a greater global exchange of knowledge. JATM is added/indexed in the following databases: • CAS • CLASE/PERIÓDICA • DOAJ • EBSCO • EZB • GOOGLE SCHOLAR • J-GATE • LATINDEX

• LIVRE • PERIÓDICOS CAPES • PKP • REDALYC • SCOPUS • SOCOL@R • SUMÁRIOS.ORG • ULRICHSWEB

Correspondence All correspondence should be sent to: Dr Ana Cristina Avelar Journal of Aerospace Technology and Management Instituto de Aeronáutica e Espaço Praça Mal. Eduardo Gomes, 50 - Vila das Acácias CEP 12228-901 São José dos Campos/ São Paulo/Brazil Contact Phone: (55) 12-3947- 5115/5004 E-mail: editor@jatm.com.br Web: http://www.jatm.com.br Published by: Departamento de Ciência e Tecnologia Aeroespacial Distributed by: Instituto de Aeronáutica e Espaço Editing, proofreading and standardization: Zeppelini Editorial Printing: RR Donnelley Edition: 500 São José dos Campos, SP, Brazil ISSN 1984-9648

In WEB QUALIS System, JATM is classified as B3 and B4 in the Interdisciplinary and Engineering III areas respectively. The journal uses CROSSCHECK to prevent plagyarism and all published articles contain DOI numbers attributed by CROSSREF. JATM is an official publication of AAB - Associação Aeroespacial Brasileira and is affiliated to ABEC - Associação Brasileira de Editores Científicos.

JATM is supported by:

Journal of Aerospace Technology and Management Vol. 6, n. 1 (Jan./Mar. 2014) – São José dos Campos: Zeppelini Editorial, 2014 Quartely issued Aerospace sciences Technologies Aerospace engineering CDU: 629.73

Historical Note: JATM was created in 2009 after the initiative of the Director of Instituto de Aeronáutica e Espaço (IAE), Brigadeiro Engenheiro Francisco Carlos Melo Pantoja. From September 2011, it has been edited by the Departamento de Ciência e Tecnologia Aeroespacial (DCTA), and it also started to be financially supported by Fundação Conrado Wessel (FCW). In order to reach the goal of becoming a journal that represents knowledge in science and aerospace technology, JATM searched for partnerships with others institutions in the same field from the beginning. In January 2014, an important step was achieved and JATM merged with Journal of Aerospace Engineering and Applications (JAESA) becoming an official publication of Associação Aeroespacial Brasileira (AAB). The copyright on all published material belongs to Departamento de Ciência e Tecnologia Aeroespacial (DCTA).

ISSN 1984-9648 ISSN 2175-9146 (online)

Journal of Aerospace Technology and Management Vol. 6 No. 1 - Jan./Mar. 2014 Editors in Chief

Executive Editor

ASSISTANT EDITOR

Ana Cristina Avelar Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil editor@jatm.com.br

Ana Marlene F. Morais Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil secretary@jatm.com.br

Roberto Gil Annes da Silva Instituto Tecnológico de Aeronáutica São José dos Campos/SP – Brazil submission@jatm.com.br

Angelo Passaro Instituto de Estudos Avançados São José dos Campos/SP – Brazil

Eduardo Morgado Belo Escola de Engenharia de São Carlos São Carlos/SP – Brazil

Mischel Carmen N. Belderrain Instituto Tecnológico de Aeronáutica São José dos Campos/SP – Brazil

Antonio Pascoal Del’Arco Jr Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

Francisco Cristovão L. Melo Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

Rita de Cássia L. Dutra Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

Carlos Antônio M. Kasemodel Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

João Marcos T. Romano Universidade Estadual de Campinas Campinas/SP – Brazil

Waldemar Castro Leite Filho Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

Carlos Henrique Netto Lahoz Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

José Nivaldo Hinckel Instituto Nacional de Pesquisas Espaciais São José dos Campos/SP – Brazil

Antônio F. Bertachini Instituto Nacional de Pesquisas Espaciais São José dos Campos/SP – Brazil antonio.prado@inpe.br

SCIENTIFIC COUNCIL

ASSOCIATE EDITORS Acoustics

Bert Pluymers Katholieke Universiteit Leuven Leuven – Belgium Marcello A. Faraco de Medeiros Escola de Engenharia de São Carlos São Carlos/SP – Brazil

Aerodynamics

Acir Mércio Loredo Souza Universidade Federal do Rio Grande do Sul Porto Alegre/RS – Brazil João Luiz F. Azevedo Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

Aerospace Meteorology Gilberto Fisch Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil Willian W. Vaughan University of Alabama Huntsville/AL – USA

Applied computation

Ceramic Materials

Leandro Baroni Univ. Fed. dos Vales do Jequitinhonha e Mucuri Teófilo Otoni/MG – Brazil

Circuitry

José Márcio Machado Instituto de Biociências, Letras e Ciências Exatas São José do Rio Preto/SP – Brazil

Romis R. F. Attux Universidade Estadual de Campinas Campinas/SP – Brasil

Astrodynamics

Othon Cabo Winter Faculdade de Engenharia de Guaratinguetá Guaratinguetá/SP – Brazil

José Maria Fonte Ferreira Universidade de Aveiro Aveiro – Portugal Altamiro Susin Universidade Federal do Rio Grande do Sul Porto Alegre/RS – Brazil Raimundo Freire Universidade Federal de Campina Grande Campina Grande/PB – Brazil

Computational fluid dynamics

Anna Guerman Universidade da Beira Interior Covilhã – Portugal

Joern Sesterhenn Technische Universität Berlin Berlin – Germany

Vivian Martins Gomes Universidade Federal de São Paulo São José dos Campos/SP – Brazil

John Cater University of Auckland Auckland – New Zealand

Josep J. Masdemont Universitat Politecnica de Catalunya Barcelona – Spain

Paulo Celso Greco Escola de Engenharia de São Carlos São Carlos/SP – Brazil

Composites

Metallic Materials

Robotics and Automation

Defense Systems

Photonics

Adam S. Cumming Defence Science and Technology Laboratory Salisbury/Wiltshire – England

Álvaro Damião Instituto de Estudos Avançados São José dos Campos/SP – Brazil

Sadek Crisostomo Absi Alfaro Universidade de Brasília Brasília/DF – Brazil

Energetic Materials

Polimeric Materials

Elizabeth da Costa Mattos Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

Cristina Tristão de Andrade Instituto de Macromoléculas Rio de Janeiro/RJ – Brazil

José Leandro Andrade Campos Universidade de Coimbra Coimbra – Portugal

Mirabel Cerqueira Rezende Instituto de Ciência e Tecnologia São José dos Campos/SP – Brazil

Edson Cocchieri Botelho Faculdade de Engenharia de Guaratinguetá Guaratinguetá/SP – Brazil

Fluid Dynamics and Turbulence

Vassilis Theofilis Universidad Politécnica de Madrid Madrid – Spain

Guidance, Navigation and Control Arun Misra McGill University Montreal – Canada

Daniel Alazard Institut Supérieur de l’Aéronautique et de l’Espace Toulouse – France David Murray–Smith University of Glasgow Glasgow – Scotland

Management Systems Adiel Teixeira de Almeida Universidade Federal de Pernambuco Recife/PE – Brazil

Antonio Henriques de Araújo Jr Universidade Estadual do Rio de Janeiro Resende/RJ – Brazil

Kyriakos I. Kourousis RMIT University Melbourne – Australia

Processing of Aerospace Materials Alexandre Queiroz Bracarense Universidade Federal de Minas Gerais Belo Horizonte/MG – Brazil

Propulsion and Combustion

Fernando de Souza Costa Instituto Nacional de Pesquisa Espacial São José dos Campos/SP – Brazil Carlos Henrique Marchi Universidade Federal do Paraná Curitiba/PR – Brazil

Radars and Tracking Systems Cynthia C.M. Junqueira Instituto de Aeronáutica e Espaço São José de Campos/SP – Brazil

Hugo H. Figueroa Universidade Estadual de Campinas Campinas/SP – Brazil Marc Lesturgie Office National d’Etudes et de Recherches Aérospatiales Palaiseau – France

André Fenili Universidade Federal do ABC Santo André/SP – Brazil

Structures

Sérgio Frascino M. Almeida Instituto Tecnológico de Aeronáutica São José dos Campos/SP – Brazil

Synthesis and Characterization of Aerospace Materials

Gilson da Silva Instituto Nacional da Propriedade Industrial Rio de Janeiro/RJ – Brazil Roberto Costa Lima Instituto de Pesquisas da Marinha Rio de Janeiro/RJ – Brazil

Thermal Sciences

Márcia B. H. Mantelli Universidade Federal de Santa Catarina Florianópolis/SC – Brazil Renato Machado Cotta Universidade Federal do Rio de Janeiro Rio de Janeiro/RJ – Brazil

Vibration and Structural Dynamics Carlos Cesnik University of Michigan Ann Arbor/MI – USA

Luiz Carlos S. Góes Instituto Tecnológico da Aeronáutica São José dos Campos/SP – Brazil Valder Steffen Junior Universidade Federal de Uberlândia Uberlândia/MG – Brazil

Editorial Production Glauco da Silva Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

Lucia Helena de Oliveira Depart. Ciência e Tecnologia Aeroespacial São José dos Campos/SP – Brazil

Rosely A. Montoro Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

Mônica E. Rocha de Oliveira Instituto Nacional de Pesquisas Espaciais São José dos Campos/SP – Brazil

Rosilene Maria M. Costa Instituto de Estudos Avançados São José dos Campos/SP – Brazil

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, 2014

ISSN 1984-9648 | ISSN 2175-9146 (online)

CONTENTS Editorial 5

A Stronger than ever Journal on Space Sciences, Technology, Management and Applications Ana Cristina Avelar, Antonio F. Bertachini A. Prado

ORIGINAL PAPERS 7

Aerospace Vehicle Development Applications of Atmospheric Thermodynamic Inputs William Walton Vaughan, Dale Leroy Johnson

Design, Fabrication and Flight Demonstration of a Remotely Controlled Airship for Snow Scientists Rajkumar Sureshchandra Pant

Theoretical Optimal Trajectories for Reducing the Environmental Impact of Commercial Aircraft Operations Cesar Celis, Vishal Sethi, David Zammit-Mangion, Riti Singh, Pericles Pilidis

Noise Source Distribution of Coaxial Subsonic Jet-Short-Cowl Nozzle Odenir de Almeida, João Roberto Barbosa, Juan Battaner Moro, Rodney Harold Self

Life Cicle Inventory for Lead Azide Manufacture Erick Braga F. Galante, Assed Haddad, Dieter Boer, Danielle Bonifácio

Development and Optimization of a Catalytic Thruster for Hydrogen Peroxide Decomposition Fernanda Francisca Maia, Leonardo Henrique Gouvea, Luis Gustavo Ferroni Pereira, Ricardo Vieira, Fernando de Souza Costa

Effect of Varying Design Options on the Transient Behavior of a Hybrid Rocket Motor Raed Kafafy, Muhammad Hanafi Azami, Moumen Idres

Efficiency-Multimission Comprehensiveness Balance for Platform-Based Satellite Family Otavio Luiz Bogossian, Geilson Loureiro, Roberto Vieira Fonseca Lopes, Edgardo Roggero

communication 93

Disaster Monitoring Constellation Using Nanosatellites Mohamed Kameche, Haider Benzeniar, Ayhane Bey Benbouzid, Redha Amri, Nadir Bouanani

INSTRUCTIONS TO AUTHORS 101 Instructions to Authors

doi: 10.5028/jatm.v6i1.335

Editorial A Stronger than ever Journal on Space Sciences, Technology, Management and Applications Ana Cristina Avelar1, Antonio F. Bertachini A. Prado2

his first JATM issue of 2014 is a very special one, and will be a landmark in the history of the journal. Starting on this number, an association was made between JATM and JAESA (Journal of Aerospace Engineering and Applications), the journal created by the Associação Aeroespacial Brasileira (AAB). Both periodicals will combine their efforts and merge into a single journal. This combination will allow a better use of financial and human resources, which were, before the present issue, divided into two journals that cover about the same topics, were based in neighboring institutions, and had many editors and referees in common. Publications made until the year 2013 will be preserved in their original archives. From January 2014, the joint publication will appear under the name of JATM. Submissions to the merged journal should be made at http://www.jatm.com.br. The editors and referees will form a single team. It was also decided to keep both editors in chief, as each one brings experience and individualized vision, which certainly will enrich the discussions and make decision

on publication of articles easier, since aerospace areas present specific and varied aspects. Looking back in time, the JAESA was created in 2007 as a technical journal, objective of which was publishing papers of research and development in aerospace engineering and space sciences, as well as applications in both the areas developed internationally. It was organized to cover topics related to space programs around the world and their applications, as well as theoretical developments in those areas and related ones, like astronomy, numerical methods, control systems, and so on. The first number of the journal is dated January–April 2008, and a total of 12 issues were published in the last 6 years. The geographical distribution of authors, referees, and editors was very large, what assured a really international journal. Several important steps were made by the journal, including the important indexation at SCOPUS. On the other side, JATM was created in 2009 in the Instituto de Aeronáutica e Espaço (IAE), with the purpose of providing the scientific community with the high-quality results of research

1.Instituto de Aeronáutica e Espaço, São José dos Campos/SP, Brazil | Ana Cristina Avelar received a Mechanical Engineering degree from the Universidade Federal de Itajubá (UNIFEI), in 1994. She received the MSc and Doctor degree in Mechanical Engineering in the area of Thermal and Fluids Sciences in 1997 and 2001, respectively, by the Universidade Estadual de Campinas (UNICAMP). She started in the Instituto de Aeronáutica e Espaço (IAE), as a researcher in 2002. She has been actuating in the area of experimental methods in wind tunnel, mainly with the optical techniques of particle image velocimetry (PIV) and pressure sensitive paint (PSP). From September 2010 to September 2011 she carried out and a post-doc stage in the Laboratoire de Mécanique de Lille, Lille, France, working with Tomographic PIV. Today she is also a professor in the post-graduation program in Sciences and Space Technologies, PG-CTE, in the Instituto Tecnológico de Aeronáutica (ITA). Email: editor@jatm.com.br 2.Instituto Nacional de Pesquisas Espaciais, São José dos Campos/SP, Brazil | Antonio F. Bertachini A. Prado has 25 years of experience in research and educational activities in the aerospace field. He obtained five academic degrees: Ph.D. (1993) and Master (1991) in Aerospace Engineering from the University of Texas at Austin (USA), Master in Space Science/Orbital Mechanics (1989) from the Instituto Nacional de Pesquisas Espaciais (INPE) in Brazil, BA in Physics (1986) and Chemical Engineering (1985) from Universidade de São Paulo in Brazil. In 1989 he also participated in the summer session of the International Space University, in Strasbourg, France. He is currently President of the Board of the Graduate School at INPE. He also has the following positions: Advisor in the field of Astrodynamics for Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) since 1995, Full Professor of the Universidade Estadual Júlio de Mesquita Filho, and Associate Fellow of American Institute of Aeronautics and Astronautics (AIAA). Email: antonio.prado@inpe.br

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.5-6, Jan.-Mar., 2014

Avelar, A.C. and Prado, A.F.B.A.

and development in the aerospace area, offering public access to all of its contents and generating a greater global exchange of knowledge. In 2011, JATM became an official publication from Departamento de Ciência e Tecnologia Aeroespacial (DCTA) and started receiving financial support from the prestigious Fundação Conrado Wessel (FCW). This financial support has been a very important means for guaranteeing the journal periodicity and punctuality, essential for a high-quality job. Today, almost 6 years after its creation, JATM publishes, without charging any fee from the authors or readers, papers from important institutions around the world. It has an Editorial Board composed of recognized researchers from several countries in different specialties, and it is indexed in 16 important databases, among them SCOPUS, LATINDEX, REDALYC, EBSCO Publishing, and PERIÓDICOS CAPES. JATM is quarterly published, and its website has more than a thousand of hits per month and, more important, from different countries. Based on the individual success, discussions related to the association of both journals have been made for several years. This action would be beneficial for the divulgation of science, the ultimate goal of both publications. In the end of 2013, it was clear that merging the publications would be the best thing to do. A single journal with a stronger editorial committee would increase the quality of all the steps involved in the production of the journal, from recruiting authors, evaluation and selecting papers, publication of the issues and divulgating it. After the decision of the unification was taken, it was

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.5-6, Jan.-Mar., 2014

necessary to plan the next steps. The first action required was the decision about the name of the new journal. A different title would require starting over again several important steps, since all the indexations and citations accumulated in the last 6 years would be lost. Therefore, it was decided to keep the title as JATM, which is a bit more generic, because it includes the “management” activities, very important in the space activities today. So, despite of the title, the publication is essentially a result of the combination of both the journals and is now fully supported by the DCTA, FCW, and AAB. After that, a combination of the editorial members from the two journals was made, trying to find the best geographical affiliation and technical distribution for the editorial board. The new Editorial Board of JATM very well shows the power of the combined journal: there are now 49 editors, from 12 different countries, covering 25 fields of expertise. Those numbers show a strong international journal able to cover all the fields of space activities. As a final step, a strong effort has been carried out in divulgating the news for all possible authors and readers, in an attempt to get an increase in the number of submissions. We sincerely hope that this unification will create a stronger journal and that the future will prove that this decision was right. Once again, we thank all our collaborators: authors, referees, and editors of both the journals for their important work in the last years, and we ask them to continue supporting our unified journal.

doi: 10.5028/jatm.v6i1.279

Aerospace Vehicle Development Applications of Atmospheric Thermodynamic Inputs William Walton Vaughan1, Dale Leroy Johnson2

ABSTRACT: Aerospace engineering has always had the need for various natural environment parameters to be used as inputs in research and engineering analyses used in the design and development of aircraft and launch/reentry vehicles. Although winds are indeed the main natural environment parameter used as inputs in vehicle design, the thermodynamic atmospheric parameters and models are also of great value and much needed as inputs. This paper will help the design engineer, chief engineer, or project manager understand the role that these thermodynamic parameters/models play. KEYWORDS: Aerospace meteorology, Launch vehicle development, Mission operations, Atmospheric thermodynamic parameters/models.

INTRODUCTION This paper is a follow-up to the article “Aerospace Meteorology: An Overview of Some Key Environmental Elements”, published in the Journal of Aerospace and Technology Management (Vaughan and Johnson, 2013). It has presented an overview with background on how the natural environment plays a major role in the design and development of launch vehicles, with an emphasis on what is presented in the NASA technical report, “Terrestrial Environment (Climatic) Criteria Guidelines for Use in Aerospace Vehicle Development” (Johnson, 2008), as a help to the design engineer. This paper presents a few specific examples of how the atmospheric thermodynamic parameters (pressure, temperature and density) along with how thermodynamic model atmospheres play a significant role as input in engineering vehicle design and analysis. Surface and upper air winds are usually the main natural environment driver used in launch vehicle design and development. A launch vehicle’s flight control and structural systems are sensitive to extreme wind speed, turbulence, wind gust, and wind shear that may occur during launch and ascent (or lift-off) throughout the Earth’s terrestrial atmosphere (from 0 to 90 km altitude). However, the three thermodynamic parameters of the atmosphere (pressure, temperature and density) are also very important in aerospace vehicle planning, design, development, testing and launch/reentry. This paper presents a historical look at the use of some of these thermodynamic procedures/models. The NASA Terrestrial Environment (Climatic) Criteria Guidelines Technical Memorandum (Johnson, 2008); hereafter referred to

1.University of Alabama – Huntsville/AL – USA 2.NASA (retired) – USA Author for correspondence: William W. Vaughan | 301 Sparkman Drive Northwest | 35899 Huntsville/AL – USA | Email: vaughan@nsstc.uah.edu Received: 06/16/2013 | Accepted: 11/15/2013

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.7-17, Jan.-Mar., 2014

Vaughan, W.W. and Johnson, D.L.

as TM (Johnson, 2008) has over the years been a major natural environments initial source document. It contains 13 key technical natural terrestrial environment parameters that can be used in the engineering design and development of launch/space vehicles. It presents the surface and in-flight thermodynamic parameters of the atmosphere in a statistical and modeling mode. The applicable model should be selected for design use based on the operational requirements for the aerospace vehicle. Mean and extreme values of these thermodynamic parameters can be used in application to many aerospace vehicle design and operational problems, such as (1) research planning and engineering design of remote Earth sensing systems, (2) vehicle design and development, and (3) vehicle trajectory analysis, dealing with vehicle thrust, dynamic pressure, aerodynamic drag, aerodynamic heating, vibration, structural and guidance limitations, and reentry analysis. Model atmospheres have also been used and improved, starting with use of the U.S. Standard Atmosphere 1976 (Anon., 1976), and its predecessors, for any U.S. site in general. However, if sufficient surface and aloft atmospheric data (measurements) exist for the exact launch site location, a Range Reference Atmosphere (RRA) can then be developed specifically for that site. Finally a Global Reference Atmospheric Model (GRAM-2010) (Leslie and Justus, 2011), has been developed so that thermodynamic values versus altitude can be obtained for any site on planet Earth. A launch or a re-entry trajectory can also be run through GRAM. Atmospheric density normally has been the main thermodynamic parameter affecting launch vehicle development for flight within the terrestrial environment. Initially, mean or median values of density (from standard or reference atmospheres for a specific site) were used as input for certain engineering calculations. Extremes of density, i.e. vertical profiles of maximum and minimum have also been used for all altitudes within the terrestrial atmosphere. However, this is very unrealistic in the real atmosphere as density is not an extreme at all terrestrial altitudes. Therefore, this brought about the construction of hot (summer) and cold (winter) atmosphere development for the various launch sites of interest to NASA, specific sites such as Kennedy Space Center (KCS)/FL, Vandenberg Air Force Base (VAFB)/CA, and Edwards Air Force Base (EAFB)/CA, etc. Finally, the NASA MSFC Global Reference Atmospheric Model (Leslie and Justus, 2011) provides in-flight atmospheric thermodynamic variables for all global geographical sites. Another unique thermodynamic procedure developed over the years is also presented within this paper. The Buell statistical relationships (Buell, 1954; Buell, 1970) between the independent J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.7-17, Jan.-Mar., 2014

variables of atmospheric pressure (P), temperature (T), and density (ρ) has been developed. This method allows one to obtain simultaneous values of two thermodynamic variables at discrete altitude levels, given that the third variable is an extreme value. Whenever an extreme thermodynamic parameter like ρ is given, the associated P and T values can then be calculated from this statistically developed procedure (equations). Likewise, also for the other two atmospheric thermodynamic parameters, should they be extreme values. Finally, if meteorological balloon and rocket data exist only up to about 55-km altitude, there is another statistical technique which extrapolates the thermodynamic data from the last level of measurement up to the 90 km terrestrial altitude level, with acceptable accuracy (Graves et al., 1973). This extrapolation procedure is not presented within this paper, but can be obtained directly from Graves, et al.(1973). The Standard Atmosphere section of TM (Johnson, 2008) presents a more detailed and complete overview of all atmospheric thermodynamic models and procedures than the ones presented here. Most of what is presented in this paper was taken from the Johnson (2008), and much of the description and text presented here has been taken directly from all the various references cited. Keep in mind that the TM (Johnson, 2008) is a terrestrial natural environment engineering applications document that has been maintained and updated by NASA for over the last fifty years, as it provides to the engineer or program/project manager all the various natural terrestrial environment statistics and models that can be used in the planning, design and development of aerospace vehicles and payloads. What is presented within this paper is a discussion of (1) atmospheric density versus altitude, (2) the atmospheric thermodynamic parameters and how they were applied within the Space Shuttle Program design, (3) the Standard and Reference atmospheres, (4) typical hot and cold atmospheres, (5) the Earth Global Reference Atmospheric Model, and (6) the application of the Buell statistical relationships regarding an extreme thermodynamic parameter and the two associated parameters.

DISCUSSION A general discussion of atmospheric density, atmospheric pressure, pressure decrease with altitude, atmospheric temperature, extreme cold compartment temperatures, and

Aerospace Vehicle Development Applications of Atmospheric Thermodynamic Inputs

In-flight Atmospheric Density at Altitude The density of the atmosphere decreases rapidly with height, decreasing to one-half of the surface value at ~7 km altitude at mid latitudes. Density is also variable at a fixed altitude, with the greatest relative variability occurring at ~70 km altitude in the high northern latitudes (60°N). Other altitudes of maximum density variability occur around the surface and 16 km. Altitudes of minimum variability occur around 8, 24 and 90-km altitude. Figure 1 shows these altitude density tendencies for the Kennedy Space Center/FL area along with the extreme density envelope, and hot/cold density values as a percent of the Patrick Reference Atmosphere 1963 (Smith and Weidner, 1964). These extreme density values approximate the ±3 σ (corresponding to the normal distribution) density values. Figure 2 provides the associated virtual temperature profiles for the KSC extreme, and hot/cold, and PRA-63 Models. Density varies with latitude in each hemisphere, with the mean annual density near the surface increasing toward the poles. In the region around 8-km altitude in the Northern Hemisphere;

120 Envelope of Maximum and Minimum Density

Altitude (km)

100

COLD

HOT

100

PRA-63 Hot Atmosphere Cold Atmosphere Extreme Temperature Envelope

Altitude (km)

atmospheric density (surface and at altitude) variability is presented in detail in Johnson (2008). The other key or unique thermodynamic parametric relationships for this paper are discussed below.

60 40

20 0 150

170

190

210

230

250

270

290

310

Temperature (K)

Figure 2. Extreme KSC virtual temperature profiles and the KSC hot & cold temperatures shown with the PRA-63 Temperature (Johnson, 2008).

e.g. the density variation with latitude and season is small. Above 8-km to ~28-km, the mean annual density decreases toward the north. Mean monthly densities between 30-km and 90-km increase toward the north in July and toward the equator in January (Smith, 1964; Johnson, 2008). Drag on a reentering spacecraft, which is a direct function of atmospheric density at a given altitude for a specific vehicle, like the Space Shuttle, has varied up to 19% over a few seconds of flight time, resulting from “patchy” density variations (density “pot holes”). The designer must recognize that atmospheric density variations do occur, and they will highly influence engine performance, specific fuel consumption, drag, and flight control. GRAM-2010 (Leslie and Justus, 2011) has been designed to reproduce typical density variations that can be encountered along a given flight path and should be considered in vehicle design, both ascent and reentry.

0 -30

-20

-10

(%)

Figure 1. Relative deviations (%) of extreme KSC density profiles with respect to PRA-63 density. KSC hot & cold density also shown (Johnson, 2008).

Thermodynamic Parameter Requirements Used in the Space Shuttle Program The Space Shuttle Requirements document, NASA NSTS 07700 Book 2, Volume X, Appendix 10.10 (Anon., 1999), contains very specific atmospheric wind and thermodynamic parametric requirements to use for the design and development of the Space Shuttle launch vehicle. Requirements are given with regard to the atmospheric thermodynamic parameters, as shown in the paragraph below, taken directly from cited document. Key models and purposes are presented. Most of these requirements refer back to the Terrestrial Environment J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.7-17, Jan.-Mar., 2014

Vaughan, W.W. and Johnson, D.L.

(Climatic) Criteria Guidelines document of 1973 (Daniels, 1973), which was base-lined early-on for the Space Shuttle program. Since the Earth GRAM was not developed when the Shuttle Requirements Document (Anon., 1999) was first assembled, it was not included initially. NASA NSTS 07700 (Anon., 1999), contains the neutral atmosphere requirements. The following atmospheric models are base-lined for the Space Shuttle as indicated for the following engineering functions: • NASA-GRAM: used in Vehicle Design (for Ascent/for Reentry, except at lower altitudes) along given flight path; used as subroutine in Trajectory, Orbit Propagator, or Simulations of In-flight programs. • NASA-Hot & Cold Atmospheres: used in Ascent Design for all Altitudes; used in Reentry Studies from 30 km to Surface; used in Design Calculations (aerodynamic heating, engine performance, and ferry operation); used for aerospace ferrying vehicles, in conjunction with hot or cold day design ambient air temperatures over runways, producing the extreme atmosphere resulting in the maximum vehicle design requirement. • Cape Kennedy Reference Atmosphere (PRA-63): used as Nominal Criteria for Surface to Orbit Insertion, Abort Trajectory and Analyses for launches; used as nominal criteria 30 km altitude to surface, for vehicle reentry; used for return to landing site (RTLS), and External Tank analysis and disposal design. • Buell Extrapolation Technique: used for design analyses requiring a knowledge of the two atmospheric variables that are associated with a third extreme variable at discrete altitudes. • U.S. Standard Atmosphere 1976: used as a Standard Day for purposes of Engine Ratings and Comparisons Thereof (Sea level values); used in Orbiter Entry (90 km down to 30 km altitude); used for non-RTLS External Tank analysis and disposal design. Standard Atmosphere A standard atmosphere is a vertical description of atmospheric temperature, pressure and density that is usually established by international agreement and taken to be representative of the Earth’s atmosphere. The first standard atmospheres established by international agreement were developed in the 1920s, primarily for the purposes of pressure altimeter calibrations and aircraft performance calculations. Later, some countries, J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.7-17, Jan.-Mar., 2014

notably the United States, also developed and published standard atmospheres. The term standard atmosphere has, in recent years, also been used by national and international organizations to describe vertical descriptions of atmospheric trace constituents, the ionosphere, aerosols, ozone, atomic oxygen, winds, water vapor, planetary atmospheres, etc. (Vaughan, 2010). The standard sea-level values of temperature, pressure, and density that have been used for decades are: temperature of 288.15 K, or 15°C; pressure of 1013.25 mbar or 760 mmHg; and density of 1225.00 g/m3 (Anon., 1976). The history of standard and reference atmospheres are presented and summarized in Vaughan (2010). Key atmospheric engineering models are given in Johnson (2008). The 1966 U.S. Standard Atmosphere Supplements (Anon., 1966) present different latitudinal atmospheres for the United States. It includes tables of temperature, pressure, density, sound speed, viscosity, and thermal conductivity for five northern latitudes (15, 30, 45, 60, 75 degrees), for summer and winter conditions. Reference Atmospheres The term reference atmosphere is used to identify vertical descriptions of the atmosphere for specific geographical locations or globally, such as the Range Reference Atmospheres (RRA) and the Earth-GRAM-2010 version 2 (Leslie and Justus, 2011). These RRA were developed by organizations for specific applications, especially as the aerospace industry began to mature after World War (Vaughan, 2010). In design and preflight analysis of aerospace vehicles, average atmospheric models are used to represent the mean or median thermodynamic conditions with respect to altitude. For general worldwide design, the U.S. Standard Atmosphere, 1976 (Anon., 1976) has been used but site-specific atmosphere models are needed at each launch location. A group of 17 RRA from the RCC/MG as documented in Anon. (1984) have been prepared to represent the thermodynamic medians within the first 70-km altitude at various ranges and launch locations. So far, a total of over 29 different site RRA have been issued. Anon.( 1966), Cole and Kantor (1978) – the supplemental atmospheres – together with GRAM (Leslie and Justus, 2011), are also useful in this regard. The NASA Marshall Earth – Global Reference Atmospheric Model – 2010 Version (Leslie and Justus, 2011) was constructed such that it provides a close approximation to the respective RRA. For the derivation of GRAM-2010, a total of 21 RRA from different sites have been utilized within GRAM-2010. These include the sites:

Aerospace Vehicle Development Applications of Atmospheric Thermodynamic Inputs

• Argentia, Newfoundland (St. Johns Airport) • Ascension Island, Atlantic • Barking Sands, Hawaii (Lihue) • Cape Canaveral, Florida • China Lake Naval Air Weapons Center, California • Dugway Proving Ground (Salt Lake City), Utah • Edwards Air Force Base, California • Eglin AFB, Florida • El Paso, Texas • Fairbanks, Alaska

Currently, some of the most commonly used standard and reference atmospheres used in the U.S. include: • COSPAR International Reference Atmosphere (CIRA), 1986 • ISO Standard Atmosphere, 1975 • NASA Earth Global Reference Atmosphere Model (GRAM), 2010 • NRL MSIS Reference Atmosphere, 2000 • RCC/MG Range Reference Atmospheres (RRA) • U.S. Standard Atmosphere, 1976 • U.S. Standard Atmosphere Supplements, 1966

• Ft. Huachuca Elec Prvng Grnd (Tucson), Arizona • Great Falls, Montana • Kwajalein Missile Range, Pacific • Nimes-Courbessac, France (STS TAL Site) • Nellis AFB, Nevada (Mercury) • Point Mugu Naval Air Weapons Center, California • Taguac, Guam (Anderson AFB) • Vandenberg AFB, California • Wallops Island, Virginia (NASA) • White Sands Missile Range, New Mexico

A detailed listing and description of many worldwide reference and standard atmospheric models is given in Vaughan (2010). In 1996, the American Institute of Aeronautics and Astronautics first published a Guide to Reference and Standard Atmosphere Models (Vaughan, 2010). This document has been updated since then and provides information on the principal features for over 70 global, regional, middle atmosphere, thermosphere, test ranges, Earth and planetary reference and standard atmospheric models.

• Yuma Proving Ground, AZ (San Diego, California)

A major new feature of the GRAM-2010 (Leslie and Justus, 2011) is the optional ability to use data (in the form of vertical profiles) from a set of RRA as an alternate to the usual GRAM climatology. With this feature, it is possible, for example, to simulate a flight profile for an aerospace vehicle that takes off from the location of one RRA site, e.g. EAFB/CA using the range reference atmospheric data to smoothly transition into an atmosphere characterized by the GRAM climatology, then smoothly transition into an atmosphere characterized by a different RRA site, e.g. WSMR/NM, to be used as the landing site in the simulation. The user can also prepare data for any other site desired for use in this mode. The GRAM-2010 model will be discussed in more detail later in this paper. The Patrick Reference Atmosphere (PRA-63) (Smith and Weidner, 1964) is a more extensive site specific annual reference atmosphere presenting data to 700-km altitude for KSC/FL. Because of the utility of this atmosphere, a simplified version is given extracted from Johnson (2008). Reference atmospheres are also available for VAFB (Carter and Brown, 1971; Johnson, 2008) and EAFB (Johnson, 1975; Johnson, 2008). These provide an annual reference atmosphere model to 700 km and have been designated as computer subroutines VRA-71 and ERA75, respectively.

Extreme Hot and Cold Atmospheric Profiles for KSC, VAFB, and EAFB Johnson (2008), gives the two extreme density profiles that correspond to the summer (hot) and winter (cold) extreme atmospheres for KSC, VAFB and EAFB. See Johnson (1973) for VAFB and Johnson (1975) for EAFB, for detailed information pertaining to these extreme atmospheres. The associated values of temperature and pressure versus altitude are also tabulated. These extreme atmospheric profiles should be used in ascent design analyses at all altitudes. For reentry studies, they are to apply only from 30 km to the surface for vehicles to be used at KSC, VAFB, or EAFB. For those aerospace vehicles with ferrying capability, design calculations should use these extreme profiles in conjunction with the hot or cold day design ambient air temperatures. The extreme atmosphere producing the maximum vehicle design requirement should be utilized to determine the design. The extreme envelopes of density deviations given in Fig. 1 imply that a typical individual extreme density profile may be represented by a similarly shaped profile, e.g. deviations of density are either all negative or all positive from sea level to 90-km altitude. However, examination of many individual density profiles shows that when J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.7-17, Jan.-Mar., 2014

Vaughan, W.W. and Johnson, D.L.

large positive deviations of density occur at the surface, correspondingly large negative deviations will occur near 15-km altitude and above. Such a situation occurs during the winter season (cold atmosphere). The reverse is also true — density profiles with large negative deviations at lower levels will have correspondingly large positive deviations at higher levels. This situation occurs in the summer season (hot atmosphere) and is presented in Fig. 1. The two extreme (hot & cold) KSC density profiles of Fig. 1 are shown as percent deviations from the Patrick Reference Atmosphere, 1963 density profile (Smith and Weidner, 1964). The two profiles obey the hydrostatic equation and the ideal gas law (i.e. the hypsometric relation). The extreme density profiles shown up to 30-km altitude were observed/measured in the atmosphere. The results shown above 30-km altitude are somewhat speculative because of the limited data from that region of the atmosphere. Quasi-isopycnic levels (levels of minimum density variation) are noted at approximately 8 and 86-km. Another level of minimum density variability is seen at ~24 km, and levels of maximum variability occur at zero, ~15 and ~68-km altitude. The associated extreme hot and cold virtual temperature profiles for KSC are given in Fig. 2. Temperatures below ~10-km altitude are virtual temperatures. Virtual temperature includes moisture to avoid computation of the specific gas constant for moist air (Johnson, 2008). NASA-MSFC Earth GRAM-2010 Reference or standard atmospheric models have long been used for design and mission planning of various aerospace systems. The NASA Marshall Space Flight Center Global Reference Atmospheric Model (Leslie and Justus, 2011) was developed in response to the need for a design reference atmosphere (using empirical data bases) that provides complete global geographical variability and complete altitude coverage (surface to orbital altitudes), as well as complete seasonal and monthly variability of the thermodynamic variables and wind components. Figure 3 provides a graphical summary of the data sources and height regions of GRAM-2010. In addition to providing the geographical, height, and monthly variation of the mean atmospheric state, it includes a perturbation model that has the ability to simulate spatial and temporal perturbations in these atmospheric parameters, if dispersions are desired (e.g., fluctuations due to turbulence and other atmospheric perturbation phenomena). When a large number of Monte J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.7-17, Jan.-Mar., 2014

MET2007, MSIS, or JB2008 Thermosphere model

120 100 Altitude, km

Satellite data

Thermosphere Fairing between MAP and Thermosphere model

80 Mesosphere

60 40 Stratosphere

20 0

Troposphere

Middle Atmosphere Program (MAP) Data bases

Rocket and remote sensing data

Fairing between MAP and NCEP Data National Centers for Environmental Prediction (NCEP)

Balloon, aircraft, & satellite remote sensing data

Figure 3. Summary of the three atmospheric regions in the GRAM-2010 program, sources for the model, and data on which the mean monthly GRAM-2010 values are based. See GRAM-2010 for more details (Leslie and Justus, 2011).

Carlo type model profile dispersions are generated at any location, the mean and standard deviation of these data will match those of the observations. The model is statistically equivalent to available measurements. The ±2 σ envelopes encompasses ~95.45% of the observations, while ±3 σ encompasses ~99.73% of the observations (according to the normal distribution). This perturbation feature makes Earth GRAM especially useful for Monte Carlo dispersion analyses of guidance and control systems, thermal protection systems, and similar applications. Some of these applications have included operational support for Space Shuttle entry, flight simulation software for other vehicles, entry trajectory and landing dispersion analyses for the Stardust and Genesis missions, planning for aerocapture and aero-braking for Earth-return from lunar and Mars missions, 6 degree-of-freedom entry dispersion analysis for the Multiple Experiment Transporter to Earth Orbit and Return (METEOR) system, and the Crew Exploration Vehicle (CEV). GRAM can also input a spacecraft trajectory and output the various atmospheric parameters along this trajectory path. The atmospheric model recommended for all reentry analyses, except lower altitudes specified in Johnson (2008), is GRAM-2010 (Leslie and Justus, 2011). GRAM is also suitable for use as a subroutine in a trajectory code or orbit propagator program or other programs used for simulations of in-flight or on-orbit atmospheric variability in density, temperature, or winds.

Aerospace Vehicle Development Applications of Atmospheric Thermodynamic Inputs

55 Latitude, degrees North

45 z=120km 40

z=70km t=1000s

t=1500s Circles every 10 km in height Tick marks every 100 seconds

25 20

15 130 140 150 160 170 180 190 200 210 220 230 240 250 260 Longitude, degrees East

Figure 4. January Ground Track of a Typical Re-entry Trajectory (57° Inclination Orbit) Landing at Edwards AFB, CA).

110

1.20

1.15

100 90 Height, Km

1.00

130

0.9

0.95

1.00

0.75

0.80 0.85

1.10

1.05 0.95 0.90

1.05

0.90 0.95 1.00

1.05

140

150

200

400

600

800

1000

1600

Time, seconds

1.5 1.4

Mean ± 2 Standard deviations Perturbation profile

1.3 1.2 1.1 1.0 0.9 0.8 0.7 0.6 0.5

130

140

150

160

170

180

190

200

Longitude, degrees East

210

220

230

240

t=500s

t=0

Figure 6. Typical trajectory through GRAM-99 Mean January Atmospheric Density with ±2 s Density Envelopes (95.45%) and one Monte-Carlo Density Perturbation Profile versus Longitude (as a Ratio of US76 Standard Density).

z=80km

z=90km

1.6

Density/US Standard 1976

gRAm TRAjECTORY/RE-EnTRY ExAmplE Figure 4 presents a typical re-entry spacecraft trajectory plot of its latitude-longitude location with height and time as indicated. De-orbiting in from a 57° inclination orbit to land at EAFB, in January. Figure 5 shows this same typical trajectory passing through the GRAM-99 derived mean January cross-sectional map of density as a function of height (altitude) vs. longitude (shown as a ratio of U.S.76 Standard density). The density variability along the trajectory starts at ~20% higher density than the U.S.76 Standard, then goes into a region of ~25% lower than standard, and finally ~5% higher than standard before landing at Edwards. Figure 6 presents

160

170

180

190

200

210

Longitude, degrees East

220

230

240

Figure 5. Typical trajectory through GRAM-99 Mean January Height versus Longitude Cross Section of Density (as ratio of US 76 Standard Density).

the same trajectory, and density results, as before; but now includes one Monte Carlo type density perturbation run along this trajectory. This time the trajectory is plotted showing the mean calculated GRAM-99 density, along with the ±2 σ densities, and the one perturbed density track, all plotted on a density ratio versus longitude graph. Note that the perturbed density does exceed the +-2 σ boundary from time to time, as ±2 σ represents approximately 95.45% of the observations, according to the normal distribution. SImulTAnEOuS VAluES Of KSC TEmpERATuRE, pRESSuRE, AnD DEnSITY AT DISCRETE AlTITuDES This section presents simultaneous values of atmospheric temperature, pressure, and density as guidelines for aerospace vehicle design considerations. The necessary assumptions and the lack of sufficient statistical data samples restrict the precision with which these data can currently be presented. The analysis is currently limited to KSC, FL only. The Buell (Buell, 1954; Buell, 1970) statistical relationships between atmospheric P, T, and ρ, when 1 is an extreme value, are presented here. Method of Determining Simultaneous Values An aerospace vehicle design problem that often arises in considering natural environmental data is stated by the J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.7-17, Jan.-Mar., 2014

Vaughan, W.W. and Johnson, D.L.

following question: “How should the extremes (maxima or minima) of temperature, pressure, and density be combined (1) at discrete altitude levels and (2) versus altitude?” As an example, suppose one wants to know what temperature and pressure should be used simultaneously with a maximum density at a discrete altitude. From statistical principles set forth by Buell (1970), the solution results by allowing mean density plus three standard deviations to represent maximum density and using the coefficients of variations, correlations, and mean values as expressed in Eq. 1 below (Johnson, 2008):

(1)

The associated values for pressure and temperature are the last two terms of Eq. 1, (A) and (B), multiplied by and , respectively, and then this result is added to and , respectively. Appropriate values of the thermodynamic correlation coefficients (r) and coefficients of variation (CV) are obtained from Johnson (2008). In general, the three extreme ρ, P, and T equations of interest are:

(2)

(3)

(4)

where M denotes the multiplication factor to give the desired deviation. The values of M for the normal distribution and the associated percentile levels are shown in Johnson (2008). The two associated atmospheric parameters that deal with a third extreme parameter are listed in more detail in Table 1. It must be emphasized that this procedure is to be used at discrete altitudes only, and holds for a specific site (KSC FL). Whenever extreme vertical profiles of pressure, temperature, and density are required for engineering application, the use of these correlated variables at discrete altitudes is not satisfactory. In TM (Johnson, 2008) there is a section (hot and cold atmospheres) that deals directly with this problem, since profiles of only extreme values of pressure, temperature, or density at every level from zero to 90-km altitude is unrealistic in the real atmosphere. GRAM can also be used to represent by Monte Carlo process the respective range of thermodynamic parameters for a given geological site. Hot Surface/Cold Tropopause Temperature Example The following equations from Table 1 will be used, for an Extreme Cold 18-Km Summer time Temperature example at KSC:

table 1. Associated Parameters for Extreme Density, Temperature, and Pressure (Johnson, 2008). for Extreme Density

for Extreme Temperature

for Extreme pressure

Passoc.

Tassoc.

ρassoc. Use + sign when extreme parameter is maximum and use – sign when extreme parameter is minimum. M is the 1, 2, or 3 sigma (normal) for the extreme parameter in question.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.7-17, Jan.-Mar., 2014

Aerospace Vehicle Development Applications of Atmospheric Thermodynamic Inputs

(5) and (6) And, for Extreme High Summer time Density at 18 km:

table 3. Patrick Reference Atmosphere 1963 (PRA-63) Annual Thermodynamic Parameters of T, P, and ρ at 18 km altitude (from Johnson, 2008). Altitude (km)

mean Temperature (K)

mean pressure (mb)

mean Density (kg/m3)

296.68

1017.01

1.18355

205.30

78.0974

0.132392

(7) The following example is shown for the KSC 18-km altitude level during a hot summer day when the surface value of temperature is high (hot) and surface density low. Whereas the value of temperature at the18-km (tropopause) altitude level is very low (cold) while the atmospheric density is very high. To compute the Eqs. 5 and 6 values for the associated density and pressure for this KSC extreme case of low (cold) temperature at 18-km altitude, one just needs to insert the thermodynamic CV and r values from Table 2 into Eqs. 5 (and 6) of Table 1. The associated value of 18-km density then turns out to be: ρassoc. = 0.132392 [1 -{ 3 (0.0275) (-0.7904)}] or ρassoc. = 0.141025 kg/m3 Which gives a higher density value than the annual PRA-63 density value does at 18-km. The percentage is 6.5% greater than the PRA-63 18-km density value (from Table 3).

table 2. KSC atmospheric thermodynamic coefficients of variation (CV) and correlation coefficients (r) between P, T, and ρ, at 18-km altitude (from Johnson, 2008). CV (%)

[σ ρ/ ]

[σP/ ]

[σT/ ]

2.75

1.75

1.70

(r )

r(Pρ)

r(PT)

r(ρT)

0.8036

-0.2706

-0.7904

CV = σ/mean. All parameters are unit less.

Atmospheric deviations as a percentage from the annual PRA-63 were computed by the following Eq. 8 (only density shown):

%∆ρ = ρmax or min - ρPRA63 x 100

ρPRA63

(8)

Next, the associated value for pressure at 18 km, from Eq. 6, is calculated as follows: Passoc.=78.0974 [1-{ 3 (0.0175) (-0.2706)}] or Passoc=79.20689 mb Which is 1.4% greater than the PRA-63 18-km pressure value at 18 km altitude. Next, we should compute the calculated low temperature value at 18 km altitude, given a high value maximum density condition at that same altitude level. So, using Eq. 7, for a given high density extreme, we can compute the associated value of temperature as follows: Tassoc=205.3 [1+{ 3 (0.0170) (-0.7904)}] or Tassoc=197.02 K Which is -4.0% of the PRA-63 temperature value at 18-km altitude. Finally, we should determine if these calculated values of associated density, pressure, and temperature, given an extreme parametric condition at 18-km altitude (i.e. extreme low temperature, extreme high density), are close to reality or not. We have obtained a few extremely hot summer day vertical sounding measurement taken at KSC. One sounding in particular (June 16, 1958) is very extreme at the surface (hot temperature) and at 18-km altitude (cold temperature), as presented in Table 4. By the way, this sounding was one of the key soundings used in the derivation of the KSC Hot atmosphere. Comparing the Buell calculated values of associated pressure, temperature and density at altitude of 18 km with the measured observations (Radiosondes-Raob) values gives good results as shown in Table 5. The three parametric value percent differences J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.7-17, Jan.-Mar., 2014

Vaughan, W.W. and Johnson, D.L.

are all within 0.8% of the measured Raob values. This is to be expected due to the fact that the inter- and intra-level correlations are very high at and between the surface level and the tropopause level for temperature and density.

Table 4. Actual KSC Atmospheric Raob Sounding Measurement for 1958-06-16-00Z. Altitude (km)

Temperature (K)

Pressure (mb)

Density (kg/m3)

Sfc.

307.46

1010.00

1.1366

195.5

79.7707

0.1422

SUMMARY REMARKS This paper presents some of the function and use that atmospheric thermodynamic parameters of pressure, temperature and density have had in the design and development of aerospace launch and reentry vehicles for flight through the terrestrial atmosphere. The application and use of these atmospheric models, or statistical values, will help the program/project managers and engineers with useful atmospheric inputs for engineering design, usage and trade studies. Most of the information presented here came from (Johnson and Vaughan, 2012) and especially (Johnson, 2008).

Table 5. Calculated versus KSC Measured Values of T, P, and ρ at 18 km altitude. Calculations

PRA-63 values Buell Computed (% of PRA-63)

Raob Measured (% of PRA-63)

KSC-Hot values (% of PRA-63) (% of Computed from Measured)

Temperature

Pressure

Density

205.30 K

78.09740 mb

0.132392 kg/m3

197.02 K

79.20689 mb

0.141025 kg/m3

-4.0 %

+1.4 %

+6.5 %

195.50 K

79.77020 mb

0.142200 kg/m3

-4.8 %

+2.1 %

+7.4%

200.0 K

81.36950 mb

0.141732 kg/m3

-2.6 %

+4.2 %

+ 7.1 %

+0.78 %

-0.71 %

-0.83 %

REFERENCES Anon., 1966, “U.S. Standard Atmosphere Supplements, 1966”, U.S. Government Printing Office, Washington, DC, 1966, pp. 3-36. Anon., 1976, “U.S. Standard Atmosphere, 1976”, U.S. Government Printing Office, Washington, DC, October 1976. Anon., 1984, “Range Reference Atmosphere Documents” published by Secretariat, Range Commanders Council (RCC), Meteorology Group (MG), White Sands Missile Range, NM. Anon., 1999, “NASA NSTS 07700 Natural Environment Space Shuttle Flight and Ground System Specification, Book 2, Vol. 10, Apx. 10.10”. Buell, C.E., 1954, “Some Relations Among Atmospheric Statistics”, Journal of Atmospheric Sciences, Vol. 11, No. 3, pp. 238-244. Buell, C.E., 1970, “Statistical Relations in a Perfect Gas”, Journal of Applied Meteorology, Vol. 9, No. 5, pp. 729-731.

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Carter, E.A. and Brown, S.C., 1971, “A Reference Atmosphere for Vandenberg AFB, California, Annual (1971 Version),” NASA/TM X–64590, NASA Marshall Space Flight Center, AL. Cole, A.E. and Kantor, A.J., 1978, “Air Force Reference Atmospheres”, AFGL–TR–78–0051, Air Force Surveys in Geophysics, No. 382. Daniels, G.E., 1973, “Terrestrial Environment (Climatic) Criteria Guidelines for Use in Aerospace Vehicle Development, 1973 Revision”, NASA/TMX-64757. Graves, M.E., Lou, Y.S. and Miller, A.H., 1973, “Specification of Mesospheric Density, Pressure, and Temperature by Extrapolation”, NASA/CR-2223, March 1973. Retrieved in January 23, 2013,from http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa. gov/19730012637_1973012637.pdf

Aerospace Vehicle Development Applications of Atmospheric Thermodynamic Inputs

Johnson, D.L., 1973, “Hot and Cold Atmospheres for Vandenberg AFB, California (1973 Version)”, NASA TM–64756, NASA Marshall Space Flight Center, AL.

Leslie, F.W. and Justus, C.G., 2011, “The NASA Marshall Space Flight Center Earth Global Reference Atmospheric Model-2010 Version 2”, NASA/TM—2011–216467.

Johnson, D.L., 1975, “Hot, Cold, and Annual Reference Atmospheres for Edwards Air Force Base, California (1975 Version)”, NASA/TM X–64970, NASA Marshall Space Flight Center, AL.

Smith, J.W., 1964, “Density Variations and Isopycnic Layers”, Journal of Applied Meteorology, Vol. 3, No. 3, pp. 290-298.

Johnson, D.L., 2008, “NASA/TM-2008-215633 Terrestrial Environment (Climatic) Criteria Guidelines for Use in Aerospace Vehicle Development, 2008 Revision”, Dec. 2008. Retrieved in January 23, 2013, from http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa. gov/20090022159_2009021428.pdf Johnson, D.L. and Vaughan, W.W., 2012, “How Atmospheric Thermodynamic Parameters and Model Atmospheres Have Been Used to Help Engineering in Aerospace Launch Vehicle Design & Development”, 50th AIAA 2012 Aerospace Sciences Meeting, Nashville, TN.

Smith, O.E. and Weidner, D.K., 1964, “A Reference Atmosphere for Patrick AFB, Florida, Annual (1963 Revision)”, NASA/TM X–53139, NASA Marshall Space Flight Center, AL. Vaughan, W.W., 2010, “Guide to Reference and Standard Atmospheric Models”, AIAA G-003C-2010, American Institute of Aeronautics and Astronautics, Reston, VA. Vaughan, W.W. and Johnson, D.L., 2013, “Aerospace Meteorology: An Overview of Some Key Environmental Elements”, Journal of Aerospace Technology and Management, Vol. 5, No. 1, pp. 7-14. doi: 10.5028/jatm.v5i1.188.

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doi: 10.5028/jatm.v6i1.313

Design, Fabrication and Flight Demonstration of a Remotely Controlled Airship for Snow Scientists Rajkumar Sureshchandra Pant1

ABSTRACT: A remotely controlled airship was designed, fabricated and demonstrated within a tight timespan of under a month after receiving the go-ahead. The main design requirement for this airship was to be able to operate from a Helipad located at an altitude of 6,572 feet AMSL under ISA+20 deg.C. The images of the terrain below were recorded during the flight and transmitted in real-time to a ground based system using an onboard telemetry system. The paper describes the methodology followed for sizing of the envelope and key components of the airship, and the procedure followed for in-house fabrication and testing. The major issues that cropped up during the operation of the airship in harsh weather conditions of rain and mild snow, as well as at night, are also highlighted. The demonstration established the efficacy of remotely controlled airships for aerial photography and data collection by snow scientists. KEYWORDS: Lighter-Than-Air systems, Airships, Blimps.

INTRODUCTION Aerial surveillance usually requires monitoring the activities of people and/events over a designated area and long period of time from an airborne platform. A monitoring process such as this would essentially be based on steady visuals from the area of interest. The design features and attributes that an aerial surveillance airborne platform must possess should help accomplish this mission completely and efficiently. For instance, such a system should have the ability to take-off and land vertically or from limited areas, since such airborne platforms may need to operate from remote locations, some of which might lack large flat open areas to get airborne and/or land. The onboard surveillance equipment suite has to steadily focus on the desired area on the ground, from a particular altitude. The quality of the surveillance is directly related to the level of stability that the platform can offer. Hence the airborne platform must be able to hover at an altitude and stay put without causing serious fluctuations to the dynamic surveillance data collection. The platform should be considerably inert to disturbances like crosswind, gust, wavering of the payload surveillance equipment due to maneuvering and induced vibrations by the platform itself. The platform should also require minimal expertise for operation, and the cyclic procedure from storage to deployment should be prompt and easy. Another important attribute is low cost of manufacture and operation of the system, not to mention the strict environmental regulations that need to be adhered to in this age of rapid degradation of our planet. Hence an airship is arguably the most suitable and preferred prospect

1.Indian Institute of Technology Bombay – Mumbai/Maharashtra – India Author for correspondence: Rajkumar Sureschandra Pant | Adi Shankaracharya Marg, Powai | 400076 Mumbai/Maharashtra – India | Email: rkpant@aero.iitb.ac.in Received: 12/10/2013 | Accepted: 01/06/2014

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Pant, R.S.

for an aerial surveillance system, taking into account all the above mentioned desirable attributes of such a system. Airships are aerodynamically shaped bodies filled with a “Lighter-Than-Air” (LTA) gas that displaces the ambient air, which results in a net upward force due to buoyancy. This is called static (or buoyant) lift, since it is due to the inherent buoyant force, which is generated even when airship is stationary. In addition, an airship also can generate dynamic lift due to the action of aerodynamic forces acting on it as it moves through the air, just like an airplane. Once airborne, airships can perform much like helicopters, remaining nearly geo-stationary for extended periods of time, albeit with much lesser fuel consumption, and noise and vibration levels. A remotely controlled (RC) airship is perhaps much more suitable than a remotely controlled aircraft for aerial surveillance due its long endurance loiter and lower fuel consumption. Design, fabrication and flight testing of prototypes of several LTA systems for various scientific applications has been carried out at IIT Bombay since 2002 (Gawale and Pant, 2002). Some recent projects include indoor remotely controlled airships for neural network control hardware implementation (Sangole et al., 2006), aerostats as platforms for low-cost re-locatable wireless communications systems (Gawande et al., 2007), outdoor remotely controlled airships for product promotion (Gawale et al., 2008), aerial river ferry using superheated steam-filled balloons (Banerjee et al., 2008), and aerostats for snow cover evaluation (Bhandari and Raina, 2009). The following sections highlight the design process and fabrication.

Design Requirements The key requirement of the RC airship was the ability to deploy the airship from a Helipad located at an altitude of 6,572 ft. AMSL, while carrying a payload camera that could take high resolution photographs of the terrain below and transmit them in-real time. As we shall observe later, the airship had to operate in very cold weather at a high altitude and the envelope material selection and fabrication process had to conform to such harsh environmental conditions. In addition, the airship specifications included limitations on its length and volume. As mentioned earlier, adequate stability of the airship during flight would obviously generate clear photographs for surveillance and this requires the airship to be controlled effectively. The stabilizers and fins were also designed keeping this requirement in mind. Of all these, the prime concern was of the propulsion system which needed to operate at altitudes where density of the air is lower than at sea level. Table 1 lists some of these key design requirements for the airship. Table 1. Airship design requirements. Parameters

Payload Operating altitude

An RC airship can broadly be divided into the five main components, viz., Envelope, Stabilizer and Fins (empennage), Gondola, Propulsion system and Remote Control system. The conceptual design and sizing of the airship is based on the methodology developed by Pant (2008), utilizing the aerostatic and aerodynamic analysis described in Cheeseman (1999). This methodology arrives at the baseline specifications of a manned airship with inputs on the user specified performance and operational requirements and has been suitably adapted by Gawale et al. (2008) for the design and sizing of RC airships. The scheme of arriving at baseline specifications of an airship to meet the user-specified operating and performance requirements is shown in Fig. 1.

3 kg 200 ft AGL

Base station altitude

6,572 ft AMSL

Design wind speed

15 m/s

Off Standard temperature

DESIGN PROCESS

Value

Operational time

20°C 15 days

Apart from the above mentioned requirements, which describe a more involved design process, more often than not we also have the constraint on lowering the cost of manufacturing and operation to a level acceptable to user. The following sections briefly explain the design of the important components of the RC airship. Envelope Design The envelope is the most crucial component of the airship and its design demands to optimally integrate the aspects of aerodynamics, stability and payload. Based on the constraints of length and payload, the GNVR shape was chosen for the envelope profile. The GNVR shape shown in Fig. 2 consists of three standard

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Design, Fabrication and Flight Demonstration of a Remotely Controlled Airship for Snow Scientists

Estimate Envelope Volume

Drag

Compute • Outside Geometry • Lift at min and max height • Ballonet Volume • Stabilizer Area

Aerostatic

Geometry

Change Volume Compute • Lift N

Compute • Reynolds Number • Drag Coeﬃcient

Payload = Lift - weight

Acceptable?

Compute • Cruise Drag & Power • Installed Power

Weight

Propulsion

Y Compute • Gondola Weight • Envelope Weight • Fuel Weight

Output

Figure 1. Scheme for determination of baseline specifications of an airship.

1.25d

1.625d

Ellipse

Circle d = 2623 mm

(1)

0.175d

where M is the bending moment and Pint is the internal static pressure. The pressure distribution on the outer envelope surface has been calculated to identify the points with maximum differential pressure. The total differential pressure was estimated using the equation given below:

Parabola x

3.05d Nose Ellipse - origin to 1.25d: Middle Circle - 1.25d to 1.62d:

(2)

Tail Parabola - 1.62d to 1.8d:

Figure 2. Geometrical Construct of GNVR profile.

sections, and its entire geometry is analytically parameterized in terms of its max diameter d. The length and volume of the airship GNVR envelope shape are 8 m and 26.64 m3. Experimental (Narayana and Srilatha, 2000) and numerical (Sundaram, 2000) studies have shown that this shape is very well suited for tethered aerostats operating at an altitude up to 1 km. To optimize the fabric thickness, the total load acting on the envelope was calculated as the summation of circumferential and longitudinal loads respectively as given by the equation:

where Paero is the dynamic pressure, Pint is the internal static pressure and h is the radius of the envelope. The center of pressure of this section is located at 33% of its length, which is also the point at which maximum differential pressure occurs. The flow over the hull is assumed to be turbulent and the drag is estimated in terms of drag coefficient CDV , maxitmum velocity, volume and the reference area from the formula (3)

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The envelope material has to be appropriately chosen to ensure adequate strength, durability, as well as low weight. A qualitative comparison of the properties of various fabrics that are used for load bearing was carried out and a light weight, yellow coloured PVC fabric was used, keeping in mind the weight, cost and availability constraints. The key properties of this material are listed in Table 2 (Gupta and Malik, 2002).

Table 3. Dimensions of the stabilizers.

Table 2. Properties of envelope material used for airship. Property

Parameter

Fixed surface

Moving surface

Surface area

0.61 m2 (Sf )

0.13 m2 (Sc)

CT (tip chord)

0.86 m

0.17 m

CR (root chord)

1.45 m

0.30 m

H (height)

0.86 m

B/2 (mean half span)

0.69 m

Value

H2 permeability @ 25 cm H2O column

3.75 liters/m2/day

Break strength along warp direction

1.68 kg/cm

Break strength along weft direction

1.76 kg/cm

Specific weight

0.14 kg/m2

Thickness

0.11 mm

Stabilizer and Rudder The stabilizer and rudder have been designed to have a cruciform shape on the envelope and the procedure for sizing has been adopted from Gawale and Pant (2002) by scaling down the dimensions of the stabilizer and rudder of existing airships. However, care has been taken to ensure that the scaling is done using data of airships which have envelopes of similar shapes and fineness ratios. A schematic view of the stabilizer for the airship is shown in Fig. 3 and its relevant dimensions are tabulated in Table 3.

Propulsion System A detailed overview of the power plant system, design issues in engine sizing and selection, advances in engine technology, various concepts for thrust vectoring and a methodology for sizing and selection of design features of an airship engine has been outlined by Gawale and Pant (2004). The specifications of the engine used for this airship have been listed in Table 4.

Table 4. Specifications of the engine. Type

Displacement

15.2 mm

Stroke

13.7 mm

RPM

2,500 to 18,000

B/2

Figure 3. Schematic of the stabilizer & rudder.

Mass

2.49 cc

Bore

Power output CR

0.15 LA-S (OSMG1415)

0.41 BHP @ 17,000 rpm 0.13 kg

Stability Analysis For a fully stable flight, the center of gravity (CG) of the airship should be exactly at the same location as that of the center of buoyancy (CB). In order to ensure this, a detailed mass breakup and CG analysis was carried out (Table 5). As shown in Table 5, the airship CG and CB were estimated to be located 3.79 and 3.64 m from the nose, respectively. This small difference in the CG and CB location helped to create a slightly unstable airship which would be easily maneuverable.

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Design, Fabrication and Flight Demonstration of a Remotely Controlled Airship for Snow Scientists

Table 5. Component mass and center of gravity distribution. Weight (kg)

CG location from nose (m)

Envelope

12.53

3.74

Fin velcro strip + hooks

0.59

6.60

Fins

1.32

6.97

Batons

0.70

0.50

Gondola

3.00

3.01

Airship assembly

18.14

3.82

Ballast

3.37

3.64

Total weight

21.51

3.79

Component

FABRICATION This section briefly outlines the fabrication methods of the components of the airship. Envelope The envelope is usually constructed by joining smaller sections called petals, as seen in Fig. 4, which are cut out from the fabric sheets using a shape template. The shape template consists of the cross-sectional shape of the envelope generated using the coordinates of the GNVR profile. The envelope petals were then seamed together, as shown in Fig. 5, depending on their orientations using radio frequency (RF) heat welding, thereby forming the whole envelope.

2.0 1.0 0.0

2.0

-1.0 -2.0

Figure 4. Envelope profile.

4.0

6.0

8.0

Figure 5. Radio frequency welding of petals.

Gondola The gondola was sized to accommodate the receiver, battery package, fuel tank, engine and payload. The gondola mounting slots were welded below the envelope and the gondola has been attached directly to the envelope using contoured aluminum rods going into these slots. It was carefully set up upon determining its location and mass, to have as stable an airship as possible. Stabilizer and Rudder The fabrication of stabilizer was done using flat sheet of high density foam (expanded polystyrene). Balsa wood was attached at edges to impart stiffness and avoid local damage to the foam due to impact loads. Servo controllers with boosters were fitted to each rudder in such a way that all control surfaces are free to rotate around 30° about the hinge. In order to attach the stabilizer surfaces, a velcro sheet was pasted on the envelope. A photograph of the ventral stabilizer and rudder is shown in Fig. 6.

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The airship was successfully flown during three different weather conditions, viz., dry day, mild rain and mild snowfall. The low density of air at such a high altitude could always resulted in reduced performance of a propeller engine and a small engine set up can amplify this problem. Despite this, the flight performance was quite satisfactory with comfortable take off and climbing flight. The airship was also maneuvered to undergo both level and climbing turning flight, including a touch-down and take-off, which demonstrated its control effectiveness. The completely fabricated airship is shown in Fig. 8. Figure 9 shows some of the images of the airship during its flight. Figure 6. Stabilizer and Rudder.

FLIGHT DEMONSTRATION Some of the major concerns dealt before the flight testing was of transportation and leak testing of the envelope. The envelope underwent a leak detection test, where light sources illuminated the holes, if any. Subsequent to the gas leak testing was the complete integration of all the subassemblies and the inflation of the envelope with Helium. The CAD model of the airship along with the fins and a side view of airship are shown adjacently in Fig. 7. It was observed that an additional ballast weight had to be added to the gondola in order to attain stability. Figure 8. Airship upon complete fabrication.

Figure 7. CAD model and airship side view.

The airship carried an operating payload which consisted of an autofocus Internet Protocol (IP) camera, which took live footage during its flight. The IP camera was capable of continuously taking photographs at the rate of around one photograph per second and the transmitted sequence of pictures were then combined in a video by playing them at a faster rate. Figure 10 shows the photograph of IP camera mounted on the front of the gondola. Figure 11 shows two photographs of the aerial footage taken by the onboard IP camera. Figure 12 shows a sequence of four images taken during flight when the LTA gas storage facility (pointed by the red arrow) was being monitored. The onboard camera was capable of procuring clear and crisp images of the people on the ground and layout of the area.

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Design, Fabrication and Flight Demonstration of a Remotely Controlled Airship for Snow Scientists

Figure 11. Two aerial photographs taken by onboard IP camera.

CONCLUSIONS

Figure 9. Airship during flight.

Figure 10. IP camera.

The design methodology adopted for the design of a RC airship not only covers the basic aspects of airship design but also governs the various options that can be utilized depending on the user’s constraints. The fabrication, system integration and operation of the RC airship have stablished the tremendous potential that airships possess to be a highly viable airborne platform for aerial surveillance. The images obtained are steady, clear and can be used to monitor all activities at a given location on the ground. The manufacturing costs and operational issues of such an effective aerial surveillance system have also been found to be much lower than for fixed wing aircraft. The demonstration established the efficacy of remotely controlled airships for aerial photography and data collection by snow scientists. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.19-27, Jan.-Mar., 2014

Pant, R.S.

Shot 1

Shot 2

Shot 3

Shot 4

Figure 12. Sequence of photographs taken by the onboard camera.

acKNoWlEdGmENtS The author would like to thank Dr. R. N. Sarwade, Director, and Mr. Jimmy Kansal, Deputy Director of Snow and Avalanches Study Establishment, DRDO, for inviting the team from LTA systems Lab. of IIT Bombay to carry out the demonstration flight of this airship, and express his gratitude

towards the following members Mr. Vishal Chaugule, Mr. Kaviresh Bhandari, Mr. Maxwell Rodrigues, Mr. Kushal Moolchandani, Mr. Nilesh Dhanve and Mr. V. D. Patil, who participated in this effort. The help and assistance from Mr. Y. P. Jahagirdar, Director of Research Institute for Model Aeronautics, Ahmednagar, and his team in system integration and flight demonstration is also gratefully acknowledged.

rEFErENcES Banerjee, S., Raina, A.A. and Pant, R.S., 2008, “Low Cost Transriver Aerial Ferry”, Proceedings of AIAA’s 8th Aviation Technology Integration and Operations (ATIO) Conference, Anchorage, Alaska, USA, AIAA-2008-8851. Bhandari, K.M. and Raina A.A., 2009, “Conceptual Design Of A High Altitude Aerostat For Study Of Snow Pattern”, Proceedings of International Symposium on Snow & Avalanches (ISSA-09), SASE, Manali, India.

National Conference on LTA Technologies, Aerial Delivery R&D Establishment, Agra, India. Gawale, A. and Pant, R., 2004, “Design Studies of a Powerplant System of Non-Rigid Airship”, Proceedings of the 5th International Convention of the Airship Association, Oxford, England.

Cheeseman, I., 1999, “Aerodynamics”, In: Khoury, G.A. and Gillet, J.D., Airship Technology, Cambridge Aerospace Series 10, Cambridge University Press, New York, pp. 25-38.

Gawande, V.N., Raina A.A., Bilaye, P., Pant, R.S. and Desai, U.B., 2007, “Design and Fabrication of an Aerostat for Wireless Communication in Remote Areas”, Proceedings of AIAA’s 17th Aviation, Technology, Integration, and Operations (ATIO) Conference and 17th Lighter-Than-Air Systems Technology Conference, Belfast, Northern Ireland, UK, AIAA-2007-7832.

Gawale, A.C. and Pant, R.S., 2002, “Design, Fabrication and Flight Testing of Remotely Controlled Airships”, Proceedings of

Gawale, A., Raina, A.A., Pant, R.S. and Jahagirdar, Y.P., 2008, “Design Fabrication and Operation of Low Cost Remotely Controlled

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Design, Fabrication and Flight Demonstration of a Remotely Controlled Airship for Snow Scientists

Airships in India”, Proceedings of AIAA’s 18th Aviation Technology Integration and Operations (ATIO) Conference, Anchorage, Alaska, USA, AIAA-2008-8853. Gupta, S. and Malik, S., 2002, “Envelope Details for Demo Airship”, PADD Project Report, Aerial Delivery R&D Establishment. Narayana C.L. and Srilatha, K.R., 2000, “Analysis of aerostat configurations by panel methods”, BLISS Project Document CF 0010, National Aerospace Laboratories, Bangalore, India. Pant, R.S., 2008, “Methodology for Determination of Baseline

Specifications of a Non-rigid Airship”, Technical Note, AIAA Journal of Aircraft, Vol. 45, No. 6, pp. 2177-2182. Sangole, R., Agashe, S., Palshikar, K. and Nakanekar, G., 2006, “Design, Fabrication and Testing of Remotely Controlled Indoor Airship employed for Neural Networks Hardware Experimentation”, Undergraduate Project Thesis, Mechanical Engineering Department, PVG College of Engineering, University of Pune, India. Sundaram, S., 2000, “Wind Tunnel tests on 1:7 and 1:28 scale Aerostat Models”, Experimental Aerodynamics Divisions, National Aerospace Laboratories, Bangalore, India.

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doi: 10.5028/jatm.v6i1.288

Theoretical Optimal Trajectories for Reducing the Environmental Impact of Commercial Aircraft Operations Cesar Celis1, Vishal Sethi2, David Zammit-Mangion2, Riti Singh2, Pericles Pilidis2

ABSTRACT: This work describes initial results obtained from an ongoing research involving the development of optimization algorithms which are capable of performing multi-disciplinary aircraft trajectory optimization processes. A short description of both the rationale behind the initial selection of a suitable optimization technique and the status of the optimization algorithms is firstly presented. The optimization algorithms developed are subsequently utilized to analyze different case studies involving one or more flight phases present in actual aircraft flight profiles. Several optimization processes focusing on the minimization of total flight time, fuel burned and oxides of nitrogen (NOx) emissions are carried out and their results are presented and discussed. When compared with others obtained using commercially available optimizers, results of these optimization processes show satisfactory level of accuracy (average discrepancies ~2%). It is expected that these optimization algorithms can be utilized in future to efficiently compute realistic, optimal and ‘greener’ aircraft trajectories, thereby minimizing the environmental impact of commercial aircraft operations. KEYWORDS: Trajectory optimization, Aircraft emissions, Environmental impact.

INTRODUCTION In this globalized world, where the efficient transportation of people and goods greatly contributes to the development of a given region or country, the aviation industry has found the ideal conditions for its development. These conditions have made the aviation industry one of the fastest growing economic sectors during the last decades. The growth in the aviation industry is reflected in the increase in air transport, expressed in terms of Revenue Passenger-Kilometers (RPKs), which has risen in an average annual rate of around 5% over the past 20 years (Boeing Commercial Airplanes, 2009). Market projections associated with this industry indicate that this growth will continue over the following years. Environmental issues associated with aircraft operations are currently one of the most critical aspects of commercial aviation (Green, 2003; Clarke, 2003; Brooker, 2006; Riddlebaugh, 2007). These are due to both the continuing growth in air traffic and the increasing public awareness about anthropogenic contribution to global warming. The critical nature of this problem means that currently several organizations worldwide are focusing their efforts towards large collaborative projects whose main objective is to identify the best alternatives or routes to reduce the environmental impact of aircraft operations. Particular examples of these projects are Partnership for AiR Transportation Noise and Emissions Reduction (PARTNER) project (PARTNER, 2003) and European Clean Sky JTI (Joint Technology Initiative) project (Clean Sky JTI, 2008). The Clean Sky JTI project has been demonstrating and validating different technologies, thereby making a major move towards achieving

1.Pontifícia Universidade Católica do Rio de Janeiro – Rio de Janeiro/RJ – Brazil. 2.Cranfield University, Cranfield – United Kingdom. Author for correspondence: Cesar Celis | Departamento de Engenharia Mecânica | Pontifícia Universidade Católica do Rio de Janeiro – Rua Marquês de São Vicente 225, Rio de Janeiro/RJ | CEP: 22453-900 – Brazil | Email: cesar.celis@puc-rio.br Received: 10/24/2013 | Accepted: 02/09/2014

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Celis, C., Sethi, V., Zammit-Mangion, D., Singh, R. and Pilidis, P.

the environmental goals set by Advisory Council for Aeronautics Research in Europe (ACARE). These targets for 2020 include reductions in carbon dioxide (CO2) and NOX emissions by 50% and 80%, respectively. Cranfield University (CU) and other partners of the European Aviation Industry are collaboratively participating in several areas of Clean Sky JTI, including the Systems for Green Operations (SGO) Integrated Technology Demonstrator (ITD). The SGO ITD concentrates on two key areas: (1) Management of Aircraft Energy (MAE) and (2) Management of Trajectory and Mission (MTM). One of the main contributions of CU to the SGO ITD is the development of suitable computational algorithms for the management of aircraft trajectory and mission, in particular, for carrying out aircraft trajectory optimization processes. This paper focuses on the initial stages of the development of these optimization algorithms and their applications for determining theoretical optimum aircraft trajectories. The optimization algorithms developed are a part of an optimization suite known as ‘Polyphemus’ (oPtimisatiOn aLgorithms librarY for PHysical complEx MUlti-objective problemS). As a part of the ongoing research about trajectory optimization, a methodology for optimizing aircraft trajectories has been initially devised. Then, Polyphemus has been developed and/or adapted for carrying out these aircraft trajectory optimization processes. Computational models simulating different disciplines, such as aircraft performance, engine performance, and formation of pollutants, have also been selected or developed as required. Simplified aircraft trajectory optimization processes have finally been carried out to evaluate the mathematical performance of Polyphemus primarily. Main results of these optimization processes are summarized in this paper. Aircraft Trajectory Optimization Optimization can be defined as the science of determining the best solutions for certain mathematically defined problems which are often representations of physical reality (Fletcher, 1987). There are several criteria and methodologies for classifying and solving optimization problems, respectively (Walsh, 1975; Schwefel, 1981; Bunday, 1984; Everitt, 1987; Krotov, 1996; Rao, 1996). Thus, aircraft trajectory optimization problems can be mainly classified as constrained, dynamic, optimal control, nonlinear – the functions relating inputs (design variables) and outputs (objective functions) are unknown in this work and they are presumed to be non-linear, non-smooth, and non-differentiable – real-valued

(mostly), deterministic (mostly), multimodal, multidimensional, and multiobjective. A number of optimization methods have been developed in the past, many of which are customized for a specific problem. Most important optimization methods can be grouped under three broad categories (Schwefel, 1981): (1) hill climbing methods (direct search methods, gradient methods and Newton methods); (2) random search methods; and (3) evolutionary methods. A detailed review of these methods can be found in Celis et al. (2009) and Celis (2010). Evolutionary methods are inspired by nature, biological structures and processes that can be observed in natural environments for solving technical problems. They are based on Darwin’s principles of species evolution reproduction cycle, natural selection and diversity by variation (Quagliarella, 1998). Most important evolutionary methods are evolutionary programming, evolution strategies, genetic programming and genetic algorithms (GAs). Among all evolutionary techniques, GAs are most widely used, and they have had a significant impact on optimization (Russell and Norvig, 2003). Like other evolutionary techniques, GAs are based on the principles of natural genetics and natural selection. Thus, basic elements of natural genetics (reproduction, crossover and mutation) are used in the genetic search procedure. Generally, evolutionary methods, in particular GAs, are robust, which help to solve problems in which the functions relating inputs to outputs are unknown and may have an unexpected behavior. In these situations, standard nonlinear programming techniques would be inefficient, computationally expensive, and in most cases, find a relative optimum that is the closest to the starting point (Rao, 1996). It has been argued (Betts, 1998) that evolutionary methods (including GAs and other techniques involving some sort of stochasticity during the optimization process) are not adequate to solve trajectory optimization problems and are computationally inferior when compared to methods that use gradient information. This inadequacy argument is originated from considering that trajectory optimization problems are not characterized by discrete variables. However, the results shown in this work highlight the fact that GAs are indeed suitable for this class of problems. Even more, for aircraft trajectory optimization involving multimodel integration, where the characteristics of the functions relating inputs to outputs are unknown, algorithms of this type appear to be the only practical alternative. A number of reasons that help support this point of view are as follows:

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Theoretical Optimal Trajectories for Reducing the Environmental Impact of Commercial Aircraft Operations

• GAs do not use specific knowledge of the optimization

problem domain. Instead of using previously known domain-specific information to guide each step, they make random changes in their candidate solutions and then use the fitness function to determine whether those changes result in an improvement. As GAs optimization routines are both model- and problem-independent, and they allow the users to (simultaneously) run different models for simulating different disciplines, they appear to be the ideal methods. • GAs are well-suited to solve problems where the fitness landscape is complex (discontinuous and multimodal), number of constraints and objectives are involved and the space of all potential solutions is large (particular characteristic of nonlinear problems). • GAs make use of a parallel process of search for the optimum, which means that they can explore the solution space in multiple directions at once. If one path turns out to be a dead end, they can easily eliminate it and progress in more promising directions, thereby increasing the chance of finding the optimal solution. From four main evolutionary algorithms, GAs have been initially chosen because of their large number of previous successful applications, worldwide. However, it is important to highlight that the hybridization of GAs with other optimization techniques has also been considered. This is due to fact that although GAs are an extremely efficient optimization technique, they are not the most efficient for the entire search phases (Rogero, 2002). Thus, hybrid optimization methods will be developed in future, as they have the potential to improve the performance in a given search phase; for example, GAs techniques involving the use of both a random search phase during the beginning of the optimization process (to increase the quality of the initial population) and a hill climbing phase at the end of the optimization (to refine the quality of the optimum point once the global optimum region has been found). Status of Optimization Algorithms (Polyphemus) Different numerical methods that could be used for solving the aircraft trajectory optimization problem were firstly reviewed, and a suitable optimization technique was initially selected (see “Aircraft Trajectory Optimization” section). The next step in the development of Polyphemus was reviewing the track record

of optimizers developed by CU for a range of applications, and identifying a candidate which could be used as a suitable ‘starting point’. This resulted in the decision to use GA-based optimization routines developed by Rogero (2002) as the basis for the development of Polyphemus. Rogero’s optimizer already includes several algorithms for each of the main phases involved in a GA-based optimization process; however, there are additional enhancements that can be introduced to further improve the quality of the optimizer. These improvements include the use of adaptive GAs (e.g., ‘master-slave’ configurations), which would allow using optimum GA parameters (e.g., population size, crossover ratio, mutation ratio, etc.) during the optimization processes; and also inclusion of the concept of Pareto optimality (Pareto fronts), which would improve its capabilities when performing multiobjective optimization processes. These improvements can be made based on successful past experiences of these concepts as part of previous optimizers (Gulati, 2001; Sampath, 2003) developed by CU. As the development of Polyphemus is continuous, only a brief description of the main aspects characterizing its current status is presented here. Polyphemus has been implemented using Java as the main programming language. Its core has been developed based on the basic structure of ‘SGA Java V1.03’ from Hartley (1998), which involves a Java implementation of the ‘simple GA’ (SGA) from Goldberg (1989). However, the original model has been recoded and extensively modified to both adapt to engineering design optimization problems and to maximize its performance. The main modifications made help to improve both the optimization performance, through an adaptation to the application domain, and the technique and genetic operators utilized during the optimization process. The optimizer application domain considered was engineering design. Thus, the chromosome modules have been developed in a way to support real-number parameter encoding in conjunction with a defined allowable range for the parameters (genes). In addition, algorithms for keeping a historical record of all created chromosomes and for preventing the creation of duplicate ones have been implemented. In order to improve the GA technique, concepts such as elitism (preservation of the genetic material of the best members through generations), steady-state replacement (partial replacement of the newly-generated chromosomes to avoid loss of potentially good genetic material), and fitness scaling (trade-off between premature convergence and genetic drift by keeping the selection pressure relatively constant along the whole optimization process) have been introduced.

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Celis, C., Sethi, V., Zammit-Mangion, D., Singh, R. and Pilidis, P.

Another phase of the optimization performance improvement involved the implementation of more advanced and efficient GA operators (crossover, mutation and selection) (Rogero, 2002). Thus, several crossover techniques suitable for realnumber encoding have been implemented, including weighted averaging crossover method, blend crossover BLX-a method and simulated binary crossover SBX method. The simulated binary crossover SBX method involves the creation of solutions within the whole search space. For instance, for a problem with ‘k’ design variables (or genes), a real-number vector (chromosome) can be given by: (1) Then, one of the most simple ways of performing a crossover operation using any two parent chromosomes, X1 and X2, involves the combination of the two vectors representing them, which is as follows:

As creep mutation is basically operated by adding or subtracting a random number to a gene of the chromosome selected for mutation, the mutation of a given gene, xi, using this method is limited to a creep range centered on its original value (Davis, 1991). A creep mutated gene, xiʹ, is then computed as follows: (4) (5) In Eqs. (4) and (5), ∆max is the maximum size used for the creep mutation, δ is the range ratio, and r is a random number from [0,1]. The level of disruption produced by the mutation process is controlled by the creep size δ. In the creep mutation with decay method, the creep size is altered as a function of the stage of the search process as follows: (6)

(2) Here, the multipliers λ1 and λ2 (subject to the condition λ1 + λ2 = 1) represent the weights randomly selected during the crossover process, and X1’ and X2’ are the child chromosomes. Depending on the permissible values of the multipliers λ1 and λ2, different subtypes of crossover methods can be derived. The (weighted) averaging crossover corresponds to the special case, in which λ1 = λ2 = 0.5. The averaging crossover suffers from contraction effects because it allows the creation of offspring only along the line generated between the two parental chromosomes. This problem is solved to some extent in the blend crossover BLX-a method, which uses an exploration factor (α) to increase the exploration capability of the crossover operator. In addition to the standard random mutation operator, others such as creep mutation, with and without decay, and Dynamic Vectored Mutation (DVM) – have also been implemented. In general, for a given parent chromosome X, Eq. (1), if its element (gene) xi is selected for mutation, a (random) change in the value of this selected gene within its domain, given by a lower LBi and upper UBi bound, will result in the following transformation:

(3)

In Eq. (6), γ represents the creep decay rate and t is the generation number. This type of implementation allows the use of large values of δ in the beginning of the search process and small values at the end; the exploration and exploitation capabilities required during the process are balanced in this way. Details about the DVM method can be found in Rogero (2002). Selection operators implemented in the optimizer include a modified roulette wheel selection operator (limiting the number of chromosome instances) and the Stochastic Universal Sampling (SUS) technique. Roulette wheel selection, in which an area proportional to its fitness is allocated to each chromosome on a virtual roulette wheel, is the best known selection method. The selection process is carried out by spinning the wheel a number of times equal to the number of chromosomes to be selected (each time a single chromosome is selected). One drawback associated with this selection method is that it has a tendency to select a large number of copies of the best chromosome, which can lead to loss of diversity. This problem can be solved to some extent by using the SUS selection method. In this method, like in the roulette wheel selection, a chromosome occupies on the wheel an area proportional to its fitness. However, instead of spinning the wheel several times for selecting chromosomes, a single spin of the wheel identifies all parent selections simultaneously. This is possible because there is another wheel on the outside of the

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.29-42, Jan.-Mar., 2014

Theoretical Optimal Trajectories for Reducing the Environmental Impact of Commercial Aircraft Operations

roulette wheel containing a number of equally spaced pointers equal to the number of chromosomes to be selected (Callan, 2003). With regard to replacement operators, tournament replacement and ranked replacement have been improved and implemented as replacement operators. Finally, Polyphemus uses a unique optimization method based on Wienke’s idea of target vector optimization (Wienke et al., 1992). In this method, designers can define, for each parameter, a target to be attained, a range within which this parameter should remain, and the requirement to maximize or minimize the given parameter. Accordingly, the quality of the design is determined by the level of both the achievement of the targets and the violation of the parameters ranges. This approach enables designers to have total control over the optimization process with neither having to know much about the optimization algorithms, nor having to devise a fitness function (Rogero and Rubini, 2003). The optimization results presented in the following sections were obtained using the current version of Polyphemus, whose main characteristics have been summarized above. Trajectory Optimization Case Studies In this section, computational models utilized, flight profiles optimized and the methodology followed for optimizing these flight profiles have been particularly emphasized.

Computational Models In the aircraft trajectory optimization processes, three computational models, i.e., aircraft performance simulation model (APM), engine performance simulation model (TurboMatch) and emissions prediction model (Hephaestus), have been utilized. Figure 1 illustrates the general arrangement of these models, as well as different parameters exchanged among them. The APM (Long, 2009) is a generic tool that determines flight path performance for a given aircraft design. It uses steady-state performance equations to resolve aerodynamic lift and drag and to determine the thrust required for a given kinematic flight state. In order to easily identify the behavior of Polyphemus, airspeed limitations – such as critical Mach number (M), never-exceed speed and wave drag at transonic M – have not been implemented in the model. As APM uses endpoints to compute performance, the user must declare a trajectory segment in terms of ground range and altitude intervals, whereby a constant flight path angle is then defined. Flight conditions are then assumed to be constant over that segment. The aircraft modeled in this work corresponds to a typical mid-sized, single-aisle, twin turbofan airliner with a maximum takeoff weight (MTOW) of about 72,000 kg and a seating capacity of about 150 passengers.

Flight Conditions (M, h, FPA, R) Aircraft Configuration (Initial Mass)

Models Engine Configuration Component Characteristics

APM (Aircraft Performance) Flight Conditions (M, h) Thrust Required

Time

TURBOMATCH (Engine Performance)

Engine Fuel Flow Combustor (air) Inlet Conditions (W, T, P)

Combustor Geometry

Flight Conditions (h) EINOX, EICO2, EIH20

Total Flight Time Total Fuel Consumed Aircraft Mass NOX Mass CO2 Mass H2O Mass

HEPHAESTUS (Emissions)

Figure 1. Computational models configuration and exchange of parameters. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.29-42, Jan.-Mar., 2014

Celis, C., Sethi, V., Zammit-Mangion, D., Singh, R. and Pilidis, P.

The performance of the engines was simulated using TurboMatch (Palmer, 1999), which is the in-house CU gas turbine performance code that has been developed and refined over a number of decades. TurboMatch performance simulations range from simple steady-state (design and off-design point) to complex transient performance computations. Finally, the gaseous emission predictions have been performed using the CU emissions prediction software, Hephaestus. An integral part of Hephaestus constitutes the emissions prediction model described in Celis et al. (2009), which follows an approach based on the use of a number of stirred reactors for modeling combustion chambers and estimating the level of pollutants emitted from them. Additional details of these computational models can be found in Celis et al. (2009) and Celis (2010). Flight Profiles It is clear that in order to demonstrate the suitability of an optimizer for optimizing aircraft trajectories, an extensive validation process of the algorithms that are implemented needs to be carried out using different analytical problems with known optimal values. In the case of Polyphemus, this part of the validation process has already been performed (Rogero, 2002) and is therefore not repeated here. In order to provide insight into the results that can be expected using Polyphemus, simplified aircraft trajectory optimization processes using this optimizer have been performed. It is relevant to note that the main objective of these processes was evaluation of the mathematical performance of Polyphemus rather than the generation of realistic aircraft trajectories. Consequently, simplifications (in terms of number of flight segments, design variables, constraints and objective functions, etc.) have been introduced when optimizing the aircraft flight profiles. Indeed, the results discussed in this work correspond to single-objective optimization processes only, which means that the determination of non-dominated or Pareto optimal solutions that characterize multi-objective optimization processes is out of the scope of this work. In this work, the aircraft flight profiles have been divided into only a small number of segments, as illustrated in Fig. 2. This helps getting a greater visibility on the characteristics of the Polyphemus performance when assessing results. This would have been more difficult if the trajectory had been divided into a greater number of segments. These hypotheses are a simplification of real cases but provide numerical solutions that are used to commission the methodology. In order to obtain meaningful results in terms of actual optimum trajectories, the flight path needs to be divided into a much larger number of segments, each small enough so

that the errors associated with the assumptions made within each segment will be cumulatively insignificant. All the optimization processes carried out involved only vertical profiles. Therefore, only three parameters have been used to define a given aircraft trajectory: (1) flight altitude (h); (2) aircraft speed: true airspeed (TAS), equivalent airspeed (EAS) or Mach number (M); and (3) range (R): the horizontal distance flown by the aircraft. One of the main uses of Polyphemus involves the optimization of aircraft trajectories between city pairs. Thus, R has been usually kept constant during the optimizations, and only altitude and aircraft speed vary (i.e., used as design variables) to compute optimum aircraft trajectories that minimize, separately, total flight time, fuel burned and NOx emissions. Several aircraft flight profiles have been optimized in order to assess the mathematical performance of Polyphemus. The optimization results associated with three of these flight profiles are summarized in this paper. A brief description of these profiles, which were analyzed as part of three separate case studies, is presented as follows: • Case 1: Simple Climb Profile Optimization. (i) Flight profile has been divided into four segments (Fig. 2). (ii) Climb segments have been defined by arbitrary segment lengths (range, R). (iii) Overall climb has been defined by the cumulative range, start and end altitudes, and Mach numbers. (iv) Variation in intermediate Mach numbers (initial M in segments 2 and 3) and altitudes (initial altitude in segments 2, 3 and 4) has been allowed during the optimization processes. (v) Only explicit constraints have been utilized, i.e., range of permissible values of the design variables (h and M) are limited. (vi) Lower and upper bounds for these permissible ranges have been set at 457 m (1,500 ft) and 10,668 m (35,000 ft), respectively, for h (profile start and end altitudes); and 0.38 and 0.80, respectively, for M. (vii) International Standard Atmosphere

Speed and Altitude (h)

4 3 2 1

Figure 2. Generic aircraft flight profile.

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Range

Theoretical Optimal Trajectories for Reducing the Environmental Impact of Commercial Aircraft Operations

(ISA) conditions are assumed. (viii) Range of flight path angle (FPA) allowed: [0, 7.5] deg. Data corresponding to this particular profile optimization are summarized in Table 1. • Case 2: Implicitly Constrained Climb Profile Optimization. (i) Flight profile is similar to the profile used in Case 1; however, aircraft speeds have been specified at the start and end of each climb segment allowing continuity in aircraft speed. (ii) Climb schedule has been described as follows; 1st seg.: climb at constant EAS from 1,500 ft (457 m) up to 10,000 ft (3,048 m); 2nd seg.: EAS acceleration at 10,000 ft (level flight); 3rd seg.: climb at constant EAS up to a segment final altitude where (cruise) M is about 0.8; 4th seg.: climb at constant M from this altitude up to 35,000 ft (10,668 m). (iii) Design variables and their range of permissible values: initial EAS in segment 1 (EAS1i [89.0, 128.6] m/s), final EAS in segment 2 (EAS2f [133.8, 221.2] m/s), initial altitude in segment 3 (h3i [3048, 4400] m), and initial altitude in segment 4 (h4i [3048,10668] m). (iv) Implicit constraint: initial M in segment 4 (M4i) – allowable range ±0.5% of its nominal value, 0.8. (v) ISA conditions have been assumed. (vi) FPA range allowed: [0, 7.5] deg. Additional details about this case study are shown in Table 2. • Case 3: Quasi-Full Flight Profile Optimization. (i) Flight profile (involving climb, cruise and descent) has been divided into eight segments. (ii) Profile has been defined following a similar approach to that used in Case 2. (iii) Flight schedule has been described as follows; 1st seg.: climb at constant EAS

from 1,500 ft (457 m) up to 10,000 ft (3,048 m); 2nd seg.: EAS acceleration at 10,000 ft (level flight); 3rd seg.: climb at constant EAS up to a segment final altitude where (cruise) M is about 0.8; 4th and 5th seg.: level flight cruise at constant M; 6th seg.: descent at constant EAS to 10,000 ft (3,048 m); 7th seg.: EAS deceleration at 10,000 ft (level flight); and 8th seg.: descent at constant EAS from 10,000 ft (3,048 m) to 1,500 ft (457 m). (iv) Design variables and their range of permissible values: initial EAS in segment 1 (EAS1i [89.0, 128.6] m/s), final EAS in segment 2 (EAS2f [117.1, 184.6] m/s), initial altitude in segment 3 (h3i [3048, 4400] m), initial altitude in segment 4 (h4i [6096, 12192] m), initial altitude in segment 7 (h7i [3048, 4400] m), and initial EAS in segment 8 (EAS8i [89.0, 128.6] m/s). (v) Implicit constraint: initial M in segment 4 (M4i) – allowable range ±0.5% of its nominal value, 0.8. (vi) ISA conditions have been assumed. (vii) FPA range allowed: [0, 7.5] deg during climb and cruise, and [-7.5, 0] deg during descent. Table 3 summarizes the data associated with this flight profile optimization. The lower and upper bounds of the range of permissible values of the design variables were in general defined in a way to reduce the computational time of the optimization processes, to take into account typical air traffic control (ATC) restrictions, and/or to avoid the aircraft losing (gaining) altitude during climb (descent) processes. For instance, below 10,000 ft, the EAS lower and upper bounds usually correspond to, respectively, the aircraft stall speed (89.0 m/s

Table 1. Case 1 (simple climb profile) – Baseline trajectory and design variables. Seg. No.

hi (m)

hf (m)

R (km)

Design variables

457

3048

0.38

–

3048

0.46

0.38 ≤ Mi ≤ 0.80; 457 ≤ hi ≤ 10668

3048

7000

0.58

0.38 ≤ Mi ≤ 0.80; 457 ≤ hi ≤ 10668

7000

10668

0.80

100

457 ≤ hi ≤ 10668

Table 2. Case 2 (implicitly constrained climb profile) – Baseline trajectory and design variables. Seg. No.

hi (m)

hf (m)

EASi (m/s)

EASf (m/s)

R (km)

Design variables

457

3048

–

128.6

89.0 ≤ EASi ≤ 128.6

3048

–

128.6

164.6

133.8 ≤ EASf ≤ 221.2

3048

7724

–

164.6

3048 ≤ hi ≤ 4400

7724

10668

0.80

–

100

3048 ≤ hi ≤ 10668

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Celis, C., Sethi, V., Zammit-Mangion, D., Singh, R. and Pilidis, P.

Table 3. Case 3 (quasi-full flight profile) – Baseline trajectory and design variables. Seg. No.

hi (m)

hf (m)

EASi (m/s)

EASf (m/s)

R (km)

Design variables

457

3048

–

128.6

89.0 ≤ EASi ≤ 128.6

3048

–

128.6

164.6

117.1 ≤ EASf ≤ 184.6

3048

7724

–

164.6

160

3048 ≤ hi ≤ 4400

7724

0.80

–

230

6096 ≤ hi ≤ 12192

7724

0.80

–

230

–

7724

3048

–

164.6

140

–

3048

–

164.6

128.6

3048 ≤ hi ≤ 4400

3048

457

–

128.6

89.0 ≤ EASi ≤ 128.6

EAS for the particular aircraft modeled) and the maximum EAS permissible below this altitude (according to ATC restrictions), i.e., 250 kts EAS or 128.6 m/s. In Case 3, in particular, the range of values in which the initial altitude in segment 4 can vary was established in a way to allow the aircraft cruising at altitudes between 20,000 ft (6,096 m) and 40,000 ft (12,192 m). Thus, EAS2f permissible values were limited to those speeds that yield Mach numbers of about 0.8 at these cruise altitudes. Similar considerations were made in the other case studies analyzed in this work. Optimization Process According to the methodology followed in this work for optimizing a given aircraft trajectory, Polyphemus first randomly changes the values of the design variables (altitude and/or aircraft speed in one or more trajectory segments) in order to create a group of potential solutions. For a given potential solution, by making use of the initial aircraft weight (aircraft empty weight plus fuel on-board, constant), the APM carries out the computations related to the first segment of the aircraft trajectory, and determines the thrust required, flight time, etc. (Fig. 1). TurboMatch subsequently uses the flight conditions and the thrust required to determine the engine operating point, thereby establishing the engine fuel flow and the combustor inlet conditions among others. Hephaestus then makes use of the combustor inlet conditions and combustor geometric parameters to calculate the emission indices for the main pollutants. Based on the fuel flow and flight time, the fuel burned during the first trajectory segment and the new aircraft weight (i.e. the initial weight less fuel burned) are calculated. Computations continue in a similar fashion for all the remaining trajectory segments. When all the segments have been computed, among other calculations, the total flight time, fuel burned and gaseous

emissions produced during the whole aircraft trajectory are also computed. This process is repeated for all the potential solutions, and for all generations of potential solutions that Polyphemus utilizes in order to determine an optimum trajectory according to given criteria initially specified by the designer. The results were obtained following a procedure similar to that described before.

RESULTS AND DISCUSSIONS Main results of the optimization processes corresponding to three case studies indicated above are summarized in this section. In these processes, the minimization of total flight time, fuel burned, and NOX emissions have been considered as the objective functions. Case 1: Simple Climb Profile Optimization The baseline climb profile for this case study as well as the optimum trajectories computed using Polyphemus and two commercial optimizers (MATLAB®, 2008) are illustrated in Fig. 3. Two different approaches used within the commercial package were (i) a pattern search algorithm called mesh adaptive search (MADS), and (ii) GAs. Both Polyphemus and the commercial optimizers yielded very similar results (Fig. 3a). Even though this first optimization case study (climb profile) corresponded to a hypothetical one, the reasonable agreement among the optimizers (average discrepancies ~2%) confirmed the validity of the approach. Figure 3c shows that in order to minimize the time spent during climb, Polyphemus suggests a solution where the aircraft flies at the highest M permissible, which was fixed at 0.38 and 0.80 in the first and fourth segment, respectively, and free to rise to 0.8 in the remaining middle two. Polyphemus also

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Theoretical Optimal Trajectories for Reducing the Environmental Impact of Commercial Aircraft Operations

Baseline Time - GAs Fuel - GAs

12000

Time - Polyphemus Fuel - Polyphemus NOx - Polyphemus

Time - MADS Fuel - MADS

Baseline

Altitude (m)

6000 4000

100

125

Ground Range (km) (a)

150

175

1500 1200

0.5

1600

Climb Segment (c)

100

125

150

Ground Range (km) (b)

175

200

Baseline Time - Polyphemus Fuel - Polyphemus NOx - Polyphemus

900 600

Baseline Time - Polyphemus Fuel - Polyphemus NOx - Polyphemus

700

1400 1300 1200 1100 1

Climb Segment

(d)

1500

1000

Relative (to Baseline) Values (%)

Turbine Entry Temperature (K)

1700

300

0.4

1800

4000

1800

Baseline Time - Polyphemus Fuel - Polyphemus NOx - Polyphemus

0.6

0.3

6000

200

Fuel Burned (kg)

Mach Number (--)

0.7

8000

2000

0.8

NOx - Polyphemus

10000

8000

0.9

Fuel - Polyphemus

12000

10000

Time - Polyphemus

600 500 400 300 200 100 0

-100

Climb Segment (e)

694.2 Time - Polyphemus Fuel - Polyphemus NOx - Polyphemus

-200

3.7 17.1 -16.2

Flight Time

55.6

50.5 -6.7 -1.1

Fuel Burned

-19.3 -43.6

Oxides of Nitrogen

(f)

-7.0-1.3

Carbon Dioxide

55.7 -6.9-1.4

Water (vapour)

Figure 3. Case 1 – Simple climb profile optimization results.

suggests that the aircraft should fly at low altitudes for as long as possible before climbing rapidly to the target end altitude (Fig. 3b). This is mathematically correct because the speed of sound is the highest at sea level, thus enabling the aircraft to fly faster (maximization of TAS) if it could actually achieve M 0.8 at this level. This solution, however, does not represent practical flight profiles because never exceed speed (VNE) is much lower than M 0.8 at sea level, thus restricting large transport category aircraft from approaching such high Mach numbers. Nevertheless, it is an interesting solution, confirming that the optimizer is working correctly in the absence of M (or TAS) constraints.

Figure 3 also illustrates that in order to reduce fuel burn, the optimizer suggests flying slower (Fig. 3c) and higher (Fig. 3b) than the reference trajectory (segment 3). This is again conceptually correct given the current reference trajectory. It is interesting to note that the fuel optimized trajectory proposes second and third segments affording a greater fuel burn (relative to the baseline) (Fig. 3d) in order to gain height (Fig. 3b), which then subsequently yields a lower fuel burn in the last segment and an overall lower fuel burn for the climb profile as a whole. In terms of flight profile, one could conclude from Fig. 3b that the trajectories optimized for minimum fuel burned and NOX emissions are similar. However,

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Celis, C., Sethi, V., Zammit-Mangion, D., Singh, R. and Pilidis, P.

there are significant differences between these two trajectories. The main difference is related to the fact that the NOX emissions optimized trajectory is flown at relatively lower Mach numbers than the fuel burned optimized trajectory (Fig. 3c). These lower Mach numbers result in lower engine thrust settings, i.e., the thrust required to fly a given segment is lower, which in turn results in lower engine turbine entry temperature (TET) values (Fig. 3e). Consequently, as one of the main factors determining the level of NOX emissions produced (besides the fuel burned) is TET, the trajectory optimized for minimum NOX emissions produces a significant reduction in the amount of NOX emitted (~-43%). Interestingly, Fig. 3e shows that in order to minimize NOX emissions, the optimizer proposes a trajectory in which the engine TET remains almost constant (~1,400–1,500 K) for the entire climb profile. It is relevant to note in this discussion that the level of NOX formed at temperatures near to and above 1,700–1,800 K increases exponentially with temperature. An aspect to be highlighted in Fig. 3f is the level of gaseous emissions (NOX, CO2 and H2O) associated with the optimum trajectories relative to the reference climb trajectory. As expected, variations in CO2 and H2O are directly proportional to the variations in the amount of fuel burned (species in chemical equilibrium). However, the aircraft trajectory optimized for total flight time significantly increases the amount of NOX emissions. One of the main factors responsible for this significant increase in NOX emissions (besides the increase in fuel burn) is the increase in TET resulting from the higher thrust settings. Fig. 3f also illustrates the increase in total flight time associated with the trajectory optimized for minimum NOX emissions. Although this parameter increases, the total fuel burned slightly decreases as a consequence of the lower thrust settings (i.e., lower engine fuel flow relative to the baseline trajectory). Additional details about the results analyzed in this first case study can be found in Celis et al.(2009). In the following two case studies, complexities (in terms of operational constraints, number of segments, number of trajectory flight phases, etc.) were included gradually. This gradual approach afforded greater visibility of the mathematical performance of Polyphemus when assessing results, which would have been more difficult if the analysis had been initiated with very complex trajectories. Case 2: Implicitly Constrained Climb Profile Optimization Results obtained in the second case study (Fig. 4) are in general similar to those obtained in the first case study. Thus, when minimizing the time spent during climb, i.e., maximizing

TAS, Polyphemus suggests a solution where the aircraft flies the first segment at the highest EAS permissible (fixed at 128.6 m/s) (Fig. 4c). This is conceptually correct because in the first segment, since the flight altitude is fixed, TAS increases with the increase in EAS. The optimizer also suggests that the aircraft should accelerate in the second segment to the highest EAS permissible (fixed at 221.2 m/s), and fly the following segments at low levels (Fig. 4b) as long as possible before climbing rapidly to the target end altitude. This is again mathematically correct because, firstly, as previously indicated, once the flight altitude has been established, the TAS increases with increase in EAS; and, secondly, for a given M, TAS increases with the decrease in altitude (speed of sound is the highest at sea level). Clearly, the influence of the third and fourth segments on the total climb time is more important than the corresponding second segment. Otherwise, the initial altitude in segment 3 would be the highest permissible. Figure 4 also shows that in order to reduce the climb fuel burned, Polyphemus suggests flying mostly slower (Fig. 4c) and higher (Fig. 4b) than the reference trajectory. In particular, it suggests flying the first segment at the highest EAS permissible. It is clear that in order to minimize the total amount of fuel burned, the total energy required by an aircraft to describe a given flight profile (aircraft energy change plus path-dependent energy required to impart that change) must be minimized. Thus, in this particular case, the total energy required to climb must also be minimized. It means that the total aircraft kinematic energy change needs to be minimized. The aircraft kinetic energy change is minimized when the initial kinetic energy is maximized. It implies, in turn, maximization of the initial aircraft speed, i.e., TAS. This TAS maximization results in the maximization of the EAS at the first segment, as highlighted. One of the main factors driving the minimization of the fuel burned during a given flight profile is the aircraft mass change. Different factors affect the fuel burned and, consequently, the changes in the aircraft mass. The aircraft speed and flight altitude constitute two of these main factors. Reducing the speed and increasing the altitude reduce drag and, consequently, the thrust required to fly a given segment. This lower thrust requirement translates into a lower engine thrust setting, and, consequently, a lower fuel burn. However, neither altitude nor speed can be increased or decreased arbitrarily. Speed reductions imply in general, an increase in flight time, which can negatively affect the total fuel burned. In addition, in order to quickly achieve higher altitudes, higher engine thrusts (i.e. higher thrust settings) are required. These higher thrust settings require higher fuel flow, which

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.29-42, Jan.-Mar., 2014

Theoretical Optimal Trajectories for Reducing the Environmental Impact of Commercial Aircraft Operations

Baseline Fuel - Polyphemus NOx - GAs

12000

Time - Polyphemus Fuel - GAs

Time - GAs NOx - Polyphemus

Altitude (m)

8000 6000 4000

100

125

Ground Range (km) (a)

150

175

175 150

Baseline Time - Polyphemus Fuel - Polyphemus NOx - Polyphemus

125 0

100

125

Ground Range (km) (c)

150

175

1400 1200 2

Climb Segment (e)

100

125

150

Ground Range (km) (b)

175

200

Baseline Time - Polyphemus Fuel - Polyphemus NOx - Polyphemus

750 500 250

250

1600

200

Baseline Time - Polyphemus Fuel - Polyphemus NOx - Polyphemus

Fuel Burned (kg)

200

Relative (to Baseline) Values (%)

True Airspeed (m/s)

Turbine Entry Temperature (K)

4000

1000

225

1000

6000

1250

250

1800

NOx - Polyphemus

8000

200

275

2000

Fuel - Polyphemus

2000

100

Time - Polyphemus

10000

Baseline

12000

200

Climb Segment (d) 245.3

Time - Polyphemus Fuel - Polyphemus NOx - Polyphemus

150 100 50 0 -50

6.6 9.6 -11.6

Flight Time

26.1 -5.0-0.3

Fuel Burned

27.3 0.4 -12.8

Oxides of Nitrogen

(f)

-5.2-0.5

Carbon Dioxide

27.3 -5.2-0.5

Water (vapour)

Figure 4. Case 2 – Implicitly constrained climb profile optimisation results.

negatively affects the fuel burned during the process. Therefore, a compromise between aircraft flight altitude and speed, which directly affect the changes in the aircraft mass, needs to be achieved at some stage. The fuel-optimized trajectory computed is a typical example of the referred compromise. It is interesting to note in Fig. 4d that this fuel-optimized trajectory proposes second and third segments affording a greater fuel burn (relative to the baseline) in order to gain height (Fig. 4b), which, then, is translated into a lower fuel burn in the last segment and an overall lower fuel burn for the whole climb profile. Regarding the trajectory optimized for minimum NOX emissions, the results show that, similar to the fuel optimized one, this trajectory

is flown mostly slower (Fig. 4c) and higher (Fig. 4b) than the baseline trajectory. In general, lower speed and higher altitude lead to a reduction in the thrust required to fly the climb segments. These lower thrust requirements are in turn translated into lower engine TET values (Fig. 4e), which in turn result in a reduction in the level of NOX emissions (Fig. 4f). As in the first case study, Fig. 4e illustrates that in order to minimize NOX emissions, the aircraft describes a trajectory in such a way that the engine TET remains almost constant along the whole climb profile. As before, CO2 and H2O vary proportionally to the variations of fuel burned (Fig. 4f). Even though the NOX emission-optimized trajectory increases total flight time, as a consequence of the lower engine thrust settings

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.29-42, Jan.-Mar., 2014

Celis, C., Sethi, V., Zammit-Mangion, D., Singh, R. and Pilidis, P.

utilized, the total amount of fuel burned is slightly reduced. In Fig. 4f, it should also be noticed that the aircraft trajectory optimized for minimum flight time significantly increases the amount of NOX emissions. This is partially because of the large amount of thrust required to increase both aircraft kinetic energy in segment 2 and potential energy in segment 4. This higher engine thrust requirement is translated into higher TET values (Fig. 4e), and consequently into a significant increase in the level of NOX emissions. Case 3: Quasi-Full Flight Profile Optimization As highlighted before and illustrated in Fig. 5, which shows the main results obtained for this particular case study, minimization of total flight time implies maximization of TAS (Fig. 5c). Baseline Fuel - Polyphemus NOx - GAs

12000

Time - Polyphemus Fuel - GAs

Time - GAs NOx - Polyphemus

Altitude (m)

2000

2000 0

100

200

300

400

500

600

Ground Range (km) (a)

275

700

800

900

100

200

300

400

500

600

Ground Range (km) (b)

1500

195 155 Baseline Time - Polyphemus Fuel - Polyphemus NOx - Polyphemus

115 0

100

200

2000

300

400

500

Ground Range (km) (c)

600

700

800

Baseline Time - Polyphemus Fuel - Polyphemus NOx - Polyphemus

1800 1600 1400 1200 1000 1

Flight Segment (e)

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.29-42, Jan.-Mar., 2014

900

600 300

150 125 100

Flight Segment (d)

Time - Polyphemus Fuel - Polyphemus NOx - Polyphemus

126.7

75 50 25 0 -25 -50

8.8 9.8 -5.2 Flight Time

14.5 -13.3 -17.2 Fuel Burned

14.7

-24.7 -35.1 Oxides of Nitrogen

(f) Figure 5. Case 3 – Quasi-full flight profile optimization results.

800

900

700

Baseline Time - Polyphemus Fuel - Polyphemus NOx - Polyphemus

1200

235

Fuel Burned (kg)

True Airspeed (m/s)

NOx - Polyphemus

4000

Turbine Entry Temperature (K)

Fuel - Polyphemus

6000

800

Time - Polyphemus

8000

Baseline

12000 10000

10000

Thus, when determining the minimum flight time-optimized trajectory, Polyphemus suggests a solution in this case, where the aircraft flies the first and the last segments at the highest EAS permissible (fixed at 128.6 m/s). This is conceptually correct because the first and last segments are flown at fixed altitudes, where TAS increases with the increase in EAS. Polyphemus also suggests that the aircraft should accelerate in the second segment to the highest EAS permissible (fixed at 184.6 m/s), and start the third segment as high as possible, and the fourth one as low as possible. This is again mathematically correct because, firstly, TAS increases with the increase in both flight altitude and EAS (segments 2 and 3); and, secondly, for a given Mach number, the TAS increases with the decrease in altitude (segments 4 and 5).

Relative (to Baseline) Values (%)

-13.5 -17.4 Carbon Dioxide

14.7 -13.5 -17.4 Water (vapour)

Theoretical Optimal Trajectories for Reducing the Environmental Impact of Commercial Aircraft Operations

As a consequence of the larger distance covered by the cruise segments 4 and 5, their influence on the total flight time is more important than that associated with the third and sixth segments. This is emphasized by the fact that the aircraft has a tendency to cruise at low altitude levels as observed in Fig. 5b. Regarding the fuel-optimised trajectory, it is observed that in order to reduce the total fuel burned, the optimiser suggests flying mostly slower (Fig. 5c) and higher (Fig. 5b) than the reference trajectory. In particular, it suggests flying the first segment at the highest EAS permissible. This situation is similar to that encountered in the second case study. In order to minimize the total fuel burned during the flight profile, the total energy required during the process must be minimized. In this case, it implies, in turn, minimization of the aircraft kinetic energy change; or, more specifically, maximization of the initial aircraft speed and minimization of the final one (in terms of TAS). As in the second case study, this TAS maximization makes the aircraft fly the first segment at the highest EAS permissible. Results also show that even though the aircraft arrives to the endpoint at a low speed, this does not correspond to the lowest EAS permissible (fixed at 89.0 m/s), as it could be expected. It is believed that one aspect that might be influencing this particular result is the path-dependent energy, which has a direct relationship with the aircraft speed and also needs to be minimum. In the foregoing analysis, the aircraft mass changes were not considered. These mass changes cannot be ignored however because in reality they are one of the main factors driving the minimization of the fuel burned during the optimization process. As highlighted before, there are two main parameters that affect the fuel burned and, consequently, the changes in the aircraft mass: the aircraft speed and the aircraft flight altitude. These two parameters directly or indirectly affect, in turn, other parameters, such as drag, thrust required, flight time and engine thrust setting (consequently, fuel flow, TET, etc.), among others. It implies that a fuel-optimized trajectory represents in fact a tradeoff among all these parameters, some of which conflict with each other. The flight profile optimized for minimum NOx emissions is flown similar to the fuel-optimized one, i.e., mostly slower (Fig. 5c) and higher (Fig. 5b) than the baseline trajectory utilized. In general, the relative lower speed and higher altitude utilized to fly this trajectory lead to a reduction in the thrust required to fly the trajectory segments. These lower thrust requirements are in turn translated into lower engine TET values (Fig. 5), which ultimately result in a reduction in the level of NOX emissions produced (Fig. 5f). Fig. 5e shows, in particular, that from all TET

values, those corresponding to the NOX emissions, optimised trajectory values are the lowest ones. This is expected, of course, because this parameter has a direct influence on the level of NOX emissions produced. In Fig. 5f, it can also be observed that the changes in CO2 and H2O present the expected behavior, and, even though the NOX emissions optimized trajectory increases the total flight time, the total amount of fuel burned is largely reduced. This is a consequence of the lower engine thrust settings utilized to fly this trajectory. As in the first two case studies, the aircraft trajectory optimized for minimum flight time significantly increases the amount of NOx emissions (Fig. 5f). This is partially due to the large amount of thrust required to fly some of the segments of this particular optimized trajectory.

CONCLUSIONS Initial results obtained using optimization algorithms (i.e., Polyphemus) capable of performing multidisciplinary aircraft trajectory optimization processes were described in this work. A short description of both the rationale behind the initial selection of a suitable optimization technique and the status of Polyphemus was firstly presented. The Polyphemus optimizer was subsequently utilized to analyze three different case studies involving the optimization of one or more phases of actual aircraft flight profiles. Results related to optimum trajectories obtained using Polyphemus, minimization of total flight time, fuel burned and NOX emissions were presented and discussed. The results obtained using Polyphemus and other commercially available optimization algorithms presented a satisfactory level of agreement (average discrepancies ~2%). Further developments on Polyphemus are currently underway in order to identify and efficiently compute optimum and ‘greener’ aircraft trajectories, which help minimize the impact of commercial aircraft operations on the environment. It is worth emphasizing that the main objective of different case studies analyzed was the evaluation of the mathematical performance of Polyphemus rather than the generation of realistic aircraft trajectories. As, in general, these different case studies provided mathematically and conceptually correct solutions, it is concluded that the approach utilized in this work for carrying out the aircraft trajectory optimization processes is a valid one. This, of course, provides the necessary motivation for continuing with the development of the Polyphemus optimizer.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.29-42, Jan.-Mar., 2014

Celis, C., Sethi, V., Zammit-Mangion, D., Singh, R. and Pilidis, P.

ACKNOWLEDGMENTS During this work, Cesar Celis was partially supported by the Programme Alβan, the European Union Programme of High Level Scholarships for Latin America, Scholarship No. E07D400097BR. Source of financing: European Union.

The authors would like to extend their gratitude to both Mr. Hasan Zolata, who using commercially available algorithms produced some of the results mention here, and Dr. Jean-Michel Rogero, whose PhD research at CU has provided a foundation for the Polyphemus optimizer used in this work. Special thanks also to Mr. Richard Long, who developed the aircraft performance model utilized.

REFERENCES Betts, J., 1998, “Survey of Numerical Methods for Trajectory Optimization”, Journal of Guidance, Control, and Dynamics, Vol. 21, No. 2, pp. 193–207. Boeing Commercial Airplanes, “Market Analysis, Boeing Current Market Outlook 2009 to 2028”, Available from: www.boeing.com/cmo. Accessed on: 16 August 2009.

Hartley, S.J., 1998, “Concurrent Programming: The Java Programming Language”, Oxford University Press, New York, US. Krotov, V.F., 1996, “Global Methods in Optimal Control Theory”, Marcel Dekker, New York, US. Long, R.F., 2009, “An Aircraft Performance Model for Trajectory Optimisation”, School of Engineering, Cranfield University, UK (unpublished).

Brooker, P., 2006, “Civil Aircraft Design Priorities: Air Quality? Climate Change? Noise?”, The Aeronautical Journal, Vol. 110, No. 1110, pp. 517–532.

MATLAB®, 2008, “The Language of Technical Computing, Version 7.7 (R2008b)”, The MathWorks, Inc. Available from: www.mathworks.com.

Bunday, B.D., 1984, “Basic Optimisation Methods”, Edward Arnold, London, UK.

Palmer, J.R., 1999, “The TurboMatch Scheme for Gas-Turbine Performance Calculations, User’s Guide”, Cranfield University, Cranfield, UK.

Callan, R., 2003, “Artificial Intelligence”, Palgrave Macmillan, New York, US. Celis, C., 2010, “Evaluation and Optimisation of Environmentally Friendly Aircraft Propulsion Systems”, Ph.D. Thesis, School of Engineering, Cranfield University. Celis, C., Long, R., Sethi, V. and Zammit-Mangion, D., 2009, “On Trajectory Optimisation for Reducing the Impact of Commercial Aircraft Operations on the Environment”, 19th Conference of the International Society for Air Breathing Engines, ISABE-2009, Montréal, Canada. Celis, C., Moss, B. and Pilidis, P., 2009, “Emissions Modelling for the Optimisation of Greener Aircraft Operations”, Proceedings of GT2009, ASME Turbo Expo 2009, Power for Land, Sea and Air, Orlando, Florida, USA. Clarke, J.P., 2003, “The Role of Advanced Air Traffic Management in Reducing the Impact of Aircraft Noise and Enabling Aviation Growth”, Journal of Air Transport Management, Vol. 9, No. 3, pp. 161–165. Clean Sky JTI (Joint Technology Initiative), 2009, Available from: www. cleansky.eu. Accessed on: 18 August 2009. Davis, L. (editor), 1991, “Handbook of Genetic Algorithms”, Van Nostrand Reinhold, New York, US. Everitt, B., 1987, “Introduction to Optimization Methods and their Application in Statistics”, Chapman and Hall, London, UK. Fletcher, R., 1987, “Practical Methods of Optimization”, 2nd Edition, John Wiley, Chichester, UK. Goldberg, D.E., 1989, “Genetic Algorithms in Search, Optimization and Machine Learning”, Addison-Wesley, Reading, MA, US. Green, J. E., 2003, “Civil Aviation and the Environmental Challenge”, The Aeronautical Journal, Vol. 107, No. 1072, pp. 281–299. Gulati, A., 2001, “An Optimization Tool for Gas Turbine Engine Diagnostics”, Ph.D. Thesis, School of Engineering, Cranfield University.

PARTNER, 2009, “Partnership for AiR Transportation Noise and Emissions Reduction”, Available from: web.mit.edu/aeroastro/ partner/. Accessed on: 18 August 2009. Quagliarella, D., 1998, “Genetic Algorithms and Evolution Strategy in Engineering and Computer Science, Recent Advances and Industrial Applications”, John Wiley & Sons, Ltd., Chichester, UK. Rao, S.S., 1996, “Engineering Optimization: Theory and Practice”, 3rd Edition, John Wiley, New York, US. Riddlebaugh, S.M. (editor), 2007, “Research & Technology 2006, NASA/TM – 2007-214479”, NASA Glenn Research Center, Cleveland, Ohio, USA. Rogero, J.M., 2002, “A Genetic Algorithms-based Optimisation Tool for the Preliminary Design of Gas Turbine Combustors”, Ph.D. Thesis, School of Mechanical Engineering, Cranfield University. Rogero, J.M. and Rubini, P.A., 2003, “Optimisation of Combustor Wall Heat Transfer and Pollutant Emissions for Preliminary Design Using Evolutionary Techniques”, Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, Vol. 217, No. 6,pp. 605–614. Russell, S. and Norvig, P., 2003, “Artificial Intelligence: A Modern Approach”, 2nd Edition, Prentice Hall, New Jersey, US. Sampath, S., 2003, “Fault Diagnostics for Advanced Cycle Marine Gas Turbine Using Genetic Algorithms”, Ph.D. Thesis, School of Engineering, Cranfield University. Schwefel, H.P., 1981, “Numerical Optimization of Computer Models”, John Wiley, Chichester, UK. Walsh, G.R., 1975, “Methods of Optimization”, John Wiley, London, UK. Wienke, D., Lucasius, C.B. and Kateman, G., 1992, “Multicriteria Target Vector Optimization of Analytical Procedures Using a Genetic Algorithm. Part I. Theory, Numerical Simulation and Application to Atomic Emission Spectroscopy”, Analytica Chimica Acta, Vol. 265, No. 2, pp. 211–225.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.29-42, Jan.-Mar., 2014

doi: 10.5028/jatm.v6i1.258

Noise Source Distribution of Coaxial Subsonic Jet-Short-Cowl Nozzle Odenir de Almeida1, João Roberto Barbosa2, Juan Battaner Moro3, Rodney Harold Self3

ABSTRACT: The noise source distribution of a short-cowl coaxial jet operating at different velocity ratios is described in this work. This was motivated by an ongoing research about noise prediction of coaxial jets through Acoustic Analogy with purposes of industrial engine application. This research has been carried out between Universidade Federal de Uberlândia (UFU), Brazil and the Institute of Sound and Vibration Research (ISVR) at Southampton University, UK. The numerical approach employed is originally based on Lighthill Acoustic Analogy. This technique, although likely known, is associated with an improved energy transfer time-scale, used in the turbulence two-point correlation function, in order to enhance the source model. The source model is coupled with the aerodynamic calculation of flow through turbulence quantities evaluated by using a standard k-ε turbulence modeling. The Computational Fluid Dynamics data have also been used to provide complementary information about the coaxial jet noise production mechanisms. Experimental data were used in order to corroborate the results from the current model. Good agreement has been found, showing that high and low frequency contributors to the radiated noise for low velocity ratio are aggregated in a region about seven to ten secondary diameters downstream, while at higher velocity ratios sources are continuously spread from about one up to ten secondary diameters from the jet exit. KEYWORDS: Aeroacoustics, Acoustic analogy, Turbulence, Noise, Nozzle.

INTRODUCTION Measuring and locating noise sources in jet flows is doubtless a difficult task since it requires special equipments and refined techniques. However, the understanding of noise production and radiation mechanisms in these flows would perfectly hold up by such achievement. Indeed, knowing exactly all the patterns behind the sources of jet noise and their location would lead towards a new phase of developing noise suppression devices and optimization of different concepts for industrial applications, for example, aero-engines. One of the most common methods for investigating the noise radiated by a jet flow is the determination of the source strength distribution along the jet axis. For single jet flows, this has been experimentally done by different techniques, using microphone phase array system and/or by using a highly directive microphone system like an elliptic mirror (Laufer et al., 1976; Fisher et al., 1977; Harper-Bourne, 1999). The problem of noise source distribution intensifies when considering coaxial jets due to the nature or structure of the coaxial flow and mainly because the introduction of a number of additional variables, such as area ratio (AR), velocity ratio (VR) and temperature ratio (TR). Nevertheless, many experimental works were set in the last years in order to provide source location data of such flows (Battaner-Moro, 2003; Femi and Bridges, 2004). Concomitantly, numerical research has been evolved through some different approaches in order to overcome the lack of information in this field (Groschel et al., 2006; Tinney et al., 2006; Eschricht et al., 2008). One of the first works towards the use of acoustic analogy in order to evaluate noise sources is attributed to Ribner (1958).

1.Universidade Federal de Uberlândia – Uberlândia/MG – Brazil 2.Instituto Tecnológico de Aeronáutica – São José dos Campos/SP – Brazil 3.University of Southampton – Southampton/United Kingdom Author for correspondence: Odenir de Almeida | Avenida João Naves de Ávila, 2.121 – Santa Mônica | CEP 38.408-100 Uberlândia/MG – Brazil | Email: odenir@mecanica.ufu.br Received: 06/16/2013 | Accepted: 10/30/2013

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.43-52, Jan.-Mar., 2014

Almeida, O., Barbosa, J.R., Moro, J.B. and Self, R.H.

Despite the work was quite simple, the most important finding was that the overwhelming bulk of the jet noise is emitted in the region of eight up to ten nozzle diameters downstream the jet exit plane. Chen (1976), also presented a simple analytical model for predicting sound power spectra density of coaxial jets at ambient temperature. It is shown that for coaxial jets the sound source strength distribution along the jet axis is constant in the initial mixing region, decaying like x-7 in the fully turbulent region. Based on the Lighthill (1952) acoustic analogy, Self (2004) introduces a jet noise source model in which a new feature is taken into account that is the inclusion of frequency dependence for the time and length scales used in the turbulence two-point correlation function. It is found that allowing for this dependence markedly improves the agreement of the prediction of the model with experimental data. This model has been utilized to describe the noise source distribution in a subsonic single jet with potential to other applications. Thus, an extension of this model is presented in this work as part of an ongoing research about noise prediction of coaxial jet through acoustic analogy. Clearly, the source model can be generalized to use Reynolds Averaged Navier-Stokes (RANS) and other Computational Fluid Dynamics (CFD) data to predict jet noise, for single and coaxial jets and also for more novel nozzle geometries. Based on that, the main goal was to identify and quantify noise sources for coaxial jets for industrial application, through the development of a simpler model, which predicts the source noise distribution mainly based on simple turbulence scales quantities. Although the approach may be simplistic from the acoustic and flow interaction standpoint, since the predictions are limited to 90° to the jet axis, the source model shows promising results and contribute for the physical understanding of noise generation, being useful to address practical problems in industry. The mathematical modeling is coupled and processed with the aerodynamic flow computation by turbulence quantities like turbulent kinetic energy (k) and turbulent dissipation rate (e). These quantities have been calculated by using CFD RANS approach with a standard k-e turbulence model – such details are given in sections; Acoustic model, The polar correlation technique, Test article and operating conditions and Computational fluid dynamics. The CFD simulations in this work were employed with two fold purposes: first, to provide turbulence quantities to the acoustic model and, second, to give support in understanding the jet noise production mechanism

through the analysis of the pattern of the turbulence in the flow over different operating conditions. The numerical results were compared against experimental data collected from Coaxial and Jet Noise (CoJeN) project (European Union Research Programme – FP6), which undertook several measurements of various types of coaxial nozzles at different working conditions. The experimental validation data is available from the work of Battaner-Moro, carried out in 2008 (not published yet), which undertook coaxial jet source location measurements using the Polar Correlation Technique (Battaner-Moro, 2003). These measurements were performed at QinetiQ’s Jet Noise Facility in Farnborough, UK, and consist of source images and frequency distribution. This large facility (Fig. 1) is able to generate modelscale cold and hot jets in an anechoic environment.

ACOUSTIC MODEL In this section, the acoustic model is described. The mathematical model for jet noise source distribution is based on the Lighthill acoustic analogy as shown in the following equations. In order to make predictions of the noise for coaxial jet flows, it is necessary to couple the acoustic model to CFD-turbulence results as an input for the source modeling. It is important to emphasize that although the Lighthill acoustic analogy includes convective amplification effects, such theory will not itself account for flowacoustic interaction. This means that an accurate prediction of the far-field pressure distribution is limited to an angle of 90° to the jet axis, in which such effects are unimportant.

Figure 1. QinetiQ’s Jet Noise Facility set up for the Coaxial and Jet Noise (CoJeN) test programs.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.43-52, Jan.-Mar., 2014

Noise Source Distribution of Coaxial Subsonic Jet-Short-Cowl Nozzle

The model itself is quite simple and easy to be numerically implemented. The whole mathematical description will not be presented herein and additional details about the mathematical formulation can be seen in Self (2004). The overall intensity as an integral over the axial extent of the jet is considered according to Eq. 1: (1) where Ix is the overall sound intensity, R is the radial distance, w is the angular frequency and x is the microphone distance. Following Goldstein (1976), Eq. 1 defines the axial source distribution for a specific frequency (w) at any radial distance (R). Since the aim is to identify the source distribution for a 90° observer, the following relation can be readily found from Lighthill’s equation: (2) where Ln are the characteristic correlation length scales in the three axial directions, characterizing the size of the eddies. In this case it is assumed L1 = L2 = L3. The time-scale is represented by t and u is a velocity characteristic of the turbulence. The angular frequency, 2πƒ, is represented by w, c0 is the mean sound speed, ϕ is the azimuthal location and r is the radial location. As it has been pointed out by several authors, a crucial factor for describing the turbulence in a flow is t . The definition of t in the current model plays an important role for predicting the location and the contribution in the frequency range. It has been shown by experiments of Harper-Bourne (1998), that both length and time-scales are frequency dependent, which means that for a good spectrum prediction such dependence must be taken into account. The frequency dependence in both length and time-scales can control both the location of the peak-frequency and also the shape of the spectrum at higher frequencies. In this work, an energy transfer time-scale (TET) has been used in order to enhance the predictions in the source model. This time-scale that is based on the transfer of turbulent energy between different wave numbers of the turbulent fluctuations is associated with the inertial sub range where the noise production mechanism is largely governed by the energy transfer phenomenon. Thus, t in Eq. 2 assumes the following relation (Azarpeyvand et al., 2006):

(3) where aT is an empirical constant that must be adjusted. This coefficient aT is currently utilized to correct the shape of the spectrum and set the peak frequency location. A complete study about the influence of this factor aT was presented in Almeida (2009). In this work, a range of values aT = [0.38 – 0.45] was applied showing satisfactory results. These values are in agreement with the work of Almeida (2009). In Eq. 3, td is the traditional time-scale based on the turbulence decay rate k/e. Λ denotes the size of the eddy which can be either found from experimental results or be estimated. In this work, a range of values of Λ=[1.8 – 2.0] has been used. Finally, l is the length-scale, as defined by: (4) Equation 4 gives the values for L1 = L2 = L3. It is important to observe at this time that a model for anisotropy could be taken into account to describe the relationship among the three axial directions. The use of the TET time-scale has provided a good agreement with the experimental results, showing a more physically realistic shape of source distribution. The acoustic model presented herein is fed in with CFD RANS results. Both t and l are evaluated by using k and e values provided by the aerodynamic simulation of the subsonic flow through a short-cowl nozzle as seen in aero-engines. Details of CFD simulations and analysis of results will be described in section Computational fluid dynamics.

THE POLAR CORRELATION TECHNIQUE Phased microphone arrays have been the tool of choice to determine the spatial distribution of aircraft engine noise, both in static tests or in experimental benches (rigs) such as model scale jet noise facilities. A standard method used in aero-acoustic testing is the Polar Correlation Technique, developed by Fisher et al. (1977). Cross-spectrum measurements in the far field of a jet can be processed to form an equivalent line source image along the jet axis. The Polar Correlation Technique relies on the inverse Fourier relationship between the normalized cross-spectrum (C)

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.43-52, Jan.-Mar., 2014

Almeida, O., Barbosa, J.R., Moro, J.B. and Self, R.H.

and an equivalent line-source strength distribution (S) along the jet axis y (Fig. 2). For an array of incoherent sources, the following relation holds: (5) where a is the polar angle from the reference microphone at 90°. In principle, (6) This Fourier relationship holds for uncorrelated far field sources. The following assumptions can then be taken: 1 The distances from each source to the jth microphone rj (y) and to the reference microphone rref (y) are equal to the array radius, i.e. rref (y) = rj (y) = r. 2 All the source/microphone angles are equal, i.e. for the directivity terms di (θj (y)) = di (θref (y)) = constant. 3 The phase term, rref (y)– rj (y) may be approximated by y sin (aj). It is important to emphasize that successful polar correlation imaging is based on careful consideration to the design of the microphone array. This design must take into account

S(y,ω)

y αN

α2 1

the characteristics of what are going to be measured and the frequency of interest. This is so because all practical microphone array techniques are subjected to geometric constraints which limit the available resolution and introduce undesired spatial aliasing. For the CoJeN nozzle, a ± 30° aperture microphone polar array with 63 microphones was used, as detailed in Table 1. Further details about the experimental setup can be found in Battaner-Moro (2003). The microphones are positioned downstream the nozzle exit, covering a spherical arc, and numbered as described on Table 1 and Fig. 2. Source Images The experimental approach by using Polar Correlation Technique provided source images assumed to be in the form of a line distribution and the noise intensity/meter of both the measured and the fitted data are plotted as a function of axial distance along the centerline of the jet. Figure 3 is an example, in which the measured image, obtained by taking a Fourier transform of the measured coherence data, is shown as a solid line. When source images of model coaxial exhaust jets were first studied, an important phenomenon was observed. Over a wide frequency range, the images were seen to display a double peak (Strange et al., 1984). This double peak phenomenon was seen consistently over a range of coaxial nozzles of different size, showing the presence of a secondary source downstream the jet. Further investigation, as presented by literature, showed that the appearance of this secondary downstream source was strictly related to the flow aerodynamics and the velocity ratio parameters. As presented by Strange et al. (1984), for a coaxial flow at velocity ratio equal to 1, only a single distributed source appears in the image. The emergence of the downstream source from its subdominant role, at a velocity ratio of 0.8, to the dominant source, at a velocity ratio of 0.62, gives a very clear indication that what is being observed in the source image is closely related to the flow conditions.

N Table 1. Polar microphone array angular spacing (CoJeN).

S: equivalent line-source strength distribution; N: microphone number; α: polar angle.

ω: angular frequency;

Figure 2. Polar microphone array and equivalent line jet noise source distribution. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.43-52, Jan.-Mar., 2014

Mic spacing

Mic positions

Mic units

0.25°

60° to 64° and 86° to 90°

32 mics

0.5°

64° to 68° and 82° to 86°

16 mics

1.0°

68° to 82°

15 mics

Noise Source Distribution of Coaxial Subsonic Jet-Short-Cowl Nozzle

TEST ARTICLE AND OPERATING CONDITIONS The test article consists of a short-cowl nozzle which is representative of a large modern high bypass ratio aero-engine, assuming a nominally 1/10th scale. It has a secondary nozzle diameter of 274.4 mm, followed by a primary nozzle and a centerbody (Fig. 4). The experimental acoustic data used in this section, for comparison purposes, are collected from the CoJeN project (European Union Research Programme – FP6) Contract no. AST3-CT-2003-502790. The current work has been processed at

Institute of Sound and Vibration Research (ISVR) at Southampton University in 2009 and 2010. Table 2 presents the flow conditions for the coaxial jet flows investigated in this work. Both primary and secondary streams where considered isothermal, i.e. the static temperature in the nozzle exit plane, Tj, is equal to the static temperature of the ambient air, T0. The nozzle operating velocities have been changed to match three different velocity ratios VR=1.0, VR=0.75 and VR=0.63. The VR has been defined as the ratio between the secondary and primary stream (Vs/Vp).

COMPUTATIONAL FLUID DYNAMICS

Single distributed source

Noise intensity per meter

VR = 1.0

Figure 3. A typical coaxial jet source image (Strange et al. 1984).

DS = 0.274 m Dp = 0.136 m VS Vp

DS: secondary diameter; Dp: primary diameter; VS: velocity at secondary stream; Vp: velocity at primary stream.

Figure 4. Short-cowl nozzle geometry.

Table 2. Operating conditions – isothermal flow. Condition

Vp (m/s)

Vs (m/s)

217.2

1.0

217.2

162.9

0.75

217.2

136.8

0.63

Vp: velocity at primary stream; Vs: velocity at secondary stream; VR: velocity ratio; P0: ambient pressure =101,325 Pa); T0: ambient temperature = 288,15K.

CFD through RANS methodology has been applied in this research in order to provide the mean flow quantities necessary to the acoustic model, as shown in section Acoustic model. The turbulence modeling is also a key point in the whole formulation, since quantities like k and e are used to compute the noise source strength, and is of great importance for understanding the underlying physics of the noise production mechanisms. It is clear that the CFD RANS alone does not reveal the frequency content of the sources. However, it is still possible to interpret CFD results by knowing that the high frequency sources are often aggregated in the close vicinity of the jet exit (between the potential core and the surrounding medium) and the low frequency sources further downstream (mixing region). The dual-stream isothermal jets operating at the velocity ratios mentioned in Table 2 have been considered in this study and a RANS scheme using a finite volume scheme with a standard k-e turbulence model (Launder and Spalding, 1972) was used to calculate the mean and turbulence quantities. The dimensions and boundary conditions for the computational domain used in the RANS simulations are illustrated in Fig. 5. Figure 6 shows part of the shortcowl mesh used for the CFD calculations. For such a kind of approach, there is no need to process a 3D full model, since RANS is only able to provide an average flow-field for the parameters of interest quantities like k and e . A comparison between 3D and 2D simulations has shown a very good agreement, as presented in Almeida (2009). It must be remembered that the analysis is performed by a noise source distribution in a centerline at the exit plane of the nozzle. Such approach is useful for industry application J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.43-52, Jan.-Mar., 2014

Almeida, O., Barbosa, J.R., Moro, J.B. and Self, R.H.

since it requires less computational power and good results can be achieved. A well-defined axisymmetric 2D simulation is a compromise between time of processing and reasonable flow prediction. With modern hardware availability, such analysis could provide results around one and two days of work, being the mesh generation the most complex part of the process. The f inal axisymmetric computational domain discretization consisted of a block structured mesh with 12 blocks and a total of 203,942 elements. The mesh points were concentrated to the shear layer region and clustered in the nozzle wall following a 7th- law turbulent boundary layer approach in order to provide a Y+ of approximately 30 in the near wall regions. A linear growing law is used to increase the elements in axial and radial direction. A sharper mesh jump is avoided during block transitions. It is important to mention that, despite the fact that the Y+ is not too large, some additional tests provided in Almeida (2009), showed the need to include near wall treatment for the short-cowl nozzles simulations.

Axisymmetry 50 DS

Outlet

15 DS

Upstream

Entrainment boundary

Inlet: total pressure and enthalpy; Entrainment: static pressure and temperature; Outlet: static pressure and temperature; Axis: axisymmetry.

Figure 5. Computational Fluid Dynamics domain and boundary conditions – short-cowl nozzle (application).

AERODYNAMICS AND SOURCE DISTRIBUTION RESULTS The numerical results in this section are separated between the flow prediction through RANS simulations and the noise source distribution through the acoustic modeling. The section Aerodynamic Calculation describes the CFD results for different subsonic short-cowl coaxial jet flow operating at different VR of 1.0, 0.75 and 0.63, in accordance with Table 2. Following, the section Source distribution presents the predicted noise distribution at each frequency for the cases, respectively. Aerodynamic Calculation Aerodynamic calculations for the short-cowl nozzle jet operating at the VR mentioned in Table 1 have been considered in this study by employing a RANS scheme using a standard k-e turbulence model to evaluate the mean and turbulence quantities. Figure 7 presents the contours of Mach and k for the different VR investigated. It is possible to observe that as the velocity ratio is close to 1.0 the coaxial jet flow resembles a single jet flow with the noise source region (high kinetic energy) spread over two up to ten diameters. As the VR is decreased, it can be identified that the source region is moving downstream. At VR=0.63, the noise source is placed between 5 ≤ x/DS ≤ 10. Figure 8 presents the axial distribution of k, l and τ for three different radial positions (r/Dj= 0.0; r/Dj=0.5; r/Dj=1.0) comparing VR=1.0 and VR=0.63. As can be seen, the k varies from an upstream distributed source at VR=1.0 to a downstream dominant source when VR=0.63. However, it can be seen that the source peaks at a different axial location, which for the short-cowl nozzle is approximately seven to eight secondary

y x

Figure 6. Short-cowl mesh refinement over the domain – 203,942 quadrilateral elements. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.43-52, Jan.-Mar., 2014

5 4 3 2 1 0 0

1200 1000 800 600 400 200

4 3 2 1

x/DS

(a) VR = 1.0 1200 1000 800 600 400 200

4 3 2 1

4 x/DS

1200 1000 800 600 400 200

5 4 r/DS

r/DS

(b) VR = 0.75 5 4 3 2 1 0 0

6 x/DS

x/DS

0.6 0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05

5 r/DS

5 4 3 2 1 0 0

r/DS

0.6 0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05

r/DS

0.6 0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05

r/DS

Noise Source Distribution of Coaxial Subsonic Jet-Short-Cowl Nozzle

3 2 1

x/DS (c) VR = 0.63

x/DS

Figure 7. Mach number (M) (left) and turbulent kinetic energy (k) (right) – short-cowl nozzle (AR = 3).

diameters. Finally, based on τ and l, it can be inferred that the large structures (big eddies) also start to dominate after 15 DS. Source Distribution Figure 9 shows the axial locations of peak of the IX at different frequencies for an observer located at 90° and a radial distance of R=65.4 DS. This result indicates a good agreement for the whole range of frequency (200 Hz up to 10 kHz) by using the source model associated with a TET time-scale. In the left hand side, the contours (images) of the sources can be visualized. On the right hand side, it is illustrated a comparison of the source distribution over a transversal line placed at the origin – line plot. For VR equal to 1 and 0.75, the sources are not well distributed, peaking at different axial locations. The second downstream source, appearing for

VR=0.75, seems to peak earlier and with a higher frequency. At VR=0.63, the downstream source appears to be more “compact” and peaks at approximately 6 DS. For cases of VR equal to 1.0 and 0.63, the numerical and experimental data agreed satisfactorily. However, a close look on Fig. 9b reveals a discrepancy between experimental and numerical data. The reason for that discrepancy is associated to the presence of some “external” noise content coming either from internal sources in the rig or due to the presence of some solid surface close to the jet. Additional noise tests (experiments) may be necessary to confirm this observation. Although the appearance of extra noise sources in the experimental data (it needs further investigation), the numerical location of the sources are reasonably predicted. Such approach is clearly important for study and investigation of regions of

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800

r/Dj = 0.0 r/Dj = 0.5 r/Dj = 1.0

700 600 500 400 300 200 100 0 -5

10 15 x/Dj

1400 1000 800 600 400 200 0

1.E+00

1.E-01

1.E-02 1.E-03 1.E-04

r/Dj = 0.0 r/Dj = 0.5 r/Dj = 1.0

10 15 x/Dj

1.E+01

1.E-02 1.E-03 1.E-04 1.E-05 1.E-06

10 15 x/Dj

1.E-03 1.E-04

r/Dj = 0.0 r/Dj = 0.5 r/Dj = 1.0

-5

10 15 x/Dj

r/Dj = 0.0 r/Dj = 0.5 r/Dj = 1.0

1.E+00 Time-scale (s)

1.E-01

1.E+01

r/Dj = 0.0 r/Dj = 0.5 r/Dj = 1.0

1.E+00

-5

1.E-02

1.E-05

r/Dj = 0.0 r/Dj = 0.5 r/Dj = 1.0

1200

1.E+00

1.E-05 -5

Time-scale (s)

Turbulent Kinetic Energy (m2/s2)

Almeida, O., Barbosa, J.R., Moro, J.B. and Self, R.H.

Length-scale (m)

Turbulent Kinetic Energy (m2/s2)

1.E-01 1.E-02 1.E-03 1.E-04 1.E-05

-5

10 15 x/Dj

1.E-06

(a) VR = 1.0

-5

10 15 x/Dj

(b) VR = 0.63

Figure 8. Flow quantities distribution at different radial locations – short-cowl nozzle (AR = 3; VR = 1.0 and VR = 0.63). In the plot, the origin (x = 0) is placed at the end of the plug.

noise source in coaxial jet flows. Especially for a VR=0.63, the numerical source location matched well the experimental results. The final words in this section will be devoted to highlight the potential for using the acoustic model in the source location. However, as previously discussed, the exact

characterization of the coaxial jet spreading and its levels of shear stresses may impose some restrictions in the accuracy of the predict spectra, for VR different than unity. Moreover, such failure in capturing, especially the high frequency content, may lead to an inexact source location matching.

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Noise Source Distribution of Coaxial Subsonic Jet-Short-Cowl Nozzle

104

10000

Frequency

8000 6000

103

4000 2000 0 -2

y/Dsec

102

(a) TEST 47 – AR = 3 and VR = 1.0

10000

y/Dsec

104

Frequency

8000 6000

103

4000 2000 0 -2

y/Dsec

102

(b) TEST 48 – AR = 3 and VR = 0.75

10000

y/Dsec

104

Frequency

8000 6000

103

4000 2000 0 -2

y/Dsec

102

y/Dsec

Figure 9. Coaxial source location – short-cowl nozzle; left: image; right: axial source strength/m: (¡) experimental; (l) prediction.

Based on this affirmation and in all the results presented in this work, it is possible to affirm that the acoustic model is promising to deal with coaxial jet flows. However, the model seems to be much more dependent on the aerodynamic results (CFD) when the VR is decreased below 1.0. This is mainly due to the shifting in the noise source location inside the jet plume, which may not be correctly captured with a coarse mesh at downstream positions.

CONCLUSION The noise source distribution of a short-cowl coaxial jet operating at different velocities ratios was investigated in this work. The numerical approach employed is originally based on Lighthill Acoustic Analogy. This technique is associated with an improved energy transfer time-scale (TET), used in the turbulence two-point correlation function, in order to enhance the source model. The

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Almeida, O., Barbosa, J.R., Moro, J.B. and Self, R.H.

main observations were registered as: a) high and low frequency contributors to the radiated noise for low velocity ratio are aggregated in a region about seven to ten secondary diameters downstream, while at higher velocity ratios sources are continuously spread from about one up to ten secondary diameters from the jet exit; b) as the velocity ratio is decreased, the noise sources move downstream the jet axis. This has been corroborated by experimental results. The numerical technique exposed herein is useful for industrial application and can be surely applied to investigate noise source regions in short-cowl nozzles, as seen in modern aircrafts.

ACKNOWLEDGMENTS Prof. Odenir Almeida thanks the financial support provided by Coordenação de Aperfeiçoamento de Pessoal de Ensino Superior (CAPES), Brazil, for the development of this work. He also thanks for all the support received at the Institute of Sound and Vibration for the conclusion of his Ph.D thesis and the Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG).

REFERENCES Almeida, O., 2009, “Aeroacoustics of Dual-stream Jets with Application to Turbofan Engines”, Ph.D. thesis, Instituto Tecnológico da Aeronáutica, São José dos Campos, Brazil.

Harper-Bourne, M., 1998, “Radial Distribution of Jet Noise Sources Using Farfield Microphones”, 4th AIAA Aeroacoustics Conference, Toulouse, France (AIAA Paper 98-2357).

Azarpeyvand, M., Self, R.H. and Golliard, J., 2006, “Improved Jet Noise Modeling Using a New Acoustic Time Scale, No. AIAA/CEAS Aeroacoustics Conference, 2006-2598”, 12th Cambridge, MA, USA.

Harper-Bourne, M., 1999, “Jet near field noise prediction, No. 20022554”, 5th AIAA/CEAS Aeroacoustics Conference, Seattle, WA, USA. Laufer, J., Schlinker, R. and Kaplan, R.E., 1976, “Experiments on Supersonic Jet Noise”, AIAA Journal, Vol. 14, No. 4, pp. 489-497.

Battaner-Moro, J., 2003, “Report on Automated Source Breakdown for Coaxial and Single Jet Noise Measurements”, ISVR Internal Report No. 03/10.

Launder, B.E. and Spalding, D.B., 1972, “Lectures in Mathematical Models of Turbulence”, Academic Press, London, England.

Chen, C.Y., 1976, “A Model for Predicting Aeroacoustic Characteristics of Coaxial Jets”, AIAA Paper 76-4.

Lighthill, M.J., 1952, “On Sound Generated Aerodynamically. I. General Theory”, Proceedings of the Royal Society, Vol. 211, No. 1107, pp. 564-587. doi: 10.1098/rspa.1952.0060

Eschricht, D., Yan, J., Michel, U. and Thiele, F., 2008, “Prediction of Jet Noise from a Coplanar Nozzle, No. 2008-2969”, 14th AIAA/CEAS Aeroacoustics Conference, Vancouver, Canada.

Ribner, H.S., 1958, “Strength Distribution of Noise Sources along a Jet”, Journal of the Acoustical Society of America, Vol. 30, No. 9, pp. 876.

Femi, A.A. and Bridges, J., 2004, “Jet Noise Source Localization Using Linear Phased Array”, NASA TM-2004-213041.

Self, R.H., 2004, “Jet noise prediction using the Lighthill acoustic analogy”, Journal of Sound and Vibration, Vol. 275, No. 3-5, pp. 757768. doi: 10.1016/j.jsv.2003.06.020

Fisher, M.J., Harper-Bourne, M. and Glegg, S.A.L., 1977, “Jet Engine Noise Source Location: The Polar Correlation Technique”, Journal of Sound and Vibration, Vol. 51, No. 1, pp. 23-54. doi: http://dx.doi. org/10.1016/S0022-460X(77)80111-9 Goldstein, M.E., 1976, “Aeroacoustics”, McGraw-Hill, New York, USA. Groschel, E., Schroder, W. and Meinke, M., 2006, “Noise Sources in Single and Coaxial Jets”, ECCOMAS CFD Paper, T.U Delft, Netherlands.

Strange, P.J.R., Podmore, G., Fisher, M.J. and Tester, B.J., 1984, “Coaxial jet noise source distributions”, American Institute of Aeronautics and Astronautics, NASA, 9th Aeroacoustics Conference, New York, USA. (AIAA Paper N. 84-2361). Tinney, C.E., Jordan, P., Guitton, A. and Delville, J., 2006, “A Study in the Near Pressure Field of Co-axial Subsonic Jets, No. 2006-2589”, 12th AIAA/CEAS Aeroacoustics Conference, Cambridge, MA, USA.

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doi: 10.5028/jatm.v6i1.289

Life Cicle Inventory for Lead Azide Manufacture Erick Braga F. Galante1, Assed Haddad2, Dieter Boer3, Danielle Bonifácio4

ABSTRACT: Like any other manufactured chemical compounds, explosives are produced using chemical reactants and other utilities (steam, heat, compressed air, feed water and electricity) and generate a set of environmental impacts (waste water, solid and water residue and waste heat, for example). On top of that, one can count the intrinsic hazard characteristic of explosives and the possibility of accidents involving these compounds. Within this framework, explosives present themselves as chemical compounds suitable for both LCI (Life Cycle Inventory) and LCA (Life Cycle Assessment). This LCI study takes into account all the raw materials, utilities and wastes taking place during the production process. In this particular article, lead azide has its processed mapped and inventoried under the scope of ISO 14040. ISO 14040:2006 describes the principles and framework for life cycle assessment (LCA), including: definition of the goal and scope of the LCA, the life cycle inventory (LCI) analysis phase, the life cycle impact assessment (LCIA) phase, the life cycle interpretation phase, reporting and critical review of the LCA, limitations of the LCA, the relationship between the LCA phases and conditions for use of value choices and optional elements. The Lead azide was chosen due its singular explosive characteristics (very sensitive, which makes lead azide the explosive of choice as a primer in several applications. The results and conclusions of this study are drawn from the review of the process, its analysis, as well as from the application of life cycle inventory methods upon the manufactory of lead azide, a highly sensitive primer explosive, providing solid ground for the further studies, such as a full LCA assessment. Furthermore, for explosives, most LCA research works aims towards disposal, not addressing manufacturing, which is the main strength of this work. KEYWORDS: Lead azide, Manufactory process, Life Cycle Inventory, Life Cycle Impact Assessment, Life Cycle Assessment.

INTRODUCTION Explosives are chemical compounds capable of undergoing a very rapid decomposition (detonation), which generates heat and produces gases, which can be converted in usefull work (thermodinamically defined). Overall, explosives have many applications, ranging from military to civilian. While the military applications of explosives are straightforward in its nature (bombs, mortars, missiles, payloads, for example), the quantities of explosives destined to the civil applications are significantly larger, due to its usage in infrastructure (mining operations, oil extraction and demolitions, for example). Like all other manufactured chemical compounds, explosives are produced using chemical reactants and other utilities (steam, heat, compressed air, feed water, and electricity) and generate a set of environmental impacts (waste water, solid and water residue and waste heat, for example). On top of that, one can count the intrinsic hazardous characteristic of explosives and the possibility of accidents involving these compounds. Within this framework, explosives present themselves as chemical compounds suitable for LCI (Life Cycle Inventory) and LCA (Life Cycle Assessment) studies. This study is a LCI and takes into account all the raw materials, utilities and wastes taking place during the production process. Hence, in this particular study, lead azide was the chosen explosive to have its manufactory process studied. Lead azide was chosen due to its explosive characteristics (very sensitive, which increase risks of detonations but makes it a mandatory compound in almost every explosive use), intense use and the presence of lead in the raw materials (in the form of lead nitrate) and residues.

1. Instituto Militar de Engenharia – Rio de Janeiro/RJ – Brazil 2. Universidade Federal do Rio de Janeiro – Rio de Janeiro/RJ – Brazil 3. Universitat Rovira i Virgili – Tarragona – Spain 4. Indústria de Material Bélico do Brasil – Magé/RJ – Brazil Author for correspondence: Erick Braga F. Galante | Instituto Militar de Engenharia – Departamento de Engenharia Química – SE/5 – Praça Marechal Tibúrcio 80 CEP 23070-200 – Rio de Janeiro/RJ – Brazil | Email: egalante@ime.eb.br Received: 11/02/2013 | Accepted: 12/28/2013

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Galante, E.B.F., Haddad, A., Boer, D. and Bonifácio, D.

Therefore, for this article, the manufactory process of lead azide was mapped using the fundamentals of ISO 14044, qualitatively analysed and had its environmental impacts identified and categorized (midpoint and endpoint). ISO 14040:2006 describes the principles and framework for life cycle assessment (LCA) including: definition of the goal and scope of the LCA, the life cycle inventory (LCI) analysis phase, the life cycle impact assessment (LCIA) phase, the life cycle interpretation phase, reporting and critical review of the LCA, limitations of the LCA, the relationship between the LCA phases and conditions for use of value choices and optional elements. ISO 14040:2006 covers life cycle assessment (LCA) studies and life cycle inventory (LCI) studies. It does not describe the LCA technique in detail, nor does it specify methodologies for the individual phases of the LCA. Furthermore, for explosives, several LCA works (Alverbro et al., 2009; Hochschorner et al., 2006; Hochschorner and Finnveden, 2006) aim towards disposal and applications, not addressing manufacturing, which is the focus of this study. This study aims to disclose the life cycle inventory of a largely used primary explosive (lead azide), aiming to help other researches addressing the environmental impacts in manufacturing lead azide, as well as other leadbased explosives. Thus, this study aims to review one of the processes for manufacturing lead azide, as well as to build the life cycle inventory of the overall process as a preparation for modelling the life cycle of lead azide manufacture.

Background Information This section provides useful background regarding LCA, explosives, lead azide and its manufacture processes. Life Cycle Assessment (LCA) Sonnemann and Castells (2004) presents a list of some analytical tools for environmental managing, in which LCA ranks first as a standardized method for product-oriented environmental impact assessment (ISO 14001; ISO 14040; ISO14040:2006; Zharen, 1998). Alverbro et al. (2009) defined Life cycle assessment (LCA) as a method for assessing the potential environmental impacts and resources used throughout a product’s life from raw material acquisition, production, use and waste management (ISO 14040:2006).

It is important to determine that, under ISO, the term ‘product’ can also include services such as waste management. The purpose of the LCA study of a particular product is to identify the main environmental impacts caused by the product or service evaluated to better determine the actions required for mitigation of impacts. According to ISO 14.040:2006, the steps of LCA are: goal and scope definition, inventory analysis, impact assessment, and interpretation. Modern work (Clift et al., 2000) stated that LCAs are commonly carried out to compare alternative ways of delivering some function. The basis for comparison, common between all alternatives, is termed the Functional Unit for the study. Life Cycle Inventory Life cycle inventory is a part of larger initiatives towards environmental management (ISO 14001; Zharen, 1998) under the guidance of ISO standards such as ISO 14001, ISO 14040 and ISO 14040:2006. To perform a life cycle inventory, it is necessary to determine inputs and outputs within a given system, collect data as well as looking at environmental aspects. According to recent work (Haddad et al., 2013), a LCI study must be carried out for each subsystem involved, materials and energy, generation of products and by-products, as well as pollutants. This inventory is known as LCI. Furthermore, as addressed by Passer et al. (2012) for an environmental construction, LCI is one step prior to the a full LCA. While the LCA aims to determine an indicator that assesses key environmental aspects, the LCI aims to obtain a condensed and reliable array of data. Explosives Overview Explosives are substances of great importance in the human development, being applied in many key areas, such as in the oil industry (Galante et al., 2013). In addition to its traditional use in the military industry, it is usage is noticeable in great engineering projects, such as the construction of tunnels and exploitation of natural resources, like mining and oil exploration. Explosives are chemical substances or mixtures of substances that react rapidly by heating or attrition, generating huge volumes of gas and heat. The general formula for an explosive is CxHyNwOz (Kinney and Graham, 1985). Explosives can be classified by several standards; however, the most

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Life Cicle Inventory for Lead Azide Manufacture

common way of classifying explosives is according to its detonation velocity and activation energy. Table 1 presents a classification matrix with some examples. Table 1. Explosives Classification matrix (compiled by Authors). Classification due to detonation velocity

Classification due to activation energy

High explosive

Low explosive

Primers

Lead azide

Lead styphnate

Boosters

RDX, TNT, HMX

Gun powder Black powder

In general, to allow a full detonation to occur, a set of different explosives is required. The simplest configuration starts with a primer, which is ignited by spark, friction, flame or impact and provides energy to ignite other more powerful explosives. Among the primers, lead azide can be counted as the most used both in military and civilian applications. The Lead Azide - Primer Explosive The lead azide is an explosive initiator, being more efficient than mercury fulminate. It requires a higher temperature for spontaneous combustion and does not decompose during long storage periods at moderately elevated temperatures. When compressed into a capsule, lead azide ignites detonating or exploding the spark (Ramaswamy et al., 1981), but not as immediately as mercury fulminate. For this reason, the main charge initiation of a blasting cap composed of Pb(N3)2 is generally mixed in defined proportions with lead styphnate or other sensitizers to cause the explosion more easily, though with a less violentflame, which serves to initiate the explosion of azide. The sensitivity to shock and friction rapidly increases in proportion as the particle size increases. Crystals of 1 mm in length are subject to a spontaneous explosion due to the energy content that they possess. Lead azide should not have needleshaped crystals with a length greater than 0.1 mm. Lead azide with dextrin may appear to be safely stored under water for a long time. However, there is a belief that crystallized azide becomes more sensitive when stored under water due to an increase in the size of the crystals. Lead azide is produced in two crystal forms: orthorhombic (α), density 4.71 and monoclinic (β), density 4.93. The formation of the orthorhombic form, very

sensitive, prevented the mixture of a diluted solution of lead nitrate and sodium azide, with which lead azide is prepared (Allan et al., 1963; Imbel, 2013; Leslie, 1966). Lead azide with dextrin can be soluble in cold water to extent measure of 1% because of the presence of dextrin. Both forms are almost insoluble in ether and acetone. Ethanol has little cold solvent action on the product and lead azide can be stored when wetted with a mixture of equal volumes of water and ethanol. Lead azide is soluble in aqueous solutions with ammonium acetate 10%. When exposed to an atmosphere with 90% of relative humidity at 30°C, lead azide in crystal form is hygroscopic and coated with a measure of 0.03 and 0.8%, respectively, of dextrin (Akhavan, 2011; Allan et al., 1963; Cooper, 1996; Holloway et al., 1965; Leslie, 1966; Meyer et al., 2007; Urbanski, 1984). Dry lead azide does not react with or corrode iron, steel, nickel, aluminium, lead, zinc, copper, tin or cadmium. Coatings of acid-proof black ink, shellac or iron surfaces, do not show any effect. In the presence of moisture, it corrodes copper and zinc and, in the latter case, it forms the dangerous and highly sensitive copper azide. For this reason, lead azide is not loaded or placed in contact with uncoated copper or their alloys (bronze, brass and other metals) (Silva and Iha, 2010; Urbanski, 1984). Lead azide is obtained as a white precipitate, by reaction of a solution of sodium azide in a solution of lead acetate or lead nitrate. It is absolutely necessary that the process be conducted so that the precipitate formed creates very very small crystals. There are several approaches to the manufactory process. Lead azide manufactory is well known in technical literature (Leslie, 1966) and can be obtained with dextrin (Allan et al., 1963; Holloway et al., 1965) or without it. From Meyer et al. (2007) as well as other sources (Akhavan, 2011; Cooper, 1996; Date et al., 2009), one can design the process for dextrinated lead azide as presented in Fig. 1. A standard plant that is able to hold a production unit as described in Fig. 1 contains vessels for the preparation of solutions (mainly solutions of sodium azide and lead nitrate), jacked vessels for the precipitation of lead azide, as well dosing pumps, filtering and drying installations. Due to the standard reaction using lead nitrate in excess, the wastewater is full of lead, and requires treatment before the final disposal. This treatment can be carried out using precipitation or other methods (like ionic resin). Since the simpler method is precipitation using sulphate, this study considers it in the inventory.

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Galante, E.B.F., Haddad, A., Boer, D. and Bonifácio, D.

Sodium Azide

Lead Nitrate

Distiled Water

NaOH

Sodium citrate

Sodium Azide Solution

Lead Nitrate Solution

Dextrin

Precipitation Vessel

Lead Azide (wet)

Filter

Wastewater

Figure 1. Manufactory process for lead azide (based on (Allan et al., 1963; Holloway et al., 1965; Imbel, 2013; Leslie, 1966)).

The steam generator consists of a set of equipment for producing water vapour. The water vapour produced is intended to distil the water from the solution, heating the reactive mixture and drying the lead azide produced.

lead azide: LIFE CYCLE INVENTORY The mapping starts with the identification of the manufacturing process and inventory stages of raw material inputs, energy inputs and service inputs themselves or other factors that may cause environmental impacts. With these data, enough information can be collected to understand the situation, qualitatively and quantitatively.

In the following sections of this chapter, the parameters of the four stages of the the manufacturing process life cycle of lead azide are defined, according to the goal and scope defined by ISO 14040:2006, from the inventory analysis of the life cycle assessment, life cycle impact and interpretation of the life cycle. This inventory was compiled from the processes described by (Allan et al., 1963; Holloway et al., 1965; Leslie, 1966) and used by (Imbel, 2013). Production Systems The main part of the production system under study consists of both phases directly and indirectly linked to the production process of lead azide, as presented in Fig. 1. The operation and procedures taking place can be folded into subsystems. Each subsystem is described, quantified, analysed and summarized in Table 2.

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Table 2. Subsystems for production of lead azide. Subsystem

Description of the operation

Subsystem for the production of sodium azide

The production starts with 450 L of water (free of impurities and salts) being placed in a plastic container, in which are dissolved 4.9 kg of sodium azide with manual stirring. The pH of the solution is checked; if other than 10, 1.5 L of sodium hydroxide solution 14% are added. After this, it is linked to continuous stirring for several minutes, and then the solution is ready.

Subsystem for the production of lead nitrate

The production starts with 450 L of water (free of impurities and salts) being put into a plastic container, in which are dissolved in 33.7 kg of dried and crystallized lead nitrate. After that, the solution is stirred for several hours and allowed to sit for 8 h. The density of the solution should be 1.05 g/cm3 and the pH should be between 5 and 6 before being transferred by gravity to the intermediate reservoirs.

Subsystem for the production of sodium hydroxide

For this solution, 1.4 kg of sodium hydroxide are dissolved in 10 L of water under agitation.

Subsystem for the production of dextrin aqueous solution

The coating agent (dextrin) is dissolved in filtered water at a proportion of 2 kg of agent for every 5 L of water. The solution is placed under heating (100°C) for 4 h with manual stirring every 30 min. After that, the solution must be cooled naturally and the density should be 1.154 g/cm3.

Subsystem for the production of lead azide

The reactor is fed manually with 6 L of the lead nitrate solution prepared above; this is measured on a solution of sodium azide at a rate of of 5L/40min. During the reaction, it should be kept under constant stirring, heated to 80°C and 50mL should be added to the corn-starch dextrin solution. After the end of the dosage of sodium azide, the circulation of hot water is closed in the reactor jacket and the solution is allowed to sit for 1min. After this, the supernatant mother liquor is poured into a plastic container and the precipitate lead azide - is filtered on a Büchner–Kitassato flask with the vacuum turned on. 500 ml of ethyl alcohol GL 96% are added to the ending process of the already filtered lead azide in order to optimize the drying process of the product.

Subsystem for effluent treatment

15 L of a sulphuric acid solution 50% are inicially placed in the tanks of the treatment plant. Then, the mother liquor effluent from each reaction is added and 1 L of a sodium nitrite solution 7% is added to the 2 cargo tank poured into the effluent. Sodium nitrite is explosive, and a distortion of the sulphuric acid precipitation is in the form of lead sulphate. The tank of the treatment plant may receive up to 12 loads of effluent, 6 L of sodium nitrite and 15 L of sulphuric acid. There is no stirring or heating. All of the loading and unloading of the solutions is done manually.

The following subsystems are indirectly linked to the cycle of the manufacturing process of lead azide, but were not included in this study: • Production of fuel oil (boiler fuel); • Production of electricity; • Condensing of steam for use of process water; • Production of steam; • Manufacture of sodium azide; • Port logistics (because the company model sodium azide is an imported product); • Manufacture of lead nitrate; • Manufacture of dextrin; • Manufacture of sodium citrate;

• Manufacture of sodium hydroxide; • Manufacture of ethyl alcohol 96% GL; • Transportation: sodium azide, lead nitrate, corn-starch dextrin,

sodium citrate, sodium hydroxide and ethanol 96% GL. Product function and Functional Unit The product function is to allow the explosive lead azide to load fuses, a primer accessory used for initiation of the explosive train. Due to its high sensitivity, it is not necessary for bulk loading of the primer. The functional unit for the development of the study was fixed at 1.0 kg of lead azide produced. This value is the basis for the standardization of data in the preparation stage of the Life Cycle Inventory.

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Galante, E.B.F., Haddad, A., Boer, D. and Bonifácio, D.

System Borders The product system is related with environmental aspects by the following steps: Consumption of natural resources (direct): crude oil and water; Energy consumption: electricity and fossil fuels; and Generation of waste: atmospheric emissions, liquid effluents and solid waste. A full set of borders and subsystems included in the study, as well as the excluded, are listed in Table 3.

Lead nitrate is a salt of colourless crystals. It begins to decompose at 200°C, giving off gaseous nitrous oxide and lead. It is soluble in water. Lead nitrate is much less hygroscopic than the other nitrates, except potassium nitrate. While the Table 4. Subsystems balances. Subsystem

System Balances From information, it is possible to consolidate inputs and outputs for each subsystem. These results are presented in Table 4. Further analysing the subsystems (Table 4), it was necessary to determine the quantities of each raw material involved in the production of one kilogram of dry lead azide. This information is presented in Table 5, which was compiled using processes described by several sources (Allan et al., 1963; Holloway et al., 1965; Leslie, 1966) and under production in some industries (Imbel, 2013). Furthermore, it is possible to determine that the two main raw materials are sodium azide and lead nitrate. Sodium azide, being a salt is not hygroscopic. It can be white or yellow. Its density ranges from 1.846 at 20°C in 100 parts of water to 40.7 at 15.2°C in parts of dissolved sodium azide. It is insoluble in ether and 100 g of absolute ethanol at 0°C can dissolve 0.22 g of sodium azide. The aqueous reaction is basic. When heated on a metallic plate, it does not melt, remaining unchanged up to 350°C, and detonates at higher temperature. When heated in vacuum, it decomposes into Na and N2. Table 3. Summary of the borders and limitations. Subsystems considered

Subsystems outside this study

- Subsystem for the production of sodium azide - Subsystem for the production of lead nitrate - Subsystem for the production of sodium hydroxide - Subsystem for the production of dextrin aqueous solution - Subsystem for the production of lead azide - Subsystem for effluent treatment

- Production of fuel used for steam generation - Production of electric energy; - Production of steam - Manufactory of sodium azide - Manufactory of lead nitrate - Manufactory of dextrin - Manufactory of sodium citrate - Manufactory of sodium hydroxide - Manufactory of alcohol - Port logistics and transports

Input

Output

Product

Subsystem for the production of sodium azide

Sodium azide (powder) Process water Electricity

Heat

Sodium azide 99%

Subsystem for the production of lead nitrate

Lead nitrate (powder) Process water Electricity

Heat

Lead nitrate 98%

Subsystem for the production of sodium hydroxide

Sodium hydroxide (powder) Process water Electricity

Heat

Sodium hydroxide 93%

Subsystem for the production of dextrin aqueous solution

Dextrin Process water Electricity Heat

Heat

Dextrin (solution)

Subsystem for the production of lead azide

Ethanol 96% GL Sodium azide Heat 99% Wasted water Sodium citrate (with high Sodium concentration hydroxide 93% of lead) Dextrin Lead nitrate 98%

Lead azide

Subsystem for effluent treatment

Waste water Waste water (with high (contaminated concentration of with diluted lead) lead) Sulphuric acid

Lead sulphate

Table 5. Material balances.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.53-60, Jan.-Mar., 2014

Raw materials

Quantities

Ethanol 96% GL

0.84 L

Sodium azide 99% Sodium citrate

0.51 Kg trace

Sodium hydroxide 93%

0.01 kg

Dextrin

0.07 kg

Lead nitrate 98%

1.38 Kg

Life Cicle Inventory for Lead Azide Manufacture

alkali nitrates and alkaline clay turn intoexplosion carbonates or oxides, lead nitrate’s boiling point is at about 160°C. Lead nitrate, similarly to ammonium, has the property of being completely volatile, although only at high temperatures. Environmental Impacts The environmental impacts of using lead azide occur from its manufacture to its disposal. The wastewater contains heavy metals and extremely low alkalinity, and there are also air emissions and solid waste generation. The environmental impacts arising from the manufacture of lead azide can be observed along all subsystems included in the study, in a greater or lesser extent, involved in the life cycle. Energetic resource consumption In the manufacture of lead azide, energy consumption comes from fossil fuels (used by the boiler to generate steam), and especially from electricity, which feeds the agitation, vacuum and product classification equipment, as well as the boiler. In the energy transportation steps, consumption comes from fossil fuel, used only by their respective transportation vehicles. Atmospheric emissions The main emissions of the system under study are mainly liquids and solids residues, due to the chemical reaction being a precipitation, taking place in water at temperatures below vaporization point. The majority of atmospheric emissions come from the steam production (CO2, H2O, NOx), which varies depending on the fuel used. Liquid effluents For the manufacture of lead azide, excess lead nitrate is used to ensure complete consumption of sodium azide, themost expensive raw material in the manufacturing process. Thus, the mother liquor at the end of the reaction contains excess solubilized lead nitrate and traces of lead azide remaining in solution. This is necessary in the effluent treatment process, in which precipitation can lead to disfigurement and explosion. After addition of these components, a pH adjustment is made and the final effluent is discarded, considering pH and the legal emission limits of 0.01 mg/L for lead (Conama, 2000). Solid residues The solid residue is generated only in the effluent treatment step, in which sulphuric acid is later added to the mother

liquor for the precipitation of lead sulphate. The recovered lead sulphate is a precipitated solid, which can be treated by co-processing (Curtin et al., 2013; Garg et al., 2013; HasanogÍlu et al., 2012), used in cement or other environmental ways of disposing solid residues.

CONCLUSION From this study, it can be concluded that the Life Cycle Inventory for a standard lead azide manufactory unit was developed in full, allowing further analysis and a full LCA. Thus, considering the limitations and assumptions made during the study, it is understood that the objectives have been achieved with the desired quality. The raw material considered in this study did not have their manufactory and transportation taken into account. Regarding energy and utilities, this may vary according to the actual facility under study. Regarding limitations, one of the most important of this study is the allocation and use of lead azide, due to the complexity of use of the explosive after being loaded. Given the wide range of environmental aspects raised in an LCI and LCA, Haddad et al. (2013) suggest the application of selection criteria that allow the separation of only the most significant environmental aspects from the total surveyed. From this study, it is possible to summarize the inputs and outputs of the lead azide manufactory process, which were presented in Table 6. Table 6. Summary of inputs and outputs.

Materials

Energy

Utilities

Inputs

Outputs

Ethanol 96% GL Sodium azide 99% Sodium citrate Sodium hydroxide 93% Dextrin Lead nitrate 98%

Lead azide Wastewater Lead sulphate

Electricity

Heat

Fuel (petrol based)

CO2 H2O NOx Heat Steam Hot water

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Galante, E.B.F., Haddad, A., Boer, D. and Bonifácio, D.

Furthermore, this study was successful in filling some of the gaps in the literature regarding the manufacturing of explosives, as well as their life cycle inventory. The environmental impacts

were mapped and the process was inventoried, which allows researchers to use this study as background information on lead azide manufactory, as well as its LCI, in future researches.

REFERENCES Akhavan, J., 2011, “The Chemistry of Explosives”. Third Edit ed. Norfolk: Biddles Ltd., Kings Lynn, Norfolk. Allan, G.B., Anders, M.L.L. and Stig, Y.E., 1963, “US 3095268 A Process for the preparation of lead azide”. No. United States. Alverbro, K. et al., 2009, “A life cycle assessment of destruction of ammunition”. Journal of hazardous materials, Vol. 170, No. 2-3, pp. 1101–1109, doi:10.1016/j.jhazmat.2009.05.092. Clift, R., Doig, A. and Finnveden, G., 2000, “The Application of Life Cycle Assessment to Integrated Solid Waste Management”, Part 1ÐMethodology. Institution of Chemical Engineers, Vol. 78. Conama, 2000, Res. Conama, No. 357. Cooper, P., 1996, “Explosives Engineering” [S.l.]: Wiley-VCH, Inc. United States of America. Curtin, V. et al., 2013, “Reducing mechanical activation-induced amorphisation of salbutamol sulphate by co-processing with selected carboxylic acids”. International Journal of Pharmaceutics, Vol. 456, No. 2, pp. 508–516. Date, P. et al., 2009, “Modelling the risk of failure in explosion protection installations”. Journal of Loss Prevention in the Process Industries, Vol. 22, No. 4, pp. 492–498, doi:10.1016/j.jlp.2009.03.007. Galante, E., Haddad, A. and Marques, N., 2013, “Application of Explosives in the Oil Industry”. International Journal of Oil, Gas and Coal Engineering (OGCE), Vol. 1, No. 2, pp. 16–22, doi:10.11648/j. ogce.20130102.11. Garg, N., Dureja, H. and Kaushik, D., 2013, “Co-processed excipients: A patent review”. Recent Patents on Drug Delivery and Formulation, Vol. 7, No. 1, pp. 73–83. Haddad, A.N. et al., 2013, “Quality Indicators for Life Cycle Inventory: Real Cases Exploratory Application”. Applied Mechanics and Materials, Vol. 431, pp. 350–355, doi:10.4028/www.scientific.net/ AMM.431.350. HasanogÍlu, A. et al., 2012, “Co-processing of Turkish high-sulfur coals and their blends with a petroleum asphalt. Part 1: In the absence of a catalyst”. Energy and Fuels, Vol. 26, No. 12, pp. 7220–7229. Hochschorner, E. et al., 2006, “Environmental life cycle assessment

of a pre-fragmented high explosive grenade”. Journal of Chemical Technology and Biotechnology, Vol. 81, No. 3, pp. 461–475, doi:10.1002/jctb.1446. Hochschorner, E. and Finnveden, G., 2006, “Life cycle approach in the procurement process: The case of defence materiel”. International Journal of Life Cycle Assessment, Vol. 11, No. 3, pp. 200–208, doi:10.1065/lca2005.10.230. Holloway, K.J. et al., 1965, “US 3173818 A - Manufacture of dextrinated lead azide”. No. USA, 1965. IMBEL, “IMBEL”, from www.imbel.gov.br. Accessed inb 17 jul. 2013. Kinney, G. and Graham, K., 1985, “Explosive Shocks in Air”, 2nd edition, USA, Springer-Verlag. Leslie, J.P.M., 1966, “US 3264150 A - Explosive lead azide process”. No. United States of America. Meyer, R., Köhler, J. and Homburg, A., 2007, “Explosives ”, 6th, Complete edition, Weinheim: Wiley-VCH Verlag GmbH, Weinheim. Passer, A., Kreiner, H. and Maydl, P., 2012, “Assessment of the environmental performance of buildings: A critical evaluation of the influence of technical building equipment on residential buildings”, International Journal of Life Cycle Assessment, Vol. 17, No. 9, pp. 1116–1130, doi:10.1007/s11367-012-0435-6. Ramaswamy, C.P., Patwardhan, W.D. and Mukherjee, A., 1981, “EP 0070932 B1 - Initiatory explosive for detonators and method of preparing the same”, No. [s.n.]. Silva, G.D. and Iha, K., 2010, “Polimorfismo: caracterização e estudo das propriedades de uma fase cristalina”, Journal of Aerospace Technology and Management, Vol. 2, No. 3, pp. 331–338, doi:10.5028/jatm.2010.02038310. Sonnemann, G. and Castellis, F., 2004, “Integrated Life-Cycle and Risk Assessment for Industrial Processes”, [S.l.]: CRC Press, Lewis Publisher. Urbanski, T., 1984, “Chemistry and tecnology of explosives, Vol 4”, New York, USA, Pergamon Press. Zharen, W.M., 1998, “ISO 14000 — Understanding the Environmental Standards”, Rockville, MD.: Published by GaoLi.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.53-60, Jan.-Mar., 2014

doi: 10.5028/jatm.v6i1.286

Development and Optimization of a Catalytic Thruster for Hydrogen Peroxide Decomposition Fernanda Francisca Maia1, Leonardo Henrique Gouvea1, Luis Gustavo Ferroni Pereira1, Ricardo Vieira1, Fernando de Souza Costa1

ABSTRACT: Hydrogen peroxide is a non toxic, low cost, green monopropellant with a significant potential for applications in satellite microthrusters. It presents a density-specific impulse similar to hydrazine. A new bulk mixed oxide catalyst for decomposition of hydrogen peroxide has been developed and tested in a 2 N thruster with modular parts, allowing the use of catalytic beds with different diameters and lengths. A method of experiment design with a star configuration was implemented to optimize the catalytic bed geometry, in order to yield complete peroxide decomposition, reduce thruster size and volume and provide maximum thrust. KEYWORDS: Satellite propulsion, Hydrogen peroxide, Mixed oxide catalyst, Catalytic bed optimization.

INTRODUCTION Nowadays, most satellites in Earth orbit utilize hydrazine catalytic decomposition within their propulsion systems. Hydrazine (N2H4) is an expensive and highly toxic (carcinogenic) monopropellant. In the last decade, due to growing concerns about the environment, there has been a significant interest for storable and nontoxic liquid propellants. Hydrogen peroxide (H2O2) is one of the most important candidates for application as a non-toxic, low cost, green-propellant in satellite propulsion systems (Wernimont, 2009). The traditional catalyst used for H2O2 decomposition is made of superposed silver gauzes which form the catalytic bed. The use of solutions with high concentration of H2O2 leads to the increase of the adiabatic decomposition temperature of the monopropellant (632°C for 85%; 755°C for 90%; 953°C for 98% in mass), making not viable the application of pure silver or silver coated catalysts in long duration operations. The melting point of silver (962°C) is very close to the decomposition temperature of H2O2 and, besides, the formation of silver oxide does not allow the extended use of this type of catalyst (Kappenstein et al., 2002). However, it is notorious that the H2O2 purity level, meaning the amount of stabilizers present in the monopropellant, has high influence in the efficiency of the catalytic system. The reaction of H 2O 2 decomposition for propulsion applications is very fast, where the induction velocity is very dependent on the active sites exposed to the reaction. Since this

1.Instituto Nacional de Pesquisas Espaciais – São José dos Campos/SP – Brazil Author for correspondence: Fernando de Souza Costa | Laboratório Associado de Combustão e Propulsão – INPE | Rodovia Presidente Dutra, Km 40 | CEP 12.630-000 Cachoeira Paulista/SP – Brazil | Email: fernando@lcp.inpe.br Received: 10/21/2013 | Accepted: 12/30/2013

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.61-67, Jan.-Mar., 2014

Maia, F.F., Gouvea, L.H., Pereira, L.G.F., Vieira, R. and Costa, F.S.

reaction is extremely exothermic, a few miliseconds after the

EXPERIMENTAL METHODOLOGY

contact of the monopropellant with the catalyst, the catalytic bed reaches a temperature high enough to make the reaction almost entirely of thermal nature instead of catalytic. A large number of transition metal oxides are catalytically active for H2O2 decomposition (Goldstein, 1974). Nevertheless, a catalyst frequently considered for H2O2 decomposition, is MnO2. However if there is oxidation of this oxide, it will change to Mn2O3, that presents a low catalytic activity. On the other hand, higher temperatures lead to the formation of Mn3O4,

which is as active as MnO2 (Hasan et al., 1999).

Rusek (1996) proposed manganese oxides, supported on alumina and silica, to replace the traditional silver catalysts. The results obtained suggest that the catalytic properties of manganese oxides are influenced by the type of support, hence the interest in new, supported catalyst, containing high concentration of surface active species, which can be stable under reaction conditions, typical of space propulsive applications. Mixed oxides, containing cations of transition metals, are frequently used as bulk catalysts in different reactions. These oxides are generally prepared by thermal decomposition of different precursors, such as hydroxides, carbonates, nitrates

Hydrogen peroxide concentration Stabilized 50% H2O2, supplied by Solvay Chemicals, was concentrated to 90%, in mass, by a counter flow of hot dry air. A deionization treatment may be done before concentration, in order to remove the stabilizers present in the peroxide. Stabilizers can reduce catalytic stability, however, in this work, stabilized peroxide has been used to evaluate the new catalyst. Catalyst preparation Extruded material was prepared from a bulk catalyst, based on manganese and cobalt oxides, capable to spontaneously decompose cold H2O2. The catalyst was prepared by co-precipitation of solutions of cobalt, manganese and aluminum nitrates (Sigma-Aldrich) in a Na2CO3 solution at room temperature, keeping a constant pH of 10, by addition of a NaOH solution. A mole ratio of 4:1:1 (Co:Mn:Al) was used. The product of this reaction was filtered, washed, dried and macerated. The powder was then peptized with HNO3 solution and finally extruded, followed by stove drying and calcinations at 900°C temperature. The final product presented a cylindrical form with approximate dimensions of 2 mm diameter and 3 mm length.

and oxalates (Vaccari, 1998). Taking into account these considerations and the results of preliminary tests, bulk catalysts were developed. Extrudates of cobalt/manganese based mixed oxides were prepared for H2O2 decomposition, employed in satellite microthrusters. The materials were tested in a modular 2 N microthruster, specially designed and built with catalytic beds of variable sizes (diameter and length), for catalytic decomposition of stabilized H2O2, concentrated to 90% in mass. A method of experiment design, using a star configuration, was employed to optimize the catalytic bed dimensions, aiming to obtain complete decomposition of propellant, the reduction of thruster volume and mass, and to yield maximum thrust. The modeling of the process was made by the response surface method, adjusting quadratic models to the experimental results, obtained from a factorial planning. Through this modeling, the response sensitivity to the variables can be estimated, besides determining the levels where the response is optimal. The interest factors studied were diameter and length of the catalytic bed, and the response of interest was the thrust generated by the system.

Microthruster Performance parameters of the new mixed oxide bulk catalyst for H2O2 decomposition were obtained by tests with a 2 N thruster, manufactured with modules, including different chambers, nozzles and ring adapters built with 316 stainless steel (Fig.1). To vary the dimensions of the catalytic bed, three chamber modules were designed and manufactured, besides two nozzles and several rings of different lengths and thicknesses, to hold the catalytic beds in place. The NASA CEA rocket performance code (McBride & Gordon, 1996) was used to calculate the thruster theoretical performance. Chamber pressure was chosen as 5 bar, and the nozzle expansion ratio was calculated as 1.45, to adapt the nozzle at 600 m altitude, where the tests were performed. Assuming frozen flow, with 90% H2O2, the calculated specific impulse was 1040 m/s, theoretical characteristic velocity was 940 m/s and ideal thrust coefficient was 1.106. Figure 2 shows a computer view of thruster and thrust balance. Pressure transducers and thermocouples were placed before and after injection and after the catalytic bed, as can be seen in Fig. 3. Thrust was measured by a 5 N load cell. A graphical user interface, written with LabView software, was used to monitor and control the tests.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.61-67, Jan.-Mar., 2014

Development and Optimization of a Catalytic Thruster for Hydrogen Peroxide Decomposition

TESTS Before tests, the mass flow rate of propellant, as a function of differential pressure in the feeding line, was determined to estimate the peroxide tank pressure required to provide the desired mass

flow rate during each test. After this procedure, the microthruster was assembled and mounted on the thrust balance, the load cell HBM 1-PW4C3/500G-1 (error±0.1g) was calibrated using known weights and, finally, continuous and pulsed test firings were performed.

Chamber D10

Post-chamber and Adapter

Chamber D15

D15L23

Post-chamber D10L25

Post-chamber D10L35

Chamber D22

Post-chamber and Adapter

Post-chamber

D08L30

Post-chamber

D20L25

Post-chamber and Adapter

D15L30

Post-chamber D15L37

D20L35

Post-chamber D22L30

Figure 1. Photography of disassembled thruster parts. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.61-67, Jan.-Mar., 2014

Maia, F.F., Gouvea, L.H., Pereira, L.G.F., Vieira, R. and Costa, F.S.

table 1. Plan of experiments.

Figure 2. Computer view of thruster and thrust balance.

Pressure transducers

Thermocouples

Figure 3. Cut view of thruster with thermocouples and pressure transducers.

rESultS aNd diScuSSioN

Test

Var 1

Var 2

L (mm)

D (mm)

F (n)

–1.00

1.03

1.00

–1.00

1.06

–1.00

1.00

1.21

1.00

1.61

0.00

2.33

0.00

2.14

0.00

2.24

–1.41

0.00

1.52

1.41

0.00

1.69

0.00

–1.41

0.71

0.00

1.41

1.59

L = catalytic bed length; D = catalytic bed diameter; F= thrust.

From this model, the influences of the variables in the test conditions were represented by level lines of the response surface, as depicted in Fig. 4. The responses are indicated on z-axis (thrust), with a color bar, as function of the independent variables indicated in the x (diameter) and y (length) axes. Table 2 shows the regression analysis to fit the response function to experimental data. As seen on Table 2, L and LD present lower coefficients than D, indicating that bed diameter presents a more significant influence than bed length in this experiment. The analysis of variance, depicted on Table 3, demonstrates that the model is significant,

The mixed oxides bulk catalysts prepared in this work had high mechanical strength and low specific area (~6 m2/g) due to the high temperatures in which they were calcined.

Nevertheless, they showed no significant mass loss during tests

2.5

in the microthruster. Thrust (N)

The plan of experiments followed the methodology described by Barros-Neto et al. (1995). Table 1 presents the plan of experiments with a star configuration built from the factorial

0.5

planning 23 (2 factors and 3 study levels), added to three

0 22

replicates at the central point, totalling 11 tests. A quadratic model was obtained using a factorial design, describing the influences of catalytic bed length (L) and catalytic

0.5 20

Diameter (mm)

bed diameter (D) on thrust (F): F = 2.22 + 0.084L + 0.247D - 0.344L2 + 0.0925LD - 0.573D2

1.5

37 0

Length (mm)

Figure 4. Contour graphic for the variable response studied: thrust F (N).

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Development and Optimization of a Catalytic Thruster for Hydrogen Peroxide Decomposition

table 2. Regression analysis using a 23 full factorial central composite design. 2.5

Coefficient

Constant

2.2172

0.0815

27.19

0.000

0.0839

0.0500

1.68

0.154

0.2471

0.0500

4.94

0.004

–0.3444

0.0597

–5.77

0.002

0.0925

0.0706

1.31

0.247

–0.5732

0.0597

–9.61

0.000

p-value

2.0

1.0

0.0 0

10 Time (s)

Figure 5. Thrust (N) obtained with CoMnAl based catalyst: D = 15 mm and L = 30 mm (Pc ~ 5.3 bar).

table 3. Analysis of variance. Degrees of freedom

Regression

2.6110

0.5222

26.17

0.001

Error

0.0997

0.0199

Total

2.7107

120

Isp = 97 s

100 80 Isp (s)

Source

1.5

0.5

SS = sum of squares; MS = mean square; F and P = statistical parameters which indicate how far away a result is from the hypothesis

as evidenced from the p-value (0.001) obtained. The model showed a high determination coefficient, which explains 96% of the variability in the response. According to the contour graphic (Fig. 4), monopropellant microthrusters using the new bulk mixed oxide catalyst for rocket grade stabilized H2O2 decomposition yield optimum thrust when the catalytic bed has length from 29 to 33 mm and diameter from 15 to 17 mm. Thrust, specific impulse and characteristic velocity curves obtained within these optimized dimensions (D = 15 mm and L = 30 mm), with chamber pressure Pc ~ 5.3 bar, are shown, respectively, in Figs. 5, 6 and 7. The propellant mass flow rate ( ) was determined from injection pressure (ΔP) considering the following polynomial equation, obtained from a regression of 16 experimental data (up to 9 bar) using water injection, prior to fire tests in the thrust balance: (1)

60 40 20 0

(2)

1000

12 Time (s)

c* = 822.3 m/s

800 600 400 200 0

The thruster specific impulse (Isp) was then determined by:

Figure 6. Specific impulse obtained with CoMnAl based catalyst: D = 15 mm and L = 30 mm (Pc ~ 5.3 bar).

c* (m/s)

t-value

Thrust (N)

Predictor

Standard error coefficient

12 Time (s)

Figure 7. Characteristic velocity obtained with CoMnAl based catalyst: D =15 mm and L = 30 mm (Pc ~ 5.3 bar).

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.61-67, Jan.-Mar., 2014

Maia, F.F., Gouvea, L.H., Pereira, L.G.F., Vieira, R. and Costa, F.S.

where F is the measured thrust and go = 9.8065 m/s2 is the

2.5

standard gravity acceleration (Sutton and Biblarz, 2000). An average experimental specific impulse of 97 s was

2.0

obtained (Fig. 6). This value is close to 106 s, the value Thrust (N)

calculated with the NASA CEA code (1996). Therefore, the experimental result is satisfactory, since various losses occur in the process. The experimental characteristic velocity was obtained by:

is the nozzle throat area. Since the characteristic velocity is a function only of

1.0 0.5

(3) where Pc is the measured chamber pressure and At = 3.6 mm2

1.5

0.0

20 25 Time (s)

Figure 8. Thrust curve obtained during continuous 30-s test with CoMnAl based catalyst (Pc ~ 5 bar).

temperature and gas properties in the combustion chamber, one can use this parameter to examine the chamber independently

2.5

of the nozzle. The theoretical value obtained was 940 m/s

2.0

and the experimental average value was 822.3 m/s (Fig. 7), Thrust (N)

corresponding to a characteristic velocity efficiency of 87%, which indicates incomplete catalytic decomposition. However, this can be considered a high c* efficiency for hydrogen peroxide decomposition, when compared to results of Pasini et al. (2007)

1.5 1.0 0.5

as depicted on Table 4.

0.0

Finally, a continuous firing of 30 seconds (Fig. 8) and

a sequence of 12 pulsed firings of 5 seconds, at intervals of

5 seconds (Fig. 9), were performed to check the stability of the CoMnAl catalyst, both tests adjusting Pc ~ 5 bar. There was no evidence of catalyst deactivation with respect to time, despite

60 Time (s)

100

120

Figure 9. Thrust curve during pulsed test, 5s on/5s off, with CoMnAl based catalyst (Pc ~ 5 bar).

the use of stabilized hydrogen peroxide. No fragmentation of the catalyst grains was observed after tests. Pressure drops 7

constant (Fig. 10). However, long duration and shorter pulse

tests, at various mass flow rates and pressures, will still be

Pressure (bar)

in the catalytic bed were about 0.5 bar and approximately

performed to evaluate thruster performance on a broader operational range.

Ps PInj

4 3 2

table 4. Comparison of c* efficiencies. c* efficiency (%)

Catalyst

Platinum supported in alumina

Silver screen

Present work

1 0

60 Time (s)

100

120

Figure 10. Pressure curves obtained during a pulse test, 5s on/5s off, with CoMnAl based catalyst.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.61-67, Jan.-Mar., 2014

Development and Optimization of a Catalytic Thruster for Hydrogen Peroxide Decomposition

CONCLUSIONS Bulk catalysts based on mixed oxides, containing Co, Mn and Al, were prepared and showed capable of promoting various cold starts in the decomposition reaction of H2O2 in a 2 N microthruster. The catalyst showed high catalytic activity, despite its low specific surface. The catalytic bed sizes, for rocket grade H2O2 decomposition using the new CoMnAl based catalyst, were optimized to obtain maximum thrust in a modular microthruster. A design of experiments methodology allowed the determination of the optimum dimensions of the catalytic bed: length from 29 to 33 mm and diameter from 15 to 17 mm. For an optimized catalytic

bed, the measured specific impulse was 97s, and the characteristic velocity efficiency was about 87%. Additional tests will be performed to evaluate thruster performance in a broader operational range.

ACKNOWLEDGMENTS The authors acknowledge the Fundação de Amparo à Pesquisa do Estado de São Paulo, the Agência Espacial Brasileira and the Conselho Nacional de Desenvolvimento Científico e Tecnológico for supporting this research.

REFERENCES Barros-Neto, B., Scarminio, I.S. and Bruns, R.E, 1995, Planejamento e Otimização de Experimentos, UNICAMP, Campinas, SP, Brazil (in Portuguese). Goldstein, J.R. and Tseung, A.C.C., 1974, “The kinetics of hydrogen peroxide decomposition catalyzed by cobalt-iron oxides”. Journal of Catalysis, Vol. 32, No. 3, pp. 452-465.

Pasini, A., Torre, L., Romeo, L., Cervone, A., d’Agostino, L., Musker, A.J. and Saccoccia, G., 2007, “Experimental characterization of a 5 N hydrogen peroxide monopropellant thruster prototype”, AIAA 2007-5465, 43rd AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, Cincinnati, OH, USA.

Hasan, M.A., Zaki., M.I., Pasupulety, L. and Kumari, K., 1999, “Promotion of the hydrogen peroxide decomposition activity of manganese oxide catalysts”. Applied Catalysis A, Vol. 181, No. 1, pp. 171-179.

Rusek, J.J., 1996, “New decomposition catalysts and characterization

Kappenstein, C., Pirault-Roy, L., Guérin, M., Wahdan, T., Ali, A.A., Al-Sagheer, F.A. and Zaki., M.I., 2002, “Monopropellant decomposition catalysts: thermal decomposition and reduction of permanganates as models for the preparation of supported MnOx catalysts”. Applied Catalysis A, Vol. 234, No. 1, pp. 145-153.

Sutton, G.P. and Biblarz, O., 2000, Rocket Propulsion Elements,

McBride, B.J. and Gordon, S., 1996, “Computer program for calculation of complex chemical equilibrium compositions and applications, II-Users manual and program description”, NASA-RP-1311.

Wernimont, E. and Ventura, M., 2009, “Low temperature operation

techniques for rocket grade hydrogen peroxide”. Journal of Propulsion and Power, Vol. 12, No. 3, pp. 574-579.

7a ed., John Wiley & Sons. Vaccari, A., 1998, “Preparation and catalytic properties of cationic and anionic clays”. Catalysis Today, Vol. 41, No. 1-3, pp. 53-71.

of hydrogen peroxide gas generators: verification testing and possible applications”, AIAA Paper 09-4617.

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doi: 10.5028/jatm.v6i1.268

Effect of Varying Design Options on the Transient Behavior of a Hybrid Rocket Motor Raed Kafafy1, Muhammad Hanafi Azami1, Moumen Idres1

ABSTRACT: Hybrid rockets provide compelling features for use in atmospheric and space rocket propulsion. One of the prominent applications of hybrid rockets which foster on its characteristics is the propulsion of micro air launch vehicles. In this paper, a set of design options of a hybrid rocket motor is evaluated for propulsion of micro air launch vehicles. In order to evaluate the various design options of a hybrid rocket, we developed design and performance simulation codes. A simulation code is based on a legacy interior ballistic model. MATLAB® environment was used to develop the design and performance analysis codes and to visualize the temporal variation of performance characteristics and grain geometry during burning. We employ the developed codes to assess the replacement of solid rocket motors which are typically used in Air Launch Vehicles by hybrid rocket motors. A typical Micro Air Launch Vehicle mission to launch a 20-kg payload into a 400-km circular polar orbit is assumed. The results show that a hybrid rocket is a suitable candidate for micro air launch vehicles. The performance is improved in terms of specific impulse and thrust with smaller size in the same mission. Several design parameters of hybrid rocket motors were also evaluated and analyzed, including different fuel port geometry, type of fuels and oxidizers, number of ports, nozzle design and initial mass flux. These design parameters bring a significant effect on hybrid rocket performance and size. KEYWORDS: Hybrid rocket, Micro air launch vehicle, Internal ballistics, Regression rate.

INTRODUCTION Hybrid rockets are featured with restarting capability, increased safety, high performance and relatively moderate cost. This unique combination of compelling features poses them as prominent candidates to replace solid rocket motors in air-launched tactical missiles and launch vehicles, besides liquid rocket engines in ground-launched strategic missiles and launch vehicles in the near future. A hybrid rocket is comprised of fuel and oxidizer in different physical states, typically a solid fuel and a liquid or gaseous oxidizer. It represents a compromise between a solid rocket motor and a liquid rocket engine (Mingireanu, 2009). In general, hybrid rockets have specific impulse higher than solid rockets and specific impulse density greater than liquid bi-propellant rockets (Kannalath et al., 2003). Wax based hybrid rocket motors are non-toxic, nonhazardous, shippable as freight cargo, potentially carbon neutral and they can be throttled for thrust control or shut down in case of in-flight anomaly then restart on demand (DeSain et al., 2009). In 2004, the Ansari X Prize has put hybrid technology to a brighter future with the successful launch of SpaceShipOne (David, 2004). SpaceShipOne was succeeded by SpaceShipTwo, which is as nearly twice in size and passed the first powered test flight in April 2013. Despite the advantages of hybrid rockets, there are several shortcomings which are specifically related to overall performance, reliability and cost effectiveness. The overall performance of a hybrid rocket is affected slightly by combustion instabilities and low solid fuel regression rates which require a relatively large fuel surface area to attain the required thrust level (Connell-Jr et al., 2009).

1.International Islamic University Malaysia – Selangor – Malásia Author for correspondence: Raed Kafafy | IIUM-Mechanical Engineering Department | Jalan Gombak, 50728 | Selayang/Selangor – Malásia | Email: rkafafy@iium.edu.my Received: 07/31/2013 | Accepted: 12/17/2013

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Kafafy, R., Azami, M.H. and Idres, M.

The source of combustion instabilities is due to a complex coupling of thermal transients in the solid fuel, the wall heat transfer blocking due to fuel regression rate, and the transients in the boundary layer that forms on the fuel surface (Karabeyoglu et al., 2005). The oxidizer and fuel are unable to mix quickly in a typical hybrid rocket motor, which results in low propellant burning rates causing a reduction in performance (Jacob, 2007). Problems related to regression enhancement have become a main focus of many researchers. Moreover, there are issues of fuel web burn out, combustion efficiency, combustion stability, throttling characteristics, and nozzle throat material response (Venuopal et al., 2011). Mainly, the performance of a hybrid rocket depends on the regression rate of the motor, which is defined as the rate at which the solid fuel regresses normal to the surface (Kumar and Kumar, 2012). The regression rate can hardly be accurately measured due to high scattering effects. According to interior ballistic model, during combustion, the heat is transferred to the solid surface by convection and radiation, where the solid phase fuel is decomposed. Zilliac and Karabeyoglu (2006) developed a model to predict the regression rate behavior. However, their model is applicable to vaporizing fuels in a cylindrical grain configuration that do not form significant char or melt layers. In addition to regression rate, there is a fundamental need to evaluate the pressure and the temporal changes of the combusting gas in the engine chamber because they are related to various phenomena such as ignition and extinction, shape change of a fuel grain due to the combustion, instability due to vortices, acoustics, and injectors, and so on. Several methods were proposed to improve hybrid rockets regression rate such as adopting swirl flow of oxidizers, doping metal additives in fuel composition, using fuel which has melted layer on the fuel surface and designing optimal grain geometry. The swirl flow can increase the residence time (or contact time) of oxidizer stream with fuel surface along the port. Results showed that the average regression rate increases up to 200% as swirl number increases. However, the enhancement of regression rate is severely localized near the inlet of fuel port. Later, the enhancement of regression technique is by employing vortex tube, which have 150% increase in regression rate where the swirl flow dominates over the entire fuel port (Lee et al., 2005). Employing fuel additives, such as ammonium perchlorate (AP), ammonium nitrate (AN) and other nitro-organic compounds, will lower

the heat vaporization effectively. This will allow more solid fuel to combust into the flow of oxidizer. The regression rates were also affected by the addition of activated aluminum powder with 20% by weight to increase the fuel mass flux by 70% over that of pure hydroxyl-terminated polybutadiene (HTPB) (Chiaverini et al., 2000). Studies by Karabeyoglu et al. (2004) showed that the regression rate of paraffin-based fuels can be increased up to 400% compared to classic fuels such as HTPB (Nakagawa and Hikone, 2011). It is observed that meltedfuel droplets entraining to the gas phase and melted fuel are flowing along the solid-fuel surface. When the oxidizer flows at high speed over the upper side of the melting fuel surface, the surface of the liquid layer becomes unstable, minute wave is formed and tiny droplets are produced at the tips of the wave and supplied to flame zone. This process occurs almost without vaporization (Ishiguro et al., 2011). It is proven experimentally that the melted-fuel flow layer is created on the solid-phase fuel surface and entraining droplets actually exist. The amount of this melted fuel contributes to the high regression rate because it needs less heat flux from the combustion gas flow to promote regression (Nakagawa and Hikone, 2011). The regression rates of the paraffin-based fuels increase as the melted-fuel viscosity decreases (Nakagawa and Hikone, 2011). Numerous analytical works have been conducted to describe the gaseous flow during the combustion process. Culick (1996) described the mean gaseous flow through the combustion chamber. Saad and Majdalani (2009) developed an analytical model for basic flowfield in hybrid rockets by employing an arbitrary headwall injection which can be used for benchmarking to test large-scale numerical simulations. However, there are limitations because of burning rate sensitivity, complex fluid dynamics and interactions with heat transfer from the flame zone and the fuel surface, viscosity effects on pressure and the mixing of the two streams (Chiaverini and Kuo, 2007). At Stanford, a group of postgraduate students had launched their 3-inch diameter nitrous oxide/aluminized paraffin hybrid rocket. They have optimized their design by using Gauss-Newton Nonlinear Least Squares (NLSQ) algorithm and used a 4th-order Runge-Kutta method to integrate the equation of motion (McCormick et al., 2004). A genetic algorithm called HYROCS code, developed by researchers at Purdue University, used a different approach to design and optimize hybrid rockets (Schoonover et al., 2000). Therefore, designing a hybrid rocket engine is not a

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.69-82, Jan.-Mar., 2014

Effect of Varying Design Options on the Transient Behavior of a Hybrid Rocket Motor

straight forward process. It is remains challenging because of complexity which involve numerous continuous parameters and variables. There are few published propulsion system design codes available for the industry but many of them employ exhaustive searches in order to optimize continuous variables (Schoonover et al., 2000). During engine operations, both the exposed surface area and perimeter of fuel ports change with time, the mixture ratio will tend to shift even if the oxidizer mass flow is held fixed. High mass flux is desirable in performance to achieve higher volumetric efficiency within the combustion chamber. However, this desire is restricted by the fact that efficient combustion may not be possible at these high mass fluxes and that mixtureratio shifts become more severe under these conditions (Vonderwell et al., 1995). We used another approach to examine the performance of a wheel-typed hybrid rocket motor through simulation of different design parameters. This can be integrated with the micro launch air vehicle used in Pegasus. We used the same mission requirement such as same payload, initial mass, required velocity change (ΔV) and altitude to test the hybrid rocket code. We also have evaluated the performance of wheel-typed grain design with the recent used of multiple circular ports. The result showed an improvement on the regression rate. The following section provides a brief description of the design code, followed by a description of the generic simulation algorithm to estimate the performance of the designed hybrid rocket. The next section describes the analysis of the designed hybrid rocket performance through simulation of varying designed parameters. Evaluation section discloses the effects on the regression rate with different geometries.

propellant including specific heat ratio, the molecular mass of combustion products, flame temperature and characteristic velocity. The thermochemical data for LOx/HTPB is obtained from software developed by the National Aeronautics and Space Administration (NASA) which is known as the Chemical Equilibrium with Application (CEA). Frozen-flow approximation is adopted in the model. This should give an optimum oxidizer to fuel ratio (O/F). We further assume that the regression rate is constant at every location during burning while the oxidizer mass flow rate will remain fixed throughout the combustion process. The flowchart of design procedure is shown in Fig. 1.

O/F ηc Pc TWRat ρfuel

Gox a m Pa ε λ

minitial t N n Δv

Thermochemical evaluation using CEA

Sizing the system

Initial & final port configuration

Performance estimation

PRELIMINARY DESIGN OF HYBRID ROCKET The preliminary design of a hybrid rocket is based on a legacy internal ballistic model (Humble et al., 1995). The algorithm can handle fuel grains with multiple ports of both circular and wheel types. At first, the basic design requirement and reasonable design margins are selected. The design module starts with thermochemical analysis of the hybrid rocket propellant to estimate the main properties of the

Display fuel grain configuration and its size

Figure 1. Hybrid rocket preliminary design flowchart.

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Kafafy, R., Azami, M.H. and Idres, M.

CEA software requires input of the major and minor species of the chemical reaction, changes of enthalpy and entropy formation of both fuel and oxidizer. The output is the variation of specific heat ratio, molecular weight of the species and the flame temperature with the propellant O/F. Polynomial functions are fitted to the data calculated by CEA for later use in the design and simulation algorithms. The values of specific heat ratio, molecular weight and flame temperature obtained from CEA are used to calculate the characteristic velocity assuming 98% combustion efficiency.

(1) where c* is the characteristic velocity, ηc* is the combustion efficiency, γ is the specific heat ratio, R is the gas constant (J/kg.K) and Tf is the flame temperature (K). The nozzle exit Mach number (Me) is calculated from the relation of nozzle expansion ratio (ε):

(2)

(5) where mﬁnal is the final mass (kg) and minitial is the initial mass (kg). (6) where mprop is the propellant mass (kg). (7) where mfuel is the fuel mass (kg) and O/F is the oxidizer to fuel ratio. (8) where Gf is the fuel mass flux (kg/m2.s) and Gox is the oxidizer mass flux (kg/m2.s).The grain design of wheel type port configurations can be calculated from the initial oxidizer mass flux and the number of ports. The geometric design of a wheel type fuel grain can be calculated from the following geometric relations. Figure 2 illustrates the geometric parameters used in the geometric relations.

The exit pressure (Pe) is calculated using isentropic of chamber pressure (Pc) relation: (3) Given g0 is the standard acceleration of gravity, the rocket specific impulse (Isp) is calculated from:

(4) where λ is the nozzle efficiency and Pa is the ambient pressure. The combustion chamber pressure is assumed here as a design parameter of the hybrid rocket. The sizing of the overall propulsion system needs the initial mass and total ΔV. For the mission selected here, the mission analysis of the micro air launch vehicle gives a total initial mass of 1,287.34 kg and ΔV of 2,624 m/s (Aldheeb et al., 2012). These values are used to calculate the final mass, propellant mass, fuel mass and fuel mass flux of the hybrid rocket stage using the following relations:

Figure 2. Geometry of a wheel type grain with triangular ports.

(9) where Api is the initial port area (m2) and ṁoxpp is the oxidizer mass flow rate in each port (kg/s).

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Effect of Varying Design Options on the Transient Behavior of a Hybrid Rocket Motor

(10) where θ is the port half angle (degree) and N is the

number of ports.

where ṁfuel is the fuel mass flow rate (kg/s), m is the fuel length exponent, a is the regression rate coefficient, and n is the mass flux exponent. The layout of a wheel typed fuel grain with 8 ports, which is obtained by the design code, is shown in Fig. 3.

(11) where h is the triangle height (m). (12) where b is the length of triangle base (m). (13) where l is the length of the side triangle (m). (14)

Figure 3. Layout of a wheel type hybrid rocket fuel grain with 8 ports obtained by the design code.

(15)

exit velocity and thrust are calculated using the following

where Ppi is the initial port perimeter. Performance parameters such as the regression rate, relations. Empirical constants such regression rate where w is the web thickness (m), ρfuel is the fuel density (kg/m3) and Lp is the port length (m).

coefficient (a), mass flux exponent (n) and length exponent (m) are taken from previous experiment. These values, a = 0.0002, n = 0.75, m = -0.15 and λ=0.9 are obtained

(16)

from (Humble et al., 1995).

where rh is the center hole radius (m).

(20) (17)

where Rdot is the fuel regression rate (m/s).

where rg is the grain radius (m). (21) (18) where Athroat is the throat area (m2) and ṁprop is the propellant

where ve is the exit velocity, To is the chamber temperature and Po is the stagnation pressure (Pa).

mass flow rate (kg/s). The length of the fuel grain can be calculated from the following empirical relation:

(22) (19)

where F is the thrust force (N) and Ae is the nozzle exit area (m2).

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Kafafy, R., Azami, M.H. and Idres, M.

MICRO AIR LAUNCH VEHICLE USING HYBRID ROCKET MOTOR

SIMULATION OF HYBRID ROCKET PERFORMANCE

We have evaluated hybrid rocket motor performance and size as shown in Table 1. We set the same initial condition and mission requirement as in existing solid rocket motor in MALV for Pegasus XL. For an optimal design of HTPB/LOx, the oxidizer-to-fuel ratio is 2.1, as shown in the thermochemical analysis using CEA. The volume of the oxidizer tank was calculated from mass and density equation and by assuming that the tank is cylindrical. The details of the initial condition and the performance of the designed hybrid rocket motor are given in Table 1.

The generic simulation algorithm shows the analytical performance of the designed hybrid rocket. Over the period of time, the surface fuel port area will increase due to burning. We assumed that the burning surface will burn perpendicularly and the corner of each port started to have a fillet shape. According to the interior ballistic model, O/F and the geometry of the ports will vary with time. The flowchart of the code is illustrated in Fig. 4.

Table 1. Parameters of the designed hybrid rocket motor for MALV for the first stage. Parameter

Hybrid Rocket Motor

Optimal O/F

2.1

Specific heat ratio

1.231

Flame temperature (K)

3593.0

Characteristic velocity (m/s)

1747.3

Chamber pressure (MPa)

7.515

Oxidizer mass flux (kg/s/m ) 2

39.27

Motor length (m)

3.01

Throat area (m2)

0.0029

Exit area (m2)

0.1708

Nozzle expansion ratio

58.40

Fuel diameter (m)

Using interior ballistic model for: • Variation of O/F • Variation of port geometry over time

• • •

300

Thrust (kN)

Burning time (s)

Get data from design process

45 0.66

Initial mass (kg)

1287.34

Propellant mass (kg)

806.97

Specific impulse (s)

316.42

Average regression rate (m/s)

0.00194

Thermochemical properties change Port configuration change Performance changes

Plot: • •

Performance over time Web thickness

Figure 4. Flowchart of hybrid rocket simulation algorithm.

The simulation algorithm starts by getting values from the output of the design algorithm. During burning, the web thickness decreases continuously and the change in the web section can be estimated from:

(23)

where Δw is the change of web thickness (m), wi is the initial web thickness (m) and Δt is the period of time (s).

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Effect of Varying Design Options on the Transient Behavior of a Hybrid Rocket Motor

Angles are formed by assuming that the two side lines and the base is burnt perpendicular leaving each corner a small fillet. The following equations give the angle at each corner of the triangle. (24)

where Apnew is the new total port formed (m2). When the port geometry changes, the O/F will vary and, accordingly, the thermochemical properties will change. By using an internal ballistic model for a non-circular type of fuel, the new O/F can be obtained from Eq (28).

where ɸ is the top fillet angle formation (degree).

(28) (25)

where ψ is the side fillet angle formation (degree). As time progresses, the web decreases, forming a new burning surface with varying perimeter and cross sectional area in each port that can be calculated from:

where O/Fnew is the new oxidizer to fuel ratio, ṁox is the oxidizer mass flow rate and ρ is the density of the fuel. The variation of O/F will affect the performance of the hybrid rocket as well. For simplicity, we assume the oxidizer mass flow rate remains constant throughout burning whereas the fuel mass flow rate is allowed to vary according to this relation:

(26) . where Perinew is the new perimeter formed (m). (27)

(29)

The hybrid rocket O/F, instantaneous web, exit velocity, thrust-to-weight ratio, regression rate and specific impulse are plotted with respect to the time as shown in Fig. 5.

Figure 5. Transient performance of a wheel type hybrid rocket. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.69-82, Jan.-Mar., 2014

EFFECT OF VARYING INITIAL DESIGN PARAMETERS ON HYBRID ROCKET PERFORMANCE We have investigated some of other design features such as nozzle designs with different expansion ratio, different initial oxidizer mass flux, number of ports used and different oxidizers and types of fuels used. These design parameters are based on the user’s input. For the purpose of comparison, we maintained the same mission requirement as for micro air launch vehicle. From the results, it proved that these designs will vary its basic performance. Figures 6 and 7 show that, the higher the nozzle expansion ratio, the higher the specific impulse as well as exit velocity. An increase in the nozzle expansion ratio would reduce the exit pressure towards ambient pressure, increasing the thrust coefficient, providing a higher level of thrust, and forcing the system to converge with the predicted results (Terrence et al., 2009).

340 330 320 310 300 290 280 270 0

nozzle expansion = 120 nozzle expansion = 90 nozzle expansion = 60

15 Time (s)

Figure 6. Transient behavior of specific impulse for various nozzle expansion ratios.

340 330 320 310 300 290 280 270

Exit velocity (m/s)

A decrease in web thickness is plotted in the top left hand side of the figure. The decreasing in web thickness results in the increase of port area. The changes in the geometry will result in the variation of oxidizer-to-fuel ratio. Notice that the oxidizer-to-fuel ratio increases although the oxidizer mass flow rate is held constant because the port diameter increases with time. The burning rate depends on the regression rate and it is the function of the total mass flux. The regression rate decreases exponentially with time due to changes in mass flux. It is convenient to maintain the regression rate constant along the fuel length at each time step to obtain a small variation of port diameter. The thrust-to-weight ratio increases sharply during the early stage because of the rapid increase in O/F and corresponds to the characteristic velocity. This progressive burning is mainly due to the increase in burning area. However, both specific impulse and exit velocity have a maximum point then started to decrease. The increase in specific impulse at the early stage is dominated by the propellant mass flow rate which is low and, after the increase in the port area, this mass flow rate will start to rise, resulting in a decrease in the total specific impulse. The same goes to the variation of exit velocity which is highly dependent on the chamber pressure and exit conditions.

Isp (s)

Kafafy, R., Azami, M.H. and Idres, M.

nozzle expansion = 120 nozzle expansion = 90 nozzle expansion = 60

15 20 Time (s)

Figure 7. Transient behavior of exit velocity for different expansion ratios.

The variation of initial oxidizer mass flux, Goi, will affect the fuel dimension the most. Based on Fig. 8, specific impulse will have less sustainability at higher initial mass flux. The fuel mass flux will have a rapid increase at the early stage and will decrease at some period of time. According to Eq. 20, the regression rate is the function of total mass flux. The total mass flux is the summation of both fuel and oxidizer mass flux. Figure 9 also shows that at higher initial mass flux gives a better thrust-to-weight ratio.

340 330 320 310 300 290 280 270 0

Exit velocity (m/s)

nozzle expansion = 120 nozzle expansion = 90 nozzle expansion = 60

15 Time (s)

Figure 8. Transient behavior of specific impulse variation for different initial mass fluxes.

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Effect of Varying Design Options on the Transient Behavior of a Hybrid Rocket Motor

TWRat

4.5 4

Goi = 300 Goi = 200 Goi = 100

3.5 3 2.5 0

15 Time (s)

Figure 9. Transient behavior of thrust-to-weight ratio for different initial mass fluxes.

The effect of number of ports on a wheel fuel grain design is discussed and analyzed. The results are shown in Table 2. Multiple ports in fuel grain are desirable to produce larger fuel surface area. The overall performance of hybrid rocket motor is critically depends on the flow mixing degree in the combustion chamber. These multiple combustion ports promote better combustion efficiency due to turbulent mixing environment in the mixing chamber downstream (Sutton and Biblarz, 2001). For many ports design, it showed that the regression rate is improved. However, this requires an increase in the design of grain diameter. Significant increase in chamber diameter indicates a strong increase in regression rate due to the possibility of boundary layer growth with less temperature gradient and more intense radiation heat transfer (Chiaverini and Kuo, 2007).

Figure 10 showed specific impulse variation of different port number. At early burning stage, specific impulse tends to increase rapidly at higher number of ports but it will not sustain longer. The same trend is also shown in Fig. 11 for the exit velocity. Fewer number of ports have its own capability to prolong its impulse but had a slightly decrease. Less number of ports will have larger web thickness, thus have more fuel to keep the fuel burning. The pressure slightly decreased throughout the major portion of the hybrid engine test firing due to volumetric expansion within the combustion chamber and nozzle erosion. The change in cross-section grain area also reduced oxidizer mass flux, which in turn reduces burning rate. Thus, gas production is reduced (Terrence et al., 2009).

Isp (s)

330 320 310 300 290 280 270 0

port 8 port 7 port 6 port 9 port 10

15 Time (s)

Figure 10. Transient behavior of specific impulse for different number of ports.

Table 2. Variation of performance parameters with number of ports. 6 ports

7 ports

8 ports

9 ports

10 parts

Fuel length (m)

1.826

1.656

1.506

1.376

1.264

Web (m)

0.069

0.068

0.067

Fuel volume per port (m3)

0.064

0.055

0.048

0.043

0.039

Final area of port (m2)

0.042

0.037

0.035

0.034

0.033

Final perimeter of port (m)

0.705

0.682

0.666

0.654

0.645

Final surface area of port (m2)

1.288

1.129

1.003

0.881

0.816

Radius of center hole (m)

0.068

0.088

0.109

0.129

0.153

Radius of grain (m)

0.292

0.311

0.332

0.352

0.373

Regression rate (cm/s)

0.208

0.211

0.214

0.217

0.219

Performance parameter

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Kafafy, R., Azami, M.H. and Idres, M.

3100 3000 2900 2800 2700 0

2.2x 10 2.1 2 1.9 1.8 1.7 1.6 0

-3

port 8 port 7 port 6 port 9 port 10

Rdot (m/s)

Exit velocity (m/s)

3200

15 Time (s)

Figure 11. Transient behavior of exit velocity for different number of ports.

port 8 port 7 port 6 port 9 port 10

15 Time (s)

Figure 12. Transient behavior of regression rate for different number of ports.

Many ports have better regression rate and more fuel can be decomposed as shown in Fig. 12, but it requires more weight for the structural design. Fewer ports have lighter weight on its structural design, but the regression rate is lower. These conditions resulted in little variation between different port designs. The effect of varying the oxidizer on performance is analyzed. Different oxidizers with HTPB fuel were considered, namely oxygen (LOx), hydrogen peroxide (H2O2) and nitrogen tetroxide (N2O4). The results show that the performance was mainly influenced by the thermo chemical properties of the fuel/oxidizer mixture. Liquid oxygen has lower boiling point and density compared to hydrogen peroxide. The HTPB and liquid oxygen provides a relatively high regression rate with a relatively stable

burn (Mingireanu, 2009). Thermochemistry properties and curve fit relation for hydrogen peroxide and nitrogen tetroxide adopted from Humble et al. (1995) and CEA. The properties of candidate oxidizers and fuels are given in Tables 3 and 4. Figure 13 shows that liquid oxygen oxidizer gives the highest specific impulse. Nevertheless, it has an optimum value until it drops at a certain time. However, hydrogen peroxide has better thrust-to-weight ratio compared to other oxidizers as shown in Fig. 14. Liquid oxygen gives a better thrust during the early stage while nitrogen tetroxide gives the lowest value during the burn. Liquid oxygen and hydrogen peroxide give a better improvement on the regression rate. This proved that the choice of oxidizers bring impact on regression enhancement.

Table 3. Properties of oxidizers. Oxidizer

Chemical formula

TFP (K)

TBP (K)

Pvap (MPa)

Density (kg/m3)

Heat of formation (cal/g)

Oxygen

5.07 @ 154K

1142

Hydrogen peroxide

H2O2

267.4

419

0.345 @ 298K

1414

-1440 (90%HP) -1340 (98%HP)

Nitrogen tetroxide

N2O4

261

294

0.765 @ 344K

1440

-50.9

Nitrous oxide

N2O

182.29

184.67

5.15@ 293K

102

443

TFP: freezing point temperature; TBP: boiling point temperature; Pvap: vapour pressure.

Table 4. Properties of fuels. Fuel

HTPB Paraffin wax

Chemical formula

TMelt (K)

TBP (K)

Density (kg/m3)

Enthalpy of formation (kJ/mol)

C10H15.4 O0.O7

514.15

N/A

920

-51.9

C28H58

327.55

438.89 @ 1atm

809

-1438.2

TMelt: melting temperature; TBP: boiling point temperature.

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Effect of Varying Design Options on the Transient Behavior of a Hybrid Rocket Motor

Figure 16 shows an inverse effect of using different types of fuels. Paraffin fuel gives a decrease in specific impulse over time. This trend occurs because of thermochemical properties of both types of fuels. Values of a and n parameters, appearing in Eq. 20 for N2O/Paraffin and N2O/HTPB are a = 0.488, n = 0.62 (Karabeyoglu et al., 2004) and a = 0.198, n = 0.325 (Lee and Tsai, 2009).

350 Isp (s)

300 250

LOX/HTPB H2O2/HTPB N2O4/HTPB

200 150 0

15 Time (s)

Specific impulse (s)

Figure 13. Transient behavior of specific impulse for different types of oxidizer.

7 LOX/HTPB H2O2/HTPB N2O4/HTPB

TWRat

6 5 4 3 2 0

15 Time (s)

Figure 14. Transient behavior of thrust-to-weight ratio for different types of oxidizer.

Rdot (m/s)

2.4

N2O/Parafin N2O/HTPB

2.2

N2O/Parafin N2O/HTPB

20 25 Time (s)

EVALUATION OF WHEEL-TYPED AND MULTIPLE CIRCULAR PORTS PERFORMANCE

We have also compared the performance with different types of fuels such as Paraffin and conventionally used HTPB with nitrous oxide as an oxidizer. Paraffin based fuel shows regression rate enhancement as in Fig. 15. This is numerically proven on experimental works carried out by previous researchers at Stanford University. This fast burning fuels form a hydro dynamically unstable liquid layer over the surface which encourages high regression rate (Karabeyoglu et al., 2004).

-3

320 310 300 290 280 270 260 250 0

Figure 16. Transient behavior of specific impulse for different types of fuels.

2.6 x 10

Table 5 shows that wheel-typed port requires a larger grain diameter but less fuel length for the desired thrust. Wheel-typed port with thicker web has an advantage for longer burning time. Moreover, wheel-typed ports have better burning port surface with at least 30% increase. This results show 5% increase of the average regression rate. However, studies showed that for short fuel grains, the boundary layer will not fully develop along the port longitudinal axis and some of the downstream parts of the fuel grain will gasify without complete burning while long grains, the entire oxidizer will be consumed before reaching the end of the grain (Kannalath et al., 2003). To estimate the final grain diameter at each port we use:

2 1.8 1.6 0

Dpf = 5

20 25 Time (s)

Figure 15. Transient behavior of regression rate for different types of fuels.

4mfuel 2 + Dpi �LpρN

(30)

where Dpf is the final diameter of port (m) and Dpi is the initial diameter of port (m). J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.69-82, Jan.-Mar., 2014

Kafafy, R., Azami, M.H. and Idres, M.

Table 5. Comparison of multiple-circular ports and wheeltyped ports design for 8 ports. Wheeltyped

0.6

0.66

Fuel length (m)

2.073

1.506

Web thickness (m)

0.06

0.0673

Final area of each ports (m2)

0.027

0.035

1.197

1.003

0.00204

0.00214

Grain diameter (m)

Final surface area of each ports (m ) 2

Average regression rate (m/s)

340 Isp (s)

Multiplecircular

360 320 300

Wheel-typed port Multiple-circular port

280 260 0

15 Time (s)

Figure 17. Transient behavior of specific impulse for different fuel grain types.

2.2 x 10 D –D w = pf pi 2

(31)

The analytical solution for circular ports with constant oxidizer mass flow is:

(32)

Rdot (m/s)

-3

Wheel-typed port Multiple-circular port

2 1.8 1.6 1.4 0

15 Time (s)

Figure 18. Transient behavior of regression rate for different fuel grain types.

where Dp is the diameter of port. For plotting at any time, the variation of grain diameter uses this relation:

(

Dp = a(4n +

. 4m 2) ( πox )n Lmp t

1 2n + 1 2n + 1 pi

)

TWRat

(33)

(34)

Figures 17, 18 and 19 show a comparison of both grain designs in terms of its specific impulse, the regression rate and thrust-to-weight ratio. In Fig. 17, multiple circular ports show better improvement of specific impulse. This is probably caused by the fast change in the geometrical area. Figure 18 shows a clear improvement on the regression rate of the wheel-typed grain. As the time goes, the surface

Wheel-typed port Multiple-circular port

Using the ballistic model, the variation of O/F for circular ports is:

0 0

15 Time (s)

Figure 19. Transient behavior of thrust-to-weight ratio for different fuel grain design.

burning area becomes much larger compared to the circular ports, but both designs show a decline trend due to a decrease of mass flux. Both designs show a regressive burning where the thrust decreases over the time. However, wheel-typed grain has an increase on thrust-to-weight ratio compared to the circular ports. More fuel is burnt in the wheel-typed grain due to a larger burning surface area.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.69-82, Jan.-Mar., 2014

Effect of Varying Design Options on the Transient Behavior of a Hybrid Rocket Motor

CONCLUSION The designed code and generic algorithm analytical performance simulation of hybrid rockets was developed and successfully demonstrated. Prior to the code, we have shown the effect of varying design parameters which can continuously change the performance. Hybrid rocket designers have to properly select and decide to meet the mission requirements. Since this code is developed for the design of propulsion system, it will require further modifications to include all factors needed by space mission analysts and designers. Based on the obtained results, the choice of initial mass flux determines its performance sustainability. According to the temporal variation, higher initial mass flux will give a

rapid increase on its performance, but will not sustain longer. The choice of number of ports also plays an important role in determining the hybrid rocket performance. Many ports have better regression rate and thrust but encounter structural integrity problem. In general, the wheel-typed grain design has higher regression rate and higher thrust-to-weight ratio compared to the multiple circular ports, but it requires a larger grain diameter. As the results showed, grain geometry has a profound effect on regression rate. Larger surface area is preferable but it has structural limitations. A conventionally used multiple-circular port has better structural integrity but lack of performance. Hybrid rocket designers have to compromise these effects to achieve the mission requirement for a micro air-launch vehicle.

REFERENCES Aldheeb, M.A., Kafafy, R.I., Idres, M., Omar, H.M. and Abido, A.M., 2012, “Design Optimization of Micro Air Launch Vehicle Using Different Evolution”, Journal of Aerospace Technology and Management, Vol. 4, No. 2, pp. 185-196. doi: 10.5028/ jatm.2012.04020112. Chiaverini, M.J. and Kuo, K.K., 2007, “Fundamentals of Hybrid Rocket Combustion and Propulsion”, American Institute of Aeronautics and Astronautics, Virgina, USA. Chiaverini, M. J., Serin, N., Johnson, D. K., Lu, Y.-C., Kuo, K. K. and Risha, G.A., 2000, “Regression Rate Behaviour of Hybrid Rocket Solid Fuels”, Journal of Propulsion and Power, Vol. 16, No. 1, pp. 125-132. Connell-Jr, T.L., Santi, S.A., Risha, G.A., Muller, B.A. and Batzel, T.D., 2009, “Experiment and Semi-Experiment Modeling of LabScale Hybrid Rocket Performance”, 45th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit. Denver, Colorado. Culick, F. E. C., 1966, " Rotational axisymmetric mean flow and damping of acoustic waves in a solid propellant rocket", AIAA Journal, Vol. 4, No 8, pp. 1462-1464. David, L., 2004, “SpaceShipOne Wins $10 Million Ansari X Prize in Historic 2nd Trip to Space”, Retrieved in January 10, 2012, from http://www.space.com/403-spaceshipone-wins-10-million-ansariprize-historic-2nd-trip-space.html. DeSain, J.D., Brady, B.B., Metzler, K.M., Curtiss, T.J. and Albright, T.V., 2009, “Tensile Tests of Paraffin Wax for Hybrid Rocket Fuel Grains”, 45th AIAA/ASME/SAE/ASEE Join Propulsion Conference & Exhibit, Denver, Colorado. Humble, R.W., Henry, G.N. and Larson, W.J., 1995, “Space Propulsion Analysis and Design”, Ed. McGraw-Hill, USA. Ishiguro,T., Keizi Sinohara, K.S. and Nakagawa, I., 2011, “A Study on Combustion Efficiency of Paraffin-based Hybrid Rockets”, 47th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, San Diego, California.

Jacob, E., 2007, “The Effect of Oxidizer Laced Hybrid Rocket Regression Rates and Performance”, American Institute of Aeronautics and Astronautics (AIAA) Southeastern Regional Student Conference, Savannah, Georgia. Kannalath, R., Kuznetsov, A. and Natan, B., 2003, “Design of a Lab-scale Hydrogen Peroxide/Hydroxyl Terminated Polybutadiene Hybrid Rocket Motor”, 39th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit. Alabama. Karabeyoglu, A., Zilliac, G., Cantwell, B. J., DeZilwa, S. and Castellucci, P., 2004, “Scale-Up Tests of High Regression Rate Paraffin-Based Hybrid Rocket Fuels”, Journal of Propulsion and Power, Vol. 20, No. 6, pp. 1037-1045. doi: 10.2514/1.3340. Karabeyoglu, M., Zilwa, S., Cantwell, B. and Zilliac, G., 2005, “Modelling of Hybrid Rocket Low Frequency Instabilities”, Journal of Propulsion and Power, Vol. 21, No. 6, pp. 1107-1116. doi: 10.2514/1.7792. Kumar, C. P. and Kumar, A., 2012, “A Numerical Study on the Regression Rate of Hybrid Rocket Motors Using a Combination of Enhancement Techniques”, 48th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, Atlanta, Georgia. Lee, C., Na, Y. and Lee, G., 2005, “The Enhancement of Regression Rate of Hybrid Rocket Fuel by Helical Grain Configuration and Swirl Flow”, 41st AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, Tucson, Arizona. Lee, J., Moon, H., Sung, H., Kim, J. and Park, S., 2011, “Combustion Characteristics of Initial Port Diameter Variation of Solid Fuel in Hybrid Rocket Motor”, 47th AIAA/ASME/ SAE/ASEE Joint Propulsion Conference & Exhibit. San Diego, California. Lee, T.-S. and Tsai, H.-L., 2009, “Fuel Regression Rate in a Paraffin-HTPB Nitrous Oxide Hybrid Rocket”, 7th Asia-Pacific Conference on Combustion, Taipei.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.69-82, Jan.-Mar., 2014

Kafafy, R., Azami, M.H. and Idres, M.

McCormick, A., Hultgren, E., Lichtman, M., Smith, J., Sneed, R. and Azimi, S., 2004, “Design, Optimization, and Launch of a 3” Diameter N2O/Aluminized Paraffin Rocket”, AIAA.

Sutton, G.P. and Biblarz, O., 2001, “Rocket Propulsion Element”, Ed. John Wiley & Sons, Canada.

Mingireanu, F., 2009, “Hybrid rocket motor internal ballistic model and oxidizer dopping. Applications”, 4th International Conference on Recent Advances in Space Technologies – RAST ‘09, Istanbul.

Terrence, L., Connell, J., Santi, S.A., Risha, G.A., Muller, B.A. and Batzel, T.D., 2009, “Experiment and Semi-Experiment Modeling of Lab-Scale Hybrid Rocket Performance”, 45th AIAA/ASME/SAE/ ASEE Joint Propulsion Conference & Exhibit, Denver, Colorado.

Nakagawa, I. and Hikone, S., 2011, “Study on the Regression Rate of Paraffin-Based Hybrid Rocket Fuels”, Journal of Propulsion and Power, Vol. 27, No. 6, pp. 1276-1279. doi: 10.2514/1.B34206.

Venuopal, S., Rajesh, K. and Ramanujachari, V., 2011, “Hybrid Rocket Technology”, Defense Science Journal, Vol. 61, No. 3, pp. 193-200.

Saad, T. and Majdalani, J., 2009, "Rotational flowfields in porous channels with arbitrary headwall injection", Journal of Propulsion and Power, Vol.25, No 4, pp. 921-929.

Vonderwell, D.J., Murray, I.R. and Heister, S.D., 1995, “Optimization of Hybrid-Rocket-Booster Fuel-Grain Design”, Journal of Spacecraft and Rockets, Vol. 32, No. 6, pp. 964-969. doi: 10.2514/3.26716.

Schoonover, P.L., Crossley, W.A. and Heister, S.D., 2000, “Application of a Genetic Algorithm to the Optimization of Hybrid Rockets”, Journal of Spacecraft and Rockets, Vol. 37, No. 5, pp. 622-629. doi: 10.2514/2.3610.

Zilliac, G. and Karabeyouglu, M., 2006, “Hybrid Rocket Fuel Regression Rate Data and Modelling”, 42nd AIAA/ASME/SAE/ ASEE Joint Propulsion Conference & Exhibit, California.

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doi: 10.5028/jatm.v6i1.312

Efficiency-Multimission Comprehensiveness Balance for Platform-Based Satellite Family Otavio Luiz Bogossian1, Geilson Loureiro1, Roberto Vieira Fonseca Lopes1, Edgardo Roggero2

ABSTRACT: This paper aims to present the Comprehensiveness Balance for Efficiency (CBfE) method for Platform-Based Satellite Family. The lack of a penalty measurement to assess the performance loss of using a platform could reduce significantly the family performance. The method, taking into account the comprehensiveness of space missions and the platform characteristics defined at the conception phase, assesses the platform inefficiency, in terms of the additional mass required by the platform equipment to cope with the worst environment factors. The method covers the aerodynamic drag and torque, the Earth’s magnetic field, the eclipse and Sun energy absorption, the cumulated radiation dose absorbed by the electronic components and the effect on the structure to be prepared for several launchers. Based on this assessment and on an interactive process, the platform designer tunes the comprehensiveness with the suitable level of efficiency. A real case, the Brazilian MultiMission Platform project (PMM), is presented as an example of application. The method covers an existing gap on the platform development process for space applications. KEYWORDS: Platform, Product family, Satellite family, Development process, Platform efficiency.

INTRODUCTION The family of products concept became relevant with the transformation of the mass production concept into mass customization aiming to comply with individual client needs (Pine, 1993). The family is a set of similar products obtained from a common platform, given to each product the functionalities required by specific clients (Meyer and Lehnerd, 1997). A product platform is a set of subsystems and interfaces developed to form a common structure (or core) from which a stream of derivative products can be efficiently developed and produced (Meyer and Lehnerd, 1997). Several authors have been working on defining a family of products and the corresponding platform for general applications. The segmentation market grid based on platform was introduced as the way to leverage the family of products across different market niches (Meyer and Lehnerd, 1997). Meyer and Utterback (1993) attempt to map the evolution of a given product family based on platform by means of extensions and upgrades. Bogossian and Loureiro (2011) grouped the family definition in three classes: based on design methods (modularity, platform based, configurational or scalable), based on generation of product variety to target market niches and based on technical aspects for improving the product process, stock reduction and component reutilization promotion. Space Context The space context has specific characteristics such as the complexity of the products and the very low production volume. It was remarked that space products are designed to comply with a particular mission (Gonzalez-Zugasti et al., 2000). The space product (the spacecraft) is designed for its particular mission (independent development). This contrasts with products for general applications in which they are designed for a niche market.

1.Instituto Nacional de Pesquisas Espacias – São José dos Campos/SP – Brazil 2.Comisión Nacional de Actividades Espaciales – Buenos Aires – Argentina Author for correspondence: Otavio Luiz Bogossian | Instituto Nacional de Pesquisas Espaciais | Avenida dos Astronautas, 1.758 – Jardim da Granja | CEP 12.227-010 São José dos Campos/SP – Brazil | Email: otavio.bogossian@inpe.br Received: 12/10/2013 | Accepted: 01/06/2014

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.83-92, Jan.-Mar., 2014

Bogossian, O.L., Loureiro, G., Lopes, R.V.F. and Roggero, E.

A preliminary question stated during the development process is the possibility to define a satellite family and its platform (the common core), taking into account that each mission has specific objective and environment. Based on these specificities, it seems preliminary that the platform will pay a significant penalty. This paper aimed to present and justify the elements of a method named Comprehensiveness Balance for Efficiency (CBfE) to conceive a low Earth orbit satellite family with the scope defined in terms of mission attributes like orbits, wings number and pointing (mission comprehensiveness) and the platform characteristics to cope with the mission attributes and environment like reaction wheel mass and battery mass (efficiency). This paper is organized as follows: Development Process Section presents a literature review on platforms development approach, mainly for space application; The Penalty and its Unit of Measurement section presents a literature review on penalty and its unit of measurement; The Method section presents the CBfE method and its implementation; Application Case section presents an application case and Conclusions and Further Work section draws some conclusions and sets up some further work. Development Process The satellite platform concept was adopted by some space programs to exploit common aspects of the space missions, requiring satellites of the same category. In general, the space agencies do not have a complete view of the satellite family to be developed before the platform design. However, they aim to increase the reuse of the common part (platform) as much as possible when future missions are defined. Boas and Crawley (2006) classified the platform development in two approaches, a parallel and a sequential one. The parallel approach corresponds to designing the platform based on a known family of products. The sequential approach corresponds to develop the platform based only on the first product. They stated also that for complex systems, the platform and the first variant (product) are often developed simultaneously, with the follow-on variants developed and deployed in sequence. Bogossian and Loureiro (2011) concluded that the sequential approach is often applied to the development of multi-mission satellite platforms, demonstrated by missions like Jason 1 with CNES PROTEUS platform (Aerospatiale and Sextant, 1995; Dechezelles and Huttin, 2000; Grivel et al., 2000), like Demeter with CNES Myriade Product Line platform (Bouzat, 2000; Cussac et al., 2004; Alary and Lambert, 2007), previously called as Ligne de Produits Micro-satellite (Buisson et al., 1998), like SkyMed/

COSMO with ASI/Alenia PRIMA platform (Galeazzi, 2000) and by SSR with INPE PMM platform (INPE, 2001). During or after the platform design, the space agencies define a certain number of space missions based on platform comprehensiveness in terms of missions, flexibility and constraints (Galeazzi, 2000; INPE, 2001; Dechezelles and Huttin, 2000) such as covered orbits, pointing accuracy, launchers, lifetime, mass and power limits for payloads. The Penalty and its Unit of Measurement Muffatto (1999) has remarked that adopting the platform concept has several benefits but also some drawbacks; one of them is the open architecture necessary to define new products. It will produce heavier products. Gonzalez-Zugasti and Otto (2000) mentioned that the main benefits of a platform adoption are the development, manufacturing and operation costs by means of the reuse and scale economy. As a drawback a lower performance or efficiency is obtained when compared with an independent development. They also remarked the need of flexibility to comply with new requirements and also of economical feasibility. Boas and Crawley (2007) stated that the benefits are tempered by performance penalties and the true benefits and penalties of platform-based product development are difficult to address. According to academic literature, these topics have not been properly addressed in the academic literature and represent an opportunity for improving managerial understanding of platform making. Gonzalez-Zugasti and Otto (2000) stated that many set of products, such as spacecraft, do not have a single always-increasing desired performance attribute that describes them completely.

THE METHOD Premises and Method Scope To develop the method it was necessary first to determine the development process adopted in the platform design for a low orbit satellite family. Based on real cases as shown on Section Development Process, it was concluded that platform designers usually adopt a sequential development process. The fact of unknowing the family of products tends to increase the platform inefficiency due to the inclusion by the designers, generalities to cover as much as possible, future unknown missions. The absence of penalty measurement does not give to the designers, means to determine the price of these generalities.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.83-92, Jan.-Mar., 2014

Efficiency-Multimission Comprehensiveness Balance for Platform-Based Satellite Family

Another aspect that was necessary to establish before the method development was the definition of the unit of penalty, described on The Penalty and its Unit of Measurement section. It was stated as premise that the method should be easily applicable and should provide to the platform designer a quick and simple response. All necessary simulations should be performed previously during the method development. When necessary, the method would interpolate the available data to cover the specific case of the platform under design. As a consequence of these premises, the amount of necessary work to develop the method was significantly increased. For this reason, it was necessary for the first version, to limit the method scope. In order to define the method scope some platforms were considered, reducing, wherever possible, the number of cases and conflicts. The platforms considered were Myriade from Centre National d’Études Spatiales – CNES (Bouzat, 2000; Alary and Lambert, 2007), Proteus from Centre National d’Études Spatiales – CNES (Dechezelles and Huttin, 2000; Grivel et al., 2000), Plattaforma Riconfigurable Italiana Multi-Applicativa – PRIMA from Agenzia Spaziale Italiana – ASI/Alenia Aerospazio (Galeazzi, 2000) and Plataforma Multi-Missão – PMM from the Brazilian National Institute for Space Research – INPE (INPE, 2001). The method scope is: • Circular orbits only (low eccentricity). • Altitudes from 400 to 1,500 km. • Low inclination orbits from 0° to 25°. • Sun Synchronous Orbit (SSO) orbits from 400 to 1,500 km with descending node crossing time at 10:00 AM and 12:00 AM. • Two pointing target, nadir and Sun. • Satellite configurations with one or two solar wings. • Cubic platform and parallelepiped shape satellite. Only significantly affected equipment by the perturbation in the space and launching environments were considered in the present version. The environmental effects and the corresponding affected equipment are the following: • Drag/orbit decay - tank (kg of fuel). • Drag/torque - reaction wheels (angular momentum in Nms). • Magnetic field/unload the reaction wheels – torque rods (Am2). • Solar irradiation/energy capture – solar array generator (surface m2). • Solar eclipse/continuous energy providing – battery (Ah). • Radiation belts/Total ionizing dose (TID) – electronic components and equipment radiation capacity (krad). • Launching/quasi-static and decoupling – structure (kg).

For the present version, the method captures the inefficiency only for these phenomena, dimensioning the equipment with the corresponding unit. Method Description Each mission requires a specific orbit for the satellite and therefore it will be submitted to a particular environment. In principle, the platform equipment is the same for all missions and the method needs to determine its capacity for the complete set of mission attributes covered by the platform (mission comprehensiveness). From the required capacity for each orbit, the method will determine the best and the worst cases for each equipment. The method adopted the equipment mass as unit of penalty. It was considered that, for a space mission, the mass is always increasing and is limited by the launcher capacity. The more mass the platform needs the less mass the customized part of the product (payload) will carry. For most of the equipment and, with the same technology, the on-board equipment needs more mass to cope with the increase of environment perturbation. An exception is the radiation effect that increases the cost, reason why it was considered in this work as an indirect mass. For those equipment and components whose capacity at first is not defined by its mass, it will be necessary to convert them in equipment mass. Table 1 shows an example of the specific capacity used to implement this conversion for the tank. Three commercial tanks could be used (based on the last column only the configuration 3 is being used) and the specific mass corresponds to the rate between the tank mass and the propellant mass. The last column can be used to assign a relative weight to each configuration. The platform inefficiency will be determined by the platform mass difference. The mass of all covered equipment and required for each mission attribute (comprehensiveness) will be determined. The highest and lowest platform masses will be considered as the worst and best cases respectively. Bogossian and Loureiro (2012) presented a preliminary version of the method. To determine the platform efficiency for a given comprehensiveness, the method uses four different models according to platform equipment. Only the basic model is presented here (Fig. 1). This model applies to the tank, reaction wheels, torque rods and battery. According to Fig. 1, the method obtains the worst dimensioning and the best dimensioning cases for three altitudes per inclination. A minimum equipment mass value could be adopted according to what is available in the market. The mass calculation in Fig. 1 corresponds to the conversion of the equipment capacity into equipment mass, using specific mass table as previously explained.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.83-92, Jan.-Mar., 2014

Bogossian, O.L., Loureiro, G., Lopes, R.V.F. and Roggero, E.

The dimensioning is significantly dependent on the highest and lowest altitudes, reason why the method interpolates the altitude range of the platform being assessed with respect to the three altitudes considered by the method. Considering all inclinations, the inefficiency is the difference between the worst and the best cases multiplied by the number of onboard equipment (by one if no redundancy) and divided by the number of different configurations (scalability concept). The more over dimensioned with respect to the minimum value, the less efficient is the platform. The scalability has a significant effect on the platform efficiency for a given comprehensiveness. As the required component capacity varies according to the mission parameters, the utilization of different hardware configurations will enable to reduce the inefficiency by choosing the appropriate configuration for each mission. The method has a specific treatment for some platform equipment. For the solar wings it is always possible to use a symmetric configuration with two identical wings or a free configuration with one or two wings. The panels, as the main wing component, have a standard size (informed as input) and a maximum number of them in the wings.

If the equipment is dimensioned exclusively by one of the environment factors considered by the method (without being based on a budget with several factors) and if it is an off-theshelf equipment, it is possible to adopt a minimum value to exclude from the inefficiency determination capacity values not available in the market. The inefficiency related to the TID absorved by the electronic components of the developed equipment and absorbed by the purchased equipment does not affect directly the mass, but the cost instead. The components with higher TID capacity are more expensive. The method requires the platform designer to establish a level of TID for the components and equipment that is between the best and the worst cases, according to what is available in the market. Based on this level, the method determines the amount of additional shielding (with respect to existing boxes and platform structure) necessary to keep the TID under the maximum supported level for the lifetime. The method captures the inefficiency from the structure considering the dimensioning for several launchers. It considers only two environmental factors, the quasi-static acceleration

Table 1. Specific mass table. Equip. Model

Propelant mass (m1) (kg)

Tank mass (m2) (kg)

136,22 80,50 46,44

6,4 6,4 6,0

Conf 1 OST 31/1 Conf 2 OST 31/0 Conf 3 PSI 80274-1 Mean (weighed) specific mass

Altitude 1

Dimensionning

Altitude 2

Adopt market minimum value

Specific mass (m2/m1)

Relative weight

0,047 0,080 0,129 0,129

0 0 1 1

Mass calculation

Altitude 3

Inclination 1 result

Interpolation

Per inclination

Obtain worst and best cases

Determine difference

Inclination N result

Figure 1. Approach to inefficiency capture.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.83-92, Jan.-Mar., 2014

Multiply by equipment number

Devide by config. number

Obtain equipment inefficency

Efficiency-Multimission Comprehensiveness Balance for Platform-Based Satellite Family

and the decoupling of the first platform mode longitudinal frequency from the launcher corresponding one. Each launcher has specific values for these two factors and the platform has to consider the worst and the best cases. For the quasi-static effect the method determines the face sheet width of the top, bottom and lateral panels. The width difference between the best and the worst cases determine the amount of additional mass between both cases and corresponds to the inefficiency due to this factor. With respect to the decoupling, the method determines the bottom panel rigidity defining the panel width for the best and the worst cases. From the difference between them it is possible to obtain the inefficiency due to this factor. The mass resulting from the sum of these two effects corresponds to the structure inefficiency due to the flexibility of the platform to be launched by a set of launchers. The Method in the Platform Lifecycle The method aims to help the platform designer at the conception phase. In space project lifecycle (ECSS, 2009) it will be used at Phase A, as shown in Fig. 2. In this phase, the

Phase 0 needs identification

Orbits Pointings Lifetime

Mission definition

Layout Mass distrib. Volume

Mechanical architecture

Power Wings

Hardware

Conception

platform is conceived, the launchers are defined and the mission comprehensiveness established. With these inputs the method assesses the platform and provides consolidated results (level of inefficiency) that could be used by the platform designer to modify the preliminary results. All required data are available at Phase A such as dimensions, number of wings and wheels, required power and layout. Implementation The logic and calculation necessary for the method were implemented by using a set of spreadsheets. Twelve spreadsheets were required, one for each considered environment factor (seven at total), one for the results, two for constants, one for specific capacities and one for the inputs. For each case, the method shall determine the dimensioning of the equipment to cope with the corresponding environmental factor, based on well-known equations or simulation results. The method covers satellite orbital altitudes from 400 up to 1,500 km. The highest and lowest altitudes are essential to determine the worst and the best cases. If the altitude range

Phase B preliminary definition

Phase A feasibility

Phase C detailed definition

Mass budget Launchers

Consolidated results

Power budget

Electrical architecture

Inputs

Orbit and attitude control Inputs

Method (dimensionning)

Figure 2. Positioning of the method in the platform lifecycle.

J. Aerosp. Technol. Manag., SĂŁo JosĂŠ dos Campos, Vol.6, No 1, pp.83-92, Jan.-Mar., 2014

Bogossian, O.L., Loureiro, G., Lopes, R.V.F. and Roggero, E.

implemented by the method is higher than the platform altitude range under evaluation, the method will interpolate the corresponding values with the best curve. The next items describes, for each environment factor, the implementation characteristics. Drag The atmospheric drag is one of the environmental factors (the others are not considered by the method) that will affect the amount of propellant necessary to maintain the nominal orbit. Based on the drag force, the method determines the amount of propellant needed for each case during a year timeframe, considering the mean orbit attack angle, the air density based on Jacchia model (Jacchia, 1977), the satellite external surface, the orbital speed, the drag coefficient and the accepted orbit error. The minimum mean attack angle was determined by computing simulation (STK, version 8.12). Aerodynamic Torque The aerodynamic torque is one of the environmental factors that affect the reaction wheels dimensioning. The angular momentum capacity is dimensioned by the method for each case. The drag force is also used for torque determination, based on the position of the center of pressure with respect to the center of mass. The reaction wheel mass is strongly dependent on the capacity to store the angular momentum, for a given maximum rotation speed. The angular momentum determination is based on the center of pressure (wings and body), center of mass, number of wings and number of orbits to store the momentum. The required wheel capacity is determined by the torque integration in a specified number of orbits, per axis. All wheels are dimensioned with the same capacity, being considered the highest axis value. Magnetic Field The Earth magnetic field defines the torque rods dimensioning necessary to unload the reaction wheels. By simulation (STK, version 8.12) is obtained, for each case and axis, the minimum annual value of the mean orbit magnetic field. The rods must produce a torque that generates a negative angular momentum with respect to the stored momentum. The unload process considers the same number of orbits accounted in the wheels dimensioning. All rods will be dimensioned with the highest axis value.

Sun For each case considered, the method determines the solar panel surface area necessary to provide the minimum power established by the platform, always using the same surface area per panel. The method considers configurations with always two wings or one or two wings. A fixed mass is considered per wing (e.g. yoke, hold-down, SADA). By simulation (STK, version 8.12) it is obtained the worst orbit case in a year period. All necessary efficiencies were considered in the implementation. Eclipse For each case, the annual maximum eclipse duration is determined by simulation (STK, version 8.12), dimensioning the battery capacity. Some inputs required by the method are efficiencies, minimum bus voltage, required power and mean Depth of Discharge (DoD). Total Ionizing Dose Based on simulations (SPENVIS, 2011), the method included several TID tables. Based on these tables the method determines the equipment/components absorbed TID, considering only the basic shielding (platform structure and equipment boxes for the components). When the dose exceeds the component/equipment specification, the shielding is increased by including aluminum plates in parallel with the electronic boards, ensuring that the absorbed dose will remain within the specification. The method requires inputs such as lifetime, component/equipment TIDs and quantity and size of the boxes. Launchers The implementation for the quasi-static acceleration is based on analytical solution for sandwich plates, supported by a rigid frame. The top panel is in simple square supported (by the frame) configuration with an uniform distributed load on the panel. This implementation is based on the launcher’s maximum acceleration and the platform layout that informs in which panel each equipment is located. The relation between the dimensioning between two launchers (best and worst cases) produces the face sheet width difference. From the panel geometry and the adopted material, it is obtained the additional mass between the worst and the best cases. For the lateral panels, the same process is applied taking into account the lateral acceleration. For the bottom panel, the launcher-satellite interface (circle) is the supported region. The load of the platform upper panels is distributed around the square perimeter. The equipment placed directly on the bottom panel is also considered as presenting

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.83-92, Jan.-Mar., 2014

Efficiency-Multimission Comprehensiveness Balance for Platform-Based Satellite Family

an uniformely distributed load. The mass directly placed over the interface is ignored. For the first mode minimum frequency (decoupling factor), an analytical solution is also considered. The launcher-satellite interface is a rigid supported circle. The dimensioning result is the bottom panel width necessary to place the first longitudinal frequency above a specified value, for a given launcher. Taking into account the worst and the best cases, it is possible to obtain the mass difference between both cases, considering also the informed geometry and adopted material. Method Outcomes The method outcome is the inefficiency mass of each onboard equipment, the total direct and indirect masses and the inefficiency percentage with respect to the platform and the satellite (see example in Application Case section). These percentages could be used as a subjective absolute value or relative value to assess the actions efficacy. A recommended approach is the variation of several orbits and platform parameters to determine the effect of each one (sensitivity analysis). The parameters that slightly reduce the comprehensiveness and increase significantly the efficiency are the most suitable to be adopted. Bogossian et al. (2011) presented a preliminary version of this method without considering scalability and structure.

that corresponds to the fraction of the tank necessary to store the amount of propellant required to keep the orbit within the established error limit for the lifetime. Table 2 shows data only for the applicable cases (two-wing configuration, W = 2). It is remarked in yellow, the interpolation for the PMM orbit range. Considering that the PMM range of altitudes (600–1,200 km) is lower than the method range altitudes (400–1,500 km). Figure 4 shows the interpolation curve to estimate the required tank mass for the PMM altitude range. In the last two lines, Table 2 informs that PMM has on board only one tank, and it informs also that was considered by the project, only one tank size (# Config.). Table 3 shows the required panel surface area for each case and Table 4 presents the total number of solar panels (considering a 1m2 panel) and wings mass. Table 5 shows the consolidated results. The first seven rows show the equipment mass difference between the worst and the best cases, representing the inefficiency of each one (obtained from Tables 1 to 4). Row 8 represents the total direct mass (excluding only the shielding mass) and line 9 includes all equipment. Rows 10 and 11 present the platform and satellite (Amazonia-1) masses

APPLICATION CASE The PMM project began in 2001 at INPE with the objective of providing the necessary means of producing low Earth orbit satellites in a reduced time and cost. The first satellite is the Amazonia-1, shown in Fig. 3, a remote sensing satellite planned to be launched in 2014. The satellite dimensions are 2.35 x 0.95 x 0.95 m. The total mass is 550 kg and the platform mass is around 300 kg. The satellite is pointed to nadir and it has always two wings with a total surface of 6.3 m2 with a SADA (Solar Array Drive Assembly) to rotate the wings. Considering that the PMM (INPE, 2001) platform is already designed and it is too late to be balanced by the method, the application case was included in this paper only to exemplify the method outputs. A sub set of tables is shown in this work to give a real meaning of the method outcomes. Table 2 shows the result of the tank dimensioning process for the considered cases including different orbits (three altitudes and some inclinations), pointing targets and quantity of solar wings. The method determines the mass value

Figure 3. Amazonia -1 satellite.

Tank Mass

Kg 30.0 25.0 20.0 15.0 10.0 5.0 0.0

27.25

0.70 0

500

1000

0.00 1500

1020 Km

Figure 4. Interpolation curve.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.83-92, Jan.-Mar., 2014

Bogossian, O.L., Loureiro, G., Lopes, R.V.F. and Roggero, E.

Table 2. Tank dimensioning. Tank mass Orbit Class

Inclination

Pointing

Equator

Nadir

Low inc

Nadir

Low inc

Nadir

Equator

Solar

Low inc

Solar

Low inc

Solar

SSO 10H

SSO 12H

97/98

# Wings

Nadir

100 97/98

Nadir

100

Max: 3.47

Orbit altitude

PMM

400 (km)

700 (km)

1500 (km)

600 (km)

1200 (km)

27.25 km

0.70

0.00

3.47 km

0.09 km

27.25 km

0.70

0.00

3.47 km

0.09 km

27.25 km

0.70 km

0.00 km

3.47 km

0.09 km

0.88 km

0.01 km

1.39 km

0.04 km

0.88 km

0.01

1.37 km

0.04 km

0.86 km

0.01 km

1.36 km

0.04 km

27.25 km

0.70 km

0.00 km

3.47 km

0.09 km

27.25 km

0.70 km

0.00 km

3.47 km

0.09 km

Min: 0.04

Unit: 3.43

# Config: 1

Inef: 3.43

# tanks: 1

Table 3. Required surface for solar panels. Solar panel (surface) Orbit altitude Orbit Class

Inclination

PMM

Pointing

400 (km)

700 (km)

1500 (km)

600 (km)

1200 (km)

Equator

Nadir

3.49 m2

3.29 m2

2.98 m2

3.38 m2

3.08 m2

Low inc

Nadir

3.78 m2

3.57 m2

3.19 m2

3.66 m2

3.32 m2

Low inc

Nadir

4.43 m2

4.00 m2

3.44 m2

4.23 m2

3.62 m2

Equator

Solar

3.01 m2

2.74 m2

3.05 m2

2.84 m2

Low inc

Solar

2.92 m2

2.61 m2

2.95 m2

2.72 m2

Low inc

Solar

2.75 m2

2.64 m2

2.76 m2

2.68 m2

SSO 10H

Nadir

3.77 m2

3.43 m2

3.19 m2

3.64 m2

3.25 m2

SSO 12H

Nadir

3.26 m2

3.09 m2

2.83 m2

3.17 m2

2.92 m2

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.83-92, Jan.-Mar., 2014

Efficiency-Multimission Comprehensiveness Balance for Platform-Based Satellite Family

Table 4. Required panels and wings mass. Always two wings # Panels (*) Orbit Class

Inclination

Equator Low inc Low inc Equator Low inc Low inc SSO 10H SSO 12H

0 12 25 0 12 25

Pointing

Nadir Nadir Nadir Solar Solar Solar Nadir Nadir

Fx mass

600

1200

(#)

4 4 6 4 4 4 4 4

4 4 4 4 4 4 4 4

(kg)

Total kg 600 (km)

9.596 24.69 9.596 24.69 9.596 32.24 2.856 17.95 2.856 17.95 2.856 17.95 9.596 24.69 9.596 24.69 Max: 32.24 Min: 17.95 Min. Without Sada 24.69 Inef: 7.55

1200 (km)

24.69 24.69 24.69 17.95 17.95 17.95 24.69 24.69

(*) Always even

Table 5. Consolidated results. Total

PMM

Component

Mass (kg)

Tank

3.43

Wheels

0.74

Magnetic torque rods

1.88

Solar wings

7.55

Battery

1.81

Structure

7.29

Shielding (indirect mass)

6.67

Inefficiency direct mass

22.7

Inefficiency total mass

29.4

Platform mass

295

Satellite mass

557.0

% Inef. Direct platform

7.7%

% Inef. Direct satellite

4.1%

% Inef. Total platform

10.0%

% Inef. Total satellite

5.3%

Total ineff. Mass lowest orbit

28.68

% Tot. Ineff. Lowest orbit - platform

9.7%

% Tot. Ineff. Lowest orbit - satellite

5.1%

used as reference. Rows 12 to 15 present the relative inefficiencies in terms of percentage with respect to the platform and satellite masses. Rows 16 to 18 show the best cases for all in the same orbit, corresponding to the orbit with the lowest total equipment mass. In order to demonstrate how the method is used for balancing the comprehensiveness in terms of altitude with the efficiency, the lowest altitude was reduced from 600 to 550 km as a first case and increased from 600 to 650 km as second case. The result for the first case was an increase in the total inefficiency mass from 29.4 to 31.9 kg (increase of 2.5 kg) that corresponds to an inefficiency increasing from 10.0 to 10.8%. For the second case, the total mass decreased to 27.5 kg (decrease of 1.9 kg) and that corresponds to an inefficiency reduction from 10,0 to 9.3%.

CONCLUSIONS AND FURTHER WORK The main result of this work is the demonstration that it is possible to obtain an objective platform penalty measurement. It enables, for the products developed by using the sequential approach, to have a feedback from the adopted comprehensiveness with respect to the efficiency. The method itself represents a tool to help space platform designer to balance efficiency with comprehensiveness. The method scope was reduced for this first version based on the real cases, and it requires further work to augment its scope in terms of space and launching environments as well as platform configuration. It is also recommended that some of the implemented models be upgraded to improve the process of capturing the inefficiencies. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.83-92, Jan.-Mar., 2014

Bogossian, O.L., Loureiro, G., Lopes, R.V.F. and Roggero, E.

REFERENCES Aerospatiale and Sextant, 1995, “Aerospatiale/Sextant Avionique brochure”, Filière Proteus CNES.

European Cooperation for Space Standardization (ECSS), 2009, “System Engineering General Requirements”, ECSS. [S.1].

Alary, D. and Lambert, H., 2007, “The Myriade product line, a real success story”, ACTA Astronautica, Vol. 61, pp. 223-227.

Galeazzi, C., 2000, “Prima: A new, competitive small satellite platform”, Acta Astronautica, Vol. 46, No. 2-6, pp. 379-388.

Boas, R.C. and Crawley, E.F., 2006, “Extending Platforming to the Sequential Development of System Families”, INCOSE 2006, 16th Annual International Symposium Proceedings. Orlando, Florida.

Gonzalez-Zugasti, J.P. and Otto K.N., 2000, “Platform-Based Spacecraft Design: A formulation and implementation Procedure”, Aerospace Conference Proceeding, IEEE.

Boas, R.C. and Crawley, E., 2007, “Parallel and Sequential Development of Complex Platform-Based Product Family”, Engineering Management Conference, IEEE.

Gonzalez-Zugasti, J.p., Otto K.N. and Baker J.D., 2000, “A Method for Architecting Product Platforms”, Research in Engineering Design, Vol. 12, No. 2, pp. 61-72.

Bogossian, O.L. and Loureiro, G., 2011, “Attributes Balance on the Adoption of Platform Based Solutions for Satellites”, Concurrent Engineering Proceedings, MIT, Cambridge, MA, USA.

Grivel, C., Doullet, F., Huiban, T., Sainct, H., Bailion, Y., Terrenoire, P. Schrive, J. and Lazard, B., 2000, “Proteus: European Standard for small satellites”, Small Satellites Systems and Services, 5th International Symposium, La Boule France.

Bogossian, O.L. and Loureiro, G., 2012, “Attributes Balance on the Adoption of Platform Based Solutions for Satellites”, Journal of Aerospace Engineering, Sciences and Applications, Vol. 4, No.1. pp. 110 - 115. Bogossian, O.L., Loureiro, G. and Lopes, R.V.F., 2011, “Architecting Method to Assess Conceptual Design of Platform Based Satellites”, International Astronautical Congress, Cape Town, South Africa. IAC-11.D1.6.3. Bouzat, C., 2000, “CNES Microsatellite Product Line, an approach for innovation”, Small Satellites Systems and Services, 5th International Symposium, La Boule France. Buisson, F., Cussac, T., Lassalle-Balier, G., Laurens, A., Ledu, M., Llorens, J.C. and Chadoutaud, P., 1998, “La ligne de produits Micro-satellite du CNES”, Small Satellites Systems and Services, 4th International Symposium, Antibes, San Juan Les Pains, France. Cussac, T., Buisson, F. and Parrot, M., 2004, “The Demeter Program: Mission and Satellite Description – Early in Flight Results”, 55th International Astronautical Congress, 2004 IAC-04-IAA.4.11.2.04. Vancouver, Canada. Dechezelles, J.J. and Huttin, G., 2000, “PROTEUS: A Multimission Platform for Low Earth Orbits”, Air & Space Europe, Vol. 2, No. 1, pp. 77-81.

National Institute for Space Research (INPE), 2001, “Multimission Platform: Data Package for System Requirement Review”, INPE’s Internal Document. Jacchia, L.G., 1977, “Thermospheric Temperature, Density and Composition: New Model”, Research in Space Science SAO Special Report No. 375, Smithsonian Institution. Pine, B.J., 1993, “Mass customization: The new frontier in business competition”, Harvard Business School Press, Boston. Meyer, M. and Lehnerd, A.P., 1997, “The power of product platform – building value and cost leadership”, Free Press, New York. Meyer, M. and Utterback, J., 1993, “The product family and the dynamics of core capability”, Sloan Management Review, Vol. 34, No. 3, pp. 29-47. Muffatto, M., 1999, “Introducing a platform strategy in product development”, International Journal of Production Economics, Vol. 6061, pp. 145-153. doi: 10.1016/S0925-5273(98)00173-X Software Satellite Took Kit (STK Analytical Graphics, Inc.) version 8.12 Expert Edition. Space Environmental Information System (SPENVIS), 2011, ESA, Retrieved in January 13, 2014, from http://www.spenvis.oma.be/intro.php.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.83-92, Jan.-Mar., 2014

doi: 10.5028/jatm.v6i1.281

Disaster Monitoring Constellation Using Nanosatellites Mohamed Kameche1, Haider Benzeniar1, Ayhane Bey Benbouzid1, Redha Amri1, Nadir Bouanani1

ABSTRACT: In this paper, a constellation of four low earth orbit nanosatellites for disaster monitoring is presented; their small size, low cost, and short time development give them the opportunity to be widely selected. The space segment is based on existing subsystems that are assembled on the bottom part of a developed structure and present acceptable performance for the mission. The payload is a multispectral camera which fulfills the mission’s requirements (remote sensing). The ground segment is based on an existing modular ground station, used for the ALSAT-1 DMC, which will be adapted to fit with the specifications of the mission. KEYWORDS: Nanosatellite, Constellation, Earth observation, LEO orbit.

INTRODUCTION African and Asian developing countries need support to face natural disasters such as floods, forest fires, desertification, and locust monitoring. The problems of desertification and locust are especially related to African countries, which are not able to handle them. This paper describes a mission idea, to develop a low cost disaster monitoring constellation using four Nanosatellites, which allows different Asian and African developing countries to access satellite images, in order to face different natural disasters. The designed system consists on a space segment which is composed by two main parts. The bottom part of the structure – also called platform – has the same dimensions used for the U6 Cubesat platform structure to hold the existing different functions – Attitude and Orbit Control Systems (AOCS), Radio Frequency (RF), power, thermal, propulsion, On-Board Data Handling (OBDH), etc. The upper part of the structure – also called payload structure – is used in order to hold the camera (optic and electronic subsystems). The ground segment is based on an existing modular ground station, used for the ALSAT-1 DMC, which will be adapted to fit with the mission requirements. The paper is subdivided into six sections. In the first section, the Mission Objectives are presented, followed by the Concept of Operations, and then the mission Key Performance are highlighted. The Space Segment, Orbit and Constellation are described further on. The last section of this work is dedicated to the Implementation Plan.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 1, pp.93-100, Jan.-Mar., 2014

Kameche, M., Benzeniar, H., Benbouzid, A.B., Amri, R. and Bouanani, N.

miSSioN oBJEctiVES The main objective is to launch the first low cost disaster monitoring constellation, which allows different Asian and African developing countries to access the satellite images, in order to face different natural disasters. • Floods: analysis and assessment of damage caused by floods, ability to restore decisions of the authorities with the necessary key information for emergency measures to be undertaken; • Locust biotopes monitoring: analysis of ecological conditions in the regions of desert locust breeding; • Forest fires: characterization of forest, prevention against forest fires, and aid for decision taking by the authorities; • Desertification: tracing of different maps of desertification sensitivity for many African countries. The other main objective of this mission is to encourage the African developing countries to acquire the space technology, to develop low cost constellation for their economic development and to obtain a multidisciplinary team which, by the end of the project, shall be able to design, to integrate and to operate a full nanosatellite mission. In absence of disaster, the constellation is dedicated for educative and scientific purposes.

To support the four nanosatellites of the constellation, an existing modular ground station used for the ALSAT-1 DMC will be adapted to fit the requirement of the mission (Amri, 2002). The existing ground station supports the communication in VHF/UHF/S-band (Fig. 1). It is implemented in the “Centre of space techniques building”, located in Arzew, west of Algeria. It includes Yagi-Uda antennas and radio for the bidirectional UHF communications and for receiving the VHF beacon (Fig. 2) and a 3.7 m parabolic dish and radio, for receiving and transmitting the S-band signal (Fig. 3). The ground station software will be modified and adapted to meet the mission’s requirements. Regarding the operational concept, three operation phases are summarized below: BEfORE nOmInAl OpERATIOn The Launch and Early Operations Phase (LEOP) starts after the satellite separation from the launcher. This phase includes

TLE

UHF/VHF

GPS TIME TRACKING

TM/TC