Vol.5 N.4 - Journal of Aerospace Technology and Management by Journal of Aerospace Technology and Management

Journal of aerospace technology and management

JOURNAL OF AEROSPACE TECHNOLOGY AND MANAGEMENT Vol. 5 N. 4 Oct./Dec. 2013 ISSN 1984-9648 ISSN 2175-9146 (online)

www.jatm.com.br

V.5, n. 4, oct./dec., 2013

Journal of Aerospace Technology and Management

General Information Journal of Aerospace Technology and Management (JATM) is a techno-scientific publication serialized, published by Departamento de Ciência e Tecnologia Aeroespacial (DCTA) and aims to serve the international aerospace community. It contains articles that have been selected by an Editorial Committee composed of researchers and technologists from the scientific community. The journal is quarterly published, and its main objective is to provide an archival form of presenting scientific and technological research results related to the aerospace field, as well as promote an additional source of diffusion and interaction, providing public access to all of its contents, following the principle of making free access to research and generate a greater global exchange of knowledge. JATM is added/indexed in the following databases: SCOPUS - Elsevier; CAS - Chemical Abstracts Service; DOAJ - Directory of Open Access Journals; J-GATE - The e-journal gateway from global literature; LIVRE - Portal to Free Access Journals; GOOGLE SCHOLAR; SUMÁRIOS.ORG - Summaries of Brazilian Journals; EZB- Electronic Journals Library; ULRICHSWEB- Ulrich´s Periodicals Directory; CLASE/PERIÓDICA- Indice de Revistas Latinoamericanas en Ciencia; SOCOL@R- China Educational Publications; LATINDEX-Regional Cooperative Online Information System for Scholarly Journals from Latin America, the Caribbean, Spain and Portugal; REDALYC - Red de Revistas Científicas de América Latina y el Caribe, España y Portugal; EBSCO Publishing, PKP-Public Knowledge Project and PERIÓDICOS CAPES. 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 affiliated to ABEC - Brazilian Association of Scientific Editors.

Correspondence All correspondence should be sent to: Dr Ana Cristina Avelar Journal of Aerospace Technology and Management Instituto de Aeronáutica e Espaço Praça Mal. Eduardo Gomes, 50 - Vila das Acácias CEP 12228-901 São José dos Campos/ São Paulo/Brazil Contact Phone: (55) 12-3947- 5115/5122 E-mail: editor@jatm.com.br Web: http://www.jatm.com.br Published by: Departamento de Ciência e Tecnologia Aeroespacial Distributed by: Instituto de Aeronáutica e Espaço Editing, proofreading and standardization: Zeppelini Editorial Printing: RR Donnelley Edition: 500 São José dos Campos, SP, Brazil ISSN 1984-9648

JATM is supported by:

Journal of Aerospace Technology and Management Vol. 5, n.4 (Oct./Dec. 2013) – São José dos Campos: Zeppelini Editorial, 2013 Quartely issued Aerospace sciences Technologies Aerospace engineering CDU: 629.73

Historical Note: JATM was created in 2009 after the iniciative of the diretor of Instituto de Aeronáutica e Espaço (IAE), Brigadeiro Engenheiro Francisco Carlos Melo Pantoja. In order to reach the goal of becoming a journal that could represent knowledge in science and aerospace technology, JATM searched for partnerships with others institutions in the same field from the beginning. 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. The copyright on all published material belongs to Departamento de Ciência e Tecnologia Aeroespacial (DCTA)

ISSN 1984-9648 ISSN 2175-9146 (online)

Journal of Aerospace Technology and Management Vol. 5 No. 4 - Oct./Dec. 2013 Editor in Chief

Executive Editor

ASSISTANT EDITOR

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

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

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

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

Francisco Carlos M. Pantoja Diretoria de Engenharia da Aeronáutica Rio de Janeiro/RJ – Brazil

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

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

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

Paulo Tadeu de Melo Lourenção EMBRAER São José dos Campos/SP – Brazil

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

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

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

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

Marco A. Sala Minucci Vale Soluções em Energia São José dos Campos/SP – Brazil

SCIENTIFIC COUNCIL

ASSOCIATE EDITORS Acoustics

Applied computation

Ceramic Materials

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

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

José Maria Fonte Ferreira Universidade de Aveiro Aveiro – Portugal

Aerodynamics

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

Circuitry

Bert Pluymers Katholieke Universiteit Leuven Leuven – Belgium

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

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

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

Astrodynamics

Antonio Sergio Bezerra Sombra Universidade Federal do Ceará Fortaleza/CE – Brazil

Altamiro Susin Universidade Federal do Rio Grande do Sul Porto Alegre/RS – Brazil

Antonio F. Bertachini Instituto Nacional de Pesquisas Espaciais São José dos Campos/SP – Brazil

Raimundo Freire Universidade Federal de Campina Grande Campina Grande/PB – Brazil

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

Composites

Edson Cocchieri Botelho Faculdade de Engenharia de Guaratinguetá Guaratinguetá/SP – Brazil Flamínio Levy Neto Universidade de Brasília Brasília/DF – Brazil

Computational fluid dynamics

Joern Sesterhenn Technische Universität Berlin Berlin – Germany John Cater University of Auckland Auckland – New Zealand Paulo Celso Greco Escola de Engenharia de São Carlos São Carlos/SP – Brazil

Defense Systems

Adam S. Cumming Defence Science and Technology Laboratory Salisbury/Wiltshire – England Wim P. C. de Klerk Netherlands Organisation for Applied Scientific Research Rijswijk/SH – Netherlands

Eletromagnetic Compatibility

Alain Azoulay École Supérieure d’Electricité Gif–Sur–Yvette – France Cynthia Junqueira Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

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

Guidance, Navigation and Control

Radars and Tracking Systems

David Murray–Smith University of Glasgow Glasgow – Scotland

Marc Lesturgie Office National d’Etudes et de Recherches Aérospatiales Palaiseau – France

Daniel Alazard Institut Supérieur de l’Aéronautique et de l’Espace Toulouse – France

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

Management Systems

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

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

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

Structures

Metallic Materials

José Rubens G. Carneiro Pontifícia Universidade Católica de Minas Gerais Belo Horizonte – Brazil

Photonics

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

Polimeric Materials Cristina Tristão de Andrade Instituto de Macromoléculas Rio de Janeiro/RJ – Brazil Mirabel Cerqueira Rezende Instituto de Ciência e Tecnologia São José dos Campos/SP – Brazil

Fluid Dynamics and Turbulence

Processing of Aerospace Materials

Vassilis Theofilis Universidad Politécnica de Madrid Madrid – Spain

Robotics and Automation

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

José Leandro Andrade Campos Universidade de Coimbra Coimbra – Portugal

Marcos Pinotti Barbosa Universidade Federal de Minas Gerais Belo Horizonte/MG – Brazil

Hugo H. Figueroa Universidade Estadual de Campinas Campinas/SP – Brazil

Alexandre Queiroz Bracarense Universidade Federal de Minas Gerais Belo Horizonte/MG – Brazil

Propulsion and Combustion

Fernando de Souza Costa Instituto Nacional de Pesquisa Espacial São José dos Campos/SP, Brazil

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

Synthesis and Characterization of Aerospace Materials

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

Thermal Sciences

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

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

Valder Steffen Junior Universidade Federal de Uberlândia Uberlândia/MG – Brazil

Carlos Henrique Marchi Universidade Federal do Paraná Curitiba/PR – Brazil

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

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

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

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

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

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, 2013

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

CONTENTS Editorial 369 The Creation of a National Space Systems Integrator João Paulo Rodrigues Campos

ORIGINAL PAPERS 371 An Airplane Calculator Featuring a High-Fidelity Methodology for Tailplane Sizing Bento Silva de Mattos, Ney Rafael Secco 387 Investigations on Directed Infrared Countermeasures Risks to Fighter Aircraft Pilots Lester de Abreu Faria, Luciano Barbosa Magalhães, Roberto d’Amore 395 Analyzing the Unscented Kalman Filter Robustness for Orbit Determination Through Global Positioning System Signals Paula Cristiane Pinto Mesquita Pardal, Hélio Koiti Kuga, Rodolpho Vilhena de Moraes 409 Simulation of Ablation in a Sounding Rocket Thermal Protection System Via an Interface Tracking Method with Two Moving Fronts Humberto Araujo Machado 421 Infrared Spectroscopy Applied to Materials Used as Thermal Insulation and Coatings Natália Beck Sanches, Ricardo Pedro, Milton Faria Diniz, Elizabeth da Costa Mattos, Silvana Navarro Cassu, Rita de Cássia Lazzarini Dutra 431 Influence of Ethylene Glycol on the Mullite Crystallization Processes Analyzed by Rietveld Refinement Flaviano Willians Fernandes, Tiago Moreira Bastos Campos, Luciana de Simone Cividanes, João Paulo Barros Machado, Evelyn Alves Nunes Simonetti, Gilmar Patrocínio Thim 439 Rainy Season Features for the Alcântara Launch Center Urias Andrade Pinheiro, Marcos Daisuke Oyama 449 Observational Study of the Surface Layer at an Ocean–Land Transition Region Luiz Eduardo Medeiros, Roberto de Oliveira Magnago, Gilberto Fisch, Edson Roberto Marciotto 459 Application of the Prado-Project Management Maturity Model at a R&D Institution of the Brazilian Federal Government Luiz Aldo Leite das Neves, Luiz Eduardo Nicolini do Patrocínio Nunes, Valesca Alves Corrêa, Mirabel Cerqueira Rezende

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, 2013

THESIS ABSTRACT 466 Study of Solidification of Eutectic Alloys in Microgravity Environment Rafael Cardoso Toledo 466 National Innovation Projects: Brazilian Space Sector Practices Mariana de Freitas Dewes

467 ADHOC REFEREES 470 INSTRUCTIONS TO AUTHORS

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

Editorial The Creation of a National Space Systems Integrator João Paulo Rodrigues Campos1

leven companies took part in the international tender that lead to the selection of the satellite supplier for the Defense and Strategic Communications Geostationary Satellite System (SGDC). All of them shared the same traits: financial power with billion-dollar-plus revenues, wide range of technical skills and long tradition in leading defense or aerospace projects as prime contractors in their home countries. The international satellite market nowadays is dominated by large satellite integrators that have the strength and skills (not to mention government support) to survive in such a competitive environment. There has been a long debate in Brazil about the need to build a national satellite integrator. Historically, this role has been fulfilled by Instituto Nacional de Pesquisas Espaciais (INPE), which has played a key role in developing early space technology capabilities in the country with successful programs such as the SCD or the CBERS series. These programs also sowed the seeds of a technically capable space industry, with good understanding of most space subsystem technologies, eager for opportunities to transition from an embryonic stage towards its maturity. The establishment of a national satellite integrator may well have a catalytic effect in this process. According to this new model, public research institutes would focus on leading edge technology development, with its inherent high risk, whereas the industrial integrator would help bringing these technologies into the market in a more efficient, cost effective way once they get mature. The success bred by these programs, fulfilling their mission on schedule

and on cost, would attract more investments, which, in turn, would benefit the whole chain. Indeed, the National Space Activities Plan (PNAE) and the National Innovation, Science and Technology Strategy published by the Brazilian Space Agency (AEB) and the Ministry of Science, Technology and Innovation (MCTI), respectively, established as a priority for public policy to nurture the creation of such company as a way to develop a healthy space sector in Brazil, an idea that was quickly embraced by the industry and other government entities. The SGDC program was a perfect opportunity to make this happen. Telebras, the entity chosen by the government to lead the National Broadband Program (PNBL) infrastructure deployment and the owner of the SGDC system, would welcome support to run a program with such complexity and Embraer was the perfect partner for the venture, given its capabilities, track record and the similarities between the aeronautical and the space sectors in terms of technologies and business processes. Actually, most of the spill-overs from the space sector go into the aeronautic and defense industry, which demonstrates the resemblances between those two sectors. Visiona Space Technology was created to act as the prime contractor and manage the SGDC program as a joint-venture between Telebras and Embraer, merging space-specific technical knowledge from senior INPE engineers, among the most experienced ones in satellite integration in Brazil, with Embraer proven technical and management practices. A Technology Absorption program, set up as part of the SGDC

Visiona Space Technology - São José dos Campos/SP - Brazil 1.João Paulo Rodrigues Campos is the Contracts and Business Development Vice-President of Visiona Space Technology, an Embraer and Telebras joint-venture. Over his career, he has held various leadership positions at Embraer and TV Cabo Portugal and also worked as a strategy consultant at McKinsey & Co. He holds a Mechanical Engineering degree from Unicamp, a Master’s degree from École Centrale de Lyon/Paris and a MBA from Insead. Email: joao.campos@visionaespacial.com.br

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project, will help the consolidation of the company technical expertise. The results so far have been very encouraging with the first phase of the SGDC project being completed flawlessly in a “record lap” time and cost. Holding a significant participation in the company capital, the government has made sure at Visiona’s creation that it would have the means to exert strategic influence over the company’s decisions. The government also established technology transfer requirements for the SGDC program. Led by the Brazilian Space Agency, the SGDC Technology Transfer Program will enable the Brazilian space industry to hold a larger share in future Brazilian geostationary programs and possibly even to claim a place in the international market. Taking a broader perspective, many studies have been performed in OECD countries on the return on investment in space programs from which two conclusions can be drawn: (1) investment in space pays off. Being on the edge of technology,

the space sector is a powerful source of innovation through its spill-overs. Also, space-based infrastructure such as navigation, imaging or telecommunication systems have the ability to leverage productivity across the economy; and (2) the actual return of investments in space technologies varies substantially among different countries depending on how the sector is structured in that particular country. The new model made possible in Brazil by the SGDC program, being spearheaded by Visiona, is poised to improve local space programs effectiveness. Renewed confidence from policy makers on the sector ability to deliver on its promises is a key to assure a continuous flow of resources, benefiting all players — academia, research institutes and industry. These players working as a team to deliver programs with high socio-economical return are the key for a sustainable Brazilian space sector. We are in the right direction!

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.369-370, Oct.-Dec., 2013

doi: 10.5028/jatm.v5i4.254

An Airplane Calculator Featuring a HighFidelity Methodology for Tailplane Sizing Bento Silva de Mattos1, Ney Rafael Secco1

ABSTRACT: The present work is concerned with the accurate modeling of transport airplanes. This is of primary importance to reduce aircraft development risks and because multi-disciplinary design and optimization (MDO) frameworks require an accurate airplane modeling to carry out realistic optimization tasks. However, most of them still make use of tail volume coefficients approach for sizing horizontal and vertical tail areas. The tail-volume coefficient method is based on historical aircraft data and it does not consider configuration particularities like wing sweepback angle and tail topology. A methodology based on static stability and controllability criteria was elaborated and integrated into a MATLAB application for airplane design. Immediate advantages with the present methodology are the design of realistic tail surfaces and properly sized airplanes. Its validation was performed against data of five airliners ranging from the regional jet CRJ-100 to the Boeing 747-100 intercontinental airplane. An existing airplane calculator application incorporated the present tail-sizing methodology. In order to validate the updated application, the Fokker 100 airliner was fully conceptually designed using it. KEYWORDS: Aircraft design, Tailplane design, Aircraft stability and control.

INTRODUCTION It is of fundamental importance for any optimization framework tailored to airplane design the use of realistic airplane models. If the disciplines that are embedded in the airplane modeling are not accurate enough, it makes no sense performing any optimization tasks, because the resulting planes may be unviable to develop or deliver an acceptable performance. Thus, the present work is concerned with the development of a computational tool able to properly model transport airplane configurations. An existing computational tool that was christened Aeronautical Airplane (AA) was enhanced with an improved approach for tailplane sizing (Mattos and Magalhães, 2012). Tail surfaces are used to both stabilize the aircraft and provide control authority that is needed for maneuver and trim. For a conventional aircraft configuration, the tail often has two components, the horizontal and the vertical tails. The primary functions of these components are: take care of airplane trim and stability, and provide control by the elevator and rudder surfaces which are associated with the horizontal and vertical tails, respectively. With regard to aircraft stability, the purpose of horizontal tail (HT) is to maintain the longitudinal stability; while the vertical ones is responsible for keeping the directional stability. Here, we refer to two stability concepts: static and dynamic. Aircraft static stability is defined as the tendency of an aircraft to return to the original trimmed conditions if diverted by a disturbance. Major disturbance sources are gusts and pilot inputs on the controls. On the other hand, the dynamic longitudinal one is related to the motion of a statically stable airplane, the way that it will return

1.Instituto Tecnológico de Aeronáutica – São José dos Campos/SP – Brazil Author for correspondence: Bento Silva de Mattos | Instituto Tecnológico de Aeronáutica | Praça Marechal Eduardo Gomes, 50 – Vila das Acácias | CEP: 12.228-900 São José dos Campos/SP – Brazil | Email: bmattos@ita.br Received: 04/06/13 | Accepted: 10/10/13

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to equilibrium after suffering some kind of disturbance. Basically, there are two primary forms of longitudinal movements with regard to an airplane attempting to return to equilibrium after being disturbed. The first one is the phugoid mode of oscillation, which is a long-period and slow oscillation of the airplane’s flight path; the second is a short-period variation of the angle of attack. Usually, this oscillation decreases very quickly with no pilot input (Centennial of Flight, 2011). However, the Centennial of Flight website introduces some misconceptions about Dutch roll, when it states that: “Dutch roll is a motion exhibiting characteristics of both directional divergence and spiral divergence.” Therefore, Dutch-roll basic cause is the unbalance between lateral and longitudinal stabilities, when the latter is considerably lower than the first one. The tail surfaces of transport airplanes are usually designed to obey static stability criteria. If some undesirable dynamic behaviors become evident during a flight test campaign, fixes are provided to overcome the problems. Typical fixes are dorsal and ventral fins, vortex generators, strakes, and in some extreme cases, stablets. The results obtained with an in-house application for tailplane sizing corroborate this statement, as will be shown in the next sections of this work. As to the controllability topic, it can be stated that an airplane is fully controlled when a flight condition may be changed in a finite time by appropriate control inputs at any new flight condition. In general, a system is considered controllable if it can be transferred from selected initial conditions into chosen final states. The controllability thus describes the influence of external inputs (in general, the controlled variables) to the inner system state. About this, an important distinction must be made between output and state controllability. Output controllability is the notion associated with the system output; the output controllability describes the ability of an external input to move the output from any initial condition to any final one in a finite time interval. No relationship must exist between state and output controllability. The state of a system, which is a collection of its variables values, completely shows the system at any given time. Particularly, no information on the past of a system will help to predict the future, if the states at the present time are known. Complete state controllability (or simply controllability if no other context is given) describes the ability of an external input to move

the internal state of a system from any initial one to any other final in a finite time interval. Concerning transonic airplanes, tail surfaces should be composed of low-thickness and/or higher sweep than that adopted for the wing, in order to prevent strong shocks on the tail in normal cruise. As required for certification regulations and safety purposes, transport airplane must dive in an emergency event occurring at high altitudes. The main reason behind this is to reach another one, where no onboard oxygen is needed for the passengers to breath. In this situation, for high-speed airplanes, the airflow over the wing becomes fully detached and the airplane counts just on the horizontal tailplane to depart from the dive. This provides a good reason for airflow remaining attached to HT at this high-speed condition. Former airplane design teams used to increase the HT quarter-chord sweepback angle relative to that figure of the wing (typically a 5 degrees plus). Although providing higher sweepback angles to HT will mean a longer arm relative to center of gravity (CG) and a higher lift curve slope, modern multidisciplinary design and optimization (MDO) frameworks are able to find out the optimal planform and airfoil shapes for tailplanes. Thus, modern design airplanes not necessarily follow the +5 degrees rule to set such angle for the horizontal tail. In order to keep weight and drag as low as possible, typical values for the maximum relative thickness for both HT and vertical tail (VT) lie in the range of 8 to 11%. Besides working with lower hinging moments, airfoil shapes for the HT shall present a very low maximum camber in order to avoid the generation of undesired induced drag and to maintain interference drag with the tailcone or the VT as low as possible. It is worth mentioning that the tailcone is a region of high decelerating airflow, and the thickness of boundary layer increases fast and dramatically. Thus, interference drag is a big issue when integrating conventional HTs into the airframe. Typical aspect ratios for HT vary from four to five. T-tails present sometimes higher aspect ratios (5–5.5) to avoid aftengine/pylon wake effects (Sadraey, 2009). Usual aspect ratios for VT range from 1.2 to 1.8 with lower values for T-tails. Taper ratios of about 0.4 to 0.6 are typical for tail surfaces, since lower ones would lead to unacceptably small Reynolds numbers (Sadraey, 2009). T-tail vertical surface taper ratios are in the range of 0.85 to 1.0 to provide adequate chord for attachment of the HT and associated control linkages (Sadraey, 2009).

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.371-386, Oct.-Dec., 2013

An Airplane Calculator Featuring a High-Fidelity Methodology for Tailplane Sizing

A large variety of tail shapes has been employed on aircraft since the beginning of powered aviation in the early 20th century. These include configurations often denoted by the letters whose shapes they resemble in front view: T, V, H, +, Y, inverted V. The selection of the particular configuration includes complex system-level considerations. Most of existing MDO frameworks for aircraft design makes use of tail volume coefficient or scaling factors approach for tailplane calculation. A relatively new example of this can be seen in the paper by Grundlach (1999). The tail volume coefficient approach is based on statistical data and may lead to tailplanes that are unable to comply with stability and controllability requirements. In the present work, a more sophisticated and higher fidelity methodology for the design of tail surfaces was compiled from the specialized literature, then coded using MATLAB platform, validated by comparing calculated tail areas to those from some current airliners, and incorporated into an airplane calculator software. Static stability and controllability criteria were used for the design of HTs and VTs of airplanes under consideration.

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Loads calculations are performed with BLWF for some points in the airplane operational envelope (Fig. 1). Afterwards, the calculated loads are used to estimate the wing structural weight applying Megson’s method in some sections of the wingbox (Fig. 2). Elastic deformation is well iteratively, since the loads will vary when the wing deforms (Fig. 3). The procedure that was adopted for the wing-box sizing is described in Videiro (2012).

MEtHodoloGY AiRplAnE CAlCulATOR TOOl AA is a MATLAB® application that calculates transport airplane characteristics, performance, and layout. User must provide to it information regarding geometry, configuration, topology, wing and tailplanes airfoils, range at given payload, passenger capacity, and engine data of the configuration that is due to be analyzed. AA makes use of a wing-body full potential code with boundary-layer correction for wings employing the strip sense approach (Karas and Kovalev, 2011). This code is known as BLWF and it is able to automatically generate a multi-block mesh from user-provided configuration parameters. The approximate-factorization algorithm AF2 is used for marching in pseudo time (Holst and Thomas, 1982). Aerodynamics coefficients are calculated using Roskam Class II methodology (Roskam, 2000a) and Torenbeek’s formulation to estimate divergence Mach number of wings and HTs (Torenbeek, 1982).

Figure 1. A full-potential code that accounts for viscous effects is used by Aeronautical Airplane. It is able to handle both low- and high-wing configurations. Fuselage

Front spar

Inboard flap

Outboard flap

Auxiliary spar

Aileron Rear spar

Figure 2. Spars and ribs belong to the wing layout defined by Aeronautical Airplane for a configuration with engines positioned at aft fuselage. The green area shown above indicates availability for fuel storage.

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1.5 1

BLWF is also used in conjunction with XFOIL for the estimation of the CLmax for the configuration under study. It is used to calculate the maximum lift coefficient of some sections along the wingspan and BLWF estimates the CLmax of the configuration using those values in the technique of the critical section. The turbofan engine model employed by AA is described in Loureiro (2008), who developed a generic deck for turbofan engines based on Benson’s work (Benson, 1995). Fan diameter, bypass ratio, fan pressure ratio, engine parts efficiencies, design point, turbine inlet temperature, and overall pressure ratio are some input variables to this model. Typical outputs from the engine module are fuel flow and thrust. AA also defines the fuselage cross section (Fig. 4) and the design diagram, the latter clustering several Federal Aviation Rules (FAR – from the United States), 25 performance conditions together, enabling a better comprehension of thrust and wing loading figures to fulfill requirements (Fig. 5). Scholz (2011) provided several guidelines that were used to define the fuselage cross section as seen in Fig. 4. The methodology that was employed to generate graphs like the one displayed in Fig. 5 is described in Roskam (2000). AA calculates fuselage geometry, CG envelope, engine thrust and consumption chart, operational envelope, direct operating cost (DOC), payload-range diagram, airplane flight mechanics, takeoff path, maximum takeoff weight (MTOW) with all airplane component weights, engine emissions, and noise signature. It must be emphasized that AA does not enforce the airplane under study to obey the requirements in Fig. 4, considering that it is indeed a transport airplane analysis tool, not a design one. If the intent is to design an airplane, AA can be without great effort easily incorporated into a design and optimization framework, considering that modern airplane conceptual design must make use of optimization tools.

-0.5 -1 -1.5 -2 -1.5 -1 -0.5

0 0.5 X (m)

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2 1.5 1 0.5 Y (m)

Figure 3. Example of wing elastic bending deformation calculated by Aeronautical Airplane.

Y (m)

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Figure 4. Aeronautical Airplane output of some fuselage cross sections.

0.4 0.35 0.3 0.25 0.2 T0/W0→ 0.15

Cruise Takeoff Landing 2nd segment Missed approach Climb at ICA 300 ft/min

0.1 0.05 0

400 450

500 550 600 650 Wing loading (kg/m2)

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750

Figure 5. Design diagram of the airplane configuration being investigated (green circle). Criteria like the second segment climb and rate of climb at initial cruise altitude (ICA) are employed to verify whether the configuration comply or not with FAR design requirements (Roskam, 2000).

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.371-386, Oct.-Dec., 2013

An Airplane Calculator Featuring a High-Fidelity Methodology for Tailplane Sizing

Enhanced tail sizing method Takeoff is a critical condition for tailplanes design, and therefore some relevant aspects of this flight phase are described in the following paragraphs. Figure 6 illustrates the takeoff segments for airplanes. V1 is the takeoff decision speed, VR the rotation speed, VLof velocity of liftoff, and V2 is the flight speed over a hypothetical 35-feet obstacle. During the initial phase of the takeoff path, the airplane must be accelerated on the ground to VEF, at which point the critical engine must be made inoperative and remain like this for the rest of the takeoff. V1 means the maximum speed in the takeoff at which the pilot must take the first action, such as: apply brakes, reduce thrust, deploy speed brakes, and stop the airplane within the accelerate-stop distance. It also means the minimum speed in the takeoff, following a failure of the critical engine at VEF, at which the pilot can continue it and achieve the required height above the takeoff surface within its distance (Boeing, 2009). According to FAR 25.149, Vmca is the calibrated airspeed at which: • critical engine suddenly becomes inoperative; • control of the airplane is possible to maintain; • maintain straight flight; • bank angle shall be lower than five degrees; • Vmca may not exceed 1.2 x Vs. The conditions for determination of Vmca are:

• maximum takeoff thrust on remaining engines; • most unfavorable CG (usually aft CG), where the tail

moment arm is the shortest); • aircraft trimmed for takeoff (takeoff flap setting); • maximum TOGW; • most critical takeoff configurations along the flight path, except for landing gear retracted. Vmca is used for determining the minimum VR and V2 . The V2 must also be chosen in order to satisfy the second segment climb requirement. Vmca must be lower than the V2 and considering its importance for aircraft performance and safety operation, it was calculated after the tail surfaces had been obtained. For this purpose, the methodology elaborated by Cavanaugh (2004) was incorporated in the present work.

375

The main objective of the present paper was the calculation of the HT and VT areas through a higher fidelity method than that offered by the tail volume approach. In order to accomplish this, static stability and controllability criteria were employed for the design of the horizontal and vertical tails. The VT area is obtained by taking the larger one from that calculated employing both criteria, while the HT area is obtained with the procedure described previously. Stability derivatives that are needed for the calculation of HT and VT areas were obtained according to Roskam’s methodology (Roskam, 1971). Initial estimated values are required in order to start iterative processes for the HT and VT calculation. MATLAB’s fsolve minimization algorithm was employed for the calculation of the tailplanes areas. Indeed, no optimization of the tail-plane areas was carried out with fsolve. This MATLAB® tool is used to find out the tailplane areas, because they depend on several parameters and variables such as wing location along the fuselage. The incorporation of a more sophisticated methodology for the tail-plane sizing into AA poses a more complex task in the interactive process for MTOW calculation. The determination of the CG location is needed not only for the tail-plane area measurement, but also to position the main and nose landing gear, which is also dependent on the overall CG location, which will guide the wing placement in the configuration. The modified AA was employed to calculate the characteristics of an airplane similar to Fokker 100, a 107-seater airliner. By varying the quarter-chord wing sweepback angle, the impact on HT and VT areas was obtained. Baseline airplane Fokker 100 is a medium size twin-turbofan airliner design and market by the extinct Dutch company, Fokker. It is a complete redesign of the Fokker F-38, which rivals the BAE 146. It had longer wings, and fuselage that would seat over 100 passengers and a “glass” cockpit featuring six large displays (World’s Aircraft, 2013). In 1988, Fokker flew an uprated Rolls&Royce Tay 650 powered version, which American Airlines ordered 75 aircraft with 75 options. The baseline airplane that was chosen in the present work was Fokker 100 fitted with Rolls&Royce Tay 620 engines. Table 1 presents some relevant data for such airplane.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.371-386, Oct.-Dec., 2013

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376

Take-off flight path

Take-off distance Ground roll

1st segment

2nd segment

3rd segment

Final segment Enroute climb

Reference zero Gear up

Brake release VR VLOF

1500 ft minimum

400 ft minimum

Min. climb gradients Segment 1st 2nd Twin-Jet +ve 2.4% Tri-Jet 0.3% 2.7% Quad 0.5% 3.0%

35 ft (15 ft wet) Engines

All engines

One inoperative

Thrust

Take-off

Maximum continuous

Airspeed

V2 to final segment speed

0 to V2

Landing gear Down

Retraction

Retracted

High-lift devices Take-off setting

Final segment speed

Retraction

Retracted

Figure 6. Illustration of take-off segments and required FAR climb requirements.

Table 1. Data for the Fokker 100 fitted with Tay 620 engines.

Horizontal stabilizer

Performance

Maximum operating Mach number Service ceiling

0.77 35,000 ft

Maximum range with maximum payload

1,720 km

Maximum takeoff weight

43,090 kg Wing

Aspect ratio

8.43

Reference area

93.5 m2

Taper ratio

0.235

Mean aerodynamic chord (MAC)

3.80 m

Quarter-chord sweepback angle

17.45º

Horizontal tail

Area

21.72 m2

Aspect ratio

4.64

Taper ratio

0.390

Quarter-chord sweepback angle

26.00º

Vertical tail

Area Aspect ratio

12.30 m2 0.89

Taper ratio

0.740

Quarter-chord sweepback angle

41.00º

Design for controllability The aircraft trim must be guarantee three axes, x, y, and z, namely the lateral, longitudinal, and directional ones, respectively. When the summation of all forces in x direction (such as drag and thrust) is zero and of all moments including aerodynamic pitching moment about y axis is zero, the airplane is longitudinally trimmed. HT can be installed at airplane tail-cone or on top of vertical tails. Some airplanes like the Piaggio Avanti business turboprop are fitted with foreplanes that are located at forward fuselage. Such foreplanes are known as canards. The HT is responsible for maintaining longitudinal trim and making the forces summations to be zero, by generating a necessary lift and contributing in the summation of moments in y axis. When the summation of all forces in y direction (such as side force) is zero; and of all moments including aerodynamic yawing moment about z axis is zero, the aircraft is said to have the directional trim. In order to trim longitudinally, we obtained the airplane seen in Fig. 7.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.371-386, Oct.-Dec., 2013

An Airplane Calculator Featuring a High-Fidelity Methodology for Tailplane Sizing

xcg-ac Cmac Lwf

with Eq. 5: ach acwf

D M0wf

M0h Lh

Figure 7. Forces and moments acting for longitudinal trim.

M 0wf + M 0h + M 0e + Lwf xcg −accmac − Lh (lh + xcg −accmac ) = 0 (1) (1) where xcg-ac is the ratio between the distance of aerodynamic center of the wing to CG and the mean aerodynamic chord of the wing (static margin). Equation 1 can be further developed and we can obtain Eq. 2: ρV 2

(2)

Finally, an expression for the area ratio is derived (Eq. 3):

⎡Cm0 + Cm0 + CLwf xcg − ac ⎤ cmac Sh wf e ⎦ = ⎣ (3) S w ηh ⎡CLh ( lh + xcg −ac cmac ) − cmac Cm0 ⎤ (3) h ⎦ ⎣ Another form of Eq. 4 is as follows:

ηh Shlhʹ′CL

lh' = lh + xcg −ac cmac (5) (5) and Cm0h ~ 0, considering that horizontal stabilizers are composed of airfoil sections, which present maximum camber approximately equal to zero. The combination Shlh S c in Eq. 4 is an important w mac nondimensional parameter for the HT design, and is referred to as the “HT volume coefficient” (Eq. 6). The name is originated from the fact that both numerator and denominator have the unit of volume (e.g. m3). The numerator is a function of HT parameters, while the denominator is of wing parameters (Eq. 6): Vh =

Shlhʹ′ (6) S wcmac (6)

Table 2 shows typical Vh figures for some aircraft types. ρV (2) Swcmac ⎡Cm0wf + Cm0e + CLwf xcg −ac ⎤ = ηh Sh ⎡⎣CLh lh − cmacCm0h + CLh cmac xcg −ac ⎤⎦ ⎣ ⎦ 2 The tail volume coefficient is an indication of handling 2

ρV 2 m0e + CLwf xcg −ac ⎤ = ηh Sh ⎡⎣CLh lh − cmacCm0h + CLh cmac xcg −ac ⎤⎦ ⎦ 2

377

S wcmac

= Cm0wf + Cm0e + CLwf xcg −ac

(4) (4)

quality in longitudinal stability and control. As the Vh increases, the aircraft tends to be more longitudinally stable, and less longitudinally controllable. The fighter aircraft that is highly maneuverable tend to have a very low tail volume coefficient. Transport airplanes require a higher one because they are tailored to perform in a stable flight, for passenger and crew’s comfort. The Vh parameter is a crucial variable in HT design and is selected at the early stages of aircraft conceptual phase. Although one of the primary functions of the HT is to provide longitudinal stability, the tail volume coefficient

Table 2. Tail volume coefficients for some airplanes (Sadraey, 2009). Airplane

Cessna 172 Piper PA-46-350P Alenia G222 Fokker 100 Boeing F/A-18C Pilatus PC-12

Type

MTOW (kg)

Wing area (m2)

Overall length (m)

HT area (m2)

Light GA (piston powered) Light transport (piston powered) Military transport (turboprop) Jet airliner (R&R Tay 620) Fighter

1,100

16.2

7.9

1.94

0.76

1,950

16.26

8.72

–

0.66

28,000

22.7

–

0.85

43,090

93.5

32.5

21.72

1.07

23,400

16.8

–

0.49

4,100

25.81

14.14

–

1.08

Airbus A340-200

Multipurpose single-engine turboprop Jet airliner

257,000

363.1

59.39

72.90

1.11

Boeing 747-400

Jet airliner

396,830

525

68.63

136.60

0.81

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.371-386, Oct.-Dec., 2013

378

Mattos, B.S. and Secco, N.R.

serves as a significant parameter both in the longitudinal where: stability and longitudinal trim issues (Sadraey, 2009). 1 1 1 1 k A =k A = − − 1.7 1.7(10a)(10a) On the other hand, this parameter is usually chosen by A AR 1 +ww1.7ARw (10a) AR1w+ AR w w 10 −10 3λ− 3λ looking at similar aircraft. In fact, in the aircraft design, kλλ =kλ = ww w(10b)(10b) 7 7 (10b) each parameter depends on everything else of the z z h h configuration in a non-linear and very complex fashion. 1− 1− h b b (10c)(10c) k = hh k h = In modern conceptual design, MDO frameworks have 2 l 33 hh3 2lh a commonplace in the aircraft industry (Mattos and b b (10c) Magalhães, 2012). Therefore, the horizontal tail, as well The relationship ( CL )M ( CL )M = 0 can be calculated by as the vertical stabilizer, shall be designed concurrently using the following expression (Eq. 11): with wing and fuselage for a clean and efficient design, and picking up a tail volume coefficient makes no sense anymore. 2. . ARw CL w = (11) + + ) + (11) 50 w ( Design for stability When the derivative Cma is negative or the neutral Another design requirement must be examined: point is behind the aircraft CG, the aircraft is said to be aircraft static and dynamic longitudinal stability. The static statically longitudinally stable. The limit of the design is longitudinal stability is examined through the sign of the found when Eq. 9 is set to zero and therefore Eq. 12 is longitudinal stability derivative Cma or the location of the aircraft neutral point; the dynamic behavior is associated with obtained. autonomous motions such as Dutch roll, spiral divergence CLα wf xcg −ac Sh and other issues linked to flight quality, which will not be = (12) Sw ⎧⎪ lh ⎤ ⎫⎪ ⎛ d ε ⎞ ⎡ considered in the present work. ⎨CLαhηh ⎜1 − ⎟ ⎢ xcg −ac + ⎥ ⎬ cmac ⎦ ⎪⎭ ⎝ dα ⎠ ⎣ ⎪⎩ (12) For an aircraft with a fixed aft tail, the aircraft longitudinal w

stability derivative is determined as Eq. 7:

Cm =

dCm d Cm0wf + Lwf = d

xcg ) + Cm0h Lh [lh + ( xac 1 V 2S c w mac 2

xcg )]

d (7) (7)

Taking into account that, we have Eq. 8: d ε ⎞ ⎛ , Lh = CL 0h + CLβhηh S h ⎜1 − d α ⎟⎠ ⎝ ⎛ S ⎛ d ε ⎞ ⎞ Sh lh ⎛ d ε ⎞ = ⎜ CLα + CLα ηh h ⎜1 − (8) Cmα (8) ⎟ ⎟ xac −cg − CLα ηh ⎜1 − ⎟ Sw ⎝ dα ⎠ ⎠ Sw cmac ⎝ dα ⎠ ⎝ wf

Because the tail operates in the downwash field of the wing (for conventional, aft-tail configurations), the effective tail angle of attack is reduced. According to Roskam (1971), the parameter dε/dα can be estimated by Eq. 9:

( ) ( )

CLα w 1.19 dε = 4.44 ⎡⎣ k A kλ kh cos Ψ w 25 ⎤⎦ dα CLα w

M =0

(9) (9)

Obtaining the horizontal tail area Eqs. 4 and 12 provide two relationships between the area ratio (Sh/Sw ) and xcg-ac . These equations can now be combined into a single graph (Scholz, 2011). It should be observed that the aft center of gravity must be positioned at a safe distance to the natural stability limit. For a jet transport aircraft, Roskam established this value as 5% of MAC. However, according to Raymer (1999), this can be further reduced to 3% of MAC. The permitted areas of focus are now between the limit lines of controllability and those of the stability requirement. Between these lines now, the required CG range can thus be fitted to find out the smallest HT surface area. The region of interest lies above the horizontal green line in Fig. 8. Table 3 shows the CG variation for some airliners. The three-engine DC-10 intercontinental airliner has the lowest CG variation among all airplanes listed in Table 2. Figures for the 737-800 and Boeing 777-200 were obtained from Boeing’s training material (Boeing, 2009) and the remaining ones from Chai and Mason (1996).

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.371-386, Oct.-Dec., 2013

An Airplane Calculator Featuring a High-Fidelity Methodology for Tailplane Sizing

0.8

CG range

0.6 0.4

0.22 MAC

Sh /Sw

0.2

Controllability

-0.2 -0.4 Stability

-0.6 -0.8

-0.2

-0.1

0.1

0.2

Static Margin

0.3

0.4

0.5

Figure 8. Illustration of the calculation procedure for horizontal tail area ratio. Static margin is the variable xcg-ac. table 3. CG variation for some airliners (Chai and Mason, 1996, Boeing, 2011). Airplane

Fore/aft (% mAC)

Variation (% mAC)

Boeing 737-800

5/36

Boeing 767

11/32

Boeing 747-400

8.5/33

24.5

Boeing 777-200

14/44

DC-10

8/18

VERTiCAl STABilizER Two criteria were adopted for the vertical stabilizer sizing: static stability and controllability. The first condition is related to stability. The tailplane may be sized to fulfill a desired coefficient value that incorporates the variation of yawing moment coefficient with yaw angle, Cnβ. The fuselage and VT are the airplane components that significantly impact its directional stability. When an airplane experiences a sideslip angle, in general the fuselage alone will generate a moment that tends to increase the sideslip angle, which is a destabilizing and undesirable condition. The VT plays a major role to the static directional stability. When the airplane experiences a sideslip angle, the VT has the same aerodynamic effects as wings at angle of attack, but at different planes. Thus, it generates a moment relative to airplane’s CG, which produces a stabilizing one that tends to neutralize the sideslip angle. The VT usually has a low aspect ratio, which provides a higher stall angle than high-aspect ratio planforms. If stall occurs, a catastrophic situation may result due to the steady increase of

379

the sideslip angle. Ventral fins and stablets are good options to improve Dutch-roll characteristics without an unacceptable weight penalty. These surfaces provide a stable yawing moment at larger sideslip angles. The second consideration for vertical tailplane sizing is associated with controllability. This criterion may be determinant for a multi-engined underwing configuration. Loss of power on the number 1 engine, which are numbered from port to starboard, requires that the pilot simultaneously apply right rudder to correct the resulting yaw moment due to asymmetric thrust condition. Also, it should be applied a rolling moment to the right both to hold the starboard wing low and balance the rolling moment due to the rudder. The low starboard wing produces a side force on the airplane that balances that generated by the rudder deflection. Engine failure at takeoff poses a critical condition for the VT design. In this case, the remaining engine (or engines) must provide enough thrust to maintain the required rate of climb with the additional drag caused by rudder and aileron deflections. Trim drag due to rudder deflection and to a lesser extent aileron deflection may be critical in meeting FAR.121 climb requirements for the second segment, especially for twin-engine airplanes. An additional vertical tail-sizing requirement, which is harder to calculate, is to keep directional control while on the ground. The tail must be sized such that the minimum control speed on the ground (VMCG) is less than the takeoff decision speed (V1 ). If this is not the case, and VMCG is greater than V1 , then the situation may arise in which critical engine failure occurs above V1 . In such situation, the pilot has to continue the takeoff (because V1 has been exceeded), but the airplane lacks adequate lateral control while still on the ground. The requirements for VMCG are given in FAR 25.149(e). They are to lose the most critical engine, apply the rudder (without the use of nose wheel steering), and maintain control down the runway with a maximum of 30 feet (~ 10 m) lateral deviation from the runway centerline. In flight test, this is performed at successively lower speeds until the pilot can no longer maintain 30 feet lateral deviation. If additional control power is required for a derivative design (such as having increased engine thrust), improved rudder effectiveness may be achieved by adding vortex generators to the vertical stabilizer (as was done on the L1011 for one customer with particularly short runway, and reduced Vmca , requirements). If that does not work, a doublehinged rudder might fix the problem. This was done on the

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.371-386, Oct.-Dec., 2013

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Boeing 747SP (along with increased tail height) to make up for the shortened fuselage (and thus reduced rudder moment arm), and also on the DC-10. Design for controllability The required yawing moment coefficient to maintain steady flight with one failed outboard engine at 1.2 times the stall speed is as specified by FAR 25.149. The remaining outboard engine must be at the maximum available thrust, and the bank angle cannot be larger than five degrees. Figure 9 shows the engine-out situation for a twin-engine configuration. The engine-out constraint is established by constraining the maximum available yawing moment coefficient (Cnavailable) to be greater than the required one (Cnreq) for the engine-out flight condition (Eq. 13):

In Eq. 15, Vn is the nozzle exit velocity and Vn/V = 0.92, 0.42 for high- and low-bypass ratio turbofans, respectively. Torenbeek’s wind milling drag equation (Torenbeek, 1982) was validated against the flight test data of 747 (Grasmeyer, 1998). Torenbeek’s equation for the estimation of wind milling drag coefficient shows relatively good agreement with the flight test data over a range of Mach numbers. The maximum available yawing moment coefficient is obtained at an equilibrium flight condition with a given bank angle and a maximum rudder deflection (δr). The bank angle is limited to a maximum of five degrees by FAR 25.149, and the aircraft is allowed to have some sideslip (β) (Grasmeyer, 1998). The sideslip angle is found by summing the forces along the y-axis:

C yδ aδ a + C yδ rδ r + C yβ β + CL sin φ = 0 (16)

(16)

We considered in the present work that β = 1 and ϕ is obtained from Eq. 17:

Thrust

⎡ ( C yβ β + C yδ aδ a + C yδ rδ r ) ⎤ ⎥ φ = arcsin ⎢ − CL ⎢⎣ ⎥⎦

Drag

(17)

Considering that Cyδa ~ 0, Eq. 17 can be simplified to Eq. 18:

⎡ ( C yβ β + C yδ rδ r ) ⎤ ⎥ φ = arcsin ⎢ − CL ⎣⎢ ⎦⎥

(18)

In Eq. 14, T is the takeoff thrust and Dwindmill is the wind milling drag of the failed engine. The drag due to the wind milling of the failed engine is calculated using the method described in Appendix G-8 of Torenbeek (1982). Thus, we have

(18) If ϕ calculated by Eq. 18 is greater than 50, we set this value for ϕ in Eq. 16 and then β is obtained instead of fixing a value for ϕ. Thus, if the pilot slightly increases the sideslip angle further than one degree, he/she will have a safe margin for the increase of the Cnavailable. In addition, if one engine is inoperative, drag could become a critical issue if the sideslip angle were excessively increased. After the values for β and ϕ are known, the aileron deflection required to maintain equilibrium flight is obtained by summing the rolling moments about the x-axis:

Dwindmill = CDwindmill qSref (14)

Clδ aδ a + Clδ rδ r + Cl β β = 0 (19a)

Figure 9. Engine-out situation of a twinjet airplane.

Cnreq =

( Dwindmill + T ) qSref bw

(13)

(14)

where 2 di2 Vn V 0.075d + 1 n 1 + 0.16M 2 V Vn

δa =

2 i

windmill

SRe f

(15)

−Clδ rδ r − Cl β β Clδ a

(19b)

(19a) (19b)

The rudder deflection is initially set to the given maximum allowable steady state value, and the bank angle and aileron

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.371-386, Oct.-Dec., 2013

An Airplane Calculator Featuring a High-Fidelity Methodology for Tailplane Sizing

deflections for equilibrium flight are determined by Eqs. 19a and b. The maximum allowable steady-state deflection is typically 20 to 25 degrees. This allows for an additional fivedegree of deflection for maneuvering. The maximum available yawing moment is found by summing the contributions due to the ailerons, rudder, and sideslip (Eq. 20):

(20)

avail

(20)

Design for stability The remained condition for the design of the vertical stabilizer considers that the airplane is experiencing a sideslip angle (Fig. 10). In this situation, the yaw moment balancing when a sideslip angle is present is enforced

Terms in Eq. 23 are calculated according to Roskam’s methodology (Roskam, 1971): Cnβwing ≅ 0 (24)

CnβVT = −C yβVT

(26)

The stability derivative CyβVT in Eq. 26 can be calculated by

⎛ dσ C yβVT = −kCy βVT CLαVT ⎜1 + ⎝ d β where

kCy

kCy Moment caused by fuselage

(25)

(lv cos α + zv sin α )

0.75

1 bv

bv d f ,v +

f ,v

381

⎞ SVT ⎟ηv ⎠ S Re f

(27)

f ,v

bv d f ,v

(28)

and

d d

= 0.724 + 3.06

Sw 1 + cos

+ 0.4 1

Zw + 0.09 ARw (29) d (29)

In Eq. 30, Zw is a vertical distance between the wing quarter-chord at the location of mean aerodynamic chord and the fuselage centerline, positive downwards. Eq. 24 is analyzed at the beginning and end of the cruise. The most critical condition is then considered.

Figure 10. The vertical tail has a stabilizing effect when the airplane is experiencing a sideslip angle.

(21)

Eq. 21 can be further developed resulting in Eqs. 22 and 23. 2

2 airplane

2 VT

Cnβairplane = CnβVT + Cnβwing + Cnβ fuselage

(23)

2 wing

fuselage

(22) (23)

rESUltS Before integrating the present methodology into an airplane design application, a numerical tool written in MATLAB® language was developed for tailplane analysis only. The main reason for creating such tool was the validation of the methodology described in the preceding sections. This tool, which was named ITAIL, presents a graphical interface as shown in Fig. 11. Stability derivatives were calculated according to the methods proposed by Roskam (1971). The incorporation of ITAIL into AA will be further described.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.371-386, Oct.-Dec., 2013

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382

MTOW 43090

Wing Aspect ratio

Engine Overall pressure ratio

Inlet diameter (m)

15.8

1.118

HT Aspect ratio

Aspect ratio

0.89

4.64

Sweepback angle

CLmax, Take-off

8.43

2.17

Area

CLmax, Landing

93.5

Fan Pressure ratio

By-pass ratio

Airfoil cm0

1.33

3.04

Taper ratio

-0.015

TIT (K)

Fan eficiency

0.74

0.39

1472

0.93

Horizontal arm

Vertical arm

13.6

2.8

0.9

MAC

Eng. location

3.8 Dihedral

Number of engines

2.59 CLmax, clean 1.68 MMO 0.77 Service ceiling (ft) 35000 If 32.5 df 3.3 Range (km) 2450

Taper ratio 0.235 Sweepback angle 17.5

Ih 14.89

Rudder deflection 25

Config 1 CG range 0.3

Fokker 100

Landing flap

VT Area

Controllability: 8.20 m2 Stability: 13.03 m2 HT Area Controllability & Stability

Leave arena

21.97 m2

Calculate

Generrate figure of this screen

Figure 11. Main panel of ITAIL, a tailplane sizing application written in MATLAB®.

ITAIL ITAIL offers users the possibility of choosing some airliners from a drop-down menu. A broad range of airliners is represented from the regional jet CRJ-100 to the Boeing 747-100. Some characteristics of the airplanes used in the validation process are listed in Table 4. Both T-tail as well as the conventional tail configuration were considered. Aircraft manufacturers usually bring into market stretched or shortened variants featuring some shared components with a baseline configuration. Usually, stabilizers are designed for the baseline configuration in some cases, taking into

account characteristics of the remaining ones. Stretched versions will not be the cause of major concerns because the HT and VT arms will turn these surfaces more effective. However, shortened ones will require larger tailplanes. For this reason, the validation airplanes considered here are baseline configurations from which others were derived. Most airplane data used for ITAIL validation were taken from online sources (Jenkinson, Simpkin and Rhodes, 2011). Table 5 compares some calculated stability derivatives to values obtained from flight tests (Heffley and Jewell, 1972). Calculated values for the airliners that were taken for validation are shown in Table 6. The highest deviation found was that for the Boeing 737-100 HT area, 6.5%. The

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.371-386, Oct.-Dec., 2013

An Airplane Calculator Featuring a High-Fidelity Methodology for Tailplane Sizing

383

Table 4. Some data belonging to the validation airplanes that were inputted into ITAIL.

Boeing 757-200 Fokker 100 Boeing 747-100 CRJ-100ER Boeing 737-100

Sw (m2)

MTOW (kg)

Thrust arm (m)

cg range (% MAC)

185.25 93.5 511 48.35 102

115680 43090 333400 24091 44225

19.35 14.89 30.54 12.21 11.97

18.97 13.6 28.35 9.76 11.04

6.9 2.8 21.3 2.2 5.0

22 30 24 30 30

agreement between calculated and actual figures for the areas can be considered excellent. The factor that determined the Fokker 100 VT area was stability. Considering that this airplane has aft mounted engines, the controllability criterion is not the critical one. A post-design check in order to guarantee that the dynamic stability behavior will be satisfactory is obviously required. The results presented here corroborates that tailplanes areas of airliners can be usually determined by considering static stability requirements, with undesired dynamic behavior being fixed during flight test campaign if needed. Furthermore, design teams should be aware that aerodynamic phenomena like high interference drag, wake vortex and localized flow separation can turn tailplanes less effective and this issue must be carefully analyzed. Figure 12 displays the Vmca and stall speed variation with takeoff weight for the B-757-200 airliner. The kink in the graph can be credited to the fact that solution for the Vmca calculation is driven by two kinds of constraints: limitation of rudder or aileron deflection. In fact, the lower boundary represents 60.0% of the MTOW and may not be an operating condition. Vmca may become an important driver for the design of the VT if takeoff field length or climb requirements in second segment are very stringent.

Table 5. Comparison of calculated stability derivatives to experimental data (Boeing 747-100). Derivative

Present work H=12,200 m M=0.90 / α=1.2º

Heffley (1972) H=12,200 m M=0.90 / α=2.4º

Cnβ Clβ Cnδa Cnδr Clδr

0.196 -0.129 0.007 0.137 -0.028

0.207 -0.095 0.0027 0.0914 -0.0052

150 140 VMCA (ktas)

Airplane

130 120 110 100 90

vmca vstall

7.5

8.5 9 9.5 WEIGHT (kg)

10 10.5 11 11.5 x104

Figure 12. Vmca calculation by ITAIL (Boeing 757-200).

Table 6. Calculated tailplane areas for some airliners. Airplane

Boeing 757-200 (RB211-535E4) Fokker 100 (R&R Tay 620) Boeing 747-100 (JT9D-7A) Canadair CRJ-100ER (GE CF34-A) Boeing 737-100 (JT8D-7)

Calculated

HT area (m2) Actual

Calculated

VT area (m2) Actual

Deviation (%)

52.06

50.35

+3.40

Deviation (%)

35.61

34.27

+3.91

21.97

21.72

+1.15

13.03

12.30

+5.90

129.28

136.60

-5.35

77.99

77.10

+1.15

9.67

9.44

+2.44

9.73

9.18

+5.99

19.46

20.81

-6.50

29.77

28.99

+2.70

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Mattos, B.S. and Secco, N.R.

384

Enhanced Aeronautical Airplane After ITAIL was validated against HT and VT areas found in a large variety of transport airplane areas, its methodology was incorporated into AA. From this point on, the ability of the enhanced AA to correctly calculate VT and HT areas was put under scrutiny. For this purpose, data of the Fokker 100 airliner were input in that application. Establishing a constraint to the static margin to be exactly 12.5% of MAC, the convergence process to determination of the HT area can be seen in Fig. 13. The parameter XLE in the abscissa axis is the distance from the airplane nose of the wing leadingedge point in the fuselage centerline. Figure 14 reveals the geometry variation and the wing repositioning until the convergence was achieved. AA also provides an artistic view of the final configuration, which was displayed in Fig. 14. The HT area that was calculated by AA was 22.1 m2, a deviation of 2.3% when compared to the Fokker 100 actual HT area, which is displayed in Table 5. The VT area obtained with AA was then 13.6 m2, which obeys three criteria: • the criterion that Vmca < 1.2 Vs at MTOW, sea level takeoff resulted in an area of 6.2 m2; • the controllability requirement demanded an area of 9.7 m2; and • the static stability imposed the ultimate area of 13.6 m2 and drove the sizing.

of 22% if the sweepback angle varies from 17.5 to 33 degrees. No significant impact on VT area was recorded as expect. Sweptwing airplanes records improved directional stability because a restoring moment arises due to the differential drag forces acting on the port and starboard wings (Fig. 15 at bottom). For this reason, no significant change in VT area is expected as sweepback angle is increased taking into account that for aft-mounted engine configurations the stability criterion is critical. Figure 16 compares the calculated configurations for two configurations with distinct wing quarter-chord sweepback angles, 17.5 and 28 degrees. Static margin was fixed as 12.5% of MAC for both simulations and the vertical tail is the same for both configurations. The Fokker 100 is displayed in green

10 5 0 -5 -10

A study about the impact of the variation of the quarterchord sweepback angle on the tailplane areas was carried out with AA using the Fokker 100 as baseline airplane. The results displayed in Fig. 15 indicated a decrease of the required HT area

29 27

MT Area (m2)

Static margin (% MAC)

24 0

21 13.1 13.2

13.3

13.4 13.5 XLE (m)

13.6

Figure 13. Static margin and horizontal tail area convergence process for the Fokker 100 airliner.

13.7

Figure 14. Above: variation of the configuration geometry during the convergence process for the horizontal and vertical tails sizing of the baseline configuration. Wing area and engine data are treated as input variables. Bottom: artistic view of the designed configuration.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.371-386, Oct.-Dec., 2013

An Airplane Calculator Featuring a High-Fidelity Methodology for Tailplane Sizing

Area (m2)

22 20 18 16

HI VT

14 12

Short Period-Frequency Requirements - Category B Flight Phases 102 airplane Levels 2&3 Level 1

20 25 30 35 Quarter-chord sweepback angle

Lower drag force

colors and features a larger horizontal tail than that of the configuration with higher sweepback angle. The CG location for the empty airplane and the wing aerodynamic center do not change significantly for both configurations. AA also provides an output graph of the short period frequency requirements for longitudinal flight quality in order to verify if the design complies with (Fig. 17).

Higher drag force

Wsp [rad 1/s]

10 15

385

101

100

10-1 100

Resulting restoring moment yawing motion

101 Nza [g 1/rad]

102

Figure 17. AA analysis for the Fokker 100, which is represented by the circle between the Level 1 boundaries.

CoNClUdiNG rEMarKS Figure 15. Above: tailplane area variation with wing quarter-chord sweepback angle of the Fokker 100 airplane. Values were obtained with AA calculator. Bottom: sweptwing airplanes produce a restoring moment improving the directional stability.

Figure 16. Comparison of the HT for the Fokker 100 (green color) and a derived confi guration with 28 degrees of wing sweepback angle.

A MATLAB® application called ITAIL was developed to validate a methodology for vertical and HT sizing of transport airplanes. ITAIL employs static stability and controllability criteria for tailplane design. The highest deviation found by the ITAIL application was that for the Boeing 737-100 HT area, i.e. 6.5%. The agreement between calculated and actual figures for the areas can be considered excellent. A post-design check in order to guarantee that the dynamic stability behavior will be satisfactory is required. The results presented here corroborates that tailplanes areas of airliners can be usually determined by considering static stability and controllability requirements,, with undesired dynamic behavior being fixed during flight test campaign if necessary. The methodology embedded in ITAIL avoids a very arbitrary criterion to the sizing of transport airplanes tail surfaces, i.e. the tail volume coefficient. Thanks to the increase in computing power in the last ten years, conceptual airplane design has steadily become more sophisticated and has incorporated higherfidelity techniques to model airplane geometry and aeronautical disciplines. In this context, dropping very inaccurate methods like the tail volume coefficient fits well in this trend.

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An existing airplane calculator designated AA was enhanced with a higher fidelity approach for sizing horizontal and vertical tailplanes. AA is a MATLAB® application tailored to be incorporated into MDO frameworks. Results obtained with AA using the methodology described in this work and incorporated by ITAIL revealed an excellent agreement

with the VT and HT areas of the Fokker 100 airliner fitted with Rolls&Royce Tay 620 engines. Vmca may become an important driver for the design of the VT if takeoff field length or climb requirements in second segment are very stringent. Both ITAIL and AA applications already consider Vmca for the VT sizing.

REFERENCES Benson, T.J., 1995, “An Interactive Educational Tool for Turbojet Engines”, Cleveland, Ohio, NASA Lewis Research Center. Boeing Co., 2009, “Takeoff Performance”, Boeing Electronic Training Material, Seattle. Cavanaugh, M.A., 2004, “A MATLAB m-File to Calculate the Single Engine Minimum Control Speed in Air of a Jet Powered Aircraft”, VMCmav1.m User’s Manual, Virginia Tech, VA. Centennial of Flight, “Dynamic Longitudinal, Directional, and Lateral Stability,” Retrieved on November 12 2011, from http://www.centennialofflight. gov/essay/Theories_of_Flight/Stability_II/TH27.htm Chai, S.T. and Mason, W., 1996, “Aircraft Landing Gear Integration in Aircraft Conceptual Design”, MAD Report, MAD 96-09-01, Virginia, September. Grasmeyer, J., 1998, “Stability and Control Derivative Estimation and Engine-Out Analysis,” Virginia Tech Department of Aerospace and Ocean Engineering Report, VPI-AOE-254. Grundlach IV, J.F., 1999, “Multidisciplinary Design Optimization and Industry Review of a 2010 Strut-Braced Wing Transonic Transport,” Master of Aerospace Engineering Thesis, Virginia Polytechnic Institute and State University, Blacksburg, Virginia. Heffley, R.K. and Jewell, W.F, 1972, “Aircraft Handling Qualities Data”, NASA CR 2144. Holst, T.L. and Thomas, S.D., 1982, “Numerical Solution of Transonic Wing Flow Fields”, AIAA paper # 82-105. Jenkinson, L., Simpkin, P., Rhodes, D., 2001, “Civil Jet Aircraft Design”, Retrieved on November 2 2011, from http://www.elsevierdirect.com/ companions/9780340741528/case-studies/default.htm Karas, O.V. and Kovalev, V.E., 2011, “BLWF 28 User’s Guide”, Moscow.

LIST OF SYMBOLS AR = Aspect ratio b = Span cmac = Mean aerodynamic chord CL = Lift coefficient CLα = Variantion of the lift coefficient with angle of attack CLmax = Maximum lift coefficient of wing or airplane configuration Cme = Moment coefficient due to engine Cm0 = Moment coefficient Cm0wf = Moment coefficient of the wing - body combination d = diameter D = Drag force M∞ = Freestream Mach number MAC – Mean aerodynamic chord l = length L = Lift force q = Dynamic pressure S = Area T = Engine thrust V = Velocity W = weight

Loureiro, V., 2008, “Optimal design of Transport Airplane with a Realistic Engine Model in the Loop,” Undergraduation Thesis, Instituto Tecnológico de Aeronáutica, São José dos Campos, Brazil. Mattos, B.S. and Magalhães, P.E.C.S., 2012, “Conceptual Optimal Design of Airliners with Noise Constraints”, 50th Aerospace Sciences Meeting and Exhibit, Nashville, TN. Raymer, D.P., 1999, “Aircraft Design: A Conceptual Approach”, 3rd Ed, Washington D.C., AIAA. Roskam, J., 1971, “Methods for Estimating Stability and Control Derivatives of Conventional Subsonic Airplanes,” Roskam Aviation and Engineering Corporation, Lawrence, Kansas. Roskam, J., 2000a, “Airplane Design, Part I: Preliminary Sizing of Airplanes,” The University of Kansas, Lawrence, DARcorporation. Roskam, J., 2000b, “Airplane Design, Part VI: Preliminary Calculation of Aerodynamics, Thrust and Power Characteristics,” The University of Kansas, Lawrence, DARcorporation. Sadraey, M., 2009, “Aircraft Performance Analysis,” Verlag Dr. Müller. Scholz, D., 2011, “PreSTo - Aircraft Preliminary Sizing Tool – From Requirements to the Three-view Drawing”, EWADE 2011 – 10th Workshop on Aircraft Design Education, Naples, Italy, May. Toreenbeek, E., 1982, “Synthesis of Subsonic Airplane Design,” Delft University Press, Delft, the Netherlands. Videiro, P.L., 2012, “Wing Structural Optimization in Aircraft Conceptual Design,” undergraduation thesis, Instituto Tecnológico de Aeronáutica, São José dos Campos, Brazil. “World’s Aircraft, “Fokker 100,” 2013, Retrieved on June 3 2013, from http://www.aer.ita.br/~bmattos/mundo/airliner/fo100.htm

a = Angle of attack b = Sideslip Angle e = Downwash angle at horizontal tail h = Ratio between local and freestream dynamic pressure f = Bank angle r = Air density Ψ = Sweepback angle SUBSCRIPTS 50 Half chord ac Aerodynamic CG Center of gravity e Engine h Horizontal tail mca Minimum control speed in the air mcg Minimum control speed in the ground ref Reference (area, length, etc…) s Stall w Wing ∞ Freestream

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doi: 10.5028/jatm.v5i4.267

Investigations on Directed Infrared Countermeasures Risks to Fighter Aircraft Pilots Lester de Abreu Faria1, Luciano Barbosa Magalhães2, Roberto d’Amore1

ABSTRACT: Directed Infrared Countermeasures (DIRCM) are used to protect aircrafts against missiles with infrared (IR) guidance. They are employed by military and civilian aircrafts, drawing away the guidance system of a missile as it attempts to lock onto the IR signature of the turbines. Unfortunately, the protection provided by these devices generates risks and challenges that must be overcome. In this paper, investigations on DIRCM risks to fighter aircraft pilots are carried out. Different kinds of lasers employed in actual DIRCMs are analyzed and the results show that, depending on their frequency (wavelength), damages can occur up to a distance of 4.8 km. The transmittance through the canopy of an F-5 fighter aircraft is evaluated and its effects on the IR propagation are predicted by the use of software called Counter-Measurements in PYTHON (CMePy). Results show that, even when there are interfaces between the pilot and the source of radiation, damages can occur, showing the importance of this investigation to the right understanding of this subject and future mitigations. KEYWORDS: Aerospace, Safety, Defense systems, Laser, DIRCM, Infrared.

CONTEXTUALIZATION Since September 11th, 2001, the entire world, but specially the USA, focuses its attention on the use of man portable air defense system (MANPADS) as the main terrorist’s weapon for striking civilian and military aircrafts. Today, there are approximately 500,000 shoulder-fired missiles in military arsenals around the world, while there are from 5,000 to 150,000 in the hands of up to 30 non-state organizations, according to a report by the Congressional Research Service (CRS) (Bolkcom et al., 2003). An analysis provided by the CRS indicates that, in the last 30 years, there were, at least, 35 civilian aircraft attacks through the use of MANPADS, of which 24 were shot down (Laurenzo, 2005; Nakagawara and Montgomery, 2008). Since then, there has been a trend not only for military but also for civilian aircrafts to be equipped with some kind of countermeasure that can draw away the guidance system of a missile as it attempts to lock onto the infrared (IR) signature of the turbines of the aircraft. Flare-based C-MANPAD systems were a main solution for military aircrafts, but the fear of collateral damage from these kinds of countermeasures, should the flares be deployed by mistake, makes it non-optimum. Therefore, some alternative countermeasure systems were developed to be employed as infrared countermeasures (IRCM). These systems disrupt the guidance systems of surface-to-air missiles (SAM), using multiple-wavelength lasers that emit radiation in the IR portion of the electromagnetic spectrum. Unfortunately, new

1.Instituto Tecnológico de Aeronáutica – São José dos Campos/SP – Brazil 2.Comando-Geral de Operações Aéreas – Rio de Janeiro/RJ – Brazil Author for correspondence: Lester de Abreu Faria | Praça Marechal Eduardo Gomes, 50 – Vila das Acácias | CEP 12.228-900 São José dos Campos/SP – Brazil | Email: lester@ita.br Received: 12/07/13 | Accepted: 30/08/13

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Faria, L.A., Magalhães, L.B. and d’Amore, R.

systems generate new risks and new challenges to scientific and medical areas that must be overcome. IR emissions from these laser systems can be hazardous to ocular tissues and skin under certain circumstances. Depending on factors like energy delivered per unit area, exposure time, and wavelength, several damages can occur. The major danger in IR emissions to the human vision is the lack of response of the ocular system to this type of radiation. The human eye can detect only wavelengths in the visible optical range and, in the case of the IR radiation, it is only noticed when some kind of damage has already happened (Nakagawara and Montgomery, 2008). Figure 1 indicates which wavelengths are mainly absorbed in the different parts of the eye: the cornea, the lens, or the retina (Brunetaud and Hill, 2012). Concerning the IR wavelengths, if the source is within the nominal ocular hazard distance (NOHD) of the pilot, near-IR (NIR) (780–1400 nm) laser radiation may damage the retina, while middle-IR (MIR) and far-IR laser radiations (>1400 nm) can injure the cornea and, to a lesser extent, the crystalline lens.

On the other hand, although skin burns are not generally very serious if a person is able to feel the damage and leave the radiation area, it becomes much more serious if people cannot react to the exposure, as the situation where a pilot is engaged in a combat arena (Brunetaud and Hill, 2012). Table 1 demonstrates the potential injuries resulting from the excessive exposure to visible and IR radiation. Excessive exposure to optical radiation in different ranges is currently a concern to industrial hygienists, safety engineers, and public health officials in many developed countries around the world for their potential hazard to health and safety (Nakagawara and Montgomery, 2008). In the literature, a lot of information and works can be found about dangers associated with exposure to excessive levels of visible and UV radiation in the USA National Airspace System (NAS) (Diffey and Roscoe, 1990; Caidin, 1992; West et al., 1998) and other airspace systems (NASA, 1998; Wisegeek, 1999; Setlow, 2003). There are, as well, many studies on the transmittance of polycarbonate in different ranges of IR. However, only a few works conjugate both information and deal with the potential hazards to human health from the exposure to high levels of IR radiation, in free space or through an additional interface (Nakagawara and Montgomery, 2008). Above all, such kind of research has not yet been performed in Brazil, leading to a lack of information on professional diseases and impacting the performance of the pilots. The same results found in this work can be expandable to the “occupational health and safety”, for all of those who work with laser emissions. This paper shows a study made by the Electronic Warfare Laboratory (LAB-GE), at Technological Institute of Aeronautics (ITA), Brazil, concerning to the IR and visible radiation risks to the pilot’s health and the protection provided by aircraft canopies. Transmittance properties of a fighter aircraft canopy from the visible to the IR range

cornea 0.4–1.4 µm

retina

1.2–1.4 and 1.6–1.8 µm lens

1.4–14 µm

Figure 1. Parts of the eye at risk from different wavelengths (adapted from Brunetaud and Hill, 2012).

Table 1. Potential injuries from excessive exposure to visible and infrared radiation. Spectral band

Wavelenght (nm)

Visible

NIR

MIR

FIR

380–780

780–1400

1400–3000

3000–106

Retinal burns

Corneal burns Cataracts

Potential injury

Color and night vision degradation Thermal skin burns

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Investigations on Directed Infrared Countermeasures Risks to Fighter Aircraft Pilots

389

(670–12000 nm) are presented. Further discussion on the electromagnetic spectrum leads to a “big picture” of the environment where the pilot operates and the risks to which he is submitted.

DIRECTED INFRARED COUNTERMEASURES Directed Infrared Countermeasures (DIRCM) is used to protect aircraft against missiles with IR guidance, especially the portable ones that can be shoulder-launched (also known as MANPADS). Its operation is given in conjunction with missile approach warning system (MAWS), which is responsible for the detection of an approaching missile, determining if it is a threat to the aircraft, and providing the approaching direction of threat to DIRCM. This signal activates the laser transmitter, which will engage the threat and transmit energy modulated beams to deflect it from the aircraft. Those equipments can be classified in two kinds of systems: directional and non-directional. Considering this work, we will focus on the directional DIRCM, once they are the most advanced and the most efficient ones recently in use. The directional DIRCM operates by emitting radiation beams with small angular apertures, as lasers. This feature leads to low operation power and low response time. Besides that, they show spectral and spatial power densities much higher than any other typical source, due to the low divergence found in the optical laser beam (order of milliradians). Its main disadvantage is the need of using turrets to direct the beam emission against the missile, requiring high pointing accuracy and integrated communication with the MAWS. An example of this kind of equipment is AN/AAQ-24(V) NEMESIS, from Northrop-Grumman (GlobalSecurity.org. 2011), which is shown in Fig. 2. There are also multi-spectral DIRCMs, which can handle multiple threats fired from any direction, and are adaptable to threat changes. These equipments are able to operate in different IR bands (Elbit Systems of America , 1999). In order to analyze the emission of a directional DIRCM, it must be considered the main characteristics of a laser beam, such as: high spectral and spatial power density, high directivity, low operating power, low bandwidth beam, and pulse operation.

Figure 2. AN/AAQ-24(V) NEMESIS, from Northrop-Grumman.

GENERAL CONSIDERATIONS Given the possibility of physiological tissue damage, a study was performed considering the potential effects that IR countermeasure equipment radiation could cause not only to human vision but also to human skin. Therefore, initially, some actual DIRCM data were collected from the literature and their potential risks to human health were dimensioned. After that, the transmittance of the canopy of an F-5 fighter aircraft was measured from visible to IR band, since this structure can be perceived as a filter, or a first barrier, between the emitting source and the pilot. The measurements were performed in LAB-GE/ITA. In order to predict the potential risks before and after the insertion of interfaces (canopy or any others), it was developed and used a software called Counter-Measurements in PYTHON (CMePy) , which calculates the optical power that reaches the eye, or the skin, of a pilot, helping us to dimension the problem. An aircraft canopy is the transparent enclosure over the cockpit. Its main function is to provide a weatherproof and reasonably quiet environment for the aircraft’s occupants, protecting them during flight. The canopy must be as aerodynamically shaped as possible to minimize drag and as transparent as possible to improve the outside visibility (AS 2211, 1991; Nakagawara and Montgomery, 2008). A high-performance aircraft’s

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canopy is vital not only for the previous features but also for the enhancing and protecting of the pilot’s vision. High-performance aircrafts from the Brazilian Air Force use polycarbonate (plastic) as canopy compound. The transmittance of a canopy compound can affect pilot’s visual performance while providing protection from harmful electromagnetic radiation. On the other hand, transmittance may be defined as the ratio of the total radiant flux that is transmitted, to that incident on the surface of the aircraft canopy. Higher is the ratio, higher is the transmittance.

MEASUREMENTS AND EXPERIMENTAL RESULTS Once it was understood how the energy generated by the DIRCM can interact with the human eye, it was necessary to perform some transmittance measurements of aircraft canopies, in order to figure out how radiation is transmitted through these interfaces. To perform this, it was collected random samples of the material used in the canopy of the F-5 fighter aircraft. The measurements were performed using a Spectrum 400 spectrometer, manufactured by PerkinElmer. This is a device that relies on the concepts of the Michelson interferometer to perform the measurement of transmittance of the sample at the wavelengths of interest. It allows measurements in the band from 0.67 to 18 μm. The measurements were conducted in a controlled environment in terms of temperature (23±0.1oC) and relative humidity (65±1%), for wavelengths from 0.67 to 12 μm. The samples were placed inside the spectrometer using a support, which was set an incidence angle of 90°. Figure 3 shows a sample being measured in the spectrometer. The measurement results are calculated, as a function of wavelength, using the ratio between the intensity of radiation that reaches the detector and the one emitted by the source. A correction factor is applied considering the background emission. The actual result of the measurement performed is shown in Fig. 4. As seen in Fig. 4, the canopy presents a high transmittance, from the initial wavelength (0.67 μm) up to 1.6 μm with some absorption valleys around 1.19 and 1.4 μm.

Figure 3. Canopy sample inside the spectrometer in order to perform the measurements.

From 1.6 μm on, transmittance drops sharply close to zero, presenting peaks around 1.8 and 2 μm, showing a transmittance level around 40%. Thereafter, the measured values are almost zero, up to the limit of the measurement, 12 μm, although the graph is presented only up to 3 μm. We must highlight the presence of valleys in 1.19, 1.4, and 1.9 μm, which correspond to the wavelengths where a high absorption by the water molecules in the air occur (Hudson, 1969). On the other hand, it is estimated that the observed absorption at 1.7 μm refers to the polycarbonate, once, synthesized from hydrocarbons, it will present absorption bands around the wavelengths of 1.68, 1.70, 1.72, and 1.78 μm (Araújo and Kawano, 2001). The results presented in Fig. 4 show that there is no transmission through the canopy for emission whose wavelengths are above 2.2 μm (except for a small region around 2.6 μm), because these wavelengths are completely absorbed or reflected by the material. This result agrees with other ones found in the literature (Nakagawara and Montgomery, 2008). Therefore, the canopy shows high effectiveness against directional DIRCM MIR emissions. However, if the DIRCM is multi-spectral and presents spectral components in the NIR, the canopy does not serve as protection, since the transmittance is around 90%, and only 10% of the incident power is reflected or absorbed by it. Based on these results, a theoretical analysis was performed, focusing on possible damages to the pilot’s health.

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100

Transmittance (%)

0 0.6

0.8

1.2

1.4

1.6

1.8

2.2

2.4

2.6

2.8

Wavelenght (microns) Figure 4. Experimental results of visible, near-infrared, and middle-infrared transmittance.

THEORETICAL ANALYSIS Due to the high transmissivity of the canopy in the NIR range, it was made a theoretical analysis of possible damages to the pilot’s vision, given the main features of well-known lasers and safety parameters for human vision. Therefore, some definitions are necessary, as shown in AS 2211: • Maximum permissible exposure (MPE): maximum level of radiation to which human tissue can be exposed without suffering injury or damage immediately after exposure or after some time; • Nominal ocular hazard distance (NOHD): minimum safety distance where the radiant intensity of the ocular tissue (retina) is considered to be below the level defined as MPE; and • Nominal skin hazard distance (NSHD): minimum safety distance at which the radiant intensity on the skin is below the level defined as MPE. Although the aim of this work is to analyze the possible damage to vision, some calculations on the potential damage to the human skin were included, once the procedure for

performing both calculations is similar and the used software (CMePy) provides the complete information on them (with appropriate changes in reference tables). The analysis starts by using the equation that calculates the intensity of a laser beam (W/m²) at a distance z from the laser source, which is given by (Lelek, 2007):

4 P0 exp ( -zµ ) π ( 2 w + z θ )2

(1)

In Eq. 1, P0 is the power of the source (in Watts), or eventually the total energy carried by one pulse (in Joules), considering a Gaussian beam; w is the waist of the Gaussian beam (m) at a distance where the beam intensity is 1/e² of P0; θ is the divergence of the beam (rad); μ is the atmospheric absorption (usually neglected) (Nakagawara and Montgomery, 2008); and z is the distance between the source and the target. Since, for safety reasons, it is necessary to calculate the NOHD of a laser with respect to the retina, variable z should be replaced by NOHD in Eq. 1; while the variable I must be replaced by MPE. Thus, considering the transmittance of

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any interfaces (such as the canopy) as τm and a multiplicative factor of 2.5 for non-Gaussian beams, one arrives to (AS 2211; Lelek, 2007):

4 P0τm 2.5 NOHD =

πMPE θ

-2 w

laser source beam must be determined. By selecting the appropriate MPE table, use the laser wavelength (nm) and exposure time (t) to locate the MPE value or formula on the table and calculate it. • For a pulsed laser source, the pulse length, the pulse repetition rate, the laser wavelength, the exposition time, and source beam size must be determined. Based on these data, (1) calculate the MPE for a single laser pulse, using the pulse length as the duration time. Then, (2) calculate the MPE for a repeated laser pulse by dividing the MPE for a single pulse by N0.25, where “N” is the actual number of pulses. Finally, (3) calculate the single pulse MPE again, using the duration time and divide this result by the number of pulses (N) to determine the average MPE for the pulsed laser source. Choose the smallest of the three values as the MPE for the pulsed laser source.

(2)

Equation 2 was applied on data of Table 2, achieving the results presented in Table 3. The calculation of the MPE is required for each one of the lasers. It was obtained by following the instructions and tables provided in AS 2211; Nakagawara and Montgomery (2008), Northwestern University (2012), and Fred Seeper (2012), considering always the most restrictive value of this parameter, as established by the AS 2211. In general, the procedures for the calculation of the MPE can be summarized as follows: • For a continuous wave (CW) laser source, the wavelength of the CW laser beam, the duration time, and the size of the

Besides considering the parameters shown in Table 2 for the calculations of each one of the lasers, the following assumptions were made (Nakagawara and Montgomery, 2008):

Table 2. Lasers technical specifications used in simulations. Wavelength (nm)

FRP (Hz)

P0 (W)

Beam divergence (mrad)

Pulse width (ns)

Beam waist (mm)

Exposition time (s)

Gaussian beam?

GaAs

840

300×10-3

0.4

Nd:YAG

1064

300×10-3

0.4

Nd:YAG

1330

300×10-3

0.4

Cr2+:CdSe

2600

300×10-3

0.4

HeNe

3390

300×10-3

0.4

CO2

10600

300×10-3

0.4

Laser*

*Nakagawara and Montgomery (2008)

Table 3. Safety distances for the lasers under test. Wavelength (nm)

MPE eye (J/m2)

MPE skin (J/m2)

NOHD (m)

NSHD (m)

GaAs

840

2.72×10-3

108.89

4834.98

23.38

Nd:YAG

1064

14.29×10-3

285.74

2110.10

14.13

Nd:YAG

1330

114.30×10−3

285.74

745.51

14.13

Cr2+:CdSe

2600

28.57

46.40

HeNe

3390

28.57

46.40

CO2

10600

28.57

46.40

Laser*

*Nakagawara and Montgomery (2008)

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Investigations on Directed Infrared Countermeasures Risks to Fighter Aircraft Pilots

• Negligible atmospheric attenuation (equals to zero); • Transmittance of the optical interfaces equal to 100%; • The beam is stationary during each one of the analysis; and • Only one wavelength is emitted.

The previous assumptions were established in order to simplify the analysis. Although being slightly different from the ones found in an actual environment, they serve as a basis for the conclusions to which we want to reach. The results presented in Table 3 were calculated with the aid of a program called CMePy developed specifically for this purpose, in a PYTHON environment. It is able to calculate both the MPE and NOHD for eye and skin in atmosphere transmission, with or without the presence of any interfaces, considering that the spectral transmissivity of that interface is known. The analysis of Table 3 shows that lasers operating in the NIR (as may be the case for multi-spectral DIRCMs) present a high danger to the pilot’s eye, since the calculated NOHD shows that, even at distances of 4834.98 m (GaAs, 840 nm), the laser beam irradiance is sufficient to cause damage to the retina. For higher wavelengths, the danger is reduced, since the NOHD and NSHD are much smaller and highly improbable to occur in actual situations. Although being improbable in an actual flight situation, these data must be considered if a ground test is being conducted. This conclusion leads to the necessity of using specific personal protection equipment (PPE) during the tests. It is important to mention that the procedure followed for the calculation and analysis of results is the same of that one published by the Laser Institute of America (LIA) under number ANSI Z136.

393

Once the NOHD and NSHD are known, in free space, it is important to calculate these parameters considering the influence of the canopy, according to Fig. 4, since it works as a filter. Therefore, the values of transmittance shown in Fig. 4 were inserted in Eq. 2 as the variable τm. The results are shown in Table 4. The results show that the IR attenuation provided by the canopy reduces NOHD of the three different types of lasers operating in the region of danger to the retina (<1.4 μm) in only 10%, in average. On the other hand, for wavelengths above 2.2 μm, in regions of MIR and FIR, the canopy acts as a perfect filter, protecting the pilot completely, except laser Cr2+:CdSe (2600 nm), which, despite of suffering a strong attenuation, still presents some danger, depending on the distance of emission. The same conclusions can be extended to NSHD measurements. However, in this last case, the calculated distances are very low, showing to be highly improbable to occur in practice.

CONCLUSIONS In this paper, we presented an investigation on the DIRCM risks to fighter aircraft pilots. Through experimental results and using a computational tool, it was showed that the F-5 fighter aircraft canopy provides only partial protection against IR radiation. Measurements performed using a spectrometer showed that the average transmittance for canopies in the NIR range (0.67–1.3 μm) was around 90% (around 10% of absorption and reflection of canopy), presenting valleys at 1.19 and 1.4 μm.

Table 4. Safety distances for lasers when interfaced by the canopy. Wavelength (nm)

NOHD (m)

NSHD (m)

Transmittance

GaAs

840

4535.56

21.88

0.88

Nd:YAG

1064

2013.06

13.44

0.91

Nd:YAG

1330

570.09

10.61

0.60

Cr2+:CdSe

2600

5.87

0.02

HeNe

3390

-0.80(1)

0.00

CO2

10600

-0.80(1)

0.00

Laser*

Negative values do not have physical meaning and must be considered “0”. For these mathematical results, laser is shown to be safe at any distance (AS 2211) *Nakagawara and Montgomery (2008)

(1)

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Faria, L.A., Magalhães, L.B. and d’Amore, R.

From 1.5μm on, transmittance drops sharply close to zero, presenting two peaks at 1.8 and 2 μm (around 40%). For wavelengths higher than 2.2 μm, transmittance show to be almost zero. Due to the high transmissivity in NIR, additional theoretical calculations were performed, which showed that the safety distances for certain lasers operating in this range can reach up to 4.8 km, depending on some parameters of operation. Even with the canopy as an interface, this distance does not seem to be considerably reduced (≈4.5 km).

These results demonstrate that the F-5 canopy offers only limited protection against IR emissions, especially against DIRCMs operating at NIR wavelengths, to which the NOHD can reach more than 4.5 km. In order to complement this work, it is suggested that the same measurements are performed to visors of helmets used by pilots (mandatory item in fighter aircraft), as well as on other kinds of canopies, in order to have a more comprehensive picture of the problem, since different materials with different thicknesses will infer different spectral transmittances to the canopies

REFERENCES Araújo, S.C. and Kawano, Y., 2001, “Espectro vibracional no infravermelho próximo dos polímeros poliestireno, Poli (Metacrilato de Metila) e Policarbonato”, Polímeros Vol.11, No. 4, pp. 213-221.

Lelek, M., 2007, “Laser and non-linear optics – Laser safety”. Retrieved in January 15, 2012, from http://www.optique-ingenieur. org/en/courses/OPI_ang_M01_C02

Bolkcom, C., Elias, B. and Feickert, A., 2003, “Homeland Security: Protecting airlines from terrorist missiles”, Congressional Research Service Report for Congress, The Library of Congress, USA, 27 p.

Nakagawara, V. B. and Montgomery, R. W., 2008, “Infrared radiation transmittance and pilot vision through civilian aircraft windscreens”, Federal Aviation Administration, (DOT/FAA/AM-08/15), 18 pp.

Brunetaud J.M. and Hill, S., 2012, “Risks from lasers”. Retrieved in July 20, 2012, from http://www3.univlille2.fr/safelase/english/ haza _en.html

NASA, 1998, “Ask an Astrophysicist”. Retrieved in July 20, 2012, from http://imagine.gsfc.nasa.gov/docs/ask_astro/answers/ 980119b.html

Caidin, M., 1992, “Growing threat of UVR”, Aviation Safety, Vol. 12, No. 3, pp.1-15.

Northwestern University, 2011, “Laser Safety Handbook”, 20pp. Retrieved in July 19, 2012, from http://www.research.northwestern. edu/ors/forms/laser-safety-handbook.pdf

Diffey, B. L. and Roscoe, A. H., 1990, “Exposure to solar ultraviolet radiation in flight”, Aviation Space and Environmental Medicine, Vol. 61, No. 11, pp. 1032-1035. Elbyt Systems, 1999, “Sensor and electro-optics solutions. MUSIC”. Retrieved in July 04, 2013, from http://www.elbitsystems-us.com/ sensor-electro-optics-solutions/airborne-maritime/survivability/music GlobalSecurity.org, 2011, “AN-AAQ-24 Directional Infrared Countermeasures (DIRCM)”. Retrieved in May 31, 2012, from http://www.globalsecurity.org/military /systems/aircraft/systems/ an-aaq-24.htm

Seeber, F., 2012, “Fundamentals of Photonics”, Module 1.2 - Light Sources and Laser Safety, Retrieved in July 19, 2012, from http://spie. org/Documents/Publications/00%20STEP%20Module%2002.pdf Setlow, R.B., 2003, “The hazards of space travel”. Retrieved in July 20, 2012, from http://www.nature.com/embor/journal/v4/n11/ full/embor7400016.html

Hudson, R.D., 1969, “Infrared system engineering”, New York: John Wiley & Sons, USA, 642 p.

West, S.K., Duncan, D.D., Munoz, B., Rubin, G.S., Fried, L.P., Bandeen-Roche, K. and Schein, O.D.,1998, “Sunlight exposure and risk of lens opacities in a population-based study: the Salisbury Eye Evaluation project”. The Journal of the American Medical Association, Vol. 280, No. 8, pp. 714-718.

Laurenzo, R., 2005, “Antimissile systems for airliners”. Aerospace America: American Institute of Aeronautics and Astronautics, Vol. 43, Ed. 3, pp. 33-37.

Wisegeek, 1999, “How Dangerous is UV Radiation?”. Retrieved in July 20, 2012, from http://www.wisegeek.com/how-dangerous-isuv-radiation.htm

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doi: 10.5028/jatm.v5i4.252

Analyzing the Unscented Kalman Filter Robustness for Orbit Determination Through Global Positioning System Signals Paula Cristiane Pinto Mesquita Pardal1, Hélio Koiti Kuga2, Rodolpho Vilhena de Moraes1

ABSTRACT: The nonlinear unscented Kalman filter (UKF) is evaluated for the satellite orbit determination problem, using Global Positioning System (GPS) measurements. The assessment is based on the robustness of the filter. The main subjects for the evaluation are convergence speed and dynamical model complexity. Such assessment is based on comparing the UKF results with the extended Kalman filter (EKF) results for the solution of the same problem. Based on the analysis of such criteria, the advantages and drawbacks of the implementations are presented. In this orbit determination problem, the focus is to analyze UKF convergence behavior using different sampling rates for the GPS signals, where scattering of measurements will be taken into account. A second aim is to evaluate how the dynamical model complexity affects the performance of the estimators in such adverse situation. After solving the real-time satellite orbit determination problem using actual GPS measurements, through EKF and UKF algorithms, the results obtained are compared in computational terms such as complexity, convergence, and accuracy. KEYWORDS: Orbit determination, Nonlinear Kalman filters, GPS measurements, Real time.

INTRODUCTION This work points out the nonlinear unscented Kalman filter (UKF) robustness assessment for a real-time satellite orbit determination problem, using Global Positioning System (GPS) measurements. This evaluation is based on comparing the UKF performance with the extended Kalman filter (EKF) for different sampling rates of the measurement from GPS signals. One-second analysis takes into account the dynamical model complexity effects on the performance of the two estimation techniques. The main subjects for the comparisons between the estimators are: convergence speed, divergence occurrence, faults, and statistical shortcomings. Based on the analysis of such criteria, the advantages and drawbacks of each estimator are exhibited. The orbit determination of an artificial satellite is done using real data from the GPS receivers. In the orbit determination process of artificial satellites, the nature of both the dynamic system and the measurements equations are nonlinear. As a result, here it is necessary to manage a fully nonlinear problem in which the disturbing forces as well as the measurements are not easily modeled. This orbit determination problem lies in estimating the variables that completely specify a satellite trajectory in the space, processing a set of information (in this case, pseudo-range measurement) related to such body. As far as this work is concerned, the more accurate GPS phase measurements are not used here, because the main goal is not the search for accuracy, but a comparison of performance under

1.Instituto de Ciência e Tecnologia – São José dos Campos/SP – Brazil 2.Instituto Nacional de Pesquisa Espacial – São José dos Campos/SP – Brazil Author for correspondence: Paula Cristiane Pinto Mesquita Pardal | Rua Eng. João Fonseca dos Santos, 158, Apto. 163B – Vila Adyana | CEP 12.243-620 São José dos Campos/SP – Brazil | Email: paulacristiane@gmail.com Received: 27/05/13 | Accepted: 18/08/13

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Pardal, P.C.P.M., Kuga, H.K. and Moraes, R.V.

different sampling rates of the measurements from GPS. Furthermore, if carrier phase measurements were used, the ambiguity resolution algorithm or any other artifacts to overcome such hindrance could eventually mask the results, misguiding the conclusions. A spaceborne GPS receiver is a powerful resource to determine orbits of artificial Earth satellites by providing many redundant measurements which ultimately yields high degree of the observability to the problem. The Topex/Poseidon (T/P) satellite is a nice example of using GPS for space positioning. Through an onboard GPS receiver, the pseudo-ranges (error corrupted distance from satellite to each of the tracked GPS satellites) can be measured and used to estimate the full orbital state. The EKF is very likely the most widely used realtime estimation algorithm for nonlinear systems (Maybeck, 1982). However, the experience from the estimation community has shown that the EKF is difficult to implement, requiring some skill to get tuned since depends very much on the nearness of the initial conditions to the true values; and the linearity on the time scale of the filter working updates. Many of these difficulties arise from the linearization required by the EKF method. Specifically for the orbit estimation problem, under inaccurate initial conditions (Pardal et al., 2011) and scattered measurements, the EKF implementation can lead to unstable or diverging solutions. Therefore, there is a strong need for a method that is probably more accurate than linearization, but that does not be liable to neither the implementation nor additional computational costs of other higher order filtering schemes. To overcome this limitation, the unscented transform (UT) was developed as a technique to propagate mean and covariance information through nonlinear transformations. The UKF is one of the sigma-point Kalman filters (SPKF), a new family of estimators that claims to yield equivalent or better performance than the EKF and elegantly to extend to nonlinear systems, without the linearization steps (van der Merwe, 2004; Julier and Uhlmann, 1997, 2004). This family of algorithms presents a new approach to generalize the KF for nonlinear dynamics and observation models. Assessment between EKF and UKF was studied before by these and other authors, with different focus. Soken and Hajiyev (2011) compared two different robust Kalman

filtering algorithms: Robust EKF and Robust UKF for the case of measurement malfunctions. In both filters, by the use of defined variables named as measurement noise scale factor, the faulty measurements were taken into the consideration with a small weight and the estimations were corrected without affecting the characteristic of the accurate ones. Proposed robust KFs were applied for the attitude estimation process of a pico satellite and the results are compared. El-Sheimy et al. (2006) studied which Kalman filtering design works best for GPS and micro-electromechanical (MEMS) inertial systems, since both have complementary qualities that make integrated navigation systems more robust. Jose (2009) implemented an UKF for integrating inertial navigation system (INS) with GPS and compared the results with the EKF approach, in performance and robustness. In a loosely coupled integrated INS/GPS system, inertial measurements from an inertial measurement unit IMU (angular velocities and accelerations in body frame) were integrated by the INS to obtain a complete navigation solution and the GPS measurements were used to correct for the errors and avoid the inherent drift of the pure INS system. Pardal et al. (2011) compared between the EKF and the nonlinear SPKF for a real-time satellite orbit determination problem, using GPS measurements for degraded initial conditions. The main subjects for the comparison between the estimators are convergence speed and computational implementation complexity. The aim was: to analyze the filters robustness; and to know the way such inaccuracies affect the performance of the estimators. In this orbit determination problem the core is to analyze the convergence behavior for each filter in situations where there are different sampling rates of the measurement. Indeed there are small up to larger intervals between the processing of two GPS signals, and such intervals affect the performance of the estimators. A second goal evaluates the dynamical model complexity effects in the performance of the estimators for this case, especially during the adverse situations of larger reception intervals between two measurements. Therefore, the performance evaluation of the EKF (the most widely used estimation algorithm) and the UKF (supposedly the most appropriate estimation algorithm for nonlinear problems) to orbit determination problems in real time is due and justified.

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Analyzing the Unscented Kalman Filter Robustness for Orbit Determination through Global Positioning System Signals

397

SiGMa-PoiNt KalMaN FiltErS When the system dynamics and the observation model are of linear nature, the conventional KF is the optimal solution and must be used fearlessly. However, not rarely, the system dynamics and/or the measurement models are nonlinear, and convenient extensions of the KF, like the EKF, have been used. The SPKF is a new family of estimators that allows similar performance to the KF for linear systems and elegantly extends to nonlinear systems, without need of the linearization procedures. This family of algorithm is a new approach to generalize the KF for nonlinear process and observation models (Julier and Uhlmann, 1997, 2004; van der Merwe et al., 2004). A set of weighted samples, the sigma-points, is used for computing mean and covariance of a probability distribution. Such algorithms include the UKF that is based on the UT, which is a nonlinear transformation of mean and covariance. The SPKF represents a technique claimed as to lead to a more accurate and easier way to implement filter than the EKF or a second order Gaussian filter. Its approach is described, as follows (van der Merwe, 2004): • A set of weighted samples is calculated deterministically based on the decomposition of the covariance and mean of a random variable. • The sigma-points are propagated through the real nonlinear function, using only functional estimation, that is, analytical derivatives are not used to generate a posteriori set of sigma points. • The later statistics are calculated using propagated sigma-points functions and weights. In general, they assume the form of a simple weighted average of the mean and the covariance. Following, it will be separately explained the UT and the UKF, the filter stemming from this transformation. unSCEnTED TRAnSFORm Essentially this is a manner of calculating the statistics of a random variable that passes through a nonlinear transformation. The UT approach is illustrated in Fig. 1 (Julier and Uhlmann, 1997; van der Merwe, 2004): select a suitable set of points (sigma-points) so that their mean and covariance are x and Pxx, respectively (Julier and Uhlmann, 1997, 2004). In turn, the

non linear transformation

Figure 1. Unscented transform.

nonlinear function is applied to each point of the set to yield a cloud of transformed points. The statistics of the transformed points (mean y and covariance Pyy) can then be calculated to form an estimate of the nonlinearly transformed mean and covariance. The sigma-points are carefully and deterministically chosen so that they exhibit certain specific properties, that is, they are not drawn at random like common Monte Carlo methods. Besides, they can be weighted in ways that are inconsistent with the distribution interpretation of sample points like in a particle filter (Julier and Uhlmann, 1997; van der Merwe, 2004). The n-dimensional random variable x, with x mean and Pxx covariance, is approximated by 2n+1 weighted points, the so known sigma-points, given by: χ0 = x χi = x + √(n+λ) Pxx χi+n = x - √(n+λ) Pxx

(1)

in which λ=α2(n+k)-n includes scaling parameters. The constant parameter α controls the size of the sigma-points distribution (0≤α≤1), and k provides an extra degree of freedom used to fine-tune the higher order moments; k = 3 -n for a Gaussian distribution (Wan and van der Merwe, 2001). In Eq. 1, each element of the n-dimensional random variable x is replaced by a set of sigma-points generated from the mean and the covariance of x. Thus, the vector variable x has become a matrix of n x i dimension. The transformation occurs as follows: • Transform each point through the nonlinear function to yield the set of transformed sigma-points:

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Pardal, P.C.P.M., Kuga, H.K. and Moraes, R.V.

398

yi = f [χi] •

(2)

The observations mean is given by the weighted average of the transformed points: 2n

y= •

∑W y i=0

(3)

table 1. Unscented Kalman filter algorithm. 1. initialization (t = k–1):

2. Sigma-points:

The covariance is the weighted outer product of the transformed points: 2n

Pyy =

∑ W [y - y][y -y] i=0

(4)

Wi is the weight associated to the i-th point given by: k (n + k) 1 , i = 1,..., n Wi = 2(n + k) 1 , i = 1,..., n Wi+n = 2(n + k)

3. propagation:

W0 =

(5)

unSCEnTED KAlmAn FilTER Using UT, the following steps are processed in the KF: • Predict the new state system and its associated covariance, taking into account the effects of the Gaussian white noise process. • Predict the expected observation and its residual innovation matrix considering the effects of the observation noise. • Predict the cross correlation matrix. In order to lead to the new filter, the UKF, these steps are arranged in the EKF, re-structuring: the dynamics; the state vector; and the observations model. Table 1 presents an algorithm for the UKF. In the filter ^ initialization, the mean, x^ k-1, and the covariance matrix, Pk-1 , of the state vector x are calculated, in reference to the prior instant, tk-1. Following, the set of sigma-points is generated, from the mean and the covariance matrix, previously calculated. In the propagation step, the generated state sigma-point set is propagated to the instant tk, using the nonlinear dynamics equation (a), and the predict mean and covariance matrix are calculated (b). During the update cycle: the observations sigma-points are generated (a), propagated through the nonlinear observations equation (b), and its vv mean is obtained (c); the predict matrices of innovation, Pk , xy and correlation, Pk , are computed (d); and finally the Kalman gain is calculated, in order to update the state x^ k, and the

4. update:

covariance matrix, Pk. They are used as a priori information in the next instant, tk+1, to generate the new set of sigma-points.

EXtENdEd KalMaN FiltEr If the dynamical system and the observations model are linear, the KF is the recursive estimator most used at

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Analyzing the Unscented Kalman Filter Robustness for Orbit Determination through Global Positioning System Signals

the present day since it is easy to implement and to use on digital computers. Its recursiveness leads to lesser memory storage, which makes it ideal for real-time applications. The EKF is a nonlinear version of the KF that generates reference trajectories which are updated at each measurement processing, at the corresponding instant (Maybeck, 1982; Brown and Hwang, 1985). Because it is very difficult to accurately model the artificial satellites orbit dynamics, the EKF is generally used in works of such nature. Its algorithm always brings updated reference trajectory around the most current available estimate. Exploiting the assumption that all transformations are quasi-linear, the EKF simply linearizes all nonlinear transformations and substitutes the Jacobian matrices for the linear transformations in the KF equations. The EKF consists of phases of time and measurement updates. In the first one, state and covariance are propagated from one precedent instant to a posterior one, which means that they are propagated between discrete instants of the system dynamics model. In the second one, state and covariance are corrected from the measurement obtained in the posterior instant of time, through the observations model. Therefore, the method nature is recursive, so it does not need to store previously the measurements in large matrices. Following, the step for the EKF time update (or propagation) cycle is presented: .

xk = f(x^ k-1) ^

Pk = φk,k-1Pk-1φTk,k-1 + Qk

(6)

399

In Eq. 7, G(t) is the white noise addition matrix. In Eq. 8, hk is a nonlinear vector function modeling the measurements; Hk is the corresponding partial derivative matrix ∂hk ∂x ;Kk

is the Kalman gain; Rk is the observations noise matrix; x^ k and ^ Pk are respectively the state vector and the covariance updated for the instant k; yk is the observations vector corresponding

to the instant k.

Notwithstanding, the EKF has limitations. First: linearization can produce highly unstable filters if the assumptions of local linearity are violated; second: the derivation of the Jacobian matrices is nontrivial in most applications, and often leads to significant implementations difficulties (Julier and Uhlmann, 1997); third: analytical Jacobian matrices can be a very difficult and error-prone process. Summarizing, linearization, as applied in the EKF, is widely recognized to be inadequate, but the alternatives incur substantial costs in terms of derivation and computational complexity. Hence, there is a strong need for a method that is probably more accurate than linearization but does not incur costs of implementation and computational of higher order than the other filters. The sigma-point algorithms were developed to meet these needs (Julier and Uhlmann, 1997).

ORBIT DETERMINATION The orbit determination process consists of obtaining values of the parameters that completely specify the motion

where f is a nonlinear vector function modeling the orbit motion, xk and Pk are respectively the propagated state and the covariance for tk; φk,k-1 is the state transition matrix between tk-1 e tk; Qk is the dynamics noise matrix given in Eq. 7. It is required the Jacobian matrix (∂f/∂x) for the transition matrix computation, which can be either simplified or very difficult to obtain.

of an orbiting body (here, an artificial satellite), based on a set of observations of the body. It involves nonlinear dynamics and nonlinear measurement systems, which depends on the tracking system, and estimation technique (for instance, KF or least squares (Maybeck, 1982; Brown and Hwang, 1984)). The dynamical system model consists of the description for the dynamics of the satellite orbital motion, measurements

Qk = ʃt φ(t, tk-1)G(t)Q(t)G (t)φ (t, tk-1)dt (7)

models, Earth’s rotation effects, and perturbation models.

The equations for the EKF measurement update cycle are:

these models depend on a variety of parameters which

k-1

Kk = PkHk (HkPkHk +Rk) T

Pk = (I-KkHk) Pk x^ k = xk + Kk [yk-hk(xk)]

Furthermore the state variables defining the initial conditions, affect both the dynamic motion as the measurement process (Montenbruck and Gill, 2001). Due to the complexity of the

-1

(8)

applied models it is hardly possible to solve such models equations directly for any of these parameters from a given set of observations.

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The observation may be obtained from the ground station networks using laser, radar, Doppler, or by space navigation systems, as the GPS. The choice of the tracking system depends on a compromise between the goals of the mission and the available tools. In the case of the GPS, the advantages are global coverage, high precision, low cost, and autonomous navigation resources. The GPS may provide orbit determination with accuracy at least as good as methods using ground tracking networks. The later provides standard precision around tens of meters and the former can provide precision as tight as some centimeters. The GPS provides, at a given instant, a set of many redundant measurements, which makes the orbit position observable geometrically. After some advances of technology, the single frequency GPS receivers provide a good basis to achieve fair precision at relatively low cost, still attaining the accuracy requirements of the mission operation. The GPS allows the receiver to determine its position and time geometrically anywhere at any instant with data from at least four satellites. The principle of navigation by satellites is based in sending signals and data from the GPS satellites to a receiver located onboard the satellite that needs to have its orbit determined. This receiver measures the travel time of the signal and then calculates the distance between the receiver and the GPS satellite. If the clocks are not synchronized, four measurements are required to obtain its position. Those measurements of distances are called pseudo-ranges. The instantaneous orbit determination using GPS satellites is based on the geometric method. In such method, the observer knows the set of GPS satellites position in a reference frame, obtaining its own position in the same reference frame. However, sequential orbit determination makes use of the orbital motion modeling to predict between measurement times and measurement model to update the orbit by processing of measurements from GPS. This gives rise to recursive and real-time KF estimator for the orbit determination (Brown and Hwang, 1985). FILTER DYNAMIC MODEL In the case of orbit determination via GPS, the ordinary differential equations which represent the dynamic model are in its simplest form, given traditionally as follows:

r=v .

v = -μ .

b=d

r + a + wv (9) r3

d = 0 + wd wherein the variables are placed in the inertial reference frame. In Eq. 9, r is the vector of the position components (x, y, z); v is the velocity vector; a represents the modeled perturbing accelerations; wv is the white noise vector with covariance Q; b is the user satellite GPS clock bias; d, the user satellite GPS clock drift; and wd the noise associated with the GPS clock. The GPS receiver clock offset was not taken into account, so as not to obscure the conclusions drawn in this paper due to introduction of clock offset models in the filters. Indeed, the receiver clock offset was beforehand obtained and used to correct the GPS measurements, so that the measurements are free from the error derived from receiver clock offset. FORCE MODEL The main disturbing forces of gravitational nature that affect the orbit of an Earth’s artificial satellite are: the nonuniform distribution of Earth’s mass; ocean and terrestrial tides; and the gravitational attraction of the Sun and the Moon. There are also the non-gravitational effects, such as: Earth atmospheric drag; direct and reflected solar radiation pressure; electric drag; emissivity effects; relativistic effects; and meteorites impacts. The disturbing effects are in general included according to the physical situation presented and to the accuracy that is intended for the orbit determination. Here, we include only a minimum set of perturbations which enable us to assess the performance of both filters, namely geopotential and third body point mass effect of Sun and Moon. The Earth is not a perfect sphere with homogeneous mass distribution, and cannot be considered as a material point. Such irregularities disturb the orbit of an artificial satellite and the keplerian elements that describe the orbit do not behave ideally. The geopotential function can be given by (Kaula, 1966): U(r, ϕ, λ) =

μ ∞ r n=0

∑∑

m=0

RT P (sin ϕ)(C cos mλ + S sin mλ) (10) nm nm r nm

where µ is Earth gravitational constant; RT is mean Earth radius; r is the spacecraft radial distance; ϕ is the geocentric

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Analyzing the Unscented Kalman Filter Robustness for Orbit Determination through Global Positioning System Signals

latitude; λ is the longitude on Earth fixed coordinates system; Cnm and Snm are the harmonic spherical coefficients of degree n and order m; Pnm are the associated Legendre functions. The constants µ, RT, and the coefficients Cnm, and Snm determine a particular gravitational potential model. Another gravitational perturbation source is due to the Sun and Moon attraction. They are more meaningful at larger distance from Earth. As the orbital variations are of the same type, be the Sun or the Moon the attractive body, they are normally studied without distinguishing the third body. The Sun–Moon gravitational attraction mainly acts on node and perigee, causing precession of the orbit and on the orbital plane. The general three-body problem model is here simplified to the circular restricted three-body problem, where the orbital motion of a third body (satellite), which mass can be neglected, around two other massive bodies is studied. The force acting on the third body (the satellite) in the inertial reference frame can be expressed as (Prado and Kuga, 2001; Guan, 2013): :

r3 = -Gm1

r13 r

3 13

-Gm2

r23 3 r23

(11)

where r13 = r3 -r1, r23 = r3 -r2, and ri, i=1,2,3 corresponds to the i-th body distance vector to the center of mass of the system; and m1 and m2 are the masses of the Sun and the Moon, respectively.

OBSERVATIONS MODEL The nonlinear equation of the observation model is: yk = hk (xk, t) + vk (12) where, at time tk, yk is the vector of m observations; hk(xk) is the nonlinear function of state xk, with dimension m; and vk is the observations errors vector, with dimension m and covariance Rk. For the present application, one only uses the ion-free pseudo-range measurements from the GPS receiver of T/P satellite. Also, the receiver clock offset was computed before and used to correct the pseudo-range measurements. In addition, the nonlinear pseudo-range measurement was modeled according to Chiaradia et al. (2003).

401

MEASUREMENTS SAMPLING RATES IMPACT IN THE ORBIT DETERMINATION Previous presented studies (Pardal et al., 2009b, 2010 and Pardal, 2011) showed that the accuracy improvement for the dynamics models did not better the errors resulting between the references from precise orbit ephemeris from JPL/NASA (POE/JPL) and the values stemming from filters estimation process. This means that the magnitude of the errors obtained through the UFK or EKF is not reduced when increasing the complexity of the dynamic model: from a model for the geopotential with high degree and order to a complex model containing the three major disruptive effects of the orbit. Further, such results showed an equivalent competitiveness between the estimators, since the errors are of the same order of magnitude. It might have occurred because, in those results, the orbit determination process was done for small sampling intervals of the measurements. Taking this into account, before concluding that a simpler dynamics modeling can be indiscriminately adopted, there is a need for another test (Pardal et al., 2010). Such test has two well determined purposes: to examine carefully the benefits of increasing the adopted dynamics model accuracy; and to investigate the apparent competitiveness between the estimators. This test rested in executing the orbit determination process considering different intervals between the GPS observations (pseudo-range) sampling. The intervals of sampling were 10, 30, 60, 300, 600, 1200, and 1800 seconds, in other words, conditions were swept from one very small range (10 seconds) to a range extremely high (1800 seconds) between the prediction and the subsequent correction of the predicted values to complete the cycle of the estimation process. With the gradual spacing of the interval between two measurements, the intention was to verify the dynamics models complexity, and the application of each filter in the time update cycle. In such situation, the propagation has it effects raised and the modeling accuracy becomes more significant in the accuracy of both filters estimative.

RESULTS The tests and the analysis for the EKF and the UKF algorithms are presented. To validate and to analyze

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the methods, real GPS data from the T/P satellite are used. The filters estimated position and velocity are compared with T/P POE from JPL/NASA. The test conditions consider real ion-free pseudo-range data, collected by the GPS receiver onboard T/P, on November 19, 1993, at different sampling rates, presenting on average between 5 and 6 GPS satellites tracked. The GPS data were previously preprocessed to remove the outliers so they cannot mislead the filters or mask different data rejection policies of each filter. The tests have covered a long one day period of orbit determination. The force model goes from a simple geopotential up to order and degree (2×2), with harmonic coefficients from JGM-2 model, to a model including perturbations due to geopotential up to order and degree (28×28) and due to the Sun–Moon gravitational attraction (Pardal et al., 2009a, 2010,

2011). The pseudo-range measurements were corrected to the first order with respect to ionosphere. As already pointed, this work is not a search for results accuracy. It aims at UKF robustness assessment, which is done through the comparison of performance between UKF and EKF estimators under different sampling rates. There are peculiar interest for speed convergence, and divergence occurrence. Table 2 shows the analysis for the predicted pseudo-range residuals convergence, which is measured in terms of time span, Dtsam, of data processed. The convergence is assumed when the residuals achieve similar statistics of the reference solution residuals. When small samplings intervals, such as 10, 30, and 60 seconds are used, convergence occurs instantaneously after the estimation process starts, for both UKF and EKF algorithms,

Table 2. Pseudo-range residuals convergence speed. Model

Geo2

Geo5

Geo10

Geo28

S-M

∆tsam (s)

UKF convergence time (h)

EKF convergence time (h)

10-30-60

300

no convergence observed

600

–

1200

–

1800

–

10-30-60

300

no convergence observed

600

no convergence observed

–

1200

–

1800

–

10-30-60

300

no convergence observed

600

–

1200

–

1800

–

10-30-60

300

600

1.5

1200

1800

no convergence observed

10-30-60

300

600

1.5

1200

1800

no convergence observed

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Analyzing the Unscented Kalman Filter Robustness for Orbit Determination through Global Positioning System Signals

and for any dynamics model adopted. For this reason, results obtained for 10, 30, and 60 seconds of sampling interval will be placed in the same line of Table 1. The model of geopotential up to low order and degree 2×2 (Geo2) starts diverging for 300 seconds of sampling interval, regardless of the estimator applied. The improved geopotential up to order and degree 5×5 (Geo5) dynamics model stops converging when UKF is the filter for 600 seconds, while EKF is not able to converge at all since 300 seconds. From an improved geopotential model and on, there is always occurrence of divergence when EKF is the algorithm, and convergence keeps occurring if UKF is the chosen algorithm. That is to say: for geopotential up to order and degree 10×10 (Geo10), divergence is detect at 300 seconds; and for geopotential up to high order and degree 28×28 (Geo28), and for a model that compounds geopotential up to order and degree 28 with Sun-Moon gravitational attraction (S-M), there is occurrence of divergence at 1800 seconds. The convergence time (consequently the convergence speed) is the same for the Geo28

403

and the S-M models up to 300 seconds of sampling interval, for both estimators, as shown in Table 1. The filters convergence time starts to be different at 600 seconds of interval between two measurements, and this difference keeps the same for each test case (600, 1200, or 1800 seconds), whether the model is Geo28 or S-M. At this point it is possible to pinpoint a model limitation for convergence analysis. Table 3 shows the convergence analysis for the position RNT (radial, normal, and along-track) components error, which is again measured in terms of data time of processing. When small intervals of sampling are used, such as 10, 30, and 60 seconds, convergence is detected instantaneously after the starts of the estimation process, for the two estimators, and for any choice of dynamics model. From 300 seconds on, the behavior is the same described in Table 2. The Geo2 model errors start diverging for 300 seconds of sampling interval, regardless of the estimator applied. The improved Geo5 dynamics model errors stop converging when the UKF is the filter for 600 seconds, while

Table 3. Errors in position convergence speed. Model

Geo2

Geo5

Geo10

Geo28

S-M

∆t sam (s)

10-30-60 300 600 1200 1800 10-30-60 300 600 1200 1800 10-30-60 300 600 1200 1800 10-30-60 300 600 1200

UKF convergence time (h) N

0 0

0 0 0 0 1 0 0 0 0

0 no convergence observed – – – 0 0 no convergence observed – – 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0.5 1

1800

10-30-60 300 600 1200

0 0 0 0

0 0 0.5 1

1800

0 0

EKF convergence time (h) N

0 0 no convergence observed – – – 0 0 no convergence observed – – – 0 0 no convergence observed – – – 0 0 0 0 0.2 0.3 3.5 3.2 no convergence 9.3 observed 0 0 0 0 0.2 0.3 3.5 3.2 no convergence 9.3 observed

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reference: UKF - ∆ tsam = 60s

200 150

residuals (m)

100 50 0 -50

-100 -150 -200

UKF - ∆ tsam = 1800s

200 150 100

residuals (m)

the EKF is not able to converge at all since 300 seconds. For the Geo10 dynamic model, divergence is detected at 300 seconds; and for the Geo28, and S-M models, there is occurrence of divergence at 1800 seconds. Again, for models Geo28 and S-M, it can be noticed that filters convergence time difference starts at 600 seconds of sampling interval, and this difference keeps the same for each test case (600, 1200, or 1800 seconds), no matter the model. So far, a model limitation for convergence analysis might be clear. Another statistical check is done, in order to confirm that the algorithms effectively reached convergence. The reference pseudo-range residuals statistics (mean and standard deviation) for each model and filter are available in the yellow lines of Table 4. As the three most improved dynamics models (Geo10, Geo28, and S-M) statistics for 10, 30, and 60 seconds considerably resemble (Pardal et al., 2009b, 2010), “reference” in Table 4 refers to a 60-s sampling interval, and is representative of the three intervals. Now, in the analysis of the two poorest dynamics models (Geo2 and Geo5), all the sampling intervals are explicit in Table 4 in order to show poor dynamics behavior as the sampling intervals increase. It is clear that poor dynamics models are more sensitive to the intervals enlargement, since Geo2 model stops converging at 300 seconds, and Geo5, at 300 or 600 seconds, depending on the estimator applied. Through Table 4 it is also noticeable that if the model is too poor (e.g., Geo2), neither UKF nor EKF are able to keep convergence for larger sampling intervals, and divergence behavior is detected at 300 seconds for both. However, if the model is slightly improved (for instance, Geo5), UKF implementation shows more robustness than EKF one, because still converges at 300 seconds, while EKF starts diverging. From Table 3, it becomes evident that the estimators really reached convergence, since their statistical values remain nearly the same as the reference ones. In order to portray such findings, Fig. 2 illustrates the reference residuals (small sampling intervals of 60 seconds) behavior, and the 1800 seconds sampling interval case behavior for both the EKF and the UKF estimators, using S-M as the dynamics model. It clearly indicates clues of EKF’s divergence for such larger sampling intervals of measurements. Proceeding the investigation, Table 5 shows total Root Mean Square (RMS) position error, where the reference values are again listed in the first row (yellowed). Again, in the analysis of the two poorest dynamics models (Geo2 and Geo5), all the sampling intervals are registered in Table 5 in order to show poor dynamical behavior as the sampling

50 0 -50

-100 -150 -200

EKF - ∆ tsam = 1800s

200 150 100

residuals (m)

404

50 0 -50

-100 -150 -200

time (h)

Figure 2. Pseudo-range residuals convergence and divergence occurrences.

intervals increase. It is clear that poor dynamics models are more responsive to the intervals increasing: the Geo2 model results stop converging at 300 seconds while the Geo5, at 300 or 600 seconds, according to the estimator applied. Through Table 5 it is also perceptible that for an excessively poor model (Geo2, for example), UKF and EKF are unable to converge for larger sampling intervals, and divergence occurs at 300 seconds for both. However, if the model is slightly improved (for instance, Geo5), UKF implementation still

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Analyzing the Unscented Kalman Filter Robustness for Orbit Determination through Global Positioning System Signals

converges at 300 seconds, while EKF starts diverging, which indicates more robustness of UKF when compared to EKF. UKF and EKF resulting RMS errors are only computed after assumed convergence time. For Table 5, it is also verifiable

405

that the estimators really reached convergence, since their RMS values remain nearly close to the reference ones. Next, Fig. 3 depicts a particular case where a relatively poor Geo5 dynamics model is adopted. While the UKF filter

table 4. Pseudo-range residual statistics, after convergence. model

∆tsam (s)

uKF residuals mean±SD (m)

0.218±18.504

0.155±18.531

0.081±26.992

-0.003±27.103

Geo2

-0.091±37.687

-0.276±37.916

300 to 1800

no convergence observed

Geo5

0.138±13.064

0.076±13.133

-0.007±14.602

-0.087±15.058

-0.251±16.575

-0.426±17.914

300

-1.604±25.216

no convergence observed

600 to 1800

no convergence observed

–

reference

-0.187±14.690

-0.277±16.221

300

-1.272±18.766

no convergence observed

Geo10

600

-2.401±19.786

–

1200

-4.193±23.873

–

1800

-7.714±33.229

–

reference

-0.091±14.363

-0.180±15.942

300

-1.234±17.489

1.861±45.886

600

-2.379±18.242

-3.557±27.425

1200

-4.216±22.295

-19.831±90.423

1800

-7.929±32.163

no convergence observed

reference

-0.109±14.021

-0.201±15.639

300

-1.294±15.872

-1.021±43.197

600

-2.397±16.542

-3.6126±25.945

1200

-4.163±20.518

-19.814±91.237

1800

-7.641±31.156

no convergence observed

Geo28

S-M

Errors - Geo5 - ∆ tsam=300s

UKF EKF

1.E+07

1.E+04 1.E+03

1.E+05

1.E+02

r (m)

1.E+06

v (m/s)

1.E+04

1.E+01 1.E+00

1.E+03 1.E+02

1.E-01

1.E+01

1.E-02

1.E+00

EKF residuals mean±SD (m)

12 16 time (h)

1.E-03

12 16 time (h)

1.E+04 Figure 3. Convergence and divergence behavior for Geo5 dynamics model. 1.E+03

v (m/s)

1.E+02 1.E+01 1.E+00 1.E-01

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Table 5. Error in position total Root Mean Square, after convergence. Model

Geo2

Geo5

Geo10

Geo28

S-M

∆tsam (s)

UKF total RMS(m)

EKF total RMS(m)

34.336

34.391

49.834

49.703

68.774

69.837

300 to 1800

no convergence observed

20.521

20.642

20.050

20.301

22.481

27.303

300

35.562

no convergence observed

600 to 1800

no convergence observed

–

reference

18.150

23.953

300

22.273

no convergence observed

600

22.658

–

1200

24.000

–

1800

28.962

–

reference

17.135

23.159

300

19.217

21.798

600

19.386

29.487

1200

20.943

no convergence observed

1800

26.420

–

reference

16.253

22.520

300

15.437

18.416

600

15.398

26.489

1200

16.403

no convergence observed

1800

23.990

–

remains converging, a divergence behavior is shown in the EKF implementation, for 300 seconds of sampling interval between two measurements. This result indicates that even if the model is not adequately chosen, the EKF believes that the model and linearization are correct. The UKF does not have linearizations and, in adverse situations, such as the ones of larger sampling intervals between two data samples or inaccurate initial conditions (Pardal et al., 2011), behaves more adequately. However, if the dynamics model is extremely truncated, such as the Geo2 analyzed in this work, neither the UKF nor the EKF will reach convergence for large sampling intervals, as shown in Tables 1–4. In Fig. 3, ∆r and ∆v represents, respectively, the absolute value of the errors in position and in velocity, in the inertial reference frame coordinates. Figure 4 shows the errors in the RNT components for the UKF and EKF reference cases (small 60-s sampling interval, left side) and the larger 1800 seconds error case results for the

EKF and the UKF estimators (right side). The outstanding behavior in the right side happens again in any “no convergence observed” case pointed out in Table 4. It indicates signs of the EKF divergence for such a very large sampling interval, while UKF reaches the convergence zone, not much later than the left side results. So far, the results showed that the performance of the filters decreases as increasing the sampling intervals (according to assays of the error in position, the pseudorange residuals, and the convergence presented previously). These results point to the advantage of using the nonlinear theory for orbital dynamics, in small intervals of the UKF and the EKF algorithms. In order to finish the results analysis, it is to be said that even considering the convergence for small sampling intervals, where UKF and EKF present similar performance, the algorithm is very sensitive to the initialization of the covariance matrix. This means that the algorithm convergence depends on the proper choice of such matrix. Therefore, the

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Analyzing the Unscented Kalman Filter Robustness for Orbit Determination through Global Positioning System Signals

Errors - ∆ tsam=60 s Errors - ∆ tsam=60 s Errors - ∆ tsam=60 s

radial radial (m)(m)(m) radial

20 20 20 10

10 100 0 -100 -10 -10 -20

-50 -50 -100

normal normal (m)(m)(m) normal along-track along-track (m)(m)(m) along-track

-100 -100 100 100 100 50

30 30 30 15 15 150

50 500

0 -150 -15 -15 -30

0 -500 -50 -50 -100

-30 -30 50 50 50 25

-100 -100

100 100 100 50

25 250 0 -250

-50 0 -50 0 0

UKF UKF EKF EKF UKF EKF

0 -500

-20 -20

-25 -25 -50

Errors - ∆ tsam=1800 s Errors - ∆ tsam=1800 s Errors - ∆ tsam=1800 s

UKF UKF EKF 100 EKF UKF 100 EKF 100 50 50 500

407

50 500

4 4 4

8 12 16 20 8 time 12 16 20 (h) (h) 8 time 12 16 20 time (h)behavior of the errors Figure 4. Convergence and divergence

results are not general, and the algorithm was adjusted for this type of orbit determination application.

CoNClUSioNS The robustness to increasing sampling intervals of two nonlinear estimators, namely the EKF and UKF was assessed for a real-time satellite orbit determination problem using real GPS measurements. One day (24h) of GPS receiver measurements of T/P satellite at different sampling rates were processed. The emphasis was to characterize each filter

0 -500 -50 -50 -100

24 -100 0 24 0 -100 24 0

4 4 4

8 12 16 20 8 time (h) 12 16 20 8 time (h) 12 16 20 time (h) in RNT (radial, normal, and along-track) components.

24 24 24

convergence behavior in situations where the sampling rates vary from small to larger intervals between two measurements. Different dynamical models were analyzed, in order to establish the modeling effects in the orbit determination process. Results showed that when small sampling intervals are used, UKF and EKF yield similar performance, with high performance, for almost all dynamics models. The exception is made if the model is extremely truncated (Geo2) or slightly improved (Geo5), where in the later Geo5 case, UKF maintains convergence and in the former Geo2 case, none of the estimators reach convergence even for 60-s sampling interval. As expected, increasing the sampling intervals decreases the

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filters performance. As larger is the interval more difficult is for EKF and UKF to reach convergence. When UKF is compared with EKF, in all cases of larger intervals, the UKF always attains convergence first. The rupture threshold for this application in particular occurs to all modeling complexities if EKF was the used algorithm. Only in two situations (the two poorest models adoption) for the UKF implementation, divergence occurred. Therefore it is to be said that the UKF is more robust than the EKF for larger sampling interval between measurements, for this type of orbit determination application.

ACKNOWLEDGMENTS The authors wish to express their consideration to the Instituto Nacional de Pesquisas Espaciais (INPE) that kindly provided everything necessary for this paper to be developed. The authors are also grateful to Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) for the support, under contracts No. 2013/00960-4, No. 2012/21023-6; to CAPES; and to CNPq.

REFERENCES Brown, R. G. and Hwang, P. Y. C., 1984, A Kalman filter approach to precision GPS geodesy, Navigation: Global Positioning System, Vol. II, pp. 155-166. Brown, R. G. and Hwang, P. Y. C., 1985, Introduction to random signals and applied Kalman filtering, 3rd ed. New York: John Wiley & Sons. 502 p.

Pardal, P. C. P. M., Kuga, H. K. and Vilhena de Moraes, R., 2009b, Non linear sigma point Kalman filter applied to orbit determination using GPS measurements, 22nd International Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2009), 22–25 Sept., Savannah, USA.

Chiaradia, A. P. M., Kuga, H. K. Kuga and Prado, A. F. B. A., 2003, Single frequency GPS measurements in real-time artificial satellite orbit determination, Acta Astronautica, Vol. 53, n. 2, pp. 123-133.

Pardal, P. C. P. M., Kuga, H. K. and Vilhena de Moraes, R., 2010,

El-Sheimy, N., Shin, E-H. and Niu, X., 2006, Extended vs. Unscented Kalman Filters for Integrated GPS and MEMS Inertial. Available from: http://www.insidegnss.com/auto/0306%20Kalman.pdf

GNSS 2010), 21-24 Sept., Portland, USA.

Guan, T., 2013, Special cases of the three body problem, [online Colorado University database]. Available from: http://inside.mines.edu/ fs_home/tohno/teaching/PH505_2011/Paper_TianyuanGuan.pdf Jose, J. M., 2009, Performance comparison of Extended and Unscented Kalman Filter implementation in INS-GPS integration, Ph.D thesis. Czech Technical University in Prague, Prague (in English). Available from: http://epubl.ltu.se/1653-0187/2009/095/LTUPB-EX-09095-SE.pdf

Comparing the extended and the sigma point Kalman filters for orbit determination modeling using GPS measurements,23rd International Meeting of the Satellite Division of the Institute of Navigation (ION

Pardal, P. C. P. M., Real time orbit determination through non linear sigma-point Kalman filter, 2011, Ph.D thesis, National Institute for Space Research (INPE), São José dos Campos. (In Portuguese). Pardal, P. C. P. M., Kuga, H. K.and Vilhena de Moraes, R., 2011, Robustness assessment between sigma point and extended Kalman filter for orbit determination,. Journal of Aerospace Engineering, Sciences and Applications, v. III, pp. 35-44. doi: 10.7446/ jaesa.0303.04 Prado, A. F. B. A. and Kuga, H. K. (Eds.), 2001, Space Technology

Julier, S. J. and Uhlmann, J. K., 1997, A new extension of the Kalman filter for nonlinear systems, International Symposium on Aerospace/ Defense Sensing, Simulation and Controls. SPIE.

Fundaments, São José dos Campos: INPE. 220 p. (In Portuguese).

Julier, S. J.and Uhlmann, J. K., 2004, Unscented filtering and nonlinear estimation. IEEE Transactions on Automatic Control, Vol. 92, n. 3, Mar 2004.

Faults, 5th International Conference on Recent Advances in Space

Kaula, W.M., 1966, Theory of Satellite Geodesy. Blasdell Pub. Co. Walthmam, Mass.

van der Merwe, R., 2004, Sigma-Point Kalman filters for probabilistic

Maybeck, P.S., 1982, Stochastic models, estimation, and control. Vol. 2, Academic Press, NY.

& Science University, Portland.

Montenbruck, O. and Gill, E., 2001, Satellite orbits: models, methods, and applications, Springer-Verlag, Berlin Heidelberg NewYork. 369 p.

Kalman filters for nonlinear estimation and sensor-fusion applications

Pardal, P. C. P. M., Kuga, H. K. and Vilhena de Moraes, R., 2009a, A discussion related to orbit determination using nonlinear sigma point Kalman filter, Mathematical Problems in Engineering, v. 2009, 12 p. Hindawi Publishing Corporation, doi:10.1155/2009/140963.

Soken, H. E. and Hajiyev, C., 2011, REKF and RUKF Development for Pico Satellite Attitude Estimation in the Presence of Measurement Technologies (RAST), 9–11 June 2011. pp. 891-896. doi: 10.1109/ RAST.2011.5966972

inference in dynamic state-space models, Ph.D thesis. Oregon Health

van der Merwe, R., Wan, E.A. and Julier, S.J., 2004, Sigma-point to integrated navigation, AIAA Guidance, Navigation, and Control Conference and Exhibit, 16-19 Aug. 2004, Rhode Island. Wan, E. A. and van der Merwe, R., 2001, The unscented Kalman filter, Kalman Filtering and Neural Networks. Haykins, S. (Ed.), John Wiley & Sons, New York, Chap. 7.

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doi: 10.5028/jatm.v5i4.262

Simulation of Ablation in a Sounding Rocket Thermal Protection System Via an Interface Tracking Method with Two Moving Fronts Humberto Araujo Machado1

Abstract: In this work, an interface tracking method is employed to simulate the ablative process in the region near the stagnation point of the VSB-30 sounding rocket Thermal Protection System (TPS). The ablation model considers the presence of two-fronts, the char-melting and the pyrolysis fronts. The results for the proposed model are compared to the ones obtained from the traditional one-front model, which supposes a constant ablation temperature. Results show that the one-front model overestimates the ablation period, mass loss and the internal temperature after the flight. The increase in the accuracy with this model shall provide a better dimensioning of the TPS, reducing its weight and cost. KEYWORDS: TPS, ablation, moving boundary, computational simulation, sounding rocket.

Introduction Sounding rockets are extensively used to provide microgravity environments for scientific experiments. The Institute of Aeronautics and Space (Instituto de Aeronáutica e Espaço – IAE) of Brazil has designed, built and launched hundreds of rockets along the past 40 years. The VSB-30 sounding rocket is a two-stage unguided solid propellant rocket used by the Brazilian Space Agency (AEB) and European Space Agency (ESA). Figure 1 shows a schematic representation of VSB-30. It has a total length of 13 m and a diameter of 0.6 m. It is equipped with two solid propellant motors, namely S31 and S30. S31 acts as a booster during its 15 s burning time, whereas S30 burns for about 30 s, reaching an apogee of 250 km, for a payload mass of 400 kg. VSB-30 was developed by IAE in cooperation with the German Space Agency (DLR). So far, several VSB-30 successful flights occurred, from Alcântara Launch Center (CLA), in Brazil, and from Esrange, Sweden (Garcia et al., 2011), Fig. 2. During its flight, VSB-30 provides 6 minutes of microgravity environment. It is worth mentioning that an S30 motor developed and fabricated by IAE equipped the VS-30-Orion rocket used by Sharp Edge Flight Experiment (SHEFEX) (Turner et al., 2005). Figure 3 presents the nominal altitude-velocity map for a VSB-30 atmospheric flight. According to Fig. 3, VSB-30 1st Stage

2 nd Stage

Payload

Fins Figure 1. VSB-30 sounding rocket. 1.Instituto de Aeronáutica e Espaço – São José dos Campos/SP – Brazil Author for correspondence: Humberto Araujo Machado | Praça Marechal Eduardo Gomes, 50 – Vila das Acácias | CEP 12.228-904 São José dos Campos/SP – Brazil | Email: humbaman@uol.com.br Received: 27/06/13 | Accepted: 23/10/13

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Machado, H.A.

reaches the speed of 7,500 km/h (2,100 m/s) while still flying within the atmosphere. For practical purposes the dense part of the atmosphere is assumed to be 90 km thick. Beyond 90 km, vacuum conditions are assumed. As a result of such very high speeds, aerodynamic heating arises as a major problem in the VSB-30 design. This problem is larger near the stagnation regions, such as those existing at the nose cap and the fins leading edges, where temperature surpasses 2,100°C (Machado and Pessoa-Filho, 2007). Indeed, it is mandatory to obtain an accurate evaluation of the reached heat flux and the temperatures, in order to correct size the thermal protection. Along the years ablative materials have been effectively used as TPS of space vehicles. This is the case of the nose cap of VSB-30, where a composite material, like Si-phenolic resin, is used (Da Costa et al., 1996; Tick et al., 1965), Fig. 4.

Figure 2. VSB-30 takes off from Esrange, Sweden.

2500

280

2000

200

Velocity, m/s

Altitude, km

240

160 120 80

1000 500

40 0

1500

100

200

300

400

500

100

200

300

400

500

Figure 3. VSB-30 altitude-velocity maps.

36º R 10.0

41.2 63.6

Figure 4. VSB-30 nose cap. J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.409-420, Oct.-Dec., 2013

Simulation of Ablation in a Sounding Rocket TPS Via an Interface Tracking Method With Two Moving Fronts

The conventional ablation model considers one front and treats ablation as single phase change process at constant temperature. In this work a model for the ablative process with two moving fronts (char-melting and pyrolysis) is applied in the computational simulation of the ablation in the thermal shield (TPS) of the VSB-30 sounding rocket, in the vicinity of the stagnation point. The objective is to compare the results of the two models, in order to evaluate the difference in design parameters, like external temperature and surface variation. This work is an extension of the previous work of Machado (2012a), in which the validation of the two-fronts model and the numerical method for ablation can be found. In that work, the two-fronts model has been already compared with some experimental results, presenting a much better agreement than the one-front model. Such procedure will allow a more accurate dimensioning of the rocket thermal protection system, contributing for project optimization. The interface tracking method proposed by Unverdi and Tryggvason (1992) was employed to solve the moving front problem. This method is based in a hybrid EulerianLagragean approach that presents the advantage of using a regular homogenous mesh and represents the interface as accurate as more points are added to the grid. The equations of mass and energy conservation in their differential form are used to represent the thermal and ablative behavior of the heat shield with the time related to local temperature and thickness variations and solved through the finite volume method. In the following sections, the physical problem and the method of solution are described, and the final results for the ablation are presented.

PHYSICAL PROBLEM AND mathematical model Aerodynamic heating To predict the heat transfer on VSB-30, it is necessary to know the pressure, temperature and velocity fields around the rocket. That can be accomplished by numerically solving the boundary layer equations. However, such a procedure is expensive and time consuming. In the present work a simpler, but reliable, engineering approach is used, which allows to obtain the convective heat transfer coefficient and the representative environment temperature for heat exchange without needing

411

of a numerical solution of the boundary layer equations. The following simplifying assumptions are made: • zero angle of attack; • the rotation around its longitudinal axis is neglected; • atmospheric air is considered to behave as a calorically and thermally perfect gas (no chemical reactions). Although those hypotheses are not exact, they provide a good approximation to the real situation for the case of a ballistic reentry and for VSB-30 flight in particular, as shown by Machado (2012b). Such hypotheses should be revised in the case of planetary reentry. The free stream conditions ahead of the nose cap are those given by v∞, T∞, p∞, corresponding, respectively, to velocity, temperature and pressure. By knowing v∞ and altitude, as function of time, together with an atmospheric model (U.S. Standard Atmosphere, 1976), it is possible to evaluate the free stream properties. For supersonic flow (M∞>1), a bow shock wave appears ahead of the nose. By using the normal shock relationships (Anderson Jr., 1989), it is possible to calculate v1, T1 and p1 after the shock, in the centerline. The heat flux over the external surface was calculated through the Zoby’s method (Zoby et al., 1981; Miranda and Mayall, 2001). This method employs the heat flux calculation at the stagnation point, obtained through the method of Cohen, as a starting point to integrate the temperature distribution inside the thermal boundary layer over the external surface. That distribution is computed by relating heat transfer to a skin-friction relation based on the momentum thickness through a modified Reynolds analogy form. Corrections for the compressibility (using the Eckert’s reference enthalpy relation) and turbulence are added to the calculation. The velocity, temperature and pressure outside the boundary layer are assumed to be that estimated after the shock. The method can be employed in constant and variable entropy-edge conditions and both, reacting and non-reacting gas mixtures. Details of the solution can be found in the work of Machado (2008). Using the Zoby’s method, the convective heat transfer coefficient, H, is calculated along the y-coordinate that is measured along the body’s surface: y = 0 corresponds to the stagnation point, and R is a geometric parameter shown in Fig. 5, in which the red line represents the nose cap surface. It should be pointed out that such a procedure is performed along the nose cap’s surface (following the y-coordinate), for different trajectory times. Therefore, H = H(y,t). The variations of the convective heat transfer coefficient and the adiabatic wall

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temperature (Taw), also called recovery temperature (Tr), at stagnation point are shown in Fig. 6. An energy balance at the surface, accounting the radiative heat transfer, provides the heat absorbed by the wall. hEAT COnDuCTiOn AnD ABlATiOn Once the convection heat transfer and the adiabatic wall temperature are known, wall temperature distributions can be determined solving the heat transfer equations. VSB-30 nose cap is covered with a composite material (Si-Phenolic resin), which works as an ablative TPS. Until the ablation temperature is reached, a transient heat conduction process

occurs. Once the TPS surface reaches the ablation temperature, its thickness is reduced; therefore, a transient, coupled conduction, moving boundary problem appears. In this work, the ablation model proposed for a composite material will consider the presence of two layers: the virgin material and the char layer that appears after the pyrolysis front. Two moving fronts will be accounted for: the pyrolysis front and the ablation front of the char layer, both starting at constant temperatures. The set of equations used to represent the physical problem is written according to the interface tracking method (Juric, 1996). The nose cap and the surrounding airflow are represented as parts of a continuous domain of calculation. The application of the energy conservation principle to an infinitesimal volume element in the mathematical domain leads to a partial differential equation for the temperature, namely:

(1) z

y=0

where r is the density, Cp is the specific heat, T is the temperature, t is the time, K is the thermal conductivity and Q is a source term that accounts for the net heat exchange at the fronts, which will be mathematically represented as interfaces: (2)

Figure 5. Coordinate system.

H - Convective heat transfer coefficient Tr - Recovery temperature

H, W/m2K and Tr, K

2400 2000 1600 1200 800 400 0

100

200

300 time, s

400

500

Figure 6. Recovery temperature and convective heat transfer coefficient at stagnation point, obtained through the Zoby’s Method, during VSB-30 trajectory.

where x is the position in the coordinate system, xF is the interface position, A is the area, and q is the source term of energy per unit of surface of the interface and must be adapted to the physical model used to represent the behavior of each interface. The following hypothesis will be assumed to build the mathematical model for the ablative and heat conduction processes in the structure: • solid materials are considered isotropic with constant properties; • the pyrolisis zone is considered a zero thickness front. Pyrolisis enthalpy and temperature are considered constant; • the char layer recession occurs through oxidation or sublimation, at constant temperature. The aerodynamic removing of material is neglected; • absence of melting layer; • full reaction of the gases and perfect mixing with the air in the boundary layer around the external surface, with negligible influence over the air physical properties; • air is treated as an ideal gas;

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.409-420, Oct.-Dec., 2013

Simulation of Ablation in a Sounding Rocket TPS Via an Interface Tracking Method With Two Moving Fronts

• the flow field around the surface is not affected by the

change in the surface geometry and gas injection; • radiation is absorbed or emitted for surface, but not

413

A similar jump condition appears in the pyrolisis front (the pyrolisis temperature, Tp, replacing TA).

transmitted. All these assumptions have being used in previous studies (Machado, 2012a), with successful results in representing the physical process. The second assumption, in particular, was studied by Sias (2009), which has concluded it was accurate enough, when compared to more complex models for ablation. All the chemistry occurring in the ablative process is implicit and is included in the ablation parameters (heat and temperature of pyrolysis). Although the hypothesis of perfect gas behavior for air might yield inaccurate results for the recovery temperature, it occurs in altitudes where the convective heat transfer coefficient approaches to zero, resulting in a negligible heat flux over the surface, and should not affect the ablation. According to these hypothesis, the heat balance in the external surface yields: (3) where V is the interface velocity, L is the heat of ablation of the char layer, H is the convection heat transfer coefficient, TF is the interface temperature and Taw is the adiabatic wall temperature, also called recovery temperature of the air, ε is the emissivity and σ is the Boltzman constant. One should note that this term might exist in every moving interface. In the pyrolisis front it is simplified, once there is no convection to or from the external flow and the radiative heat transfer is supposed to not occur between the layers (since there is no transmission): (4) In this case, Lp is the heat of pyrolisis. The flux of injection gases is also neglected due its low specific mass, when compared to the solid material. It is remarkable that the specific mass that appears in Eqs. 3 and 4 is the interface specific mass. Although the airflow is included in the domain, its effects are implicit in the convection coefficient H. As a consequence, this region is considered adiabatic, and the heat capacity and thermal conductivity are assumed to be null. Once ablation temperature (TA) is reached, the interface condition becomes: (5)

MEtHod oF SolUtioN The moving boundary problem was solved by the Interface Tracking Method, introduced by Unverdi and Tryggvason (1992), and employed by Juric (1996) in the solution of phase change problems. In this method, a fixed uniform Eulerian grid is generated, where the conservation laws are applied over the complete domain. The interface acts as a Lagragean referential, where a moving grid is applied. The instantaneous placement of the interface occurs through the constant remeshing of the moving grid, and each region of the domain is characterized by the Indicator Function, which identifies the properties of the wall and the air around it. This method allows for the representation of any geometry used in the TPS, and also the characterization of every layer separately. It is accomplished without a high increase in the computational cost and does not need any pre-processing (construction of unstructured grid or coordinate transformation). In this work, this method is employed to estimate the ablative performance of the TPS, considering a two-dimensional approach in both, the heat conduction and the moving boundary problem. Although here an axisymmetric simulation is performed, the method can be applied to any 2-D geometry. The method is detailed described in the previous work of Machado (2012a), and will be briefly summarized as follows. The interface is represented as a parametric curve, R(u), where the normal and tangent vectors and curvature are extracted from. The interface points are interpolated by a 4th order Lagrange polynomial that uses two points before and two after the point to be interpolated, which allows one to analytically obtain the geometric parameters and remeshes the curve, keeping the distance d between curve points within the interval 0.9 < d/h < 1.1, where h is the distance among the fixed grid points, as shown in Fig. 7. The phase distribution in the domain of calculation is represented with the Indicator Function, I(x,t). For the simple case of only two phases, it varies from 0 (air) to 1 (solid), and it is numerically constructed using the interface curve to determine a source term G(x). The jump of the indicator function across the interface is distributed over the fixed grid points, yielding a gradient field in the mesh:

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Machado, H.A.

(6)

where n is the normal unitary vector, and the term δ(x - xf) should be zero, except over the interface, as represented by the Dirac delta function, δ. However, such a representation is not convenient for a discrete number of points, since the Lagrangian points, xk, do not necessarily coincide with the Eulerian grid points, xi,j. The Distribution Function is used to represent the interface discontinuity as a continuous and smooth function. Such a function is similar to a Gaussian distribution function and its value depends on the distance |xij - xk| between the Lagrangean and Eulerian points:

R (u)

n Lagrangean point at interface

I,J d

Figure 7. Eulerian and Lagrangean meshes.

(7) where Dij is the Distribution Function for a point k in the Lagrangean mesh with respect to a Eulerian point (xi,yj). One should note that increasing h results in a thicker interface. The function f is the probability distribution related to the distance h as: f1 ( x ) f ( x ) = 1/2 0

f1 ( 2

if x x)

(11)

if 1 < x < 2 if x

thick region at the interface. In a similar manner, this function is used to interpolate the field variables from the Eulerian grid to the interface. The equations used to distribute the source term in the field and interpolate variables to the interface are:

(8a)

(12)

(8b) The divergence of the gradient field is found by numerical derivation of Poison’s equation: (9) Despite being considered constants in each phase, the properties inside the domain must be treated as variable in the formulation. A generic property f (r, Cp or K) is expressed as: (10) where f0 andf1 are the property values for phases 0 and 1 (according to their values of indicator function), respectively. The coupling between the moving mesh and the fixed grid is done at each time step, through the Distribution Function, used to represent the source terms in the balance equations and to interpolate the infinitesimal discontinuities into a finite

where Dsk is the average of the straight line distances from the point k to the two points on either side of xk, and corresponds to the area concerned to the point xk in the interface surface. Equation 11 is the dicretized form of Eq. 2, where the Dirac delta function was replaced by the distribution function, Di,j, which is also done in Eq. 6. The initial interface shape, R(u), is first specified and then the Indicator Function is constructed. From the initial conditions, the property and temperature fields are determined. Out of the ablative period, the interface temperature keeps bellow the ablation temperature, and the energy equation is solved as a pure heat conduction problem, via the Finite Volume Method, employing the well-known FTCS discretization in an explicit time marching schedule (Patankar, 1980). As the interface reaches the ablation temperature at a given point, an iterative process starts up, in order to determine the interface velocity at each time step, which must satisfy the temperature condition, Eq. 5, at that interface point. The process goes on as far as the point temperature is equal to ablation temperature. The steps to be followed are:

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.409-420, Oct.-Dec., 2013

Simulation of Ablation in a Sounding Rocket TPS Via an Interface Tracking Method With Two Moving Fronts

1. using the current value of interface velocity, V n (result from

2. 3. 4.

5. 6. 7.

the previous time step), the interface points are transported to a new position, calculated explicitly through the equation V n = (dxf /dt).n, where n is normal unitary vector; density and specific heat are calculated at the new interface position; V n+1 is estimated via Newton iterations, using a numerical relaxation schedule, Eq. 13; heat flux q crossing the interface is calculated through Eqs. 3 or 4, depending on the interface, and distributed into the fixed grid through Eq. 11; according to the boundary conditions, energy equation, Eq. 1, is used to obtain the temperature at time step n +1; temperature is interpolated to find TF at the interface, using Eq. 12; the temperature jump condition is tested and if it is lower than the reached tolerance, the fields of viscosity and conductivity are updated for the new position, and one step in time is advanced. If that is not the case, a new estimate for V n+1 is calculated and the process returns to step 5.

The convergence criterion used in step 7 is the residual in Eq. 5. Once it has reached the desired tolerance, convergence for interface velocity is assumed. Otherwise, the velocity is corrected via Newton Iterations, given as: V n+1 = V n - ω.R(T)

415

where NFC is the number of interfaces. The Lagrangean grids for all interfaces have the same values for the parameters h and d, shown in Fig. 1, and are constructed from a particular parametric curve Rm(u). Igi is the Global Indicator Function for a region m, obtained from the Indicator Function of each interface (calculated as described before). It is given as: (15a) (15b) If there are more than one moving interface, the source term Qm for every interface has to be extracted from a modified form of Eq. 2: (16) Actually, according to the numerical method, Eq. 11 will be used to calculate the source term in every interface. The total amount of heat generated will be the summation of the heat sources of all interfaces: (17) The convergence criterion and velocity correction are the same as those for the case of one interface, but they are extended to all interfaces at each time step.

(13)

where ω is a relaxation factor and R(T) is the residual for the temperature jump condition at the interface. Iterations are repeated until R(T) in every point become smaller than the prescribed tolerance. The optimum value for ω is found by numerical experimentation, at the beginning of the calculation. The method was compared with the analytical solution for a simple phase change problem resulting in an excellent agreement (Ruperti Jr., 1991). In the case of more than one interface, an Indicator Function, Im, is created for each interface, in order to characterize every region concerned to the interfaces individually. Therefore, in a region m (that corresponds to a specific phase or material), a generic property is estimated as:

(14)

rESUltS The results were obtained for the region near the stagnation point of VSB-30, Fig. 8, which corresponds to a circular semisphere with radius of 280 mm. Note that the Y-coordinate has a different meaning of y-coordinate shown in Fig. 3 (y). Since the flight is considered with zero angle of attack, the problem is considered to be axy-symmetric, and only the half of that region has to be simulated. A 20x20 points grid over a domain of 12x12 mm was employed to simulate the heat transfer and moving boundary problem, with a tolerance of 10-6 for the residual in Eq. 5. A resulting 26 points Lagrangean mesh was obtained for the interface used to represent the external surface. The convergence of the domain representation by the Indicator Function is shown in Fig. 9, for a cutting view of the domain in the diagonal starting from the axes origin.

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The more points are added to the mesh the more the thickness reduces. When the number of points reaches 20x20, the interface representation is assumed to be satisfactory, since the rise of the processing time would not compensate the correspondent reduction of interface thickness. Figure 10 shows the final interface position according with the number of mesh points used, where the results for 15x15 and 20x20 meshes are very close. In Fig. 11, the variation of external temperature with time at the stagnation point shows an even better convergence between these two meshes. Considering these results, the 20x20 mesh was considered refined enough for the purposes of this study. The results were compared with those from the one-front model (Machado, 2008), using the same numerical parameters. Due to the difference between the models, the composite material (Si-phenoic resin) was characterized in a different way. The properties of resin employed in each model are shown in Table 1. Figure 12 shows the characterization of every region in the domain through the global indicator function, at t = 0. In this case, the two interfaces corresponding to the pyrolisis and carbonization fronts are placed at the same initial position, over the external surface, since the ablative process did not start yet. The colored region corresponds to the interfaces and do not represent exactly the discontinuity, presenting a slope and a finite thickness (about 0.6 mm). This thickness could be reduced through the increase of the number of grid points, which was not considered necessary, according to the convergence analysis. Figure 13 shows the 3-D view of temperature distribution at various moments. Temperature peaks occur in the external surface, in direct contact with the heated air, during the ascension,

at 35 seconds (Fig. 13a) and at 500 seconds during the reentry (Fig. 13c). Between these times, at 100 seconds (Fig. 13b) and after the reentry heating, at 524 seconds (Fig. 13d), a surface cooling occurs. In this case, the external surface becomes a heat sink and yields a “valley” in the temperature profile. One can observe that the temperature of fusion of the char is not reached. Indeed, this surface does not move, since there is no phase change and only the pyrolysis front moves when the pyrolisis temperature of the resin is reached. The region correspondent to the air does not present relevant temperature changes, once it is considered to be adiabatic with zero thermal capacity. Temperature distribution and the external surface position correspondent to these profiles are shown in Fig. 14. The results were compared to those obtained through the one layer model, where ablation is treated as single-phase change process. Figure 15 shows temperature of internal and external surfaces, both at R=0, along the symmetry axis (at Y=10 mm and Y=0, respectively), what corresponds to the stagnation point line. The two temperature peaks correspond to the ascension and reentry of the vehicle in the atmosphere, including a period between then where a cooling occurs, due the heat losses by radiation, in the absence of convection (H=0, according to Fig. 4). It is noticeable that the ablative process begins when the external surface reaches the pyrolisis temperature, and the ablation front starts to move. The temperature of fusion of the char is not reached at both peaks, even in the points of maximum temperature. For the one layer model, the temperature of ablation is lower, what means the process begins earlier and takes more time. Due the presence of the char layer in the two-fronts model, the temperatures reached in the internal surfaces are lower, what

Nose top

1 Stagnation point

0.5

rTPS = 10 mm 12 mm

Indicator Function

5x5 10x10 15x15 20x20

x=0,y=0

12 mm

Figure 8. Domain of calculation, regions (TPS in blue) and dimensions.

Figure 9. Indicator Function profile observed in the diagonal trough the axes origin, for diverse number of points used in the regular mesh.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.409-420, Oct.-Dec., 2013

Simulation of Ablation in a Sounding Rocket TPS Via an Interface Tracking Method With Two Moving Fronts

0.01

700

0.009

600

0.007

500

0.006

400

0.005

Initial position Final position 20x20 15x15 10x10 5x5

0.004 0.003 0.002 0.001 0

T, ºC

Y,m

0.008

0.0025

0.005

417

20x20 15x15 10x10 5x5

300 200 100

0.0075

0.01

100

200

300

400

500

Time, s

R.m Figure 10. Interface position for the virgin material at the final time, for diverse number of points used in the regular mesh.

Figure 11. External surface temperature with time, for diverse number of points used in the regular mesh.

Table 1. Properties of Si-phenolic resin. Property

Two-front model

One-front model

Virgin material

Char

Thermal conductivity (W/m°C)

0.485

0.428(2)

Specific heat, Cp (J/kg °C)

1,256

(1)

1,256

879.5(2)

Specific mass, ρ (kg/m3)

1,730(1)

1,300(2)

(1)

Emissivity, ε

0.8

Heat of ablation (MJ/kg)

12(4)

–

Temperature of ablation (°C)

538(4)

–

Heat of pyrolisis (MJ/kg)

–

0.78(5)

–

Temperature of pyrolisis (°C)

–

599 (5)

–

Heat of fusion/Sublimation (MJ/kg)

–

10.5(3)

Temperature of fusion (°C)

–

3,700(3)

0.8

(1)

Da Costa et al. (1996);

(1)

Gregori et al. (2008);

(4)

Tick et al. (1965);

(5)

0.8(3)

(1)

Williams and Curry (1992);

(2)

Savvatimskii (2003).

(3)

0.012

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

0.008

0.8

Y, m

Indicator Function – I

0.01

0.6 0.4

0.006 0.004

0.2

0.005 0.005 Y, m

R, m

0.01

0.002 0

0.002

0.004

0.006

0.008

0.01

0.012

R, m

Figure 12. Global Indicator Function for every region: Thermal Protection System in red and air in blue. J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.409-420, Oct.-Dec., 2013

Machado, H.A.

1000

800

600

T, ºC

418

400 200 0

200

Char Surface

0.0

0 0.0

R, m

0.0

1 0.0

0 12

0.0

400

0.01 0.01 2 0.0 0.00 08 0.00 6 0.00 4 Y, m 2

800

600

T, ºC

1000

400 Char Surface

0.0

06 0.0 .008 1 R, m 0 0.0 120 0.0

0.0

06 0.0 .008 1 R, m 0 0.0 120 0.0

0.01 0.01 2 0.00 0.00 8 0.00 6 0.00 4 Y, m 2

(b) t=100 s

1000

0.0

(a) t=35 s

200

Char Surface

400 200

0.01 0.01 2 0.0 0.00 08 0.00 6 0.00 4 Y, m 2

Char Surface

0.01 0.01 2 0.0 0 0.00 08 02 0.0 .004 6 0.00 6 0 0 0.00 4 Y, m 0.0 .008 2 1 R, m 0 0.0 120 0.0

(d) t=524 s

Figure 13. 3-D view of temperature field during the trajectory, with the Global Indicator Function for every region: Thermal Protection System in blue and air in red.

indicates that this layer works as a thermal barrier for the virgin material of the TPS. Figure 16 shows the interface position with the time, after the two ablation periods (ascension and reentry). According to the results, the ablation is more intense closer the stagnation point for both models. However, the two-fronts model indicates a lower material consumption than the one layer model. The initial position of the external surface keeps constant in the twofronts model, since the temperature of fusion/sublimation of the char is not reached. The displacement starts after the formation of the pyrolisis front (considered to be initially at 0.1 mm after the external surface, for calculation purposes).

conclusion In this work, the two-dimensional transient aerodynamic heating and ablation processes in the vicinity of the stagnation point of the VSB-30 sounding rocket TPS were simulated through an interface tracking method considering the presence of two moving fronts, the pyrolisis and the carbonization fronts, and two resulting layers, the virgin material and the char layer. Preliminary results demonstrated that the method is able to capture the temperature peaks and to represent the ablation process as a moving boundary problem, in the presence of more

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.409-420, Oct.-Dec., 2013

Simulation of Ablation in a Sounding Rocket TPS Via an Interface Tracking Method With Two Moving Fronts

949.969 886.637 823.306 759.975 696.644 633.312 569.981 506.65 443.319 379.987 316.656 253.325 189.994 126.662 63.3312

0.01

Y, m

0.008 0.006 Char surface

0.004 0.002 0

0.002 0.004 0.006 0.008 R, m

0.01

0.012

0.008 0.006 Char surface

0.004 0.002 0

0.012

0.002 0.004 0.006 0.008 R, m

(a) t=35 s

Y, m

0.008 0.006 Char surface

0.002 0

0.002 0.004 0.006 0.008 R, m (c) t=500 s

0.012

0.01

0.012

392.428 366.266 340.104 313.942 287.781 261.619 235.457 209.295 183.133 156.971 130.809 104.647 78.4856 52.3237 26.1619

0.01 0.008 Y, m

849.45 792.82 736.19 679.56 622.93 566.3 509.67 453.04 396.41 339.78 283.15 226.52 169.89 113.26 56.63

0.01

(b) t=100 s

0.012

0.004

283.678 264.766 245.854 226.942 208.031 189.119 170.207 151.295 132.383 113.471 94.5594 75.6475 56.7356 37.8237 18.9119

0.01

Y, m

0.012

419

0.006 Char surface

0.004 0.002 0

0.012

0.002 0.004 0.006 0.008 R, m

0.01

0.012

(d) t=524 s

Figure 14. Temperature distribution during the trajectory.

1100

External suface temperature One-front model Two-front model – Inteface char-Si-Phenolic Two-front model – Char layer

1000 900 800

0.01

Initial profile at t = 0

0.008

500 400

0.007 0.006 0.005

One-front model, t = 524 s

0.004

300

0.003

200

0.002

100

0.001

One-front model, t = 200 s

0.009

Y, m

600

Two-front model, t = 200 s

0.011

Internal suface temperature One-front model Two-front model

700

T, ºC

0.012

0 0

50 100 150 200 250 300 350 400 450 500

Time, s

Figure 15. Comparison between the results of the ablation models for temperatures in the stagnation point, R=0.

Two-front model, t = 524 s

0.002

0.004

0.006

R, m

0.008

0.01

0.012

Figure 16. Comparison between the results of the ablation models for interface position with time.

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than a single moving front, which allows representing diverse physical simultaneous processes. When compared with the one-front model, it results in lower temperatures out of the periods of heating, shorter periods of ablation and less consumption of protective material. This analysis can be extended to more regions of the rocket, more layers and other shapes, including more moving fronts, if it is necessary. A more realistic physical model for the ablation in the composite material may now replace the one used in this work. The inclusion of the flow field effects, like injection of mass due to sublimation, shall also be incorporated into the simulation. Since the two-fronts model has presented better accuracy than the one-front model, as shown in previous studies

already mentioned, it seems to be a better option to estimate TPS performance for other vehicles and might provide a more accurate dimensioning and consequently reducing its weight and cost.

acknowledgments The author would like to thank the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), the Brazilian Federal Agency for Science and Technology, for the financial support during this work.

REFERENCES Anderson Jr., J.D., 1989, “Hypersonic and High Temperature Gas Dynamics”, McGraw-Hill, Blacklick, Ohio, USA.

Patankar, S.V., 1980, “Numerical Heat Transfer and Fluid Flow”, Taylor & Francis, New Delhi.

Da Costa, L.E.V.L., De Mello, F.C. and Pardini, L.C., 1996, “Viability Study of Thermal Protection for SARA Platform”, IAE/CTA, Technical note NT-130ASE-N/96, IAE/CTA, São José dos Campos, São Paulo, Brazil (in Portuguese).

Ruperti Jr., N.J., 1991, “Solution of a One-Dimensional Ablation Model”, Master thesis, Instituto Nacional de Pesquisas Espaciais, São José dos Campos, São Paulo, Brazil (in Portuguese).

Garcia, A., Yamanaka, S.S.C., Barbosa, A.N., Bizarria, F.C.P., Jung, W. and Sheuerpflug, F, 2011, “VSB-30 Sounding Rocket: History of Flight Performance”, Journal of Aerospace Technology and Management, Vol. 3, No.3, pp. 325-330. doi: 10.5028/JATM.2011.03032211

Savvatimskii, A.V., 2003, “Melting Point of Graphite and Liquid Carbon”, Physics-Uspekhi, Vol. 46, No. 12, pp. 1295-1303. doi:10.1070/ PU2003v046n12ABEH001699

Gregori, M.L., Barros, E. de A., Petraconi Filho, G., Costa, S.F. and Pardini, L.C., 2008, “Properties of Quartz-Phenolic Composites for Thermal Protection Systems”, 59th IAC Congress, Glascow, Scotland. Juric, D., 1996, “Computations of Phase Change,” Ph.D. thesis, University of Michigan, Michigan, 166 p. Machado, H.A. and Pessoa-Filho, J.B., 2007 , “Aerodynamic Heating on VSB-30”, ESA Symposium, Visby, Sweden. Machado, H.A., 2008, “Two-dimensional Simulation of Ablation due to Aerodynamic Heating in a Sounding Rocket”, 40th AIAA Thermophysics Conference, Seattle, USA. Machado, H.A., 2012a, “Simulation of Ablation in a Composite Thermal Protection System via an Interface Tracking Method”, Journal of Aerospace Technology and Management, Vol. 4, No. 3, pp. 331340. doi: 10.5028/jatm.2012.04030312 Machado, H.A., 2012b, “After-Flight Thermal Analysis of the VSB30 Sounding Rocket Payload”, VII National Congress of Mechanical Engineering – CONEM, São Luís, Maranhão, Brazil (in Portuguese). Miranda, I.F. and Mayall, M.C de M., 2001, “Fluxo de Calor Convectivo em Micro-Satélites em Reentrada atmosférica”, Graduate Dissertation, Instituto Tecnológico de Aeronáutica, São José dos Campos, São Paulo, Brazil, 66 p.

Sias, D.F., 2009, “Hibrid Solutions for Heat Transfer on Ablative Thermal Protection Systems”, Master thesis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil (in Portuguese). Tick, S.J., Huson, G.R. and Griese, R., 1965, “Design of Ablative Thrust Chambers and Their Materials”, Journal of Spacecraft and Rocket, Vol. 2, No. 3, pp. 325-331. doi: 10.2514/3.28179 Turner, J., Hörschgen, M., Turner, P., Ettl, J., Jung, W. and Stamminger, A., 2005, “SHEFEX – The Vehicle and Sb-Systems for a Hypersonic Re-entry Flight Experiment”, 17th ESA Symposium in European Rocket and Balloon Programmes and Related Research, Sandfjord, Norway. U.S. Standard Atmosphere, Washington, D.C., U.S. Government Printing Office, 1976. Unverdi, S.O. and Tryggvason, G., 1992, “A Front-Tracking Method for Viscous, Incompressible, Multi-fluid Flows”, Journal of Computational Physics, Vol. 100, No. 1, pp. 25-37. Williams, S.D. and Curry, D.M., 1992, “Thermal Protection Materials – Thermophysical Property Data”, NASA Reference Publication 1289. Zoby, E.V., Moss, J.N. and Sutton, K., 1981, “Approximate Convective Heat Equations Hypersonic Flows”, Journal of Spacecraft and Rockets, Vol. 18, No. 1, pp. 64-70.

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doi: 10.5028/jatm.v5i4.265

Infrared Spectroscopy Applied to Materials Used as Thermal Insulation and Coatings Natália Beck Sanches1, Ricardo Pedro1, Milton Faria Diniz2, Elizabeth da Costa Mattos2, Silvana Navarro Cassu2, Rita de Cássia Lazzarini Dutra2

ABSTRACT: It is very important to control the characteristics of a polymer used in rubber compositions and paints in aerospace applications. Hence, the development of simple and fast methodologies that allow the identification of these compositions becomes attractive to researches carried out in this area. This study has evaluated infrared (IR) techniques, such as transmission, universal attenuated total reflection (UATR), and attenuated total reflection (ATR), for the characterization of elastomers and paints. It takes into consideration the characteristics of surface techniques, such as the depth of penetration of the IR beam into the sample. The presence of additives in low concentrations on paints was only detected by UATR after grinding the components. Results show that it is possible to differentiate rubber mixtures with similar IR spectra and to detect small amounts of additives in the surface of coatings. KEYWORDS: Rubbers, paints, infrared, FT-IR, ATR, UATR.

Introduction Polymers are used in many industrial areas, such as the aerospace (Dutra et al., 2002; Oliveira et al., 2011). Their different types, with specific characteristics, are used in propellant (Sciamareli et al., 2012), composites, rubbers (Ferrari et al., 2012; Santos et al., 2013), and coatings (Blackford, 1999). The resin most often utilized in aerospace applications is hydroxyl terminated polybutadiene (HTPB), which is used in propellants and flexible insulation protection (Lourenço et al., 2006; Crespim et al., 2007). It is known that phenolic resins are employed in composites for rigid thermal insulation, while epoxy are used in adhesive compositions for these same protections and in polymer arrays for the aeronautical sector (Pardini, 2000). Rubbers are used in flexible thermal insulation, and the most cited ones in literature for using in aerospace applications are copolymers of acrylonitrile and butadiene (NBR) and ethylene-propylene-diene rubber (EPDM) (Moraes et al., 2007). Traditionally, aircraft paints are coatings with specific characteristics and requirements, because in extreme cases the use of a defective one may contribute to injuries in a catastrophic scale (Blackford, 1999). The requirements are associated with the environment in which modern aircrafts are used and how they are painted, among other factors. Thus, there are significant differences in formulations used for the aerospace sector compared to other industrial areas, allowing the use of products that can cause damage to people’s health, such as chromate pigments, or to the environment, like large amounts of solvents.

1.Instituto Tecnológico de Aeronáutica – São José dos Campos/SP – Brazil 2.Instituto de Aeronáutica e Espaço – São José dos Campos/SP – Brazil Author for correspondence: Rita de Cássia Lazzarini Dutra | Praça Marechal Eduardo Gomes, 50 – Vila das Acácias | CEP 12.228-904 São José dos Campos/SP – Brazil | Email: ritarcld@iae.cta.br Received: 11/07/13 | Accepted: 18/10/13

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Generally, solvents are used to decrease viscosity and facilitate the application of paints. The vehicle or binder is the chemical component that will generate a film on the surface, besides being responsible for keeping the other constituents scattered and clumped. Fillers are materials that provide specific properties to paints, for example, the magnetic nanoparticles that turn aircrafts non-detectable to radars. The additives are compounds used from storage to product application. For example, microbicides preserve the paint during its storage and catalysts reduce the time of film formation during product application (Mello et al., 2012). With regard to the analysis of polymers used in the aerospace industry, there are studies in the literature (Sanches et al., 2006; ASTM D3677-10, 2010; Romão et al., 2006; Hori et al., 1990) that employ mid infrared (MIR) spectroscopy for identification, characterization, and quantification of materials. In these investigations, analyses of materials using Fourier transform infrared (FT-IR) techniques with variation of the parameters, in different layers of the surface, were not explored. It is also known that some components present in paints cannot be directly analyzed by using conventional FT-IR techniques, because the characteristic bands overlap with absorptions of other components often seen in high concentrations. Although the study of bands overlapping could be done by using mathematical methods, such as derivatives and deconvolution to quantify components, these ones, in some cases, are complex and depend on the thickness of the analyzed samples. This limitation becomes even more relevant when additives are present in extremely low concentrations. A specific methodology must then be studied. The FT-IR literature emphasizes that, in the analysis of multiple components systems, there are factors to be considered: influence of sample preparation, total or partial analyses of composition (Allen, 1992; Dutra et al., 1995), and the technique used to obtain the spectrum, single or coupled (Mattos et al., 2004; Almeida et al., 2002; Mateo et al., 2009). In these cases, it is helpful to perform the evaluation of characteristic functional groups, the query to the database of reference spectra (Smith, 1979) using spectra software (Szafarska et al., 2009), and the interpretation of similar studies to highlight the limitations and potential of techniques (Miliani et al., 2002).

Different FT-IR techniques may be suitable for the study of many kinds of materials, according to their characteristics. The most common surface technique is the attenuated total reflection (ATR), which can be used for the analysis of liquid and dried paint films (Mazzeo et al., 2007; Zhang et al., 2009). In the ATR spectroscopy, the radiation passing through the crystal reflects totally on its internal surface (Waltham, 2005). When a radiation absorbing material is placed in contact with a crystal, the infrared (IR) beam penetrates the thin layer of the sample surface and loses energy, causing the attenuated total reflectance. In this technique, good contact between the sample and the crystal is essential (Pandey and Kulshreshtha, 1993). The penetration depth of the IR beam into the sample depends on the IR radiation angle of incidence, the radiation wavelength, and the refractive indices of the crystal and sample. Ge crystals are able to analyze thinner surfaces (Pandey and Kulshreshtha, 1993). In the ATR technique, different species of the sample surface and interior can be revealed depending on the analysis procedure. The literature reports (Pandey and Kulshreshtha, 1993; PerkinElmer, 2005) and experiences indicate that this aspect must be considered if the study requires selective analysis of the very superficial layer. Ultimate FT-IR techniques, such as the universal ATR (UATR), have found prominence in the analysis of different materials (PerkinElmer, 2005; Abidi and Hequet, 2005). This technique of internal reflection is able to perform nondestructive analysis of solids, powders, liquids, and gels. In it, the IR beam passes through an ATR element composed of ZnSe (refractive index 2.67) or diamond-KRS-5 (refractive index 2.42), with high refractive index, and reaches the surface of the sample. One of the prerequisites of the UATR technique is good contact between the crystal and the sample surface. The probe strength can be adjusted to obtain the most suitable contact, since different pressure levels influence directly on the intensities of the obtained spectra. Because the IR beam does not penetrate deeply into the sample, this technique is suitable for analyzing surfaces with thickness of only a few microns (Pandey and Kulshreshtha, 1993). It was observed in the literature some gaps related to the use of surface FT-IR techniques, varying the degree of sampling depth. Hence, it was concluded that it is essential the development of new characterization methodologies that

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enable rapid identification of the different materials used in the aerospace industry. The aim of this work was to show the new results obtained by the Chemical Department of the Instituto de Aeronáutica e Espaço (AQI/IAE), in the characterization of different types of materials by using FT-IR reflection techniques, such as ATR and UATR. In addition, the transmission FT-IR technique was applied to identify rubber mixtures.

Experimental The nitrile rubber (NBR) and rubber mixtures used in this study were prepared in AQI/IAE. The paint and additives were provided by Rohm and Haas. The chemical structure of the additives carbendazim (2-methoxycarbonylamino-benzimidazole), Diuron (N’-(3,4-dichlorophenyl)-N, N-dimethylurea)) and OIT (2-N-octyl-4-isothiazolin-3-one) is shown in the text. Portions of the samples, before and after extraction with acetone, were also subjected to Beilstein and acidresistance tests (Dutra and Diniz, 1993). A treatment with heated ortho-dichlorobenzene was performed as well. Evaluation of transmission and reflection FT-IR techniques for characterization of rubbers NBR was analyzed qualitatively by transmission, preparing the sample by pyrolysis (Smith, 1979) after extraction in methanol for eight hours, using the liquid film technique. For the UATR analysis, the NBR sample was placed in contact with the surface of the crystal and a force of 80 N was applied with the aid of an articulated arm. For ATR (KRS-5 or Ge) analysis, the NBR sample was placed on both sides of the crystal. In this study, KRS-5 (TlBr-crystal mixture TlI) with refractive index 2.4 and Ge with refractive index 4.0 crystals were utilized. The filler was analyzed by transmission, preparing the specimen with the KBr disc technique. Evaluation of transmission FT-IR techniques for characterization of rubbers and their mixtures An unknown mixture of rubbers was analyzed qualitatively by transmission, preparing the sample by pyrolysis after extraction for eight hours in acetone, using

423

the liquid film technique (Smith, 1979). All analysis were performed using the spectrometer Spectrum One PerkinElmer (resolution 4 cm-1, gain 1, spectral range 4000– 400 cm-1, 40 scans). UATR analysis of painted surface A paint film was applied on a flat glass substrate. After drying, the film was removed by scraping and then analyzed. The paints additives were analyzed directly, without the need of sample preparation. UATR analysis was performed using the spectrometer Spectrum One, PerkinElmer, DTGS detector (resolution 4 cm-1, spectral range 4000–550 cm-1, gain 1, 20 scans). Spectra were obtained using the accessory UATR and applying a force of 80 N.

Results and Discussion This section presents the evaluation of FT-IR techniques for the analysis of elastomers and paints, using methods traditionally applied and developed in AQI/IAE laboratories. Applicability of transmission and reflection FT-IR techniques for characterization of rubbers It is known that conventional FT-IR techniques can be applied to the characterization of rubbers (Matheson et al., 1994; Wake et al., 1983; Williams and Besler, 1995). In this work, different information is obtained by using independently transmission (liquid phase of pyrolyzate) and reflection (ATR and UATR) techniques. These were used for the characterization of the polymer and the filler, respectively. Figure 1 shows the FT-IR spectra obtained from NBR analysis by using different techniques. It is observed that the analysis of the liquid phase of the pyrolyzate resulted in a spectrum (Fig. 1a) with the base polymer characteristic absorptions. The band observed at 2237 cm-1 is assigned to the stretching of the C-N group, and the peaks in the region of 1000–900 cm-1 are assigned to the wagging bending vibration of vinyl and trans C=C groups. In the spectra obtained by reflection using different crystals (Figs. 1b, 1c and 1d), the band at 2237 cm-1 showed lower intensity and there was a contribution of Si-O groups in the region of 1100–900 cm-1,

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the latter related to the load used in the formulation (Smith, 1979). Figure 1e shows these load-related bands, analyzed by transmission (KBr pellet), after rubber calcination. Although the spectra obtained by using reflection techniques did not clearly evidence the absorptions of NBR, they were able to indicate the filler used in the formulation. In order to obtain the same filler related information by using transmission techniques, it is necessary to apply supplementary sample preparation techniques, such as KBr pellet after rubber calcination. The use of reflection techniques can also be applied to the detection of silica filler in silicone rubbers, instead of using a large number of techniques to achieve the same goal. This is important because applying some sample preparation techniques for transmission analysis, such as calcination, can induce the formation of silicon oxide in this type of rubber.

(a) 2237.91

993.70 968.22 914.10

2236.92

(b)

795.07 967.30 1074.95

(d)

967.02 1071.52

2237.56

802.87 969.01 1099.58

(e)

4000

807.43

3200

2400

1800

cm-1

1400

1000

600 400

Figure 1. FT-IR spectra obtained from analysis of the nitrile rubber through different techniques: (a) Nitrile rubber transmission pyrolyzed (liquid film), (b) Nitrile rubber – UATR, force 80, (c) Nitrile rubber – ATR/KRS-5, (d) Nitrile rubber – ATR/Ge, and (e) residue obtained after calcination of rubber on Bunsen burner – transmission, KBr pellet.

It might lead to misinterpretation, since not all silicone rubbers have silica as filler. AppliCABiliTY OF FT-iR TRAnSmiSSiOn TEChniquES FOR ThE ChARACTERizATiOn OF RuBBERS AnD ThEiR mixTuRES As it is known, pyrolysis of rubbers without solvent extraction is performed only to determine the base polymer and the best solvent for its extraction. Because additives bands are also observed, it is necessary to extract the rubber sample in order to obtain a spectrum that shows only characteristic bands of the polymer (Smith, 1979; Wake et al., 1983). The identification of rubber mixtures by using FT-IR is possible, providing that their characteristic bands are different, without the occurrence of overlapping. Figure 2 illustrates the application of the FT-IR transmission technique (pyrolysis without control of temperature) to an unknown mixture of rubbers, with and without extraction. The main absorptions observed in the spectrum of the pyrolyzed rubber without extraction (Fig. 2a) and their attributions (Smith, 1979) are: 1376 cm -1, angular bending vibration (δ) of CH 3 groups; 991 and 909 cm -1, wagging bending vibration (ω) of vinyl group; 964 cm -1, wagging bending vibration of trans groups; and 887 cm -1, wagging bending vibration of vinylidene groups. These absorptions positions, shapes, and intensities suggest the presence of EPDM and/or NR (poly cis-isoprene or natural rubber) elastomers, which requires the use of acetone solvent for removal of the plasticizer and other additives (Wake et al., 1983). The sample was extracted for eight hours in Soxhlet apparatus, using acetone as the solvent. Then, it was pyrolyzed and analyzed as a liquid film (Fig. 2b). The same main bands were observed, as expected, because plasticizers used in EPDM and NR formulations are apolar and their groups do not contribute with different absorptions in the spectrum of the pyrolyzates without extraction. The band at 887 cm-1 is characteristic of NR rubber, but it is also present in the EPDM rubber. To further elucidate the chemical structure of the sample, a comparison was made between the IR spectrum of this sample and that of known EPDM and NR rubbers, pyrolyzed after extraction in acetone (Figs. 2b and 2c).

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(a)

425

(a)

(b)

%T %T

(b)

(c)

4000

(d)

4000

3200

2400

1800 1400 cm-1

1000

3200

2400

1800

cm-1

1400

1000

600 400

Figure 3. FT-IR spectra of (a) pyrolyzed insoluble portion, after ortho dichloro benzene treatment (previously extracted in acetone for eight hours), and (b) pyrolysed EPDM (reference).

600 400

Figure 2. FT-IR spectra of (a) sample pyrolyzed without extraction, (b) sample pyrolyzate after extraction with acetone, (c) spectrum of known EPDM, pyrolyzed after extraction with acetone, and (d) spectrum of known NR, pyrolyzed after extraction with acetone.

To better identify the EPDM bands, it was also applied a treatment with hot ortho-dichlorobenzene to the sample, which was then pyrolyzed to obtain the IR spectrum. The resultant IR spectrum (Fig. 3a) showed more clearly the presence of EPDM rubber (bands marked with an asterisk), as can be visualized in Fig. 3b. The Beilstein test was performed before and after extraction in acetone to confirm the absence of halogen compounds, expected because EPDM and NR do not present halogens in their compositions. The negative result corroborates the hypothesis of the presence of these elastomers. The acid-resistance test, which basically classifies the rubber resistance when treated with an acid mixture at 40°C and 70°C, was also performed in the sample to elucidate the mixture composition. The sample did not show degradation at 40°C in the time established for the test, i.e. 15 minutes (Dutra and Diniz, 1993). At 70°C, it was observed degradation in around three minutes, confirming that the rubber has a certain content of EPDM. If the sample contained only NR, it would resist for only a few seconds, for less than one minute.

Basically, FT-IR analysis of the liquid pyrolysis products, associated with Beilstein and acid resistance tests (Dutra and Diniz, 1993), within the detection limits of the FT-IR technique, indicated that the unknown rubber consists of NR and EPDM. The utilization of acid-resistance test to characterize rubber mixtures (Dutra and Diniz, 1993), also developed in IAE/AQI, is important especially in EPDM mixtures. This elastomer is considered as a replacement for NBR in the aerospace industry. The addition of NR in a rubber mixture may be intended to lower the cost, but it certainly affects properties. Hence, we can conclude that appropriate methodologies for the quality control of materials, contemplating the application of different tests and analyses, are fundamental to achieve the outlined goals, essentially in the aerospace industry, where materials require specific properties. AppliCABiliTY OF TRAnSmiSSiOn AnD REFlECTiOn FT-iR TEChniquES FOR ChARACTERizATiOn OF pAinTS As mentioned, aeronautical coatings may contain hazardous products, such as chromate pigments (Blackford, 1999). The identification of chromate anions (CrO42-) can be performed by IR spectroscopy in the region of 800 to 900 cm-1 (Smith, 1979). Copper chromite is used in the aerospace industry as a catalyst precursor (Faillace et al., 1999). It was characterized by transmission (KBr disc), reflection, and

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photoacoustic detection (PAS) FT-IR techniques in an article published by the IAE/AQI group (Campos et al., 2003). It was observed that FT-IR techniques are able to indicate the catalyst method of synthesis. This is possible by using surface analysis techniques, with different degrees of sampling depth, without the need for other characterization methods. Therefore, the techniques used in this study (Campos et al., 2003) have emerged as new alternatives for characterizing other types of catalysts or materials containing these kinds of compounds, such as chromium-based anions. Certain additives, as OIT, diuron, and carbendazim, are added in small contents to preserve coatings during storage (Mello and Suarez, 2012). In this study, the methodology of chromium-based compounds (Campos et al., 2003) was applied to the analysis of paints, with different degrees of sampling depth. AnAlYSiS OF pAinTS BY uATR Figure 4 shows the UATR spectra of additives OIT, carbendazim, and diuron, painted with and without preservatives, dried resin, and filler. The main bands observed in the FT-IR spectrum of OIT (Fig. 4a) are: 2924, 2855 and 722 cm-1 (stretching and rocking bending vibration of the CH2 aliphatic group), 1618 cm -1 (stretching of the C=O group), as well as at 1263 and 781 cm -1 (vibration of five-membered heterocyclic ring). The spectrum of carbendazim (Fig. 4b) showed bands at: 3320 cm -1 (stretching of N‒H secondary group), 1710 cm-1 (stretching of C=O group), 1627 cm -1 (stretching of the C=O group and of the C=N 5-membered heterocyclic ring), 1591 cm-1 (stretching of the C‒C aromatic and C=N groups), 1266 and 1093 cm-1 (stretching of the C‒O group), and 726 cm -1 (vibration of five-membered heterocyclic ring and of the C‒H aromatic substitution group). The spectrum of the additive Diuron (Fig. 4c) shows bands at: 3280 cm-1 (stretching of the N‒H group), 1651 cm-1 (stretching of C=O and C=N groups), 1584 cm-1 (stretching of the C-C aromatic group), 1473, 1298 and 1186 cm-1 (stretching of the C‒N group), 1132 cm-1 (wagging of C‒Cl aromatic group), as well as 864 and 813 cm-1 (bending vibration of the CH‒tri-aromatic substitution group) (Smith, 1979; Silverstein et al., 2005.

(a)

O S

(b)

(c)

H3C

nC8H17 N N H

NH O

CH3

CH3 N NH O Cl

(d)

%T (e) (f)

(g)

4000

3200

2400

1800

cm-1

1400

1000

550

Figure 4. UATR spectra of: (a) OIT, (b) carbendazim, (c) diuron, (d) paint without preservatives, (e) paint with preservatives, (f) dry resin, and (g) calcium carbonate.

The additives OIT, carbendazim and diuron presented intense peaks that could be potentially used as analytical bands. However, the direct analysis of these compounds in the paint, by using UATR, was not possible because spectra of the paint, without (Fig. 4d) and with (Fig. 4e) preservatives are virtually identical. The resin (Fig. 4f) and filler (Fig. 4g) are present in high concentrations, so these ingredients could be the major interferences to the direct analysis. In fact, the main bands observed are associated with the resin (Fig. 4f): 1728 cm-1 (stretching of the C=O group), 1159 cm-1 (stretching of the C‒O group), and 698 cm-1 (bending vibration of C‒H aromatic substitution group). Although the characterization of preservatives in the surface of paints by UATR was not possible in the studied conditions, it is known that they are added to the compound to control proliferation of microorganisms (Lindner, 2005). Thus, we can suppose that some content of preservatives is present on the film surface.

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It is known that the diffusion phenomenon is a process that depends on the size of the involved particles. The rate of diffusion is inversely proportional to the size of the particle. Hence, a smaller particle will diffuse faster and migrate easier to the surface. Preservatives were grinded to obtain different particle sizes. These samples were studied to investigate the effect of particle size on surface analysis results. Evaluation of the influence of particle size in the detection of preservatives at the painted surface by UATR Results of grinded preservatives are presented in Table 1 and show that preservatives reduce their size up to one fifth, when grinded for six minutes. The values represent the average of two determinations. Stabilizers (wetting agents and dispersants) were added to the samples to prevent agglomeration of crystals. Although the resulting particle sizes are very close, there seems to be a tendency of increasing the particle size with time, indicating agglomeration of particles (Welin-Berger and Bergenstahl, 2000). This increasing is negligible compared to the magnitude and extent of the uncertainty in its determination, and therefore it was neglected. The smaller the particle size, the most effective the preservative should be in the microbiological control.

427

The explanation for this is that with smaller particle sizes, preservatives cover a larger surface area and can be used in lower concentrations. In smaller sizes, these additives also migrate more easily to the surface, becoming more bio-available to control microbes. UATR analysis after grinding of preservatives The

UATR

paint

spectra,

with

and

without

preservatives and grinding, compared to UATR reference spectra of the additives (OIT, diuron, and carbendazim), are shown in Fig. 5. The band with low intensity around 3320 cm–1 (N‒H group), detected in the spectra of paints containing grinded preservatives (specially the six-minute grinded sample), and the peaks at 1625 cm-1 (C=O group and C=N 5-membered heterocyclic ring), and around 1330 cm-1 (C–N group), may indicate the presence of carbendazim. The absorption around 1625 cm-1 might have also contributed to the OIT bands around 1620 cm-1 (C=O group). The band around 870 cm-1 (C–H aromatic substitution group) might be from the additives diuron and/or carbendazim, and the absorption observed at 782 cm-1 (vibration of five-membered heterocyclic ring), from the additive OIT.

Table 1. Particle sizes of preservatives. Particle size (µm) Sample

Without grinding

Grinding for 1 minute

Grinding for 6 minutes

Zero time

3 months

Zero time

3 months

Zero time

3 months

32.073±0.422

33.148±0.234

14.711±0.102

14.900±0.101

4.963±0.023

5.234±0.036

31.834±0.357

35.133±0.289

16.529±0.115

16.987±0.078

6.487±0.109

6.610±0.101

32.189±0.309

33.390±0.415

11.870±0.111

13.041±0.099

5.992±0.197

6.123±0.099

32.558±0.178

32.098±0.238

4.570± 0.098

15.067±0.121

5.336±0.136

5.897±0.020

31.845±0.310

33.022±0.102

15.460±0.201

16.001±0.309

4.196±0.099

4.223±0.021

32.012±0.284

32.917±0.287

15.649±0.193

16.122±0.235

5.971±0.099

5.992±0.113

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(a) 2873.42

1728.47

1601.93 1395.26 1583.23 1252.76 1493.69 1066.54 1452.86 1159.36

2957.39 2930.51

(b)

(c)

(d) %T

1795.31

2957.01 2930.80 2873.19

2954.54 2924.78

(f)

3319.70

1409.04

1618.85

1509.07

2855.15

1711.28 1627.69

2949.32

2935.22

1474.48 1442.17

1651.43 1584.74 1523.98

3600

3200

2800

2400

2000

871.43

1225.96 871.71 753.61 1266.68 1180.13 782.55 1327.70 1028.24 725.39

1464.40 1313.50 1238.04 1263.10 1114.10

(g)

4000.0

1027.42

1404.82

1591.37 3279.67

782.48

1266.11 1161.51

1728.37 1622.93

1800 cm

1600

759.48

1028.28

1408.62

1794.20

2856.13 2926.47

871.57

1159.69

1795.22 1729.59 1626.65

3074.70

(e)

1257.61

1728.96

2856.86 3320.50 3027.67 2956.15 2927.11 3319.83 3027.60 2955.09

1601.88

759.71

1266.65 1327.22 1224.83 1234.71

1406.55

1400

1268.47

1200

1052.82

781.60 722.72 752.84

726.86 1093.87 791.79 955.80 869.84

1132.151027.05

1186.68

1000

724.52

864.76

755.55

800

550.0

-1

Figure 5. Spectra of UATR – (a) paint without preservatives, (b) paint with preservatives, without grinding, (c) paint with preservatives, grinded during one minute, (d) paint with preservatives, grinded during six minutes, (e) OIT, (f) carbendazim, and (g) diuron.

CoNClUSioNS The evaluation of IR techniques for the characterization of elastomers and coatings indicated that: • Transmission and reflection techniques applied to the same rubber sample, NBR in this study, can provide different information, highlighting only the base polymer characteristic absorptions (by pyrolysis/transmission) or the filler ones (by KBr pellet/transmission and ATR or UATR/ reflection). This methodology can be applied to other types of rubber, particularly silicone formulations that might or might not contain silica filler. This aspect can be easily verified by using reflection analysis (ATR or UATR). • The studied EPDM/NR mixture can be identified only by using the pyrolysis technique and after treatment

with ortho-dichlorobenzene. This method is suitable only for mixtures whose characteristic bands do not overlap. • The UATR technique, without grinding of preservatives, did not allow the direct analysis of the painted dry film, for the samples analyzed. However, after the preservatives had been grinded, it was possible to observe bands around 3300 and 1330 cm-1 (carbendazim), 1625 cm-1 (carbendazim and/or OIT), 870 cm-1 (diuron and/or carbendazim), and 782 cm-1 (OIT). • By using different FT-IR techniques, the preservatives carbendazim and OIT allow better visualization of the painted surface after grinding, because of their characteristics bands (medium and intense bands of functional groups and/or higher diffusion).

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REFERENCES Abidi, N. and Hequet, E., 2005, “Fourier Transform Infrared analysis of trehalulose and sticky cotton yarn defects using ZnSe-Diamond UATR”, Textile Research Journal, Vol. 75, No. 9, pp. 645-652. Allen, T.J., 1992, “Paint sample presentation for Fourier transform infrared microscopy”, Vibrational Spectroscopy, Vol. 3, No. 3, pp. 217-237. Almeida, E., Balmayore, M. and Santos, T., 2002, “Some relevant aspects of the use of FTIR associated techniques in the study of surfaces and coatings”, Progress in Organic Coatings, Vol. 44, No. 3, pp. 233-242. American Society for Testing and Materials – ASTM, 2010, “Book of ASTM Standards”, Philadelphia, D 3677-10, Standards Test Methods for Rubber-Identification by Infrared Spectrophotometry. Blackford, R., 1999, “Performance demands on aerospace paints relative to environmental legislation”, Pigment & Resin Technology, Vol. 28, No. 6, pp. 331-335. Campos, E.A., Diniz, M.F., Reis, T.B., Dutra, R.C.L., Rezende, L.C. and Iha, K., 2003, “Aplicação de Técnicas FT-IR na Caracterização de Catalisador Cromito de Cobre. Utilizado na Indústria Aeroespacial”, Anais Associação Brasileira de Química, Vol. 52, No. 1, pp. 22-25. Crespim, H., Azevedo, M.F.P, David, L.H., Cassu, S.N. and Lourenço, V.L., 2007, “Substituição de amianto por silicato de alumínio e grafite expansível em compósito de poliuretano utilizado em motor-foguete”, Polímeros: Ciência e Tecnologia, Vol. 17, No. 3, pp. 228-233. Dutra, J.C.N., Massi, M., Otani, C., Dutra, R.C.L., Diniz, M.F., Urruchi, W.I., Maciel, H. and Bittencourt, E., 2002, “Surface Modification of EPDM Rubber by Reactive Argon-Oxygen Plasma Process”, Molecular Crystals and Liquid Crystals, Vol. 374, No. 1, pp. 45-52. Dutra, R.C.L. and Diniz, M.F., 1993, “Resistência à degradação oxidativa e comportamento aos solventes como indicadores da composição de sistemas elastoméricos vulcanizados mistos”, Polímeros: Ciência e Tecnologia, Vol. 3, No. 3, pp. 25-28. Dutra, R.C.L., Diniz, M.F., Takahashi, M.F.K., 1995, “Importância da Preparação de Amostras em Espectroscopia no Infravermelho com Transformada de Fourier (FTIR) na Investigação de Constituintes em Materiais Compostos”, Polímeros: Ciência e Tecnologia, Vol. 5, No. 1, pp. 41-46. Faillace, J.C., Medeiros, M.E., Filho, A.M.F., 1999, “Comportamento Térmico do Acelerador de Queima de Propelentes Sólidos, Anais do III Encontro Técnico de Materiais e Química, 3th ETMQ, Rio de Janeiro, Brazil. Ferrari, V.C.G.M., Lourenço, V.L., Dutra, R.C.L., Diniz, M.F., Azevedo, M.F.P., David, L.H., 2012, “Caracterização de um Pré-Impregnado Aeronáutico por FT-IR e Análise Térmica”, Polímeros: Ciência e Tecnologia, Vol. 22, No. 4, pp. 369-377.

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doi: 10.5028/jatm.v5i4.273

Influence of Ethylene Glycol on the Mullite Crystallization Processes Analyzed by Rietveld Refinement Flaviano Willians Fernandes1, Tiago Moreira Bastos Campos1, Luciana de Simone Cividanes1, João Paulo Barros Machado1, Evelyn Alves Nunes Simonetti1, Gilmar Patrocínio Thim1

ABSTRACT: Mullite is an excellent structural material due to its high temperature stability, high electrical insulation capabilities and creep resistance. This material has a number of technological applications, such as rocket nozzles used in the aerospace industry. In this work, mullite was obtained by sol-gel process, using silicic sol, aluminum nitrate and ethylene glycol, besides the following volume ratios of silica sol dispersion to ethylene glycol: 1/0; 1/1; 1/2; and 1/3. After drying, the samples were thermal treated at temperatures of 1,000; 1,100; 1,200 and 1,250°C. The samples were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD) and specific surface area (Bruner-Emmett-Teller – BET). SEM showed that mullite particles are fine and nearly equiaxed. The sample without ethylene glycol showed 3/2 mullite after heat treatment at 1,250°C. The sample with intermediate ethylene glycol concentration presented two crystallization processes: the first at 1,000°C forming mullite and spinel phases, and the second at 1,250°C forming only 3/2 mullite. However, the sample with the highest ethylene glycol concentration crystallized directly to mullite at 1,000°C with the highest yield. There is a strong dependence on the specific surface area with temperature. The Rietveld refinement showed that the a cell lattice of mullite and the Al/Si molar ratio in the mullite formula depend on the ethylene glycol presence and on the calcination temperature. The lattice parameters b and c are not dependent on the alumina content, but the parameter a increases with the increase in the alumina content. Samples prepared with higher ethylene glycol concentrations reached higher mullite yields at lower temperatures. KEYWORDS: Mullite, Sol-gel, Ethylene glycol, Rietveld refinement.

INTRODUCTION Mullite has the nominal composition of 3Al2O3. 2SiO2 (3:2 mullite) and is the only stable crystalline phase in the SiO2Al2O3 binary system at atmospheric pressure (Schneider et al., 2008). Mullite is known as an important material for electronic, optical and high-temperature structural applications because of its excellent properties, such as high-temperature strength, creep resistance, low thermal expansion coefficient and good dielectric properties, even at elevated temperatures and high oxidative atmosphere (Chakraborty, 2008; Cividanes et al., 2010a, 2011). Mullite has a number of technological applications, such as rocket nozzles used in the aerospace industry (Schneider et al., 2008). The mullite structures consist of chains of distorted edge-sharing Al−O octahedra at the corners and center of each unit cell running parallel to the c-axis. The chains are crosslinked by Si−O and Al−O corner-sharing tetrahedrons. The mullite crystal system is orthorhombic - dipyramidal class (H-M Symbol 2/m 2/m 2/m); Space Group: Pbam (Schneider and Komarneni, 2005; Shackelford and Doremus, 2008). Mullite is a solid solution of silica and alumina and its stoichiometry is based on the alumina/silica molecular ratio (Campos et al., 2012; Chakraborty, 2008; Cividanes et al., 2010b; Fischer et al., 1996; Gerardin et al., 1994; Schneider et al., 2008). The chemical formula of mullite is often given by Al4+2xSi2-2xO10-x, where x=0, x=0.25 and x=0.4 correspond to sillimanite, 3:2 mullite and 2:1 mullite, respectively (Campos et al., 2012; Chakraborty, 2008; Cividanes et al., 2010a; Fischer et al., 1996). The amount of oxygen vacancies per unit cell is represented by x, which is related to the

1.Instituto Tecnológico de Aeronáutica – São José dos Campos/SP – Brazil Author for correspondence: Gilmar Patrocínio Thim | Praça Marechal Eduardo Gomes, 50 – Vila das Acácias | CEP 12.228-900 São José dos Campos/SP – Brazil | Email: gpthim@gmail.com Received: 27/08/13 | Accepted: 27/10/13

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replacement of silicon ions (Si4+) by aluminum ions (Al3+) in the tetrahedral sites of the mullite structure (Mazza et al., 2008). With increasing alumina content, the cation Si4+ is replaced by the cation Al3+ and the anion (oxygen) and the oxygen vacancies are created to maintain charge neutrality (Campos et al., 2012; Cividanes et al., 2011; Okada, 2008). In order to accommodate the structural defects, significant distortions in the aluminum and silicon polyhedra are caused. In mullite, there are three tetrahedral “chains” in the unit cell. Therefore, the distorted alumina tetrahedrons have to be arranged in an oxygen-deficient tri-cluster (three tetrahedrons sharing single corner-bridging oxygen). These clusters constitute a distinctive element of mullite crystal structure (Schneider and Komarneni, 2005; Shackelford and Doremus, 2008). Oxygen vacancies tend to cluster with short-range order along specific crystallographic directions which depend on the alumina content in mullite formula (Cividanes et al., 2010a). Moreover, oxygen vacancies tend to correlate in parallel with the lattice parameter a and, in a lesser extent, with b, considering that mullite with lower alumina concentration tends to show more random vacancy distribution (Fischer et al., 1996; Gerardin et al., 1994; Yabuki et al., 2002). One can assume that the lattice parameter a shows a linear dependence on the Al2O3 content (Fischer et al., 1996; Gerardin et al., 1994; Yabuki et al., 2002). The mullite crystallization sequence depends on the homogeneity of silicon and aluminum in the precursor (Aksaf and Pask, 1975; Campos et al., 2012; Cividanes et al., 2010b; Inoue, 2004; Richardson et al., 1988). When the precursor is homogeneous, the mullite crystallization temperature is lower, and the alumina content in the mullite structure is the same of the starting material. Therefore, the control of the hydrolysis and condensation rate of the starting materials is very important to increase the precursor homogeneity (Chakraborty, 2008). Otherwise, phase segregation can occur, which not only promotes the crystallization of undesirable phases, such as α-alumina and spinel, but also determines a higher mullitization temperature. In the literature, many ceramic synthesis methods are described, such as: mixtures of solid reagents, coprecipitation of mixed salts, sol-gel, spray pyrolysis etc. (Cividanes et al., 2010a; Schneider et al., 1994). Depending on the application and value of the final product, each method can be justified, since each one has its peculiarity. The sol-gel process has been used for the mullite synthesis to generate products with high purity and homogeneity. In addition, it is a versatile and good reproducible method (Cividanes et al., 2010b). Moreover, the temperature required for the mullite crystallization is low (1,000–1,350°C) compared to traditional methods, such as

mixing of reactive powder (1,500–1,700°C) as a consequence of the high homogeneity degree of the precursor (Cividanes et al., 2010b; Hong and Messing, 1998). The tetraethylorthosilicate (TEOS) in alcoholic medium is one of the most frequently used silica source for synthesizing mullite by sol-gel (Oliveira et al., 2010; Cividanes et al., 2010b). One of the purposes of this study was the replacement of TEOS by silicic acid (aqueous medium), with the aim of reducing costs. The use of silicic acid as silica source leads to higher mullite crystallization temperature. Thus, chemical additives may be used to reduce this temperature, which can overcome the most sol-gel process shortcoming that is to control the hydrolysis and condensation reaction rates of the precursors. This difference in the reactivity of the starting materials can result in phase segregation prior to gelation process. However, the sol homogeneity level can be controlled by the action of chemical additives, such as carboxylic acids, β-diketones or functional alcohols, which act as chelating agents and modify the precursor reactivity (Brandhuber et al., 2005; Campos et al., 2012; Chakraborty, 2008; Inoue, 2004; McMahon et al., 1999; Richardson et al., 1988). Rietveld proposed a method for refinement of crystal structures based on mathematical algorithms, which consists in comparing the experimental data obtained from X-ray diffraction with calculated data (Rietveld, 1969). This method can be used for the unit cell refinement, crystal structure refinement, microstructure analysis, quantitative analysis of phases and determining the preferred orientation (Le Bail, 2004; Langford et al., 2000). In this work, the Rietveld refinement is used for the determination of a, b and c cell lattices of mullite synthesized at various temperatures. As the parameter a is related to the alumina content of mullite formed, the Al/Si molar ratio and the x factor of mullite formula can be calculated. Therefore, in this paper, mullite was obtained by sol-gel process using silicic acid and aluminum nitrite as silica and alumina precursors. Ethylene glycol was used as an agent to control the homogeneity level. The effect of ethylene glycol was analyzed by means of Rietveld refinement method.

EXPERIMENTAL S i l i c a s o l an d a lu m i nu m n it r at e n on a hyd r at e (Al(NO 3 ) 3 .9H 2 O; Vetec) were used as sources of silica and alumina, in molar ratio Al/Si = 3/1. Ethylene glycol

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Influence of Ethylene Glycol on the Mullite Crystallization Processes Analyzed by Rietveld Refinement

(Vetec) was used as a chemical additive in four volumetric proportions in relation to silica sol dispersion: 1/0, 1/1, 1/2 and 1/3, and the samples were named as S-0, S-1, S-2 and S-3, respectively. The procedure of obtaining silica sol and the mullite precursor gel/xerogel were described elsewhere (Campos et al., 2012). The xerogels were pre-calcined at 430°C (for removal of organic matter), and then were calcined at 1,000; 1,100; 1,200 and 1,250°C for 5 hours. The amounts of mullite crystallized in these samples were determined by a calibration curve constructed using the X-ray diffraction (XRD) techniques described below. Calcined samples were analyzed by XRD, in a Philips X-ray diffractometer, PW 1830/1840 model, using CuKα radiation and operating at 40 kV and 25 mA. The XRD analysis was performed between 10° and 90°, with a scan step time of 10.1600 s and with a step size of 0.0170°. The Rietveld refinement was performed with the data obtained from the standard reference material LaB6. This refinement led to a set of instrumental function parameters and the results are obtained by a validation method using a profile matching method. A modified pseudo-Voigt function (TCHZ) was used to fit the profiles. A calibration curve for mullite was used to quantify the amount of crystallized mullite in the samples calcined at 1,000; 1,100; 1,200 and 1,250°C. This curve is based on XRD analysis of solid mixtures of pure CaF2 and pure mullite, where CaF2 is used as internal standard. The XRD intensities at 26° (related to mullite) and 28° (related to CaF 2) were determined, and the relative yield of mullite crystallization was determined according to Eq. 1: x 100

(1)

where I26 stands for the intensity of the XRD proﬁle at 26° (2θ) divided by the mass of the calcined sample and I28 stands for the intensity of the same XRD proﬁle at 28° (2θ) divided by the mass of CaF2 introduced into the calcined sample. Rietveld refinements were used to analyze the XRD data with the GSAS/EXPGUI software, and the a, b and c cell lattices were determined. Then, the molar concentration of alumina in the mullite (m) was obtained with the linear relationship between the lattice parameter a and the concentration of alumina (m), according to Eq. 2 (Fischer et al., 1996): m = 144.5 x a-1029.5

(2)

433

After calculating the molar concentration of alumina (m), the parameter x of the mullite equation (Al4+2xSi2-2xO10-x) was determined with the Eq. 3 (Fischer et al., 1996; Schneider et al., 2008): (3) Then, the “concentration” of Al and Si in this material (4+2x and 2-2x, respectively) was determined. Therefore, the molar ratio Al/Si (mullite stoichiometry) can be obtained. Scanning electron microscopy (SEM) analyses were made using the Jeol JSM-5310 microscope, in order to observe the influence of heat treatment on the microstructure of ceramic powders. The surface area analysis of the materials calcined at 1,000; 1,100; 1,200 and 1,250°C were made by Bruner-EmmettTeller (BET) specific surface area measurements, using a Quantachrome NOVA-1200 equipment and nitrogen as the gas.

rESUltS Figure 1 shows the X-ray diffraction of samples S-0, S-1, S-2 and S-3 calcined at 1,000; 1,100; 1,200 and 1,250°C for 5 hours. Figure 1(a) shows the crystallization of only α-alumina after firing sample S-0 at 1,000°C, while samples S-1, S-2 and S-3 formed mullite and spinel phase after firing at the same temperature. The intensities of mullite peaks at 26° of the samples S-1, S-2 and S-3 are practically the same. Using these peaks as references, one can observe the decrease of the intensity in the spinel peak, in 45°, as the ethylene glycol (EG) content is increased. Therefore, EG should increase the homogeneity of mullite precursors and should make it possible to mullite crystallizes at 1,000°C, which is the mullite crystallization temperature of single phase precursor (the most homogeneous one). This increase in the homogeneity was observed in a previous work of our research group (Campos et al., 2012). This work showed, using images of a scanning electronic microscope with energy dispersive X-ray (EDXelemental mapping), that the samples prepared with EG are constituted by silicon and aluminum particles whose sizes are much smaller than those present in the sample prepared without EG. This previous work (Campos et al., 2012) also showed that the Al(IV) and Al(V) contents in the mullite precursors can be a decisive factor for crystallizing mullite at 1,000°C.

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(a)

1,000ºC

1,100ºC M

Relative intensity

M MM M M e M M

(b)

M MM M M MM

(c)

1,200ºC

M M

M M M

1,250ºC M M

Relative intensity

(d)

MM M

S0 10

2θ

Figure 1. X-ray diffraction patterns of samples S-0, S-1, S-2 and S-3 calcined for 5 hours at: (a) 1,000°C; (b) 1,100°C; (c) 1,200°C and (d) 1,250°C; M: mullite; e: spinel; α: α-alumina.

Sample without EG had low contents of aluminum with these coordinations (Campos et al., 2012). Figure 1(b) shows that α-alumina is still the only crystalline phase in the sample S-0 and samples prepared with EG crystallized mullite. Figure 1 also shows that the sample with the highest EG concentration, S-3, did not show the presence of spinel phase, but the other two samples prepared with EG did. Figure 1(c) shows the disappearance of the spinel phase in all samples fired at 1,200°C and the formation of mullite phase. Thus, mullite was formed by consumption of spinel. One can still observe that while the samples S-1, S-2 and S-3 formed only mullite at 1,200°C, the sample S-0 formed mullite

and α-alumina. Mullite could be obtained from sample S-3 at 1,200°C, which is close to the crystallization temperature for mullite obtained from TEOS (Oliveira et al., 2010; Ban et al., 1996). Figure 1(d) shows the mullite formation in all samples calcined at 1,250°C, but sample S-0 continues to form the α-alumina phase together with mullite phase. Table 1 shows the parameters obtained from Rietveld refinements for mullite crystallization at 1,000°C. There are no data for sample S-0 since mullite did not crystallized at this temperature from this sample. One can see that sample S-1 crystallized mullite with the major percentage of alumina (70%) and samples S-2 and S-3 show practically the same percentage (~ 67%). However, the mullite yield

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Influence of Ethylene Glycol on the Mullite Crystallization Processes Analyzed by Rietveld Refinement

is quite different between the samples, where the sample prepared with the highest EG concentration showed the highest yield (52%), while the sample prepared with the lowest EG concentration showed the lowest mullite yield (35%). The cell parameters b and c do not depend on the alumina content in the mullite formula, as reported in the literature. Therefore, they are virtually constant in all experiments. The parameter a determined for mullite from all samples is very similar too. It is important to note that the alumina content showed in Table 1 is the Al2O3 molar content that is present in the mullite structure, according to the chemical formula of mullite: Al4+2xSi2-2xO10-x. In this table, 3/2 mullite has 60% of Al2O3 molar content, and 2/1 mullite has 67%. Table 2 shows that alumina contents in all samples treated at 1,100°C are lower than that treated at 1,000°C. Therefore, samples were initially rich in alumina and the thermal treatment at 1,100°C provoked the incorporation of silica into

435

mullite formula. Samples prepared with higher concentration of EG lead to mullite with higher contents of alumina and higher crystallization yields. The lattice parameters b and c are not depended on the alumina content, but the parameter a increases with the increase of alumina content. Table 3 shows that S-0 crystallized mullite with 53% of alumina, while all samples prepared with EG reach alumina contents closer to mullite 3/2 (64%). However, mullite at this temperature prepared from all samples with EG always formed mullite with virtually the same value of alumina content. One can conclude that the alumina content of mullite depends on the presence of EG, but does not depend on its concentration. However, the mullite yield is strongly dependent on the EG concentration. Samples prepared with higher EG concentration showed higher yield than samples prepared with lower concentration. Table 4 shows that at 1,250°C all samples crystallize mullite with composition near to Al/Si = 3/2 and all samples that

Table 1. Mullite crystallization for samples calcined at 1,000°C. Sample

Al2O3 (% mol)

Yield (%)

a (Å)

b (Å)

c (Å)

S-1

70±2

0.48±0.08

35±4

7.61±0.01

7.72±0.01

2.891±0.003

S-2

67±2

0.41±0.08

40±4

7.58±0.01

7.72±0.01

2.888±0.002

S-3

68±2

0.43±0.08

52±4

7.59±0.01

7.72±0.01

2.890±0.002

Table 2. Mullite crystallization for samples calcined at 1,100°C. Sample

Al2O3 (% mol)

Yield (%)

a (Å)

b (Å)

c (Å)

S-1

62±2

0.29±0.08

37±4

7.552±0.007

7.675±0.005

2.879±0.001

S-2

64±2

0.34±0.08

51±4

7.568±0.005

7.693±0.003

2.887±0.001

S-3

67±2

0.41±0.08

62±4

7.589±0.006

7.673±0.003

2.884±0.001

Table 3. Mullite crystallization for samples calcined at 1,200°C. Sample

Al2O3 (% mol)

Yield (%)

a (Å)

b (Å)

c (Å)

S-0

53±2

0.1±0.1

41±4

7.493±0.008

7.687±0.007

2.872±0.002

S-1

64±2

0.33±0.08

69±4

7.563±0.002

7.690±0.002

2.8885±0.0007

S-2

64±2

0.35±0.08

81±4

7.568±0.003

7.700±0.002

2.8877±0.0007

S-3

65±2

0.36±0.08

90±4

7.575±0.003

7.699±0.003

2.8891±0.0008

Yield (%)

a (Å)

b (Å)

c (Å)

Table 4. Mullite crystallization for samples calcined at 1,250°C. Sample

Al2O3 (% mol)

S-0

63±2

0.32±0.08

66±4

7.560±0.004

7.696±0.004

2.886±0.001

S-1

65±2

0.36±0.07

100±4

7.571±0.001

7.696±0.001

2.8886±0.0002

S-2

65±2

0.36±0.07

100±4

7.572±0.001

7.691±0.001

2.8878±0.0003

S-3

64±2

0.33±0.07

100±4

7.563±0.001

7.695±0.01

2.8869±0.0003

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were prepared with EG reached a yield of 100%, while the sample prepared without EG reached a yield of 66%. All cell lattices are practically the same in the mullite crystallized at this temperature. Schneider and Komarneni (2005) showed the influence of the Al2O3 content of mullite ceramics on the mechanical properties of the material. They showed that 3/2 mullite (60 mol % Al 2O 3) has about 380 MPa of bending strength at 1,300°C in air, while 2/1 mullite (67 mol % Al2O3) has about 240 MPa at the same conditions. They also showed that 3/2 mullite has about 3.4 MPa.m1/2 of fracture toughness and 2/1 mullite has about 2.4 MPa.m1/2, both at 1,300°C in air. Therefore, one can conclude that 3/2 mullite shows better mechanical properties than 2/1 mullite, which can increase the life time and the reliability of the 3/2 mullite-based pieces for using in aerospace applications. The 2/1 mullite-based pieces can fail during application in a shorter time than 3/2 mullite, because of the Al2O3 content. Therefore, Rietveld method can be used as an analytical tool to ensure that the alumina content would not interfere with the mullite properties, since it can determine a cell lattice of mullite and this parameter is related to alumina content. The results obtained by BET for samples S-0, S-1, S-2 and S-3 calcined at 1,000 and 1,200°C are shown in Table 5. Samples S-1, S-2 and S-3 show higher surface area than S-0 when they were fired at 1,000°C. However, these surface areas are practically the same after firing at 1,200°C, since the surface area of samples S-1, S-2 and S-3 decreased substantially when they were fired at 1,200°C. These results can be correlated to the results previously expressed in Tables 1 to 4, which showed that EG increased the homogeneity of the mullite precursors, accelerating the incorporation of silica into mullite formula, decreasing the alumina contents or increasing the mullite yield. EG molecules should act during the sol-gel stage decreasing the polymerization kinetics of silica, resulting in small silica clusters. These small silica clusters may be more homogeneously mixed with those of alumina. Single phase mullite gels are very homogeneous because they have aluminum and silicon mixed in an atomic level. Therefore, EG increases the homogeneity of the precursor, favoring the overall mullite kinetics. The higher surface area of the samples prepared with EG calcined at 1,000°C should be correlated with the homogeneity of these samples.

Campos et al. (2012) also showed that samples prepared with EG were constituted by silicon and aluminum particles whose sizes are much smaller than those present in the sample prepared without EG. Samples prepared with EG also showed good homogeneity, since they crystallizes mullite in lower temperatures compared to the sample prepared without EG. Figure 2 shows the SEM micrographs of samples S-0, S-1 and S-3 calcined at 1,000°C for 5 hours (Fig. 2a, b and c, respectively) and 1,250°C for 5 hours (Fig. 2d, e and f, respectively). SEM micrograph of sample S-2 is not shown because it was very similar to the samples S-1 and S-3. It can be seen from the images, and in agreement with the BET results, that samples with the same EG/silanol volumetric proportion and calcined at 1,000°C present smaller particles compared to samples calcined at 1,250°C. Furthermore, for both temperatures (1,000 and 1,250°C), it is clear that S-0 sample presents particles with irregular shapes and samples S-1 and S-3 contain particles with near-angular shapes. This changing in the morphology of the particles is explained by the changing in the phases (from α-alumina to mullite, or from mullite with different alumina contents) present in the samples.

Table 5. Effect of heat treatment on mullite amount and surface area. 1,000°C Sample

Al2O3 content

Yield % (mol/mol)

Surface area (m2/g)

S-0

–

S-1

S-2

S-3

1,200°C Sample

Al2O3 content

Yield % (mol/mol)

Surface area (m2/g)

S-0

S-1

S-2

S-3

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(a)

(b)

(c)

(d)

(e)

(f)

Figure 2. Scanning electron microscopy micrographs of samples S-0, S-1 and S-3 calcined at: 1,000°C for 5 hours (a, b and c, respectively) and 1,250°C for 5 hours (d, e and f, respectively).

CONCLUSIONS EG has a positive effect in the crystallization of mullite, with the suppression of undesirable phases like α-alumina. The experimental methodology used here showed to be effective for obtaining mullite powder with fine particles, and in the temperature near 1,200°C (near the crystallization temperature when using TEOS as precursor), with a lower cost than the methods that use TEOS as silica source. These fine powders have adequate characteristics to be applied in the sintering process of heat insulation or heat exchanges parts. The X-ray analysis and the Rietveld refinement showed that a cell lattice of mullite and the Al/Si molar ratio in the mullite formula depend on the presence of EG and on the calcination temperature. However, different EG concentrations influence

the a cell lattice virtually in the same way. The samples prepared with higher concentration of EG lead to mullite with higher crystallization yields at lower temperatures. The Rietveld method showed to be efficient to analyze the Al2O3 content in the mullite, which is associated to the mechanical properties of mullite. Then, this method can be used to ensure that the mullite ceramics will not fail during aerospace application.

ACKNOWLEDGMENTS The authors gratefully acknowledge CNPq, FAPESP and CAPES for financial support.

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Fernandes, F.W., Campos, T.M.B., Cividanes, L.S., Machado, J.P.B., Simonetti, E.A.N. and Thim, G.P.

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Langford, J., Louër, D. and Scardi, P., 2000, “Effect of a Crystallite Size Distribution on X-ray Diffraction Line Profiles and Whole-powderpattern Fitting”, Journal of Applied Crystallography, Vol. 33, No. 2, pp. 964-974. doi: 10.1107/S002188980000460X

Ban, T., Hayashi, S., Yasumori, A., and Okada, K., 1996, “Characterization of Low Temperature Mullitization”, Journal of the European Ceramic Society, Vol. 16, No. 2, pp. 127-132. doi: 10.1016/0955-2219(95)00131-X

Le Bail, A., 2004, “Monte Carlo Indexing with McMaille”, Powder Diffraction, Vol. 19, No. 3, pp. 249-254. doi: 10.1154/1.1763152

Brandhuber, D., Torma, V., Raab, C., Peterlik, H., Kulak, A., and Hu, N., 2005, “Glycol-Modified Silanes in the Synthesis of Mesoscopically Organized Silica Monoliths with Hierarchical Porosity”, Chemistry of Materials, Vol. 17, No. 16, pp. 4262-4271. doi: 10.1021/ cm048483j Campos, T.M.B., Cividanes, L.S., Brunelli, D.D., Sakane, K.K., and Thim, G.P., 2012, “Effect of ethylene glycol on the mullite crystallization”, Journal of the European Ceramic Society, Vol. 32, No. 4, pp. 835-842. doi: 10.1016/j.jeurceramsoc.2011.09.028 Chakraborty, A.K., 2008, “Si-incorporated alumina phases formed out of diphasic mullite gels”, Journal of Materials Science, Vol. 43, No. 15, pp. 5313-5324. doi: 10.1007/s10853-008-2775-y Cividanes, L.S, Brunelli, D.D., Bertran, C.A., Campos, T.M.B., and Thim, G.P., 2011, “Urea effect on the mechanism of mullite crystallization”, Journal of Materials Science, Vol. 46, No. 23, pp. 7384-7392. doi: 10.1007/s10853-011-5699-x Cividanes, L.S, Campos, T.M.B., Rodrigues, L.A., Brunelli, D.D., and Thim, G.P., 2010a, “Review of mullite synthesis routes by sol–gel method”, Journal of Sol-Gel Science and Technology, Vol. 55, No. 1, pp. 111-125. doi: 10.1007/s10971-010-2222-9 Cividanes, L.S, Campos, T.M.B., Bertran, C.A., Brunelli, D.D., and Thim, G.P., 2010b, “Effect of urea on the mullite crystallization”, Journal of Non-Crystalline Solids, Vol. 356, No. 52-54, pp. 30133018. doi: 10.1016/j.jnoncrysol.2010.05.076 Fischer, R.X., Schneider, H. and Voll, D., 1996, “Formation of Aluminum Rich 9:1 Mullite and its Transformation to Low Alumina Mullite upon Heating”, Journal of the European Ceramic Society, Vol. 16, No.2, pp. 109-113. Gerardin, C., Sundaresan, S., Benziger, J., and Navrotsky, A., 1994, “Structural Investigation and Energetics of Mullite Formation from SolGel Precursors”, Chemistry of Materials, Vol. 6, No. 2, pp 160-170. doi: 10.1021/cm00038a011 Hong, S.H. and Messing, G.L., 1998, “Anisotropic Grain Growth in Diphasic-Gel-Derived Titania-Doped Mullite”, Journal of the American Ceramic Society, Vol. 81, No. 5, pp. 1269-1277. doi: 10.1111/ j.1151-2916.1998.tb02478.x Inoue, M., 2004, “Glycothermal synthesis of metal oxides”, Journal of Physics: Condensed Matter, Vol. 16, No. 14, pp. S1291-S1303. doi:10.1088/0953-8984/16/14/042

Mazza, D., Ronchetti, S., and Costanzo, A., 2008, “Atomistic simulations on mullite Al2(Al2+2xSi2−2x)O10−x in a variable range of composition”, Journal of the European Ceramic Society, Vol. 28, No. 2, pp. 367-370. doi: 10.1016/j.jeurceramsoc.2007.03.003 McMahon, C.N., Alemany, L., Callender, R.L., Bott, S.G. and Barron, A.R., 1999, “Reaction of Al(tBu)3 with Ethylene Glycol: Intermediates to Aluminum Alkoxide (Alucone) Preceramic Polymers”, Chemistry of Materials, Vol. 11, No. 11, pp. 3181-3188. doi: 10.1021/ cm990284q Okada, K., 2008, “Activation Energy of Mullitization from Various Starting Materials”, Journal of the European Ceramic Society, Vol. 28, No. 2, pp. 377-382. doi: 10.1016/j.jeurceramsoc. 2007.03.015 Oliveira, T.C., Ribeiro, C.A., Brunelli, D.D., Rodrigues, L.A. and Thim, G.P., 2010, “The Kinetic of Mullite Crystallization: Effect of Water Content”, Journal of Non-Crystalline Solids, Vol. 356, No. 52-54, pp. 2980-2985. doi: 10.1016/j.jnoncrysol.2010.05.078 Richardson, J.W., Pluth, J.J., Smith, J.V. and Dytrych, W.J., 1988, “Conformation of Ethylene Glycol and Phase Change in Silica Sodalite”, The Journal of Physical Chemistry, Vol. 92, No. 1, pp. 243-247. doi: 10.1021/j100312a052 Rietveld, H.M., 1969, “A Profile Refinement Method for Nuclear and Magnetic Structures”, Journal of Applied Crystallography, Vol. 2, pp. 65-71. doi:10.1107/S0021889869006558 Shackelford, J.F. and Doremus, R.H., 2008, “Ceramic and Glass Materials”, Springer, New York, USA. Schneider, H., Voll, D., Saruhan, B. and Schmücker, M., 1994, “Constitution of the γ-alumina Phase in Chemically Produced Mullite Precursors”, Journal of the European Ceramic Society, Vol. 13, No. 5, pp. 441-448. doi: 10.1016/0955-2219(94)90022-1 Schneider, H. and Komarneni, S., 2005, “Mullite”, Wiley-VCH, Weinheim, Germany. Schneider, H., Schreuer, J. and Hildmann, B., 2008, “Structure and Properties of Mullite – A Review”, Journal of the European Ceramic Society, Vol. 28, No. 2, pp. 329-344. doi: 10.1016/j. jeurceramsoc.2007.03.017 Yabuki, M., Takahashi, R., Sato, S., Sodesawa, T., and Ogura, K., 2002, “Silica-Alumina Catalysts Prepared in Sol-Gel Process of TEOS with Organic Additives”, Physical Chemistry Chemical Physics, Vol. 4, No. 19, pp. 4830-4837. doi: 10.1039/B205645C

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doi: 10.5028/jatm.v5i4.266

Rainy Season Features for the Alcântara Launch Center Urias Andrade Pinheiro1, Marcos Daisuke Oyama2

ABSTRACT: The rainy season features for the Alcântara Launch Center region (2°S-3°S; 44°W-45°W), located at the northern coast of Brazil, were obtained by using the Climate Prediction Center/National Oceanic and Atmospheric Administration daily precipitation data from 1979 to 2012 accumulated to pentads. The rainy season onset (demise) was defined as the first pentad when precipitation is greater (lower) than the climatological annual average, and this behavior lasts for three out of the four following pentads. The average rainy season features were: 28 January as onset day; 16 June as demise day; 140 days as length; 1527 mm as total precipitation (about 80% of the annual value); and 10.9 mm day -1 as intensity (rain rate). The uncertainty on these climatological values due to the use of different precipitation datasets was estimated as few days for the onset/demise days and length, 100 mm for the total precipitation and about 1 mm day -1 for the intensity. Except for intensity, the rainy season features showed large interannual variability: standard variation of about one month for onset/demise days, and coefficient of variation of 33 and 40% for length and total precipitation, respectively. The three-week period between 24 March and 13 April belonged to the rainy season of all years. In general, longer (shorter) duration was related to early (late) onset, late (early) demise, and higher (lower) total precipitation. Within the rainy season, on an average, precipitation was lower than 0.1 mm day -1 in only four to five days; therefore, the occurrence of “no-rain” days was rather uncommon. KEYWORDS: Aerospace meteorology, Precipitation, Climatology, Brazilian space program.

INTRODUCTION The rainy season is usually defined as the period of the year when precipitation is higher (than a given threshold) due to atmospheric conditions that favor the occurrence of rain events. It is described by five features: onset and demise days, length, total precipitation, and intensity (Table 1). Interannual variability of these features can severely affect socio-economic activities; for instance, in the semi-arid Northeast Brazil, people “anxiously await the annual arrival of the rainy season and its promise of an adequate harvest for that year; in the event of drought, [...] agricultural production is compromised and immense human suffering prevails” (Lemos et al., 2002, p. 479). Here, we focus on the rainy season features for the Alcântara Launch Center [Centro de Lançamento de Alcântara (CLA)] region (2°S-3°S; 44°W-45°W; Fig. 1). It is a specific region located at the northern coast of Brazil, and its importance is based on the fact that CLA is the main rocket launching center of the Brazilian space program. For the CLA region, climate is classified as tropical humid (IBGE, 2002), annual precipitation is ~2000 mm, and the seasonal cycle shows maximum (minimum) precipitation in austral autumn (spring) (Pereira et al., 2002). This cycle is closely related to the seasonal latitudinal migration of the Atlantic Intertropical Convergence Zone (AITCZ): during austral autumn, AITCZ attains its southernmost position and directly affects the CLA region (Molion and Bernardo, 2002). Other atmospheric systems, such as coastal squall lines (CSL) (Cohen et al., 1995, 2009; Oliveira, 2012), easterly waves (Alves et al., 2008; Machado et al., 2009), and upper tropospheric cyclonic vortices (UTCV) (Kousky and Gan, 1981; Silva, 2005; Ferreira et al., 2009), also affect the precipitation amount (Barros

1.Instituto Nacional de Pesquisas Espaciais – São José dos Campos/SP – Brazil 2.Instituto de Aeronáutica e Espaço – São José dos Campos/SP – Brazil Author for correspondence: Marcos Daisuke Oyama | Praça Marechal Eduardo Gomes, 50 – Vila das Acácias | CEP 12.228-901 São José dos Campos/SP – Brazil | Email: marcos.oyama@ymail.com Received: 12/07/13 | Accepted: 27/09/13

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Table 1. Definition of the rainy season features; pk is the total precipitation (mm) in day k. Unit

Symbol

Definition

Onset day

Rainy season feature

Julian day

–

Demise day

Julian day

–

Length

Day

L = Tf - Ti + 1

Total precipitation

P = ∑ pk

mm day-1

I = P/L

Intensity (or average rain rate)

EQ 1S

2S 3S 4S 5S 50W

49W

48W

47W

46W

45W

44W

43W

42W

41W

40W

k=Ti

In this study, a detailed account on the rainy season features for the CLA region is given. The report is organized as follows. The precipitation dataset and the method to identify the rainy season onset and demise are described in the Data and Methodology section. The climatology and interannual variability of the rainy season features for the CLA region, their sensitivity to the use of different precipitation datasets, a discussion on the meteorological factors related to the rainy season onset, and a brief analysis on the dry days/spells within the rainy season are presented in the Results section. A summary of main findings of the study, as well as possibilities of future work, are given in the Concluding Remarks section.

Figure 1. CPC-G precipitation data grid points. Central small circles indicate grid points for which precipitation data exist. The precipitation for the Alcântara Launch Center region is calculated as the average over the gray 1° x 1° box. Alcântara Launch Center position is indicated by the red circle.

DATA AND METHODOLOGY

and Oyama, 2010). For the CLA region, interannual variability of precipitation is related to sea surface temperature anomalies (SSTA) in tropical Pacific and Atlantic oceans (Kayano and Andreoli, 2006; Marques and Fortes, 2012). From the aerospace meteorology perspective (Vaughan and Johnson, 2013), knowledge about the rainy season features for the CLA region is particularly important and useful for the planning of rocket launching missions, since absence of rainfall (“no-rain” condition) is usually necessary for rocket-related activities in CLA (Marques and Fisch, 2005). Preliminary information about the rainy season climatology for the CLA region can be derived from the literature: the onset would take place from December to January; the demise, from May to June; and the length would be within the range of five to seven months (Marengo et al., 2001; Liebmann et al., 2007; Silva et al., 2007). More precise information is not available, since the cited studies focused on larger scales (Amazonia or South America) and used different methods for rainy season identification.

PRECIPITATION DATA Daily precipitation from the Climate Prediction Center (CPC)/National Oceanic and Atmospheric Administration (NOAA) dataset called “CPC Unified Gauge-Based Analysis of Global Daily Precipitation” (CPC-G), for the period from January 1979 to December 2012 (34 years), are used. The data are based on rain gauge measured precipitation and gridded on a 0.5° x 0.5° mesh using an optimal interpolation (OI) technique (Xie et al., 2007). Daily precipitation for the CLA region is calculated as the area-average precipitation over the four grid points that surround the CLA (Fig. 1). The daily data are accumulated to pentads for rainy season identification. The choice of OI technique as the interpolation procedure for the CPC-G data is based on a comprehensive assessment over global land areas carried out by Chen et al. (2008). They found that, compared to other two widely used interpolation methods, the OI technique consistently performed the best for all situations (regions, seasons, and network

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Rainy Season Features for the Alcântara Launch Center

RAINY SEASON IDENTIFICATION In the literature, various methods to identify the rainy season features are found (the most common methods are briefly reviewed in Alves et al., 2005, p. 386). Here, we adopt a simple condition adapted from Gan et al. (2004), but based solely on pentad precipitation. The rainy season onset (demise) pentad is defined as the first pentad when precipitation is greater (lower) than the climatological annual average, and this behavior lasts for at least three out of the four following pentads. For a given year, the search for the onset pentad starts from pentad 63 (07–11 November) of the previous year and for the demise pentad, from pentad 20 (06–10 April) of the given year (this pentad is in the middle of the climatological rainy season, as will be shown in the next section). The onset (demise) day is defined as the middle day of the onset (demise) pentad, and the other features — length, total precipitation, and intensity — are calculated from expressions given in Table 1. The identification of the onset pentad is illustrated in Fig. 2. From pentad −10 (which is the pentad 63 of the previous year) to pentad 5, precipitation is lower than the annual average. Pentads 6 and 8, albeit having aboveaverage precipitation, are not the onset pentad, because in only two out of the following four pentads precipitation is greater than the annual average. The onset condition is firstly met by pentad 10.

RESULTS RAINY SEASON FEATURES The climatological rainy season features for the CLA region are shown in Table 2. On average, the rainy season extends from the end of January to mid-June, thus lasting for ~4½ months. Compared to the literature, the onset and demise days are within the range of December–January and May–June, respectively, but the length is slightly shorter than the range of five to seven months (Marengo et al., 2001; Liebmann et al., 2007; Silva et al., 2007). The precipitation amount in the rainy season accounts for ~80% of the total annual average. The average intensity, of ~10 mm day -1, is quite high; its value is comparable to the annual mean rain rate over the rainiest parts of Amazonia (Marengo and Nobre, 2009) and may be classified as transition between light and heavy precipitation (Sun et al., 2006). Except for the intensity, interannual variability of the rainy season features is large: standard deviation for the onset and Pentad precipitation anomaly (mm)

densities). Moreover, the OI technique led to a relatively stable performance statistics over regions covered by fewer gauges. This result is particularly suited for this study, as it minimizes the uncertainty related to the sudden drop in the number of gauges used for interpolation over the CLA region in the 2005–2012 period: the number of gauges within the CLA region varies from six to ten in the 1979–1988 period; from five to seven in the 1989–2004 period; and decreases to about two or three in the 2005–2012 period (see also Silva et al., 2007, p. 851).

441

15 10 5 0 -5 -10 -15

-10

-8

-6

-4

-2

Pentad Figure 2. Illustration of the method to identify the rainy season onset. Light (dark) blue bars refer to positive (negative) pentad precipitation anomaly, that is, pentad precipitation greater (lower) than the climatological annual average. Negative pentads refer to pentads of the previous year: pentad p, p<0, refers to pentad p + 73 of the previous year. The black arrow points to the rainy season onset pentad.

Table 2. Climatological rainy season features for the Alcântara Launch Center region (1979–2012). Feature

Onset day Demise day Length Total precipitation Intensity

Average

Standard deviation

Coefficient of variation (%)

28 January 16 June 140 days 1567 mm 10.9 mm day-1

27 days 28 days 46 days 622mm 1.8 mm day-1

– – 33 40 16

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demise days is ~1 month; for the length, ~1½ month; and for the total precipitation, 40% of the average value. The smaller variability of the intensity — 16% of the climatological value — is related to the existence of a clear direct proportionality between total precipitation and length. The linear regression equation between these two features without the intercept term is (Fig. 3a):

Total precipitation (mm)

P = 11.3 × L (1)

3000

y=11.344x R2=0.8789

2500 2000 1500 1000 500 0

100

150

200

250

lenght (days) Onset day anomaly (days)

(a) 90

y=-0.4934x+68.98 R2=0.7082

and the coefficient of determination value is very high (~90%). The slope term of Eq. 1, as expected, is close to climatological intensity value. The temporal evolution of the rainy season from 1979 to 2012 is shown in Fig. 4. Marked changes from one year to the next (e.g., 1986–1988; 1993 and 1994) lead to the high value of standard deviation obtained previously for the onset/demise days and length, and illustrate the lack of lag-1 autocorrelation (almost zero) for these features. There are periods, however, when the rainy season length shows lower variability: for instance, from 1999 to 2004 (from 1979 to 1983, but 1982), the length is longer (shorter) than the average. The three-week period between 24 March and 13 April belongs to the rainy season of all years — this information could be useful for planning purposes. In general (~60% of the years), late (early) onset corresponds to early (late) demise, thus leading to shorter (longer) rainy season length. This behavior is ratified by the linear regression analysis involving these features. Considering length as an independent variable, the equations for onset and demise anomalies are given respectively by (Fig. 3b–c): ∆Ti = –(0.49 × L – 69.0);

(2)

∆Tf = 0.51 × L – 70.8

(3)

30 0 -30 -60 -90

100

150

200

250

For both equations, the coefficient of determination value is ~70%, which indicates good fitting skill.

lenght (days)

Demise day anomaly (days)

(b) 90

y=0.5066x-70.814 R2=0.719

60 30 0 -30 -60 -90

100

150

200

250

lenght (days)

(c) Figure 3. Relation between rainy season length and total precipitation (a), onset day anomaly (b), and demise day anomaly (c). The linear regression line is drawn in blue color. The equation and the coefficient of determination (R2) are shown. In (a), regression analysis is carried out without the intercept term.

2011 2009 2007 2005 2003 2001 1999 1997 1995 1993 1991 1989 1987 1985 1983 1981 1979 3-Dec 31-Dec 28-Jan 25-Feb 25-Mar 22-Apr 20-May 17-Jun 15-Jul 12-Aug

Figure 4. Temporal evolution of the rainy season from 1979 to 2012. The horizontal bars represent the rainy season within each year. Dark (light) blue bars refer to the years when the anomalies of onset and demise days have opposite (the same) sign.

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SENSITIVITY TO THE USE OF A DIFFERENT PRECIPITATION DATASET The climatological rainy season features may be sensitive to the precipitation dataset used. To evaluate the degree of uncertainty related to this sensitivity, the rainy season features for the CLA region from 1997 to 2012 derived using daily precipitation data from a different dataset — the National Aeronautics and Space Administration (NASA) “Global Precipitation Climatology Project (GPCP) 1-Degree Daily Combination” (GPCP-1DD) (Huffman et al., 2001) — are compared to those derived using the CPC-G data. The GPCP-1DD dataset is a satellite-based spatial and temporal downscaling of the GPCP monthly precipitation estimate (outcome of merging microwave and infrared precipitation estimates and rain gauge data) on a regular 2.5° x 2.5° global mesh (Adler et al., 2003), and so does differ from the CPC-G dataset. The average rainy season features for the 1997–2012 period derived from the two datasets show good overall agreement, as the bias values are low (Table 3). This implies the robustness of the climatological features obtained previously (cf. Table 2). The uncertainty may be estimated as few days (1–10 days) for the onset/demise days and length, 100 mm for the total precipitation, and ~1 mm day -1 for the average intensity. The temporal evolution of the onset/demise days and length derived from both datasets are shown in Fig. 5 [the total precipitation and average intensity time series are not shown and are not analyzed because intensity is almost constant (coefficient of variation < 15%) and, consequently, total precipitation is closely proportional to length]. The overall agreement between the time series derived from the two datasets is excellent for the onset day (Pearson linear correlation coefficient, r ~ 0.9), and moderate for the demise day (r ~ 0.5) and length (r ~ 0.7). For the onset day, large differences (~1 month) are found in three years (2006, 2008, and 2009), and the bias value results mostly from these specific

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differences. For the demise day, systematic differences are found until 2004, the largest absolute differences (1–2 months) are found in three years (1999, 2010, and 2012), and the bias value results mostly from the systematic differences (because the largest differences do not have the same sign and partially cancel each other out). For the length, the time series pattern is similar to the demise day’s, and the differences have smaller absolute value, although large differences (>1 month) are still found in two years (1999 and 2010). Therefore, the use of different precipitation datasets may lead to high uncertainty in the rainy season features for specific years, but the average features would be only marginally affected by these temporally localized large differences and, therefore, would be almost the same (i.e., low bias). The rainy season features derived using the rain gauge data collected at the meteorological station in CLA (this dataset is hereafter referred to as STN) (Marques and Fisch, 2005) are also compared to those derived using CPC-G and GPCP-1DD data. STN dataset should be regarded as the outcome of an independent station, because both CPC-G and GPCP-1DD datasets do not use STN data. The comparison is carried out for the short period from 2003 to 2012, when STN data are more reliable. For this period, the average rainy season features derived using STN data are close to those derived using CPC-G data and GPCP-1DD data (not shown). The temporal evolution of the rainy season features shows good overall agreement among the different datasets (Fig. 5). • For the period 2005–2012, there is very good agreement. It adds reliability to the results derived using CPC-G data for this period, when uncertainty of CPC-G data are higher due to the small number of stations used for interpolation within the CLA region. • The largest differences (one to two months) are found in 2003 for the demise day and length. STN data show below-average precipitation during May 2003, and the

Table 3. Average rainy season features for 1997–2012 derived from CPC-G and GPCP-1DD precipitation datasets. Bias refers to GPCP-1DD minus CPC-G-derived values. Feature

Onset day Demise day Length (days) Total precipitation (mm) Intensity (mm day-1)

Average value for 1997–2012 derived from CPC-G dataset

GPCP-1DD dataset

28 January 14 June 138 1531 11.0

24 January 06 June 134 1631 12.4

Bias

-4 days -8 days -4 +100 +1.2

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rainy season ends up prematurely, although above-average precipitation is found in the first half of June. For CPC-G and GPCP-1DD data, the below-average precipitation period is much shorter and rainy season demise is postponed to mid-June (not shown). This kind of difference is expected when single station and interpolated data are compared (Silva et al., 2007).

GPCP-1DD

STN

Onset day

31-Mar 1-Mar 30-Jan 31-Dec 1-Dec

1997

2000

2003

2006

2009

2012

(a)

CPC-G

Demise day

12-Aug

GPCP-1DD

STN

13-Jul 13-Jun 14-May 14-Apr 15-Mar

1997

2000

2003

2006

2009

2012

(b)

CPC-G

210

Lenght (days)

DISCUSSION ON THE METEOROLOGICAL FACTORS What are the meteorological factors that shape the high interannual variability of the rainy season features for the CLA region? For the 1979–2010 period, Pinheiro (2013) partially addressed this question by focusing on the rainy season onset. The conclusion was that early (late) onset would be mainly related to factors that favor (inhibit) precipitation occurrence over the CLA region: AITCZ located to the south (north) of the mean position and/or with high (low) intensity, negative (positive) SSTA in Nino 3.4 region [according to Trenberth, 1997, El-Niño (La-Niña) events refer to at least six consecutive months when the five-month running means of SSTA in Nino 3.4 region are positive (negative) with absolute value >0.4°C], and negative (positive) cross-equatorial SSTA gradient in tropical Atlantic. Other factors related to atmospheric systems that directly affect the CLA region, such as higher/ lower frequency of occurrence of UTCV and/or CSL, would be important to explain the onset day anomalies for specific years (Marques and Fortes, 2012). For instance: • In 2001, the longest length in the 1997–2012 period for both CPC-G and GPCP-1DD datasets took place (Fig. 5). It resulted mainly from a pronounced early onset related to favorable conditions for precipitation occurrence over the CLA region in January 2001: more intense AITCZ located to the south of the mean position, La-Niña event in tropical Pacific, and negative cross-equatorial SSTA gradient in tropical Atlantic. • In 1989, the onset day was the same as in 2001 (Fig. 4). Similarly to January 2001, there were a La-Niña event in tropical Pacific and negative cross-equatorial SST gradient in tropical Atlantic in January 1989; however, AITCZ position and intensity were close to the mean. The earliness was also related to higher frequency of occurrence of CSL over the northern coast of Brazil in January 1989. • In 2010, the latest onset day in the 1997–2012 period for both CPC-G and GPCP-1DD datasets took place (Fig. 5).

CPC-G

30-Apr

GPCP-1DD

STN

180 150 120 90 60 30

1997

2000

2003

2006

2009

2012

(c) Figure 5. Temporal evolution of the rainy season onset day (a), demise day (b), and length (c) derived from CPC-G (light blue), GPCP-1DD (dark blue), and STN (dotted) precipitation datasets.

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The lateness was the result of unfavorable conditions for precipitation occurrence over CLA from mid-February to mid-March of 2010: weak (less intense) AITCZ located to the north of the mean position, El-Niño event in tropical Pacific, positive cross-equatorial SST gradient in tropical Atlantic, and high frequency of occurrence of UTCV over Northeast

Rainy Season Features for the Alcântara Launch Center

Brazil. An example of UTCV that inhibited cloud formation over CLA in 11 March 2010 is shown in Fig. 6. The analysis for specific years, carried out by Pinheiro (2013), was useful to identify the factors related to rainy season onset, but further studies are needed to assess the relative importance of the factors statistically. Preliminary analysis indicates that AITCZ features (position and intensity), taken together, are able to explain ~20% of the onset day variance. This percentage seems to be consistent, because it is close to the average fraction of monthly precipitation variance explained by AITCZ features between January and February (Oyama and Carvalho, 2012). However, the percentage is rather low due to the difficulty in properly representing the magnitude of the onset day anomalies, even though the anomalies sign could be better predicted from the anomalies of the AITCZ features (proportion correct of about 75%). For the oceanic indices, SSTA in the Nino 3.4 region are

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able to explain ~5% of the onset day variance; cross-equatorial SSTA gradient in tropical Atlantic, ~25%. These fraction values are consistent with the results of Kayano and Andreoli (2006), who showed that interannual variations of the Northeast Brazil climate are more closely related to the tropical Atlantic variability modes than to the tropical Pacific’s. The role of SSTA in the Nino 3.4 region seems to increase for higher onset day variations: in more than half of the years in which the onset day occurs earlier (later) than the first (third) quartile, the rainy season begins under negative (positive) SSTA in Nino 3.4 region. Future work is also needed to unravel the factors that influence the rainy season demise. DRY DAYS AND DRY SPELLS WITHIN THE RAINY SEASON In the rainy season, as the atmospheric conditions are favorable to precipitation, the occurrence of dry days (days

Figure 6. Water vapor satellite image for 21 UTC, 11 March 2010 (source: Centro de Previsão de Tempo e Estudos Climáticos, Instituto Nacional de Pesquisas Espaciais, Brazil). The position of the UTCV center, according to the Climanalise bulletin for March, 2010 (http://climanalise.cptec.inpe.br/~rclimanl/boletim/pdf/pdf10/mar10.pdf ), is indicated by the blue circle. The subsidence region associated to the UTCV directly affects Northeast Brazil. J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.439-448, Oct.-Dec., 2013

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Number of dry spells (year-1)

without precipitation or with small amounts of precipitation) or dry spells (consecutive dry days) would not be expected. But they do exist and their frequency could be regarded as an additional rainy season feature. Dry days are defined as those with precipitation lower than a given threshold. Here, two thresholds commonly found in the literature (e.g., VicenteSerrano and Beguería-Portugués, 2003, p. 1107) — 1 and 0.1 mm day -1 — are used to identify the dry days within the rainy season for the CLA region. Considering the higher threshold (1 mm day -1), on average, dry days account for about 11% (~15 days) of the rainy season. About half of the dry days are grouped in dry spells, and the amount of dry spells decreases sharply as its length (i.e., the number of consecutive dry days) increases (Fig. 7). The return period is longer than 1 year for dry spells lasting for three days or more. The results presented here are close to the values shown in INMET (2009) for a city near CLA (São Luís). For aerospace meteorology purposes, the near-zero threshold (0.1 mm day -1) is important because it is more related to the “no-rain” condition needed for rocket-related activities in CLA. Using this threshold, the amount of dry days/spells is much lower: on average, there are only four to five dry days within the rainy season, and dry spells of any length have return periods longer than one year (Fig. 7). Therefore, the “no-rain” condition is rather difficult to be met during the rainy season for the CLA region.

1 mm 0.1 mm

CONCLUDING REMARKS The rainy season features — onset and demise days, length, total precipitation, and intensity — for the CLA region were obtained by using the CPC-G daily precipitation from 1979 to 2012 (34 years). For each year, the rainy season was identified objectively from the pentad precipitation data derived from the daily’s. The main conclusions of this study were: • The climatological rainy season features were: 28 January as onset day; 16 June as demise day; 140 days as length; 1527mm as total precipitation; and 10.9 mm d -1 as intensity. • The uncertainty on these climatological values due to the use of different precipitation datasets (e.g., the GPCP-1DD and STN datasets) was estimated as few days for the onset/demise days and length; 100mm for the total precipitation and ~1 mm day -1 for the intensity. • Except for the intensity, the rainy season features showed large interannual variability: standard variation of about one month for onset and demise days, and coefficient of variation of 33 and 40% for length and total precipitation, respectively. • The three-week period between 24 March and 13 April belongs to the rainy season of all years. • In general, longer (shorter) duration was related to early (late) onset, late (early) demise, and higher (lower) total precipitation. • The occurrence of dry spells within the rainy season is rather uncommon; on average, in only four or five days of the rainy season, precipitation is lower than 0.1 mm day -1. The results presented here may be regarded as a first step towards a comprehensive understanding of the rainy season for the CLA region. Follow-up steps could include studies on the meteorological factors/systems and oceanic indices that drive the anomalies of the rainy seasons features (e.g., by expanding the work of Pinheiro, 2013), as well as on the average atmospheric conditions related to the rainy season onset and demise (like Marengo et al., 2001; Barbieri, 2005).

Dry spells lenght (consecutive dry days) Figure 7. Number of dry spells within the rainy season for the Alcântara Launch Center region per year as function of the dry spell length (i.e., the number of consecutive dry days) for two thresholds: 1 and 0.1 mm day-1.

ACKNOWLEDGMENTS This work is part of the first author’s MSc dissertation under the guidance of the second author. The authors are grateful to the three anonymous reviewers for their useful comments and suggestions.

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doi: 10.5028/jatm.v5i4.244

Observational Study of the Surface Layer at an Ocean–Land Transition Region Luiz Eduardo Medeiros1, Roberto de Oliveira Magnago2, Gilberto Fisch1, Edson Roberto Marciotto1

ABSTRACT: High-frequency measurements of wind, and temperature were made during the dry season of 2008 to study the development of an internal boundary layer at the main Brazilian space launching centre, Centro de Lançamento de Alcântara at Alcântara, Maranhão, Brazil. Turbulence measurements taken at the coast, in two different points 227 m apart show different daily cycles of turbulent kinetic energy friction velocity (u*), and buoyancy flux w’ Tν’ . Surface roughness change, surface heating change, and a gap in the natural vegetation seem to be the causes for the variation in these turbulent parameters. The mean wind cycle also shows distinct patterns. It seems that, first, internal boundary layers develop when the oceanic surface layer reaches the continent, and a second when the first internal boundary layer’s flow encounters the gap. A direct implication is that turbulence is not horizontally homogeneous and measurements taken at single places are not spatially representative. Knowing how turbulence varies spatially is necessary information to understand the diffusion of pollutants exhausted by rockets near the coast. KEYWORDS: Coast, Internal boundary layer, Sonic anemometer, Surface layer.

INTRODUCTION The space vehicle launching centers are usually located at coastal areas due to safety reasons. Examples are the Kennedy Space Centre at Florida, Centre Spatial Guyanais at French Guiana, and Centro de Lançamento de Alcântara (CLA) at Brazil, all of which are located on the coast. CLA, for instance, has its launching pad around 650 m from the seashore. At the coastal regions there are always surface cover changes and in some case changes in topography too. Changes in surface cover create discontinuities in surface roughness and heating, which in conjunction with the topography affect the distribution of winds, temperature, and humidity. It is well known that boundary layers being advected over surface with discontinuities develop internal boundary layers (IBL) (Stull, 1988; Arya, 2001). A boundary layer is the layer of fluid near a boundary that is affected by friction against that boundary surface, and possibly by transport of heat and other variables across that surface. An IBL is a layer within the atmosphere bounded below by the surface, and above by a more or less sharp discontinuity in some atmospheric property. A surface layer is the same as a surface boundary layer, which is a layer of air of order tens of meters thick adjacent to the ground where mechanical (shear) generation of turbulence exceeds buoyant generation or consumption. In the case of roughness discontinuities, the mechanically generated IBL might reach equilibrium within or less than a kilometer downwind from discontinuity and its effects are likely to be felt only at the surface layer. In case of surface heating discontinuity, the internal boundary layer might reach equilibrium only at much larger distances (Garratt, 1990;

1.Instituto de Aeronaútica e Espaço – São José dos Campos/SP – Brazil 2.Universidade do Estado do Rio de Janeiro – Resende/RJ – Brazil Author for correspondence: Roberto O. Magnago | Departamento de Matemática, Física e Computação/Faculdade de Tecnologia/Universidade do Estado do Rio de Janeiro | Rodovia Presidente Dutra km 298 – Pólo Industrial | CEP 27.537-000 Resende/RJ – Brazil | Email: ducamobi@gmail.com Received: 21/03/13 | Accepted: 26/07/13

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Medeiros, L.E., Magnago, R.O., Fisch, G. and Marciotto, E.R.

Mahrt and Vickers, 2005) and it may affect much deeper layers. Sea and land breezes are examples of thermal IBLs. On this regard, Case et al. (2005) analyzed the sea breeze climatology at Kennedy Space Centre using a network of 44 mesonet towers and five wind profiles, while Merceret (2006) studied the rapid temporal variation of the winds using wind profilers. For the case of CLA, Gisler et al. (2011) studied the climatology of the surface winds through 5 years of data from a 70 m-high anemometric tower, in order to characterize the wind flow regime in the region. Recently, Moreira et al. (2011) described the usage of a software to study the dispersion of pollutants (or toxic gases) released at CLA. The CLA is located in the northeast (NE) of Brazil, right at the shoreline. The wind regime at its location is influenced by the trade winds and perhaps by a sea/land breeze circulation (Gisler et al., 2011). Besides these influences, there is also influence from a topographic barrier, an escarpment. The barrier is located just where the beach ends, and according to a wind tunnel study/simulation carried by Marinho et al. (2009), the flow is supposed to accelerate around the edge of the escarpment and to develop a recirculation a few hundred meters downwind from it. The limitation of this study is that the escarpment was approximated by a step-like barrier with 90 degrees inclination, which is not realistic. The understanding of how the marine boundary layer is affected, and how the surface layer is modified downwind from the escarpment, is extremely important for the evaluation of the stress caused by turbulence and wind on the structure and trajectory of rockets, well as for the dispersion of pollutants exhausted by them. For the region of CLA, mechanical and thermal IBLs should be developed when the marine boundary layer reaches the coast. Nonetheless, in this work we focus only on the mechanical effects exerted on the near-surface turbulence. The possible deeper effects of the surface heating discontinuity on the local planetary boundary layer are left for a future work.

is composed by an eroded slope about 40 m high, very heterogeneous, which at some points resembles cliffs. The slope is partially vegetated and delimitates the border of a plateau. In Fig. 1, it is shown that the slope with the surface cover change from water to land with small dense bushes typically 3 m tall. In Fig. 2 the local topography with the plateau and the surrounding slope is shown. The topographic surface (Fig. 2) was built using data from the Shuttle Radar Topography Mission provided by NASA Land Processes Distributed Active Archive Center (http://gdex. cr.usgs.gov/gdex/). The spatial resolution is 3 arc-seconds or approximately 92 m for Alcântara region.

Figure 1. The topographic barrier between the ocean and the plateau, the escarpment.

5 x Elevation range (m)

450

300 400 100 200 0

-2000 u -1000

th-

rth

-2000 -1000 0

1000 ist st. 1000 ran st D e ge 2000 2000 t-W (m Eas )

)

e (m

ng . ra

DATA Most of the data used in this work are from the Muricí II meteorological campaign (Marciotto et al., 2012) conducted at CLA, during September 17–25 of 2008. The measurements were taken right at the coast. The transition zone water-land

Figure 2. A perspective of the topographic of the region with respect to the prevailing wind direction. The taller vertical segment indicates the position of the tower plus Sonic B, and the shorter segment the mast with Sonic A. The flat part of the surface corresponds to the sea.

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Observational Study of the Surface Layer at an Ocean–Land Transition Region

Measurements were taken at approximately 675, 700, and 750 m inland at the plateau, as shown in the terrain cross sections (Fig. 3) along the direction of the prevailing wind during the time of the experiment – NE (Fig. 4). The Shuttle Radar Topography Mission data produces very similar cross

sections like the ones presented in Fig. 3; however, we preferred to use a local topographic map because there was more data in the region of the slope. The windroses presented in Fig. 4 show the prevailing wind direction in the region. For their determination we used 30 minutes average of zonal (u) and meridional (v) wind components during the period of the experiment. There are 16 directional sectors for each windrose, and every sector corresponds to a range of 22.5 degrees. At 750 m inland there was a 70 m tall tower (taller vertical segment in Fig. 2) with a wind profile composed by six propeller anemometers at the levels 6, 10, 16, 28, 43, and 70 m. A 3D sonic anemometer (Sonic B), from Campbell Scientific, Inc. (model CSAT3), was mounted on the tower at 9.5 m above ground level (AGL). Gisler et al. (2011) describes some climatological features of this tower. At 700 m inland, there was a second sonic anemometer (Sonic A – shorter segment in Fig. 2) installed at 9.5 m (AGL) on a mast. This site was located in a clearing with sparse small grass. The clearing, with Sonic A behind, is shown in Fig. 5 upper panel. The prevailing wind direction is from the bushes on the right side of the picture. The upwind natural vegetated fetch of Sonic B is presented in the lower panel of Fig. 5. The sonic boom points towards the direction from where the wind comes from. The broader view of the details of the landscape of the region, where the tower and Sonic A were located, is presented in an aerial photo of the region (Fig. 6). Inside the clearing, where Sonic A was located with respect to the direction of the

Sonic B (tower) Sonic A SoDAR

Elevation (m)

200

400

600

800

Distance (m) Figure 3. Cross section of the terrain’s elevation through Sonic A, Sonic B, and SoDAR, along the prevailing wind direction (NE). Zero in x-axis refers to the exact sensors’ position, right x-axis refers to upwind distance, and left x-axis to downwind distance. The elevation data were obtained from a local topographic map.

Sonic A (9.5 m)

Sonic B (9.5 m)

Sonic C (level 70 m)

0-3 m/s 3-6 m/s 6-9 m/s

0.25

0.5

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451

0-3 m/s 3-6 m/s 6-9 m/s

1 0.25

E W

0.5

0.75

1 0.25

E W

0.5

0.75

Figure 4. Windroses for Sonic A (700 m inland), Sonic B, and for the anemometer at the top of the tower. Sonic A was 700 m inland, and Sonic B plus tower was 750 m inland. J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.449-458, Oct.-Dec., 2013

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Sonic A

Figure 5. Sonic A mounted on a mast 9.5 m above ground level at a clearing site with sparse short grass, and lower panel shows Sonic B mounted at the same height but on a tower, with upwind fetch composed by dense bushes of 3 m height.

on cti ire d d in gw llin a ev Pr Sonic A Tower & Sonic B SoDAR

402 m

20 m

on cti ire d d in gw llin a ev Pr

227 m

Figure 6. Aerial photo of the region where the tower, the two Sonics (A and B), and the SoDAR were located. This picture was taken in 2003 just after an accident with a rocket. By 2008, the burnt vegetation (black area near Sonic A and building) had recovered and the damaged buildings were removed from the site. The dashed lines indicate the distance between the tower and Sonic B and Sonic A (227 m), between tower and SoDAR (402 m), and the upwind distance between Sonic A and natural vegtation (20 m).

prevailing wind, there is an upwind fetch of 20 m, which is half composed by sparse short grass and half by asphalt. Beyond that, the fetch is composed by same natural vegetation that is present at Sonic B’s site. At the tower there is no surface cover change for a fetch of at least 200 m long. Note that these details are also presented in Fig. 5, and we are excluding most part of the slope of the escarpment in the discussion of the fetch here. The wind profile at the tower is permanent, but the two Sonics were temporary and operated from September 17–25, 2008. This period of two weeks is within the dry season in the region, and according to Fisch et al. (2010) is when the easterly winds are the strongest (10–15 m/s). The wind speed and wind direction data from the profile are sampled at 2 seconds but only 10-minute averages are kept. All turbulent fluxes refer to correlation of velocity with velocity, and velocity with temperature during half-hour periods. The Reynolds mean removal was done using 30-minute block averages, and fluctuations were obtained by subtracting the average values from the instantaneous values. Near the observation points the surface was flat and only horizontal rotation was applied to align the x-axis of the reference frame with the direction of the mean wind. Because there was no fast response humidity and pressure measurements available, we approximated the

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.449-458, Oct.-Dec., 2013

Observational Study of the Surface Layer at an Ocean–Land Transition Region

buoyancy flux w’ θv’ by the virtual sonic temperature flux w’ Tvs’ . Where w’, qv’ and Tsv’ are, respectively, fluctuations of vertical wind velocity, virtual potential temperature, and derived sonic virtual temperature. We are aware of the error we may introduce when doing such approximation. Fluctuations of virtual temperature Tv’ are not exactly the same as Tsv’. Nonetheless, fluctuations of sonic virtual temperature are sensitive to fluctuation of humidity as does virtual temperature, but in Tv’ the influence of humidity fluctuation is slightly higher. There exists also influence of pressure fluctuation on the buoyancy flux, but we believe it is negligible. In order to simplify our notation in the whole rest of the text, we have dropped the letter “s” in the subscript of w’ Tsv’ . Wind data from an acoustic Doppler detecting and ranging device – SoDAR (from Atmospheric Systems Corporation with frequency operation range between 4500 and 5500 Hz) were used as complementary data for this study. The wind profiler (SoDAR) operated from October 11 to 14 of 2011, and was located inside a second clearing (Fig. 6) at 6 m east and 402 m north from the tower, approximately 675 m from the ocean (Fig. 3).

RESULTS

A=0.75 (Walmsley, 1989; Garratt, 1990) and a roughness length ( z0 ) of 0.35 m, and the distance from the steepest part of the escarpment to the tower along the predominant wind direction (200 m), gives an IBL of approximately of 42 m deep at the tower. The 0.35 m roughness length z0 was obtained by adjusting a neutral logarithmic wind profile to the tower (Fig. 7). Here, we have to point out that the situation is a bit more complicated than the types of IBLs discussed in the literature (e.g., Stull, 1988; Garratt, 1990; Arya, 2001). Besides the surface roughness changes, there is a topographical barrier. According to Marinho et al. (2009) the flow obstruction caused by the barrier forces the flow to accelerate and to develop a recirculation. The recirculation train extends from where the plateau levels off to about 200 m inland. However, the average wind vector field for a layer between 30 and 65 m AGL, obtained from the SoDAR data, shows a horizontal flow with no reversal in the flow direction. The main difference between this study and Marinho et al. (2009) is that the analysis presented here is based in the real topography with approximately 8 degrees inclination barrier, while in previous it was assumed step-type barrier with 90 degrees inclination. Because in the real situation

10 9

angular coef. = 1.53 intercept= 1.68 r2= 0.985 z0= 0.335 m

U (m s1)

INTERNAL BOUNDARY LAYERS The windroses (Fig. 4) obtained from the tower top level (70 m) aerovane, and from both sonics indicate that the average flow direction is approximately uniform within the height of the tower and within the horizontal extension of the experimental array size. The wind is predominantly from the sector north-north–east (NNE), with ~ 70% of the time with winds coming from NE. This result is somewhat similar to what Gisler et al. (2011) found using a longer wind data set. They showed that for dry season, for a period between the years 1996–1999, the predominant winds were 45% from NE and 40% from ENE, and the remaining 15% from NNE and E. In a scale of about 10 km, the costal line is aligned north-north-west (NNW) – south-south-east (SSE), and winds reaching the tower and Sonic A are necessarily from the ocean (Fig. 2). Because in the direction NE to SW the surface drastically changes, the lower part of the marine boundary layer develops an IBL when it reaches the coast (Arya, 2011). Using the IBL formula zIBL ≈ A z0L0.2 x 0.8 (Elliot, 1958), with

453

7 6 5 4 2.0

2.5

3.0

3.5

4.0

In (z(m)) Figure 7. Circles represent tower average wind profile as function of ln(z). The dotted is a neutral logarithmic curve, U(z) = (u*/k) ln(z/z0), adjusted to the tower profile. The angular coefficient, the linear coefficient, r2, and z0 refer to the dotted line. The upper and lower ends of the vertical solid lines are the speed standard deviation added and subtracted, respectively, from the average speed.

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the slope is significantly more gentle and the measuring points were more than 200 m inland from the steepest part of the escarpment, we believe that the recirculation zone, if existent, will be located upwind and the generated wake will be largely diffused by turbulence by the time the flow reaches the observing points. Therefore, the complicating factors added to the flow due to the barrier should not be the cause for different turbulent regimes at the observing points. In addition, because the IBL height (42 m) is above the height of both sonics (9.5 m), the IBL should be close to equilibrium at the sonics, in sense that the flow is not accelerating or decelerating.

3.5

Sonic A Sonic B

TKE (m2 s-2)

6.0

U (m s-1)

Turbulence and Mean Flow State The first result, one finds when looking to the average time series of wind, friction velocity, w’ Tv’ , and turbulent kinetic energy (TKE) for Sonic A and Sonic B (Sonic A is ~ 227 m north of Sonic B), is that the mean wind speed is stronger at Sonic B than at Sonic A (Fig. 8a, b, and c) but u* and TKE are rather weaker. The friction velocity u* was obtained through u* = -ur‘ w’. The ur and vr are, respectively, the velocity components along and perpendicular to mean wind direction, obtained through 2D-horizontal rotation, w the vertical velocity component, and primes denote perturbations. The mean wind

5.5 5.0 4.5

3.0 2.5 2.0 1.5

4.0 0

Hour (LST)

0.3

w’Tv’ (m K s-1)

U (m s-1)

0.4

0.7 0.6 0.5

0.2 0.1 0.0

Shear production and buoyancy production (m2 s-2)

u* - Sonic A u* - Sonic B buoyancy flux - Sonic A buoyancy flux - Sonic B

0.8

0.4

(b)

(a)

0.9

Hour (LST)

(c)

0.0 -0.1 -0.2 -0.3 Shear - Sonic A Shear - Sonic B Buoynacy - Sonic A Buoynacy - Sonic B

-0.4 0

Hour (LST)

(d)

Figure 8. Twenty-four-hour cycles for (a) wind speed for Sonic A (black line) and B (red line), (b) turbulent kinetic energy for Sonics A and B, (c) friction velocity u* and buoyancy flux w’ Tν’ for sonics A and B, and (d) shear production, and buoyancy production destruction terms for Sonics A and B. The vertical bars in (a) and (b) are the standard errors. Each point refers to 1- hour binaverages with 18 data points. The error bars include natural variability of the flow and instrumentation errors as well. However, differences between measurements, caused by instrumentation errors, should be minimal because sensors were kept the same. J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.449-458, Oct.-Dec., 2013

Observational Study of the Surface Layer at an Ocean–Land Transition Region

was obtained through U = ur and TKE = 1/2 ( u’2 + v’2 + w’2 ). For all these variables, we used the sonic 10 Hz data. The second result is that, shear production term (SP = ur’ w’ дU дz), and buoyancy production/destruction term (BPD = (g/Tref) Tv’ w’ ) are smaller at Sonic B than at Sonic A (Fig. 8d). The vertical gradient of the horizontal wind was determined using the U, and the sonic measuring height (9.5 m AGL). Velocity at the surface was set equal to zero. Having in mind that origin of the wind (e.g., pressure gradient and advection of momentum) was the same and that the measurements were taken at the same height AGL at Sonic A and B, these results clearly show the consequence of stronger turbulence at Sonic A. It has to be recalled that Sonic A was located in clearing and Sonic B was not. Wind speed, TKE, and BPD at the tower (Sonic B) and clearing (Sonic A) come somewhat closer only at night (1700–0500 local standard time (LST)), when the layer is weakly stable. Sonic B shows wider distribution than Sonic A for the stability parameter (z/L), indicating that the flow regime at the tower is less neutral than at the clearing as is presented in Fig. 9. Here, L is the Obukhov length (L = u*3 k (g/Tref) w’Tv’ , where k is the Von Karman constant = 0.4) and z=9.5 m, is the height AGL of the sonics. Values of z/L at Sonic A resemble more the values found within the surface layer closer to the surface, with wind shearing being the dominant source of turbulence and buoyancy a secondary source. At the tower it resembles more

# of occurrences

150

Sonic A Sonic B

100

0 -0.4

-0.3

-0.2

-0.1

0.0

0.1

z/L Figure 9. Distribution of z/L stability parameter for Sonics A and B. All cases were included.

455

the turbulence found over higher levels in the boundary layer, with somewhat diminished influence of wind shearing on the generation of turbulence. The wind speed, TKE, SP, and BPD show a clear diurnal cycle, peaking in the morning before noon and being a minimum around 1800 hours of LST. Note though that in SP there is a negative peak, with this variable almost completely mirroring wind and TKE. In the TKE budget equation there is minus sign in front of SP, which makes this terms always production term. The objective here is not to close the TKE equation, but rather to quantify the sources of turbulence generation, SP and BPD at the clearing and tower, which makes the turbulence at these places to be distinct. At the clearing, SP and TKE are in phase with each other but wind is not so. Between 0500 and 1000 LST there is a clear decreasing tendency for the wind but not so for SP. Note that just after 0500 LST the wind is maximum but SP is not. Shear Production (Fig. 8d) only becomes a maximum at 1000 LST, when the wind is weaker. The lack of clear tendency in SP and the apparent tendency for wind to decrease between 0500 and 1000 LST might be a result of averaging a small data record. Nine days of data were necessarily a small sample. In fact, when plotting the entire time, wind series (result not shown), we do see it increasing during this period for a few days. Perhaps, an entire dry season period would be enough to show that U increases between 0500 and 1000 LST. Beyond 1000 LST the wind and SP become stronger and negatively related (Fig. 8a and d), meanwhile TKE seems to be slightly less dependent on SP. Just after 1000 LST TKE decays not following the increase in decay in SP, but further on until 1500 LST it levels off while wind and SP vary. During this period the BPD is strongest and it probably helped to keep TKE near constant. At the tower there is a similar situation. Immediately after 1000 LST, wind and SP decrease forcing TKE to diminish. Beyond this point until 1500 LST, the approximately constant values of TKE are partially kept by the strong BPD. As a bottom line, we can conclude that the main difference between the clearing and the tower is that, there is stronger turbulence at the former. The fact that the U, SP, and TKE are not in phase with each other is consequence of sampling a small data record. The observed 24-hour wind cycle maybe a result of effects of variable local horizontal temperature gradient (sea breeze) interacting with the local trade winds. In a recent study conducted at the costal and central regions of Maranhão State, Medeiros and Fisch (2012) observed that 24-hour wind hodograph was the result

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of the sea breeze interacting with a large-scale southerly flow during the dry season. Spectra The normalized spectra f Sα/(u*2 ϕε2/3), where α = ur, vr, and w, reveals that at Sonic B the turbulence spectral peak is at low frequencies while at Sonic A the peak is displaced towards high frequencies. This result is clearly seen in Fig. 10, longitudinal velocity ur (along mean wind direction) and lateral velocity v r (crosswind – result not shown) but not in the vertical (w) velocity (Fig. 11). Such differences indicate that at Sonic A site turbulence is more fully developed because of higher SP. Turbulence at Sonic B is less isotropic than at Sonic A, its w-spectrum shows an inertial, but its u-spectrum has neither subinertial range nor constant slope at high frequencies. The normalized TKE dissipation rate, ϕε = kzε/u*3, was determined by adjusting through least-square method f Su/u*2 = 0.3ϕε2/3 n-2/3 (Kaimal and Finnigan, 1994) to the inertial subrange of ur-spectrum, where n is the normalized frequency (fz/U),

and f is the frequency in Hertz. The ϕε was calculated for every 30-minute spectrum and later used to obtain the 30-minute normalized spectra of ur, v r, and w, which were finally averaged (Figs. 10 and 11). Turbulence mechanically generated (e.g., through SP) has spectrum concentrated at higher frequencies interval than do turbulence generated by convection (e.g., through BPD). The SP supplies energy directly to ur and BPD to w, therefore the shifting in ur-spectrum is a direct consequence of stronger SP. For the remaining spectral components (w and v r), it is an indirect consequence, because they have their energy supplied by correlation pressure term, which is a term that works in a way to make the turbulence more isotropic by removing energy from ur and putting in w and v r. Combining the average wind speed with the inverse of the ur spectral peak frequency, one finds that typical scale size of horizontal eddies are in the order of 50 m for A and 120 m for B. Consequently the clearing, which is less than 100 m wide, should not support the typical eddy sizes present at B. Its presence not only caused higher ur’ w’ дU дz and

Longitudinal Velocity Spectra 1.00

1.00

Sonic A Sonic B

0.50

0.20

f Sw / (u.2 Φε2/3)

Vertical Velocity Spectra

0.10 0.05 0.02

0.20 0.10 0.05 Sonic A Sonic B

0.02

0.01

0.01 1e-03

1e-02

1e-01

1e+00

1e-03

1e+01

f z/U

Figure 10. Normalized u-spectral energy density as a function of normalized frequency (n = fz/U). Each point represents a binned average of all 30-minute spectra. The bins were defined by constant increments in n. The shifting in the spectral peak of u from Sonic A to Sonic B is 0.044 Hz and in ν is 0.061 Hz.

1e-02

1e-01

1e+00

1e+01

f z/U

Figure 11. Normalized w-spectral energy density as a function of normalized frequency (n = fz/U). Each point represents a binned average of all 30-minute spectra. The bins were defined by constant increments in n. The shifting in the spectral peak of w from Sonic A to Sonic B is 0.021 Hz.

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Observational Study of the Surface Layer at an Ocean–Land Transition Region

stronger mechanical turbulence, as consequence of a wake behind the trees interacting with the mean wind above, but also a restrictive factor for the size of the horizontal eddies.

457

slightly better scores for the 9.5 m are due to identical equipments, and perhaps shorter distance 227 m (Fig. 6). The results showed that 70 m wind level is more uniform across the region. Surface inhomogeneities, like the clearing where Sonic A was located and the second clearing, where the SoDAR was located (see Fig. 6 for details), have less influence at this level as one would expect. This height is near the top of the calculated IBL, and it is possible that it is above the first real IBL, which forms near the edge of the plateau. The 30-m results indicate that this level is the least spatial homogenous; however, because of the problems mentioned above we still believe that the 30 m level is spatially more homogeneous than the 9.5 m level.

WIND SPATIAL REPRESENTATIVENESS Wind measurements taken 30 and 70 m AGL at the tower and at nearby SoDAR during the dry season 2011 (October 11–14), plus the wind measurements taken by Sonics A and B at 9.5 m AGL during the dry season of 2008 (period of Muricí II experiment, September 17–25), were used to define the spatial representativeness of the wind at CLA. The 10-minute wind averages were used for the comparison. The parts of the whole data set came from two different years. However, because surface cover did not change and the wind direction and speed of the prevailing winds, during the measuring period in 2008 and 2011, were very similar, this time period difference should not compromise the analysis. The same level wind measurements were compared through the usage of the statistical indices correlation, normalized mean square error, and fractional shift presented in Table 1 (see caption for details). Although the comparison for the levels 70 and 30 m had the largest distance (402 m) and the measuring techniques were distinct (SoDAR and tower), the 70 m had the best values (scores) for the statistical indices among all. The worst scores obtained for 30 m are partially an experimental artifact caused by different equipments, and the

CONCLUSION Even though the experimental array was limited, two places with turbulence measurements, and the campaign duration was short, less than two weeks, we can still draw some qualitative and quantitative conclusions. For a distance of 200 m (Fig. 3) downwind from the edge of the escarpment, near-surface turbulence is not spatially homogeneous at CLA launching pad site. It is a consequence of the development of other IBL at the clearing, internally to a first one, which develops when

Table 1. For the 70 m and 30 m levels, the tower 70 m and 28 m wind measurement levels were compared with the 30 m and 70 m of the SoDAR. The SoDAR was located 402 m north from the tower (Fig. 6) and operated at that location from October 11 to 14, 2011. For the 9.5m level, we used two sonic anemometers 227 m apart, which operated from September 17 to 25, 2008. Spatial representativeness of the wind Zonal wind speed (U )

Meridional wind speed (V )

Measurement height (m)

9.5

Distance between measuring points (m)

227

402

227

402

Correlation*

0.81

0.75

0.83

0.87

0.76

0.83

Normalized mean square error**

0.04

0.02

0.01

0.03

0.02

Fractional shift***

0.79

-0.63

-0.32

0.34

0.21

0.08

*The correlation was determined by ∑ ( xi - x )( yi - y ) / ( σxσy ) , where xi and yi represent, respectively, the i-th measurement from two different sensors, and σx and i=1

σy standard deviations of x and y sensors. Optimum value is one. N

** Normalized mean square error was computed by ∑ ( xi - yi ) *** Fractional shift was computed by 2 (σx - σy )

i=1

N x y . Optimum value is zero.

(σx + σy). Optimum value is zero.

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the wind first encounters the escarpment. Due to our limited experimental array, we could not determine the extension of the first and second IBLs. TKE, shear, and buoyancy are different even within a distance of 227 m (Fig. 6). The point closer to the escarpment (Sonic A), but in a clearing, had stronger u*, TKE, SP (also true for momentum flux), and buoyancy flux (buoyancy production term) but weaker wind. Part of the increment of TKE at the tower and clearing during the sunlight hours, was due to stronger buoyancy flux. The stronger buoyancy at the clearing must have been a consequence of the asphalt’s higher surface temperature, upwind inside the clearing. The presence of the clearing was also the reason for higher u* and weaker wind. We suppose that a wake zone is developed behind trees, that delimit the clearing perimeter, and it interacting with the mean wind increased the removal of momentum from it. As a bottom line of this analysis, the wind, TKE, and fluxes measured at the tower are not representative of the conditions at the clearing where the rocket launcher is located. Nonetheless, in the absence of better measurements, the spatial analysis of the wind components (U,V ) showed that the tower top-level (70 m)

wind might be the least surface affected measurement. It can be used as a first approximation to represent the wind for the region at that level, relative to operational decisions concerning rocket launch and diffusion of pollutants exhausted by the rockets.

ACKNOWLEDGMENTS The authors acknowledge the financial support from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) under the grants 510159/2010-9 (Medeiros), PQ 303720/2010-7 (Fisch), 471143/2011-1 (Marciotto), and Fundação de Amparo a Pesquisa do Estado de São Paulo (2010/16510-0). The authors are also thankful to NASA Land Processes Distributed Active Archive Center (LPDAAC); ASTER L1B; USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota 2001 for providing the topographic data. The authors also acknowledge the valuable suggestions made by reviewers to make the work better.

REFERENCES Arya, S.P., 2001, Introduction to Micrometeorology. Academic Press, San Diego. Case, J.L., Wheeler, M.M., Manobianco, J., Weems, J.W. and Roeder, W.P., 2005, A 7-yr climatological study of land breezes over the Florida spaceport. Journal of Applied Meteorology, Vol. 44, No. 3, pp. 340-356. Elliot, W.P., 1958, The growth of the atmospheric internal boundary layer. Transactions of the American Geophysical Union, Vol. 39, No. 6, pp. 1048-1054. Fisch, G., Avelar, A.C., Marinho, L.P.B., Gielow, R., Girardi, R.M. and Souza, L.F., 2010, The internal boundary layer at the Alcântara space center: wind measurements, wind tunnel experiments an numeric simulations. In: Proceedings of the Fifth International Symposium on Computational Wind Engineering (CWE2010), Chapel Hill, North Carolina, USA May 23-27, 2010. Garratt, J.R., 1990, The internal boundary layer – A review. BoundaryLayer Meteorology, Vol. 50, No. 1-4, pp. 171-203. Gisler, C.A.F., Fisch, G. and Correa, S. C., 2011, Statistical analysis of wind profile in the surface layer at the Alcântara launching center. J Aerospace Management, Vol. 3, No. 2, pp. 193-202.doi: 10.5028/ jatm.2011.03022411 Kaimal J.C. and Finnigan, J.J., 1994, Atmospheric boundary layer flows – their structure and measurement. Oxford University Press, New York. Mahrt, L. and Vickers, D., 2005, Boundary-layer adjustment over small-scale changes of surface heat flux. Boundary-Layer Meteorology, Vol. 116, No. 2, pp. 313-330.

Marciotto, E.R., Fisch, G. and Medeiros, L.E., 2012, Characterization of surface level wind in the Centro de Lançamento de Alcântara for use in rocket structure loading and dispersion studies. J Aerospace Technology and Management, Vol. 4, No. 1, pp. 69-79. doi: 10.5028/ jatm.2012.04014911 Marinho, L.M.B., Avelar, A.C., Fisch, G., Roballo, S.T., Souza, L.F., Ralf, G. and Girardi, R.M., 2009, Studies using wind tunnel to simulate the atmospheric boundary layer at the Alcântara Space Center. J Aerospace Technology and Management, Vol. 1, No. 1, pp. 91-98. doi: 10.5028/jatm.200901019198 Medeiros, L.E. and Fisch, G., 2012, Low atmospheric flow at Centro de Lançamento de Alcântara (CLA) and surrounding areas of the north part of the Maranhão State. In: Proceedings of the XVII Congresso Brasileiro de Meteorologia, Gramado, RS, Brazil. Merceret, F.J., 2006, Rapid temporal changes of boundary layer winds. Journal of Applied Metorology and Climatology, Vol. 45, No. 7, pp. 1016-1020. 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. J of Aerospace Technology and Management, Vol. 3, No. 1, pp. 41-52. doi: 10.5028/jatm.2011.03010311 Stull, R.B., 1988, An introduction to boundary layer meteorology. Kluwer Academic Publishers, Dordrecht. Walmsley, J.L., 1989, Internal boundary layer height formulae – a comparison with atmospheric data. Boundary-Layer Meteorology, Vol. 47, No. 1-4, pp. 251-262.

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doi: 10.5028/jatm.v5i4.272

Application of the Prado - Project Management Maturity Model at a R&D Institution of the Brazilian Federal Government Luiz Aldo Leite das Neves1,2, Luiz Eduardo Nicolini do Patrocínio Nunes1, Valesca Alves Corrêa1, Mirabel Cerqueira Rezende2

ABSTRACT: Government institutions have sought to improve their processes in project management in an effort to elevate their maturity levels using models that clearly identify the weaknesses in the management of their projects. This article aims to show a case study by applying the Project Management Maturity Model (Prado - PMMM), developed by Darci Prado, in a Research and Development (R&D) Institution of the Brazilian Federal Government. The scores show that the maturity level in project management is weak (institutional level equal to 2.47). The main causes for this score are attributed to the lack of knowledge and the unpreparedness of some sectors and project managers. It was also observed that the dimension named Technical Competence presents the highest value (46%), considered a good score. On the other hand, the dimension involving Behavioral Competence presents the lowest value (9%), which, according to the used methodology, is considered weak, indicating that investments must be made to enhance this dimension. KEYWORDS: Maturity in management, Project management, Maturity models, Growth plan.

INTRODUCTION Managing projects is not as simple a task as many may think. Besides having well-defined beginning and end, it involves a series of steps with specific goals, which require financial, human, as well as material resources. It is also complicated by the fact that many institutions are structured as a matrix, where projects permeate several departments within. The concept of project management has gone through an evolution in the past few years, where the traditional idea of the project manager being a specialist, usually hired by engineering companies, is now considered limited. Nowadays, the concept of project management is viewed under a new perspective inside organizations, which includes more inter-relations that take into consideration varied profiles in medium and high level positions in the organizational charts (Barber, 2004). In order to remain competitive, organizations have been trying to improve their process management aiming for the success of their projects by adopting project management practices. The demand for qualified professionals in this area has increased since the project manager has become essential by providing an advantage to both public and private institutions. Public organizations are not different from their private counterparts in terms of their complexity. They are both going through the conflict arising from the changes and innovations required by the current environment against the bureaucratic dynamics of their organizational culture (Pires and Macêdo, 2006). Due to this complexity, which is a characteristic of modern organizations, studies in the area of project management have intensified, with emphasis on project handling, project

1.Universidade de Taubaté – Taubaté/SP – Brazil 2.Instituto de Aeronáutica e Espaço – São José dos Campos/SP – Brazil Author for correspondence: Luiz Aldo Leite das Neves | Praça Marechal Eduardo Gomes, 50 – Vila das Acácias | CEP 12.228-904 São José dos Campos/SP – Brazil | Email: luiz_aldo@ig.com.br Received: 07/08/13 | Accepted: 24/10/13

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management offices, maturity, strategic alignment, life cycle and risk management (Prado, 2010). In the 1990’s, Brazil saw the beginning of actions meant to create a greater alignment with the public administration management movement, gaining strength after the establishment, in 1995, of Ministry of Federal Administration and State Reform (MARE). At the time, the Brazilian government was becoming more concerned with such issues as administrative efficiency, contract handling, public service, and accountability; in other words, providing a better quality service to the most interested party, its citizens. In this context, government institutions are intensively utilizing tools that measure the maturity and handle the management of its projects with the purpose of directly contributing to the satisfaction of its customers. Using an informed diagnosis, public project managers can create guidelines to improve the administration of projects, which in turn increases their success rate at the end. These tools are being used to enable the evaluation of maturity in project management, taking into consideration the context and the management procedures in above mentioned institutions. The outcome of these evaluations is used to create a plan of action for short and long term growth, as well as to guide the management of programs and projects with the purpose of increasing their success rate. Prado and Archibald (2009) did some research with public, private, and third sector (public non-profit civil institutions) institutions and confirmed the low rate of project management maturity in public organizations of both direct and indirect type administration. In other words, the study observed that these institutions were less efficient and effective in project management than their private counterparts. In an effort to contribute to this discussion, this article aims to show the present level of project management maturity of a Research and Development (R&D) institution of the Brazilian Federal Government, using Prado-Project Management Maturity Model (Prado - PMMM) and based on the scores, make some suggestions to advance the project management maturity of the institution in question.

PROJECT MANAGEMENT MATURITY Beginning in the 1990’s, various models were developed to evaluate the maturity of organizations in managing projects, almost all of them inspired by the maturity model in software development, Capability Maturity Model Integration (CMMI),

created by Carnegie Mellon University in a partnership with the Systems Engineering Institute (SEI) (Prado, 2010). According to Kerzner (2002), Maturity in Project Management is represented by specifically designed systems and processes, which are characterized by repetitiveness, increasing the probability of success, though not guaranteeing it, in spite of this increase in probability being its main characteristic. Globalization and the changes in the economic and business scenario forced organizations to change its way of thinking in regards to project management, since they depend on their projects, together with technological advances, to guarantee their competitiveness and survival. Therefore, Project Management Maturity has advanced since it shows that the company that makes use of it has the tools, capability, and the needs to manage its projects. The advantages of Project Management have been largely advertised and the application of their methods is ever more common in companies, especially those that need to provide a swift and effective response to the current organizational and environmental issues (Carvalho and Rabechini, 2006). In this area of study, the best recommended practices are grouped in the maturity models, which try to identify the present level of maturity in organizations through the use of assessments and subsequently propose improvements by showing which practices are useful to advance their level of maturity in project management. This procedure allows for future assessments using the previous score as reference as well as the growth data of the organization during the period, increasing the chance of success in project execution. Among the models developed in the 1990’s and beyond, we have the Prado - PMMM, based on the experience of its author, Darci Prado, in the implementation of project management in dozens of Brazilian institutions (Prado, 2010).

Prado - PMMM The Prado - PMMM used in this case study is characterized by the simplicity of its questionnaire, the practical way of obtaining scores, the applicability to the various sectors of an organization as well as to the organization as a whole. It is also in alignment with our culture, since it has been used in many Brazilian institutions, and is available online. This model developed by consultant Darci Prado is comprised of 5 levels of maturity and 6 dimensions, as shown

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in Fig. 1. It takes into account the areas related to processes, people, technology, and strategies spread through the 5 levels, in accordance with the terminology used by the Project Management Body of Knowledge (PMBOK) Guide. MATURITY LEVELS According to Prado’s model, Level 1 represents the initial stage, where the department hasn’t made any coordinated effort to implement project management. In other words, in this stage projects are executed based on intuition, individual effort, and good will. There is usually no planning or control, since there are no standard procedures, and consequently there is the possibility of delay and cost overrun, and most likely the technical specifications will not be met. The first level clearly shows the total disengagement between those involved in the project and the practices of Project Management. Level 2 “Known” demonstrates that the organization regularly invests in training and has acquired project management software. There may be isolated initiatives to standardize procedures, but their use is limited because there needs to be widespread standardization in order to facilitate project planning and control. In spite of that, failures are still frequent because the lack of standards results in a diluted use of knowledge. Level 3 “Standardized” has seen the implementation of the Project Management Office, which has standardized the use of procedures that require the utilization of planning and control processes, which in turn demand more dedication on the part of those involved in the project. We can observe manager’s improvements in terms of technical, behavioral, and contextual competency. The problems affecting project

Level 5 Optimized Level 4 Managed

performance are known but haven’t yet been resolved and it is obvious that improvements are needed. Level 4 “Managed” shows that investments in behavioral competency are efficient because the project managers are better prepared to handle the behavioral aspects of their teams, such as human relationships, conflicts, and negotiations. At this level, the practice of improvement is intensified in order to boost knowledge through an emphasis in advanced course participation (such as MBA’s in project management) and visits to other organizations that have consolidated project management processes (benchmarking). Finally, Level 5 “Optimized” indicates that the company has reached a high level of project management understanding; the processes are optimized therefore accomplished in less time, at less cost, but with quality scores, due to the wide experience, knowledge, and attitude of the people involved. That is obtained through the harmonization of the Project Management Model and the Organizational Structure, which are in complete alignment with corporate business. THE DIMENSIONS The correlation between the six dimensions and the five maturity levels shows how mature the project management of an institution is. Under this perspective, Prado (2010) takes into consideration this correlation, which brings into evidence the most important characteristics of each of the maturity level of the model, as well as expected success rate of the projects. The six dimensions of the Prado - PMMM are shown in Fig. 2. In broad terms, the dimensions, or maturity factors, appear in

DIMENSIONS Strategic Alignment Behavioral Competence

Level 3 Standardized

Sucess

461

Organizational Structure

Level 2 Known

Informatization

Level 1 Initial

Methodology Technical Competence

Maturity level Source: adapted from Prado (2010).

Figure 1. Maturity Level and Dimensions – Prado - PMMM. J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.459-465, Oct.-Dec., 2013

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each level with more or less intensity, depending on the moment where the peak of maturity occurs in a specific dimension. The first dimension — Technical Competency — addresses the knowledge base of project management, which must be widespread among the project management sectors. In other words, project managers and others involved should strengthen their technical knowledge not only in their specific areas but also other aspects of project management. The second dimension — Methodology — means that the use of a unique methodology in the whole company is highly recommended. It should also allow for small variations among the different sectors, since the correct application of methods, techniques, and tools is guaranteed by carrying out a series of steps. The third dimension — Informatization — addresses the use of project management software since many aspects of the methodology need to be computerized to be used by a number of people as standard procedure. The fourth dimension — Organizational Structure — concentrates on the way a company or its sectors are organized to execute its projects. The most commonly used structures are functional, matrix, and divisional. These also allow for some variations and may coexist with each other or with complementary structures, such as in a Project Management Office. The fifth dimension — Behavioral Competence — recognizes that the execution of the work depends on the people therefore, it is essential that they are motivated to perform their tasks to the best of their ability. Conflicts among those in a team are usually detrimental to the project and should be avoided by managers.

The sixth and last dimension — Strategic Alignment — considers it fundamental to have the projects aligned with the business of the organization so that they are adequately planned and executed. Each project must have been assessed according to certain criteria, such as technical and financial evaluation, and risk analysis. ASSESSMENT The Prado - PMMM allows for the maturity assessment to be obtained through the application of a 40-item questionnaire. According to the Prado - PMMM (Prado, 2010), the final score of the maturity assessment is obtained from the answers and data of this test. This final score is given on a scale of 1 through 5, which can be interpreted according to illustration in Fig. 3. The total points obtained from the answers at each level determine the placement in a level. Each answer is measured from 0 to 100 points, or the equivalent percentage, as shown in Fig. 4. Based on these scores, the weak and strong points rated in each question can be analyzed, and a plan of action for the short, medium, and long term can be established.

Comfort Zone

Average

Weak

Very Weak

Organizational Structure

Up to 1.60 ..............................................................Very Weak Informatization

Methodology

Very Good

Good

A Platform For Project Management (PM)

Strategic Alignment

Excellent

Between 1.60 and 2.60 .................................................. Weak Between 2.60 and 3.20 ..............................................Average Between 3.20 and 4.00 ..................................................Good Between 4.00 and 4.60 .........................................Very Good

Competence

Above 4.60 ................................................................Excellent

Source: Adapted Prado (2010).

Figure 2. Dimensions of Prado-Project Management Maturity Model – PMMM.

Adapted from Prado (2010).

Figure 3. Management Maturity Scale.

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RESULTS AND DISCUSSION

Up to 20% ................................................ Weak Adherence Up to 40% ............................................ Average Adherence Up to 70% ................................................ Good Adherence Up to 90% .......................................Very Good Adherence Up to 100% ........................................ Excellent Adherence Source: Prado (2010).

Figure 4. Standards for Level Adherence Assessment.

CASE STUDY This case study was performed at a R&D Institution of the Brazilian Federal Government. This institution has a variety of projects in its portfolio, each with its own characteristics and different funding resources. Its goal is research and development directed at increasing technical and scientific knowledge in order to provide technical solutions to strengthen the country’s industrial sector, and therefore contribute to national sovereignty through research, development, and innovation. The first step in the present research was choosing the model to evaluate project management maturity, in this case the Prado PMMM. This choice was based on the advantages of this model as explained earlier. The next step was choosing the target audience to apply the Prado - PMMM questionnaire, involving 19 sectors of the chosen organization. The work related to this study started with sector meetings to demonstrate the importance of this research as well as the benefits it would bring to the organization. The target audience is very important to the organization’s projects due to their technical knowledge, their experience in project management, or for their part in their project’s team. The selected audience included upper managers, managers, future project managers, internal service suppliers, and others involved in the projects, mainly for their position and job description in each sector of the organizational structure. The tool used to collect data for this study was a questionnaire based on the Prado - PMMM developed by consultant Darci Prado, available online at “www.maturityresearch.com”. This model was chosen for its simplicity, small number of questions, and alignment with our culture. The questionnaire is comprised of 40 questions, addressing four levels of maturity (from 2 to 5) and each level has 10 questions with 5 choices (A, B, C, D, and E), which weigh 10, 7, 4, 2, and 0, respectively. The questionnaires were handed out individually to each respondent in his own sector in order to prove once again the importance of the research. A total of 78 questionnaires were given out.

In order to analyze the scores of this research, some factors were taken into consideration, such as the respondents’ profile in regards to technical knowledge, professional experience, participation in project management courses, management position, and length of service in the organization. Respondents’ profile: 100% are college graduates, 90% have a doctorate, masters, or a specialty degree, but only 30% have done training in project management. During the course of this research, most of the respondents (90%) completed the questionnaire. This positive outcome may be attributed to the direct approach used for its distribution, when each could raise concerns regarding the model being used and have these promptly addressed. Of the 78 questionnaires given out, 72 were turned in. The data collected was consolidated and analyzed using the formulas provided by the Prado’s model to calculate maturity levels (Prado, 2010). The respondents who took part in this research work at 23 different sectors of the institution under study. Its organizational structure is considered a “weak” matrix, and since it is a hierarchy, managers’ autonomy is limited. Not only do the sectors where the respondents work have different characteristics, the projects within a sector differ as well; some sectors concentrate on R&D, others work directly with the execution of projects, still others provide services, besides project department itself, and upper management. Based on the analysis of the respondents’ as well as the institutional profile, it is possible to improve our comprehension of the final score obtained by the institution in terms of management maturity, adherence to the levels, and adherence to the dimensions. The institutional Final Maturity Score (FMS) was calculated using Eq. 1 (Prado, 2010). The score obtained was 2.47, which is considered a weak level of project management maturity according to the reference levels proposed by the model (Fig. 3). According to Prado (2010), the FMS obtained by this institution demonstrates a fairly good level of knowledge, and there are isolated individual efforts; it also shows that they have initiated the implementation of a standard project management platform. Institutional FMS =

1 23 ∑ FMSsector n) 23 ( n=1

Institutional FMS =

56.88 23

(1)

Institutional FMS = 2.47

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Adherence to the four levels proposed by the model used in the research is shown in Fig. 5. An analysis of this figure shows that Level 2, also called “Known”, shows the most adherence (62%). According to Prado (2010), this is a good score, since it demonstrates isolated attempts to standardize procedures as well as create a common language. Level 3, on the other hand, also called “Standardized”, shows a 48% adherence, considered average. This level is considered very important since it is where the implementation of a project management platform is verified. Nevertheless, according to Prado (2010), even adherence levels close to 100% are no guarantee for consistent and lasting scores. Finally, Levels 4 (“Managed”) and 5 (“Optimized”) show 30% and 7% adherence levels, respectively, which are considered weak profiles by Prado’s model standards. The assessed score showing institutional adherence by levels indicate that the institution needs to improve its methods in

Level 5

Level 4

30%

Level 3

48% 62%

Level 2 0

Level Adherence Figure 5. Subject’s Percent of Adherence to the Levels of Institutional Maturity.

Dimension Adherence Strategic Alignment

31%

Behavioral Competence

12%

Organizational Structure

25%

Informatization Methodology

31%

Technical Competence

32% 49% 0

the project management area. It is also worth pointing out that the assessed scores may have been influenced by the type of organizational structure. Generally speaking, it has been noticed that the institution has fairly good adherence in certain levels based on Prado’s model standards, in spite of not having a centralized model of project management. Figure 6 represents the institutional adherence to the dimensions based on the data collected from the questionnaires. An analysis of this figure shows that the first dimension — Technical Competence — is the highest at 49%, considered good according to the Prado - PMMM (Prado, 2010). On the other hand, the second (Methodology), the third (Informatization), and the sixth (Strategic Alignment) dimensions show values considered average, i.e. 29%¸ 29 and 27%, respectively. The fourth dimension — Organizational Structure — has a value of 15%. Finally, the Behavioral Competence (fifth dimension: Behavioral Competence) shows a value of 9%, which is considered weak according to the methodology used. It can be observed from these scores that the institution presents a profile typical of Level 2, where the organization invests in training and has acquired project management software, but the values in Dimensions 4 and 5 indicate that the institution has to implement actions to enhance these dimensions.

Figure 6. Subject’s percent of Adherence to the Dimensions of Institutional Maturity.

CONCLUSION The scores obtained through this research, based on the Prado - PMMM, allowed for the identification of the level of Project Management Maturity of the subject institution. Comparing the score obtained through this case study (2.47) with the overall average scores of Brazilian companies in terms of R&D by the survey Brazil Research 2012 (available at www.maturityresearch.com), we see that the subject institution is in line with the national average of institutions in the same category (2.60). It must be emphasized that this is a low score according to the reference levels of the model used in this research (Prado - PMMM). One of the contributing factors to this score is the lack of tools for project control and follow-up, added to poor communication, which had a significant impact on the final score, since many projects are structured as a matrix therefore permeating various sectors of the institution. In addition to that, it is known that this organization has a “weak” matrix structure, i.e. it is a hierarchical institution where managers’

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decision making is limited, having a direct effect on the outcome of projects. Nevertheless, this research has identified the following as the main contributors to the score: lack of knowledge, inexperience of some project managers, the way the institution is structured, and the way resources are distributed. This organization has been going through positive changes in the past few years, such as internal restructuring, improvement in its strategic planning, better quality management. It can be asserted that this first assessment of their project management maturity is consistent with these improvements although not the object of such changes. One of the positive aspects of this research was pointing out the current level of maturity of the organization and which areas need improvement. The analysis of the dimension adherence scores will be used by the Project Office to prioritize improvements, especially in the areas where the dimension level was low. In this case study, although the FMS is considered “weak”, we can classify the institution as a Level 2 “Known” in maturity, whose final score shows a 60% adherence level, which is considered “good” according to the standards of the Prado model. This means that there are isolated initiatives towards the use of good Project Management practices, but there is a lack of a methodology, which leads to a dilution of the acquired knowledge as evidenced by levels 4 and 5, where adherence was “Average” and “Weak”, respectively. As far as dimension adherence is concerned, the organization is inadequate in all dimensions, in spite of the 49% achievement in “Technical Competence”, which is considered “Good” according to the reference scale, because the project managers are not knowledgeable in the methodologies used in project management. This behavior is made worse by the “Behavioral Competence” dimension, since managers are not encouraged to participate in conflict management training, and the “Organizational Structure” dimension, which is hierarchical. The scores point to the need of corrective measures to improve the level of project management maturity.

465

In order to better understand the score achieved by the institution, the offices were divided into groups based on the type of activity they perform, which made it possible to evaluate the differences among them in regards to project management maturity (Management Group, Research Group, and Service Group). The scores clearly show the respondents’ view of project management. The Project Management Office of this institution is still in its initial stage, which hinders the implementation of a “Methodology”. Only through its empowerment and the support of upper management can the Project Management Office improve the administration of processes and prevent the use of isolated software whose sole purpose is managing deadlines. Software to manage risk, cost, and resources is not offered by the institution, though it would help improve the “Organizational Structure” as well as the “Informatization” dimensions. Based on the scores, a plan of action aiming to develop the institution’s project management processes has been proposed, to be coordinated by the Project Management Office. The initiatives shall concentrate on how manager’s profile and organizational structure affect project management in order to develop directives meant to solve the problems affecting the performance of projects. It is important to promote awareness among upper management, project managers, and all others involved in projects, thus creating a culture that values the use of a corporate tool for the management of projects, be it through lectures, training, or even visits to other institutions included in benchmark, as well as intensive training for the present and future project managers.

ACKNOWLEDGMENTS The authors would like to thank the Project Office of the institution for their support throughout this study as well as all the respondents who promptly contributed to this research.

REFERENCES Barber, E., 2004, “Benchmarking the Management of Projects: a Review of Current Thinking”, International Journal of Project Management, Vol. 22, No. 4, pp. 301-307.

Pires, J.C.S. and Macêdo, K.B., 2006, “Cultura Organizacional em Organizações Públicas no Brasil”, Revista de Administração Pública, Vol. 40, No. 1, pp. 81-105.

Carvalho, M.M. and Rabechini, R., 2006, “Construindo Competências para Gerenciar Projetos: Teoria e Casos”, Atlas, São Paulo, Brazil.

Prado, D., 2010, “Maturidade em Gerenciamento de Projetos”, INDG Tecnologia e Serviços Ltda., Nova Lima, Brazil.

Kerzner, H., 2002, “Gestão de Projetos: As Melhores Práticas”, Bookman, Porto Alegre, Brazil.

Prado, D. and Archibald, R., 2009, “Gerenciamento de Projetos para Executivos”, INDG Tecnologia e Serviços Ltda., Nova Lima, Brazil.

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Thesis abstracts This section presents the abstract of most recent PhD thesis related to aerospace technology and management

STUDY OF SOLIDIFICATION OF EUTECTIC ALLOYS IN MICROGRAVITY ENVIRONMENT Rafael Cardoso Toledo Instituto Nacional de Pesquisas Espaciais; São José dos Campos/SP – Brazil; rafael@las.inpe.br Thesis submitted for Ph.D. degree in Engineering and Spatial Technologies, at Instituto Nacional de Pesquisas Espaciais, in 2013. ADVISORS: Doctor Chen Ying An and Doctor Jerônimo dos Santos Travelho KEYWORDS: Microgravity, Alloys solidification, Eutectic alloys, Heart transfer, Sounding rockets, Drop tube. ABSTRACT: This work aims to study the influence of gravity on the solidification using the technique of conventional and inverse vertical directional solidification (BridgmanStockbarger method), the effect of microgravity on solidification in sounding rocket (VSB-30) and the transfer heat by radiation and conduction on droplets obtained by solidification in the Associate Laboratory of Sensors and Materials of Coordination of Spatial Technologies of Brazilian Space Research Institute (LAS/CTE/INPE) 3 m drop tube. To this purpose, it was used the PbSn eutectic alloy Pb38.1Sn61.9 wt. %. The samples were analyzed by densitometry, scanning electron microscopy (SEM) and energy dispersive X-ray (EDS). The results show that the formation of dendritic structures is related to the presence of convective flows, which occur primarily in the solidification in terrestrial gravity, and that there is no dendrite formation in microgravity and the solute distribution profile is constant along the entire sample. A model for heat transfer by conduction is developed for droplets in free fall consistent with the experimental finds, which show that the greater the initial velocity of the droplets the smallest the time of solidification.

NATIONAL INNOVATION PROJECTS: BRAZILIAN SPACE SECTOR PRACTICES Mariana de Freitas Dewes Universidade Federal do Rio Grande do Sul; Porto Alegre/RS – Brazil; mfdewes@ea.ufrgs.br Thesis submitted for Ph.D. degree in Administration at Universidade Federal do Rio Grande do Sul, in 2012. ADVISOR: Doctor Antonio Domingos Padula

KEYWORDS: Space industry, Technological development, Cooperation, Public-private partnership, Public contracts, Industrial property, National projects for innovation. ABSTRACT: In markets dominated by government purchases, investment in innovation depends mainly on public subsidies whose success in application is related to a productive arrangement of the institutions involved. In this work, we investigated and analyzed practices and mechanisms associated with innovation in state coordinated projects. The research was carried out using a case study method, having been conducted in two phases: exploratory and field investigating the Brazilian space sector. Institutions and companies participating in the space sector were analyzed. The studied companies develop and manufacture satellite subsystems. In the governmental sphere, official documents related to the space sector and to the national system of innovation were examined. The different elements mapped were laws and regulations, economic subsidy, programs and policies, financial resources, labor, contracts and developed products, and intellectual property. These were studied in light of theories of institutions, national and sector systems of innovation, Sabato’s triangle, triple helix, open innovation and public procurement, being characterized as dynamic determinants that contribute to the improvement of the innovation generation process. The results of the present paper include: characterization of organizations which were in the Brazilian space sector and of contracts signed between public institutions and companies; analysis of the legal framework pertaining to innovation, confronting it with organizational practice; and identification of relevant elements in the governance and innovation mechanisms in the space sector. In particular, factors that promote and inhibit innovation were identified in the governance structure of the Brazilian satellite program, based on theoretical presuppositions that condition innovation. In the theoretical field, this work aimed at understanding forms of government, i.e. industry interaction in the context of fostering innovation through structuring an analytical tool for analyzing national projects for innovation. Future applications of this analytical framework may be in studying for other productive sectors, besides high technology areas, which may be dependent of the interaction with the State.

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ADHOC REFEREES Besides the participation of Editorial Board, the Journal of Aerospace Technology and Management had a collaboration of specialists as reviewers to evaluate the manuscripts. To them, the JATM thanks for the contribution in Vol. 5 (2013).

Adilson Cleomenes Rocha Instituto de Controle do Espaço Aéreo São José dos Campos/SP – Brazil

Arcanjo Lenzi Universidade Federal de Santa Catarina Florianópolis/SC – Brazil

Adilson Marques da Cunha Instituto Tecnológico de Aeronáutica São José dos Campos/SP – Brazil

Ariovaldo Felix Palmerio Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

Adilson Walter Chinatto Jr Universidade Estadual de Campinas Campinas/SP – Brazil

Bento Silva de Mattos Instituto Tecnológico de Aeronáutica São José dos Campos/SP – Brazil

Alexandre Kupka da Silva Universidade Federal de Santa Catarina Florianópolis/SC – Brazil

Breno de Moura Castro Embraer São José dos Campos/SP – Brazil

Alexandre Nogueira Barbosa Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

Carlos Alberto Rocha Pimentel Universidade Federal do ABC Santo André/SP – Brazil

Amaury Caruzzo Instituto Tecnológico de Aeronáutica São José dos Campos/SP – Brazil

Carlos Henrique Scuracchio Universidade Federal do ABC Santo André/SP – Brazil

Amílcar Porto Pimenta Instituto Tecnológico de Aeronáutica São José dos Campos/SP – Brazil

Cayo Prado Fernandes Francisco Universidade Federal do ABC Santo André/SP – Brazil

Amir Antonio M Oliveira Universidade Federal de Santa Catarina Florianópolis/SC – Brazil

Clive Dyer QinetiQ Farnborough – United Kingdon

Anderson Cattelan Zigiotto Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brasil

Cleber de Souza Correa Instituto de Controle do Espaço Aéreo São José dos Campos/SP – Brazil

André Felipe Simões Universidade de São Paulo São Paulo/SP – Brazil

Daniel Vila Instituto Nacional de Pesquisa Espaciais Cachoeira Paulista/SP – Brazil

André Luis da Silva Universidade Federal do ABC Santo André/SP – Brazil

Danielle Costa Morais Universidade Federal de Pernambuco Recife/PE – Brazil

Andre Plabo Lopez Barbero Universidade Federal Fluminense Niterói/RJ – Brazil

David Merodio Codinachs European Space Research and Technology Centre Noordwijk – Netherlands

Antônio Delson C Jesus Universidade Estadual de Feira de Santana Feira de Santana/BA – Brazil

Denise Barcza Stocler Pinto Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

Antônio Fábio Carvalho da Silva Universidade Federal de Santa Catarina Florianópolis/SC – Brazil

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

Antônio Gil Vicente de Brun Universidade Federal do ABC Santo André/SP – Brazil

Élcio Jerônimo de Oliveira Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.467-469, Oct.-Dec., 2013

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Adhoc Referees – 2013

Elizabete Yoshie Kawachi Instituto Tecnológico de Aeronáutica São José dos Campos/SP – Brazil

Israel da Silva Rego Universidade Federal do ABC Santo André/SP – Brazil

Enda Bigarella Embraer São José dos Campos/SP – Brazil

Jefferson J Vilela Centro de Desenvolvimento da Tecnologia Nuclear Belo Horizontre/MG – Brazil

Fariborz Motallebi University of London London – United Kingdon

Jesse J Sabatini U.S. ARMY RDECOM Picatinny Arsenal/NJ – USA

Fernando Madeira Universidade Federal do ABC Santo André/SP – Brazil

Joakin Hagvall Sjöland&Thyselius Stockholm – Sweden

Flávio D. Marques Escola de Engenharia de São Carlos São Carlos/SP – Brazil

José Luis Gomes da Silva Universidade de Taubaté Taubaté/SP – Brazil

Francis J Merceret National Aeronautics And Space Administration Titusville/FL – USA

José Atilio Fritz Rocco Instituto Tecnológico de Aeronáutica São José dos Campos/SP – Brazil

Francisco Arakaki Embraer São José dos Campos/SP – Brazil

José Carlos Góis Universidade de Coimbra Coimbra – Portugal

Frank J.Schmidlin National Aeronautics And Space Administration Wallops/VA – USA

José Maria Brabo Fundação Cearense de Meteorologia e Recursos Hídricos Fortaleza/CE – Brazil

Gilson da Silva Instituto Nacional da Propriedade Industrial Rio de Janeiro/RJ – Brazil

Kimiya Komurasaki University of Tokyo Tokyo – Japan

Grahan W Polson Defence Science and Technology Laboratory Fort Halstead – United Kingdom

Kumiko Koibuchi Sakane Universidade do Vale do Paraíba São José dos Campos/SP – Brazil

Hans – Albert Eckel German Aerospace Center Stuttgart – Germany

Lilia M Guerrini Universidade Federal do Estado de São Paulo São José dos Campos/SP – Brasil

Hélcio Francisco Villa Nova Universidade Federal de Itajubá Itajubá/MG – Brazil

Luciano Kioki Araki Universidade Federal do Paraná Curitiba/PR – Brasil

Helder Costa Universidade Federal Fluminense Niterói/RJ – Brazil

Luciene Dias Villar Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

Hélio Koiti Kuga Instituto Nacional de Pesquisa Espaciais São José dos Campos/SP – Brazil

Márcio da Silveira Luz Departamento de Ciência e Tecnologia Aeroespacial São José dos Campos/SP – Brasil

Henrique Dezani Faculdade de Tecnologia de São José do Rio Preto São José do Rio Preto/SP – Brazil

Márcio Teixera de Mendonça Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

Hilton Cleber Pietrobom Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

Maria José Pontes Universidade Federal do Espírito Santo Vitoria/ES – Brasil

Humberto Araújo Machado Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

Maurício Donadon Instituto Tecnológico de Aeronáutica São José dos Campos/SP – Brazil

Humberto Xavier de Araújo Universidade Federal da Bahia Salvador/BA – Brazil

Mário Gustavo Klaus Oliveira Universidade Federal do Rio Grande do Sul Porto Alegre/RS – Brazil

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.467-469, Oct.-Dec., 2013

469 Monique van Hulst TNO Rijswijk – Nederlands

Rosa Maria Rocha Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

Olímpio Achilles de Faria Mello Embraer São José dos Campos/SP – Brazil

Sandro da Silva Fernandes Instituto Tecnológico de Aeronáutica São José dos Campos/SP – Brazil

Ove Dullum Norwegian Defence Research Establishment Kjeller – Norway

Selma S.S. Melnikoff Universidade de São Paulo São Paulo/SP – Brazil

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

Silvana Navarro Cassu Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

Paulo Giacomo Vilani Instituto Nacional de Pesquisa Espaciais São José dos Campos/SP – Brazil

Sonia Thiboutot Defence Research and Development Canada Ottawa – Canadá

Pedro Carlos de Oliveira Escola de Engenharia de Lorena Lorena/SP – Brazil

Stanilav Moshkalev Universidade Estadual de Campinas Campinas/SP – Brazil

Pedro Paiva Brito Pontifícia Universidade Católica de Minas Gerais Belo Horizonte/MG – Brazil

Suzana F Dantas Daher Universidade Federal de Pernambuco Recife/PE – Brazil

Rafael Krummenauer Universidade Estadual de Campinas Campinas/SP – Brazil

Ulrich Teipel Technische Hochschule Nürnberg Georg Simon Ohm Nürnberg – Germany

Rafael Santos Mendes Universidade Estadual de Campinas Campinas/SP – Brazil

João Marcelo Vedovoto Universidade Federal de Uberlândia Uberlândia/MG – Brazil

Renato Semmler Instituto de Pesquisas Energéticas e Nucleares São Paulo/SP – Brazil

Vera Lucia Lourenço Instituto de Aeronáutica e Espaço São José dos Campos/SP – Brazil

Rita de Cássia M Salles Faculdade de Tecnologia de São José dos Campos São José dos Campos/SP – Brazil

Volnei Tita Escola de Engenharia de São Carlos São Carlos/SP – Brazil

Rita de Cássia Silva Universidade de Brasília Brasília/DF – Brazil

Willian Melo Silva Pontifícia Universidade Católica de Minas Gerais Belo Horizonte/MG – Brazil

Roberto Fernando da Fonseca Universidade Federal de Alagoas Maceió/AL – Brazil

Wilson Santos Instituto Nacional de Pesquisa Espaciais São José dos Campos/SP – Brazil

Roberto Lopes Instituto Nacional de Pesquisa Espaciais São José dos Campos/SP – Brazil

Willian Roberto Wolf Universidade Estadual de Campinas Campinas/SP – Brazil

Roberto Mendes Finzi Neto Universidade Federal de Uberlândia Uberlândia/MG – Brazil

Yan Hong University of Hong Kong Kowloon – Hong Kong

Rogério Sales Gonçalves Universidade Federal de Uberlândia Uberlândia/MG – Brazil

Yoshio Kawano Universidade de São Paulo São Paulo/SP – Brazil

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INSTRUCTIONS TO AUTHORS (Revised in March, 2013)

SCOPE AND EDITORIAL POLICY

PAPER SUBMISSION

The Journal of Aerospace Technology and Management (JATM) is the official publication of the Departamento de Ciência e Tecnologia Aeroespacial (DCTA), 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:

The manuscript should be digitalized using a Microsoft Word (.doc) software program and submitted electronically in English. See the instructions at http://www.jatm.com.br/ ojs/index.php/jatm/about/submissions#onlineSubmissions. If there is any conflict of interest regarding the evaluation of the manuscript, the author must send a declaration indicating the reasons so that the review process occurs fairly. After submitting the manuscript, the corresponding author will receive an e-mail with the Term of Copyright Transfer, in which the author agrees to transfer copyrights to the DCTA in case of acceptance for publication, thus being forbidden any means of reproduction (printed or electronic) without previous authorization of the Editor in Chief. If the reproduction is allowed, it is mandatory to mention the JATM. The author also declares that the manuscript is an original paper, its content is not being considered for publication in other periodicals and that all co-authors participated satisfactorily in the paper elaboration as to make public the responsibility for its content. The declaration must be printed, signed by the main author, and sent back by mailing to the following address: Instituto de Aeronáutica e Espaço/ATTN: Helena Prado/ Praça Marechal Eduardo Gomes, 50 – Vila das Acácias – CEP: 12228-901 – São José dos Campos/SP, Brazil. Papers already presented at conferences will be accepted if they were not published in complete form in the Annals of the conference or if they are extended with additional results or new findings. These articles will be evaluated as the others. Articles from guest authors will be published after approval of one specialist associate editor. The JATM does not publish translated articles from other journals.

• Acoustics • Aerodynamics • Aerospace Meteorology • Applied Computation • Astrodynamics • Ceramic Materials • Circuitry • Composites • Computational Fluid Dynamics • Defense Systems • Eletromagnetic Compatibility • Energetic Materials • Fluid Dynamics and Turbulence • Guidance Navigation and Control • Management Systems • Metallic Materials • Photonics • Polymeric Materials • Processing of Aerospace Materials • Propulsion and Combustion • Radars and Tracking Systems • Robotics and Automation

PEER REVIEW

• Structures • Synthesis and Characterization of Aerospace Materials • Thermal Sciences • Vibration and Structural Dynamics.

Manuscripts will be reviewed by at least two expert consultants, members of the Editorial Committee or external evaluators (ad hoc referees) in double blind peer review mode,

J. Aerosp. Technol. Manag., São José dos Campos, Vol.5, No 4, pp.470-472, Oct.-Dec., 2013

ensuring complete anonymity. In case of disagreement on the results of the evaluation, the manuscript will be forwarded to a third reviewer, and it will be accepted for publication only if two approvals are received. The evaluators can accept the manuscript in the form it was submitted, they can reject it or request revisions. The manuscript that requires revision will be sent to the author that is supposed to submit a new version and, in the case the author does not agree with the suggestions, it is necessary to send a “letter to editor”, explaining the reasons. The Editor will approve after verifying in the new version the adherence to the reviewers’ suggestions or will send to another evaluation round if the changes have not been sufficiently addressed. Accepted manuscripts can be edited to comply with the format of the journal, remove redundancies, and improve clarity and understanding without altering meaning. Authors are also strongly advised to use abbreviations sparingly whenever possible to avoid jargon and improve the readability of the manuscript. All abbreviations must be defined the first time that they are used. The edited text will be presented to authors for approval.

MANUSCRIPT CATEGORIES Editorial: Any researcher may write the editorial on the invitation of the Editor in Chief. Editorials should cover broad aspects of Aerospace Technology. Such manuscripts are not submitted to peer review. Review articles: These should cover subjects that are relevant to the scope of the journal. Authors should bear in mind that they are expected to have expertise in the reviewed field. The article may be of any length required for the concise presentation of the subject. Original papers: These articles should report results of the scientific research. The article may be of any length required for the concise presentation and discussion of the data, but succinct papers are favored in terms of impact as well as in readability. Communications: They should report previous results of ongoing research and should not exceed eight pages. Thesis abstracts: The journal welcomes recent Masters and PhD thesis abstracts for publication. Such contribution will not be submitted to peer review.

MANUSCRIPT STRUCTURE Whenever is possible, articles should include the following subsections, however articles from some areas should follow their usual format. Title and names of authors: The title should not contain abbreviations. All authors should be identified with full name, e-mail, institution to which they are related, city, state, and country. One of them should be indicated as the author for correspondence and his/her full address is required. Abstract: They are limited to 250 words and structured into objectives, methods, results, and conclusions. Citations or abbreviations (except internationally recognized abbreviations, such as weights, measures, and physical or chemical ones) are not permitted. Keywords: Three to six items that should be based on NASA Thesaurus volume 2 – Access Vocabulary. Introduction: It should set the purpose of the study, providing a brief summary (not a review) of previous relevant studies, and stating the new advances in the current investigation. The introduction should not include data or conclusions from the work being reported. A final sentence summarizing the novel finding to be presented is permissible. Methodology: The authors are free to use any structure in this section to fit the objectives of the work, they could also rename it (e.g. Numerical analysis, Case study, and so on), and in some cases it may be advisable to omit it. Clear and sufficient information to permit the study to be repeated by others should be briefly given. Standard techniques need only to be referenced. Previously published methods may be briefly described following the reference. Results: This section should be a concise account of the new information that was discovered, with the least personal judgment. Do not repeat in text all the data in the tables and illustrations, but briefly describe what these data comprise. Discussion: The discussion should include the significance of the new information and relevance of the new findings in light of existing knowledge. Only unavoidable citations should be included. Citations to review articles are not encouraged in this section. In some cases, it may be advisable to merge with the previous section (“Results and Discussion”). Acknowledgements: This section should be short, concise, and restricted to acknowledgements that are necessary. The financial support received for the elaboration of the manuscript must be declared in this item.

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References: Acceptable references include journal articles, numbered papers, books, and submitted articles, if the journal is identified. References must be restricted to directly relevant published works, papers, or abstracts that have been accepted for publication. References from private communications, dissertations, thesis, published conference proceedings, and preprints from conferences should be avoided. Self-citation should be limited to a minimum. Authors are responsible for the accuracy and completeness of their references. References in text: The references should be mentioned in the text by giving the last name of the author(s) and the year of publication. Either use “Recent work (Smith and Farias, 1997)” or “Recently Smith and Farias (1997)”. With three or more names, use the form “Smith et al. (1997)”. If two or more references have the same identification, distinguish them by appending “a”, “b”, etc., to the year of publication. Standards should be cited in text by the acronym of entity followed by the number, and they do not need to appear in the reference list. Reference list: References should be listed in alphabetical order, according to the last name of the first author, at the end of the article. Only citations that appear in the text should be referenced. Unpublished papers, unless accepted for publication, should not be cited. Work that is accepted for publication should be referred to as “In press”. It is recommended that each reference contains the digital object identifier number (DOI). References retrieved from the Internet should be cited by the last name of the author(s) and the year of publication, or n.d., if not available, followed by the date of access. Some examples of references are as the following ones: 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.2009.0102265272 Paek, 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.pdf Silva, 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. Tables: Tables should be constructed using the table feature in the word processor or using a spreadsheet program, such as Microsoft Excel. They should be numbered in order of appearance in the text, using Arabic numerals. Each table should have a title and an explanatory legend, if necessary. All tables must be referenced and mentioned in the text as “Table” and succinctly described in the text. Under no circumstances should a table repeat data that are presented in an illustration. Statistical measures of variation (i.e., standard deviation or standard error) should be identified, and decimal places in tabular data should be restricted to those with mathematical and statistical significance. Authors should take notice of the limitations set by the size and layout of the journal. Therefore, large tables should be avoided. Figures: All illustrations, line graphs, charts, schemes, photographs, and graphs should be referred as “Figure” and submitted with good definition. Number figures consecutively using Arabic numerals in order of appearance. References should be made in the text to each figure using the abbreviated form “Fig.”, except if they are mentioned in the beginning of the sentences. Captions should be descriptive and should allow the examination of the figures, without reference to text. The size of the figures (including frame) should be 8 cm (one column) or 17 cm (two columns) wide, with maximal height smaller than 22 cm. Equations: Type them on individual lines, identifying them by Arabic numerals enclosed in parenthesis. References should be made in the text to each equation using the abbreviated form “Eq.”, except in the beginning of the sentences, where the form “Equation” should be used.

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