vol. 6 n.2 - journal of aerospace technology and management

JOURNAL OF AEROSPACE TECHNOLOGY AND MANAGEMENT

Vol. 6 N. 2Apr./Jun. 2014

ISSN 1984-9648ISSN 2175-9146 (online)

Journal of Aerospace Technology and Management

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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.

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CorrespondenceAll correspondence should be sent to:Dr Ana Cristina AvelarJournal of Aerospace Technology and Management Instituto de Aeronáutica e Espaço Praça Mal. Eduardo Gomes, 50 - Vila das AcáciasCEP 12228-901São José dos Campos/ São Paulo/Brazil

ContactPhone: (55) 12-3947- 5115/5004E-mail: [email protected]: http://www.jatm.com.br

Published by: Departamento de Ciência e Tecnologia Aeroespacial Distributed by: Instituto de Aeronáutica e EspaçoEditing, proofreading and standardization: Zeppelini EditorialPrinting: RR DonnelleyEdition: 500São José dos Campos, SP, BrazilISSN 1984-9648

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).


Vol. 6, n. 2 (Apr./Jun. 2014) – São José dos Campos: Zeppelini Editorial, 2014Quartely issued

Aerospace sciencesTechnologiesAerospace engineering

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EDITORS IN CHIEF

Ana Cristina AvelarInstituto de Aeronáutica e EspaçoSão José dos Campos/SP – Brazil [email protected]

Antônio F. BertachiniInstituto Nacional de Pesquisas Espaciais São José dos Campos/SP – Brazil [email protected]

EXECUTIVE EDITOR

Ana Marlene F. MoraisInstituto de Aeronáutica e EspaçoSão José dos Campos/SP – Brazil [email protected]

ASSISTANT EDITOR

Roberto Gil Annes da SilvaInstituto Tecnológico de AeronáuticaSão José dos Campos/SP – Brazil [email protected]

JOURNAL OF AEROSPACE TECHNOLOGY AND MANAGEMENTVol. 6 No. 2 - Apr./Jun. 2014

ISSN 1984-9648ISSN 2175-9146 (online)

SCIENTIFIC COUNCIL

Angelo PassaroInstituto de Estudos AvançadosSão José dos Campos/SP – Brazil

Antonio Pascoal Del’Arco JrInstituto de Aeronáutica e EspaçoSão José dos Campos/SP – Brazil

Carlos Henrique Netto LahozInstituto de Aeronáutica e EspaçoSão José dos Campos/SP – Brazil

Eduardo Morgado BeloEscola de Engenharia de São CarlosSão Carlos/SP – Brazil

Francisco Cristovão L. MeloInstituto de Aeronáutica e EspaçoSão José dos Campos/SP – Brazil

João Marcos T. RomanoUniversidade Estadual de CampinasCampinas/SP – Brazil

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

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

Rita de Cássia L. DutraInstituto de Aeronáutica e EspaçoSão José dos Campos/SP – Brazil

Waldemar Castro Leite FilhoInstituto de Aeronáutica e EspaçoSão José dos Campos/SP – Brazil

ASSOCIATE EDITORS

ACOUSTICSBert PluymersKatholieke Universiteit LeuvenLeuven – Belgium

Marcello A. Faraco de Medeiros Escola de Engenharia de São CarlosSão Carlos/SP – Brazil

AERODYNAMICSAcir Mércio Loredo SouzaUniversidade Federal do Rio Grande do SulPorto Alegre/RS – Brazil

João Luiz F. AzevedoInstituto de Aeronáutica e EspaçoSão José dos Campos/SP – Brazil

AEROSPACE METEOROLOGYGilberto FischInstituto de Aeronáutica e EspaçoSão José dos Campos/SP – Brazil

Willian W. VaughanUniversity of AlabamaHuntsville/AL – USA

APPLIED COMPUTATIONLeandro Baroni Universidade Federal do ABC Santo André/SP – Brazil

Romis R. F. AttuxUniversidade Estadual de CampinasCampinas/SP – Brasil

ASTRODYNAMICSOthon Cabo WinterFaculdade de Engenharia de GuaratinguetáGuaratinguetá/SP – Brazil

Anna Guerman Universidade da Beira InteriorCovilhã – Portugal

Vivian Martins GomesFaculdade de Engenharia de GuaratinguetáGuaratinguetá/SP – Brazil

Josep J. Masdemont Universitat Politecnica de Catalunya Barcelona – Spain

CERAMIC MATERIALSJosé Maria Fonte FerreiraUniversidade de AveiroAveiro – Portugal

CIRCUITRYAltamiro SusinUniversidade Federal do Rio Grande do SulPorto Alegre/RS – Brazil

Raimundo FreireUniversidade Federal de Campina GrandeCampina Grande/PB – Brazil

COMPUTATIONAL FLUID DYNAMICSJoern SesterhennTechnische Universität BerlinBerlin – Germany

John CaterUniversity of AucklandAuckland – New Zealand

Paulo Celso GrecoEscola de Engenharia de São CarlosSão Carlos/SP – Brazil

EDITORIAL PRODUCTION

Glauco da SilvaInstituto de Aeronáutica e EspaçoSão José dos Campos/SP – Brazil

Lucia Helena de OliveiraDepart. Ciência e Tecnologia AeroespacialSão José dos Campos/SP – Brazil

Mauricio Andrés Varela Morales Instituto Tecnológico de AeronáuticaSão José dos Campos/SP – Brazil

Mônica E. Rocha de OliveiraInstituto Nacional de Pesquisas EspaciaisSão José dos Campos/SP – Brazil

Rosely A. MontoroInstituto de Aeronáutica e EspaçoSão José dos Campos/SP – Brazil

Rosilene Maria M. CostaInstituto de Estudos AvançadosSão José dos Campos/SP – Brazil

COMPOSITESEdson Cocchieri BotelhoFaculdade de Engenharia de GuaratinguetáGuaratinguetá/SP – Brazil

DEFENSE SYSTEMSAdam S. CummingDefence Science and Technology LaboratorySalisbury/Wiltshire – England

ENERGETIC MATERIALSElizabeth da Costa MattosInstituto de Aeronáutica e EspaçoSão José dos Campos/SP – Brazil

José Leandro Andrade CamposUniversidade de CoimbraCoimbra – Portugal

FLUID DYNAMICS AND TURBULENCEVassilis TheofilisUniversidad Politécnica de MadridMadrid – Spain

GUIDANCE, NAVIGATION AND CONTROLArun MisraMcGill UniversityMontreal – Canada

Daniel AlazardInstitut Supérieur de l’Aéronautique et de l’EspaceToulouse – France

David Murray–SmithUniversity of GlasgowGlasgow – Scotland

MANAGEMENT SYSTEMSAdiel Teixeira de AlmeidaUniversidade Federal de PernambucoRecife/PE – Brazil

Antonio Henriques de Araújo JrUniversidade Estadual do Rio de JaneiroResende/RJ – Brazil

METALLIC MATERIALSKyriakos I. KourousisRMIT University Melbourne – Australia

PHOTONICSÁlvaro DamiãoInstituto de Estudos AvançadosSão José dos Campos/SP – Brazil

POLIMERIC MATERIALSCristina Tristão de AndradeInstituto de MacromoléculasRio de Janeiro/RJ – Brazil

Mirabel Cerqueira RezendeInstituto de Ciência e TecnologiaSão José dos Campos/SP – Brazil

PROCESSING OF AEROSPACE MATERIALSAlexandre Queiroz BracarenseUniversidade Federal de Minas GeraisBelo Horizonte/MG – Brazil

PROPULSION AND COMBUSTIONFernando de Souza CostaInstituto Nacional de Pesquisa EspacialSão José dos Campos/SP – Brazil

Carlos Henrique MarchiUniversidade Federal do ParanáCuritiba/PR – Brazil

RADARS AND TRACKING SYSTEMSCynthia C.M. JunqueiraInstituto de Aeronáutica e EspaçoSão José de Campos/SP – Brazil

Hugo H. FigueroaUniversidade Estadual de CampinasCampinas/SP – Brazil

Marc LesturgieOffice National d’Etudes et de Recherches AérospatialesPalaiseau – France

ROBOTICS AND AUTOMATION André FeniliUniversidade Federal do ABCSanto André/SP – Brazil

Sadek Crisostomo Absi AlfaroUniversidade de BrasíliaBrasília/DF – Brazil

STRUCTURESSérgio Frascino M. AlmeidaInstituto Tecnológico de AeronáuticaSão José dos Campos/SP – Brazil

SYNTHESIS AND CHARACTERIZATION OF AEROSPACE MATERIALSGilson da SilvaInstituto Nacional da Propriedade IndustrialRio de Janeiro/RJ – Brazil

Roberto Costa LimaInstituto de Pesquisas da MarinhaRio de Janeiro/RJ – Brazil

THERMAL SCIENCESMárcia B. H. MantelliUniversidade Federal de Santa CatarinaFlorianópolis/SC – Brazil

Renato Machado CottaUniversidade Federal do Rio de JaneiroRio de Janeiro/RJ – Brazil

VIBRATION AND STRUCTURAL DYNAMICSCarlos CesnikUniversity of MichiganAnn Arbor/MI – USA

Luiz Carlos S. Góes Instituto Tecnológico da Aeronáutica São José dos Campos/SP – Brazil

Valder Steffen JuniorUniversidade Federal de UberlândiaUberlândia/MG – Brazil

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 2, 2014 ISSN 1984-9648 | ISSN 2175-9146 (online)

EDITORIAL

109 Standards and Technical ExcellencePaul S. Gill

ORIGINAL PAPERS

111 The Update of an Aerodynamic Wind-Tunnel for Aeroacoustics TestingLeandro Dantas Santana, Micael Carmo, Fernando Martini Catalano

119 Simulation of Rocket Exhaust Clouds at the Centro de Lançamento de Alcântara Using the WRF-CMAQ Modeling SystemErick Giovani Sperandio Nascimento, Davidson Martins Moreira, Gilberto Fisch, Taciana Toledo de Almeida Albuquerque

129 A New Coupled Free Wake-CFD Method for Calculation of Helicopter Rotor Flow-Field in Hover Hamid Farrokhfal, Ahmad Reza Pishevar

149 Estimation of Pico-Satellite Attitude Dynamics and External Torques via Unscented Kalman Filter Halil Ersin Söken, Chingiz Hajiyev

159 Trajectory Control During an Aeroassisted Maneuver Between Coplanar Circular OrbitsWiller Gomes dos Santos, Evandro Marconi Rocco, Valdemir Carrara

169 Thermal Control Design Conception of the Amazonia-1 SatelliteDouglas Felipe da Silva, Issamu Muraoka, Ezio Castejon Garcia

177 Analysis of Radar Cross Section Reduction of Fighter Aircraft by Means of Computer SimulationLuiz Alberto de Andrade, Luan Silva Carvalho dos Santos, Adriana Medeiros Gama

183 Material Coding for Aircraft Manufacturing IndustryHong Xia Cai, Ming Yu Dai, Tao Yu

193 Reliability Analysis for Aviation Airline Network Based on Complex NetworkDong Bing,

COMMUNICATION

202 Fire Resistant Aircraft Unit Load Devices and Fire Containment Covers: A New Development in the Global Air Cargo IndustryGlenn Baxter, Kyriakos Kourousis, Graham Wild

INSTRUCTIONS TO AUTHORS

210 Instructions to Authors

CONTENTS

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 2, pp.109-110, Apr.-Jun., 2014

Standards and Technical ExcellencePaul S. Gill1

Technical excellence influences the development of uniformity of practices, resulting in standards which may be applied throughout an organization, resulting

in reduced costs, mitigated risks and commonality of practices. Initiatives which address the enhancement of an organization’s technical excellence are the key to an organization’s maintaining of a high level of performance in current programs and projects as well as preparing for new ones. Technical excellence is the goal of all organizations and individuals, whether in government or private industry, national or international ones. But, what do we mean by technical excellence? Most people have their own ideas and interpretation as to what constitutes technical excellence. According to the dictionary, excellence is defined as the state, quality, or condition of excelling; superiority. To excel is to be better than, or to surpass, others. We believe most, if not all, people would be comfortable with this definition. It may be appropriate to explore some statements which have been made concerning technical excellence. One author defined technical excellence as an effort to ensure that well-considered and sufficient technical thoroughness and rigor are applied to programs and projects under an uncompromising commitment to safety and mission success, while another author identified four guiding principles towards achieving technical excellence; (1) Clearly documented proven policies and procedures, (2) Effective training and development, (3) Engineering excellence, and (4) Continuous communications. According to Chris Scolese, Director of the NASA Goddard Space Flight Center, two fundamental attributes must be considered when pursuing technical excellence: (1) Personal accountability, whereby each individual must understand and believe that he or she is responsible for the success of the organization’s mission, and (2) Organizational responsibility, whereby the organization

doi: 10.5028/jatm.v6i2.370

1.National Aeronautics and Space Administration – Huntsville/AL | USA Author for correspondence: Paul S. Gill | NASA Technical Standards Program Office – NASA Marschall Space Flight Center, Huntsville | AL 35812 – USA | Email: [email protected]

provides proper training, tools, and environment. It has also been noted that due to the rapidly expanding technology and science, engineers and technologists in the 21st century must have a strong technical background in their fields as well as understand technology at the interface between traditional fields. They must be creative, skilled problem solvers who can think critically using sound principles and concepts. Technical excellence, and thus good standards, is a product of these principles. In the aerospace arena, one can certainly equate organizational technical excellence and thus have proven engineering and the use of technically proven standards to mission success. In the final analysis, technical excellence is one of, if not the most important goals of any organization. How one achieves and maintains it is a whole other matter, for which there is no simple answer. Unquestionably, an organization with recognized technical leaders who have vision, superior technical competence, and the desire to excel, will achieve technical excellence. The development of proven standards is certainly a product of this goal. Thus, technical leadership is the key for an organization’s success as well as the ability of the managers responsible for the carrying out of the organization’s mission. Technical excellence is also related to the strategic management of an organization’s human capital, and is an organization’s most critical asset in accomplishing its mission. Therefore, ensuring the continued development of scientific and technical expertise is necessary in order to preserve an organization’s — and the nations’ — role as a leader in technology. It is also significant to produce good standards and, accordingly, their application. In 2007, NASA undertook a technical excellence initiative in order to identify and solve engineering challenges. The initiative was designed as to provide quality solutions and work which will translate into an agency investment strategy for application into present and future missions. Among the attributes of this

EDITORIAL


110Gill, P.S.

initiative are the improvement of overall technical capability; the development of analysis and testing, beneficial to multiple missions, programs, and projects; the advancement towards tool/technique capability; and proven standards. In 2006, the aerospace industry released a position paper which demands for standards based on technical excellence of content rather than the source of a standard. Experts from the Aerospace Industries Association’s Strategic Standardization Forum (SSFA) for Aerospace prepared a position paper on the use of standards in response to the growing concern that certain policies and legislation may be putting the industry — and consumers — at risk. The SSFA emphasized that the aerospace industry must select standards based on safety, quality, and technical merit, rather than based on which organization developed them. Thus, the authors of the paper recognized technical excellence in relation to ensuring that proven standards are produced and applied in order for good engineering to be achieved. Technical excellence is crucial in the ensuring of compatibility and interoperability of a system’s architecture. Proven standards, also referred to as good standards, are important in order to achieve this goal. Perhaps it would be best to, once again, consult the dictionary for what is meant by the term “standard”. It means, among other things, “a degree or level of requirement, excellence, or attainment”. It is this meaning that we associate with good standards and their role in achieving the success of a program or project. The motivations for good standards and the associated enhancement of technical excellence vary considerably. One most often sees economic issues as their main motivation. Applications to regulatory matters are another strong motivation factor. Among the main motivations for good standards are international competitiveness; commodity confidence; safeguards for health, safety, and environment; risk reduction; facilitation of commercial communications; and technology transfer. However, enhancing organizational capabilities and technical excellence, although readily recognized as a key motivation, is not often seen in the list of motivations for the development and promotion of good standards. In 2012, the World Standards Cooperation Newsletter emphasized that “Good standards are technology-independent. A good standard helps companies build products that work and communicate with each other anywhere in the world. Standards are an integral part of all organizational product development efforts. Designers and

development engineers should be among the most aggressive supporters of technical standards. Standardization activities establish engineering and technical applications for processes and practices and, in doing so, enhance all organizational capabilities, further promoting technical excellence. Thus, they enable an organization to not dissipate its energies on the costly exercise of “reinventing the wheel”. The integration of good standards is one step towards the goal of significantly enhancing an organization’s technical capabilities and products. Technical excellence is the key to the nation’s future in the rapidly growing globalization of the industry. For any country to remain competitive and to maintain its technical leadership in the world, enhancing the nation’s capabilities is critical. These capabilities may be acquired only by achieving technical excellence, which is a requirement for good systems’ engineering. Good standards provide a major opportunity to achieve the goal of enhancing organizational capabilities and providing a means whereby technical excellence may be infused into the development and the manufacturing process. In many cases, the existing standards, or the requirements within them, are so well established that, without the existence of good examples highlighting a deficiency or weakness in the standard, it is hard to advocate a need for change, and therefore the use of technical excellence as to substantiate a change, and thus illustrate the importance of technical excellence. Enhancing an organization’s capabilities and products is an important product of standards, especially when coupled with allied information such as lessons learned and experiences with the use of a standard. Such must be the thrust of any viable organization. This is reinforced and expanded, based on feedback from an organization’s staff, its contractors, and on the users of its products, in order to improve the content of the standards. Such feedback, in turn, helps industry to meet demands for timely, productive, and reliable systems, as well as to contribute to improvements in efficiency and costs. Proven standards play an important role in the transfer of technical experiences, lessons learned, best practices, and infusion of new technology for the further enhancement of technical excellence within all organizations. Although technical excellence is not easy to be quantified, there is no doubt it is readily recognized, both by those involved in standards use and development activities and by those who are the “customers,” be they public, government, or industry.


ABSTRACT: This paper describes the update and characterization of a previously pure aerodynamics wind-tunnel into a facility able to simultaneously execute aerodynamics and aeroacoustics testing. It is demonstrated that the application of high-performance acoustic materials on strategic positions of the wind-tunnel circuit and punctual actions can substantially reduce the background-noise level. This paper shows efficient measures which resulted to broadband-noise reduction of up to 5 dB and practically complete removal of spectral tones. In addition, it is demonstrated that the applied acoustic treatment reduced the turbulence level, measured at the test-section at maximum operational velocity, from the previous 0.25% level to 0.21%. As a minor penalty, the acoustic treatment reduced the flow velocity in 2% for the same electric-power input. Finally, the work described in this paper resulted on a wind-tunnel with good flow quality and capacity for aeroacoustics testing.

KEYWORDS: Aeroacoustic testing, Wind-tunnel noise, Wind-tunnel acoustic treatment.

The Update of an Aerodynamic Wind-Tunnel for Aeroacoustics TestingLeandro Dantas Santana1, Micael Carmo2, Fernando Martini Catalano1

INTRODUCTION

The recent air transportation growth raised concern to authorities and civil institutions regarding its environmental consequences, among them, the noise. In order to regulate this important environmental impact, authorities established noise restrictions to aircraft certification and operations. To comply with these increasingly stringent regulations, the aeronautic industry has been, for decades, developing means to reduce the engine noise. With the current state of the art technology, the aircraft engine reached the same noise level as the airframe. Therefore, regarding noise, the engine optimization is no longer the exclusive preoccupation of the aeronautic industry, now, aeroacoustic development of airframe components also challenges the minds of engineers and aeronautical researchers.

This paper is result of a work-package from the Silent Aircraft project – sponsored by Empresa Brasileira de Aeronáutica (EMBRAER) and Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) with objective to develop critical knowledge on aeroacoustic testing and simulations.

In an international context of building new wind-tunnels for aeroacoustics testing such as the works of Kim et al. (2001), Wickern and Lindener (2000) and Sarradj et al. (2009), or for the adaptation of previously pure aerodynamic wind-tunnel into an aeroacoustic facilities (Remillieux et al., 2008; Künstner et al., 1995), it was decided that upgrading a previously pure aerodynamic facility was the optimal solution for the project requirements and constraints. The chosen wind-tunnel was the closed circuit wind-tunnel LAE-1, situated in the Aerodynamic Laboratory at Escola de Engenharia de São Carlos from the Universidade de São Paulo, Brazil. The work subject of this paper was developed in the period between October 2008 and March 2010.

doi: 10.5028/jatm.v6i2.308

1.Escola de Engenharia de São Carlos – São Carlos/SP – Brazil 2. Empresa Brasileira de Aeronáutica – São José dos Campos/SP – Brazil

Author for correspondence: Leandro Dantas Santana | Escola de Engenharia de São Carlos | Avenida Trabalhador São Carlense, 400 – Centro | CEP: 13566-590 São Carlos/SP – Brazil | Email: [email protected]

Received: 11/30/2013 | Accepted: 02/17/2014


112Santana, L.D., Carmo, M. and Catalano, F.M.

In order to define minimum required levels for noise reduction, it was decided to set, as a target experiment, the measurement of a NACA-0012 trailing-edge airfoil noise at velocity of 30 m/s. The beamforming technique was used for noise sources identification and possible quantification (Sijtsma and Holthusen, 1999; Brooks and Humphreys, 2006; Dougherty, 2005). Wind-tunnel background-noise is, in this paper, defined as the noise generated by the wind-tunnel running in stable regime and empty testing chamber.

The LAE-1 closed circuit wind-tunnel was designed originally as a 3/8 scale facility prototype of an automotive wind-tunnel to

be built in the future. The wind-tunnel was originally built in the period between 1997 and 2002, having as predominant material the naval plywood. Due to the Brazilian automotive industry difficulties in the early 2000s, and the national aeronautic industry rebirth, this previously automotive wind-tunnel, became a multi-task facility with instrumentation mainly focused on aeronautical testing, able to perform a diverse range of industrial and academic tests. Figure 1 presents the drawings of the LAE-1 closed circuit wind-tunnel.

The wind-tunnel test section dimensions are 3.00 m long, 1.30 m high and 1.70 m wide. The maximum design flow speed

Figure 1. Plan view of the LAE-1 wind-tunnel.

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113The Update of an Aerodynamic Wind-Tunnel for Aeroacoustics Testing

is 50 m/s, with turbulence level of 0.25%, nowadays, due to operational safety and components endurance, the maximum speed is limited to 45 m/s. Its 110-HP-electric motor drives an eight-blade fan, with seven straighteners located downstream the fan. In the flow stabilization section, there are two mesh screens with 54% porosity, followed by a 1:8 contraction cone designed using two 3rd order polynomials, joined at 45% from the inflection point. The relative low turbulence level, considering the installation of only two screens and no honeycomb, is attributed to the care taken during the design and construction of low angle diffusers, low-drag corner-vanes and high-efficiency rotor blades, optimized using a combination of Computation Fluid Dynamics (CFD) and semi-empirical techniques (Catalano, 2001).

THE BACKGROUND NOISE REDUCTION PROCESS

The LAE-1 background noise reduction campaign was developed in three phases: a first phase, where conceptual solutions, either innovative or inspired on literature, were multidisciplinary and evaluated as to their efficiency, feasibility and budgetary constraints. This first phase was followed by two steps of effective implementation. In the first step, the effect of pure wall treatment was evaluated, whose results indicated the need of a second step of acoustic treatment, where, in order to achieve the noise reduction target, the addition of an acoustic baffle and fan treatment was implemented.

PRELIMINARY CONCEPTS FOR THE PROJECTIn order to establish the noise reduction target, it was defined

an objective experiment to be done after the wind-tunnel adaptation. The requirements for this experiment were its representativeness of future tests campaign and the detailed availability of results in the literature to be used as a validation data, the self-noise measurement of a NACA-0012 airfoil, carried-out by Brooks et al. (1989), was chosen as an ideal experiment to fulfill those requirements .

An initial wind-tunnel noise assessment was conducted in order to determine the most critical noise condition. From this evaluation, it was found that the flow speed of 31 m/s is the most critical condition, due to the existence of fan excited structural resonance. For this reason, this speed was set as

reference for the measurements shown in this paper. Figure 2 shows a comparison of the LAE-1 baseline background noise level and the NACA-0012 airfoil noise.

At Fig. 2, the NACA-0012 self-noise was calculated using semi-empirical methods described by Brooks et al. (1989). For these calculations, it was considered a 0.20 m chord airfoil spanning the wind-tunnel test section, subjected to a 31 m/s flow speed and an angle of attack of two degrees.

Figure 2. NACA-0012 noise compared with the wind-tunnel background noise.

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From Fig. 2, it is noticed that, without post-processing for noise source separation, it is only possible to measure three octave band frequencies, localized from 1 kHz to 1.6 kHz. This clearly justifies the need of wind-tunnel background noise reduction in conjunction with the use of a microphone array to increase the measurable bandwidth. For this paper, the post-processing technique considered is the traditional delay-and-sum beamforming. For a microphone array, it is a rule of thumb used to compute the maximum signal to noise ratio (DdB), measurable by an array with M microphones as proposed by Shin et al. (2007):

∆dB = 10log10M (1)

For the current project, it was defined to use an array with M=106 microphones, consequently, it is concluded that it is possible to distinguish a source with an intensity of 20.25 dB less than the background noise by exclusively using regular post-processing techniques. If advanced decorrelation techniques of beamforming measurements are considered, it is possible to subtract 5 dB more in relation to the noise level estimated on Eq. 1 (Shin et al., 2007). Finally, it can be concluded that with the gain given by the instrumentation and signal processing, it is



possible to identify sources 25 dB less intense than the wind-tunnel background noise level. Adopting these hypotheses, it can be concluded that it is possible to measure a NACA-0012 airfoil noise sources for all the region of the orange line of Fig. 2. In addition to that, after a wind-tunnel background noise reduction of 5 dB, it would be possible to measure the full range of frequencies of interest, from 500 to 8.000 Hz, for the NACA-0012 airfoil.

THE FIRST WIND-TUNNEL NOISE REDUCTION PHASE

The first phase of acoustic treatment consisted of applying melamine foam on the selected section walls. Several regions of the wind-tunnel were considered for the foam application. Sections with adverse pressure gradient were discarded due to the boundary layer separation risk induced by increasing of the roughness, possible imperfections on the foam superficial finishing, or steps between the foam plates, which may occur on installation or during the operational life of the tunnel. The settling chamber was also discarded due to the risk of negative effects on the flow stabilization. Therefore, the remainder regions A and B from Fig. 3 were chosen as the best candidates for foam application.

effect on the wind-tunnel flow velocity, once this area has a lower flow speed and bigger cross-sectional area. To achieve the acceptable margin of 1.5% of flow velocity reduction, on the testing chamber, it was decided to apply a 2 cm thick foam on walls A and 5 cm thick foam on walls B.

THE SECOND WIND-TUNNEL NOISE REDUCTION PHASE

In order to increase the noise reduction achieved so far, the second and deeper noise reduction task was conducted.

To reduce the noise at lower frequencies, an acoustic baffle (element D from Fig. 3) was installed between the walls A, dividing the inner sections from the first and second corner vanes. With the installation of this baffle, it was expected a flow speed reduction of no more than 2%, referring to the wind-tunnel baseline conditions. The acoustic baffle construction scheme is shown in Fig.4.

Figure 3. Wind-tunnel regions that received acoustics treatments.

The next step was to define the thickness of the noise absorbent layer. It was performed an analysis of sensitivity for reducing the transversal area, due to the foam thickness on the testing chamber flow velocity. Calculations, made with the help of the wind-tunnel design model, showed that the section reduction on the region of the walls A (see Fig. 3) leads to a very sensible impact on the wind-tunnel flow velocity. The main reason for this is its comparatively high flow speed and, therefore a significant pressure loss. The reduction of the cross-section of the region of the walls B (see Fig. 3) has a minor

Figure 4. Acoustic baffle construction scheme.

Perforatedplywood

Acoustic baelateral view

10 mm20 mm

90 mm

Perforatedplywood

40 mm

Polyurethanefoam

Glass wool

For the design of the baffle, it was considered the feasibility, efficiency and budgetary constraints. This baffle was composed of a sandwich assembly filled with glass wool. The perforated plywood open area was optimized in order to generate a maximum open area to the mechanical strength relationship. The perforated plate was covered with polyurethane foam to assure surface smoothness.

A second action was taken based on the opportunity to improve the wind-tunnel fan performance and, simultaneously, reduce its noise. Due to circularity defects of the metal shield that involves the fan, the tip to wall space varies from 1.5 cm to 3 mm. This space is known to generate a tip vortex and non-uniform loading to the fan blades, which is a noise source



(Camargo et al., 2007). To avoid this problem, polyurethane foam was used as filling material in the regions where the gap was greater than 3 mm. The foam leading edge and trailing edge were shaped to guarantee smooth geometric transition. Figure 5 (a) presents the region with greater gap and (b) shows the same region, after the tip treatment, with polyurethane foam.

edge and the electric motor that drives the fan. This last noise source is mainly related with the speed controller that excites the electrical coil inside the motor producing noise.

It is quite difficult to identify and treat each noise source separately; instead it is more practical and efficient to line the noise propagation path in order to isolate a region of interest, and consequently this approach is adopted in this work.

For low flow speeds, it can be considered that the main wind-tunnel noise source is the sum of the fan and the turbulent boundary layer. Considering this, it was decided to line the region downstream and upstream of the fan, in order to treat both directions of the propagation path of the noise generated by the fan. The upstream distance from the fan to the testing chamber is smaller than the downstream one; therefore, it was preferred to add, as much as possible, acoustic treatment on the region comprised by the walls A (see Fig. 3). In the upstream direction, the settling chamber screens can be considered as a sudden increase of acoustic resistance, which tends to reflect the acoustics waves back, attenuating the noise generated by the fan propagated upstream. To better identify and differentiate the noise sources generated by the fan from the one generated by the flow, the noise spectra were analyzed on three different flow speeds: 15 m/s, 31 m/s and 37 m/s. The results are presented on Figs. 6, 7 and 8. For low speed (15 m/s), it is expected the fan noise to predominate, while for flow speeds up to 37 m/s the noise from turbulent boundary layer will be the main source. The velocity of 31 m/s is a special point of interest due to the occurrence of a structural resonance induced by the fan rotation.

Analyzing the first phase of noise reductions, regarding the Figs. 7, 8 and 9, it is noticed that for all flow speeds the foam treatment was effective on reducing the noise at frequencies from

Figure 5. Region with higher fan tip to wall gap before (a) and after (b) the treatment.

(a) (b)

The baseline wind-tunnel noise spectra, measured in the testing chamber, showed the existence of a very intense tone localized at the frequency of 5 kHz and its multiples. The investigation of the origin of this tone and harmonics showed that they were generated by the motor inverter, and they were related to the motor rotational speed controller. The inverter set-up allows changing its carrier frequency to a limit of 10 kHz. Considering that higher frequencies are better damped by the medium, the highest achievable frequency was adopted as the carrier frequency for the inverter.

WIND-TUNNEL BACKGROUND NOISE: RESULTS AND DISCUSSION

Typically the main wind-tunnel noise sources are the walls laminar/turbulent boundary layer, corner vanes, fan trailing

Soun

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Figure 6. Wind-tunnel noise spectra measured on the testing chamber for a flow velocity of 15 m/s.



300 Hz to 4 kHz. From Figs. 6 to 8, it is seen that the first phase of noise reduction was effective on absorbing the noise generated by

the fan. For lower flow speeds, the noise reduction reached 5 dB, for the frequency of 1 kHz. Opposite to that, when the flow speed increases to a higher speed (e.g. 37 m/s), the boundary layer noise becomes a significant noise source, and its reduction reaches 2 dB for the same frequency. Finally, it can be noticed that the acoustic treatment was very effective in reducing the fan noise at low flow speeds. As a side effect, it can be noticed a noise increase of up to 2 dB at high frequencies and high flow velocities.

Observing specially Figs. 8 and 9, it is noticed that the final noise treatment phase resulted on undesired noise increment at frequencies below 200 Hz. Even thought all noise level increase is undesired, this side-effect was considered acceptable because it will not affect future noise measurement since this frequency is far below the one of interest for future tests.

An important result in Fig. 6 is the vanishing of the peak at 5 kHz after the second phase of the acoustic treatment. This can be explained by the parameter modification of the inverter that controls the fan rotation. Since this carrier frequency was moved to 10 kHz, this tone almost disappeared from the spectrum.

It can be concluded that the first acoustic treatment phase acted mainly at low frequencies while the second one was more effective at high frequencies.

Figure 9 presents the Overall Sound Pressure Level (OSPL) variation as a function of the flow speed. From Fig. 9, it is remarkable that the first phase of noise treatment reduced the OSPL in an average value of 4 dB for all flow speeds. Opposed to that, the second acoustic treatment phase reduced the OSPL just for flow velocities below 20 m/s, and had no noticeable effect at high flow speeds.

THE ACOUSTICS TREATMENT EFFECT ON THE FLOW

The acoustic treatment effect on the flow is verified by the values of the turbulence intensity and the flow velocity. The turbulence was measured with the help of a DANTEC constant temperature hot-wire anemometer system. In this system, a probe model 55P01 was rigidly fixed in the center of the wind-tunnel testing chamber. This probe was calibrated using a DANTEC system described by Bruun (2002).

The hot-wire anemometer data was acquired using a sampling ratio of 6400 Hz during 5 seconds. The measured turbulence raw data was processed with in-house software that calculates the turbulence intensity and auto-power-spectra. A parametric study showed that the

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Figure 9. Overall Sound Pressure Level (OSPL) variation with the wind-tunnel flow speed.



calculation of the wind-tunnel turbulence, free of structural vibration and electrical noise influence, requires the use of a band-pass filter, which filters frequencies outside the 3 Hz and 1.000 Hz range.

Figure 10 shows the evolution of the turbulence intensity with the velocity measured in the testing chamber.

Figure 10 shows that the turbulence grows with velocity up to, approximately, 19 m/s, reaching a constant level, for velocities up to 30 m/s, and, consecutively, decays for higher velocities. This behavior is present in all phases of the acoustics treatment. The explanation to this behavior is based on the fact that this wind-tunnel was designed for a 50 m/s flow, meaning that, as the velocity increases, the flow passes from a laminar/low-turbulence to an “on design” condition, where the constant pitch blade reaches its optimum point. Since, for high velocities, the turbulence eddies, with largest length scales, have their characteristic length reduced, when compared to low velocities, which propitiates a more effective viscous dissipation. As a macro-effect of this, there is a measurable decreasing in the working section turbulence level as shown in Fig. 10.

It is desirable, for good quality wind-tunnels, that its turbulence spectrum is free of tones. It is noticed from Figs. 11 and 12 that the acoustic treatment does not introduce any effect on the turbulence spectra.

Tu (%

)

Flow Velocity (m/s)

0.25


0.20

0.1510 20 40

0.30

15 25 30 35

Figure 10. Wind-tunnel turbulence measured on the testing chamber.

A second point, coming from Fig. 10 analysis, is the fact that the acoustic treatment resulted to a positive effect on the wind-tunnel turbulence intensity levels. This is explained by the reduction of the acoustic noise, known as an excitation source on the non-linear instabilities mechanisms that lead the flow to turbulence. In addition to this is the fact that, for high Reynolds numbers, the statistics of small eddies is universally and uniquely determined by the viscosity and the rate of energy dissipation, not related to the noise, in such way that the turbulence generation has been decreased but the dissipation rate has been kept constant.

The turbulence spectrum is presented in Figs. 11 and 12.

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Figure 12. Wind-tunnel turbulence spectrum measured after background noise reduction process for a flow speed of 31 m/s.

The last analysis is the influence of the acoustic treatment to the flow velocity, for the same input power. The electric power input for the motor that drives the wind-tunnel fan, will be calculated based on the manufacturer information that a constant torque is kept with the rotational speed variation. Consequently, the flow velocity is a direct function of the pressure losses, and it is possible to compare the flow velocity reduction after the noise treatment phases. As previously mentioned, the foam installation on the walls increased its local velocity due to the cross-section reduction. Therefore, a slightly increase to the pressure loss, at the treated sections, are expected with the



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Aer 1st phase

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Figure 13. Wind-tunnel flow velocity reduction due the acoustic treatment.

related decrease of the test section velocity. Figure 13 shows the acoustic treatment phases effect on the test section velocity.

Analyzing Fig. 13 results, it is seen that the flow velocity reduction was in agreement with the expected within the two phases of noise treatment. The second acoustic treatment phase caused the most important flow speed reduction. This fact can, mainly, be attributed to the hydraulic diameter reduction, caused by the baffle installation. Considering that the pressure loss is proportional to the inverse of the hydraulic diameter, reducing the hydraulic diameter in 2 times implies on a sectional pressure loss of 8 times. Since section B pressure loss is small,

compared to the rest of the wind-tunnel, a minor effect on the total wind-tunnel velocity reduction is expected.

CONCLUSIONS

The present paper shows the process of background noise reduction for the LAE-1 wind-tunnel. This process was divided into two main phases, where, in a first phase, the effect acoustic treatment on strategic parts of the wind-tunnel circuit was evaluated. Results showed that this treatment was more effective at frequencies ranging from 400 to 4 kHz. A second phase, complementary to the first, acted mainly on frequencies above 4 kHz. These two noise treatment phases resulted on a wind-tunnel background noise reduction of up to 5 dB, and a desirable turbulence level reduction from the original 0.25% to 0.21%, with no addition of tones on the turbulence spectra. The minor drawback of this work was the reduction of 2% in the flow speed, considering a constant electric power input.

ACKNOWLEDGEMENTS

The authors acknowledge FAPESP and EMBRAER for the financial support for this project.

REFERENCESBrooks, T.F. and Humphreys, W.M., 2006, “A Deconvolution Approach for the Mapping of Acoustics Sources (DAMAS) Determined from Phased Microphone Arrays”, Journal of Sound and Vibration, Vol. 294, pp. 856-879.

Brooks, T.F., Pope, D.S. and Marcolini, M.A., 1989, “Airfoil Self-Noise and Prediction”, NASA Reference Publication 1218.

Bruun, H.H., 2002, “Hot-Wire Anemometry – Principles and Signal Analysis”, 2nd Edition, Oxford Science Publications.

Camargo, H., Remillieux, M., Burdisso, R., Crede, E. and Devenport, W., 2007, “The Virginia Tech Stability Wind Tunnel from an aerodynamic into an aeroacoustic facility”, 19th International Congress on Acoustics, Madrid.

Catalano, F.M., 2001, “The new closed circuit wind tunnel of the aircraft laboratory of University of São Paulo”, 16th Brazilian Congress of Mechanical Engineering, Vol. 6, pp. 306-312.

Dougherty, R.P., 2005, “Extension of DAMAS and benefits and limitations of deconvolution in beamforming”, 11th AIAA/CEAS Aeroacoustics Conference.

Kim M., Lee J., Kee J. and Chang J., 2001, “Hyundai Full Scale Aero-acoustic Wind Tunnel”, SAE Technical Papers, 2001-01-0629.

Künstner R., Potthoff J. and Essers U., 1995, “The Aero-Acoustic

Wind Tunnel of Stuttgart University”, SAE Technical Papers, 950625.

Remillieux M.C., Crede E.D., Camargo H.E., Burdisso R.A., Devenport

W.J. and Rasnick M., Van Seeters P., Chou A., 2008, “Calibration and

Demonstration of the New Virginia Tech Anechoic Wind Tunnel”, 14th

AIAA/CEAS Aeroacoustics Conference, AIAA 2008-2911.

Sarradj E., Fritzsche C., Geyer T. and Giesler J., 2009, “Acoustic and

aerodynamic design and characterization of a small-scale aeroacoustic

wind tunnel”, Applied Acoustics, Vol. 70, pp. 1073–1080.

Shin, H.C., Grahan, W.R., Sijtsma, P., Andreou, C. and Faszer, A.C.,

2007, “Implementation of a Phased Microphone Array in a Closed-

Section Wind Tunnel”, AIAA Journal, Vol. 45, No. 12.

Sijtsma, P. and Holthusen, H., 1999, “Source Location by Phased

Array Measurements in Closed Wind Tunnel Test Sections”,

AIAA-99-1814.

Wickern G. and Lindener N., 2000, “The Audi Aeroacoustic Wind

Tunnel: Final Design and First Operational Experience”, SAE Technical

Papers, 2000-01-0868.


ABSTRACT: In this work we report numerical simulations of the contaminant dispersion and photochemical reactions of rocket exhaust clouds at the Centro de Lançamento de Alcântara (CLA) using the CMAQ modeling system. The simulations of carbon monoxide (CO), hydrogen chloride (HCl) and alumina (solid Al2O3) pollutants emission represent an effort in the construction of a computational tool in order to simulate normal and/or accidental events during rocket launches, making possible to predict the contaminant concentrations in accordance with emergency plans and pre and post-launchings for environmental management. The carbon monoxide and the alumina concentrations showed the formation of the ground and contrail cloud. The results also showed that hydrogen chloride concentrations would be harmful to human health, demonstrating the importance of assessing the impact of rocket launches in the environment and human health.

KEYWORDS: Centro de Lançamento de Alcântara, Rocket launch, CMAQ, Dispersion modeling.

Simulation of Rocket Exhaust Clouds at the Centro de Lançamento de Alcântara Using the WRF-CMAQ Modeling SystemErick Giovani Sperandio Nascimento1, Davidson Martins Moreira2,3, Gilberto Fisch2, Taciana Toledo de Almeida Albuquerque1

INTRODUCTION

An important and singular air pollution problem is related to rocket launches. The burning of rocket engines during the first few seconds prior to and immediately after vehicle launchings results in the formation of a large cloud of hot, buoyant exhaust products near ground level, which subsequently rises and entrains ambient air until the temperature and density of the cloud reach an approximate equilibrium with ambient conditions. The United States’ space activities are conducted by military personel (Air Force) and civilians (NASA). The US Air Force has 2 rocket launching centers, being one at the east coast at Cape Canaveral Air Force Base (AFB), in Florida, and another one at the west part, at Vandenberg AFB, California. These launching centers are close to big and populated region/cities which may be affected by the gases released during the launchings. In order to estimate the risks and environmental impacts associated to the launchings (either normal or failed ones), a special model named Rocket Exhaust Effluent Diffusion Model (REEDM) was developed by Bjorklund et al. (1982). This model assumes a constant wind profile and Gaussian plume turbulence to access the movement of the clouds derived from the exhausted gases. According to Nappo and Essa (2001), despite the fact that the REEDM model was operationally used for a long time (since the 1980s), it may not be fully checked using field data collected over the US launching centers. During the period from 1995 up to 1997, a Model Validation Program (MVP) was established to address these concerns and several field experiments with special tracers made at Cape Canaveral AFB and Vandenberg AFB. Furthermore, regarding the numerical simulations for the prediction of rocket exhaust flow fields, we can cite the works of Reis et al. (1970) and Rao et al. (2001).

doi: 10.5028/jatm.v6i2.277

1.Universidade Federal do Espírito Santo – Vitoria/ES – Brazil 2.Instituto de Aeronáutica e Espaço – São José dos Campos/SP – Brazil 3. Centro Integrado de Manufatura e Tecnologia – Salvador/BA – Brazil

Author for correspondence: Davidson Martins Moreira | Instituto de Aeronáutica e Espaço | Praça Marechal Eduardo Gomes, 50, São José dos Campos/SP | CEP: 12.228-901 | Brazil | Email: [email protected]



120Nascimento, E.G.S., Moreira D.M., Fisch G. and Albuquerque T.T.A.

Centro de Lançamento de Alcântara

AlcântaraRaposa

São LuísSão José de Ribamar

D04

Centro de Lançamento de Alcântara

AlcântaraRaposa

São LuísSão José de Ribamar

D04

Figure 1. The location of the domain used in CMAQ to model the rocket launch case.

Unfortunately, there is no model fully ready to meet these demands in Brazil, or some experimental data dispersion of contaminants related to the Centro de Lançamento de Alcântara (CLA). Therefore, researches are very important as well as the developing of a modeling system designed to calculate peak concentrations, dosage and deposition (resulting from both gravitational settling and precipitation scavenging), downwind from normal and aborted launchings, to be used in mission planning activities and environmental assessments, pre-launch forecasts of the environmental effects of launch operations and post-launch environmental analysis in the Brazilian site. To this end, this paper aims at providing a framework which will allow the development of a model that considers the Brazilian site’s characteristics. Therefore, the main purpose of this study is to report a numerical study (numerical experiment) of the contaminant dispersion and photochemical reactions for the pollutants HCl, Al2SO3 and CO in CLA using the Community Multi-scale Air Quality (CMAQ) modeling system. The paper describes the main CMAQ changes needed to incorporate rocket launches in this modeling system. In fact, CMAQ is a multi-pollutant, multiscale air quality model that contains state-of-science techniques for simulating all atmospheric and land processes which affect the transport, transformation, and deposition of atmospheric pollutants and/or their precursors on both regional and urban scales (Byun e Ching, 1999). These simulations represent an effort in the construction of a computational tool for normal and/or accidental events during rocket launches, making it possible to both predict and simulate the concentration needed, in accordance to emergency plans and pre and post-launchings for environmental management.

THE MODELING SYSTEM

The CMAQ model is a fully 3-dimensional modeling system consisting of three main components (meteorology, emissions and a chemical model) and various interfaces. Therefore, with this modular structure, the CMAQ has enough flexibility to change other meteorological models and emissions modeling systems (Byun and Ching, 1999). It is important to mention that the CMAQ model is a state-of-the-art modeling tool in air quality problems.

SITE DESCRIPTIONThe CLA is the space gate of Brazil and it is located at the

North part of Brazil (2 degrees south), close to Equator (Fig. 1).

More information about the CLA, its topography and climate can be found at Pires et al. (2009).

Climatologically, there is a very distinctive rainfall characteristic: a wet season from January up to June, with highest rainfall in March and April (monthly rainfall about 300 mm/mo) and a dry season from July up to December (rainfall almost null or very low – less than 30 mm/mo). The winds follow this climatic behavior presenting high wind speed during the dry season (winds around 7-8 m/s at 70 m height) and a moderate winds (winds around 5-6 m/s) during the wet season. To represent this behavior, it was released a radiosonde in the CLA on April 2nd, 2010. The radiosonde uses a Vaisala Oy system (sonde RSV92G), which is the state-of-art in terms of upper air measurements. The winds (wind speed and direction up to 5,000 m) are presented at Fig. 2 and they are quite characteristic for the wet season: there is a linear increase of the wind speed from the surface up to a maximum value of approximately 16 m/s at 3,000 m. Between 2,000 and 4,000 m, the winds are very persistent, ranging from 14-16 m/s. The direction of the wind is NE/E (around 60º at 500 m) and then rotating southward (around 100-110º). The synoptic pattern is typical from the wet season, with the intertropical convergence zone (ITCZ) reaching the north part of Brazil, with a large cloud cluster over the ocean and inland at the CLA (Fig. 3).

Figure 4 presents the modeling domains. These domains were modeled using the Weather Research and Forecasting (WRF) Model (version 3.3) (Skamarock and Klemp, 2008) to generate meteorological fields in a one-day simulation. The horizontal


121Simulation of Rocket Exhaust Clouds at the Centro de Lançamento de Alcântara Using the WRF-CMAQ Modeling System

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INPE/CPTEC/DSA NOAA GOES-12 Canal - 5 IR 2010040221600

Figure 3. The synoptic pattern for April 2nd, 2010.

resolutions of the nested grid were 27 km, 9 km, 3 km and 1 km, and the horizontal dimensions, in grid cells, were 36 x 36, 54 x 54, 84 x 84 and 66 x 66, for domain 1 to 4, respectively. Recently, Silva and Fisch (2014) did a detailed analysis about the use of WRF for the CLA using radiosonde data collected during dry (2008) and wet season (2010) as a comparison. They found that the WRF, using the default parameterizations, can represent the wind speed at the site reasonably well (difference between the model and the observed is less than 2.0 m/s). The innermost domain, highlighted in Fig. 1, was used in order to model a hypothetical average rocket launch event in CLA using the CMAQ model, version 5.0.1 (Byun and Schere, 2006).

In order to run the WRF model to properly generate the meteorological fields, we used final analysis data from the Global Forecast System (GFS) with a resolution of 1 arc degree. Figure 5 presents the surface wind field simulated by the WRF model at the time of the launch (April 2nd, 2010 – 2-5PM). It is possible to note that the wind is predominantly blowing in the opposite direction of the large city of São Luís and in the direction of an inhabited area, and it keeps on until later in the same day, at 9PM local time,

when the wind field entirely changes and it starts blowing in the direction of the city of São Luís. This situation, which is presented in Fig. 6, shows that a major concern is to determine when launching operations should be released, in order to prevent the environmental impact and air contamination of a metropolitan area, due to the large cloud of hazardous pollutants generated by the launch. In this simulation, the pollutants were already been dispersed to out of the domain at the time when the wind field changed, meaning that there were no more environmental risk of the cloud reaching the city of São Luís, as observed in the “Results and Discussion” section.

D01

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Figure 4. The location and distribution of the domains modeled in WRF Model.



393735333129272523

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Figure 5. The surface wind field on April 2nd, 2010, at the time of the launching.

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Figure 6. The surface wind field in domain 4 at 9PM, when it changed to the direction of São Luís.

BUILDING THE EMISSIONS FILEThe emission file is the critical point in the air quality context.

To this end, it was developed the Sparce Matrix Operator Kernel System (SMOKE), which is used in order to generate the input information from emissions compatible with the model (Borge et al., 2008). In this study we did some adaptations in the SMOKE system to work with rocket launching emissions, which has some particularities that we had to address to in the system. The system has been applied to the CLA to process a hypothetical emission inventory for a test.

Figure 7 shows the launching of Titan IV, in Cape Canaveral, USA, and the formation of the so called ground-cloud and contrail-cloud (Nyman, 2009). It has been a practice in USA to consider only the emissions from ground to 3000 m in simulations, and the emission rate of a normal rocket launch is 5,2x105 g/s (Bjorklund et al., 1982).

Since SMOKE was not designed to deal with such kind of source, for each vertical level below 3,000 m, a virtual stack source emitting a certain hypothetical amount of the total emissions was configured, following the distribution presented in Table 1. These constituents are supposed to be released by the Brazilian Satellite Vehicle Launcher, for instance. Each virtual stack was positioned in the middle of its respective level. The virtual stacks located nearest to the ground have the main contribution in the emissions of simulating the ground-cloud effect. The remaining emissions were distributed along the highest levels. Input parameters, which entail significant uncertainty, were treated in a conservative fashion, in the sense that choices were made in order to favor overestimating rather than underestimating the toxic chemical concentrations being evaluated for the environmental assessment study. For example, we assume that all the material released by the rocket remains in the area below 3,000 m.



Contrail Cloud

Ground Cloud

Figure 7. Illustration of the formation of the ground and contrail clouds, during a rocket launch (Nyman, 2009).

Table 1. Hypothetical distribution of the total emissions along the levels represented by their corresponding virtual stack.

Virtual StackId

Stack Height(m)

Distribution ofTotal Emissions (%)

0 11 20

1 53 30

2 81 10

3 120 7

4 180 6

5 233 5

6 285 5

7 524 4

8 704 4

9 1446 3

10 2195 3

11 2788 3

Table 2. Basic composition of the combustion gases of the propellant.

Product % m/m

Carbon Dioxide (CO2) 4.4

Carbon Monoxide (CO) 27.6

Hydrogen Chloride (HCl) 21.6

Alumina (Al2O3 sólido) 28.2

Water Vapor (H2O) 7.0

Nitrogen (N2) 8.4

Hydrogen (H2) 2.8

Table 3. List of the options used in Community Multi-scale Air Quality, for this work.

Property Selected Option

Gas Chemistry cb05tucl_ae6_aq

Aerosol Chemistry aero6

Advection vwrf

Vertical Diffusion acm2

Solver ebi_cb05tucl

Cloud Module cloud_acm_ae6

The emissions inventory was composed of only one pollutant, representing the combustion gases of the propellant. Table 2 shows the distribution of compounds that are emitted during the combustion, and their corresponding species in the chosen chemical mechanism (Bjorklund et al., 1982). For this simulation, we considered the three major emitted compounds: carbon monoxide (CO), hydrogen chloride (HCl) and alumina (solid Al2O3). It is

important to mention that only the emissions from the rocket launch were considered in this work. Regarding temporal allocation the temporal profiles were setup in SMOKE, in order to have the entire emissions only at noon (time of the hypothetical launch).

CONFIGURING THE COMMUNITY MULTI-SCALE AIR QUALITY (CMAQ) MODEL

For this study, we used the CMAQ model, version 5.0.1. Though our work has no intention to evaluate the impact of the emissions in the region, but only to study how we can apply CMAQ to such case, we chose to evaluate how it would impact the environment in this situation. Table 3 shows the options used in order to build the CMAQ. No initial or boundary conditions were applied, and the photolysis rates were calculated by the photolysis rate preprocessor (JPROC) using a table of clear-sky photolysis rates (or J-values) for a specific date according to the selected options used in order to build the CMAQ.



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Figure 8. Sequential tile plot for PM 2.5, representing the concentrations of alumina, for the first layer.

RESULTS AND DISCUSSIONS

Figure 8 shows a sequential tile plot for alumina, which was modeled as fine particulate matter (PMFINE), and Fig. 9 shows a sequential tile plot for carbon monoxide, both at the first level, at an approximate 20 m height. In these figures, we can see how the ground cloud behaviors after the rocket launch. Initially, it presents high concentrations at the location where the launching occurred, and after the first hour it presents a sensible decrease in the concentrations.

Six hours later the ground cloud was totally dispersed from the domain.

Figures 10 and 11 show a sequential 3D plot for both alumina and carbon monoxide, respectively. As it can be observed, these figures shows the same behavior, where we can easily identify the ground cloud and contrail cloud formed after the launching. In the subsequent hours, most of the concentrations remain near the ground.

Hydrogen chloride emissions were present in a considerable amount (Fig. 12), which led to harmful



CO

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Figure 9. Sequential tile plot for CO for the first layer.

PM 2.5Abril 02, 2010 14:00 UTC350

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10)

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Figure 10. Sequential 3D plot for PM 2.5.



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HCL - Clumn 302238,222

1875,000

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es/s

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2019181716151413121110

987654321

1 3 5 7 9 11131517192123252729313335373941434547495153555759

Domain RowAbril 02, 2010 14:00:00 UTC

Laye

r

Figure 12. Vertical cross section plot of the emissions of hydrogen chloride. The emissions near ground are in the order of 2,000 moles/s.

Table 4. Health effects of respiratory exposure to hydrogen chloride (HCl).

Exposure Limits (ppm)

Health Effects

5 < Coughing

35 Throat irritation occurs after only a short time

35 < Severe breathing difficulties and skin inflammation or burns

10-50 Maximum level that can be sustained for several hours

100 < Swelling of the lungs and often throat spasm

50-1,000 Maximum possible exposure: 1 hour

1,000-2,000 Very dangerous even for a very short exposure

concentration levels in CMAQ. Table 4 (Baxter et al., 2000) presents the hydrogen chloride exposure limits, in ppm, and their impact in human health. HCl is a colourless gas with an irritating pungent odour perceivable at 0.8 ppm (Lide, 2003). Figure 13 presents the sequential tile plot for HCl at the first level. The first hour shows the higher concentrations at the launch site, with concentration levels between 100-1,000 ppm. According to Table 4, at this region, the maximum exposure time is only 1 hour, causing swelling of the lungs, throat spasm and irritation. In the surrounding area, it presents concentration levels between 35-50 ppm, and even at the second hour, the concentration still remains in a dangerous level (5-35 ppm).

Based on the results, it is considered that the use of CMAQ to model rocket launch cases is very interesting and promising. Some difficulties were faced, which had to be addressed in order to run the model. One of the biggest challenges here was on how to properly build the emissions file. Although the configuration and the running of the SMOKE, in this case, were manageable, it was considered that a specific tool should be created in order to achieve this purpose. The results of the concentrations of HCl, CO and PM 2.5 (Al2SO3) showed to be very interesting: the formation of the ground and contrail cloud and the dispersion of the ground cloud were very clear in the simulations, and the concentration levels of HCl, reported by the model, showed the importance to assess the impact of rocket launch events in the environment and human health, since chlorine has a major impact in the emissions of the combustion gases of the propellant during rocket launch events.



Figure 13. Sequential tile plot for HCl for the first layer.

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SUMMARY AND PERSPECTIVES

In this work, our main purpose was to apply CMAQ as a numerical model to simulate the dispersion of the contaminants emitted during a normal rocket launch event and to evaluate the entire CMAQ suite – model and its accessories – for this case. Since it is a first step towards providing a framework which will allow the development of a more meaningful model considering the Brazilian site characteristics, in CLA, we found that the use of the model for this case is quite promising. These results show that there is a need for thorough checking of the schemes with experimental observations planned in the region. We focus our attention in the future to evaluate the construction of the cloud released by the rocket, which can be initially defined as a single cloud that grows and moves, but remains the same during the formation of its ascending phase (ground cloud). This will address the different spatial and temporal scales of the problem more consistently. For more details about this, see the work of Moreira et al. (2011). For this

purpose, we plan to develop a tool that is capable of calculating the formation of the cloud, from the moment it is released to the moment when it stabilizes in the atmosphere, then to build the emissions file accordingly to the input into the CMAQ. Furthermore, it is important to construct numerical experiments from the CMAQ system model in order to have them compared to the concentration results of the MSDEF model (Moreira et al., 2011), which is a very good and proven tool for a fast screening of the contaminant concentration field during rocket launch events.

ACKNOWLEDGEMENTS

The authors thank the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for the partial financial support of this work.

REFERENCESBaxter, P.J., Adams P.H., Aw, T.-C., Cockcroft, A. and Harrington, J.M., 2000, “Hunter’s Diseases of Occupations”, Arnold, London, Vol. 45, No. 2, pp. 123-178. doi: 10.1016/S0003-4878(00)00067-3.

Borge, R., Lumbreras, J. and Rodrıguez, E., 2008, “Development of a High-Resolution Emission Inventory for Spain Using the SMOKE Modelling System: A Case Study for the Years 2000 and 2010”, Environmental Modelling & Software, Vol. 23, pp. 1026-1044.

Bjorklund, J.R., Dumbauld, J.K, Cheney, C.S. and Geary, H.V., 1982, “User’s Manual for the REEDM (Rocket Exhaust Effluent Diffusion Model) Computer Program”, NASA contractor report 3646. NASA George C. Marshall Space Flight Center, Huntsville, AL.

Byun, D.W. and Ching, J.K.S., 1999, “Science algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System”, EPA/600/R-99/030, Office of Research and Development, United States Environmental Protection Agency, Washington, DC.

Byun, D. and Schere, K.L., 2006, “Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3community Multiscale Air Quality (CMAQ) Modeling System”, Appl. Mech. Rev., Vol. 59, pp. 51-77.

Lide, D.R., 2003, “CRC Handbook of Chemistry and Physics”, 84th edition. CRC Press, Boca Raton, Florida.

Moreira, D.M., Trindade, L.B., Fisch, G., Moraes, M.R., Dorado, R.M. and Guedes, R.L., 2011 “A Multilayer Model to Simulate Rocket Exhaust Clouds”. Journal of Aerospace Technology and Management, Vol.

3, pp. 41-52. doi: 10.5028/jatm.2011.03010311.

Nappo, C.J. and Essa, K.S.M., 2001, “Modeling Dispersion from Near-Surface Tracer Releases at Cape Canaveral, Florida”. Atmospheric Environment, Vol. 35, pp. 3999–4010.

Nyman, R.L., 2009, “NASA Report: Evaluation of Taurus II Static Test Firing and Normal Launch Rocket Plume Emissions”.

Pires, L.B.M., Avelar, A.C., Fisch, G., Roballo, S.T., Souza, L.F., Gielow, R. and Girardi, R., 2009, “Studies Using Wind Tunnel to Simulate the Atmospheric Boundary Layer at the Alcântara Space Center”. Journal of the Aerospace and Technology Management, Vol. 1, No. 1, pp. 91-98. doi: 10.5028/jatm. 2009.01019198.

Rao, R.M., Sinha, K., Candler, G.V., Wright, M.J. and Levin, D.A., 2001, “Numerical Simulation of Atlas II Rocket Motor Plumes”, 39th AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV.

Reis, R.J., Aucoin, P.J. and Stechman, R.C., 1970, “Prediction of Rocket Exhaust Flow Fields”, Journal of Spacecraft and Rockets, Vol. 7, No. 2, pp. 155-159. doi: 10.2514/3.29891.

Silva, A.F.G. and Fisch, G., 2014, “Avaliação do Modelo WRF para a Previsão do Perfil do Vento no Centro de Lançamento de Alcântara”, Brazilian Journal of Meteorology, In press.

Skamarock, W.C. and Klemp, J.B., 2008, “A Time-Split Nonhydrostatic Atmospheric Model for Weather Research and Forecasting Applications”, Journal of Computational Physics, Vol. 227, pp. 3465-3485. doi:10.1016/j.jcp.2007.01.037.


ABSTRACT: This paper concerns a new coupled free wake-CFD method for proper calculation of aerodynamic loads on a two bladed helicopter rotor in hovering flight. Loading is computed by solving the three dimensional Euler equations in a rotating coordinate system. However, since direct simulation of the tip vortices and wake requires a very fine grid, the rotor’s wake effects are modeled by a free wake approach and included into the CFD calculation by a transpiration boundary condition at the rotor surface. An influence coefficient solution method is used to find the rotor’s wake shape, being steady in a rotating frame. Euler equations are also considered in the form of absolute flow variables and solved by a multi grid Jameson’s finite-volume method. The accuracy of the proposed method is illustrated by comparing numerical results to the available experimental results for the pressure distribution on a blade of a model helicopter rotor at different tip Mach numbers.

KEYWORDS: Free wake, Euler equations.

A New Coupled Free Wake-CFD Method for Calculation of Helicopter Rotor Flow-Field in Hover Hamid Farrokhfal1, Ahmad Reza Pishevar2

INTRODUCTION

The flow field around rotors is inherently complex. In the vicinity of the blade tip, it is highly three dimensional and vortical and it is characterized by non-uniform inflow with blade-vortex interactions and transonic conditions. In hover, the complex’ three dimensional rotor wake has a dominant influence on the blade loading (Jenny et al., 1968; Clark and Langrebe, 1971; Landgrebe, 1972).

A proper evaluation of the rotor aerodynamic loading has an important role in the analysis and design of helicopter rotor blades. There are two general approaches for simulating the flow field around a rotor and determining its loads. In the first approach, the inviscid or viscous set of governing equations is solved by a proper Computational Fluid Dynamic (CFD) method (Roberts and Murman, 1985; Chen and McCroskey, 1988; Kramer et al., 1988). Obviously, in this approach, the vortex system near the blade tips and the wake region below the rotor disk must be captured accurately for realistic pressure and load estimations. The computational cost is extremely increased for this approach (Roberts and Murman, 1985; Chen and McCroskey, 1988; Kramer et al., 1988; Ramachandran et al., 1989; Lee and Kwon, 2006; Chen et al., 1987). In the second approach, the tip vortices and the helical wake system are not solved by the CFD calculation and instead, the influence of strong tip vortices and the geometry of the wake are included by a vortex-wake model.

Solution scheme which use the later idea are often grouped under methods using wake models encompassing the potential flow (Strawn and Caradonna, 1987; Strawn and

doi: 10.5028/jatm.v6i2.366

1. Malek-Ashtar University of Technology – Isfahan – Iran 2. Isfahan University of Technology – Isfahan – Iran.

Author for correspondence: H. Farrokhfal | Department of Mechanical & Aerospace Engineering | Malek-Ashtar University of Technology – 83145-115 | Shahinshahr – Isfahan – Iran | Email: [email protected]

Received: 12/06/2013 Accepted: 02/14/2014


130Farrokhfal, H. and Pishevar, A.R.

Tung, 1986; Chang and Tung, 1985; Egolf and Sparks, 1987), the Euler (Sankar et al., 1986, Agarwal and Deese, 1987; Chang and Tung, 1987) and the Navier-Stockes methods (Agarwal and Deese, 1988; Narramore and Vermeland, 1989; Wake and Sankar, 1989; Srinivasan and MacCroskey, 1988). Several approaches have been applied to model the vortical field of rotors. The most famous of these models is the prescribed wake model, which uses experimental data to drive empirical formulas relating the rotor blade geometric parameters and thrust coefficient (CT) to the vortex’ wake geometry (Kocurek and Tangler, 1976; Landgrebe, 1972; Landgrebe and Cheney, 1972; Landgrebe et al., 1977). These schemes do not correctly model the flow characteristics and their reliance on experimental data causes to not appropriately predict blade loading for a new rotor configuration. Free wake models have been developed in an attempt to overcome these limitations by simulating in details the actual shape and motion of the wake (Crimi, 1965; Landgrebe, 1969; Clark and Leiper, 1970; Sadler, 1971; Johnson, 1980; Johnson, 1981; Summa and Clark, 1979; Summa and Maskew, 1981; Saberi, 1983; Rosen and Graber, 1988). This can be done by finding the geometry of the wake and the strength of tip and sheet vortices, which are modeled by a finite number of vortex line segments. Then, the effect of the vortex-wake is brought into CFD calculation by modifying either the boundary conditions at the blade surface (Sankar et al., 1986, Agarwal and Deese, 1987; Chang and Tung, 1987) or explicitly modifying the blade angle of attack (Wake and Sankar, 1989; Srinivasan and McCroskey, 1988).

There are some advantages for calculating the rotor aerodynamics loads by a coupled wake-CFD method when compared to the direct method. The first benefit is that accurate enough results can be obtained with significant reduction in the computational expense. This is a key factor when a low fidelity approach is required for design or multidisciplinary optimization purposes. The second benefit is to relieve the difficulties arisen in meshing the 3-D domains around the rotor. Since the tip region is not computed directly in the CFD calculation, the 3-D grid can be obtained from an assembly of two-dimensional spanwise sectional grids (Caradonna et al., 1984).

The shape of the rotor wake has an intense effect on the aerodynamic performance of a helicopter rotor in hover flight. The present research explains a new interactive wake-CFD solver for calculating the inviscid transonic

flow on a two-bladed rotor in hover flight. In this method, the CFD flow solver is coupled with a free wake model by using the surface transpiration approach, i.e., effects of rotor vortex-wake are included into CFD calculation by using the estimated downwash velocity to modify boundary condition along the blades.

In most of the previously reported works, the induced velocity is computed prior to the CFD analysis by a separate program such as Hover, B-TRIM of McDonnell Douglas Helicopter Company (Agarwal and Deese, 1987), or Host (Benoit et al., 2000). In this work, however, the induced downwash of rotor blades are computed along with the CFD solution and one can interact with it through an iterative procedure. The model is based on a lifting line theory, which provides estimation for induced downwash along the blade. In this method, the blade is substituted by a row of square vortex panels in radial direction and, at some location of blade span, a finite number of spiral vortices are shed downwards the disc rotor. Then, the positions of helices are corrected by induced velocities, which are computed by employing the Biot-Savart law and the use of the Basic Curved Vortex Element (BCVE) or Self-Induction Vortex Element (SIVE) elements (Quackenbush et al., 1989). The procedure continues until the shape of the wake converges to a steady state and therefore, the self-induced velocities become from zero up to a tolerance.

Then, the transonic flow field about the rotor is calculated by the solution of the three-dimensional Euler equations on a multi-block structured grid about the blades. However, as mentioned earlier, the three-dimensional grid is constructed from an assembly of 2D grids about the blade sections which is extended only to the blade tip and consists of two blocks that are symmetric in relation to the planform. In this study the finite-volume scheme of Jameson, Schmidt, and Turkel (Jameson et al., 1981) is used to discrete governing equations. The method is reformulated for a rotating coordinate system which is attached to the rotor blades, but equations are finally rewritten in terms of absolute-flow variables. This approach has been used previously by Holmes and Tong (1985) in order to develop an Euler solver for some applications in turbomachinery.

The iterative procedure starts by determining the spanwise circulation and the shape of the wake from the blade geometry. Then the flowfield around the blade is computed by the CFD solver, using the calculated downwash


131A New Coupled Free Wake-CFD Method for Calculation of Helicopter Rotor Flow-Field in Hover

velocity along the blade as a boundary condition. Now, a new bound circulation can be obtained from the lift of each section and therefore, it is possible to improve the wake model accordingly. The procedure is repeated until the blade bound circulation or downwash velocity remains unchanged in two subsequent iterations.

The paper is organized as follows: the methodology used for modeling the rotor wake and tip vortices is explained. Governing equations for CFD calculations are presented and the numerical procedure for discretizing these equations is described. The necessary boundary conditions and the way through which the rotor wake model interacts with the CFD solver are explained. Finally, some illustrative numerical results are presented. Calculations are performed for the flow field of a model helicopter rotor in hover at various collective pitch angles. Comparisons with the experimental data of Caradonna and Tung (1981) are also presented.

ROTOR WAKE MODELING

The rotor wake model is based on the proposition that the free wake hover problem possesses a self-preserving state solution when viewed in a rotating frame that is fixed to blades (Bliss et al., 1985). The fundamental idea in the model is that at each point on the rotor wake, when viewed in coordinates rotating with the blade, the velocity contributions due to all effects must be such that there is no convection to change the wake shape. Any velocity component in a plane, normal to the vortex filament curve, will convect the vortex to a new position, thereby changing the filament shape (Quackenbush et al., 1989).

Applying the idea, two features of the wake are properly determined: the contraction of the helical wake as it extends in the axial direction below the rotor, and the interaction of the tip vortex of two blades. For modeling the wake, the two curved vortex elements introduced in Bliss et al. (1985) and Baskin et al. (1967) are used. These elements include the BCVE and SIVE. The BCVE is used in order to compute the velocity induced at any point in the flow field, not on the element itself, and the SIVE is used to evaluate the velocity induced by a vortex filament on itself, including the effect of distributed vorticity in the bent vortex core. Figure 1 shows two types of vortex elements on a typical helical vortex filament. The BCVE

is a parabolic arc element positioned between control points whereas the SIVE is a circular arc element passing through each three adjacent control points.

The solution procedure involves a way of reducing the cross flow velocity to zero at every collocation point. Figure 2 shows the idea. In order to achieve this goal, for a given arrangement of the free wake collocation points, the net velocity in each cross flow plane is computed first. Next, the effect of small displacements of each collocation point on the velocity at every other collocation point, and the displaced point itself, are determined. These effects are known as influence coefficient.

In practice, the solution must be obtained numerically with the wake modeled by using discrete elements. The blade is represented by a set of vortex quadrilaterals in a way that

rj + 2j + 2

j - 1

j - 2

j - 3

j

Point of Evaluation

BCVE

BCVESIVE

InterpolatedPoints

BCVE

Ĵ + 2

Ĵ - 1

Ĵ - 2

Figure 1. BCVE and SIVE elements for discretizing wake vortex filaments.

Cross-ow PlanesDisplaced Plane

Initial Plane

qj + 1→

tj + 1

→

bj + 1

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nj + 1

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bj

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tj

→→ nj

r

Displaced Vortex FilamentInitial Vortex Filament

Ω

→

Figure 2. Method of displacing the vortex filament to eliminate cross flow.



their forward edges are located at the quarter chord line of the blade and the distance of their aft edges from the blade trailing edge is also one-fourth of the local chord. The vortex filaments of sheet vortex and the tip vortex are shed from the points on the aft legs of these panels, and the panels including the shed points are in the form of truncated vortices, as shown in Fig. 3.

Figure 4 shows a schematic of a typical load distribution on a rotor blade. It is assumed that the corresponding wake consists of four filaments, whose strengths are defined as shown in the figure. The locations of the release point are also computed from the Batchelor principle (Batchelor, 1967) as:

(1)

where is the distance of the release point of the vortex filament from the center of rotation.

The velocity contribution of each individual vortex element is determined analytically using the Biot-Savart law. The total velocity vector at a collocation point includes the local self-induction effect (SIVE), the contributions from the rest of the wake (BCVE’s), the velocity induced by the bound circulation on the rotor blades, and the swirl velocity component associated with the rotating coordinate system.

The inf luence matrix relates cross f low velocity perturbations to small displacements for a given wake

configuration. This matrix can be used to predict the set of collocation point displacements, which will eliminate the cross flow velocity. If the free wake location is described in terms of the positions of N collocation points, and the normal and binormal cross flow velocity components at the ith point are denoted by and , respectively; then the N-dimensional vectors based on these velocity components are defined as:

(2)

The free wake solution is achieved when a set of collocation point locations is found, such as .

The amplitudes of small displacements on the ith collocation point in the local normal and binormal directions are denoted by and . From these, changes of displacement vectors are defined as:

(3)

and the normal and binormal velocity component changes, associated with the above displacement vectors, are:

(4)

Blade Control Point

Trailing VortexCollocation

Points

TruncatedPanel

Ω Z

Z

ZZV

Ω

Υ1

Υ1 = - Γ1 Υ2 = Γ1 - Γ2 Υ3 = Γ2 - Γ3 Υ4 = Γ3

Υ2Υ3

Υ4

Γ2

Γ3

Γ1

Figure 3. Schematic of the quadrilateral vortex panels of the blade.

Figure 4. Defining the strength of trailing vortex filaments from the bound circulation.



The velocity change vectors can be related to the change of displacement vectors through the influence coefficient sub matrices, namely:

(5)

For instance, the sub matrix gives the changes in normal velocity due to binormal displacements. These matrices must be evaluated numerically. To obtain zero net cross flow velocity, the collocation point displacements must be chosen so that the velocity changes cancel the net cross flow velocity, namely, = and = , which means solving the following matrix equation for and :

(6)

Since the wake problem is actually nonlinear, this process must be repeated several times.

A free wake analysis involves only a finite number of turns of free vortex. Beyond the free wake region is an adaptive mid wake which completes the contraction process. The final part is also a semi-infinite helical wake having constant radius and constant pitch based on the momentum theory. The location and geometry of the adaptive mid wake depend on the position of the last points in the near section of the wake. In other words, the last points of near wake section are the beginning points of the mid-section, and after the mid-section the wake model is continued with the far section as shown in Fig. 5.

From the conservation of mass flow within the contracting mid wake and by applying the momentum theory, it is possible to drive the boundary shape of the mid wake (Bliss et al., 1985) as:

(7)

where and are the axial and radial locations of the last free wake points, and is the final contraction radius. In hover flight, the radial contraction exponent is given by:

(8)

where is the azimuth location of the last free wake point.When calculating two-bladed rotors, the solution is

symmetric from one blade to the next blade. This symmetry allows the induced effect of the second blade on the primary wake to be expressed in terms of the corresponding effect of the primary wake on the second blade.

Once all the different effects have been computed and summed, the resultant velocity vectors at each collocation point are expressed in the blade fixed reference frame. These resultant velocity vectors are solved into the local normal and binormal directions in order to obtain the cross flow velocity components and their changes, associated with point displacements. The velocity changes are normalized by the unit displacement magnitudes to obtain the influence coefficients. These coefficients, as described earlier, are used to predict how the collocation points should be displaced in order to reduce the velocity components in the cross flow planes. The obtained result at this stage is not the final solution due to the nonlinear nature of the problem. Therefore, starting from an initial wake shape, the solution procedure is repeated with a proper relaxation factor in quasi linear steps to improve the convergence rate (Bliss et al., 1984).

The Initial wake shape can be estimated from a contracted wake model with a contraction ratio of = 0.85, where

is the radius of the contracted far section of the wake.

Z

ΓΩ

Near WakeLast Free

Wake Point

AdaptativeMid Wake

Momentum TheoryFar Wake

xy0

(y0)

y

rb

Γf

Figure 5. Three part wake model for the hover analysis.



The circulation distribution is also obtained from an approach explained by Baskin et al. (1967); in other words, equating the 2-D lift coefficient of blade section to the lift coefficient obtained from the Kutta-Joukowski theorem. As a result, a system of linear algebraic equation is obtained by defining the sought circulation. By knowing the circulation, the convergent shape of the wake is computed as explained in the previous section. Finally, at the end, the distribution of the downwash on blade surface is computed. This downwash is then fed to the CFD solver to include the compressibility effect and to correct the circulation. The circulation at this stage is computed from the integration of the pressure field around the blade sections and by using the Kutta-Joukowski theorem. With this new distribution of the circulation, the free wake solver provides a new downwash distribution and this process is iterated until a convergent solution for the downwash is attained.

GOVERNING EQUATIONS

Let (u,v,w) denote the absolute velocity components in the rotating Cartesian coordinate system (x,y,z), as shown in Fig. 6. Compressible Euler equations can be formulated as (Agarwal and Deese, 1987):

(9)

where:

(10)

and ( , , ) denotes the rotational velocity components:

(11)

Defining the flux vector and rotational speed vector as:

and:

where ( , , ) are the unit vectors in the (x, y, z) coordinate system, Eq. (9) can be written as:

(12)

Equation (12) can be solved by the finite-volume method described in the next section.

NUMERICAL METHOD

In order to apply the finite-volume method, Eq. (12) is written in its integral form as:

(13)

The computational domain is meshed by a multiblock structured hexahedral grid. Applying Eq. (13) to a typical (i,j,k) cell, for a cell center algorithm, we obtain a system of ordinary differential equations as:

(14)

where is the cell volume, represents the net convectional flux out of the cell, and and are rotational fluxes out of each cell. We rewrite Eq. (14) as:

X

Y

Ω

Figure 6. Rotating coordinate system.



(15)

The convective fluxes can be determined by a second order central scheme. However, the resultant scheme is not dissipative, and therefore, undamped oscillations at odd and even mesh points can be developed during the computation. In order to suppress the tendency for odd-even decoupling, and to prevent the appearance of oscillations in regions containing severe pressure gradients near shock waves and stagnation points, the finite-volume scheme is modified by the addition of artificial dissipative terms as below:

(16)

where denotes the dissipative fluxes. Jameson et al. (1981) have established that an effective form of dissipative terms for flows with discontinuities is a blend of second and fourth differences, with coefficients which depend on the local pressure gradient. Therefore, dissipative terms are written as follows:

(17)

Where:

(18)

and is defined as:

(19)

Δx denotes the forward difference operator and ò(2) and ò(4) are adaptive coefficients defined as:

(20)

(21)

(22)

Typical values of the constants κ(2) and κ(4) are κ(2) = 0.5, κ(4) = 1/64. Also, is defined as:

(23)

where CFL is the courant number and ( , , ) are the spectral radii for each cell:

(24)

a is the speed of sound and (U, V, W) are the contavariant velocities:

(25)

(Ur, Vr, Wr) are the components of relative velocity obtained from:

(26)

( , , ),( , , ) and ( , , ) are the metrics of transformation for computational coordinates ( , , ). Other dissipation terms, DyWy,j,k and DzWy,j,k, in Eq. (17), are calculated in an analogous manner.

Equation (16) can be integrated in time by a multistage Runge-Kutta scheme. The conserved variables are updated to the new time level n+1 as:

(27)



where αm = 1, and D(w(k))=β(k) D(W(n+1,k))+(1-β(k))D(W(k-1)). For a fifth-stage scheme the coefficients are given as:

(28)

In this work a local time stepping and residual smoothing procedure is also used in order to improve the convergence rate.

BOUNDARY CONDITIONS

Special care must be given to the boundary conditions when solving Euler equations for rotor blades of a helicopter by a coupled free wake-CFD method. The computational domain, as shown in Fig. 7, is a cylinder with radius of 1.5 times the blade span. A far-field boundary condition is applied at the outer surface of the cylinder and also at the blade tip plane. In addition to that, at the root of the blade, the symmetry boundary condition is applied where z axis is along the blade span. At the solid surface of the blade, the transpiration condition is applied to take into account the effects of the wake in the CFD calculations. In this method, the usual velocity to the blade surface is calculated so that it exactly cancels out the effect of the wake normal downwash to the surface.

The boundary condition at the blade surface must be modified in order to include the wake effects. For invicid calculations, this becomes:

(29)

where is the wake induced velocity and is the normal unit vector of the blade surface. Thus, the induced velocity is used to modify the conventional tangency of velocity vector at the solid surface. Also, the fluxes of density and the enthalpy at the solid surface are modified accordingly to meet the new boundary condition.

It is interesting to note that, for a computational region covering only a portion of the rotor disk, the effect caused by the other rotor blades and the flow outside the finite computational region are incorporated into the calculation by employing the above transpiration velocity technique if the effects of other blades are included in the induced downwash velocity.

The treatment of the far-field boundary condition is based on the concept of Riemann invariants for a one-dimensional flow normal to the boundary (Jameson and Baker, 1983). Let subscripts ∞ and e denote far-field values and values extrapolated from the interior cells adjacent to the boundary, respectively, and let Vn and a be the velocity component normal to boundary and to the speed of sound (a). If the flow in the far field is subsonic, fixed and extrapolated Riemann invariants are introduced as:

(30)

Which are corresponding to incoming and outgoing waves. These invariants may be added and subtracted to obtain:

(31)

At an outflow boundary, we extrapolate the tangential components of velocity, which results in:

(32)

And for an inflow boundary, the tangential components are those of the free stream:

(33)

Block No. 2

Block No. 1 Y

Z X

Figure 7. Computational region is covered by two blocks of structured grids.



The procedure is completed by extrapolating the entropy at an outflow boundary S = Se or setting it equal to its free stream value at inflow boundary S = S∞. The density, energy and pressure can then be calculated from a and s.

RESULTS

In this section some illustrative examples are presented as to demonstrate the capability of the proposed method for predicting the aerodynamic load and the flow field of a model helicopter rotor in hover state at various tip speeds. The rotor is a two bladed rotor with an aspect ratio of 6, which are untwisted and have rectangular planform with the NACA-0012 airfoil section. The experiments on this rotor have been presented by Caradonna and Tung (1981) at the NASA Ames Research Center. Computations are performed on a two-block mesh with 141570 nodes, on a personal computer. Figure 7 shows the computational domain which is symmetric in relation to the blade planform.

Five orders of magnitude reduction in the density residual are considered as convergence criteria. However, in most simulations for a Courant number of 2.0, the convergence criteria is not met in less than 1,600 time steps for the first iteration of the free wake model, and it is reduced to around 800 steps for the next iterations. Some of the results are discussed in the following sub-sections.

NON-LIFTING CASEFor this case, the free wake model is switched off and only

the CFD calculation is performed. Pressure distribution on the blade upper and lower surfaces is shown in Fig. 8 for a tip Mach number of Mt = 0.52 and zero collective angle. Except near the tip, the agreement between the Euler calculation and the experimental data is obvious. The discrepancy near the tip can be caused by boundary conditions. However, the difference between numerical and excremental results is not significant.

LIFTING CASESAs stated before, for this case, the rotor downwash is first

calculated by modeling the blade vortices with the BCVE vortex elements. Then, the effect of the wake and tip vortices is fed

into the CFD solver and a new bound circulation is calculated from the sectional lift. Finally, a new iteration is started by correcting the shape of the wake and its strength according to the new bound circulation. The procedure is continued until the induced downwash velocity converges to a steady form. For the lifting cases, calculations are performed for five blade tip Mach numbers and in four different collective angles. For these simulations, the bound vortex is modeled by 32 vortex rings. In the near field part, the tip vortex is modeled by 3 turns of a helical vortex filament, each turn composed by six BCVE elements. The pitch of helix is initially set to in this region, where the initial CT is estimated from a collective angle by using the rotor momentum theory. The sheet vortices are also modeled by 4 filaments, each helical filament has 2 turns and each turn has six basic vortex elements. The mid part of the wake consists of 8 turns of helical filaments with the same axial pitch as the near wake filaments, i.e., . Also the far part of the wake consists of 50 turns with an axial pitch, which is twice the value used in the mid part.

For these simulations the number of iterations required for the convergence of induced downwash velocity is less than 300 iterations for a known circulation distribution in the free wake solver, and the number of iterations for achieving a steady distribution of downwash velocity from the combined free wake-CFD solver at low collective angles is six while it is reduced to less than 4 iterations for higher angles.

The sectional lift coefficient and the circulation distribution obtained from these calculations are shown in Fig. 9. The non-dimensional circulation (experimental or numerical) is defined as:

(34)

where C1 is the section lift coefficient at z radial station. The circulation shown in these figures is non-dimensioned byR2Ω, where Ω is the rotational velocity of the rotor and R is the rotor radius. As can be seen from these figures, the agreement with the experimental data is satisfactory. The thrust and inviscid power coefficients have also been computed and presented in Table 1. Comparison reveals that coefficients have good coincidence with experimental data (Caradonna and Tung, 1981) for all collective angles.

As a typical result, non-dimensional downwash and circulation distribution at intermediate iterations between



the free wake model and the CFD solver is depicted in Fig. 10. It should be pointed out that through this iterative process, the compressibility effects in the tip region of the blades are

included into the free wake model. Figure 10 shows that the downwash, or circulation distribution, is converged to a steady form in a few iterations.

-0.5

-0.4 -0.2 0 0.2 0.4

0.5

z/r = 0.5

1

0

Cp

x/c

-0.5

-0.4 -0.2 0 0.2 0.4

0.5

1

0

Cp

x/c

z/r = 0.68

-0.5

-0.4 -0.2 0 0.2 0.4

0.5

1

0

Cp

x/c

z/r = 0.8-0.5

-0.4 -0.2 0 0.2 0.4

0.5

1

0

Cp

x/c

z/r = 0.89

-0.5

-0.4 -0.2 0 0.2 0.4

0.5

z/r = 0.96

Euler Free Wake Calculations

Experimental Data

1

0

Cp

x/c

Figure 8. Pressure distribution in non-lifting case, Mt = 0.52, θc = 0, AR = 6.0, untwisted, untapered, NACA 0012 blade.



Table 1. The computed thrust and power coefficients and comparison with experimental data.

Collective Angle (degree)

Tip Mach No.Thrust Coefficient×103

(Computed)Thrust Coefficient×103

(Experimental)Power Coefficient×104

(Computed)

2.0 0.830 0.615 0.690 0.935

5.0 0.815 2.253 2.190 1.769

8.0 0.439 4.655 4.590 3.120

8.0 0.877 4.606 4.730 4.262

12.0 0.794 7.730 7.920 7.941

0,04

Mt = 0.749, t0 = 12

Mt = 0.877, t0 = 8

Mt = 0.439, t0 = 8Mt = 0.815, t0 = 5

Mt = 0.830, t0 = 2 ExperimentCalculation

0,02

0

0.4 0.6 0.8z/R

Nom

. Din

. Cir

cula

tion

0.6

0.5

0.4

Experiment

Mt = 0.794, T0 = 12

Mt = 0.877, T0 = 8

Mt = 0.439, T0 = 8

Mt = 0.815, T0 = 5

Mt = 0.830, T0 = 2Calculation

0.3

0.2

0.1

0

-0.1

0.4 0.6 0.8z/R

Cl

Figure 9. Lift coefficient and circulation distribution at various tip Mach numbers and collective angles.

0.03

0.02

0.01

0

-0.01 0.2 0.4 0.6 0.8

Non

Din

. Cir

cunl

atio

n

z/R

Colletive = 0.8 deg.Mt = 0.877

itr = 2

itr = 1itr = 3

itr = 4

0.03

0.02

0.01

0

-0.01 0.2 0.4 0.6 0.8 1

Non

Din

. Dow

nwas

h

z/R

Colletive = 0.8 deg.Mt = 0.877

itr = 1

itr = 2

itr = 3

Figure 10. Downwash and circulation distribution at subsequent iterations.



-2

-1.5

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.5-2

-2.5

-1.5

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.68

-2

-2.5

-1.5

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.80 -1.5

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.89

-1.5

-2

-1

-0.5

0

0.5

1

1.5-0.4 -0.2


Experimental Data

0 0.2 0.4

Cp

x/c

z/R = 0.96

Figure 11. Pressure distribution on a lifting rotor in hover, Mt = 0.830, θc = 2deg.

Pressure distribution is shown in Figs. 11-15 for different tip Mach numbers and collective angles from 2 to 12 degrees. As shown in these

figures, the agreement with experimental data is very good, especially for the position of shock wave on the upper surface of the blade.



-1.5

-2

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.5

-1.5

-2

-2.5

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.68

-1.5

-2

-2.5

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.80-1.5

-2

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.89

-1.5

-2

-1

-0.5

0

0.5

1

1.5-0.4 -0.2

Euler Free wake Calculations

Experimental Data

0 0.2 0.4

Cp

x/c

z/R = 0.96


The flow solver can also be run in a full multi-grid (FMG) mode, which accelerates the process of convergence. Figure 16 shows the results obtained by applying the FMG approach in

three levels and in a two-block cylindrical mesh. The time of computation can be decreased one order of magnitude and approximately two hours on a personal computer.



-1.5

-2

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.5

-1.5

-2

-2.5

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.68

-1.5

-2

-2.5

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.68-1.5

-2

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.89

-1.5

-2

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0


Experimental Data

0.2 0.4

Cp

x/c

z/R = 0.96




-1.5

-2

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.5

-1.5

-2

-2.5

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.5

-1.5

-2

-2.5

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.80-1.5

-2

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0


Experimental Data

0.2 0.4

Cp

x/c

z/R = 0.80

-1.5

-2

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0


Experimental Data

0.2 0.4

Cp

x/c

z/R = 0.96




-1.5

-2

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.5

-1.5

-2.5

-2

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.68

-1.5

-2.5

-2

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.80

-1.5

-2

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.89

-1.5

-2

-1

-0.5

0

0.5

1

1.5-0.4 -0.2 0


Experimental Data

0.2 0.4

Cp

x/c

z/R = 0.96




-1.5

-1

-0.5

0

0.5

1 -0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.80-1.5

-1

-0.5

0

0.5

1 -0.4 -0.2 0 0.2 0.4

Cp

x/c

z/R = 0.89

-1.5

-1

-0.5

0

0.5

1 -0.4 -0.2 0


Experimental Data

0.2 0.4

Cp

x/c

z/R = 0.96

Figure 16. Pressure distribution on a lifting rotor in hover obtained from FMG approach,Mt = 0.877, θc = 8deg, AR = 6.0.

CONCLUSION

A new coupled free wake-CFD methodology for evaluation of aerodynamic loads on a helicopter rotor in hovering condition was introduced. In this approach, the Euler equations are solved in a rotating coordinate system and the rotor wake effects are modeled by a free wake approach and included into the CFD solver by a transpiration boundary condition at the blade surface.

The obtained results from this approach for the pressure distribution on the blade surface are promising; the performance

parameters such as thrust and induced torque coefficients are particularly estimated with a rather good accuracy. The main advantage of this method is that satisfactory results can be achieved by minimum computational efforts, particularly for the grid generation problem and the required CPU time. This feature becomes important where a low fidelity accurate rotor blade flow solver is required for primarily design or optimization purposes. However, the proposed method ignores important phenomena such as the Blade-Vortex Interaction (BVI) or shock vortex interactions.



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Roberts, T.W. and Murman, E.M., 1985 “Solution Method for a Hovering Helicopter Rotor Using the Euler Equations”, AIAA Paper 85-0436.

Rosen, A. and Graber, A., 1988, “Free Wake Model of Hovering Rotors Having Straight or Curved Blades”, Proceedings of the International Conference on Rotorcraft Basic Research, Journal of the American Helicopter Society, Vol. 33, No. 3, pp. 11-19(9). doi: 10.4050/JAHS.33.11.

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Strawn, R.C. and Caradonna, F.X., 1987, “Conservative Full-Potential Model for Unsteady Transonic Rotor Flows”, AIAA Journal, Vol. 25, No. 2, pp. 193-198. doi: 10.2514/3.9608.

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Summa, J.M. and Clark, D.R., 1979, “A Lifting-Surface Method for Hover/Climb Airloads”, Proceedings of the 35th Annual National Forum of the American Helicopter Society, Washington, D.C., USA.

Summa, J.M. and Maskew, B., 1981, “A Surface Singularity Method for Rotors in Hover or Climb”, USAAVRADCOM TR 81-D-23.

Wake, B.E. and Sankar, L.N., 1989, “Solutions of the Navier-Stokes Equations for the Flow About a Rotor Blade”, Journal of the American Helicopter Society, Vol. 34, No. 2, pp. 13-23(11).doi: 10.4050/JAHS.34.13.


ABSTRACT: In this study, an Unscented Kalman Filter (UKF) algorithm is designed for estimating the attitude of a pico-satellite and the in-orbit external disturbance torques. The estimation vector is formed by the satellite’s attitude, angular rates, and the unknown constant components of the external disturbance torques acting on the satellite. The gravity gradient torque, residual magnetic moment, sun radiation pressure and aerodynamic drag are all included in the estimated external disturbance torque vector. The satellite has magnetometers and gyros onboard as the attitude sensors. Because of the inherent nonlinear dynamics and the nonlinear measurement model, the UKF, which is a nonlinear version of the Kalman Filter, is selected as the filter algorithm. Performance of the proposed algorithm is demonstrated via simulations for a cube pico-satellite and the results are analyzed for different scenarios.

KEYWORDS: Pico-satellite, Attitude estimation, Unscented Kalman filter, Disturbance torques.

Estimation of Pico-Satellite Attitude Dynamics and External Torques via Unscented Kalman Filter Halil Ersin Söken1, Chingiz Hajiyev2

INTRODUCTION

Although there are numerous researches on cubesats, and this number is increasing day by day, the investigations are still far from being concluded. The Cubesat is basically a cubic pico-satellite which has a volume of 1 liter and mass of no more than 1.3 kg. These types of satellites are the outcomes of a search for lighter, smaller and cheaper spacecrafts, and recently, they have mostly been considered as a part of research projects of organizations like universities (Toorian et al., 2008).

The biggest difficulty of cubesat applications is the limitations on the size and mass of the satellite. All the subsystems must be designed regarding these limitations and, unquestionably, a trade-off between the performance and applicability might be required. Specifically for the attitude determination and control system (ADCS), we cannot use highly accurate sensors and actuators onboard a cubesat, since they are usually heavy and large. The attitude of the satellite must be determined and controlled using miniaturized sensors and actuators, which are usually coarser and less accurate (Candini et al., 2012). In this case, one technique to increase the system performance is to properly select the onboard running ADCS algorithm.

The Extended Kalman Filter (EKF) is a nonlinear Kalman Filter (KF), which has been widely used for satellite attitude estimation (Psiaki et al., 1990; Lefferts et al., 1982). On the other hand, the EKF has some disadvantages, especially for the highly nonlinear systems. Generally, this is caused by the mandatory linearization phase of the EKF procedure and so the Jacobians derived with that purpose. For most of the applications, the generation of the Jacobians is time consuming, difficult and

doi: 10.5028/jatm.v6i2.352

1.Graduate University for Advanced Studies – Sagamihara/Kanagawa – Japan 2.Istanbul Technical University – Istanbul – Turquey

Author for correspondence: Chingiz Hajiyev | Aeronautics and Astronautics Faculty | Istanbul Technical University | Maslak, 34469 Istanbul – Turkey | Email: [email protected]



150Söken, H.E. and Hajiyev, C.

prone to human errors (Julier and Uhlmann, 2004; Sekhavat et al., 2007). Nonetheless, the linearization brings about an unstable filter performance when the time steps for the update is not sufficiently small and so the estimation or identification procedure fails as the filter diverges (Julier et al., 1995). Per contra, small time steps increases the computational burden because of the larger number of Jacobian calculations. As a result of these facts, the EKF may be efficient only if the system is almost linear on the timescale of update intervals (Julier and Uhlmann, 2004).

A relatively new Kalman filtering technique, which does not have the shortcomings of the EKF for the nonlinear systems, is the Unscented Kalman Filter (UKF). The UKF generalizes the Kalman filter for both the linear and nonlinear systems and, in case of nonlinear dynamics, it provides relatively more accurate estimation results than other known observer design methodologies such as the EKF. The essence of the UKF is the fact that the approximation of a nonlinear distribution is easier than the approximation of a nonlinear function or transformation (Julier et al., 2000). The UKF introduces sigma points for catching higher order statistics of the system. It satisfies both better estimation accuracy and convergence characteristics by securing higher order information (Sekhavat et al., 2007). Furthermore, the UKF is more robust against the initial estimation errors, so even in case of inaccurate a priori knowledge about the initial condition of the states, it performs well. The only disadvantage of the UKF is the increase in the computational burden when compared to the EKF. Yet, the computational load depends on the type of application and there are several researches aiming at making the UKF computationally more efficient (Li et al., 2013).

There are many documented studies for the UKF as an estimation algorithm in astronautics. In (Crassidis and Markley, 2003) it is used as a state estimator, while both the states and the parameters of the satellite are estimated by the UKF in Dyke et al. (2004), Sekhavat et al. (2007) and Sekhavat et al. (2009). Moreover, in Vinther et al. (2011) the UKF is preferred as a part of the inexpensive cubesat attitude estimation method, where also the magnetometer biases are estimated and in Inamori et al. (2009) it is used for in-orbit magnetic disturbance estimation and compensation. In Soken and Hajiyev (2011) and Soken and Sakai (2011), both the magnetometer and the gyro biases are estimated as well as the attitude of the satellite by using the UKF. In Soken and Sakai (2013), two different UKFs are run onboard a nanosatellite for attitude estimation, sensor calibration and residual magnetic moment compensation. In Oliveria et al.

(2014), the UKF is used for attitude estimation of a nanosatellite and the results are compared with the EKF. In Li et al. (2013), an adaptive version of the UKF is implemented as a part of the ADCS of a nanosatellite. Lastly, in Cornejo et al. (2010), the UKF is used for state estimation and fault detection purposes within the nanosatellite attitude control system.

In this study, an UKF algorithm is designed for estimating a pico-satellite’s attitude and the in-orbit external disturbance torques acting on the pico-satellite. Gravity-gradient torque, sun pressure, aerodynamic drag and residual magnetic moment are included in the estimated disturbance torque vector. The satellite, for which the algorithm is proposed, has magnetometers and gyros onboard as attitude sensors. The main contribution of this study is to show that it is possible to estimate the unknown external torques as well as the attitude dynamics parameters of the satellite via a simple UKF based algorithm. In addition to the investigations in (Soken and Hajiyev, 2009), a more detailed discussion is given for the torque estimation with an extended simulation scenario. Moreover the case for uncertainty in the satellite’s moments of inertia is covered.

The paper proceeds as follows: the mathematical model of the pico-satellite attitude dynamics is presented in the following section. In “The sensors measurement models” the sensors’ measurement models are given. “Unscented Kalman filter (UKF) for the estimation of attitude dynamics and external torques” contains information about the UKF algorithm for the attitude state and unknown external torque estimation. In “Simulations”, the performance of the proposed algorithm is demonstrated via simulations. And in the last section of this study, there is a brief summary of the obtained results and the conclusion.

PICO-SATELLITE ATTITUDE DYNAMICS MATHEMATICAL MODEL

Although the Euler angles may have singularity in some certain cases as an attitude kinematics representation technique, they are physically more significant and easier to interpret than the quaternions. Moreover, when the quaternions are used for kinematics representation, implementing the UKF becomes more difficult because of the quaternion norm constraint (Crassidis and Markley, 2003). Hence, in this study, we preferred to use the Euler angles for simplicity and presumably to reduce the computational load.

When we use the Euler angles the mathematical model of the satellite can be expressed with a 9 dimensional system vector.


151Estimation of Pico-Satellite Attitude Dynamics and External Torques via Unscented Kalman Filter

The Euler angles representing the attitude of the satellite (φ is the roll angle about x axis; θ is the pitch angle about y axis; ψ is the yaw angle about z axis), the body angular rates with respect to the inertial axis frame and the constant components of the external disturbance torques form the state vector:

(1)

where (2)

(3)

ω is the angular velocity vector of the body frame with respect to the inertial frame and Nd is the external disturbance torques vector. Since we only estimate the constant components of the torques in this study:

(4)

The dynamic equations of the satellite can be derived by the use of the angular momentum conservation law (Wertz, 1998):

(5)

where J is the inertia matrix, consisting of the main moments of inertia as J=diag(Jx,Jy,Jz) and Nc is the applied control torque. In this study it is assumed that there is no attitude controller so Nc=0

Kinematic equations of the pico-satellite in terms of Euler angles can be given as:

. (6)

Here, c (.), s (.) and t (.) are the cosine, sine and tangent functions, respectively. Besides, p, q and r are the components of the ωBR vector, which indicates the angular velocity of the body frame with respect to the reference frame (orbit frame). ωBI and ωBR can be related via:

(7)

where ω0 denotes the angular velocity of the orbit with respect to the inertial frame, found as . A represents the direction cosine matrix (Wertz, 1998):

(8)

THE SENSORS’ MEASUREMENT MODELS

The investigated cubic pico-satellite has two types of sensors onboard: the magnetometers, which measure the strength of the Earth’s magnetic field; and the gyros, which provide the angular rates of the satellite with respect to the inertial frame.

In this section, measurement models of these two sensors are presented.

THE MAGNETOMETER MEASUREMENT MODELThe Earth magnetic field vector components can be modeled

in the orbit frame as a function of time (Sekhavat et al., 2007):

(9)

(10)

(11)

Here, Me is the magnetic dipole moment of the Earth (Me = 7.943 x 1015 Wb.m); μ is the the Earth Gravitational constant (μ = 3.98601 x 1014 m3/s2); i is the orbit inclination; ε is the magnetic dipole tilt (ε = 11.70); ωe is the spin rate of the Earth (ωe = 7.29 x 10-5 rad/s); r0 is the distance between the centre of mass of the satellite and the Earth.

Three onboard magnetometers of the pico-satellite measures the components of the magnetic field vector in the body frame. Therefore, for the measurement model, which characterizes the measurements in the body frame, the magnetic field vector terms must be transformed by the use of the direction cosine matrix A. The overall measurement model may be given as:



(12)

where, H1(t), H2(t) and H3(t) represent Earth’s magnetic field vector components in the orbit frame as a function of time, and Hx (ϕ, θ, ψ, t) Hy (ϕ, θ, ψ, t) and Hx (ϕ, θ, ψ, t) show the measured Earth magnetic field vector components in the body frame as a function of time and varying attitude. Furthermore, η1 is the zero average Gaussian white noise with the characteristic of

(13)

Here I3x3 is the identity matrix with the dimension of 3 x 3, σmis the standard deviation of each magnetometer error and δkj is the Kronecker delta.

In this study, we assume that the magnetometers are calibrated using either an on-ground or in-orbit technique so no bias terms are included in Eq.(12).

THE GYRO MEASUREMENT MODELAs aforementioned, the other attitude sensor onboard the

satellite is the gyro. Widely used model for the gyro measurements is as follows:

(14)

where,ωBI,meas is the measured angular rates of the satellite, and η2 is the zero mean Gaussian white noise with the characteristic of:

(15)

Here, σv is the standard deviation of each rate gyro random error. Same as the magnetometers, the gyros are assumed to be calibrated.

UNSCENTED KALMAN FILTER (UKF) FOR THE ESTIMATION OF ATTITUDE DYNAMICS AND EXTERNAL TORQUES

The essence of the UKF is the unscented transform, a deterministic sampling technique, that we use for obtaining

a minimal set of sample points (or sigma points) from the a priori average and covariance of the states. Then, these sigma points go through nonlinear transformation. The posterior average and the covariance are determined using the transformed sigma points (Julier et al., 1995; Crassidis and Markley, 2003).

The UKF is derived for discrete-time nonlinear equations, so the system model is given by;

(16)

(17)

Here xk is the state vector and is the measurement vector. Moreover wk and vk are the process and measurement error noises, which are assumed to be Gaussian white noise processes with the covariances of Q (k) and R (k) respectively.

The UKF is based on the determination of 2n+1 sigma points with an average of x (k|k) and a covariance of P (k|k). For an n dimensional state vector, these sigma points are obtained by:

(18)

(19)

(20)

where, x0(k|k), x1(k|k) and x1+n(k|k) are sigma points, n is the state number and k is the scaling parameter which is used for fine tuning, and the heuristic for choosing this parameter is n+k=3 (Julier and Uhlmann, 2004). corresponds to the lth column of the indicated matrix and l is given as l=1... n.

The next step of the UKF process is transforming each sigma point by the use of system dynamics:

(21)

Then, these transformed values are utilized for gaining the predicted average and covariance (Crassidis and Markley, 2003; Soken and Hajiyev, 2011 )

(22)

l=1... n

l=1... n

l=0... 2n



(23)

Here, is the predicted average and P (k+1|k) is the predicted covariance. Furthermore, the predicted observation vector is:

(24)

where (25)

After that, the observation covariance matrix is determined as:

(26)

where the innovation covariance is

(27)

Here R (k+1) is the measurement noise covariance matrix. On the other hand, the cross correlation matrix can be obtained as:

(28)

The following part is the update phase of the UKF algorithm. At that phase, first by using measurements, y (k+1), the innovation sequence is found as:

(29)

and then the Kalman gain is computed via the equation:

(30)

At last, the updated states and covariance matrix are determined by:

(31)

(32)

Here, (k+1|k+1) is the estimated state vector, and P (k+1|k+1)is the estimated covariance matrix.

SIMULATIONS

The proposed UKF based estimation method is tested in this section. The simulations are performed for 40,000 s, which is the period for almost 7 orbits of the satellite. The orbit of the cubesat is a circular orbit with an altitude of r=550km and an inclination of i=97º. The inertia matrix of the satellite is J=diag(2.1x10-3,2.0x10-3,1.9x10-3)kg.m2, which corresponds to a 10cm cubic satellite with an approximate mass of 1.2kg, as mentioned.

The magnetometer sensor noise is characterized by zero mean Gaussian white noise, with a standard deviation of σm=300 nT. The rate gyro random error is taken as . As the filter parameter for the UKF, ĸ is selected as ĸ=-3, which is different than the suggested heuristic.

The initial attitude errors for all the simulations are set to few degrees. The initial estimates for the angular rates and the external disturbance torques are all zero. Besides, the initial value of the covariance matrix is taken as P0=10-10 for all the estimated states, while the process noise covariance matrix is selected as Q=10-29 for the attitude and Q=10-20 for the rest of the states. The R matrix is composed of the sensor noise covariances for the magnetometers and gyros, which means 9x10-12 T2 as 3 diagonal components corresponding to magnetometer measurements and 64 x 10-10 rad2/s2 as 3 diagonal elements corresponding to the gyro measurements. The constant torque term used for modeling purposes in the simulations (the values which will be estimated) is Nd=[5 -3 4]T x 10-1 μNm.

The algorithm was tested in two cases. We are running a dynamics based model for the estimations (in other words, since we are using the satellite dynamics in addition to the kinematics) so there might be uncertainties, mainly caused by the mismatch between the real dynamics and the model used in the filter. The only source for such uncertainty in our simulations might be the inertia terms, which are not exactly known. Hence the two scenarios for the simulations are the cases with and without the uncertainty in the inertia terms.

l=0... 2n



In the first scenario, there is no uncertainty in inertia terms. In Fig.1, an Euler angle estimation example is given. The top plot compares the estimation with the actual value, and the lower one presents the estimation error. As seen, the UKF accurately estimates the attitude, and after almost 1.5 orbits (≈11000s.) the estimation error falls below ±1deg. If the attitude determination accuracy requirement for such pico-satellite without attitude control is considered (±1deg of determination accuracy may be accepted as sufficient), then we may state that the UKF algorithm works well and provides sufficient accuracy for pico-satellite missions. Besides, as presented in Fig. 2, the UKF also has a good estimation characteristic for the angular rates.

As to the main purpose of the proposed algorithm, the Fig. 3 presents an example for the estimation of the external disturbance torques. As clearly seen, the UKF converges to the real values after 10,000th s, and gives sufficiently accurate estimations for the torque terms. Therefore, we can say that the given UKF algorithm estimates all of the states accurately. The estimation results for the rest of the parameters are similar and can be seen in Figs. 4-6. The key contribution of the method is proving that such estimation can be achieved by a simple algorithm that is computationally nondemanding and efficient enough for cubesat applications.

The second simulation is performed for a scenario where we do not know the inertia of the satellite accurately, and there are 5% and 10% uncertainty in inertia terms, respectively. For understanding sake, the results are given in table with comparison with the results obtained for the

0.50 1 1.5 2 2.5 3 3.5 4

x 104

x 104

- 100

-50

0

50

100

Phi(d

eg)

Roll Angle Estimation

Kalman EstimationActual Value

0 0.5 1 1.5 2 2.5 3 3.5 4

0

5

-5

10

Erro

r(de

g)

Time(sec)

Figure 1. Roll angle estimation.

0.1

-0.05

0

0.05

0.1

Wx(

deg/

s)

Wx Estimation

Kalman Estimation Actual Value

0 0.5 1 1.5 2 2.5 3 3.5 4x 104

-5

0

5 x 10-3 Er

ror(

deg/

s)

Time(sec)

0 0.5 1 1.5 2 2.5 3 3.5 4x 104

Figure 2. Estimation of the angular velocity about “x” axis.

-10

-5

0

5

Ny(

Nm

)

Ny Estimation


0.50 1 1.5 2 2.5 3 3.5 4x 104

0.50 1 1.5 2 2.5 3 3.5 4x 104

-10

-5

0

5 x 10-7

x 10-7

Erro

r(N

m)

Time(sec)

Figure 3. Estimation of the constant external torque about “y” axis.



first scenario, beforehand. In Table 1, root mean square errors are tabulated for 10,000 s. between 20,000th and 30,000th seconds such that;

(33)

Pitch Angle Estimation

et

a (d

eg)

Psi (

deg)

Erro

r (de

g)

Erro

r (de

g)Time (sec)

0 0.5 1 1.5 2 2.5 3 3.5x104

4Time (sec)

0 0.5 1 1.5 2 2.5 3 3.5x104

x104 x104

4

0

100

50

0

-100

-50

8000

6000

4000

2000

0

5

0

-5

5

0

-5

-10

0.5 1 1.5 2 2.5 3 3.5 4 0 0.5 1 1.5 2 2.5 3 3.5 4

Yaw Angle Estimation


As it can be seen, even for a small uncertainty in inertia terms of the satellite, the estimation error increases for all the estimated states. Yet, for the simulation case with 5% uncertainty, estimation error is still within the acceptable bounds for cubesat applications, regarding that the attitude estimation accuracy is better than ±1deg. However, if the level of uncertainty is 10%,

Figure 4. Estimation of the pitch and yaw angles.

Figure 5. Estimation of the angular velocities about “y” and “z” axes.

Wy Estimation

Wy

(deg

/s)

Wz (

deg/

s)

Erro

r (de

g/s)

Erro

r (de

g/s)

Time (sec)0 0.5 1 1.5 2 2.5 3 3.5

x1044

Time (sec)0 0.5 1 1.5 2 2.5 3 3.5

x104

x104

x10-3 x10-3x104

4

0

0.2

0.1

0

-0.1

-0.2

0.2

0.15

0.1

0.05

0

5

0

-5

2

0

1

-1

-2

0.5 1 1.5 2 2.5 3 3.5 4 0 0.5 1 1.5 2 2.5 3 3.5 4

Wz Estimation




Table 1. Root mean square error for the cases with and without uncertainty in the inertia terms of the satellite.

Without Uncertainty With 5% Uncertainty With 10% Uncertainty

φ(0) 0.1302 0.2719 3.7194

θ(0) 0.0905 0.2487 1.4792

ψ(0) 0.3100 0.3868 2.8478

ωx(0/s) 9.8034e-5 5.5424e-4 0.0041

ωy(0/s) 1.3251e-4 3.4723e-4 0.0068

ωz(0/s) 1.0042e-4 3.2553e-4 0.0056

Nx (Nm) 6.8174e-9 1.6826e-7 2.9664e-7

Ny (Nm) 4.8459e-9 1.5259e-8 8.7539e-7

Nz (Nm) 6.7456e-9 5.4487e-8 7.1730e-7

Nx Estimation

Nx

(Nm

)

Nz (

Nm

)

Erro

r (N

m)

Erro

r (N

m)

Time (sec)0 0.5 1 1.5 2 2.5 3 3.5

x1044

Time (sec)0 0.5 1 1.5 2 2.5 3 3.5

x104

x104

x10-6 x10-6

x10-6 x10-6

x104

4

0

1

0.5

0

-0.5

-1

1

0.5

0

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0

-5

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1

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0

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-1

0.5 1 1.5 2 2.5 3 3.5 4 0 0.5 1 1.5 2 2.5 3 3.5 4

Nz estimation


Figure 6. Estimation of the constant external torques about “x” and “z” axes.

then it is not possible to satisfy these requirements and the attitude estimation error becomes more than ±1deg. Further examinations show that if there is even more uncertainty in the inertia terms of the satellite, the filter may diverge in long term.

CONCLUSION

In this study, an Unscented Kalman Filter (UKF) algorithm is designed for estimating a pico-satellite’s attitude and the in-orbit external disturbance torques acting on the pico-satellite. The gravity-gradient torque, sun pressure, aerodynamic drag and residual

magnetic moment are included in the estimated disturbance torque vector. The satellite, for which the algorithm is proposed, has magnetometers and gyros onboard as attitude sensors. The main contribution of this study is to show that it is possible to estimate the unknown external torques as well as the attitude dynamics parameters of the satellite via a simple UKF based algorithm. The results show that by using the presented algorithm it is possible to estimate the attitude of the cubesat with an accuracy better than ±1deg, which is accurate enough for such satellites. Moreover the case for uncertainty in the satellite’s moments of inertia is addressed with additional simulations. It is shown that if the uncertainty is less than 5%, the requirements for the attitude estimation performance are still satisfied although the estimation error increases.



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Vinther, K., Jensen, K.F., Larsen, J.A. and Wisniewski, R., 2011, “Inexpensive Cubesat Attitude Estimation Using Quaternions and Unscented Kalman filtering”, Automatic Control in Aerospace, Vol. 4, No. 1.

Wertz, J.R., 1998, “Spacecraft Attitude Determination and Control”, Kluwer Academic Publishers, Dordrecht, Holland.


ABSTRACT: This paper presents the simulation results of an aeroassisted maneuver around the Earth, between coplanar circular orbits, from a geostationary orbit to a low orbit. The simulator developed considers a reference trajectory and a trajectory perturbed by external disturbances combined with non-idealities of sensors and actuators. It is able to operate in closed loop, controlling the trajectory (drag-free control) at each instant of time using a Proportional-Integral-Derivative (PID) controller and propulsive jets. We adopted a spacecraft with a cubic body composed of two rectangular plates arranged perpendicular to the velocity vector of the vehicle. Propulsive jets are applied at the apogee of the transfer orbit in order to keep the perigee altitude and control the rate of heat transfer suffered by the vehicle during atmospheric passage. A PID controller is used to correct the deviation in the state vector and in the keplerian elements. The U.S. Standard Atmosphere is adopted as the atmospheric model. The results have shown that the aeroassisted transfer presents a smaller fuel consumption when compared to a Hohmann transfer or a bi-elliptic transfer.

KEYWORDS: Aeroassisted maneuvers, Orbital dynamic, Trajectory control.

Trajectory Control During an Aeroassisted Maneuver Between Coplanar Circular OrbitsWiller Gomes dos Santos1, Evandro Marconi Rocco1, Valdemir Carrara1

INTRODUCTION

An orbital maneuver is the transfer of a satellite from one orbit to another by means of a change in velocity. To perform this change, the spacecraft has to engage the thrusters or use the natural forces of the environment. The Hohmann transfer and the bi-elliptic transfer are some alternatives to perform an orbital maneuver by propulsive means. In 1961, Howard London presented the approach of using aerodynamic forces to change the trajectory and velocity of a spacecraft, this new technique became known as aeroassisted maneuvers (Walberg, 1985). This type of orbital transfer can be accomplished in several layers of the atmosphere. The altitude reached by vehicle is linked to the mission’s purpose and to the maximum thermal load supported by the vehicle structure. The main advantage of this type of maneuver is the fuel economy. According to Walberg (1985), many papers on aeroassisted orbital transfer have been made in recent decades and it has been shown that a significant reduction in fuel can be achieved using aeroassisted maneuvers instead of Hohmann transfer. Consequently, the reduction of fuel provides an increase in the payload capacity of the vehicle.

Orbit transfer between two circular and coplanar orbits is very common. The technique of using atmospheric drag to reduce the semi-major axis is known as aerobraking and it was first used on March 19th, 1991, by spacecraft Hiten. The launch was conducted by the Institute of Space and Astronautical Science of Japan (ISAS). The spacecraft passed through Earth’s atmosphere at an altitude of 125.5 km over the Pacific Ocean at a speed of 11 km/s. The experience resulted in a decrease in apogee altitude of 8,665 km. In May 1993, an aerobraking maneuver was used on a mission to Venus by Magellan spacecraft, whose goal was to circularize the orbit of the spacecraft. In 1997, the probe U.S. Mars Global Surveyor (MGS)

doi: 10.5018/jatm.v6i2.351

1.Instituto Nacional de Pesquisas Espaciais – São José dos Campos/SP – Brazil

Author for correspondence: Willer Gomes dos Santos | Instituto Nacional de Pesquisas Espaciais | Avenida dos Astronautas 1758, São José dos Campos/SP | CEP: 12227-010 – Brazil | Email: [email protected]



160Gomes dos Santos, W., Rocco, E.M. and Carrara, V.

used its solar panels as “wings” to control its passage through the tenuous upper atmosphere of Mars and lower its apoapsis.

There are several missions requirements that become feasible with the use of aeroassisted vehicles, as for example, to reconfigure orbital systems that are unable to perform an orbital maneuvering (such as replacing a malfunctioning satellite by a spare), to transfer space debris to a new orbit, to operate Space Transportation Systems (STS), to use the atmospheric drag as a brake force to provide orbit capturing of the vehicle, to assist the International Space Station with the transfer of cargo between geostationary orbit (GEO) and low earth orbit (LEO), among others.

Within the context of this paper, we can cite the scientific micro-satellite Franco-Brazilian (FBM) project, in partnership between the Brazilian and French space agencies (INPE and CNES), which would be released as piggy-back on an Ariane 5, and then would perform aerobraking maneuvers to transfer the satellite to the orbit service (Furlan, 1998). The Ariane 5 rocket has the capacity to carry up to eight microsatellites with a maximum individual weight of 120 kg through the Ariane Structure for Auxiliary Payload (ASAP). However, the rocket was designed to place satellites in geostationary transfer orbits. The propellant required to transfer the FBM to a low orbit (between 800 and 1,300 km) by means of chemical propellants, would exceed the allowed amount of mass. This question has led space agencies to study the concept of aerobraking as a workaround. However, CNES, in 2003, left the program which was subsequently discontinued (Brezun et al., 2000). In another interesting study with the same context, Schulz (2001) developed an optimal control law that minimizes the fuel consumption during an aeroassisted maneuver, as well as analyzed orbital changes.

This paper will present the simulation of an aerobraking maneuver between GEO and low orbit. The study aims at examinining the effects that this kind of maneuver can cause in the orbital elements. It will also demonstrate the difference in fuel costs and the elapsed transfer time between an aeroassisted maneuver and a fully propulsive maneuver. The results show that the aeroassisted transfer has a propellant consumption lower than a Hohmann or a bi-elliptic transfer.

PROBLEM DEFINITION

In this paper, a spacecraft with a cubic body composed of two rectangular plates was adopted, called aerodynamic plates,

placed in opposite sides of the vehicle’s body. The inclination angle of the plates, regarding its molecular flow (attack angle), was fixed at 90 degrees, in order to maximize the projected area and the drag force. The spacecraft will be transferred from the GEO to a low orbit. The orbits are considered circular and coplanar. A multipass aeroabraking strategy is used to perform the transfer.

First, the spacecraft applies an impulse to take the vehicle out of the GEO and put it into an elliptical orbit with perigee within the limits of the atmosphere. After each passage at atmospheric region, a reduction of the apogee transfer orbit occurs. When the spacecraft reaches its final apogee altitude, then, a new impulse is applied to the vehicle to remove it from the transfer orbit and insert it into the final orbit. In order to control the rate of heat transfer suffered by the vehicle during the passage through atmosphere, propulsive jets are applied at the apogee, correcting the decay of perigee. This transfer strategy is shown in Fig. 1.

Figure 1. Multipass aerobraking (Walberg, 1985).

deorbit (OTV)

AtmosphereLimit

Target Orbit(LEO FOR OTV)

Final circularizationand periapsisraise maneuver

Apoapsis trimmaneuvers toadjust periapsis

GEO

In this work, it was used the Aeroassisted Spacecraft Maneuver Simulator (SAMS) based on the Spacecraft Trajectory Simulator (STRS), which uses a drag-free control architecture to control the spacecraft trajectory (Rocco, 2006, 2008, 2009, 2010). Usually, a ground based open loop control is used for maneuver correction and orbit transfer. However, in some drag-free missions (Gravity Probe B, Hipparcos, GOCE, etc) the feedback control is mandatory. The STRS and SAMS consider a reference trajectory and a trajectory perturbed by external disturbances, including the aerodynamic effects, combined with non-idealities


161Trajectory Control During an Aeroassisted Maneuver Between Coplanar Circular Orbits

of sensors and actuators. The simulator works in closed loop controlling the trajectory at each instant of time, which is one of the input parameters, using a Proportional-Integral-Derivative (PID) controller and propulsive jets. Santos (2011) used the SAMS to study how the orbital elements can be changed by an aeroassisted maneuver and how much fuel is saved comparing with a propulsive maneuver. Figure 2 shows a basic diagram of the running logic of the aeroassisted maneuver simulator. In this work, noises and non-idealities in the actuator and in the sensor were not considered.

According to Theil and Silas-Guilherme (2005), drag-free technology is essential for scientific missions which need a very low disturbance environment. Several missions have used this technology, such as Gravity Probe B (GP-B) to test the relativistic effects on a gyroscope; STEP and MICROSCOPE had the objective of testing the weak principle of equivalence; Laser Interferometer Space Antenna (LISA) for the detection of gravitational waves; and Gravity Field and Steady State Ocean Circulation Explorer (GOCE), launched in March 2009, to determine the gravity-field anomalies with high accuracy; among others.

AEROASSISTED MANEUVERThe main forces acting on a spacecraft in LEO are gravitational

force (mg), thrusters force (Ts) and aerodynamic forces (F), caused by the interaction of the satellite with the atmosphere. The spacecraft position in space determines the magnitude of aerodynamic forces suffered by the spacecraft. The higher the planetary atmospheric density is, the stronger the aerodynamic forces are. The aerodynamic force can be divided into two: the drag force (FD), whose direction is opposite to the velocity vector, and the lift force (FL), perpendicular to the drag force. According to Vinh (1981), the magnitude of these forces is given by the following equations:

(1)

(2)

where r is the atmosphere density, CD and CL are, respectively, the drag and lift coefficients on the projected area S, and V is the velocity of the spacecraft in relation to the atmosphere. The lift can also be decomposed into altitude lift force (FA) and lateral lift force (FB). The attack angle (a ) is measured between the longitudinal axis of the spacecraft and velocity in relation to the atmosphere. The magnitude of the aerodynamic force depends mainly on the attack angle, and its direction varies depending on the bank angle (s ) between the lift plane and the plane, formed by the velocity vector in relation to the atmosphere and the vector position of the spacecraft, as shown in Fig. 3.

The direction and amplitude of these forces can be calculated by the following equations (Guedes, 1997):

(3)

Figure 2. Basic diagram of the aeroassisted maneuver simulator.

Disturbance

Disturbedstate

OrbitalDynamic

ReferenceState

PIDController

Sensor

PropulsiveSystem

+-

++

BACKGROUND CONCEPTS

This section aims at presenting the main equations and concepts used in the development of this work. Firstly, the concepts about drag-free technology are presented, and then the equations of aeroassisted maneuver and trajectory control system are introduced.

THE DRAG-FREE SATELLITEDrag-free satellites have a payload that follows a geodesic

path through space. The satellite is affected by gravity and by non-gravitational forces. The control system for compensation of the non-gravitational forces is called drag-free control system. The drag-free device has an outer shell and an inner mass called proof mass. Inside the outer shell, the proof mass is floating freely and the distance between the outer shell and the proof mass is constantly measured. When a displacement of the proof mass regarding the outer shell is detected, this means that the outer shell has been influenced by non-gravitational forces and it has moved. Hence, thrusters will reposition the outer shell regarding proof mass so that it returns to the initial position.



(4)

where is the velocity in relation to the atmosphere versor; is the angular momentum versor; and is the altitude versor. The altitude lift force, the lateral lift force, the angular momentum vector and the altitude vector are calculated according to the following equations:

(5)

(6)

H = R×V (7)

N = V×H (8)

The drag coefficient (CD), altitude lift (CA) and lateral lift (CB), are calculated using the Impact Method (Regan and Anandakrishnan, 1993), according to the following equations:

CD = 2sen2 α (9)

CL = 2senα cosα (10)

CA = CL cosσ (11)

CB = CL senσ (12)

The Impact Method, or Newtonian Impact Theory (Vinh et al., 1970), is a simplified numerical technique to approximate the forces and torques acting on a body. It assumes an elastic reflection of particles in a specular surface. The normal component of impact velocity is reversed while the tangential component is unchanged. This model assumes that the particles have no random velocity component, usually associated to microscopic particles of gas (Regan and Anandakrishnan, 1993).

The U.S. Standard Atmosphere model provides the value of the atmospheric density, depending on the position of the vehicle, for the calculation of aerodynamic forces. The velocity of the spacecraft in relation to the atmosphere in the inertial system is calculated assuming that the atmosphere has the same rotation velocity of the Earth and its equation is given by Kuga et al. (2008):

(13)

where is the velocity vector in relation to the inertial system and ω is the angular velocity vector of Earth’s rotation.

Some of the major difficulties faced by the spacecraft during atmospheric maneuver are related to the heating rate and velocity deceleration. These quantities increase when the vehicle is submitted to high atmospheric densities and high velocities. In the upper atmosphere, it should be considered a form of heating known as free molecular heating. This phenomenon occurs due to the impact of free molecules against the vehicle. The rate of heat transfer as per area unit is given by the following equation (Gilmore, 1994):

(14)

where αc is the thermal accommodation coefficient (Gilmore (1994) recommends the use of αc = 1).

The orbital spacecraft state is described by the coordinates X = [r V], measured in an inertial frame centered on Earth, and the dynamic model of the spacecraft used in this paper is given by:

(15)

where μ is the central body gravitational constant (product of the central body mass and the universal gravitational constant)

Figure 3. Components of the aerodynamic forces, attack angle and bank angle (Guedes, 1997).

σα

LONGITUDINAL AXIS

FL

FA

V

FB

FD

R



and ΔVp is the velocity variation caused by propulsive thrusters when activated. The lift force is null because the attack angle of the aerodynamic plates is perpendicular to the velocity vector.

TRAJECTORY CONTROL SYSTEMA PID controller was used to correct the deviation of the

spacecraft trajectory. Most of the industrial controllers are PID due to its flexibility, low cost and robustness. The PID control action is computed by:

(16)

where KP , KI and KD are the proportional gain, integral gain and derivative gain respectively, and e(t) is the position error. Most control systems today use digital computers. Hence, to implement the PID control law in a digital computer, it is necessary to discretize the PID equation c(t). Several discretization methods can be consulted in Franklin et al. (1998). Using the discretization methodology proposed by Hemerly (2000), we can write the discrete PID control law equation, as

(17)

where T is the sample period.

RESULTS

This section aims at presenting the results of an aeroassisted maneuver simulation to transfer the spacecraft from a GEO to a low orbit of 1,000 km of altitude. The presented curves refer only to the aeroassisted transfer. Table 1 shows the initial conditions of the orbit.

The complete maneuver was performed in 58.93 days and, at the end of the period, there was a reduction of approximately 35,000 km in the apogee altitude, according to Fig. 4. The perigee altitude remained at an average of 115 km, with a variation of ± 0.5 km due to the application of jet propulsion at the apogee of the orbit, shown in Fig. 5.

There was no change in the orbit inclination because lift forces were not being applied to the vehicle. Figure 6 illustrates

Table 1. Initial condition of the transfer orbit.

Description Value Units

Apogee altitude 35786.14 km

Perigee altitude 115 km

Eccentricity 0.7332 -

Inclination 1 degrees

RAAN* 200 degrees

Perigee argument 10 degrees

Mean anomaly 180 degrees*Right Ascension of Ascending Node.

Figure 4. Apogee altitude as function of time.

0

0

Apog

ee a

ltitu

de [k

m]

40000

20000

30000

10000

105 2015

Time (days)

25 3530 4540 50 6055



Figure 5. Perigee altitude as function of time.

100

0

Perig

ee a

ltitu

de [k

m]

120

110

115

105

105 2015

Time (days)

25 3530 4540 50 6055

Figure 6. Semi-major axis deviation as function of time.

-120

0

Sem

i-maj

or a

xis

devi

atio

n [m

]

80

-40

40

-80

105 2015

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25 3530 4540 50 6055

-160

0

the deviation of the semi-major axis versus time. During the maneuver, the control system acts to reduce the error between the reference trajectory and the disturbed trajectory. The error appears when the first thrust is applied at the apogee.

Figure 7 shows the variation in the position components X, Y and Z, for the first five days of maneuvering. The variation is almost zero at the Z component due to the roughly equatorial orbit. The behavior X and Y components is related to the orbit eccentricity. As the vehicle approaches the perigee the orbital velocity increases, and vice versa.

The applied thrust at the orbit apogee versus time is shown in Fig. 8. Due to orbit circularization the trajectory path inside

low atmosphere is increased, causing perigee decay. So, while low thrusters are applied at the beginning of the maneuvering process in order to adjust the trajectory derivations, high impulses are employed at the final orbit to correct the perigee height.

Figure 9 illustrates the drag force suffered by the vehicle along the atmospheric path. It can be observed a downward trend close to the final orbit. This behavior is related to the velocity reduction and with the circularization of the orbit.

The heat transfer rate suffered by the vehicle as function of time is shown in Fig. 10. The maximum value supported by the vehicle depends on the material structure. The values obtained are within the limit given by Kumar and Tewari (2005). However,



Figure 7. X, Y and Z components of the position and velocity vectors as function of time.

0

Y po

sitio

n [m

]

4E+007

-2E+007

2E+007

-4E+00710.5 21.5

Time (days)2.5 3.53 4.54 5

6E+007

0E+000

0

Z po

sitio

n [m

]

4E+007

-2E+007

2E+007

-4E+007105 2015

Time (days)25 3530 4540 50

6E+007

0E+000

55 60

0

X v

eloc

ity [m

/s]

10000

-5000

5000

-10000

10.5 21.5Time (days)

2.5 3.53 4.54 5

15000

0

-15000

0

Y ve

loci

ty [m

/s]

10000

-5000

5000

-10000

10.5 21.5Time (days)

2.5 3.53 4.54 5

15000

0

-15000

0 105 2015Time (days)

25 3530 4540 50 55 60

Z ve

loci

ty [m

/s]

10000

-5000

5000

-10000

15000

0

-15000

0

X p

ositi

on [m

]

4E+007

-2E+007

2E+007

-4E+00710.5 21.5

Time (days)2.5 3.53 4.54 5

6E+007

0E+000

0

0

ru

st [N

]

1.6

0.8

1.2

0.4

105 2015

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25 3530 4540 50 6055

Figure 8. Propulsive thrust as function of time.

the heat rate experienced by the vehicle can be increased or decreased by controlling the perigee altitude; the lower the perigee height, the higher the heat suffered by the vehicle.

Regarding the consumption of propellant, two situations are considered. The first one illustrates a hypothetical situation: it is considered that the drag force suffered by the vehicle is

provided by the propulsion system, whose results are presented in Fig. 11; and the second situation (Fig. 12), shows the case that considers only the thrust necessary to correct the perigee altitude.

Simulations show that up to 200 kg of fuel propellant should be needed in order to perform orbit adjusting using only the propulsion system, with application of thrust at perigee of the orbit



0

0

Hea

t tra

nsfe

r [W

/m2 ]

40000

20000

30000

10000

105 2015

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25 3530 4540 50 6055

Figure 10. Rate of heat transfer as function of time.

0

0

Prop

ella

nt co

nsum

ed [k

g]

250

150

200

50

105 2015

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25 3530 4540 50 6055

100

Figure 11. Hypothetical situation: propellant required for applying a thrust equal to drag force.

0

Dra

g fo

rce

[N]

0

-20

-10

-30

105 2015

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25 3530 4540 50 6055

Figure 9. Drag force as function of time.



Table 2. Comparative table of propellant consumption and transfer time.

ManeuversPropellant

Consumption (kg)Transfer time

(days)

Hohmann 276.62 0.22

Bi-elliptic 361.25 1.48

Aeroassisted 160.92 58.93

0

0

Prop

ella

nt co

nsum

ed [k

g]

2.5

1.5

2

0.5

105 2015

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25 3530 4540 50 6055

1

Figure 12. Aeroassisted situation: propellant necessary to correct the decay of perigee.

transfer, compared to 2.11 kg of fuel, necessary to the aeroassisted, plus PID control orbit maneuvering. Furthermore, it should be taken into account the propellant needed to enter to and exit from the transfer elliptical orbit. The propellant adopted was the liquid oxygen/liquid hydrogen, whose specific impulse is 460 s.

Table 2 shows a comparison of propellant consumption and transfer time among Hohmann transfer, Bi-elliptic transfer and aeroassisted maneuver. It was considered the consumption of propellant to enter and to exit from the elliptical orbit transfer. The aeroassisted maneuver spent 160.92 kg of propellant, where 2.11 kg were used to correct the decay of perigee, and 158.81 kg to get in and out of the transfer orbit. The fuel economy of aeroassisted maneuver related on the transfer of Hohmann was approximately 116 kg.

in computations, the ideal case for calculating the transfer time of propulsive maneuvers, but when thrust is small, due to thruster size, and the number of propulsive maneuvers to reach the final orbit is increased, the time to perform the maneuver also increases.

CONCLUSIONS

It was presented the simulation of an aeroassisted maneuver to transfer a vehicle from a GEO to a low orbit altitude using propulsive jets to correct the decay in perigee altitude and to correct deviations between the reference trajectory and the disturbed trajectory. It can be concluded that the control system met expectations and that it has maintained the residual error in state vector within acceptable limits.

The control propulsive jets is able to maintain the perigee altitude within reasonable limits (115 km ± 0.5 km), avoiding the vehicle to suffer high thermal loads during the atmospheric path. This case can be compared to the mission of scientific FBM, in which there was a need to transfer the satellite from a GEO to a low orbit, by means of aeroassisted maneuvers. In general, the aeroassisted maneuvers were more advantageous in terms of fuel economy than the fully propulsive maneuvers. The closed loop control system was critical to the simulation success, without which it would not be possible to eliminate efficiently the residual errors in the trajectory.

The aeroassisted maneuver took 58.93 days to reach the final orbit, while the transfer of Hohmann and bi-elliptical, required far less time, 0.22 and 1.48 days respectively. However, it was considered,



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system for the trajectory”, XIV Colóquio Brasileiro de Dinâmica Orbital,

Águas de Lindóia, Brazil.

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the trajectory deviation for some drag-free missions”, Proceedings of

the 8th Brazilian Conference on Dynamics Control and Applications,

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ABSTRACT: Amazonia-1 is a Brazilian remote sensing satellite providing mainly images, in order to observe and monitor deforestation, especially in the Amazon region. This paper describes the thermal control design, which uses passive and active concepts. The active thermal control is based on heaters regulated by software via thermistors. The passive thermal control consists of multi-layer insulation blankets and radiators, paints, surface finishes to maintain temperature level of the overall carrier components within an acceptable value. The thermal control design is supported by thermal analysis using thermal mathematical model. The temperatures and heater power are predicted for critical cases.

KEYWORDS: Amazonia-1 satellite, Satellite thermal control, Thermal mathematical model.

Thermal Control Design Conception of the Amazonia-1 SatelliteDouglas Felipe da Silva1, Issamu Muraoka1, Ezio Castejon Garcia2

INTRODUCTION

The Amazonia-1 Thermal Control Subsystem (TCS) consists of active and passive thermal control elements in order to maintain the spacecraft’s components and structure within a controlled range of temperature throughout the mission of the spacecraft, from the Beginning of Life (BOL) to the End of Life (EOL). The payload equipment, the propulsion subsystem components and batteries, along with their operational requirements, are the main drive to develop Amazonia-1 thermal control design and analysis.

The Amazonia-1 mission’s main goal is to provide image data, improving the deforestation monitoring capability in the Amazon region. The use of a new instrument called Advanced Wide Field Imager (AWFI) with a 40-meter spatial resolution on board will improve the capability of the system (Scaduto et al., 2010). The current Wide Field Imager (WFI) camera acquires images in two spectral bands, while in the AWFI the images will be acquired in four bands. Figure 1 presents the satellite.

The remote sensing Amazonia-1 satellite is the first mission to use the Multi-Mission Platform (MMP) concept developed by the Instituto Nacional de Pesquisas Espaciais (INPE), coordinated

doi: 10.5028/jatm.v6i2.320

1. Instituto Nacional de Pesquisas Espaciais – São José dos Campos/ SP – Brasil 2. Instituto Tecnológico de Aeronáutica – São José dos Campos/ SP – Brazil

Author for correspondence: Douglas Felipe da Silva | Instituto Nacional de Pesquisas Espaciais – Av. dos Astronautas, 1.758, São José dos Campos/SP | CEP: 12.227-010 | Brazil | Email: [email protected]


Figure 1. Amazonia-1 satellite.


170Silva D.F., Muraoka I. and Garcia E.C.

by Brazilian Space Agency (AEB). The MMP platform concept provides capability to support a variety of low Earth orbit missions using the same basic three-axis stabilized platform, with different payload instruments (Santana et al., 2012). The MMP main design goal is modularity, allowing separated integration from the satellite payload module. This provides independent design, construction and test of each module before payload-to-MMP integration and final compatibility tests.

There is an extended amount of works available in the literature about satellite thermal control and, for instance, the handbook of Gilmore (2002), as well as the text books of Karam (1998) and Messeguer et al. (2012), are some of the best work presented.

FEATURES OF THE AMAZONIA-1 SATELLITE

A few salient features of Amazonia-1 satellite are as follows:• Overall dimensions of the main body: 2,200 x 950 x 950 mm;• Mass: About 500.0 kg;• Orbit: Sun-synchronous, 752.4 km high, 98.405° inclination

and passage time 10:30 a.m.;• Structure: Aluminum honeycomb structure;• Power: 420 W (average), InGAP/InGaAs/Ge solar panels,

lithium ion battery;• Stabilization: 3 axis stabilized;• Mission life: 4 years.

The exploded view of the MMP is presented in Fig. 2, and Fig. 3 presents the payload.

During nominal operation mode, the -Y face will be always pointed to Earth. In emergency mode, the satellite attitude control will point -Z, facing the Sun, in order to warm up the propulsion subsystem elements, and two rotations per orbit will be imposed around the Z axis (in negative direction), in order to distribute external heat loads equally on the lateral panels.

CONSTRAINTS AND REQUIREMENTS TO TCS

The Amazonia-1 TCS shall provide a thermal environment which ensures reliable performance of all components during all mission

phases, minimizing the thermal control mass and heater power budgets. The main functions of the Amazonia-1 thermal control are:

Battery 2

Magnetotorquer 1

TT&C Antenna 1 ACE

OBDH

SADA1TCE

TT&C1&2

GYRO

Star Sensor 2

LNA

GPSAntenna1&2

2

GPSReceiver

PCDU

SADA 2

Magnetometer1Magnetometer 2

Reaction Wheel 3

Tank

Reaction Wheel 2

Reaction Wheel 4

Reaction Wheel 1

Battery 1

Battery 3

Battery 4

Magnetotorquer2 Magnetotorquer 3

TT&C Antenna 2

Star Sensor 1

Figure 2. Exploded view of the MMP.

BPF

TWT 1TWT 2

EPC 2 EPC 1HPS

QPSK-TX

SSR

SDC

SPE 1

SPE 2

DSS

AWFI

AWDT Antenna

Figure 3. Exploded view of the payload.


171Thermal Control Design Conception of the Amazonia-1 Satellite

• To provide temperature distribution in order to have all onboard equipment operate within their designed operational temperature range, under all possible attitudes experienced during all mission phases;

• To allow the dissipation of the excess energy generated with no detrimental effect.

The Amazonia-1 thermal design conception is based on the balance of the following criteria: • Insulate, as well as possible, the satellite of external

environment. Making use of this approach, temperatures are less sensitive to the external radiation and the satellite can perform better during all mission stages. Radiators are used in order to reject internal heat dissipation;

• Minimization of thermal gradients in the satellite by using high emittance coatings on the majority of the internal surfaces (equipment and structural parts) and improving the conductive coupling between equipment and mounting panels, through thermal interface filler materials;

• For AWFI, batteries and propulsion components, which have operational temperature limits quite different from the remaining equipment in the satellite, the paragraph above is not applicable. These components shall be thermally insulated — by radiation and conduction — from the

satellite, having their temperatures controlled individually by heaters;

• Insulate the equipment placed in direct view to space from the space environment. This is necessary in order to avoid large temperature fluctuation on these equipment, through illuminated and eclipse periods;

• Insulate MMP from payload module, considering the premise that the MMP is a multi-mission platform and its thermal design shall be qualified for any payload;

• The electrical power allowance for heaters shall not exceed 30 W during mission nominal modes and 60 W during emergency mode;

• The temperature limits (operational and non-operational) and the heat dissipation for all equipment are disposed in Table 1 (payload) and Table 2 (MMP). The cases in these tables will be described further on in “Cases under evaluation”.

THERMAL CONTROL SYSTEM USED IN AMAZONIA-1

As explained before, in order to minimize temperature gradients, all internal surfaces of the satellite are coated with

Table 1. Payload equipment temperature limits and heat dissipation.

Equipment Temperature Range (°C)Heat Dissipation (W)

Case A Case B Case C

AWDT Antenna -100 ~ 80 0 0 0

BPF -30 ~ 60 0 0 10.0(1)

DSS -10 ~ 45 (-20 ~ 55)(2) 0 3.0 3.0

EPC (x2) -20 ~ 60 (-30 ~ 75)(2) 0 0.1 1.5(3)

HPS -20 ~ 50 (-30 ~ 60)(2) 0 0 3.2(1)

QPSK-TX -10 ~ 45 (-20 ~ 50)(2) 0 0 12.0(1)

SDC -10 ~ 45 (-20 ~ 55)(2) 0 0 4.0(1)

SPE1 -10 ~ 45 6.0 6.0 90.9(4)

SPE2 -10 ~ 45 22.1 22.1 101.6(4)

SSR -10 ~ 45 (-20 ~ 55)(2) 0 5.0 10.0

TWT (x2) -20 ~ 60 (-30 ~ 75)(2) 0 0 40(3)

(1)15 min of operation in illuminated period; (2)non-operational temperature range; (3)15 min of operation in illuminated period (only main unit; the redundant one is on stand-by); (4)25 min of operation in illuminated period.



high emittance black paint. All external surfaces are wrapped with 20 layers of Multilayer Insulation (MLI) blankets with areas available for positioning the radiators. In order to reduce heater power consumption during emergency mode, the internal face of the adapter cylinder, positioned at the MMP -Z panel, is kept in bare aluminum, as well as the areas of the -Z panel which are out of the cylinder circle.

The propulsion components were dealt with different approach since they shall be kept at a temperature range from +10 °C to +50 °C. The lower limit of +10 °C is so to avoid hydrazine freezing. Considering that the lower temperature limit of the propulsion subsystem is considerably higher than those of the remainder MMP components, the thermal design was set according to the following baseline: • Radiative insulation of all components (except thrusters)

wrapping them with 15 layers of MLI blanket;

• Conductive insulation of all attachment points with the mounting panel using fiber glass-epoxy composite washer;

• Electrical heating using skin and spiral heaters on all components. A total of seven heater circuits (four main and three

redundant ones) are installed on the propulsion components, using correspondently 7 switched heater lines. All lines are controlled by ON/OFF commands from the On Board Data Handling (OBDH), which uses the thermistor telemetry as input data. The thermistors which will be used are space qualified, interchangeable, NTC type with a resistance of 10 kΩ at 25 °C, and manufactured by Measurement SpecialtiesTM.

The batteries panel also received specific design since their range, 0 to 20 °C, is narrower than most of the electronic equipment in the satellite (-10 °C ~ 45 °C). The concept is to couple the battery packs to the mounting panel and to use

Table 2. MMP equipment temperature limits and heat dissipation.

Equipment Temperature Range (°C)Heat Dissipation (W)

Case A Case B Case C

ACE -10 ~ 45 18.0 14.0 20.0

Battery (x4) -10 ~ 20(1) 2.3 2.3 2.8

GPS Antenna (x2) -20 ~ 55 (-20 ~ 60)(2) 0 0 0

GYRO Elect. Module -20 ~ 60 (-40 ~ 75)(2) 0 21.6 30.0

GYRO ICU -20 ~ 60 (-40 ~ 75)(2) 0 0.1 0.2

LNA -10 ~ 45 (-20 ~ 50)(2) 0 0.6 0.6

Magnetometer (x2) -20 ~ 50 0 0 0

Magnetotorquer (x3) -10 ~ 50 2.7 0 2.7

OBDH -10 ~ 45 47 47 47

PCDU -10 ~ 45 6.3 6.3 31.8(3)

Propulsion Elements +10 ~ 50 0 0 0

Reaction Wheels (x4) -15 ~ 55 6.7 6.7 40.4(4)

SADA (x2) -20 ~ 60 4.6 5.1 6.5

Star Sensor (x2) -20 ~ 50 (-40 ~ 70)(2) 0 0.2 13.5

TCE -10 ~ 45 (-25 ~ 45)(2) 0 0 0.8

TT&C (x2) -20 ~ 50 6.2 6.2 14.6(5)

TT&C Antenna (x2) -80 ~ 55 0 0 0(1)At mounting panel; (2)Non-operational temperature range; (3)Operation in illuminated period; (4)6 min of operation in illuminated period; (5)15 min of operation in illuminated period (only main unit; the redundant one is on stand-by).



this panel as a radiator. The mounting panel is insulated, conductively (through fiber glass-epoxy washers) and radiatively (15 layers of MLI blanket), from the other parts of the satellite.

In order to avoid the temperature at battery panels below -10 °C, heaters are placed at the panel, near the batteries’ feet. The temperatures at the panel are monitored by four thermistors, placed near the heaters. The heater circuits will be activated by ON/OFF commands from the OBDH. The main circuit will turn on at -8 °C and turn off at -6°C, while the redundant ones will turn on at -10 °C and turn off at -8 °C. The temperature refers to the lowest value among the four thermistors.

The AWFI has autonomous thermal control, and it is wrapped with a MLI blanket and conductively insulated from the mounting panel through washers. Also, it has its own radiator and dedicated heaters. Since its thermal control is independent from the satellite (Scaduto et al., 2010), in this global analysis, its temperature was considered to be between 15 °C and 20 °C.

The thermal optical properties of coatings used in the satellite are presented in Table 3.

VERIFICATION OF DESIGN BY THERMAL MATHEMATICAL MODELING

The thermal modeling is based on a nodal or lumped parameter method. In this method, the satellite is divided in a number of regions, assumed isothermal, which is called nodes. These nodes exchange heat among each other by conduction and radiation, and with space by radiation. Also, they can receive heat loads from external sources or from electronic

components. The temperature of each node is the result of these interactions. The governing equation is the energy equation which consists of transient, conduction, and radiation terms, plus boundary conditions (solar radiation, albedo and Earth radiation), as a source term. This equation can be written as presented in Eq. (1) (Karam, 1998):

(1)

Where the thermal mass is mcp [J/K]. The external loads are QS, QA, and QE [W], while internal heat dissipation is QD [W]. The conduction and radiation exchange factors are represented by Kij [W/K] and Rij [m-²], respectively. The Stefan-Boltzmann constant is σ [W/m²/K4], and Ti and Tj [K] are the temperatures of nodes i and j, respectively.

The temperatures have been calculated in transient regime, considering variations on external heat loads and equipment heat dissipation profiles. The model was built using the C&R Technologies ThermalDesktop/Radcad - SINDA/FLUINT thermal software package, and details of this package can be obtained in Panczak et al. (1998).

The MMP is represented by 2,764 diffusion nodes and 1,698 arithmetic ones, while the payload is represented by 4,161 diffusion, 1,444 arithmetic and 2 boundary nodes. Thus, the whole model is composed by 6,925 diffusion nodes, 3,142 arithmetic nodes and 2 boundary ones (total of 10,069 nodes).

The arithmetic nodes are used in order to represent the MLI blankets, which have small mass, since these elements have very low thermal capacitances and respond almost instantaneously to the thermal changes of environment.

The boundary nodes are used in order to represent a component with constant temperature. In this case, the equipment is the AWFI, since it has its own active thermal control and it shall operate between +15 °C and +20 °C.

Table 3. Coatings’ thermal optical properties.

Coating Emissivity (ε)Absorptivity (α) Effective

Emissivity (εeff)BOL EOL

Bare Aluminum 0.05 0.15 0.15 -

Black Paint 0.88 0.95 0.95 -

MLI 0.80 0.41 0.51 0.02

White Paint 0.94 (BOL)0.95 (EOL) 0.20 0.33 -



CASES UNDER EVALUATION

The design philosophy is to find the critical cases and analyze them supposing that all other cases result in intermediate temperatures. Three critical cases are evaluated: cold, hot and emergency cases. • Case A: Cold case represents the nominal operational

mode in which minimum external heat loads (albedo: 0.34; Earth radiation: 208 W/m²; solar radiation: 1326 W/m²), minimum internal heat dissipation and BOL optical properties are considered. The orbit parameters are adopted for winter solstice, and the satellite -Y face will be pointing towards Earth;

• Case B: Hot case represents the nominal operational mode in which maximum external heat loads (albedo: 0.42; Earth radiation: 233 W/m²; solar radiation: 1418 W/m²), maximum internal heat dissipation and EOL optical properties are considered. The orbit parameters are adopted for summer solstice, and the satellite -Y face will be pointing towards Earth;

• Case C: Emergency case represents the satellite with attitude different from the nominal mode. In this case, the satellite -Z face is permanently oriented towards the Sun and the solar panel is oriented parallel to the -Z panel. The spin rate is two revolutions per orbit around the Z axis, in negative direction. The power consumption is minimized in this mode. Some equipment are turned off during this case and the temperature limit is the non-operation limit. The external heat loads, orbit parameters and optical properties are the same considered in case A (nominal cold case).

TEMPERATURES AND HEATERS POWER CONSUMPTION PREDICTIONS

The temperatures have been calculated in transient condition, and the stabilization criterion was considered when the temperature variation between two consecutive orbits is less than 0.1 °C in the same orbit point, for hot case. In cold and emergency cases, it is not possible to accomplish this criterion, since heaters are required and their ON/OFF dynamic does not allow the same conditions at the same orbit point. In this case, a minimum of 15 orbits are simulated.

The extreme predicted temperatures for payload (minimum for emergency case and maximum for hot case) are shown in Fig. 4. By analyzing Fig. 4, it should be noted that the predicted range of the equipment are the extremes of the central (black solid) bar. The empty space between this bar and the other two ones (minimum limit to the left and maximum to the right) is the temperature margin of the equipment.

The extreme predicted temperatures for MMP are shown in Fig. 5.

All maximum temperatures were predicted with at least 5 °C of margin, and the cold ones are controlled by heaters. It should be noted that the predicted temperatures of the solar array generator exceeded the acceptable range (-80 °C to 80 °C), reaching -84.6 °C in the emergency case and 82.1 °C in the hot case. Although its temperature does not affect the satellite thermal behavior considerably, a detailed thermal analysis will be accomplished in order to satisfy its temperature requirement.

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90

TWT 2TWT 1

SSRSPE2SPE1SDC

QPSK-TXHPS

EPC 2EPC 1

DSSBPF

AWDT Antenna

Temperature (ºC)

Figure 4. Payload predicted temperatures.



To achieve the temperatures presented in Figs. 4 and 5, it was necessary to determine radiators areas. The process to find these areas, presented in Table 4, is iterative.

The average power consumption for all heaters, during cold and emergency cases, is presented in Table 5.

For the nominal cold case (Case A), the heater power consumption is within the requirement (30 W) with 25% margin,

according to U.S. Department of Defense (1982). For emergency cases, the predicted power does not comply with the requirement (60 W). This value is under review, since in emergency cases the overall power consumption is reduced and more power is available. The emergency case is thermally critical due to its distinct attitude, in which the payload module remains in the Sun shadow, and almost all equipment are turned off.

Table 4. Radiators areas.

Radiator Location

Area (m²)

MMP Payload

+X Face 0.3624 0.2356

-X Face 0.1035 0.015

+Y Face 0.24 0.1596

-Y Face 0.432 0

Total 1.1379 0.4102

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90TT&C Antenna 2TT&C Antenna 1

TT&C 2TT&C 1

TCEStar Sensor 2Star Sensor 1

SADA 2SADA 1

Reaction Wheel 4Reaction Wheel 3Reaction Wheel 2Reaction Wheel 1

Propulsion ElementsPCDUOBDH

Magnetotorquer 3Magnetotorquer 2Magnetotorquer 1

Magnetometer 2Magnetometer 1

LNAGYRO ICUGYRO EM

GPS ReceiverGPS Antenna 2GPS Antenna 1

Battery 4Battery 3Battery 2Battery 1

ACE

Temperature (ºC)

Table 5. Heaters power consumption.

HeaterPower Consumption (W)

Case A Case C

Batteries Panel 1.44 32.42

Payload Module 12.45 44.37

Propulsion Elements 3.69 2.85

Total 17.58 79.64

Figure 5. MMP predicted temperatures.



REFERENCESGilmore, D.G., 2002, “Spacecraft Thermal Control Handbook”, 2nd Edition, The Aerospace Corporation Press, El Segundo, CA, Vol. I.

Karam, R.D., 1998, “Satellite Thermal Control for System Engineers”, Progress in Astronautics and Aeronautics, AIAA, Cambridge, Vol. 181, pp. 274.

Meseguer, J., Pérez-Grande, I. and Sanz-Adréz, A., 2012, “Spacecraft Thermal Control”, Woodhead Publishing, Cambridge.

Panczak, T., Ring, S. and Welch, M., 1997, “A CAD-based Tool for FDM and FEM Radiation and Conduction Modeling”, Cullimore and Ring Technologies, 28th ICES Conference, SAE Paper 981577.

Santana, A.C., Martins-Filho, L.S., Duarte, R.O., Arantes Jr., G. and Casella, I.S., 2012, “Attitude Control of a Satellite by Using Digital Signal

Processing”, Journal of Aerospace Technology and Management, Vol. 4, No. 1, pp. 15-24. doi: 10.5028/jatm.2012.04014611.

Scaduto, L.C.N., Carvalho, E.G., Santos, A.R., Soares, A.L., Castellar, A., Azeka, L.A., Souza, W.M., Evangelista, S., Santos, A.G., Malavolta, A.T., Vales, L.F., Segoria, D., Santos, F.S., Candeloro, L., Almeida, M.R., Yasuoka, F.M.M., Modugno, R.G., Cartolano, R., Barbalho, S., Santos, P.A., Rebolho, D., Otoboni, J.A., Correa, Y., Stefani, M.A. and Castro Neto, J.C., 2010, “The Advanced Wide Field Imaging Camera (AWFI) for the Amazonia 1 Brazilian Satellite”, International Conference on Space Optics (ICSO), Rhodes, Greece.

U.S. Department of Defense, 1982, “Test Requirements for Space Vehicles - MIL-STD-1540B”, U.S. Air Force, Washington, D.C., USA, pp.16-17.

CONCLUSIONS AND FUTURE ACTIONS

The Amazonia-1 thermal control design and analysis were presented. This design has been verified through the use of the SINDA/FLUINT thermal analyzer, and established by using passive means and heaters. All subsystems have been in the specified temperature ranges, for nominal and emergency cases. The heater power predicted for

nominal operational cases complies with the specification, however, in an emergency case, the predicted value is beyond specification. This requirement is under review and may possibly be increased.

A thermal model based in this design will be submitted to the Thermal Balance Test, in order to qualify the thermal design. Afterwards, the results will be correlated, and the thermal mathematical model validated.


Analysis of Radar Cross Section Reduction of Fighter Aircraft by Means of Computer SimulationLuiz Alberto de Andrade1, Luan Silva Carvalho dos Santos1, Adriana Medeiros Gama1

INTRODUCTION

The goal to reduce a military aircraft’s RCS is directly related to the distance at which it can be detected by hostile radars. The radar equation given below (Eq.1) provides a quantitative way to analyze the impact of a target’s RCS reduction in its distance for monostatic radars (Knott et al., 1993):

Rmax = [(PtG2λ2 σ) / (4π)3 PminL)]1/4 (1)

Where: Rmax is the maximum range of the radar detection, Pt is the radar antenna’s transmission power, Pmin is the minimum power detected by the radar, G = Gr = Gt is the radar gain, L are the losses associated to the radar electronics and the environment, and σ is the RCS (Gama and Rezende, 2010; CST, 2012).

When analyzing Eq.1, it is revealed that among the variables of the radar equation, only one possible control by the target aircraft is its RCS, all others are inherent to either the hostile radar system or the environment. Therefore, from the point of view of the aircraft, the radar parameters and the environment can be considered as a constant of the detection system. An examination of Eq.1 shows that the RCS of a target should be decreased by sixteen times, so that the maximum detection distance R falls by its half.

METHODOLOGY

The computational resource used in this study was the CATIA V5 software, responsible for the development of 3-D models of the aircraft which shall have the reduction of its

doi: 10.5028/jatm.v6i2.259

ABSTRACT: This paper presents a preliminary study of the Radar Cross Section (RCS) reduction on the fighter aircraft. First, it was studied the RCS of the aircraft from computational simulations based on prior knowledge of vulnerable areas of this aircraft to radar threats. Subsequently, the possible applications of Radar Absorbing Materials (RAM) on the surface of the aircraft were evaluated, in order to reduce its RCS. The absorber material used in the simulations was denominated FC70, which has good attenuation in the range of 10 to 12 GHz. The study of this reduction was accomplished by applying RAM in four different scenarios at the frequency of 11.1 GHz, where the material is more sensitive. The RCS simulations of the fighter aircraft and its RCS reduction by RAM application were carried out with the support of the software “Computer Simulation Technology” (CST), version 2012. Such technology makes it possible to simulate the application with an absorber material layer on the surface of the aircraft. For the study of the RCS reduction on the fighter aircraft, it was first necessary to develop a detailed 3D model of the fighter aircraft, and it was developed with the software “Computer Aided Three-Dimensional Interactive Application” (CATIA). In conclusion, it is impossible to make much progress attempting to retrofit stealth onto a conventional aircraft because if the shape is wrong, no amount of absorbing material treatments will reduce the RCS.

KEYWORDS: Radar cross section, Radar absorber material, Computational simulation, Radar cross section reduction.

1.Instituto de Aeronáutica e Espaço – São José dos Campos/SP – Brazil

Author for correspondence: Luiz Alberto de Andrade | Instituto de Aeronáutica e Espaço – Divisão de Materiais | Praça Marechal Eduardo Gomes, 50, Vila das Acácias, São José dos Campos/SP | CEP: 12228-904 – Brazil | Email: [email protected]



178Andrade,L.A., Santos, L.S.C. and Gama, A.M.

RCS studied. In order to study the electromagnetic scattering, the package A-Solver (asymptotic solver) of the Computer Simulation Technology software – CST 2012 version – was used (Gama and Rezende, 2005). In the interest of studying the RCS reduction, the RAM FC70 was inserted on the library of the CST (Gama and Rezende, 2010; Gama and Rezende, 2005; Gama et al., 2011),which simulated its application in parts of the surface of the aircraft, called Scenarios 1, 2, 3 and 4.

In order to facilitate the RCS’s simulations on the F-5 and to modify the aircraft, because it is still in operation, some of its characteristics were modified (CST, 2013; CATIA, 2010; EMBRAER, 2005). The simplifications were: the setting of a metallic canopy, the removal of external antennas, the withdrawal of navigation and training lights, as well as the removal of “probe” for refueling during flight, weapons and engines. Figure 1 shows, in detail, a three dimensional model

Figure 1. 3-D perspective model of the fighter aircraft developed with the CATIA software.

Figure 3. Fighter aircraft partially coated with RAM, Scenario 2.


Figure 2. Aircraft fighter aircraft partially coated with RAM, Scenario 1.

split to enable the necessary RAM application according to Scenarios 1, 2, 3 and 4 (Alves et al., 2006).

In Scenario 1, an almost covered aircraft is presented. The RAM were not applied on the canopy because it would compromise the pilot’s vision nor on the Radome’s surface because it could attenuate the signal emitted and received by its own radar.

In Scenario 2, in addition to the surfaces uncovered in Scenario 1, the turbine palettes and exhaust were not coated either.

In Scenario 3, in addition to the parts not covered in Scenario 2, the air inlet of the aircraft was not coated, and in Scenario 4, only the horizontal and vertical stabilizers were coated, in addition to the uncoated parts in Scenario 3, the wings, fuselage’s parts and aircraft’s rear.

Figures 2, 3, 4 and 5 illustrate, respectively, Scenarios 1, 2, 3 and 4, where the fighter aircraft is partially coated with RAM, noticing that coated surfaces are yellow and uncoated surfaces are grey.


179Analysis of Radar Cross Section Reduction of Fighter Aircraft by Means of Computer Simulation

Figure 6 and Table 1 show the comparison between the reflectivity measured and simulated with the CST software, and the RAM FC70 used in this study of RCS reduction.

The difference between the theoretical and the measured curve is due to inhomogeneity of the material and the variation in the thickness of the sample.

RESULTS

The preliminary study of RCS reduction on the fighter aircraft, by managed application of RAM, was performed at the frequency of 11.1 GHz, with aspect angles ranging from 0° to 360° with increments of 1°. All simulations were performed in the same frequency because RAM is more sensitive.

All RCS simulations were made using the package Asymptotic Solver (A-Solver), with a Gaussian excitation, vertical polarization and Triangular Mesh. Around 2,000,000 elements were generated. Figure 7 shows a comparison between RCS of the fighter aircraft at the frequency of 11.1 GHz, partially coated and uncoated with RAM, as shown in Fig. 2 (Scenario 1).

Figure 8 shows a comparison between RCS of the fighter aircraft at 11.1 GHz, partially coated and uncoated with RAM, as shown in Fig. 3 (Scenario 2).

Figure 9 shows a comparison between RCS of the fighter aircraft at 11.1 GHz, partially coated and uncoated with RAM, as shown in Fig. 4 (Scenario 3).

Figure 10 shows a comparison between RCS of the fighter aircraft at frequency 11.1 GHz, partially coated and uncoated with RAM, as shown in Fig. 5 (Scenario 4).

Table 2 shows a comparison between peak values of RCS at 11.1 GHz and RCS in the frontal, lateral, and rear portions of the fighter aircraft.

Tables 3 and 4 show values of RCS at 11.1 GHz in units of decibels square meter (dBsm) and square meter (m2), respectively. The average was taken in intervals of 10° for RCS in the frontal, lateral, and rear portions of the fighter aircraft.

Typical radars detect aircrafts with a frontal RCS of 5m2 within 165 km of distance. Using the Eq. 1 and the results from Table 4, the reach of radars after applications of RAM, according to the scenarios presented, was estimated. Table 5 shows the reach of radars before and after applications of RAM.


Table 1. Comparison between the measured and the simulated reflectivity of the RAM FC70.

Frequency (GHz)Reflectivity

Measured Theoretical

8.2 -5.48852 -8.28

11.1 -26.17 -22.60

11.2 -28.06 -22.06

12.4 -16.5625 -16.96

Figure 6. The reflectivity curve of the RAM FC70 used for RCS reduction.

Re

ectiv

ity (d

B)

Frequency (GHz)12.48.4

5

0

-5

-10

-15

-20

-25

-308.8 9.2 9.6 10.0 10.4 10.8 11.2 11.6 12.0

Reference Measured Simulated



Table 2. Comparison between the RCS of the fighter aircraft at 11.1 GHz frequency, uncoated and partially coated with RAM FC70.

Incidence

Peak RCS in dBsm

UncoatedScenarios (Coated with RAM)

1 2 3 4

Frontal 0º 15.20 -5.42 7.79 13.6 17.2

Lateral 90º 33.10 21.40 17.00 16.6 36.7

Rear 180º 30.50 5.41 11.30 15.3 16.2

Figure 7. Scenario 1 – Comparison between the RCS of the fighter aircraft at 11.1 GHz, partially coated and uncoated with RAM.

Rad

ar C

ross

Sec

tion

(dBs

m)

Aspect Angle (Degrees)180-180

50

40

30

20

10

0

-20

-30-135 -90 -45 0 45 90 135

-10

RCS Aircraft uncoated RCS Aircraft partially coated


Figure 8. Scenario 2 – Comparison between the RCS of fighter aircraft at 11.1 GHz, partially coated and uncoated with RAM.

Rad

ar C

ross

Sec

tion

(dBs

m)


50

40

30

20

10

0

-20

-30-135 -90 -45 0 45 90 135

-10



Rad

ar C

ross

Sec

tion

(dBs

m)


50

40

30

20

10

0

-20

-30-135 -90 -45 0 45 90 135

-10



Rad

ar C

ross

Sec

tion

(dBs

m)


50

40

30

20

10

0

-20

-30-135 -90 -45 0 45 90 135

-10


181Analysis of Radar Cross Section Reduction of Fighter Aircraft by Means of Computer Simulation

Table 3. Comparison between the fighter aircraft RCS average at 11.1 GHz frequency uncoated and partially coated with RAM FC70.

Incidence

Average RCS in dBsm


1 2 3 4

Frontal -5º to 5º 10.20 -1.11 5.51 10.80 10.9

Lateral 85º to 95º 26.30 9.70 11.00 16.00 17.50

Rear -175o to 175o 10.50 0.16 2.75 3.03 3.93

Table 4. Comparison between the fighter aircraft RCS average at 11.1 GHz frequency uncoated and partially coated with RAM FC70.

Incidence

Average RCS in m2


1 2 3 4

Frontal -5º to 5º 10.47 0.77 3.55 12.02 12.30

Lateral 85º to 95º 426.58 9.33 12.59 39.81 56.23

Rear -175o to 175o 11.22 1.04 1.88 2.00 2.47

Table 5. Comparison between the detection distance of the fighter aircraft in the frequency of 11.1 GHz to a typical radar using the average RCS, uncoated and coated with RAM FC70.

Incidence

Rmax in km


1 2 3 4

Frontal -5º to 5º 222.5 116.0 169.8 230.3 231.7

Lateral 85º to 95º 562.2 216.2 233.0 310.7 338.8

Rear -175o to 175o 226.4 124.8 144.8 147.1 155.1

After the application of RAM with a theoretical attenuation of 22.6 dB at 11.1 GHz, for Scenario 1, the average of frontal RCS presented around 10 dB of attenuation. While the maximum range of the radar detection fell from 222 km to 116 km, i.e. the aircraft fighter could get 48% closer to the target before executing the mission. For Scenario 2, whose differences are only changing by the exhaust turbine and the application of RAM for metal, the average of frontal RCS presented around 7dB of attenuation. While radar reach fell from 222 km to 169.8 km, i.e. the aircraft fighter could get 25% closer to the target before executing the mission. For Scenarios 3 and 4, the application of RAM proved to be inefficient for frontal RCS reduction.

CONCLUSIONS

Nowadays, for an aircraft to be considered stealth, it shouldn’t be detected in less than 20 km of distance. However, this value can vary considerably depending on the technological advancement of radar detection systems and stealth technology. It is impossible to make much progress attempting to retrofit stealth onto a conventional aircraft because if the shape is wrong, no amount of material absorber treatments will reduce the RCS. Consideration must be given to any part of an aircraft to which a radar wave can reach to, in order to develop a low observable aircraft. On the other hand, the first critical factor to consider in the design process is the shape of the aircraft. This element has been designed into the aircraft from the beginning.



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CATIA, 2010, Manual do Usuário, Available from: www.catia.com. Acessed on: 20 May 2013.

CST, 2013, Manual do Usuário CST Studio Suite versão 2012. Available from: www.cst.com. Acessed on: 20 May 2013.

EMBRAER. T.O. 1F - 5EM - 1, 2005, São José dos Campos, BR.

Gama, A.M. and Rezende, M.C., 2005, “The relationship between Mn-Zn ferrites with different iron ion contents and the absorption energy in X-band”, Proceedings of the IEEE, Vol. 1, pp. 322-325. doi: 10.1109/IMOC.2205.1580009.

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of microwave absorption properties on ferrite volume fraction in MnZn

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and Magnetic Materials, Vol. 323, No. 2, pp. 2782-2785. doi:

10.1016/j.jmmm.2011.05.052.

Knott, E.F., Schaeffer, J.F. and Tuley, M.T., 1993, “Radar Cross

Section”, 2nd edition, Artech House Inc., USA, Chen, W.K., 1993,

“Linear Networks and Systems”, Book style, Wadsworth, Belmont,

US, pp. 123–135.


ABSTRACT: Material coding is the basis for enterprises to perform information management. There are various kinds of materials in the aircraft manufacturing industry. In order to improve the efficiency of aircraft material management, this paper studies the aircraft material coding. The aircraft materials are divided into six categories based on some characteristics. The flexible material coding model is proposed consisting of code fields which indicate the material and code field relations in order to define the constraint relations among code fields. There are three kinds of code fields: class code, property code and flow code, while there are three kinds of relations, including the parallel relation, the subordinate relation and the dependency one. For convenient recognition and operation, the alpha-numeric combination code method is used. The material coding system in which the material code could be automatically generated was finally developed. The system has been applied in the aircraft manufacturing enterprise and it has achieved good results.

KEYWORDS: Aircraft, Material coding, Material classification.

Material Coding for Aircraft Manufacturing IndustryHong Xia Cai1, Ming Yu Dai1, Tao Yu1

INTRODUCTION

In the information age, it is necessary to establish the information system for enterprises to improve their own competitiveness in the increasingly fierce market. Establishing a unified material code of rules is the bottom for enterprises to perform information management. The unreasonable material code would lead to confusion in management. Depending on the rational material coding, we can optimize material management in order to improve the efficiency of management and to reduce the material inventory cost.

It is not, however, an easy work. The study of information classification and coding started in early 1945, in the United States, and the national material coding system was put forward in 1958. Since the 1960s, countries like Romania and Japan put a large amount of manpower and resources into studying material coding. Recently, many scholars have carried many new researches on how to encode material. Yi et al. (2006) and Wang and Wang (2008) introduced coding technology based on the ontology for information integration. Lei et al. (2008) discussed code principle structures and characteristics of material classification with group technology. Jiang (2007), Wang and Wang (2008) and Zhao et al. (2010) studied the flexible structure and the multi-segment code for the Product Data Management (PDM) system and proposed the information coding model. Material coding management is applied in different industries (Li and Xu, 2012; Xiong et al., 2010), and Xiong et al. (2010) suggested that new technologies such as Radio Frequency Identification can be applied in material coding management. Meng and Kong (2013) studied the enterprise material coding in detail, and proposed the purchasing material coding scheme and method,

doi: 10.5028/jatm.v6i2.315

1.Shanghai University – Shanghai – China

Author for correspondence: Hongxia Cai | Shanghai Key Laboratory of Mechanical Automation and Robotics | School of Mechatronic Engineering and Automation | Shangai University | Yanchang Road 149, Zhabei District | 200072 Shanghai/China | Email: [email protected]



184Cai, H. X., Dai, M. Y. and Yu, T.

based on the K3 auxiliary production software. Wang et al. (2011) put forward the flexible classification and coding system for the rapid design of aircraft tooling components. But there have been few researches on the aircraft material coding, especially for the aircraft manufacturing industry. Due to the characteristics of aircraft material and material management requirements, it is of great importance to study aircraft material coding.

ANALYSIS OF AIRCRAFT MATERIAL CODING

Material coding, in a few words, is a material management method which uses symbols or numbers in order to represent specifications or categories of materials, so that it is easy to recognize, track and monitor them. These materials cover not only raw materials and parts, but also semi-finished and finished products, purchased parts, packaging materials, product brochures, even including all the tooling resources, labor insurance supplies and energy for manufacturing or services.

As the special discrete manufacturing industry, the materials in the aircraft industry have their own characteristics: various in kinds and quantities, many self-made parts and design changes, strict quality control and tracking measure.

Lots of materials have special customization demand; they must be producted in a single piece or in small batches in order to meet the requirements. Due to its particularity and complexity, material coding is different in the aircraft manufacturing industry.

The general material coding process is shown in Fig. 1. First, sort out all materials. Describe them using a standard pattern and determine the categories for their classification. Then, according to the material classification and its coding standards, it is established a coding structure and a set of encoding rules are generated. Finally, the coding system is developed, so that the material can be automatically coded to be used in the information system.

Because of the particularity of the aircraft manufacturing industry, its material coding presents some difficulties, as shown in Fig. 1, with the red boxes, which are:

AIRCRAFT MATERIAL CLASSIFICATIONAircraft material classification should consider the variety

of the materials. The material coding process covers almost all departments in the enterprise. The materials have complicated properties, and there is a cross impact between the various materials. How to determine the classification boundary and to make a reasonable classification without any ambiguity to distinguish aircraft material is a difficult task. There are two main classification methods, one is a surface classified method and the other one is line classified. The basic principles of

According tocomplementarity,compability and

similarity of material

Datacollection

Normalizeddescriptionmaterials,

data analysis

Refer to relateddomestic and

international standard

Dene codestructure

Materialclassication Code rules

Materialcoding system

Dene codestandard

Figure 1. Flow chart of material coding.


185Material Coding for Aircraft Manufacturing Industry

classification are meant to ensure that the classification system embraces the features of comprehension, systematization and expansibility. The material code should be separated into several segments. Taking the overall information of material into consideration and arranging them within the different code segments. Taking the natural material attributes as basis. Reserving enough space to code new attributes, in order to ensure the extensibility of material system.

CODING STRUCTURE AND CODING RULESThe coding rules should define how long the code is and

what information should be included. Because there are so many material properties, it is difficult that all attributes are represented in the material coding. Arguments will be arisen in different departments. The important information of the material is taken into account, while the changeable or configuration information should be avoided. When the length is determined, we should consider reserving the space for the demand of the product expansion, enterprise extension, long-term planning, etc. Of course, the setting of such long material code is discouraged.

MATERIAL CODING SCHEME

AIRCRAFT MATERIAL CLASSIFICATIONMaterial classification is the process of seeking common

ground while putting aside differences (Wang and Tong, 2006). It distinguishes and classifies materials through certain principles and techniques, according to properties, attributes or characteristics. In order to make sure high efficiency and convenient maintenance is kept, the material classification should follow the principle of “good compatibility, moderate information capacity, simple and standard”.

Taking one aircraft manufacturing company as an example, the statistics show that the aircraft material library is very large. Nowadays, there are more than 100,000 kinds of materials, among which are raw materials, non-metallic materials and composite materials, and so on. The aircraft materials can be classified in different ways based on the requirements. According to the production requirement, they are classified into three categories: large parts (including the fuselage, wing, engine hanger, etc.), aircraft related material, non-production aircraft material (including

general consumables, tools, and work clothes, etc.). While in inventory management, the classification is: airborne system finished-products, airborne structural components, metal raw materials, parts, standard parts, auxiliary materials and flying materials (referring to the material used on the plane and being taken away on the plane).

The classification methods above are suitable for materials for aircraft manufacture needs, but they do not include all the materials used in the aircraft manufacturing process. The material code is applied to all production and inventory material in the material coding system. Therefore tools, equipments, fittings and others should be contained. Currently, a simple classification is the AB classified control method in the aircraft industry. They are divided into two major groups called A and B: group A is the kind of material for aircraft manufacture needs, such as raw material, parts, standard parts, airborne system finished-products, airborne structural components, and so on, and group B consists of auxiliary materials, including tools, equipment, office suppliers, labor protection, etc.

The main research focuses on a detailed classification of group A. Small classes sharing the same characteristics were grouped into one large class based on the analysis from the early chapter, with normal material classification standard. Thus, metal raw materials, chemical materials, parts and standard parts can fall into one class. Group A is divided into five classes: raw material, airborne system finished-products, airborne structural components, self-made parts and equipment delivered together with the aircraft from suppliers. The aircraft classification is shown in Fig. 2 as follows:

AIRCRAFT MATERIAL CODING STRUCTUREThe material coding structure is generally designed as

subordinate relation or material attribute. Also, it can use drawing number as part of the code. The simplest way to do it is sequential coding, which uses Arabic numbers and/or Latin alphabet letters in sequential order as to identify material, such as the department code. The subornation coding has advantages such as clear structure relations, defined information and being easy to remember, but it does not work well to various types of material. Classification coding is easy to search and control, but hard to determine boundary classification. Sequence code is rarely used alone, because it fails in reflecting the relation between the materials. In recent years, with the increasing study on coding technology, it is



a trend that code structure is flexible in both code length and code layer relation.

There are some influence factors on flexible code structure. Jiang et al. (2008) evaluated the influence factors of material code structure, including code length, features description, structure stability, standard compatibility, organization rationality and structure uniformity, by using an analytic hierarchy process (AHP). The paper concluded that the material code structure should be designed based on the integration of these six factors. Considering one or a few of them and ignoring the other factors will lead to code structure instability. Therefore, the material code structure should be designed in accordance with the enterprise’s material properties and its own requirements. It is not feasible to just copy other enterprises’ coding system.

The most common material code structure is defined as: routine code, occupy code or flow code (Yi et al., 2006; Zhao et al., 2010). Based on the aircraft material classification and on essential factors for structure, the composite aircraft code structure is proposed, which consists of three kinds of code fields: class code, property code and flow code, as shown in Fig. 3.

The meta-model is composed of code fields, and the code field relation, in which the code field is the basic

unit of code, is classified by the meaning of every code segment, and the code field relation defines the constraint relations among code fields. There are three kinds of code fields: class code, property code and flow code. Based on the classification coding principle, each class code, with the same code structure, has a strong correlation, and it includes class value and class name, which cannot be repeated and contains all material object. Property code is to encode material attributes. Flow code is to distinguish the same kind of material with a sequence of numbers. The initial value and step length of flow code is freely defined. Code fields are not independent and they have constraint among them. There are three relationships in our aircraft material coding structure. The parallel relation is simple, like the same level property code. The subordinate relation refers to generating material code in a certain order. The dependency relationship regulates the generation of code values. Direct dependence is where the front class code decides the later code value. Union dependence is that the flow code relies on the class code and on the property code. The property code depends on the class code, called extensive dependency relation.

The coding structure model is the basis of the code rules. This code structure model can meet the flexible coding requirements with high applicability.

Aircra material

Group A

Rawmaterial

Airbonesystem

nished-products

Airbonestructural

componentsSelf-made

parts

Equipmentdelivered

together withthe aircra

from suppliers

Other

Metal raw

material

Non-m

etalic rawm

aterial

Standard parts

Com

posite material

Other

Airbone system

Finished-systemW

ork opackage

Smallnished-

products

... ...

... ...

... ...

... ...

O

ce supplies

Non-m

etalicm

anufacturingprogram

Soundproof cottonm

anufactoringprogram

Machined parts

Tool

Machinery and

electronic products

Group B

Divides byattributes

Divides byparts

Divides byattributesNo sub-class

Divides byinstalled system

Divides byaircra project

Figure 2. Aircraft material classification.



AIRCRAFT MATERIAL CODING RULESMaterial coding should follow some basic principles, such

as uniqueness, integrity, stability, simplicity, etc. The aircraft material coding rules is established based on the material coding principles, in reference to the national relevant material coding’s standard regulations, while considering both the characteristics of the aircraft industry and the aircraft material features.

Generally, there are three coded forms, the number code, the letter code, and the alpha-numeric code. The number and the letter codes have theirs virtues and shortcomings. The letter code has such advantages as explicit material meaning, convenience of recognition, being easy to remember, but complicated and heavy workload when the material library is large. The number code is easy to operate with, it has quick input capacity, and it obviously improves efficiency, however, it is difficult to identify. The hybrid code method, the alpha-numeric encoding form, takes the best of both approaches and is widely used. The aircraft materials are large and include very complex information. For convenient recognition and operation, the alpha-numeric combination code method is used, with numbers from 0 to 9 and letters from A to Z, in random permutation.

Based on the code structure and on the alpha-numeric coding form of aircraft material above, the material coding rule is proposed in Fig. 4.

In the aircraft material code rules, the first class code, the second sub-class code, and the last sub-class code length are two bits. One bit cannot satisfy coding requirements, and it represents poor extensibility. While three bits may bring ill consequences, like over long length and a waste of code space. Property code length is different, and we set it according to the different properties of the aircraft material. Flow code length is determined not only to meet the coding requirements, but also to consider the total code length. Code length with a big gap, will bring management trouble. In the first class code, a small number of equipments from the suppliers use the code “U8” to represent them. And other materials in group B are represented with the number “99”.

The characteristics above about code rules are that all materials can be well indicated according to category, function, features and attributes. It is scientific and reasonable, avoiding material coding missing and ambiguity.

MATERIAL CODING SYSTEM

MATERIAL CODING SYSTEM MODELThe material coding system mainly consists of three parts:

definition of code rules, generation of code value, and code management.

Level 1 code:class code

First classcode

Second sub-class code

(1) (1) (1)

(2)

(3)

ird sub-class code ... ...

Subordinate relantionship

Shape Color Function ... ...

Flow code eld

Parallel relationship

Dependency relationship:

(1) Direct dependence(2) Extensive dependence(3) Union dependence

Level 2 code:property code

Level 3 code:ow code

Figure 3. Material code meta-model.



......

First class code: 01 raw material03 airbone structural components99 Other classU8 equipment those delivered together with the aircra from suppliers

02 airbone system nished-products04 self-maded parts

Flow code

Property code

Last sub-class code

Second sub-class code

First class code

Figure 4. Aircraft material coding rules.

Based on the code structure meta-model, each code field and relation among them should be well-defined in the rule editor. The constraint relationships among code fields should be fixed. Those code rules are put in the code rule library.

Each code should be associated with a corresponding library code. When applying to generate material code, it selects the code rule accordingly, sends the code request to the code generator, and it generates an unique material code. As soon as the code is generated, it is stored in material code library, along with related material information.

Last, the system can enable operators to query or modify code and other management functions in order to maintain conveniece. The aircraft material coding system model is shown in Fig. 5.

KEY CODE TECHNOLOGYThe key technology of designing coding system is the code

metadata model. Based on the code structure meta-model, metadata information model is represented as follows:

The model consists of “Material Object”, “Code Rule”, “Function” and “Operation”. “Material Object” refers to the material and its associated attributes. “Code Rule” includes all defined code rules. “Function” refers to the function of the relation between material and code rule. “Operation” is used to define the code rule’s operations. In the model, code rule and code field include their own elements. Code Fields Constraint is the collection of code field relations. Based on the information model, we can build a good database to manage data. Then put it forward in the system processing flow chart in Fig. 6.

CODING SYSTEM IMPLEMENTATIONTo have a better management of material coding in Enterprise

Information systems for the aircraft manufacturing enterprise, the material code management system is developed, with the Java application platform and the Oracle database technology, based on the Browser/Server structure mode. The system could generate the material code automatically, inquire the material code, search material, and manage material classification. Some main functions and interfaces of the system are discussed below.

Material code applicationInput material information and select material classification

from the drop-down list shown in the page (Fig. 7). Use Ajax techniques to refresh only material classification items instead of

Function model Database

Denite code rule

Modify code rule

Apply material code

Modify material code

Inquire material code

Material code library

Code rule library

Figure 5. Aircraft material coding system model.



the whole page. When the first class is selected, decide whether the sub-class exits or not, and then update dynamically and display sub-class items.

Material code querySelect the search criteria, such as “classification”, “material

code”, “applicant”, show the result in list (Fig. 8). The status is important, which shows the material code is either approved or waiting for approval. And the approver can edit it by clicking the button “approve” or “reject”. Through the column “application path”, the material classification is easy to be identified.

Material query This search interface (Fig. 9) shows material information,

including material code, name and so on. By selecting the search criteria, those materials which need to be managed and edited are found.

CONCLUSIONS

Material coding is of great significance for the enterprise information management. Taking the characteristic of aircraft materials into consideration, the paper studies material coding technology and proposes an aircraft material coding scheme. Firstly, it classifies aircraft material, secondly establishes code structure and code rule, and finally develops the material coding system which has been applied in the aircraft manufacturing enterprise and achieved good results. This study has greatly improved the coding efficiency and provided a classification method for aircraft material coding and powerful support in this aspect.

ACKNOWLEDGMENT

This work is supported by the Shanghai Science and Technology Committee under Grant No. 12dz1124300& 13521103604. The authors are grateful for the financial support, and they also would like to thank the anonymous reviewers and the editor for theirs comments and suggestions.

Input material information,

select material classication

Read material

information

Whether exist or not

Iterate through

material code library

Match code rule

Generate class code

Generate property code

Flow code generator

Generate ow code

Generate material code

Validate code length

Check uniqueness

Generate material code

End

Y

Y

N

N

N

Y

Figure 6. System processing flow chart.



Figure 7. The material code application interface.

Figure 8. The material code query interface.

Figure 9. The material query interface.



REFERENCESJiang, J., 2007, “An Effective Method Based on Granularity-Structure for Organizing and Utilizing Manufacturing Information Resource”, Journal of Northwestern Polytechnical University, Vol.25, No.2, pp. 245-250.

Jiang, J., Wang, J., Guo, L. and Liu, Y., 2008, “Research on Unified Coding Structural Model of Manufacture”, Mechanical Science and Technology for Aerospace Engineering, Vol.27, No.1, pp. 13-18 (in Chinese).

Lei, W., Yang, C. and Li, Y., 2008, “The Material Classification Codes System”, Group Technology & Production Modernization, Vol.25, No.1, pp. 60-63 (in Chinese).

Li, Y. and Xu, D., 2012, “Development of cutting tools information coding rules for the tools management system”, Proceedings - ICIDT 2012, 8th International Conference on Information Science and Digital Content Technology, Vol.2, pp. 397-400.

Meng, X. and Kong, Y., 2013, “Material Classification and Coding based on K3 Manufacture Assistant Software”, Shanghai Chemical Industry, Vol.38, No.2, pp.19-21 (in Chinese).

Wang, J. and Wang, B., 2008, “Research on Application of Ontological Information Coding in Information Integration”, Global Design to Gain a Competitive Edge, pp.147-155. doi: 10.1007/978-1-84800-239-5_15.

Wang, K. and Tong, S., 2006, “Manufacturing Quality Information Classification based on Group Technology and Quality BOM”, IMACS, Multi-conference on “Computational Engineering in Systems Application”, pp. 2160-2167.

Wang, R., Zhou, L. and An, L., 2011, “Research on Flexible Classification and Coding Technology for Airplane Fixture Components”, Mechanical Engineering & Automation, Vol.1, pp. 21-23 (in Chinese).

Xiong, G., Hu, L., Qin, T., Nyberg, T.R., Wang, F. and Shi Q., 2010, “Design and Improvement of the Material Coding Standardization for Power Group Enterprise”, IEEE International Conference on Automation and Logistics, ICAL, pp. 597-602.doi: 10.1109/ICAL.2010.5585353.

Yi, J., Pan, P. and Dong, J., 2006, “Ontology- based PDM coding management middleware”, Computer Integrated Manufacturing Systems, Vol.12, No.6, pp.1821-1826 (in Chinese).

Zhao, H., Liu, J., Dong, Y. and Xu, Z., 2010, “Research on Flexible Coding System Model for PDM System”, Journal of Engineering Graphics, Vol. 31, No. 5, pp. 34-38 (in Chinese).


ABSTRACT: In order to improve the reliability of aviation airline network, this paper presents an empirical analysis on the airline network structure of an aviation company in China from the perspective of complex network, and the calculation result of the statistical features and degree distribution of the network, proves that the network is a small-world network and a scale-free network. Four indicators, i.e. degree, closeness, vertex betweenness and flow betweenness, are utilized for aviation network centralization so as to distinguish the most appropriate method. The influence of nodes in local network is to be measured through the indicators. The results show that vertex betweenness can achieve the best aviation network centralization effect. Specifically, the centrality degree reaches 95.87%. On this basis, the network reliability is analyzed to discover that when two nodes with maximum degree or maximum betweenness are removed, the network performance is reduced by a half. Eventually, countermeasures are proposed for further improvement according to the results. In other words, complex network method is feasible used to analyze the topological structure and statistical features of aviation network. Based on this, a study is conduced to the network reliability and suggestions are proposed for optimizing the aviation network.

KEYWORDS: Complex network, Aviation network, Scale-free property, Reliability.

Reliability Analysis for Aviation Airline Network Based on Complex NetworkDong Bing1,2

INTRODUCTION

Aviation network refers to an airline system constituted by airlines connected in a certain way in a district, serving as a basis for the production and development of the airline company. In the studies on aviation network with network research methods, it is found that the aviation network has relevant statistical features (Guimera and Amaral, 2004; Guimera et al., 2005; Barrat et al., 2004) of “small-world network”. However, most studies (Barrat et al., 2005) focus on the analysis on physical statistical features of aviation network structure and the evolution of overall topological structure, while only a few studies are made on the analysis of route network with social network methods (Porta et al., 2006). Since there are significant differences between aviation network nodes, it is particularly necessary to conduct comparative analyses on relevant nodes and studies on centrality of aviation network. The concept of network centrality can be traced back to the idea of applied statistics in the 19th Century (Gaertler and Wagner, 2001). Typically, different centrality indicators are required for centralization for different types of networks, and the multiple centrality study method needs to be applied in combination with parameters. In China, some scholars adopt other theories and methods to study the centrality of aviation network (Dang and Li, 2011). For instance, some scholars use rank-size model to measure the air transport concentration degree so as to assess the position of hub airports; some scholars mainly adopt the dominant flow method supplemented by squared Euclidean distance method and distance-based cluster method to analyze the level and change of major cities in China in domestic passenger aviation network, based on the air passenger statistical data; moreover, some scholars (Porta et al., 2006) employ the Geographic Information System (GIS) method to study the spatial pattern

doi: 10.5028/jatm.v6i2.295

1.Southwest Jiaotong University – Chengdu/Sichuan – China 2.Civil Aviation Flight University of China – Guanghan – Deyang/Sichuan – China

Author for correspondence: Dong Bing | School of Traffic and Transportation | Southwest Jiaotong University | 610031 Chengdu/Sichuan – China | Email: [email protected]



194Bing, D.

of the domestic aviation network airport system on the basis of air flow data.

With the development of the civil aviation industry in China, the air transport network keeps booming in scale. Nevertheless, it still suffers imperfection, low reliability and low operation efficiency. On the airline company’s side, while planning aviation network, network planners basically conduct decision analysis according to experience. They only take the demand for a single airline as the primary indicator for assessing the necessity of launching an airline. Besides, they often select airlines that are basically the same in route of airlines benefiting other companies while neglecting the network reliability and its overall synergistic effect. The safety and reliability of aviation network exert on an important impact on market competitiveness and economic benefits of an airline company. Therefore, the aviation network should be planned in a systematical manner to improve its overall synergistic effect. A complex network method is proposed to analyze the topological structure and statistical features of aviation network by taking China Southern Airlines (CSA) as an example. Based on this, a study is conduced to the network reliability and suggestions are proposed for optimizing the aviation network of CSA.

COMPLEX NETWORK PROCESSING METHODS AND RELATED RESEARCH

As a small world model and a scale-free network model were proposed in the end of the 20th century, the complex network gradually became the research hotspot in different discipline. In order to facilitate the study of complex network effectively, all kinds of research software are introduced, such as Pajek, Ucinet, NetworkX and NetMiner 3.

In this paper, Ucinet is used for airline network. Ucinet is a social network analysis program developed by Steve Borgatti, Martin Everett and Lin Freeman. The program is distributed by analytic technologies. The software Ucinet involves, in the network analysis, programs such as community discovery and region analysis, ego network analysis and the hole structure analysis and so on. It also contains a large number of analysis programs, such as cluster analysis, multidimensional scaling, singular value decomposition, factor analysis and correspondence analysis, the role and status analysis, including structure, and the role and regular equivalence.

In this paper, we take airline passenger flow data as samples, the city for the network nodes, routes between cities as the network edge, aviation passenger flow between cities as

the mapping relationship between node and nodes in the network structure and construction of air traffic network, as shown in Fig. 1:

Figure 1. Network structure and construction of air traffic network.

vertex

edge

Based on Ucinet software, two kinds of simulation systems were introduced (Hongguang and Liping, 2012), the deliberate targeting system and random interference system are designed, and some simulation experiments are done. The structure diagrams of air passenger flow network are plotted with the software (Dang and Li, 2010) and analyzed from the perspective of structural characteristics, degree of distribution and network centrality. However, traditional research methods fail to thoroughly identify the complexity of aviation network spatial relations between airports in a proper way. Centrality tests for network nodes are important means for judging the importance of nodes in the network, adjusting the aviation layout, and optimizing resource allocation, which, in particular, is greatly significant for safety of aviation network.

EMPIRICAL DATA ACQUISITION AND PROCESSINGIn this paper, the domestic and international flight data

from CSA´s data centre, in 2010, are taken as samples. Usually, cargo flights are arranged at night and the characteristics of transport cargo flights are different from passenger flights. In this article, cargo flights will not be considered. According to the model, let the airport as network node, the direct airline as network edge, and the number of navigable flights among airports as weight of edge, which constituted a weighted aviation network of CSA. Furthermore, adjacency matrix (Kij)nxn (n refers to the number of nodes) is used in order to represent


195Reliability Analysis for Aviation Airline Network Based on Complex Network

the aviation network, Kij, in the matrix, refers to the number of flights from Airport i to Airport j. Due to data limitation, the network in this paper is an undirected one. That is, out-degree and in-degree are not involved.

STATISTICAL FEATURE AND THE DEGREE OF DISTRIBUTION

According to the statistics, there are 187 nodes and 1,245 edges. It is thus evident that the aviation network is concentrated and its structure is relatively complex. To show network hierarchical relationship, a backbone aviation network of CSA is built. The node weight threshold is taken as 500 in the weighted network. According to the statistics, there are 99 nodes and 369 edges.

In the complex network theory (Newman, 2003), the indicators reflecting the statistical features of network structure are mainly degree of nodes, average degree, average path length, density, clustering coefficient, etc.

The relation matrix (aij) is built for the aviation network, in which aij indicates the relation of flight numbers between city i and city j: if there are any flights between city i and city j, aij = 1; otherwise, aij = 0. Upon calculation, the statistical feature indicators of 2010 aviation network of CSA are listed in Table 1.

the network. According to Table 1, the average degree is 6.684, indicating that, in average, each city node is connected to other 6.684 city nodes.

Table 1. Statistical features of 2010, CSA aviation network.

Project Statistical Value

1 Number of Nodes 187

2 Degree of Node 1250

3 Average Degree 6.684

4 Average Path Length 2.558

5 Network Density 0.0358

6 Clustering Coefficient 0.592

Table 2. Top 10 degree values of CSA airports, in 2010.

Airport degree

1 Guangzhou 106

2 Shenzhen 48

3 Beijing Capital 48

4 Urumqi 46

5 Changsha 42

6 Dalian 39

7 Zhengzhou 38

8 Shenyang 37

9 Pudong 34

10 Wuhan 32

The degree of a node in an aviation network refers to the number of airports having direct flights with this airport (node); greater degree of a node means greater importance to some extent. Table 2 shows CSA ten airports with top degrees in 2010, among which Guangzhou ranks the first of airport degree as an airline hub of CSA. The average degree of a network refers to the average value of all degrees of nodes in

Generally, the distance between two nodes is defined as the number of edges of the shortest path between two nodes, and the average path length of a network is the average value of the distances between all node pairs. In an aviation network, the distance describes the path from one airport to another one using the minimum transit times; the shorter the distance is, the less transit times are required. Meanwhile, the average path length stands for the depth of the air transport, which is a property of the transport shortcut in the integral network; the shorter the average path length is, the less transit times are required between any two airports, bringing more convenience for the passengers. In 2010, the average path length of CSA aviation network is 2.558, which means only 1.558 transit times are required for transporting from one airport to another, favorably meeting the air transport demand.

An undirected network density is defined as the ratio of actual connection number to the maximum possible connection numbers in the figure. In an aviation network, it describes the ratio of actual number of opened segments to the number of all possible segments. The density indicates the closeness of the air connections among all cities in the network. The value is taken between 0 and 1; if the value is closer to 1, the network structure is more perfect and the connections of the air transport


196Bing, D.

are closer. In 2010, the density of aviation network is 0.0358, which is relatively small, indicating that the connections are not very strong among CSA airports.

The clustering coefficient of a node in the network stands for the ratio of the actual connection number to the maximum possible connection edges between this node and its adjacent nodes. In an aviation network, the clustering coefficient of a node indicates the average cluster degree of the local network comprised of the airport and its adjacent airports. Higher clustering coefficient means greater cluster degree of the local network, and smaller impact of this node on the adjacent airports; on the contrary, lower value means more dependence of the adjacent airports on this node. Guangzhou Airport has a clustering coefficient of 0.06, which is the smallest of all, indicating that the adjacent airports are highly dependent on Guangzhou Airport, and large numbers of flights will be affected if failure occurs in Guangzhou Airport. The clustering coefficient of the integral network is the average value of the clustering coefficients of all city nodes. As shown in Table 1, CSA aviation network, in 2010, has relatively small average shortest path length and large clustering coefficient; hence this aviation network belongs to a small-world network.

Betweenness is generally defined as the capability of a node for controlling the connection of other node pairs, i.e., the effect of a node acting as a bridge between other node pairs. Higher betweenness of a node indicates stronger effect of the node as a bridge, and more important role in the network. See Table 3 for

the betweennesses of top 10 airports of CSA Aviation Network in 2010, based on which Guangzhou Airport, as a CSA hub, has the maximum degree and betweenness, taking up the most important position in the network. Secondly, Urumqi, Beijing Capital and Shenzhen Airports also play prominent roles as bridges. Specifically, Pudong Airport ranks third in the betweenness value; although its degree value is not particularly high, it is still a significant transit in the network. If a fail occurs in important transit airports mentioned above, the connections will be greatly affected between other nodes.

The degree distribution of the nodes in the network can be described in the power-law distribution function;

p(k)=k-α (1)

The power-law distribution coefficient α shows the degree distribution characteristics of a network(Newman, 2003). The distribution p(k) is the probability of a randomly selected node of degree k.

Power-law distribution is also known as scale-free distribution; a scale-free network is a network whose degree distribution follows a power law. In the log-log plot, a straight line with negative slope can be obtained by conducting a linear fitting for the degree distribution, and the absolute value of slope is the power exponent. If the absolute value is relatively small, this network is scale-free. Correlation coefficient indicates the fitting degree of the curve; higher correlation coefficient means the curve is more favorably fitted, explaining actual problems more adequately. The degree distribution of undirected network is discussed here. By applying a double-segment fitting in the log-log plot, we obtained the power exponent and correlation coefficient variance of the degree distribution (Table 4), and degree distribution (Fig.2) of CSA aviation network in, 2010.

Based on Table 3 and Fig. 2 , at the significance level of α=0.0001, Segment 1 and Segment 2 both have excellent fitting, with correlation coefficient exceeding 0.96. Since Segment 1 and Segment 2 both have relatively small power exponents, the degree distribution of CSA aviation network is subject to double-segment power-law distribution, and thus this network is scale-free. Hence, the nodes in the aviation network are heterogeneous, certain nodes (hubs) have large numbers of connections, playing the dominant role in the network, while other large number of nodes only have small numbers of connections and are located on the edge of the network, according to the Matthew Effect.

Table 3. Betweenness values of top 10 airports of CSA aviation network, in 2010.

Airport Betweenness Values

1 Guangzhou 51.414

2 Urumqi 25.782

3 Pudong 9.61

4 Beijing Capital 8.418

5 Shenzhen 6.21

6 Harbin 5.828

7 Shenyang 5.68

8 Kunming 4.811

9 Dalian 4.571

10 Changsha 4.224



Where, W refers to the whole network, represents the centrality value of the node with the largest centrality degree.

According to the equation, if the centrality of all nodes is the same, namely the network has no center, then . In case the centrality degree of only one node is of 1 and that of other nodes is 0, will be greater, and the handful of center nodes will be more prominent, which shows that the larger the centrality difference between network nodes, the higher the centrality indicators of the handful of center nodes. Thus, the accuracy of center nodes will be higher, and so will the centrality degree.

Different network centrality (Friedkin, 1991; Newman, 2005) degrees can be figured out by substituting degree, closeness, vertex betweenness and flow betweenness into the following equations respectively.

(3)

Where CD(x), CC(x), CB(x), and CFB(x) represents the degree, closeness, vertex betweenness and flow betweenness indicator values of network nodes, respectively. In contrast, the degree indicator is more suitable for measuring the influence of nodes in local network. In global scope, however, the closeness indicator needs to be referenced. The two indicators are only applicable to static network analysis, while the betweenness indicator is more suitable for analysis of dynamic network. When the degree, closeness, vertex betweenness and flow betweenness indicators are used for aviation network centralization based on the formula above, the centrality degree under the different indicators is shown in Table 3.

Obviously, the centrality degree varies with the selected centrality indicators for network centralization. The centrality degree of closeness is relatively low, which indicates that closeness is not suitable for aviation network centralization. Moreover, the

Figure 2. Node degree distribution of CSA aviation network, in 2010.

2

slope= -0.55094

Kβ=27

logP

(k)

0log k 2

slope= -1.53157

0

Table 4. Feature values of degree distribution of domestic flight network.

ProjectStatistical

Value

1 Number of Nodes(N) 187

2 Degree of Nodes(k) 1250

3 Fitting of Segment 1

Power Exponent(|α|) 0.55094

Correlation Coefficient(|ACC|) 0.55094

4 Fitting of Segment 2

Power Exponent(|α|) 1.53157

Correlation Coefficient(|ACC|) 0.96051

COMPARISON OF CENTRALITY DEGREE UNDER DIFFERENT INDICATORS

Relevant network centrality indicators serve as the basis for measuring the centrality degree of the network. On the assumption that centrality indicators have been defined in Network CA with n nodes, the centrality degree of the network will be defined as follows (Costenbader and Valente, 2003):

(2)


198Bing, D.

top 10 nodes in the aviation network are selected to catch the distribution of indicator values for each node, as shown in Fig. 2. Therefore, degree and closeness fail to obviously distinguish nodes, while the difference of distributions of betweenness is relatively large. Through calculating the centrality degree of each indicator, and comparing the cumulative distribution of centrality data of top 10 nodes, vertex betweenness is the most suitable indicator for aviation network centralization.

NETWORK RELIABILITY ANALYSISThere are a lot of network attack means in reality (Barrat et al.,

2005; Holme et al., 2002; Kai-Quan et al., 2012; Li and Cai, 2012), where the random attack and the hostile attack are relatively representative, and the hostile attack is very destructive. As it was demonstrated before, the aviation network of CSA is a scale-free one, with associate scale-free properties, such as “Stable and Fragile” in attacks, which means it has a very strong flexibility in random attacks or unexpected malfunctions while it is very fragile in hostile attacks (Xiaohuan Wu et al., 2013).

The most important indicators to characterize the network topologic structure are the average path length and the clustering coefficient. The average path length in the aviation network represents air transport depth, the clustering coefficient represents air transport width, and the network efficiency represents overall coordination of the network. With a smaller average path length of the network, a bigger clustering coefficient, and higher network efficiency, it is indicated that the network has a better performance and a stronger fault-tolerant capability. Therefore, this paper will have the reliability analysis on the current network of CSA with the three indicators, and the calculation formulas are as follows:

(4)

In Eq.(4) N stands for network node number, dij stands for the shortest distance from Node i to Node j.

Ci=2Ei/(ki(ki-1)) (5)

In Eq. (5) ki stands for the number of edges directly connecting with airport i, and Ei stands for the number of existing connecting edges between airports in number of ki.

(6)

In Eq.(6) with 0≤E≤1 . When E=1, the network is completely connected; and when E=0, all nodes in the network are isolated.

Airport nodes in the aviation network of CSA are sorted according to the degree and the betweenness value in descending order, and then the airport based on a relatively big degree and that, based on a relatively big betweenness, are removed orderly, the average path length, the clustering coefficient, and the network efficiency of the network are calculated respectively, and changes of the average path length, the clustering coefficient and the network efficiency corresponding to the decrease of the airport number are counted and compared, then the contents indicated in Fig.4, Fig. 5 and Table 5 are respectively obtained.

As indicated by Figs. 3 and 4, the fluctuation of average path length is relatively strong when several nodes are removed, and the fluctuation of average path length based on the remove policy of degree priority is stronger than the one based on the policy of

Figure 3. The cumulative distribution of centralized data under four indicators of the top 10 nodes.

0.16 Flow Betweenness

Degree

P

0

Rank2

Closeness

1

0.18

0.140.120.100.080.060.040.02

43 65 87 109

Vertex Betweenness

Figure 4. Comparison of average path length changes.

21

Ove

r age

pat

h le

ngth

43 65

Betweenness Priority LBetweenness Priority L

Removed nodes



Figure 5. Comparison of clustering coefficient changes.

Betweenness Priority C

021

0.8

0.6

0.4

0.2

43 65

Degree Priority C

Clu

ster

ing

coe

cien

t

Removed nodes

Table 5. Centrality degree under the different indicators of CSA aviation network.

Indictor Centrality Degree

1 Degree CD 83.12%

2 Closeness CC 42.50%

3 Vertex Betweenness CB 95.87%

4 Flow Betweenness CFB 93.12%

Table 6. Comparison of network efficiency changes based on two different remove policies.

Test Procedure

Based on Degree Priority Based on Betweenness Priority

Original Network Backbone Network Impacted Flights

Original Network Backbone Network Impacted FlightsNode Edge Efficiency Node Edge Efficiency Node Edge Efficiency Node Edge Efficiency

Before Test 187 1245 — 99 369 — — 187 1245 — 99 369 — —

Remove 1 Point 186 1035 -11.21 98 267 -34.68 18.6 186 1035 -10.35 98 267 -34.68 18.6

Remove 2 Points 185 939 -18.36 97 228 -51.46 28.4 185 945 -17.54 97 237 -48.73 25.42

Remove 3 Points 184 847 -25.50 96 188 -60.78 37.9 184 882 -23.26 96 217 -56.83 34.26

Remove 4 Points 183 761 -30.64 95 160 -69.47 40.1 183 790 -29.01 95 177 -64.30 39.62

Remove 5 Points 182 685 -33.73 94 136 -76. 31 43.3 182 700 -31.43 94 142 -72.46 40.05

priority of betweenness. The average path length increases to a largest extent when Guangzhou Airport node with the greatest degree and the highest betweenness is removed, then it decreases afterwards because the network is divided into multiple connected sub-graphs, among which no connection exists. The average

path length decreases after increasing, which is a shortcoming for measuring the network performance.

However, overall, it shows that after the node with the greatest degree or the highest betweenness has been removed, the average path length fluctuates widely, which causes the network instability. As it is indicated in Fig. 4, the clustering coefficient decreases after several nodes are removed, and the decrease of the one based on the remove policy of degree priority is slightly larger than the one based on the betweenness priority; therefore, the clustering coefficient decreases and the clustering degree becomes smaller when the node with the greatest degree or highest betweenness has been removed from the network.

As shown in Table 6, when 1~5 nodes with the greatest degree and highest betweenness are removed, the network efficiency suffers a relatively great impact and the backbone network suffers an even greater one, because both of the removed airlines and the flights are of huge numbers in the original network. Especially, when 5 nodes are removed, the efficiency of the original network is decreased by more than 30% and the backbone network decreased by over 70% and the whole network is almost paralyzed. In addition, the decrease based on the remove policy of degree priority is larger than that based on the betweenness priority. In order to understand the network change condition deeply, taking the test of removing two cities one-time for example, we conduct specific analysis on the network performances, based on the two kinds of remove policies before and after the test. Before the test, backbone network of the CSA


200Bing, D.

contains 99 nodes and 369 routes; when two airports with the greatest degree value (Guangzhou Airport and Shenzhen Airport) have been removed according to the first policy, 306 routes disappear, the original network efficiency is decreased by 18.36%, the backbone network efficiency is decreased by 51.46%, and the network performance is decreased by half. When two airports with the highest betweenness (Guangzhou Airport and Urumqi Airport) are removed according to the second policy, 300 routes disappear, the original network efficiency is decreased by 17.54%, and the backbone network efficiency is decreased by 48.73%. Its decrease is slightly smaller than that of the remove policy based on the degree priority.

When two airports with the greatest degree are removed, the number of routes of the whole network in south-to-north direction is decreased substantially; when two airports with the highest betweenness are removed, most airports in the western area become isolated nodes, holding up flights, and the overall network performance is decreased by half or so. However, the network performance decreases approximately by about 25% (Dang and Li, 2011) when two nodes are removed in the America aviation network, indicating that the southern aviation network is relatively fragile when facing a selective attack and the overall network reliability is expected to be enhanced. Apart from the selective attack, the current network layout of CSA can cause heavy strike to the aviation transportation when facing random attacks, including natural disasters. Therefore, while enlarging the scale of network development, CSA should also develop its aviation network into a multi-hub system. Judging from the degree value and betweenness value of the airport node, number of airlines removed from the network, and the network efficiency decrease condition, in addition to taking Guangzhou Airport as the core hub, CSA can further plan and revise its aviation network by defining Beijing as the important hub between Europe, America and the inland of China, defining Urumqi as the regional hub between the middle Asia and the inland of China. On the other hand, the Company can promote connections in east-to-west direction, for example, establishing connections between the west and the east centered in Zhengzhou to fill up the blank in this direction. In this way, the overall performance and reliability of the network can be enhanced, ensuring fluent running of the air transport system.

CONCLUSION

In this paper, complex network theory is applied, an aviation network structure model is built for CSA, and its structure is analyzed. It is discovered that Guangzhou Baiyun Airport is the hub of the aviation network of CSA, the majority of airlines are in the south and north directions, the airlines in Western Region are distributed around Urumqi in a radial way. Moreover, analysis is conducted on the distribution of statistical features and degree distribution of the network, and it is proven that the aviation network of CSA is a small-world network and a scale-free network. Based on this, the reliability of the network is analyzed according to the degree and betweenness of nodes in the network. The results show that the backbone network performance will be reduced to a half, once 2 nodes are removed and it basically breaks down once 5 nodes are removed. The overall reliability of the network is far from high. Therefore, CSA should put more efforts in the overall programming of the aviation network as well as the construction and management of the aviation hub, seek to develop as an multi-hub airport, launch more flights in west and east directions, reasonably allocate air transport resources, and improve the overall performance of the network so as to meet the demand of sustained and healthy development of air transport of CSA.

The Complex network method is used to analyze the topological structure and statistical features of aviation network. Based on this, the suggestions are proposed for optimizing the aviation network from the analysis of the network reliability the application of the methodology (airport planning, fleet sizing, route planning, etc) isn’t mentioned in the paper. They will be discussed in other papers.

ACKNOWLEDGEMENTS

This work is supported by the Joint Funds of the National Natural Science Foundation of China and the Civil Aviation Administration of China (Grant No.U1233105), partially supported by grant J2010-03 of Science and Technology Fund of CAFUC.



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Barrat, A., Barthélemy, M. and Vespignani, A., 2005, “The Effects of Spatial Constraints on the Evolution of Weighted Complex Networks”, Journal of Statistical Mechanic, doi:10.1088/1742-5468/2005/05/P05003.

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Guimera, R., Mossa, S., Turtschi, A. and Amaral, L.A.N., 2005, “The Worldwide Air Transportation Network: Anomalous Centrality, Community Structure, and Cities’ Global Roles”, Proceedings of the National Academy of Sciences of the United States of America, Vol. 102, pp. 7794-7799.

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ABSTRACT: Unit load devices (ULDs) are pallets and containers which are used to carry air cargo, mail and passenger baggage on wide-body aircraft. This paper analyses two important recent developments in the suppression of fires on freighter aircraft – fire resistant containers and fire containment covers for palletised cargo. In July 2013, United Parcel Service (UPS) placed an industry-first order for 1,821 fire-resistant shipping containers; this represented a major milestone in aviation history, offering unprecedented protection from intense fires on the airline’s freighter aircraft. The new fire-resistant containers have been designed to withstand intense fires of four hours duration or longer, in order to provide pilots with sufficient time to land their aircraft during such an emergency. Fire containment covers have also been developed to enhance safety on freighter aircraft by isolating individual palletised cargo under special fire retardant covers that are capable of withstanding the intense heat and explosions associated with the ignition of dangerous goods.

KEYWORDS: Aircraft unit load devices, Air cargo, Fire containment covers, Fire resistant aircraft containers, Freighter aircraft, Innovation.

Fire Resistant Aircraft Unit Load Devices and Fire Containment Covers: A New Development in the Global Air Cargo IndustryGlenn Baxter1, Kyriakos Kourousis1, Graham Wild1

INTRODUCTION

Innovation initiatives are becoming exceptionally important for firms seeking greater competitiveness (Maldonado et al., 2009). In the global air transport industry, an important area of innovation in recent times has been in aircraft unit load devices (ULDs). Unit load devices are pallets and containers which are used to load passengers’ baggage, air cargo and mail onto a wide-body aircraft (Lu and Chen, 2011). The International Air Transport Association (IATA) estimates that there are currently around 700,000 ULDs in use by the world’s airlines, with an estimated replacement value of close to approximately $USD1 billion (IATA, 2013a).

As a by-product of the aeronautical design, space is created underneath the wide-body aircraft’s passenger deck, where passenger baggage, postal mail and air cargo can be carried (Dempsey and Gesell, 1997). With the introduction of wide bodied aircraft, such as the Boeing B747 and the McDonnell Douglas DC10 aircraft in the 1970s, a large volume of space was required to be filled in the lower deck belly holds of passenger flights, and a faster method of loading and unloading needed to be introduced (Morrell, 2011). Consequently, airlines utilise special containers (aircraft unit load devices) that are designed to fit the lower deck belly holds of their wide-body passenger aircraft, together with specially designed containers to fit the main deck of their freighter aircraft (Baxter, 2011; Morrell, 2011). The utilization of ULDs assists airlines in the standardization and unitization of loading and discharging passenger baggage and air cargo at airports (Lu and Chen, 2012).

doi: 10.5028/jatm.v6i2.327

1.RMIT University – Melbourne/VIC - Australia

Author for correspondence: Glenn Baxter | RMIT University | School of Aerospace, Mechanical and Manufacturing Engineering, Building 57, Level 3, 115 Queensberry Street - Carlton South/VIC - Australia 3053 | Email: [email protected]



203Fire Resistant Aircraft Unit Load Devices and Fire Containment Covers: A New Development in the Global Air Cargo Industry

Nearly 60 per cent of world air cargo traffic is carried in freighter aircraft (Boeing, 2013). Wensveen (2011) notes that a freighter aircraft is an aircraft that is expressly designed or converted to carry air cargo, express, and so forth, rather than passengers. Most air cargo is carried in containers or ULDs, with the remainder transported loose in dedicated aircraft lower deck belly-hold compartments (Nobert and Roy, 1998).

Freighter aircraft are operated by the integrators, such as FedEx and United Parcel Service (UPS), dedicated all-cargo carrying airlines, for example, Luxembourg-based Cargolux, and combination passenger airlines that operate freighter aircraft in addition to their scheduled passenger services, for instance, Lufthansa or Korean Airlines. The integrators services are underpinned by the use of dedicated, efficient global multi-modal networks – they own and operate most of their own aircraft, smaller aircraft, trucks, and automated handling and storage facilities (Milenković, 2001). FedEx and United Parcel Service (UPS) are the world’s two largest integrators (Bowen, 2012).

The main deck of a freighter aircraft has no amenities and utilises a roller deck to move cargo on and off the aircraft quickly so as to optimise the aircraft turnaround time. A roller deck is an aircraft main deck that is equipped with rollers on the floor, which enables palletized or containerised cargo/ULDs to be pushed or moved into position (Coyle et al., 2011).

In November 2012, the United States National Transportation Safety Board (NTSB) announced recommendations to reduce the harm from fires aboard freighter aircraft. The NTSB recommendations followed three fire-related freighter aircraft accidents that had occurred around the world since 2006 (Jansen, 2012; Sperry, 2012). In response to these recommendations and to suppress fires on freighter aircraft, new innovative fire-resistant containers and fire containment covers for palletized cargo have been developed and introduced into service in the world air cargo industry.

THE ROLE OF AIRCRAFT UNIT LOAD DEVICES IN AIRLINE OPERATIONS

In addition to the ULDs used to carry passenger baggage, airlines use special ULDs for the carriage of air cargo and postal mail, such as containers and pallets. An aircraft pallet

and its net and/or igloo, and/or aircraft container, enable individual pieces of air cargo and passengers baggage to be assembled into a standard size unit which expedites the loading and unloading of aircraft having compatible handling and restraint systems during the aircraft’s ground turnaround time (Allaz, 2004).

Airlines can select customized types of ULDs for matching the inner contours of the main and lower decks of various wide-bodied aircraft sizes. Since ULDs are also compatible with a variety of wide-body aircrafts, airlines generally seek the benefit of commonality to purchase as many similar types of ULDs as possible (Lu and Chen, 2012).

The International Air Transport Association (IATA) recognizes a set of standardized ULDs in the form of open pallets, igloos and ULD containers (IATA, 2013b). These ULDs are designed to be compatible with a number of aircraft types and are compatible with cargo terminal handling systems, the airport apron, and the aircraft loading equipment (Ashford et al., 2013).

The dimensions (and capacities) of the aircraft container ULDs used by the world’s airlines depend on the aircraft flown and are often closed aluminium devices. Their size and shape depend on whether they are loaded onto lower or upper aircraft decks and whether they have been designed for use on narrow or wide-bodied aircraft (Reynolds-Feighan, 2013). The Airbus A319, A320 and A321 aircraft, for example, offer airlines a containerised baggage/cargo system that saves on the aircraft ground handling turnaround time (Airbus, 2013). Some airline ULDs are customised to fit specific aircraft types, whereas others as noted are compatible with multiple aircraft types (Nobert and Roy, 1998). Also, some containers/ULDs are refrigerated (Reynolds-Feighan, 2013). For example, LD8 containers can only be utilised on Boeing B767 aircraft (Nobert and Roy, 1998), whereas LD9s can be used on Airbus A330, A340 and A380 as well as Boeing B777, 747 and 787 passenger aircraft. LD9s can also be used on Boeing MD11, B777 and 747 freighter aircraft.

The major types of ULDs used by the world’s airlines are as follows:

Containers: Rigid bodied containers are used by airlines to protect air cargo (and passenger baggage) and to ease the handling of multiple small, individual consignments of air cargo.

Pallets: Pallets are devices providing a rigid base, suitable for forklifting, on which air cargo consignments can be loaded. The load is secured by nets, and the complete load can be


204Baxter, G., Kourousis, K. and Wild, G.

manhandled, forklifted, or moved mechanically as a unit (Ashford et al., 2011: 465). Similar to container ULDs, pallet size and shape/contour will vary on the aircraft operated by the airline. Pallets can assist an airline to accommodate larger size consignments (Reynolds-Feighan, 2013). Wide-bodied aircraft can accommodate pallets with standard dimensions of 96 x 125 x 64 inches (244 x 318 x 163 cm) within their lower deck belly holds (Ashford et al., 2011). Dedicated freighter aircraft can also accommodate pallets with a 96 x 125 inch base, but up to 96 inches (Q6 Contour) or 118 inches (Q7 Contour) (244 x 318 x 243 cm or 244 x 318 x 300 cm) in height on their main deck (CPC, 2012).

Igloos: Igloos are rigid bodied pallets used principally to prevent damage to air cargo or to the interior of the aircraft, where passenger cabins are converted to all-cargo use. A structural igloo is a fully enclosed shell constructed integrally with a pallet base to ensure that cargo conforms to the required aircraft contours. The shell and the pallet base form a single structural unit. A non-structural igloo is a bottomless shell that is designed to fit over an aircraft pallet and to provide shape to loaded cargo. The shell is used in conjunction with the aircraft pallet but does not provide any structural strength (Ashford et al., 2011).

FIRE RESISTANT AIRCRAFT UNIT LOAD DEVICES

BACKGROUNDAs noted earlier, in November 2012, the United States

National Transportation Safety Board (NTSB) announced recommendations to reduce the harm from fires on board freighter aircraft. The NTSB recommended that the United States Federal Aviation Administration (FAA) require active fire-suppression systems in all cargo compartments of aircraft, the development of fire resistant containers/ULDs, and also to improve the early detection of fires within cargo containers (ULDs) and pallets (Jansen, 2012; Sperry, 2012).

The NTSB recommendations followed three fire-related aircraft accidents that had occurred around the world since 2006. In two of the accidents, fires on the aircraft resulted in the deaths of the flight crew and the loss of the aircraft (UPS Boeing B747-400F freighter aircraft in Dubai, UAE in 2010 and an Asian Cargo Boeing B747-400F freighter

aircraft that crashed into the ocean off South Korea in 2011) (Jansen, 2012). The third accident occurred in a UPS aircraft at Philadelphia Airport in 2006, in which the crew was able to escape from the aircraft with some minor smoke-inhalation difficulties, but the aircraft was significantly damaged by the fire (Sperry, 2012).

The accident investigations revealed that, during its early stages, a fire burning inside a cargo container or ULD is often concealed. Subsequently, when a fire grows, it quickly burns through the container to threaten the aircraft and its crew (Jansen, 2012). In the three accidents, the fires started within the cargo containers aboard the aircraft, but by the time the aircraft’s fire warning system alerted the pilots to the dangers of the fires, there was little time for them to react (Sperry, 2012).

In the United States, Federal regulations require the fire detection systems on cargo airline’s aircraft to alert pilots within one minute of a fire starting. However, the NTSB’s investigation discovered that current systems detected fire and smoke anywhere from two and half minutes to more than 18 minutes after the fire started on the aircraft (Sperry, 2012).

The NTSB concluded in its report that cargo containers/ULDs made of flammable materials significantly increase the intensity of the on-board aircraft (Sperry 2012).

The aviation industry, responding to these requirements has taken several actions to design safer equipment. It should be noted that one of the first attempts to mitigate the fire safety issue has originated from the Pan Am flight 103 accident (Ushynskyi, 2009). In particular, in a 1993 FAA report focusing on design of explosion resistant containers, particular requirements for fire and explosion suppression covers were defined (FAA, 1993).

The International Organization for Standardization (ISO) TC20/SC9 ‘Air cargo and ground equipment’ committee has initiated a project on defining a standard for fire safety of air cargo containers (FAA, 2013). Under this task the following standards are going to be developed and published in the coming period:• ISO 14186:2013 specifies the minimum design and

performance criteria and testing methods of fire containment covers (FCCs): either in those cargo compartments of civil transport aircraft where they constitute one means of complying with applicable airworthiness regulations, or secondly, on a voluntary basis, when deemed appropriate by operators to improve



fire protection in aircraft cargo compartments where airworthiness regulations do not mandate their use (International Organization for Standardization, 2013b).

• SAE AS6453. This SAE Aerospace Standard (AS) is identical to the ISO 14186 standard

• ISO/SAE standard for Fire Resistant Containers (FRC). The FAA is going to develop a unique ‘Technical Standards Order’ (TSO) referencing this standard (FAA, 2013). The SAE AS6453 and ISO 14186:2013 for Fire Containment Covers (FCC) standards are identical in content, covering covers used in cargo compartments of civil transport aircraft. FAA will include this in the relevant Technical Standards Order (TSO) – TSO C90 (FAA, 2013).

BASICS OF AIRCRAFT FIRESTypes of fires

There are four types of fire, classified by the fuel source, which are identified by alphabetical labels. • Class A: ordinary combustible materials, wood, cloth,

paper, or upholstery materials, etc.• Class B: flammable or combustible liquids, petroleum,

greases, solvents, or paints, etc. • Class C: energised electrical equipment.• Class D: metals (Dornan 2008).

These are the US identifiers, Europe and Austrália use different letters. Different types of fire require the use of different types of fire extinguishers, specifically, the substance used to extinguish the fire, and whether it removes the fuel, oxygen, or the heat source (or a combination of these).

Fires zonesA fire zone is an area or region of an aircraft that is designed

by the manufacturer to require specific fire protection. As with fire types, fire zones are also identified by alphabetical labels (Flynn, 2001; Tooley and Wyatt, 2009): • Class A – is the cockpit, cabin, or cargo area, where the fire

can be visually detected, reached and combated by crew, • Class B – is a cargo area, where the fire can be reached

and combated by crew, and is detected by a remote fire detection system,

• Class C – is an engine or cargo area, where automated systems are required for both the detecting and extinguishing of the fire,

• Class D – is a cargo area, on passenger aircraft, where the crew cannot reach fire, but the fire is confined within a minimally ventilated fire resistant area (no longer used),

• Class E – is a cargo area on a cargo only aircraft, where the crew cannot reach the fire and ventilation controls are required.

Fire Suppression SystemsWhile an aircraft has essential fire suppression systems

for the engine and for the lavatories, of interest to this work are portable fire extinguishers and hold fire suppression systems. Portable fire extinguishers are used in Class A and B fire zones, where the crew can use them to actively combat the fire. As with their terrestrial kin, all types of portable fire extinguishers are red in colour, with a different colour label, and each is suited to different types of fires, having a specific effect.• Red label: water, which is suitable for Class A fires,

removing the heat.• Blue label: dry powder, which is suitable for Class B fires,

isolating the oxygen. • Black label: CO2, which is suitable for Class C fires,

isolating the oxygen.• Green label: Halon 1211, which is suitable for fires of all

Classes, stopping the chemical reaction.

Hold fire suppression systems for Class C fire zones are based on Halon 1301 (the two version of Halon have different chemical compositions). Here, the Halon is used as a flooding agent to halt the chemical reaction that takes place in a fire. These suppression systems continue to supply Halon for a period of time after the fire has been extinguished to ensure the heat has dissipated such that re-ignition cannot occur. The specific requirements are an initial volume concentration greater than 5%, and a minimum concentration of 3% for 60 minutes or 180 minutes under ETOPS (Flynn, 2001).

DEVELOPMENT OF FIRE RESISTANT CONTAINERS AND FIRE CONTAINMENT COVERSFire resistant containers (FRC)

The SAE AS 6278 Standard Fire Resistant Container - Design, Performance and Testing Requirements “specifies the minimum design and performance criteria and testing methods of passive fire resistant containers (FRCs) for carriage on aircraft main deck, to be used: (a) either in those cargo compartments of



civil transport aircraft where they constitute one means of complying with applicable airworthiness regulations, (b) or on a voluntary basis, when deemed appropriate by operators to improve fire protection in aircraft cargo compartments where airworthiness regulations do not currently mandate their use”.

On June 19, 2013, the commercial aerospace and defence contractor, AAR, announced the development of a Fire Resistant Container (FRC) that has proved capable of containing an onboard aircraft fire for up to six hours of flight. The AAR subsidiary, Nordisk, partnered with DuPont, and was able to incorporate DuPontTMKevlar® brand fibre and Nomex® XF flame barrier into the innovation, making it the lightest weight FRC on the market. The new FRC is up to 30 per cent lighter than aluminium containers/ULDs (AAR, 2013).

The new FRC is a main deck container designated with the airline industry code “AAD”. The code (AAD) refers to its size and shape, which is over 485 cubic feet. The testing of the new FRC strictly measured fire containment, not blast resistance. Depending upon the aircraft’s size, anywhere from 24 to 30 FRCs could be accommodated inside the aircraft (AAR, 2013).

On July 22, 2013, United Parcel Service (UPS), one of the world’s major integrators, placed an industry-first order for 1,821 fire-resistant shipping containers. The development and introduction of these unit load devices (ULDs) represented a major milestone in aviation history, offering unprecedented protection from intense fires (UPS, 2013b). UPS took delivery of the first of these ULDs in October 2013 and will receive the final ULDs by Spring 2014 (Putzger, 2013).

The new fire-resistant ULDs containers are built with a revolutionary new panel material, MACROLite, that are stretched across an aluminium frame (Croft, 2013) (Putzger, 2013). According to United Parcel Service (2013b), “MACROLite is a fibre-reinforced plastic composite similar to the material used in ballistic body armour”. Burn testing conducted by UPS and the United States Federal Aviation Administration (FAA), and witnessed by the National Transport Safety Board (NTSB), has revealed that a ULD constructed of MACROLite panels can contain a fire with a peak temperature of 1,200 degrees Fahrenheit (649°C) for more than four hours (United Parcel Service, 2013b). Therefore, the new fire-resistant containers have been designed to withstand intense fires of four hours or longer, in order to provide pilots with sufficient time to land their aircraft during such an emergency. The units work by depriving any fires that may break out of oxygen (Putzger, 2013).

Fire containment covers (FCC)Air cargo is also carried on the main and lower-deck freighter

aircraft belly holds, on aircraft pallets. The relative volumes of cargo carried on aircraft pallets is of significance since fire mitigation measures needed to be developed to suppress fires that break out when cargo is carried on these units. As a result, fire containment covers have been developed to enhance safety on freighter aircraft by isolating individual pallets under a special fire retardant cover (Waldron, 2011).

In recent times, several firms, such as AmSafe and Newtex Industries, have responded to the industry requirement for fire containment covers. The AmSafe fire containment cover provides air cargo airlines with a safer flying environment by mitigating the risk of serious fire caused by undeclared dangerous goods. The fire containment cover operates as a passive system which keeps a fire isolated from other cargo being carried on an aircraft. The AmSafe fire containment cover has been designed for palletised loads of cargo and the cover is comprised of a patented fire retardant fabric with a detachable QuickZip pallet net. The manufacturer reports that the cover “can contain a fire with temperatures of up to 1500°F (815°C) for up to four hours”. This combination of net and fabric cover provides a two-way fire barrier that effectively isolates each cargo position on the aircraft and prevents any fire from spreading and escalating. The fire containment cover suppresses the localised threat of the fire on the aircraft through oxygen starvation (AmSafe, 2013).

The AmSafe fire containment cover has many applications but it has been principally designed for palletized loads being transported in the presently unprotected Class E compartments found on the main deck of the majority of freighter aircraft. These covers are also intended to be used to protect the remaining Class D4 lower lobe aircraft belly-holds that are still in operation (AmSafe, 2013).

Another firm that has been developing a fire containment cover is United States-based Newtex Industries, Inc. Since 2007, Newtex has been working closely with aviation experts and airline safety officials to develop a structure capable of withstanding the intense heat and explosions associated with the ignition of dangerous goods, including lithium-ion batteries. The Newtex Z-BlockTM Fire Containment Cover (Fig. 1) has been developed following the outcome of this research and development, and the firm notes that its product is the most advanced protection available for containing deadly smoke and fire in commercial aircraft cargo bays. The system is made of



Z-BlockTM, a polymer coated fabric treated with a proprietary formulation of fire retardants that is able withstand temperatures up to 1800°F (980°C) and offers exceptional resistance to fire, smoke, and weather conditions (Newtex , 2013a). Similar to all of Newtex Industries products, Z-BlockTM is formulated with inorganic materials and will not produce hazardous outgassing (Newtex, 2013b).

Z-BlockTM coating and substrate fabrics were originally designed to channel smoke and prevent the spread of fire in aircraft hangars, picture theatres, and large public gathering spaces. The firm has subsequently optimized the fabrics for use in cargo operations by creating a multi-layer system for increased strength and puncture resistance and by adding an interior membrane that contains weather and mildew resistant properties (Newtex, 2013a).

The Z-BlockTM fabric has been tested to the following standards: • ASTM D6413 – Vertical Flame Resistance• ASTM E-84 – Surface Flame Spread & Smoke Density• FAR 25, Appendix F, Part III and IV – Flame Penetration

Resistance & Smoke Density• BSS 7239 – Toxicity of Products Combustion (Newtex,

2013a).

UPS Airlines, for example, has purchased 575 fire containment covers that are able to withstand fires with a peak temperature of 650° C / 1,200° F for at least four hours. The airline is deploying these fire containment covers on palletized cargo, and on routes where there are large volumes on electronics being shipped (UPS, 2013a).

CONCLUSIONS

The carriage of air cargo has become increasingly standardised with the vast majority of world scheduled air cargo traffic now being carried in aircraft unit load devices (ULDs) (Reynolds-Feighan, 2013). Following the investigation into three fire-related freighter accidents that occurred between 2006 and 2012, the NTSB recommended, in October 2012, that the United States Federal Aviation Administration (FAA) require active fire-suppression systems in all cargo compartments of aircraft, the development of fire resistant ULDs, and also to improve the early detection of fires within cargo containers (ULDs) and pallets.

The industry has responded to this critical aviation safety issue. This paper has examined two recent innovations in response to the mitigation and suppression of fires on board freighter aircraft –fire resistant containers and fire containment covers for palletised cargo. Using the latest technologies, this equipment has been designed to suppress fires in the aircraft for around 4 hours thereby providing the pilots with sufficient time to land their aircraft during such an emergency.

ACKNOWLEDGMENTS

The authors would like to express their sincere gratitude to Newtex Industries, Inc. for permission to use the image of the firm’s fire containment cover in this paper.

REFERENCES

Photo courtesy of Newtex Industries, Inc.

Figure 1. Airline air cargo pallet fire containment cover.

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Ashford, N. J., Stanton, H. P. M.and Moore, C. A. et al. 2013, “Airport operations”, 3rd ed., McGraw-Hill, New York.

Baxter, G. S., 2011, “Restructuring air freight chains: strategic options for competitive advantage”, Ph.D. Thesis, Griffith University, Brisbane, Australia.

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Bowen, J. T., 2012, “A spatial analysis of FedEx and UPS: hubs, spokes, and network structure”, Journal of Transport Geography, Vol. 24, pp. 419-431.

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Coyle, J. J., Novak, R. A., Gibson, B. J., Bardi J. Edward, 2011, “Transportation: a supply chain perspective”, 7th ed, South-Western Cengage Publishing, Mason, OH.

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J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 2, pp.210-212, Apr.-Jun., 2014J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 2, pp.210-212, Apr.-Jun., 2014

INSTRUCTIONS TO AUTHORS(Revised in March, 2014)

SCOPE AND EDITORIAL POLICY

The Journal of Aerospace Technology and Management (JATM) is the official publication of the Departamento de Ciência e Tecnologia Aeroespacial (DCTA) and Associação Aeroespacial Brasileira (AAB), both in São José dos Campos, São Paulo State, Brazil. The journal is quarterly published (March, June, September and December) and is devoted to research and management on different aspects of aerospace technologies. The authors are solely responsible for the contents of their contribution. It is assumed that they have the necessary authority for publication. When submitting the contribution, authors should classify it according to the area selected from the following topics:

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Coimbra, A. L., 1978, “Lessons of Continuum Mechanics”, Ed. Edgard Blücher, São Paulo, Brazil, 428 p.Alves, M.B. and Morais, A.M. F., 2009, “The management of knowledge and technologies in a Space Program”, Journal of Aerospace Technology and Management, Vol. 1, No. 2, pp. 265-272. doi:10.5028/jatm.2009.0102265272Paek, S.K., Bae, J.S. and Lee, I., 2002, “Flutter Analysis of a Wraparound Fin Projectile Considering Rolling Motion,” Journal of Spacecraft and Rockets, Vol. 39, No. 1, pp. 66-72.Bae, J.S., Kim, D.K., Shih, W.H., Lee, I. and Kim, S.H., 2004, “Nonlinear Aeroelastic Analysis of a Deployable Missile Control Fin,” Journal of Spacecraft and Rockets, Vol. 41, No. 2, pp. 264-271.

Clark, J.A., 1986, “Private Communication”, University of Michigan, Ann Harbor.EMBRAPA, 1999, “Politics of R&D”, Retrieved in May 8, 2010, from http://www.embrapa.br/publicacoes/institucionais/polPD.pdfSilva, L.H.M., 1988, “New Integral Formulation for Problems in Mechanics” (In Portuguese), Ph.D. Thesis, Federal University of Santa Catarina, Florianópolis, S.C., Brazil, 223p.Sparrow, E.M., 1980a, “Forced Convection Heat Transfer in a Duct Having Spanwise-Periodic Rectangular Protuberances”, Numerical Heat Transfer, Vol. 3, pp. 149-167.Sparrow, E.M., 1980b, “Fluid-to-Fluid Conjugate Heat Transfer for a Vertical Pipe-Internal and External Natural Convection”, ASME Journal of Heat Transfer, Vol. 102, pp. 402-407.

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ContactPhone: (55) 12-3947- 5115/5004E-mail: [email protected]: http://www.jatm.com.br

Published by: Departamento de Ciência e Tecnologia Aeroespacial Distributed by: Instituto de Aeronáutica e EspaçoEditing, proofreading and standardization: Zeppelini EditorialPrinting: RR DonnelleyEdition: 500São José dos Campos, SP, BrazilISSN 1984-9648

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).


Vol. 6, n. 2 (Apr./Jun. 2014) – São José dos Campos: Zeppelini Editorial, 2014Quartely issued

Aerospace sciencesTechnologiesAerospace engineering

CDU: 629.73

JATM IS SUPPORTED BY:

The copyright on all published material belongs to Departamento de Ciência e Tecnologia Aeroespacial (DCTA).

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.

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JOURNAL OF AEROSPACE TECHNOLOGY AND MANAGEMENT

Vol. 6 N. 2Apr./Jun. 2014

ISSN 1984-9648ISSN 2175-9146 (online)


www.jatm.com.br

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