Vol. 6 N.3 - Journal of Aerospace Technology and Management by Journal of Aerospace Technology and Management

Journal of Aerospace Technology and Management

JOURNAL OF

AEROSPACE TECHNOLOGY AND MANAGEMENT Vol. 6 N. 3 Jul./Sep. 2014 ISSN 1984-9648 ISSN 2175-9146 (online)

www.jatm.com.br

V. 6, n. 3, Jul./Sep., 2014

Journal of Aerospace Technology and Management

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

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

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

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

JATM IS SUPPORTED BY:

Journal of Aerospace Technology and Management Vol. 6, n. 3 (Jul./Sep. 2014) – São José dos Campos: Zeppelini Editorial, 2014 Quartely issued Aerospace sciences Technologies Aerospace engineering CDU: 629.73

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

ISSN 1984-9648 ISSN 2175-9146 (online)

JOURNAL OF AEROSPACE TECHNOLOGY AND MANAGEMENT Vol. 6 No. 3 - Jul./Sep. 2014 EDITORS IN CHIEF

EXECUTIVE EDITOR

ASSISTANT EDITOR

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

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

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

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

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

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

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

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

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

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

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

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

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

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

SCIENTIFIC COUNCIL

ASSOCIATE EDITORS ACOUSTICS

APPLIED COMPUTATION

CERAMIC MATERIALS

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

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

CIRCUITRY

Bert Pluymers Katholieke Universiteit Leuven Leuven – Belgium

AERODYNAMICS

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

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

Leandro Baroni Universidade Federal do ABC Santo André/SP – Brazil

ASTRODYNAMICS

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

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

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

Anna Guerman Universidade da Beira Interior Covilhã – Portugal

COMPUTATIONAL FLUID DYNAMICS

Vivian Martins Gomes Faculdade de Engenharia de Guaratinguetá Guaratinguetá/SP – Brazil Josep J. Masdemont Universitat Politecnica de Catalunya Barcelona – Spain

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

COMPOSITES

METALLIC MATERIALS

ROBOTICS AND AUTOMATION

DEFENSE SYSTEMS

PHOTONICS

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

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

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

ENERGETIC MATERIALS

POLIMERIC MATERIALS

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

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

José Leandro Andrade Campos Universidade de Coimbra Coimbra – Portugal

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

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

FLUID DYNAMICS AND TURBULENCE

Vassilis Theofilis Universidad Politécnica de Madrid Madrid – Spain

GUIDANCE, NAVIGATION AND CONTROL Arun Misra McGill University Montreal – Canada

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

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

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

Kyriakos I. Kourousis University of Limerick Limerick - Ireland

PROCESSING OF AEROSPACE MATERIALS Alexandre Queiroz Bracarense Universidade Federal de Minas Gerais Belo Horizonte/MG – Brazil

PROPULSION AND COMBUSTION

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

RADARS AND TRACKING SYSTEMS Cynthia C.M. Junqueira Instituto de Aeronáutica e Espaço São José de Campos/SP – Brazil

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

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

STRUCTURES

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

SYNTHESIS AND CHARACTERIZATION OF AEROSPACE MATERIALS

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

THERMAL SCIENCES

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

VIBRATION AND STRUCTURAL DYNAMICS Carlos Cesnik University of Michigan Ann Arbor/MI – USA

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

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

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

Rosely A. Montoro 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

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

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

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, 2014

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

CONTENTS EDITORIAL 217 ABEC Brasil and JATM in the Promotion of Ethics in Scientific Publishing Silvia Regina Galleti, Sigmar de Mello Rode ORIGINAL PAPERS 219 The Compact Irradiator Modulus Designed for DNA Repair and Mutagenesis Studies in ISS Microgravity Environment Using UVA Emitted by Light-Emitting Diodes Marcelo Sampaio, Heitor Evangelista, Roberto d’Amore, Nasser Ribeiro Asad, Lídia Maria Buarque de Oliveira Asad, Adriano Caldeira de Araújo, Ana Paula Hagge Brasil, Nelson Veissid, Valeri Vlassov, Alessandra Pacini, Monique Thérèze Schulz Fontoura 231 High Power Laser Weapons and Operational Implications Nelson Alex Roso, Romero da Costa Moreira, José Edimar Barbosa Oliveira 237 An Approach to Outlier Detection and Smoothing Applied to a Trajectography Radar Data Aguinaldo Bezerra Batista Júnior, Paulo Sérgio da Motta Pires 249 Direct Numerical Simulation of the Onset of Vortex Shedding for Blunt Elongated Bodies Marcos Aurélio Ortega, Roberto da Mota Girardi, Jorge Hugo Silvestrini 267 Study of Conservation on Implicit Techniques for Unstructured Finite Volume Navier-Stokes Solvers Carlos Junqueira-Junior, Leonardo Costa Scalabrin, Edson Basso, João Luiz F. Azevedo 281 A Numerical Study on Smart Material Selection for Flapped and Twisted Morphing Wing Configurations Mauricio Vicente Donadon, Lorenzo Iannucci 291 Small Solid Propellant Launch Vehicle Mixed Design Optimization Approach Fredy Marcell Villanueva, He Linshu, Xu Dajun 301 Relative Motion Guidance, Navigation and Control for Autonomous Orbital Rendezvous Mohamed Okasha, Brett Newman 319 Location Issues of Thin Shell Parts in the Reconfigurable Fixture for Trimming Operation Hu Fuwen 333 Extending the Student Qualitative Undertaking Involvement Risk Model Jeremy Straub INSTRUCTIONS TO AUTHORS 353 Instructions to Authors

doi: 10.5028/jatm.v6i3.414

EDITORIAL ABEC Brasil and JATM in the Promotion of Ethics in Scientific Publishing Silvia Regina Galleti1, Sigmar de Mello Rode2

he Associação Brasileira de Editores Científicos, ABEC Brasil, is a nationwide and non-profitable civil society. Founded in 1985, it brings together people and companies in an attempt to develop and enhance the publishing of technical and scientific journals; to improve communication and dissemination of information; to keep up with the exchanging of ideas, the debating of issues and the defense of common interests. Since its founding, the ABEC Brasil has always had as a goal to ensure publishing standards of form and content of technical and scientific publications in the country; to keep in touch with and to assist correlated institutions and societies in the country and abroad; to regularly disseminate issues of technical and scientific editorial interest; to promote conferences, seminars and courses on its objectives; to represent the interest of national scientific journals, while always keeping an ethical standard of quality and science. In order to emphasize the role of ABEC Brasil in fulfilling the goals mentioned above, the assistance given to editors for the creation and maintenance of scientific journals is mentioned. That was how the Journal of Aerospace Technology and Management, JATM, was created, in 2009. The group of idealizer creators of this renowned journal on the aerospace field had the initiative of seeking for ABEC Brasil´s guidance on the founding of this journal. ABEC Brasil was the one that steered decision making towards the creation of a journal published exclusively in English, intended for the dissemination of research results of both Brazilian and foreign professionals, granting a international character to the publication since its creation. The fact that JATM publishes solely in English is already a facilitator in its internationalization process (Morais et al., 2012). Indexing this journal in recognized

data bases will cause this journal to consolidate itself as for its main objective, which is to be internationally recognized as a vehicle of scientific dissemination in the aerospace field. Its editors do never get tired of searching for a larger number of indexes for JATM. Already indexed by SCOPUS, DOAJ, LATINDEX and REDALYC, among others, the JATM currently attempts being indexed by SciELO. The seriousness and professionalism, with which its editorial board conducts its activities, as well as the quality of the works published in the journal, are factors which will promote, undoubtedly, the joining of JATM in this electronic library. Still within ABEC Brasil´s objectives, there is the promoting of meeting between its associates and interested parties in general, through courses, workshops and gatherings. These events are intended to provide their audience with knowledge, improvement and debates on what is latest in scientific editorial. The conducting of these events is given, preferably, in association with institutions and/or scientific journals. In order to improve the knowledge of its editors, as well as to maintain JATM aligned with international tendencies, its editors are always present in events promoted by ABEC Brasil. In addition to that, the maturity and professionalism achieved by this journal’s editorial board may be demonstrated by the initiative of JATM on promoting in the premises of the Departamento de Ciência e Tecnologia Aeroespacial, DCTA, in São José dos Campos, the XXII Course of Scientific Publishing in association with ABEC Brasil, have taken place on the 15th to the 17th of last May. The organization of this event proved that both ABEC Brasil and JATM are in sync as for the current concerns regarding scientific publishing. The choice of the central theme of this course was unanimous: “Building an ethical publication of international quality”. One must

1.Instituto Biológico – São Paulo/SP – Brazil 2.Universidade Estadual Paulista Júlio Mesquita Filho – São José dos Campos/SP – Brazil Author for correspondence: Silvia Regina Galleti – Instituto Biológico – Avenida Conselheiro Rodrigues Alves, 1.252 – Vila Mariana – CEP 04014-002 – São Paulo/SP – Brazil | Email: galleti@biologico.sp.gov.br

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.217-218, Jul.-Sep., 2014

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Galleti, S.R. and Rode, S. M.

remember that, the making of an ethical publication depends, fundamentally, on two “actors”: the author(s) and the editor. If the work submitted to publication has not been ethically conducted, we will have a loss not only on its dissemination, but also on its reproduction and credibility. We may say science, as a whole, will be at loss. On the other hand, it will have no use to have an ethically conducted and written work if, when submitting it to a journal, one comes across lack of ethics in conducting the editorial committee. The dissemination of quality science must be ethical in all aspects. There must always be integrity in research and ethics in the publication (Rode, 2011). This is an undisputable matter. The ABEC Brasil recognizes the importance of discussing this theme. This is evidenced in the programming of its events, where ethics and plagiarism are recurring themes. National

journals must always demonstrate the ethical integrity by which they conduct their works. This will ensure credibility to both authors and readers and, as a result, grant visibility to the journal (Rode and Galleti-Queiroz, 2013). JATM proves that right. Attentive to global trends, it implemented the use of a system to detect plagiarism in papers submitted to publishing. Also, as trying to ensure the ethical evaluation of the works, the JATM instructs its reviewers into providing constructive evaluations. The international projection of a brazilian journal is absolutely possible. It is a matter of professionalism, integrity and having a qualified staff. We lay here the example of JATM: a young, but yet determined, journal, under ethical conduction, seeking for the constant update and improvement of its editorial board.

REFERENCES Morais, A. M. F., Moreira, J.P., and Avelar, A. C., 2012, “Journal of Aerospace Technology and Management: conquistas e desafios para internacionalização”, Retrieved in September 15th, 2014, from http:// ocs.abecbrasil.org.br/Index.php/WEC/VIIWEC/paper/viewFile/23/17 Rode, S. M., 2011, “Integrity in scientific publication”, The Journal of

Venomous Animals and Toxins including Tropical Diseases, Vol. 17, No.1, pp 118. Rode, S. M. and Galleti-Queiroz, S. R., 2013, “Ethical publication providing social benefit: challenges of editors and the ABEC Brasil”, Brazillian Oral Research, Vol. 27, No. 2, pp. 89-90

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doi: 10.5028/jatm.v6i3.350

The Compact Irradiator Modulus Designed for DNA Repair and Mutagenesis Studies in ISS Microgravity Environment Using UVA Emitted by Light-Emitting Diodes Marcelo Sampaio1, Heitor Evangelista2, Roberto d’Amore3, Nasser Ribeiro Asad2, Lídia Maria Buarque de Oliveira Asad2, Adriano Caldeira de Araújo2, Ana Paula Hagge Brasil2, Nelson Veissid1, Valeri Vlassov1, Alessandra Pacini4, Monique Thérèze Schulz Fontoura2

ABSTRACT: This work presents the design and characteristics of a new compact ultraviolet (UV) irradiator used in a biological onboard space flight experiment. The experiment, called DRM, took place in the International Space Station research facility (ISS-13 expedition), during the Centenary Mission (Russian-Brazil) in March-April 2006. The DRM main objective was to correlate the DNA repair mechanism and mutagenesis with microgravity. A compact irradiator apparatus was designed for DRM to allow in situ induced radiation in space. This apparatus, called CIM, uses UV-A Light-emitting diodes (LEDs) with 375 nm wavelength as molecular lesions inducers on four bacterial E. coli strains. The manned space mission restrictions were focused on during the CIM main parts design. The ultraviolet dosimetry is also described in this document as DRM experiment results and the CIM operational data are reported to certify the CIM design and DRM protocol compatibility in space application. KEYWORDS: Ultraviolet radiation, Microgravity, ISS.

INTRODUCTION During the last decades, experiments using different bacteria (e.g.: Bacillus subtilis HA 101, Echerichia coli B/r, Echerichia coli PQ37, Echerichia coli 3 cp, Deinococcus radiodurans type R, Deinococcus radiodurans type R+rec30) have been conducted and their preliminary conclusions point to no significant statistical difference existing in DNA repair mechanism responses to equivalent radiation doses under microgravity or terrestrial conditions. These experiments have been developed under an approximately common basic protocol: essentially, they have used facilities of irradiation on Earth (e.g.: employing X-ray, gamma ray or UV-C radiation) to achieve a large spectrum of dose responses, followed by cooling and transportation to the microgravity environment, where the samples were incubated under controlled temperatures, ranging normally from + 20°C to + 37°C, depending on the experiment type and organism employed. In these conditions, the biological systems are available to repair damages in the DNA from naturally occurring and artificially induced radiation. After a period of time in space, they were cooled again and sent back to the Earth’s surface, where their results were compared with ground controls. One exception was the in situ space irradiation experiment, conducted by Pross et al. (2000) during the Shuttle Atlantis flight STS-84. The experiment employed a 63Ni beta source to irradiate diploid mutant rad54-3 of the yeast Saccharomyces

1.Instituto Nacional de Pesquisas Espaciais – São José dos Campos/SP – Brazil 2.Universidade do Estado do Rio de Janeiro – Rio de Janeiro/RJ – Brazil 3.Instituto Tecnológico de Aeronáutica – São José dos Campos/SP – Brazil 4.Universidade do Vale do Paraíba – São José dos Campos/SP – Brazil. Author for correspondence: Marcelo Sampaio | Instituto Nacional de Pesquisas Espaciais | Avenida dos Astronautas 1758, São José dos Campos/SP | CEP: 12.227-010 – Brazil | Email: marcelo.sampaio@dge.inpe.br Received: 03/24/2014 | Accepted: 06/11/2014

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Sampaio, M., Evangelista, H., d’Amore, R., Asad, N.R., Asad, L.M.B.O., Araújo, A.C., Brasil, A.P.H., Veissid, N., Vlassov, V., Pacini, A. and Fontoura, M.T.S.

cerevisiae. They also concluded that no significant difference was observed for either induction or repair but due to the type of radiation used in the experiment, their conclusion was restricted to double-strand breaks. Table 1 shows a brief compilation of space flight experimental conditions employing studies of DNA repair and mutagenesis. An alternative method to achieve higher dose levels over the biological material in space flights, without using artificial sources aboard, is the external exposition of the biological experiment, as performed during the EXOBIOLOGIE experiment that belonged to the French PERSEUS mission (1999) onboard the MIR station (Rettberg et al., 2002). The authors investigated the ability of microorganisms (spores of the B. subtilis DNA repair, wild-type strain (HA 101X) and a DNA-repair deficient mutant strain (TKJ 6312, uvr.4Zf.Js pl-1)) to survive high doses of extraterrestrial solar UV alone or in combination with other space parameters (e.g. vacuum, microgravity, and cosmic radiation). They reported the protective effects over microorganisms of inorganic substances like artificial or real meteorites (in a powdered state). Other microbiological experiment compilations which discuss ionized radiation conducted in Low Earth Orbit (LEO) can also be found at Olsson-Francis and Cockell (2010). If, on the one hand, there is acceptable evidence that weightlessness alters several cellular functions and effects signaling pathways associated to cell proliferation, differentiation and death (Manti, 2006), its effective influence on DNA repair still raises controversies, despite an apparent trend to invariance between ground and space results. Kiefer and Pross (1999) concluded that the radiation repair is not impaired by the space environment and, based on the premise that the repair mechanism appears to be evolutionarily conserved, they further argued that it also holds true for mammalian cells. Nevertheless, direct comparison of onboard and terrestrial experimental results still presents important constraints: the interaction of the total spectra of cosmic ionizing radiation with an organism is not fully simulated on the ground, especially in the case of HZE particles, the ones with Z > 2. They are ~ 1% of the total galactic cosmic radiation with energies high enough to penetrate at least 1 mm of the spacecraft walls. HZE particles may represent up to 50% of the effective dose in space (Ottolenghi et al., 2001). Their contribution of the radiation derived from the by-products originating from collisions of the cosmic rays primary beams with the nuclei of the materials which compose the spacecraft shielding. In the history of radiobiological experiments in space, the use of onboard radiation sources occurred between the Gemini 3

Mission and the Spacelab D1 Mission, the results of which were reported between 1967 and 1986 (Kiefer and Pross, 1999). In this period, the onboard experiments used gamma ray radiation point sources that produced doses varying from 60 to 1,600 Gy, and beta radiation of 32P and 85S”. These experiments focused on alterations in the chromosomal or hematopoietic tissue, formed by fibers and cell types that support the blood tissue-forming cells (stem cells). Nevertheless, restrictions were imposed on the use of these manufactured ionizing radiation sources, which may generate high doses and high dose rates onboard during space missions, since their use could result in increased risks to the crew members. The use of ultraviolet lamps also created safety problems due to their toxic vapor contents. More recently, when there has been a surge of techniques that would significantly improve our knowledge of space biology responses to space radiation, through molecular level research, most radiobiological protocols have still been limited to preflight irradiation conditions. A promising alternative to that is the use of unmanned, free-flying nanosatellites, like the SESLO experiment during the Space Environment Organism/Organic Exposure to Orbital Stresses (O/OREOS), mission (Nicholson et al. 2011). The mission was launched to a 650 km Earth orbit, providing a total dose rate in the experiment 15 times higher than observed at the ISS (exposed to microgravity, ionizing radiation, and heavy-ion bombardment due to its high-inclination orbit), using living organisms (B. subtillis wild-type 168 and radiation-sensitive mutant WN1087 strains). In view of the above limitations, we have developed the DRM (DNA Repair Under Microgravity) experiment to observe the biological response at the molecular level due to the combination of naturally occurring cosmic radiation and induced UV-A. We have developed a portable, hermetic UV-A irradiator, based on UV-A LED technology to conduct the DRM experiment, the Compact Irradiator Modulus (CIM). Here the UV-A irradiation was used in order to just enhance the biological response (not because it is significant for space missions) and observe if any synergistic response would occur in the presence of cosmic radiation. The equipment allowed for the study of the DNA repair and mutagenesis of E. coli K12 strains in conditions of irradiation in space (International Space Station-ISS/13th mission), during the Centenary Space Mission (Brazil-USA-Russian) in March-April 2006. Here we describe the CIM apparatus and the DRM experiment procedures and results. We have also characterized the space weather conditions during the experiment.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.219-230, Jul.-Sep., 2014

The Compact Irradiator Modulus Designed for DNA Repair and Mutagenesis Studies in ISS Microgravity Environment Using UVA Emitted by Light-Emitting Diodes

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Table 1. A brief summary of recent radiobiological studies involving DNA repair in bacteria, yeast and human lymphoblastoid during flight missions. Biological system

Type of radiation

Site of irradiation

Dose

Flight payload; Mission

Reference

Saccharomyces cerevisiae

X-ray, 80 kV

Ground

> 140 Gy

STS-42; IML-1

Pross et al., 1994

Bacillus subtilis

UV-C, 254 nm

Ground

> 335 J m-2

STS-65; IML-2

Horneck et al., 1996

Escherichia coli B/r

X-ray, 150 kV

Ground

120 Gy

STS-65; IML-2

Horneck et al., 1996

Escherichia coli PQ37

Gamma ray, 60Co

Ground

300 Gy

STS-65; IML-2

Horneck et al., 1996

Deinococcus radiodurans

Gamma ray, Co

Ground

2-12 kGy

STS -65; IML-2

Kobayashi et al., 1996

Saccharomyces cerevisiae

X-ray, 80 kV

Ground

140 Gy

STS-76; SMM-03

Pross and Kiefer, 1999

Deinococcus radiodurans

Gamma ray, Co

Ground

1-12 kGy

STS 79

Kobayashi et al., 2000

Saccharomyces cerevisiae

Beta ray, 63Ni

Space

Not mentioned

STS 84

Pross et al., 2000

Escherichia coli KY396

X-ray, 150 kV and UV-C, 254 nm

Ground

150 Gy and 2 J m-2

STS-91

Takahashi et al., 2001

Saccharomyces cerevisiae

UV-C, 254 nm

Ground

30-300 J m-2

STS-91

Takahashi et al., 2001

Saccharomyces cerevisiae

X-ray, 150 kV

Ground

100-750 Gy

STS-91

Takahashi et al., 2001

Saccharomyces cerevisiae

X-ray, 150 kV

Ground

100-750 Gy

STS-91

Takahashi et al., 2001

Spores of the Bacillus subtilis wildtype strain (HA 101X) and DNA-repair deficient mutant strain (TKJ 6312, uvrA10 spl-1)

Full extraterrestrial UV spectrum

Space

36.8-48.7 mGy

MIR; EXOBIOLOGIE/ Perseus

Rettberg et al., 2002

Deinococcus radiodurans and Bacillus sp. (PS3D)

Extreme UV, 30.4 nm

Space

6x1016 photons m-2

Terrier Black Brant rocket; SERTS

Saffary et al., 2002

Wild type Saccharomyces cerevisiae strain (ATCC 18824)

Satellite environment radiation / Not provided

Space

Not mentioned

Practice 8 recoverable satellite

Yi et al., 2011

Human lymphoblastoid cell lines: TSCE5 (wtp53 gene status) and WTK1 (mp53 gene status)

Full extraterrestrial radiation spectrum

Space

71.2 mSv

ISS

Takahashi et al., 2011

Space

1.0-1.6 Gy

O/OREOS nanosatellite

Nicholson et al. 2011

Space

54 mSv

ISS

Yatagai et al., 2012

Bacillus subtilis (wild-type Satellite environment 168 and and mutant radiation WN1087) (650 km Earth orbit) Human lymphoblastoid TK6 cells

Full extra-terrestrial radiation spectrum

STS: Space Shuttle Missions.

A specific hardware was designed owing to attend the experiment, the specifications and flight restrictions: weight, dimensions, biological barriers protocols and temperature safety limits. The bacterial samples were isolated in slots wile being

irradiated. Each slot is submitted to the environmental cosmic radiation and induced UV-A radiation generated by an array of 18 UV-A 375 nm LEDs. The use of UV-A LEDs in compact biological experiments presented several advantages: it promotes

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a closer contact of the biological material with the irradiation sources improving the dosimetric model; the geometry of each radiation slot as a small rectangular hermetically closed box resulted in a relatively well distributed dose to the biological material inside them; UV-A LED generates 375 nm irradiation with 10 nm bandwidth, at full spectrum half width. They are more resistant to vibration than conventional lamps. Unlike the previous experiments, the CIM made it possible to irradiate the bacterial strains with a predetermined dose of radiation while in the microgravity of space. Thus, it was possible to carry out both irradiation and cell repair in the same environment without having to manipulate the biological material in the confined environment of the spacecraft. Therefore, it is a new experimental process.

REQUIREMENTS AND TECHNICAL CONSTRAINTS The CIM apparatus was transported to the ISS by the Soyuz TMA-8 spacecraft. Once onboard the ISS, it was operated in the Russian Segment (RS) of the space station before returning to Earth aboard Soyuz TMA-7 on April 9, 2006. The requirements and constraints imposed on biological experiments onboard the space shuttles (Cogoli,1996) are basically the same as those of the ISS/Soyuz. The CIM design takes into account all of the safety restrictions of this manned satellite, the Soyus-TMA transport limitations, and the established conditions for the DRM project (SSP41163, 1999; SSP50094, 2000; SSP50146, 1998). The CIM was powered by a DC power supply of the ISS module power supply system (PSS), with a 28.5 ± 0.5 VDC (3A) nominal voltage. The mass was limited to 3 kg for transport aboard the Soyuz to the ISS and to 1.5 kg for the return. Using the appropriate LINS (Line Impedance Stabilization Network), the conducted electromagnetic emissions were found to be between 10 and 100 Hz, and the radiated electromagnetic emissions emitted during CIM operation were between 10 kHz and 1000 MHz. The integrity of the characteristics and the functioning of the CIM system were checked to comply with the expected pressure, temperature, and humidity constraints of the Soyuz and ISS environments. Several Environmental Compatibility requirements had to be met: touch temperature exposed parts; off-gassing (a quantitative analysis of gases that may be generated by non-metallic materials); biological barriers

(the guarantee of containment of biological material); external characteristics (no sharp edges, corners and shatterable materials are allowed, in order to avoid any injury during microgravity handlings. The fan blades are protected by 3 mm grid holes); and ergonomy (protection against erroneous operations).

THE COMPACT IRRADIATOR MODULUS (CIM) SYSTEM AND OPERATION The total CIM hardware is divided into 4 different modules, as shown in Fig. 1 and Fig. 2: the Irradiator Module, with four slots, loaded with biological material that is irradiated during the DRM experiment; the Fan Module, responsible for maintaining the biological material’s proper temperature during irradiation period; the Memory Module, which stores all relevant data; and the Electronics Module that controls the proper functioning of all the equipment. Only the Irradiator Module and the Memory Module return to Earth. All CIM electronic components are Commercial Off-The-Shelf (COTS), except for the DC/DC converters, the Input EMI filter, connectors and the DC power cable. Irradiator Module (IM): The main body of the IM contains the biological material distributed in four 1 mL square wells called “Irradiated slots,” where the irradiation takes place; four 25 μL “background slots”, where no irradiation occurs; and one fence, designed to contain two thermoluminescent solid state dosimeters (TLD-100 - LiF : Mg,Ti), used to quantify the environmental ionizing radiation. Each one of the 4 irradiated slots has 18 UV-A LEDs (NICHA Co. model NSHU550A UV-A 375 nm) facing its surfaces. The LEDs are distributed over its upper and bottom halves (Fig. 3), containing 9 units on each side. These slots are isolated from the environment by biological barriers that are implemented with silicon and nitrilic rubber o-rings and Scotch Weld® 2216 epoxy adhesive. The Fan Module (FM): When switched on, each UV-A LED dissipated about 35 mW. Therefore, extra thermal power was generated while a total of 72 LEDs were switched on during the experiment. This process was responsible for a temperature increase of the equipment. The FM was designed to be connected to the irradiation module in order to maintain the biological material temperature never above 39°C; in fact, during the irradiation aboard the ISS, it was always maintained

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Fixed at this side

Fan cable

Fan Module

223

Fan Irradiator

Eletronic Module

Upper UV Irradiator cable

Bottom

C Memory Module

ISS Ground

Power Cable Female Connectors Male Connectors

A Fixed at this side

Memory

Power

Irradiator Module

ISS Ground

Irradiator and Fan Grounging

ISS Power bus

Figure 1. CIM block diagram. B A

B D

Upper D

Bottom

UV irradiator cable Fan cable

Figure 2. Compact Irradiator Modulus interconnected. (A) Irradiator (IM); (B) Fan (FM); (C) Connector to attach Memory (MM); and (D) Electronic (EM).

(a)

(b)

Figure 3. (a) Bottom half; (b) Upper half of inner Irradiator Module. (A) One Irradiated slot with the silicon rubber o’ring; (B) One background slot with the silicon rubber o’ring; (C) Thermoluminescent dosimeter fence; (D) nitrilic rubber o’rings. The screws which fasten the two halves are passed through nitrilic o’rings. The picture presents (4x) 9 UVA LEDs facing the upper half and (4x) 9 LEDs facing the bottom half of the irradiated slot. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.219-230, Jul.-Sep., 2014

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below 30°C. Convective cooling was generated by two 60 mm ball bearing type fans (one redundant). Pre-flight temperature controlled tests demonstrated that, while fan cooling was applied, the irradiation slot temperature did not exceed 8°C above room temperature, merely reaching an equilibrium with room temperature. Memory Module (MM): The MM is composed of two serial EEPROM IC memories (one redundant), mounted in a military/high reliability specification connector and encapsulated with a silicone rubber compound. This module is attached to the Electronic Module by a faced external connector (Fig. 2 c). All relevant experiment data is recorded in it. After integration and initialization of the experiment, this module recorded the 8051 firmware status and the following relevant parameters: (1) the temperature within the slots; (2) the temperature of the irradiation module itself; (3) the irradiation status (on/off) and; (4) the voltage values in the memory module. Time resolution of acquisition data was 1 minute. This data was recovered on Earth at the mission’s end. Electronic Module (EM): This module was composed of the 8051 family microcontroller, which commanded both the IM and FM. The power supply used by the fan module was provided by the ISS DC bus. It had two internal DC/DC converters which provided the DC power to all the other modules of the experiment. The ISS safety requirements specify that maximum temperature of any external surface of an experiment device should not exceed 40°C. To meet this requirement, the aluminum case (alloy AL 6063 T6), used in the EM was deeply anodized in order to increase its heat emissivity (to a depth of not less than 0.82). The less dissipating electronics were placed on the bottom-front panel, while the upper part of the irradiator module was loaded with the 5 most dissipating components: two DC/DC converters, two Voltage Regulators, and one EMI Filter (for a total of 7.6 Watts during the irradiation period). UV: UV-A dosimetry was conducted by measuring the spatial distribution of light intensity inside all irradiation slots, since the bacteria will occupy this volume randomly during the experiment. The irradiation conditioning was simulated by filling up the 1 mL slot with the same saline solution used in the flight experiment. Parallel planes within the slot composed by the array of 9 LEDs at the upper surface and the same at the bottom of the Irradiator Module were analyzed. UV-A spatial distribution was obtained experimentally by screening the light intensity horizontally and vertically inside the slot, using a photocell covered by a mask with a 1 mm orifice diameter at

its center. This photocell was previously calibrated by a UV-A sensor (ORIEL 70260 Radiant Power Meter and ORIEL 70282 Photodiode Absorber Head, that provide sensitivity between 3 mW to 300 mW for a 375 nm wavelength lighting source). Vertical resolution of each step of measurement was 0.1 mm and 0.2 mm for the horizontal axis. Figure 4 depicts the configuration of the isopleths of irradiance at 4 different distances from the LED array (upper or bottom part of the irradiation slot). Taking into account both surfaces with 9 LEDs, we obtain each plane’s irradiance distribution. This procedure made it possible to compile a complete description of the UV-A intensity distribution inside the irradiation slot. The statistical analysis of the results is presented in Fig. 5. The median value of this collection is 4.38 mW.cm-2, this value was used as total volume slot irradiance (Sampaio, 2007). Operation and procedures: Aboard the ISS, the astronaut had to follow the unpacking procedures, assemble the Memory and Fan cables; then interconnect the module cables to the EM, and the power cable to the ISS DC bus. Once it was switched on, the EM firmware started a CIM checkup. The Memory and Fan cables were verified and two LEDs on the EM front panel indicated the test condition. After this automatic test, the UV Irradiation was begun by an astronaut command through an EM front panel pushbutton. The IM with FM attached was 154 mm in length, 149 mm wide, and 65 mm high, weighing 1.45 kg. The EM with MM was 149 mm x 130 mm and 100 mm (l,w,h), weighing 1.1 kg. All flight models, including cables, extra fuses and the spare MM, were disposed of in three different Nomex® bags. The overall CIM launch dimensions were 260 mm x 200 mm x 250 mm (l x w x h) and the total mass was 3.08 kg. The return mass was 1.24 kg. During the irradiation period, the maximum CIM consumption was 16.6 W (28 VDC, 0.59 A). After the irradiation period, the equipment was shut down and disabled. The IM and MM were packaged for return to Earth and all other parts were prepared for disposal.

BACTERIAL STRAINS, TRANSPORT, FLIGHT AND EXPERIMENTAL STRATEGY The biological material employed in the experiment was composed of bacterial strains derived from E. coli K 12 with the following DNA repair phenotypes: AB1157 (wild type), AB2463 (as AB1157, but recA13 — deficient in the repair by recombination

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A Irradiance 0.1 3.5 7.0 10.5 14.0 mWcm-2 2 4

8 10 12 14 16 18

Lateral slot (mm) (a)

Lateral slot (mm)

2 4

18 16 14 12 10 8 6 4 2 0

8 10 12 14 16 18

Lateral slot (mm) (b)

D Irradiance 0.1 1.3 2.5 3.8 5.0 mWcm-2

Irradiance 0.1 2.0 4.0 6.0 8.0 mWcm-2

Irradiance 0.1 1.0 2.0 3.0 4.0

Lateral slot (mm)

C 18 16 14 12 10 8 6 4 2 0

18 16 14 12 10 8 6 4 2 0

Lateral slot (mm)

18 16 14 12 10 8 6 4 2 0

225

8 10 12 14 16 18

mWcm-2 0

2 4

Lateral slot (mm) (c)

8 10 12 14 16 18

Lateral slot (mm) (d)

Figure 4. Isopleths plots for layer irradiance distribution inside one slot: horizontal plane at (a) 0.5 mm from LED surface; (b) 1.3 mm; (c) 2.1 mm and (d) 2.9 mm. These layers present partial contribution for one slot surface with 9 LEDs.

8% Median Value 4.38 mWcm-2

Relative frequency

6% 5% 4% 3% 2% 1% 0% 0

Irradiance inside the slot (mW cm-2)

Figure 5. Histogram of Irradiance values inside the irradiated slot. The median value of this collection represents a total slot Irradiance of 4.38 mW.cm-2. J. Aerosp. Technol. Manag., SĂŁo JosĂŠ dos Campos, Vol.6, No 3, pp.219-230, Jul.-Sep., 2014

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and SOS), AB2480 (as AB1157, but uvrA-recA — deficient in nucleotide excision repair, recombination and SOS) and DNA polymerase I deficient strain P3478, a polA1 mutant derivative from E. coli wild-type W3110 strain. They were transported from Brazil (Rio de Janeiro State University) directly to the desert of Baykonur (Kazakhstan). The transport was conducted under room temperature and bacterial strains were fixed in Millipore filters of 47 mm diameter, enclosed in a sterilized plastic holder. In Baykonur, a compact biological laboratory was adapted in an installation of the Russian Flight Center, in order to maintain basic conditions concerning the handling and inoculation of the the biological material in the experimental apparatus. Figure 6 depicts typical experimental setups for preirradiation (ground irradiation) and onboard irradiation (mostly aboard the ISS, MIR, nanosatellites and Shuttle missions) as indicated in Table 1. The basic difference in protocol steps is where the irradiation takes place. In both cases, it is assumed that the initial phases of the biological repair mechanism occur in microgravity. The inoculation of the biological material took place 6 hours before the launch of the Soyus TMA-8 spacecraft. On March 29th, the biological material was boarded onto the Reentry Module and on March 31st, 2006 (02:30 h GMT), Soyus was launched from Baykonur. The experimental apparatus was transferred to the ISS on March 31st and the total period of stay

aboard the ISS was 9 days and 12 hours. The equipment was installed and operated close to the air conditioning outflow on the “PIRS docking compartment”, between the Zarya and Zvezda modules. Indoor temperature at that site was maintained constant, during the 9 days, at ~ +21°C. The apparatus, containing the bacterial strains, returned to Earth on April 9th (GTM: 20:04, April 8th), using the identification “Module of Biological Urgency” and immediately transported to Moscow/ Russia where it was analyzed, in the same compact biological laboratory once installed in Baykonur. A detailed chronology of the experiment is presented in Table 2.

EXTERNAL RADIATION DOSE AND SPACE WEATHER CONDITIONS DURING THE EXPERIMENT Even in low Earth orbits, such as the orbital altitude of the ISS, any biological material is under the influence of Solar Cosmic Rays (SCR) and Galactic Cosmic Rays (GCR), which are formed by particles whose energies vary from 10-1 to 108 MeV. Besides these contributions, trapped particles, especially protons (Reitz et al., 2005), by the Earth’s magnetosphere in the Van

Table 2. Chronology of the experiment during the Centenary Mission (March-April 2006) Date

Time GMT

Experiment chronology

Temperature amplitude

Inoculation of bacterial strains in the irradiator in Baykonur

March, 29th

06:00 h

00:00 h

~ +25oC

Pre-flight tests

March, 29th

06:30 h

00:30 h

~ +21oC

Refrigeration of the Irradiator with the bacterial strains

March, 29th

07:00 h

01:00 h

~ +10oC

Boarding the apparatus in the Soyus TMA-8

March, 29th

12:20 h

06:20 h

~ +22 to +23oC

Lauching of Soyus TMA-8

March, 30th

02:30 h

20:30 h

~ +22 to +23oC

Soyus TMA 8 docked to the ISS

April, 1st

04:18 h

2 days + 22:18 h

~ +21oC

Begin of UV-A irradiation

April, 7th

13:40 h

9 days + 07:40 h

~ +21oC *

End of UV-A irradiation

April, 7th

17:40 h

9 days + 11:40 h

~ +21oC *

Soyus TMA 7 undocked from the ISS

April, 8th

20:23 h

10 days + 14:23 h

~ +21oC

Reentry at Earth’s atmosphere

April, 8th

23:47 h

10 days + 17:47 h

~ +25oC

Delivery of the apparatus to the research team at Moscow

April, 9th

08:00 h

11 days + 02:00 h

~ +10 to +25oC

Begin of survival and mutagenesis tests

April, 9th

12:40 h

11 days + 06:40 h

~ +25oC

Experimental procedure

(*) a maximum, of short duration, +30oC was reached inside the irradiation module, during the 4 hours of irradiation.

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Aboard-irradiation setup

Pre-irradiation setup S P A C E E A R T H Pre-irradiation

DNA Repair

Step 1a

Step 2a

Cell analysis

Step 3a

Aboard irradiation Step 1b

Internal exposure

Radioactive source Simulated solar spectrum

External exposure (direct solar/cosmic radiation)

DNA Repair Step 2b

Cell analysis Step 3b

enhenced close (artificially) naturally occuring space dose

Figure 6. “Classical setup” for DNA repair experiments in microgravity (steps 1a-3a) and the in situ biological experiment (steps 1b-3b) during the USA-Russian-Brazil centenary flight mission in March-April 2006.

Allen belts, may also contribute to doses. During the flight experiment, we monitored the dose at the irradiation slots by two thermoluminescent solid state dosimeters: TLD-100 (LiF : Mg,Ti), measuring 3.2 mm x 3.2 mm x 0.9 mm. The dosimetry based only on passive TLD detectors is not enough to describe the total interaction of ionizing radiations with the biological material on board the ISS, but may allow an indication of any abnormal radiation event occurring during the flight. The integrated effective dose for the ~10 days of flight was 3.7 mSv, if a mean radiation quality factor of 2.2 is assumed (Reitz et al., 1996). This value is slightly higher than the TLD dosimetric value (3.8 mSv during 15 days in orbit) obtained during the space experiment REPAIR and KINETICS, conducted by Horneck et al. (1996), performed during the IML-2 mission in July 1994 (also during a period of solar minimum). In addition, during the present mission, solar X-ray emissions detected by the GOES satellite did not registered any important enhancement related to solar events higher than a C 9.0 flares (correspondent to a peak flux of 9 x 10-6 W.m-2, in the 1-8 A energy channel). During the hours that the UV-A

radiation was induced in the experiment, only a B5.0 solar flare was measured by the GOES detectors, which is considered a minor event (correspondent to a peak flux of 5 x 10-7 W.m-2). Moreover, there was not report any CME event or anomalous galactic cosmic ray flux interacting with the earth. In short, there was no relevant anomaly in the parameters that characterize the geo-space behavior during the experiment.

RESULTS AND DISCUSSION The analysis of the ionizing irradiation carried out during ISS orbit, the performance of the CIM system, and the results of the biological experiment follow. Memory Module data results: After an analysis of Irradiance follow-up data stored in the MM, it was noted that the sets of LEDs in the two slots turned off after 25 minutes, contrary to the 240 minutes programmed in the firmware, due to an undetermined

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with 6.6 J.cm-2, while the strains uvrA recA (AB2480) and recA (AB2463) mutants followed the original irradiation protocol, with of 63.6 J.cm-2. The above irradiation periods were recorded in the CIM memory module. Ground control experiments were conducted after the mission, employing the same irradiation module of the flight and similar irradiation periods and temperature conditions, as recorded. Considering the relative differences in survival frequency, the wild type strain (AB1157) was more sensitive onboard the ISS than on Earth. In contrast, the uvrA recA mutant (AB2480) was more resistant on ISS than on Earth. It has been reported that UV-A induces high quantities of cyclobutane pirimidine dimmers (CPD), which are lesions repaired via NER (Tyrrell, 1973; Douki et al., 2003 and Rochette et al., 2003). On the other hand, the uvrA recA mutant (AB2480) lacking NER would produce less DNA breaks than the wild type strain. In contrast, on Earth, where there are relatively lower doses of environmental ionizing radiation, the uvrA recA mutant was more sensitive than the wild type strain, since the absence of NER can represent a severe injury to the cells. The AB2463 (recA) and P3478 (polA) strains did not show different results in UV-bacterial survival experiments carried out on Earth or on ISS. With the exception of the wild type strain (AB1157), the mutants presented higher or equivalent “spontaneous

failure. The temperature data for the EM was coherent with this observation, showing an inflection at 25 minutes into the recording, as shown in Fig. 7 (T3 - EM cover temperature). Since the EM did not return to Earth, and the IM did not present any signs of failure in later tests carried out with the engineering model of the EM and firmware from the flight, nor were any problems found after a detailed analysis of the electronic circuits, watchdog registers and firmware design, we suspect there was a momentary failure due to radiation in the environment, such as SCR - Solar Cosmic Rays and GCR - Galactic Cosmic Rays, creating a SEU (Single Event Upset), associated with the use of COT components of medium-scale integration. Electronic failures of the ISS orbit are generally due to proton flow from the South Atlantic Anomaly– (SAA) (Kuznetsov, 2005). It was not possible to correlate the time of failure (April 7, 2006, 14:06 GMT) with the possible passage of the ISS through the SSA, in virtue of limited access to orbital data. Considering the mean UV irradiance value found for each slot was 4.38 mW.cm-2, and the time the LEDs were turned on, the irradiation dose for the AB1157 and P3478 strains was 6.6 J.cm-2 and the dose for the AB2463 and AB2480 strains was 63.6 J.cm-2. Observed biological response to UV-A radiation in a microgravity environment: In the experiment, the wild type strain (AB1157) and polA mutant (P3478) were irradiated

30 29 28

Temperature (ºC)

27 26 25

T1 (IM Inner temp. sensor)

T2 (IM Inner temp. sensor)

Telec (EM international top surface sensor)

22 21 240

230

220

210

200

190

180

170

160

150

140

130

120

110

100

Time (minutes) Figure 7. Temperature data during DRM experiment in the ISS. (T1) Irradiator module upper half, (T2) Irradiator module Botom half and the internal top surface (Telec) of the Electronic module. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.219-230, Jul.-Sep., 2014

The Compact Irradiator Modulus Designed for DNA Repair and Mutagenesis Studies in ISS Microgravity Environment Using UVA Emitted by Light-Emitting Diodes

mutation” frequency on ISS. This could be explained by the elevated doses of cosmic radiation on ISS. Nevertheless, when irradiated by UV-A on ISS, the wild type strain (AB1157) and uvrA recA mutant (AB2480) decreased in mutation frequency. Taking into account that UV-A inhibits the protein synthesis, leading to cell growth delay (Favre et al., 1985), this agent could inhibit mutagenesis due to the local ionizing radiation, probably through blocking the DNA-replication, which would suppress the expression of the mutant phenotype. Yet, polA (P3478) and recA (AB2463) mutants presented growth delay when irradiated with UV-A. This effect would not interfere on mutation frequency of these strains.

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the market. In the microgravity experiment we could observe that, besides the environmental high cosmic ionizing radiation, compared to conditions on Earth, the induced UV-A irradiation could inhibit mutagenesis, probably because it inhibits the protein synthesis, leading to cell growth delay. Yet, polA (P3478) and recA (AB2463) presented growth delay when irradiated with UV-A. Nevertheless, these results suggest that more experiments would be conducted using the above experimental conditions in order to better understand the biological responses under high elevated irradiation environments.

ACKNOWLEDGMENTS CONCLUSIONS The reduction of environment radiation effects on electronic devices should be better evaluated in a future version of this experiment. Techniques, such as redundancy and the use of components that are less susceptible to SEE, should be employed as Dodd and Massengill (2003) and LaBel et al. (1996) experiments conclusions. The irradiance modeling of the CIM irradiation wells, displayed in “UV irradiance”, is of fundamental importance for future comparisons (even if in other systems) with the results obtained in the DRM experiment. Moreover, the techniques used in the modeling may be employed for other geometries and other, more powerful types of LEDs already on

The authors are grateful to the Brazilian Space Agency (AEB) under Microgravity Program for sponsoring this project and to the Instituto de Aeronáutica e Espaço (IAE) and Instituto Nacional de Pesquisas Espaciais (INPE), especially the Engineers Agnaldo Eras, Narli B. Lisboa and Dilmar Vieira dos Santos for their great help in the development of the instrumentation; we also thank the Brazilian astronaut Marcos Cesar Pontes, who performed the Brazilian experiment aboard the ISS, and the technical and scientific staff of the Laboratório de Integração e Testes (LIT) from INPE, who provided the performance tests and, finally, the technical staff of ENERGIA, who clarified several details on building an onboard experiment in the ISS.

REFERENCES Cogoli, A., 1996, “Biology under microgravity conditions in Spacelab International Microgravity Laboratory 2 (IML-2)”, Journal of Biotechnology, Vol. 47, No 2-3, pp. 67-70. doi: 10.1016/0168-1656(96)01413-7. Dodd, P. E. and Massengill, L.W., 2003, “Basic Mechanisms and Modeling of Single-Event Upset in Digital Microelectronics”, IEEE Transactions on Nuclear Science, Vol. 50, No 3, pp. 583-602. doi: 10.1109/TNS.2003.813129. Douki, T., Reunaud-Angelin, A., Cadet, J. and Sage, E., 2003, “Bipyrimidine photoproducts rather than oxidative lesions are the main type of DNA damage involved in the genotoxic effect of solar UVA radiation”, Biochemistry, Vol. 42, No 30, pp. 9221-9226. doi: 10.1021/bi034593c. Favre, A., Hajnsdorf, E., Thiam, K. and Araujo, A.C., 1985, “Mutagenesis and growth delay induced in Escherichia coli by nearultraviolet radiations”, Biochimie, Vol. 67, Issue 3-4, pp. 335-342. doi: 10.1016/S0300-9084(85)80076-6.

Horneck, G., Rettberg, P., Baumstark-Khan, C., Rink, H., Kozubek, S., Schäfer, M. and Schmitz, C., 1996, “DNA repair in microgravity: studies on bacteria and mammalian cells in the experiments REPAIR and KINETICS”, Journal of Biotechnology, Vol. 47, No 2-3, pp 99-112. Kiefer, J. and Pross, H.D., 1999, “Space radiation effects and microgravity”, Mutation Research, Vol. 430, Issue 2, pp. 299-305. doi:10.1016/S0027-5107(99)00142-6. Kobayashi, Y., Kikuchi, M., Nagaoka, S. and Watanabe, H.,1996, “Recovery of Deinococcus radiodurans from radiation damage was enhanced under microgravity”, Biological Sciences in Space, Vol. 10, No 2, pp. 97-101. doi:10.2187/bss.10.97. Kobayashi, Y., Watanabe, H., Kikuchi, M. and Narumi, I., 2000, “Effect of the space environment on the induction of DNA-repair related proteins and recovery from radiation damage”, Advances in Space Research, Vol. 25, No 10, pp. 2103-2106.

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Sampaio, M., Evangelista, H., d’Amore, R., Asad, N.R., Asad, L.M.B.O., Araújo, A.C., Brasil, A.P.H., Veissid, N., Vlassov, V., Pacini, A. and Fontoura, M.T.S.

Kuznetsov, N. V., 2005, “The Rate of Single Event Upsets in Electronic Circuits onboard Spacecraft”, Cosmic Research, Vol. 43, No 6, pp. 443–451. doi:10.1007/s10604-005-0066-9. LaBel, K. A., Gates, M.M., Moran, A.K., Marshall, P.W., Barth, J., Stassinopoulos, E.G., Seidleck, C.M. and Dale, C.J., 1996, “Commercial Microelectronics Technologies for Applications in the Satellite Radiation Environment”, In: Proceedings Aerospace Applications Conference, Vol. 1, pp. 375-390. doi: 10.1109/AERO.1996.495897. Manti, L., 2006, “Does reduced gravity alter cellular response to ionizing radiation?”, Radiation and Environmental Biophysics, Vol. 45, No 1, pp. 1-8. doi: 10.1007/s00411-006-0037-4. Nicholson, L.W., Ricco, A.J., Agasid, E., Christopher, B., Diaz-Aguado, M., Ehrenfreund, P., Friedricks, C., Ghassemieh, S., Henschke, M., Hines, J.W., Kitts, C., Luzzi, E., Ly, D., Mai, N., Mancinelli, R., McIntyre, M., Minelli, G., Neumann, M., Parra, M., Piccini, M., Rasay, M.R., Ricks, R., Santos, O., Schooley, A., Squires, D., Timucin, L., Yost, B. and Young, A., 2011, “The O/OREOS Mission: First Science Data from the Space Environment Survivability of Living Organisms (SESLO) Payload”, Astrobiology, Vol. 11, No 10, pp. 951-958. doi: 10.1089/ast.2011.0714. Ottolenghi, A., Ballarini, F. and Biaggi, M., 2001, “Mechanistic bases for modelling space radiation risk and planning radiation protection of astronauts”, Physica Medica, Vol. 17, Suppl 1, pp. 272-277. Olsson-Francis, K. and Cockell, C.S., 2010, “Experimental methods for studying microbial survival in extraterrestrial environments”, Journal of Microbiological Methods, Vol. 80, pp. 1-13. doi:10.1016/j.mimet.2009.10.004. Pross, H.D., Kost. And Kiefer, J., 1994, “Repair of radiation induced genetic damage under microgravity”, Advances in Space Research, Vol. 14, Issue 10, pp. 125-130. doi:10.1016/0273-1177(94)90461-8. Pross, H.D., and Kiefer, J., 1999, “Repair of cellular radiation damage in space under microgravity conditions”, Radiation and Environmental Biophysics, Vol. 38, No 2, pp. 133-138. doi:10.1007/s004110050149. Pross, H.D., Casares, A. and Kiefer, J., 2000, “Induction and Repair of DNA Double-Strand Breaks under Irradiation and Microgravity”, Radiation Research, Vol. 153, No 5 Pt 1, pp. 521-525. Reitz, G., Beaujean, R., Benton, E., Burmeister, S., Dachev, Ts., Deme, S., Luszik-Bhadra, M. and Olko, P., 2005, “Space radiation measurements on-board ISS-the DOSMAP experiment”, Radiation Protection Dosimetry, Vol. 116, No 1-4 Pt 2, pp. 374-379. doi:10.1093/rpd/nci262. Reitz, G., Beaujean, R., Kopp, M., Leicher, M. and Strauch, K., 1996, “Dosimetric mapping in BIORACK on IML2”, Journal of Biotechnology, Vol. 47, Issue 2-3, pp. 83-88. doi: 10.1016/0168-1656(96)01372-7. Rettberg, P., Eschweiler, U., Strauch, K., Reitz, G., Horneck, G., Wänke, H., Brack, A. and Barbier, B., 2002, “Survival of Microorganisms in space protected by Meteorite material: Results of the experiment ‘EXOBIOLOGIE’ of the Perseus mission”, Advances in Space Research, Vol. 30, No 6, pp. 1539-1545. doi: 10.1016/S0273-1177(02)00369-1.

Rochette, P.J., Therrien, J.P., Drouin, R., Perdiz, D., Bastien, N., Drobetsky, E.A. and Sage, E., 2003, “UVA-induced ciclobutane pyrimidine dimers from predominantly at thymine dipyrimidine and correlate with the mutation spectrum in rodent cells”, Nucleic Acids Research, Vol. 31, No 11, pp. 2786-2794. Sampaio, M., 2007, “Design, Dosimetry and performance of a UV irradiator system used in space biology”, Ph.D. Thesis, Technological Institute of Aeronautics, São José dos Campos, Brazil, 158p. (In Portuguese). Saffary, R., Nandakumar, R., Spencer, D., Robb, F.T., Davila, J.M., Swartz, M., Ofman, L., Thomas, R.J. and DiRuggiero, J., 2002, “Microbial survival of space vacuum and extreme ultraviolet irradiation: strain isolation and analysis during a rocket flight”, FEMS Microbiology Letters, Vol. 215, No 1, pp. 163-168. SSP41163, 1999, “International Space Station Program: Russian Segment Specification, Revision G. National Aeronautics and Space Administration & Russian Space Agency”, Houston, Texas, USA & Moscow, Russia, 452 pp. SSP50094, 2000, “NASA/RSA JOINT SPECIFICATIONS: Standards Document for the ISS: Russian Segment. Revision A. National Aeronautics and Space Administration & Russian Space Agency”, Houston, Texas, USA & Moscow, Russia, 532 pp. SSP50146, 1998, “NASA/RSA Bilateral Safety and Mission Assurance Process Requirements for the ISS Revision A. National Aeronautics and Space Administration & Russian Space Agency”, Houston, Texas, USA & Moscow, Russia, 159 pp. Takahashi, A., Ohnishi, K., Takahashi, S., Masukawa, M., Sekikawa, K., Amano, T., Nakano, T., Nagaoka, S. and Ohnishi, T., 2001, “The effects of microgravity on induced mutation in Escherichia coli and Saccharomyces cerevisiae”, Advances in Space Research, Vol. 28, Issue 4, pp. 555-561. doi: 10.1016/S0273-1177(01)00391-X. Takahashi, A., Suzuki, H., Omori, K., Seki, M., Hashizume, T., Shimazu, T., Ishioka, N. and Ohnishi, T., 2011, “Expression of p53-regulated genes in human cultured lymphoblastoid TSCE5 and WTK1 cell lines after spaceflight in a frozen state”, Advances in Space Research, Vol. 47, Issue 6, pp. 1062-1070. doi: 10.1016/j.asr.2010.11.002. Tyrrell, R.M., 1973, “Induction of pyrimidine dimers in bacterial DNA by 365 nm radiation”, Photochemistry and Photobiology, Vol. 17, Issue 1, pp. 69-73. doi: 10.1111/j.1751-1097.1973.tb06334.x. Yatagai, F., Honma, M., Ukai, A., Omori, K., Suzuki, H., Shimazu, T., Takahashi, A., Ohnishi, T., Dohmae, N. and Ishioka, N., 2012, “Preliminary results of space experiment: Implications for the effects of space radiation and microgravity on survival and mutation induction in human cells”, Advances in Space Research, Vol. 49, Issue 3, pp. 479-486. doi: 10.1016/j.asr.2011.10.015. Yi, Z.C., Li, X.F., Wang, Y., Wang, J., Sun, Y. and Zhuang, F.Y., 2011, “The postmitotic Saccharomyces cerevisiae after spaceflight showed higher viability”, Advances in Space Research, Vol. 47, Issue 11, pp. 2049–2057. doi:10.1016/j.asr.2011.02.006.

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High Power Laser Weapons and Operational Implications Nelson Alex Roso1, Romero da Costa Moreira1, José Edimar Barbosa Oliveira1

ABSTRACT: The operational implications of high power laser weapons are constantly growing in countries with advanced military technological level. As well as on progress in integration with air, land and naval platforms, this paper discusses the necessary development and implementation of the operational concepts into Armed Forces, which should target orientation in the improvement process of the appropriate warfare material, i.e. the laser, as well as remodeling the existing combat’s doctrine. Finally, we highlight some capabilities and limitations inherent in the technology of lasers and present some applications in defense and attack operations enabled by the implementation of laser weapons. KEYWORDS: Weapons systems, Laser, Operational applications, Photonics.

INTRODUCTION The experimental demonstration of the first laser was performed by Theodore Maiman on May 16, 1960, and preceded numerous scientific and technological areas, which have provided great advances in telecommunications, instrumentation and armaments industries, among others (Hecht, 2010). In Brazil, it is noteworthy that the formation of human resources and the development of research on laser within the Departamento de Ciência e Tecnologia Aeroespacial (DCTA) — former Centro Tecnológico da Aeroespacial (CTA), are performed by the Instituto Tecnológico de Aeronáutica (ITA) and the Instituto de Estudos Avançados (IEAV) for over thirty years. In these institutes, significant results have been obtained in the areas of high power laser (HPL), optical sensors and communications links with optical fiber, among others (Monteiro et al., 1994). History reveals that, in areas of defense interest, conventional weapons have dominated warfare for centuries. Nowadays, the primacy of cartridge is replacing the projectiles for HPL, as technological advances in photonics introduce systems capable of destroying materials hardness, as well as protecting centers of Command and Control with early warning systems integrating lasers. Projectiles, e.g., mortars and missiles, are passive of interception and destruction by lasers that concentrate large amounts of electromagnetic energy (EME) in very small areas, destroying control systems of guided weapons or causing previous detonation. The coexistence of conventional weapons systems and concentrated EME weapons is not nearly over, since these complement one another. Emerging possibilities of available technological resources require the fighters, scientists and commanders to have a comprehension of their implications,

1. Instituto Tecnológico de Aeronáutica – São José dos Campos/SP – Brazil Author for correspondence: Nelson Alex Roso | Instituto Tecnológico de Aeronáutica | Praça Marechal Eduardo Gomes 50, São José dos Campos / SP | CEP: 12.228-901 – Brazil | Email: nelsonalexroso@gmail.com Received: 10/03/2013 | Accepted: 06/29/2014

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since the war is won with the best job proficiency, e.g., the troops with greater efficiency will dictate the asymmetry of the conflict. Within the Brazilian Air Force (FAB), the thematic “laser weapons” has received attention as reveal issues on the Air General Commander (COMGAR) Spectrum Magazine, published in 2000, 2001 and 2012. This paper proves the success of graduate programs and research in the area of defense, provided by FAB for over ten years, making insight into operational implications of laser weapons. Initially, the growing capacity in developing these types of weapons, as well as progress in integration with air, land and naval platforms, is discussed. The need to develop operational concepts into the Armed Forces is also highlighted, mainly to guide the development of laser weapons; as well as a parallel is made between the capabilities and limitations of such weaponry. In addition to that, an approach of attack and defense enabled by the use of laser weapons is made; and finally, a retrospective completion of the main elements addressed is presented.

DEVELOPMENT AND INTEGRATION OF HIGH POWER LASER WEAPONS Laser weapons are being created and tested throughout the world, specially in Armed Forces with notable weapons capabilities such as the United States, China, France and Russia, among others. The current example is the Tactical High Energy Laser (THEL), developed for use by the U.S. Army against tactical missiles with short and medium range, as well as the Anti-missile Airborne Laser (ABL) platform used by Boeing 747 U.S. Air Force (USAF) against intercontinental ballistic missiles (Dunn, 2005). The major challenge in understanding the operational implications of the progress on laser weapons technology is the great variety of characteristics of such weaponry. Technologically, the type of laser that has been developed for military purposes was the chemical type employed in the THEL and ABL, which produces an intense beam of infrared radiation from chemical reactions between its solutes. Also, the solid state lasers (SSL), electrically energized, are less potent, and they depend on crystals which are responsible for originating their beams, but continue in constant evaluation, promising great benefits in light weapons used in the front battle. In a statement, the

Department of Defense of the U.S. (DoD), through the high power solid-state laser development program — announced in December 2004 —, had already reached a 25 kW of power with SSL in the laboratory, although, according to the DoD, it is way far from the power required to be tactically effective, which should be greater than 100 kW (Dunn, 2005). The system’s weight is also a problem, since it has been estimated at about 11,000 Kg for a laser of 100 kW. On the other hand, the American defense industry estimates that the technological development of SSL can reduce this weight to less than 4,000 Kg (Dunn, 2005), allowing the use of lighter and more agile platforms. However, power level is not the only feature to improve. An effective laser gun system must be created, a strong integration between subsystems for acquiring and tracking targets must be made, as well as the system base should monitor and provide enough energy power in several engagements at combat. Another type of laser with potential applicability in weapons systems is the Free Electron Laser (FEL), whose base of operation is in free motion of a relativistic electron beam through a fixed magnetic structure (Federation of American Scientists, 2013). This type of laser has larger area than others and can be easily tuned to a specific wavelength bandwidth. The FEL technology is being evaluated by the U.S. Navy (U.S.N.) as a good candidate in the introduction of direct energy weapons against missiles and aircrafts. Significant progress has been achieved in increasing the power level of the FEL, and it is possible to consider, in the near future, the construction of compact weapons with this type of laser with megawatt’s power. Recently, the North American Thomas Jefferson National Accelerator Facility (2013) — Jefferson Lab — showed that it has reached the power of 14 kW. With emphasis on this expectation, on June 9, 2009, the office of the Research Department of the U.S.N., responsible for promoting science and technology programs of the U.S.N. and U.S. Marine Corps, announced the contract closing with Raytheon Company, for the development of an experimental 100 kW FEL (Popular Mechanics Journal, 2009). Researchers have also been made to improve the quality of the laser beam at high powers. The laser’s feature called Beam Quality (BQ) essentially measures the scattering cross section of the laser beam at a given distance, measuring the ability to focus the laser on the target to form a point of intense light from the platform which employ its. In many cases, the intensity of the focused spot is proportional to 1/BQ2, where BQ equals 1, meaning a perfect focus. Therefore, a small increase in

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BQ could result in a decrease of the intensity of the laser energy deposited on the target. Hence, the maintenance of a near BQ equals 1 is a very important requirement in the development of the laser system. The integration of HPL on air, land and naval platforms must progress in order to suit the needs of the missions focus. As mentioned before, the ABL suited to the B-747 USAF platform, to execute strategic missions, was necessary multiple structural changes on the aircraft in order not to lose the features of the platform performance. Another example of adaptation was the modification on THEL, towards implementing it as a fighting system known as High Energy Laser Rockets and Mortars Artilhary (HELRAM), which may be used to destroy multiple types of threats, including artillery rockets and light mortars (Dunn, 2005). Several other concepts using the laser from military platforms have been developed, but the limitations in space and weight still remain. Unmanned Aerial Vehicles (UAV), bombers, fighters, ships, submarines, satellites, tanks and many other weapon system platforms may involve HPL, serving as offensive and defensive weapons in the Operation Theatre (OT).

OPERATIONAL CONCEPT BASED ON CAPABILITIES AND LIMITATIONS It is necessary that the Armed Forces inform the defense industry theirs requirements, in order to provide the best performance possible. The investment at HPL technologies must be made in order to promote research’s development in technology institutes, as well as justifying budget on National Security. At FAB, according Oliveira (2002), the Operational Applications Postgraduate Program (PPGAO), that commissioned militaries from the Air Force, the Navy and the Army, develops operational research on these technological innovations, integrating operational and scientific communities, in order to clarify doubts and to maximize the conceptual and operational outcomes. Acting as Electronic Protective Measures (Brasil, 2006), selfdefense systems such as the Direct Infrared Countermeasure System (DIRCM) (Northrop Grumman, 2013), are capable to divert the spotlight of laser-guided missiles, confusing their trajectories. Acting as Electronic Attack Measure (Brasil, 2006), lasers can be used in conjunction with weapon systems of AF to appoint or engage

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multiple targets, increasing the offensive capability. This function is extremely desirable against fast maneuverable targets such as UAV, surface-to-air missiles (SAM), air-to-air missiles (AAM), rockets and mortars. Notwithstanding this, the currently laser designation, carried out in the FAB by Litening System (Fig. 1), enables the use of “dumb bombs” (with Lizard control kit) with a high degree of accuracy (Northrop Grumman, 2013). Compatible Energy consumption is proportional to the consumption of chemical fuel (COIL – Chemical Oxygen Iodine) or electricity (SSL) of the system, according to the type of laser. This is a tactical advantage, since the production of electric power at aircrafts, tanks and warships is constant, then the systems will be limited to components heating. The chemical lasers, for example, can operate according to the autonomy of its chemical cartridges, as is the case of HELRAM, which can be used between 10 and 20 times without being refilled, and furthermore, it is possible to operate with serials cartridges such as USAF B-747 ABL, improving the reload speed. In the SSL lasers, the number of shots is limited by the dissipate heat ability and batteries capacity on board. This limitation is called Duty Cycle Limit (Dunn, 2005). The HPL has low cost per shot, improving the military availability and replacement when compared to guided missiles, that are expensive to manufacture because of the embedded technology involved (rocket motors, guidance systems, avionics, seekers and others). The HPL has a cost directly proportional to the used energy, which can be produced without additional costs. For instance, the GBU-31 Joint Direct Attack Munition (JDAM) costs around U$25,000.00, one AGM-154C Joint Standoff

Figure 1. Litening Pod Designator of FAB AMX aircraft (adapted from aereo.jor.br, 2009).

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Weapon (JSOW) costs around U$720,000.00, one AGM-158 Joint Air-to-Surface Standoff Missile (JASSM) approximately U$700,000.00, one AIM-120 Advanced Medium Range Air-toAir Missile (AMRAAM) is estimated at around U$320,000.00 and the AGM-65 Maverick may reach the value of U$158,000.00 (depending on the variation) (Federation of American Scientists, 2013). In contrast to the values of the weapons above, the cost of the chemical fuel by shot in B-747 ABL, which uses the COIL as laser fuel, is around U$10,000.00 (Dunn, 2005), indicating operational feasibility of this weaponry. The accuracy of these systems allows the operator to choose the impact place of the laser beam on the target and to adjust it according to the desired level of damage. This adjustment may be inserted in the beam Dwell Time, setting the duration of laser illumination, reducing the collateral damage. The combat logistics for the maintenance of HPL is reduced in comparison with conventional systems. For example, guns and launchers need to be reloaded with projectiles and missiles after the shot, while laser weapons need only chemical fuel or electricity to generate energy for their systems. The ability to adapt to several types of missions, controlling the Dwell Time of the laser beam, makes the HPL weapon platforms a Multi mission crafts, reaffirming the Air Force flexibility feature. However, the technical limitations of laser systems must also be understood by the operational community, some of them are listed below: • Turbulence and atmospheric attenuation are factors that can affect the propagation of the laser beam, since dust particles, aerosols, water vapor and atmospheric instability lines, absorbing or scattering the laser energy, reducing operational range of application; • Dependence of line-of-sight with targets, because the laser beam does not make ballistic trajectories; • Due to the high concentration of energy in a point with a relatively small cross section, the lasers beams are more adjusted for engaging targets with small areas. For example, against combat vehicles, the laser will be effective in components such as antennas, control surfaces, sensors and fuel tanks; • While chemical lasers generate wavelengths beams which are safe for human vision, the SSL are dangerous regarding these wavelengths. In case of HPL use against a target that does not fully absorb the energy issued, the spread energy by reflection can blind or damage the allied troops at OT if they are not properly protected.

Once the capabilities and limitations are known, the consequences of HPL weapons should be properly discussed.

OPERATIONAL IMPLICATIONS The approach of operational implications in the use of HPL weapons does not underestimate the complex doctrinal study for development of military tactics and strategies, therefore, a synthetic implication in offensive and defensive operations on the ground, water, air and space OT’s will be explored. OFFENSIVE OPERATIONS Offensive operations using HPL should consider that features of laser itself, such as accuracy, speed and the number of engagements, will be more important than destruction power itself. For example, the same laser that provides an active defense for an aircraft can be used against ground targets, providing a significant air-to-ground capability. Combining these capabilities with new aerial platforms and new sensors, it is possible to increase its potential use, increasing the airpower effectiveness in counterinsurgency and counterterrorism missions, as was done in Pakistan by the USAF. Moreover, the reduction of collateral damage is remarkable, because damage may decrease from many meters to some centimeters. Strategic air targets such as radio, television and satellite antennas, power transformers, refrigeration and heating systems, which depend on these sites, may be blocked without complete destruction. It is also possible to assume that the SSL development, will change a lot of conventional weapons loaded in modern attack aircraft by HPL systems, which are capable of being used hundreds of times, setting a new limit on station combat, i.e., the human limit and the electromechanical system limits of air platforms. At this point it is possible to foresee the efficient union of SSL lasers and combat UAV. On surface warfare context, the offensive ground operations should use the HPL weapons on precision missions that require high speed on engagement, adjustability and minimal collateral damage, such as missions against snipers. Laser weapons can be associated with optical and acoustic sensors to locate and to neutralize the sniper instantly, before they perform a shooting. Lasers do not have time of flight compared to the missiles and conventional weapons. However, the stealth features of lasers

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are also used in benefit of sniper weapons, since they are only visible through target damage. The same limiting factors of laser weapons in ground operations will also limit the naval operations. A good example for the use of these weapons is the integrated laser on submarine periscope, which may engage surface targets such as ships, boats and aircrafts flying at low altitude with greater precision and less collateral damage, not revealing their positions when launching missiles and torpedoes. In addition to conventional air, land and sea scenarios, there is also the space OT, a perfect environment for the lasers, because a space platform can engage, for example, a spy satellite or ground targets with the use of HPL; however, the reverse is true (Brasil, 2006). An example of this, according to Dunn (2005), occurred in 1984, when the Union of Soviet Socialist Republics (USSR) used a Ground Based HPL (GBL) against the U.S.’ space shuttle, the Challenger, causing malfunctioning of the onboard equipment and stress on the crew. DEFENSIVE OPERATIONS In defensive operations, the HPL can provide self-defense measures for the air, land and naval platforms, as well as protect other platforms without protective systems. Lasers can be used against missiles, aircraft, satellites, UAV, bombs, artillery projectiles, rockets or mortars, neutralizing these threats before they reach their targets. The first use of the laser for self-defense purposes occurred in 1973, when the USAF Weapons Laboratory (AFWL) hit a drone flying at 200 Kts, using a CO2 laser based on ground. In 1983, the same laboratory carried out tests against the AIM-9B Sidewinder missile, which was kept at 2000 Kts (Dunn, 2005). In air operations, lasers incorporated into self-defense systems are responsible for increasing the survivability of many aerial platforms, particularly the subsonic platforms, such as the E-8 Joint STARS (Joint Surveillance Target Attack Radar System), the E-3 Sentry AWACS (Airborne Warning and Control System) and the ABL B-747 (Federation of American Scientists, 2013). These aircrafts are considered strategic targets in OT and are called High Value Airborne Asset (HVAA). Furthermore, the use of laser systems for self-defense in attack aircraft against SAM and AAM missiles reduces the suppression of enemy air defense workload, making the OT less widespread. In other words, an air campaign can begin with interdiction actions before obtaining air superiority stage, reaching the

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enemy gravity centers in a shorter time, which also will result in lower costs and less collateral damage. In land operations, HPL can increase the survival of the troops, as well as enable greater land mobility. These systems can be mounted on tanks to intercept missiles, artillery shells, rockets and mortars, increasing the OT self-defense. An example, previously mentioned in this paper, is the THEL type system, which has the ability to destroy rockets, mortars and other artillery ballistic projectiles. Several features that benefit the air defense and land operations also provide naval defense operations, increasing the fleet flexibility. For instance, lasers can provide an effective shield from ballistic or cruise missiles, allowing the permanence of warships greater proximity to the enemy coastline within the firing range of tactical missiles. One of the biggest threats of naval forces is the Supersonic Sea-Skimming Missile Antiship (ASCM) (Federation of American Scientists, 2013), which may be undetected until seconds before impact. In this case, HPL can offer a high rate of engagement of these threats. Strategically, as well as in offensive space operations with HPL, defensive operations by satellite based lasers are able to monitor and defend large areas on Earth’s surface against ballistic missile much faster than land protection systems, because this type of threat can be engaged shortly after launch and at higher altitudes.

CONCLUSIONS The presentation of the operational implications of HPL weapons is the first step to aware for operational and scientific community of the evolution in this area and it is used in order to alert the authorities about the military importance given on the nations with high technologies developed. Initially, this paper described the capacity of HPL, as well as the progress in its integration with air, land and naval platforms. It also was presented the attractive costs and benefits ratio. Thereafter, it was explained the necessity of operational concepts development on Armed Forces, in order to guide appropriate manufacturing laser weapons, highlighting the HPL capabilities and limitations. Finally, it was presented an overview of offensive and defensive operations enabled by use of HPL weapons.

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Therefore, according to the evidence of operational applications with laser weapons systems in many conflict situations, a new age in military and civilian community is possible, where postgraduate

programs as the PPGAO can guide the development of concise operational concepts, joining the scientific knowledge with operative know-how, since one complements the other and vice-versa.

REFERENCES Brasil, Ministério da Defesa do. Comando da Aeronáutica, 2006, “Comando-Geral de Operações Aéreas. NSCA 500-1: Sistema de Guerra Eletrônica da Aeronáutica”, Brasília: COMGAR. Dunn, R.J., 2005, “Operational Implications of Laser Weapons”, Retrieved in June 22, 2013, from http://www.northropgrumman.com/AboutUs/ AnalysisCenter/Documents/dfs/Operational_Implicationsof_La.pdf. Federation of American Scientists, 2013, “Website about science and technology in the fields of biosafety, technology analysis and strategic security”, Retrieved in June 20, 2013, from http://www.fas.org/. Hecht, J., 2010, “A Short History of Laser Development”, Optical Engineering, Vol. 49, No 9. Retrieved in June 25, 2013, from http:// www.if.ufrgs.br/~jgallas/pub/FisAtoMol/hecht_short_-history_of_ laser_development_OE2010.pdf. Monteiro, E.C., Geraldo, J.A. and Oliveira, J.E.B., 1994, “Fotônica e Optoeletrônica: Pesquisas Desenvolvidas na Divisão de Engenharia Eletrônica do ITA”, Revista ITA Engenharia, Vol. 1, No 1, pp. 13-16.

Northrop Grumman, 2013, “DIRCM, Directed Infrared Countermeasures”, Retrieved in June 24, 2013, from http:// w w w. n o r t h r o p g r u m m a n . c o m / C a p a b i l i t i e s / D I R C M / P a g e s / default.aspx. Oliveira, J.E.B., 2002, “Ciência, Tecnologia e Inovação em Áreas de Interesse da Defesa”, Retrieved in June 22, 2013, from https:// www.defesa.gov.br/arquivos/pdf/ciencia_-tecnologia/palestras/ ctidefesa.pdf. Poder Aéreo, 2009, “Pod Litening no A-1 da FAB na Operação Laçador”, Retrieved in June 25, 2013, from http://www.aereo.jor.br /2009/11/19/pod-litening-no-a-1-da-fab-na-operacao-lacador/. Popular Mechanics Journal, 2009, “The Navy’s New 100 kW laser weapons”, Retrieved in June 25, 2013, from http://www. popularmechanics.com/technology/gadgets/4321422. Thomas Jefferson Laboratory, 2013, “Free Electron Lasers”, Retrieved in June 21, 2013, from https://www.jlab.org/FEL/.

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An Approach to Outlier Detection and Smoothing Applied to a Trajectography Radar Data Aguinaldo Bezerra Batista Júnior1, Paulo Sérgio da Motta Pires2

ABSTRACT: The tracking of aerospace engines is reasonably achieved through a trajectography radar system that generally yields a disperse cloud of samples on tridimensional space, which roughly describes the engine trajectory. It is proposed an approach on cleaning radar data to yield a wellbehaved and smooth output curve that could be used as basis for instant and further analysis by radar specialists. This approach consists on outlier detection and smoothing phases based on established techniques such as Hampel filter and local regression (LOESS). To prove the effectiveness of the approach, both filtered and unfiltered data are submitted to an extrapolation method, and the results are compared. KEYWORDS: Trajectory, Radar, Filtering, Smoothing, Outlier detection.

INTRODUCTION Trajectography radar systems play an important role on the tracking process of an aerospace engine. During the whole flight of a target, a radar system is able to retrieve linear distance, azimuth and elevation data of the flying engine to radar operators and trajectography subsystems. The acquisition processes of a trajectory radar system deliver to data analysts a sparse cloud of samples, which is used to sketch, albeit crudely, the trajectory of the engine through the three-dimensional space. This occurs because radar systems suffer from a broad variety of perturbations that negatively influences the acquisition process and cannot be easily modeled, such as atmospheric issues, refraction and reflection, Earth’s curvature, presence or absence of transponder, device calibration, antenna servomechanism interactions, SNR, target volume and position, etc. These intrinsic characteristics of trajectory radar systems may adversely affect any subsequent data analysis. In some cases, the presence of outliers (aberrant or implausible values) in retrieved data, the high dispersion in collected samples, and the inherent noise of the process may hinder the assimilation of the actual trajectory of an engine. As a consequence, further assumptions from this raw data may lead to inaccuracies and misunderstandings related to the actual trajectory of the flying engine. To address this problem, it is proposed an approach on filtering acquired data in order to produce as outcome a smooth and outlier-free curve, that is expected to be a better approximation of the real trajectory of the aerospace engine.

1.Centro de Lançamento da Barreira do Inferno – Natal/RN – Brazil 2.Universidade Federal do Rio Grande do Norte – Natal/RN – Brazil Author for correspondence: Aguinaldo Bezerra Batista Júnior | Rodovia RN 063 - Km 11 PO Box 54 | CEP: 59.140-970 | Parnamirim/RN – Brazil | Email: aguinaldoabbj@clbi.cta.br Received: 01/21/2014 | Accepted: 06/24/2014

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The proposal consists, basically, on the implementation of this approach to a moving-window filter which will be applied to incoming radar data or to offline data. Once interest data became outlier-free and smoothed, further analysis and processing will become more accurate, it will be possible to apply curve fitting techniques to get the trajectory’s function parameters, driving out to more accurate calculations of speed and acceleration of the target, probable impact area, and to make more concise extrapolations when signal retrieval is somehow interrupted. The core of the proposed filter is based on modern implementations of classical statistic methods with proven effectiveness, in particular, in the fields of outlier detection and data smoothing. The remainder of the paper is structured as follows. The next Section outlines the background concepts of interest in this research. The subsequent Section presents our approach on radar data filtering. Then the accomplished results are discussed and some derived discussion is proposed on the final Section.

PAPER BACKGROUND The task of excerpting meaningful information from imperfect data has always been a very common problem and topic of interest on data analysis. There are several families of techniques and approaches to accomplish it, such as time series and statistical analysis, digital signal processing, artificial neural networks, reinforcement learning, to name a few. The approach based on statistical analysis caught most attention in preliminary study phases due to the large amount of flexible and effective techniques suitable for general data cleaning problems. In this context, data smoothing and outlier detection techniques, especially when combined, seemed well suited for use our radar data filtering problem. DATA SMOOTHING A trajectography plot can be viewed as the evolution of tracking signal measurements as a function of time. Seen as a physical process, the trajectory of an spatial engine, like many other experiments, may be viewed as a discrete signal whose amplitudes change rather smoothly as a

function of time, whereas many sorts of noise are noted as rapid, random, abrupt, and sometimes aberrant changes in amplitude from point to point within the signal. For some aerospace engines like rockets, the trajectory data is not supposed to fit models because the trajectory may change during flight. Nonetheless, it may be convenient not to force data into models but just to attempt to moderate frantic data by using a classic and straight-forward statistical approach known as data smoothing. In few words, smoothing is a process where the samples of a signal are adjusted so that individual points that are higher than the immediately adjacent points are moderated, and points that are lower than the adjacent points are raised, leading to a naturally smoother signal output. Considering that the actual signal is smooth in nature, it will not be much distorted, though some noise could be substantially lessened. Such a rough approach is sometimes evaluated as poorly suited because it may be close to data cooking (falsifying or selectively conditioning data in an attempt to prove an hypothesis), as it may inadvertently suppress some important information on data stream (Wilson, 2006). However, as we are interested mainly on short terms trends of an engine’s trajectory and uncovering data, minor variations in target’s position are not a major concern. Literature shows that smoothing may be distinguished from the closely related and partly overlapping concept of curve fitting. It happens because the former outcomes an uppermost idea of relatively slow changes of values, provided by a smooth function which approximately fits the data with little concern to the close matching of values, while the latter concentrates on achieving as exact a match as possible (best fit) by using an explicit function form for the result. The main goal of most smoothing methods is not to specify a parametric model for the mean function but to afford a more flexible approach that allows the data points themselves to suggest the appropriate functional form of the smoothed curve. Smoothing methods provide a bridge between making no assumptions on a formal structure (a purely non-parametric approach) and making very strong assumptions (a parametric approach) by making only a relatively weak assumption: the targeted data might represent a smooth curve (Simonoff, 1998). There are several ways to achieve data smoothing, especially through statistical techniques. Among these approaches based on regression analysis, more precisely

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An Approach to Outlier Detection and Smoothing Applied to a Trajectography Radar Data

local regression, and the employment of splines, more incisively smoothing splines are great stand outs, since they are classic, well established, simple to understand and interpret and proven effective when applied to general smoothing problems. Local Regression Local regression is an approach to fitting curves and surfaces to data in which a smooth function may be well approximated by a low degree polynomial in the neighbourhood of a point (Loader, 2012). Early work on using the underlying principles of local regression to smooth data are dated from the late 19th century and continued in a reticent fashion until mid 20th century, mainly because they were too computationally intensive for that time. Hopefully, from the 1970s on, hitchhiking on great advancements in computer hardware/ software and scientific computing, the local regression subject enjoyed a reborn. Since then, several relevant works on extending, modernizing and generalizing local regression have been developed, including the use of the method in other branches of scientific literature (Cleveland and Loader, 1996). A modern, proficient and widely used local regression algorithm is the LOESS (short for LOcal RegrESSion) procedure. LOESS is a nonparametric local regression method pioneered by Cleveland (1979), and further developed by Cleveland and Devlin (1988), in which a smooth function may be properly fitted by a low degree polynomial in a chosen neighborhood (subset) of any point of a dataset in a moving window fashion. This method employs weighted least squares (WLS) to fit linear or quadratic polynomial functions of the predictors at the centers of neighborhoods in order to build up a curve which describes the deterministic part of the variation in the data, point by point. LOESS is a weighted polynomial regression procedure where more weight is given to points near the target point and less weight is given to points further away. The radius of each neighborhood contains a specified fraction of data points, known as smoothing parameter or bandwidth, which is the main parameter of the method and controls the smoothness of the estimated surface in each local surroundings. (Cohen, 1999; NIST/SEMATECH, 2012). A chief advantage of this method is that the data analyst is not compelled to specify a global function to fit a model to the data, but only to fit pre-defined low-order polynomials

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to small segments of the data. LOESS is considered a versatile and coherent choice when it is demanded to model complex processes for which there are no theoretical models. In most implementations of LOESS, there are few knobs to deal with. In general, the smoothing parameter value and the degree of the local polynomial are the user-specified inputs, though in some cases the weight function may also be a flexible parameter. The traditional weight function used by LOESS is the popular tri-cube weight function (NIST/SEMATECH, 2012). The smoothing parameter or bandwidth, q, is a number between (d+1)/n and 1, with d denoting the degree of the local polynomial and n denoting the number of data points. The value of q is the proportion of data used in each fit and the subset of data used in each WLS fit is comprised of the nq (rounded to the next largest integer) points, whose explanatory variables values are closest to the point at which the response is being estimated (NIST/ SEMATECH, 2012). The smoothing parameter controls the flexibility of the LOESS regression, then large values of q yields smoother functions which would soften fluctuations in the data, while a smaller q value will make the regression function more conforming to the data and may eventually capture undesirable data oscillations. The selection of these parameters is normally an empirical task that depends on the dataset, but typical values lie in the range of 0.25 to 0.5, for most applications. Although fixed selection of the bandwidth may provide good fits in many cases (Cleveland and Loader, 1996), several criteria and procedures for intelligent, automated and adaptive bandwidth selection have been developed (Cleveland and Devlin, 1988; Cleveland and Loader, 1996). The local polynomial degree d is either locally linear or locally quadratic. A zero degree polynomial turns LOESS into a weighted moving average while the use of higher-degree polynomials is unrecompensed, because they tend to overfit local data and are numerically unstable. Besides, one of the main goals of LOESS is to approximate any function in a small neighbourhood by fitting low-order polynomials (NIST/SEMATECH, 2012). In fact, locally quadratic fit is recommended in the early smoothing literature because it may provide a sufficiently good approximation when there are rapid shifts in the slope, peaks and valleys (Cleveland and Loader, 1996). It is a well known fact that the LOESS smoothing procedure, like other statistical analyses based on least squares fitting, is considerably sensitive to the presence of

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even a small proportion of outliers in the dataset. Depending on their location and deviation level, such inconsistent observations may fairly distort regression coefficients and corrode regression analysis, making the data fitting not representative of the bulk of the data. A good approach to verify how suspected outlying data may influence the results is to perform the targeted processing (in our case the application of LOESS) on data, both with and without these outliers, in order to examine their specific impact on the results and finally conclude if these outlying points should be worked out or not. In Fig. 1, the tridimensional plot (a) shows 2,177 reasonably well behaved trajectory data points (with no extreme outliers) obtained from radar tracking of the International Space Station (ISS) while plot (b) shows the result of a 5% artificial outlier contamination procedure applied to the original data. Plot (c) shows the resulting smoothed curve from LOESS application over the original points from plot (a) while plot (d) shows the distorting effects caused by these outliers to the resulting LOESS smoothed curve, using the same smoothing parameters as in plot (c). In this work, all original test datasets were purposely artificially contaminated for better illustration of outlier effects and effectiveness of the techniques used in this work. To overcome the influence of the outliers, a robust version of the LOESS has been referenced in the literature. The robustification consists, basically, in iterative reweighting

processes that involve building robust weights with a specified robustness weight function by using current residuals and updating those on each iteration, until the residuals remain unchanged. However, these additional steps add much more computation complexity to the LOESS and dramatically decrease its performance (Cleveland, 1979; Cleveland et al., 1990; Garcia, 2010). In our preliminary tests, robust LOESS was proved unattractive because it took too long to converge and neglected to several extreme outliers. Smoothing Splines (Penalized Least Squares) Another popular and established non-parametric regression is smoothing splines, which is based on the optimization of a penalized least squares criterion whose solution is a piecewise polynomial or a spline function (Loader, 2012). This approach employs fitting a spline with knots at every data point, so it could potentially fit perfectly into data, but the function parameters are estimated by minimizing the usual sum of squares plus a roughness penalty defined by the penalized sum of squares criterion (Garcia, 2010). An amount of penalty is imposed according to the magnitude of the tuning parameter (also known as degree of freedom) of the method, so that the lower is the parameter the closer is the data fit, which could lead to a noisy curve, as it follows every detail in data. The higher is the parameter, the smoother is the solution curve, which could end up in a very poor fit to data.

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Figure 1. Influence of outliers on LOESS smoothing procedure. (a) Original data; (b) Original data with 5% artificial outliers contamination; (c) LOESS applied to original data; (d) LOESS applied to outlier contamined data. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.237-248, Jul.-Sep., 2014

An Approach to Outlier Detection and Smoothing Applied to a Trajectography Radar Data

It is a well known fact that the results obtained with smoothing splines are very similar to LOESS results. Moreover, the main drawback of smoothing splines is the proved sensitivity to outliers (Garcia, 2010). These facts were confirmed in preliminary tests with our trajectory datasets. Generally, even using a higher tuning parameter, the method tends to overfit misbehaving data (dispersed and highly-contaminated), always leading to a less smoothed output than the LOESS’ output for the same scenario. Although it is a good, fast and well-referenced method, it proved to be less effective than LOESS when considering our trajectory datasets. OUTLIER DETECTION Although there is no single, unanimously accepted or rigid mathematical definition of what an outlier is, there is a consensus on referring to outliers as a statistical term for an observation that is numerically much deviant from the behaviour obser ved in the majority of the data. In statistics, there is a wide and classic discussion regarding the characterisation and categorisation of these unusual observations in outliers, high-leverage points and influential points. However, since this kind of analysis is not the focus of this work, we will not go into the merits of these differentiations. Outliers are perhaps the simplest and best-known type of data anomaly (Pearson, 2005), highly common in most applied and scientific scenarios involving data collection and analysis. In terms of our trajectography dataset, outliers can be seen as points that disobey the general pattern of smooth variation seen in the data sequence, which represents the flight of an aerospace engine which is bound to the laws of physics. These data anomalies demand close attention, because they are observations that do not follow the statistical distribution of the bulk of the data, and consequently, may lead to erroneous results regarding statistical analysis (Liu et al., 2004). The presence of even a few of these anomalies in a large dataset can have a disproportional influence on analytical results (Pearson, 2005) and may cause general distortions, estimation biasing and inflated error rates that could lead to false alarms, improper decision making, faulty conclusions, model misspecification, etc. In general, outliers may be treated merely as an extreme manifestation of the random variability inherent of the data, hence, they have to be retained and processed in the same

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manner as the other observations (ASTM International, 1980), or as a representation of some disorder or unexpected conditions in the system, such as gross measurement, sampling, computing or recording errors, transient malfunctioning, noise, missing data, human errors, etc. In the latter case, the identified implausible values may eventually be rejected, statistically adjusted and/or held for further analysis. Discussion regarding what to do with identified outliers is a common and controversial topic in outlier detection literature, but there is a consensus that outliers should not be simply discarded, since they may carry important (or key) information and insights about the process. After all, “one person’s noise could be another person’s signal”. Outlier detection has been suggested to detect implausible behaviour points for numerous applications such as business transactions, clinical trials, voting, network intrusion, weather prediction, geographic information systems, chemical data processing, industrial process monitoring, and so forth (Pearson, 2005; Nurunnabi and Nasser, 2008). Although dealing with outliers is an old and well-known problematic, there is no ultimate outlier identification procedure which is able to cover all kinds of outlier scenarios. Even so, since there are different types of outliers emanating from various sources and influencing data analysis in different ways, as well as there is no rigid formalisation of what constitutes an outlier, the identification of these doubtful observations is an arduous and ultimately a matter of interpretation, or at least previous knowledge of the data. Several outlier detection criteria, procedures and guidelines have been actively developed for centuries (since the 19th century), using different diagnostic statistics. Most of them attempt to segregate the data into an outlier-free subset and a supplementary subset containing all potential outliers. These procedures can be grouped across a wide taxonomy of detection approaches, which include univariate, multivariate, parametric, non-parametric, distributionbased, distance-based, density-based, deviation-based and cluster-based methods (Pearson, 2005; Lee, 2008; Nurunnabi and Nasser, 2008). For the sake of time and study limitations, a historical review or a comparative study of outlier detection procedures diverges from the goal of this work. Thus, we briefly describe only some of the most known and referenced methods. Before that, it is crucial to highlight two concepts which are closely related to

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outlier detection: masking and swamping. Masking is the inability of a procedure to detect actual outliers while the swamping is the detection of a legit observation as an outlier. Literature shows that these opposing effects are faced by all outlier identification methods in a complementary fashion: a procedure which performs well with respect to masking is more susceptible to swamping and vice-versa (Pearson, 2005; Pearson, 2011). Cook’s distance is a general method for assessing the local influence of single data point against least squares regression analysis. The goal of the method is to detect influential points in regression coefficients, but it cannot be considered a conclusive test to detect outliers because it is very prone to both masking and swamping effects (Rancel and Sierra, 2000; Adnan et al., 2003; Nurunnabi and Nasser, 2008). Other widely known method is the Dixon’s Q for outlier detection, mainly because of its simplicity. It is based on a comparison between the suspect value and its direct or close neighbour with the overall or modified range. A point is flagged as outlier if its calculated Q value exceeds the critical Q-value presented in a static table at the chosen significance level. Although it was originally recommended by ISO for inter-laboratorial tests, this test should be used just once in a dataset to detect a unique outlier, because it is highly prone to masking effects (Massart et al., 1997). The Grubbs’ test for outliers is a commonly used procedure which replaced Dixon’s Q test on ISO recommendations (Horwitz, 1995). It is suggested in order to detect outliers in a univariate dataset assumed to come from an approximately normally distributed population. The test is based on the difference of the mean of the sample and the farthermost data, considering the standard deviation. Although it has been modified and adapted (Horwitz, 1995; NIST/ SEMATECH, 2012), this procedure still suffer from series of limitations, like the normality assumption, the use of nonrobust characterisation tools, such as mean and standard deviation as core statistics, and the inability of reasonably detecting multiple outliers (Zhang et al., 2004; Solak, 2009; NIST/SEMATECH, 2012). Another popular approach to outlier identification is the well-known 3σ edit rule, based on the idea that, if data sequence is assumed to be approximately normally distributed, the probability of observing a point farther than three standard deviations from the mean is only about 0.3% (Pearson, 2002). Despite its historical importance and intuitive appeal, this outlier

detection procedure tends to be ineffective in practice. The basic weakness is that the presence of outliers in the dataset can cause substantial errors in both estimated mean and standard deviation in which the procedure is based. It makes outliers harder to point out and, consequently, too few outliers are detected (Pearson, 2005). From the described so far, it is noticeable that there is the need for a versatile outlier detection procedure, which could work independently from the distribution type assumption, capable of sanely detecting multiple outliers without a process model or assumptions and based on robust (outlier-resistant) statistical tools. The Hampel filter, regarded as one of the most robust and efficient outlier identifier (Liu et al., 2004), satisfies these requirements by running through data a moving window cleaner centred at the current data point, using robust core statistics. A robust adaptation of the 3σ edit rule, named Hampel identifier, is then applied to this window to characterise each point regarding a local neighbourhood of preceding and subsequent samples, producing the replacement of the data point declared to be an outlier with a more representative value, according to other data points in the immediate vicinity, otherwise the data point is unchanged (Pearson 2005; Pearson, 2011). The Hampel identifier, base processing of the Hampel filter, consists of replacing the original data location and data standard deviation estimates in the 3-σ edit rule. The mean is replaced with the median and the standard deviation is substituted with the MAD (Median Absolute Deviation). Because median and MAD are both less sensitive to outliers than the mean and standard deviation, respectively, the Hampel identifier behaves much more effectively than the 3 σ edit rule in a majority of outlier scenarios (Pearson, 2005; Pearson, 2011). The main drawback of these replacements is that the overall outlier detection procedure becomes more aggressive and, consequently, legit data may be declared as outliers. Then, this identifier is naturally more sensitive to the swamping effect than to masking effects. Davies and Gather (1993) reasonably described in details the overall Hampel functioning, including the employed criteria used in order to evaluate if a sample is an outlier. The Hampel filter has only two tuning parameters: the half-width K of the window and the threshold parameter t (Pearson, 2005). The former defines the bandwidth for the cleaning window while the latter determines the aggressiveness of the filter in considering the suspect points as outliers. It is important to note that the filter remains well defined

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An Approach to Outlier Detection and Smoothing Applied to a Trajectography Radar Data

for t = 0, for which it is reduced to the median filter, since the target sample will always be replaced with the median of the window. In the other extreme, if t is large enough, the target sample will stay unmodified unless the MAD scale estimate is zero. This condition only occurs when a majority of values in the moving window are exactly the same, for example, when processing a coarsely quantised dataset (Pearson, 2005), which is not the case in our experiments. The behaviour of the Hampel filter, applied to our trajectory datasets, was very satisfactory. For example, when the filter was processed over a test trajectory dataset, this procedure was able to detect about 90% of those artificial outliers, which purposely contaminated the original dataset (10% of the total samples). It also pointed out some evident abehant samples within the original data. Figure 2 plots illustrates the Hampel filter response to the previously contaminated ISS trajectory data, using K=3 and t=3. Plot (c) shows that most outlying samples were detected and replaced with the median of their surroundings. This is a good example of small but beneficial distortions introduced by this filter to an outlier contaminated dataset.

RADAR DATA FILTERING The main interest in this research is to clean up tracking data of aerospace engines collected from a trajectory radar

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system, producing as result a smooth and outlier free curve that would afford much more accurate results regarding online and offline analyses. Due to the proven effectiveness and popularity of LOESS and the Hampel filter in their respective fields, they were chosen to integrate our proposal as base statistics. PROPOSAL The proposal consists on submitting trajectory radar data to a data filtering system founded on outlier detection/ substitution and smoothing phases. The outlier detection pre-processing phase is borne to the Hampel filter while LOESS takes the charge of the main process of smoothing. It is important to highlight that, for illustration purposes, all original tracking datasets were submitted to an outlier contamination process based mostly in the application of additive Gaussian white noise to some randomly chosen samples, in order to simulate a slight distortion of the signal. These signal distortions could be caused, for example, by adverse weather conditions, malfunctioning of some radar subsystem or channel interferences. The first approach to address the issue of cleaning radar data is to run both procedures sequentially on offline radar data. This would yield an after flight quality plot of the just tracked aerospace target to radar experts, helping them on reporting clean plots, extracting meaningful trends from radar data, detecting disagreements regarding nominal trajectories, modelling trajectories, etc. The second approach

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Figure 2. Hampel filter work over outlier-contaminated trajectory data. (a) Original data; (b) 10% Outlier contamination e (c) Hampel filtered data. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.237-248, Jul.-Sep., 2014

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is supposed to be online and it involves establishing a moving window with parameterized bandwidth to be applied to incoming radar data, producing an on flight outlier-free and smoothed curve, which could be used for an accurate calculation of instant speed and acceleration of the target, determination of a less dispersed area of impact and establish concise extrapolations on the trajectory in case of radar malfunctioning or tracking loss.

step and the overall processing is applied to it. The step size could be the whole window bandwidth or a fixed proportion of it. The overall processing has to be accomplished as fast as data arrives, once the online approach is supposed to support radar analysts’ decisions and derivative processing during the flight of the engine.

IMPLEMENTATION In these early research phases, it is important to prioritize a proper evaluation of the effectiveness of the methods under study over the achieved performance. After the validation of the effectiveness of the methodology, performance considerations should be taken into account. Given that, it is convenient in study and prototype phases to use numerical computing environments for the sake of simplicity, abstraction and flexibility, despite the well-known performance issues inherent to these environments. Several environments such as Matlab, R and Scilab were used in our experiments since most building blocks used in our approach are already satisfactorily implemented in these numerical environments. The dataset is basically composed by tridimensional Cartesian coordinates converted from azimuth, elevation and distance coordinates of trajectory data generated by the radar during the tracking of a target. As they are obtained from different radar subsystems, they can be considered independent measures. Then, each trajectory coordinate is separately treated as a univariate time series, which is conjoined with the others only for plotting purposes. The offline approach was implemented in a straightforward way. It consists on the sequential submission of the target dataset to both Hampel filter and LOESS procedures with predefined parameter values to get as result an outlier-free and smoothed curve which may better reflect the trajectory of the aerospace engine. The online approach rendered somewhat more work, since a moving window to be applied to the incoming data needs to be established. To ensure the online scenario, a 20 Hz sampling rate to the already existent trajectory datasets is simulated. Thus, a sliding window is established, taken into account a fixed proportion of the full dataset points to set the window size. To step the window through data, it is required to fill a buffer with the latest incoming points. Thus, at each fulfilment of the buffer, the window moves a

RESULTS EFFECTIVE COMBINATION In most tests, the filtering process combining outlier detection and smoothing successfully delivered an outlier-free and smooth curve that, according to radar specialists, does not affront rocket models and nominal trajectories. The results are quite satisfactory as both procedures on which our approach is based performed very well in their actuation fields. The Hampel filter played an important pre-processing role as the routine preceding the LOESS smoother, especially in scenarios of high contamination. In most tested datasets, Hampel filter could show an identification rate exceeding 80% in relation to artificial outlying data. It was also able to ensure that the most significant outliers would not render much interference in local regression. Figure 3 shows a comparative plot set for offline and online filtering approaches. Figure 3 (a) presents a 10% artificial outlier contamination on a certain rocket trajectory data and Fig. 3 (b) reveals the offline results regarding the application of the filtering processes, using the parameters K=3, t=3 (Hampel filter) and g=0.5, d=2 (LOESS). A locally quadratic fit (d=2) was used in all LOESS tests. Regarding the smoothing phase, it was observed significant differences between offline and online results. Offline results were quite impressive, since the output curve was indeed smooth, reflecting better the actual trajectory of the target flying engine. On the other hand, the results from the online implementation pointed out that there is a serious trade-off related to the size of the moving window and the effectiveness of the LOESS smoothing procedure. In general, online outputs reveal a drop in performance and effectiveness of the method when the bandwidth of the moving window is narrow, when compared to the size of the whole dataset. In comparison with the offline method, the online approach always leads to a less smooth curve, obtained from a temporally and computationally more costly process.

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An Approach to Outlier Detection and Smoothing Applied to a Trajectography Radar Data

It happens because LOESS requires a fairly large and densely sampled datasets in order to produce good models. That is, it needs good empirical information on the local structure of the process in order to perform the local fitting (NIST/SEMATECH, 2012). When the dataset is partitioned in windows that move as data arrives, much less data is delivered to each iteration of the LOESS procedure, making the smoothing process much more local when considering the whole dataset. Besides, as the LOESS procedure is invoked more often (at least in each window iteration), a performance drop is also justifiable. Substantially increasing the moving window bandwidth is not the solution to this problem, because this would make this online approach gradually closer to the offline approach. Since the methods are computationally intensive, limited computational capabilities becomes a crucial barrier in the online approach. Figure 3 (c ) also shows the results when applying the online method on the contaminated dataset, using the same parameters used on offline experiments (for Hampel filter and LOESS) and a window size w=100 (one hundred samples) for the moving window applied to incoming data. The online method outputs a slightly less smooth curve than the offline results and it is much more computationally intensive.

applied to this smooth and well-behaved data would render better results when compared to the processing of raw radar data. From existing filtered source data, extrapolation could play an important role by completing the missing portion of the signal in a tracking loss scenario or by predicting with better accuracy the impact point of a falling aerospace engine. To prove this hypothesis, we decided to extrapolate both filtered and unfiltered data using a well known and effective extrapolation method and comparing the results. A proven effective and established method that could fit well to the problem was chosen. A linear predictive strategy using autoregressive modeling was preferred as its attractiveness stems, among others, from the fact that the numerical algorithms involved in the processing are rather simple and it depends on a limited number of parameters which are estimated from the already measured data (De Hoon et al., 1996). However, this strategy produces coefficients which are not well suited for numerical computation and models which are not always stable. To overcome these constraints, the preferable algorithm to estimate the autoregressive parameters is the Burg’s method, due to its reliability and accuracy on parameter estimates and because the estimated autoregressive model is guaranteed to be stable (De Hoon et al., 1996). To sum up very briefly, we used the strategy mentioned above to extrapolate a time series (each of the three

EXTRAPOLATING FILTERED DATA Because of the good results after applying the filtering processes, it was expected that any further processing

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Figure 3. A comparative plot set for offline and online filtering approaches. (a) Original data with 10% outlier contamination; (b) Offline filtered curve; (c) Online filltered curve. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.237-248, Jul.-Sep., 2014

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signal components) by fitting a linear model to the time series, in which each sample is assumed to be a linear combination of previously observed samples. Hence, the predicted samples are the next time samples of the input time segment. The following figures illustrate the results of applying Burg’s extrapolation over both unfiltered and filtered radar data, obtained from the tracking of a certain rocket. Again, for illustration purposes, the base data used in these extrapolation experiments is the original tracking data with 5% of samples artificially contaminated by outliers. Figure 4 shows a tridimensional plot comparing the entire base data to its extrapolation, starting from the 3501st of a 5820 sample total. A noticeable deviation between the unfiltered samples curve and the extrapolated samples curve can be noted. On the other hand, the tridimensional plot of Fig. 5 clearly shows that the deviation between the filtered data and its extrapolation is much lower.

DISCUSSION AND FUTURE WORK This work represents just a primer approach to trajectory radar data cleaning, supported by a long-standing demand brought about by radar specialists. Preliminary results were considered quite satisfactory, mainly regarding the

offline processing. The problem of outlying samples was reasonably surpassed and data analysts can rely on a smoother and more representative curve for subsequent analysis. Online processing demands more work regarding the optimal strategy on processing incoming radar data as the efficient use of a stepping moving window limits the amount of data delivered to smoothing processes and, consequently, degrades the quality of the smoothed curve. Besides, for effectiveness and performance reasons, it is essential to implement online processing methods in lowlevel programming languages, since the online approach needs several iterations of already costly processes, and the processed results should be instantly available. As for what to be done with the filtered data, the possibilities are vast. It was shown that, from a smooth and well behaved trajectory curve, extrapolation strategies could be readily applied in case of radar malfunctioning and target loss. Also, it is possible to adjust parametric models to the curve which can propitiate accurate speed and acceleration calculations on specific points of the trajectory. These approaches could also be used in a scenario where it is needed to determine precisely the area of impact of a tracked flying engine. The parameters of the processes used in this study were chosen based on literature indications and empiricism. A future work could comprise the use of an automated

Base data x 10 8 7 6 5 4 3 2 1 0 -1 1000

Extrapolated data

Start of extrapolation

-1000

-2000

-3000

-4000

-5000

-6000

-7000

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Figure 4. Extrapolation of unfiltered radar data.

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7 x 104

An Approach to Outlier Detection and Smoothing Applied to a Trajectography Radar Data

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Fltered data Extrapolated data

x 10 8

7 6 5 4 3 2 1 0 -1 1000 0

Start of extrapolation

-1000

-2000

-3000

-4000

-5000 -6000

-7000

-8000

-1

7 x 104

Figure 5. Extrapolation of filtered radar data.

parameter tuning algorithm to select the best parameters given the trajectory shape and quality of radar data. Also,

analyses data and nominal trajectory behavior data for a specific engine.

the independent trajectory coordinates problematic is likely to be a good candidate for parallel or distributed processing approach in order to speed up the overall process, especially for the online filtering process. A lot of validation work has to be done in order to verify if the output model really befits the actual trajectory of a tacked aerospace engine. This validation work may consider past tracking datasets, analytic physical

ACKNOWLEDGMENT We would like to thank Eng. Edson Duarte, from Centro de Lançamento da Barreira do Inferno (CLBI), for providing several radar tracking datasets and invaluable explanations about trajectory radar functioning.

REFERENCES Adnan, R., Mohamad, M.N. and Setan, H., 2003, “Multiple Outliers Detection Procedures in Linear Regression”, Matematika, Vol. 19, No 1, pp. 29-45. ASTM International, 1980, “Standard Practice for Dealing with Outlying Observations”, Active Standard E-178-08. Cleveland, R.B., Cleveland, W.S., McRae, J.E. and Terpenning I., 1990, “STL: A seasonal-trend decomposition procedure based on LOESS”, Journal of Official Statistics, Vol. 6, No 1, pp.3–73.

Cleveland, W.S., 1979, “Robust Locally Weighted Regression and Smoothing Scatterplots”, Journal of the American Statistical Association, Vol. 74, No 368, pp. 829–836. doi:10.1080/0162 1459.1979.10481038. Cohen, R. A., 1999, “An Introduction to PROC LOESS for Local Regression”, Proceedings of the 24th SAS Users Group International Conference, Paper 273.

Cleveland, W.S. and Devlin, S.J., 1988, “Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting”, Journal of the American Statistical Association, Vol. 83, No 403, pp. 596-610.

Davies, L. and Gather, U., 1993, “The identification of multiple outliers”, Journal of the American Statistical Association, Vol. 88, No 423, pp. 782–792. doi:10.1080/01621459.19 93.10476339.

Cleveland, W.S. and Loader, C.L., 1996, “Smoothing by Local Regression: Principles and Methods”, Statistical Theory and Computational Aspects of Smoothing, pp. 10-49, Haerdle and M. G. Schimek, Springer, NY.

De Hoon, M.J.L., van der Hagen, T.H.J.J., Schoonewelle, H. and van Dam, H., 1996, “Why Yule-Walker should not be used for autoregressive modeling”, Annals of Nuclear Energy, Vol. 23, No 15, pp. 1219–1228. doi:10.1016/0306-4549(95)00126-3.

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Garcia, D, 2010, “Robust smoothing of gridded data in one and higher dimensions with missing values”, Computational Statistics & Data Analysis, Vol. 54, No 4, pp. 1167-1178. doi:10.1016/ j.csda.2009.09.020. Horwitz, W., 1995, “Protocol for the Design, Conduct and Interpretation of Method-performance Studies”, Pure & Applied Chemistry, Vol. 67, No 2, pp. 331-343. doi:10.1351/pac199567020331. Lee, J., Han, J. and Li, X., 2008, “Trajectory Outlier Detection: A Partition-and-Detect Framework”, Proceedings of the IEEE 24th International Conference on Data Engineering (ICDE ‘08), pp. 140-149. Liu, H., Shah, S. and Jiang, W., 2004, “On-line outlier detection and data cleaning”, Computers & Chemical Engineering, Vol. 28, Issue 9, pp. 1635–1647. doi:10.1016/j.compchemeng.2004.01.009. Loader, C., 2012, “Smoothing: Local Regression Techniques”, Handbook of Computational Statistics, Ch. 20, pp. 571-596. doi:10.1007/978-3-642-21551-3_20. Massart, D. L., Vandeginste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J. and Smeyers-Verbek, J., 1997, “Handbook of Chemometrics and Qualimetrics: Part A”, Data Handling in Science and Technology, Elsevier Science, Vol. 20A. NIST/SEMATECH, 2012, “e-Handbook of Statistical Methods”, Retrieved in May 27, 2013, from http://www.itl.nist.gov/div898/handbook/. Nurunnabi, A.A.M. and Nasser, M., 2008, “Multiple Outliers Detection: Application To Research & Development Spending and Productivity Growth”, BRAC University Journal, Vol. V, No 2, pp. 31-39.

Pearson, R. K., 2002, “Outliers in process modeling and identification”, IEEE Transactions on Control Systems Technology, Vol. 10, No 1, pp. 55-63. doi:10.1109/87.974338. Pearson, R.K., 2005, “Mining Imperfect Data: Contamination and Incomplete Records”, SIAM.

Dealing

with

Pearson, R.K., 2011, “Exploring Data in Engineering, the Sciences, and Medicine”, Oxford University Press, 2011. Rancel, M.M.S. and Sierra, M.A.G., 2000, “Procedures for the Identification of Multiple Influential Observations Using Local Influence”, The Indian Journal of Statistics (Sankhy ), Series A, Vol. 62, No 1, pp. 135-143. Simonoff, J.S., 1998, “Smoothing Methods in Statistics”, 2nd edition, Springer. Solak, M.K., 2009, “Detection of Multiple Outliers in Univariate Data Sets”, Schering-Plough Research Institute, Summit, NJ, paper SP06-2009, Retrieved in May 27, 2013, from http://www. lexjansen.com/pharmasug/2009/sp/SP06.pdf. Wilson, D.I., 2006, “The Black Art of Smoothing”, Electrical and Automation Technology, June/July Issue. Zhang, M.H., Luypaert, J., Pierna, J.A.F., Xu, Q.S. and Massart, D.L., 2004, “Determination of Total Antioxidant Capacity in Green Tea by Near-infrared Spectroscopy and Multivariate Calibration”, Talanta, Vol. 62, Issue 1, pp. 25-35. doi: 10.1016/S00399140(03)00397-7.

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doi: 10.5028/jatm.v6i3.365

Direct Numerical Simulation of the Onset of Vortex Shedding for Blunt Elongated Bodies Marcos Aurélio Ortega1, Roberto da Mota Girardi1, Jorge Hugo Silvestrini2

ABSTRACT: This research project focuses upon the wake behind a two-dimensional blunt-trailing-edged body. Data are obtained numerically by means of a Direct Numerical Simulation code. The body has an elliptical nose followed by a straight section that ends in a blunt base. The present paper is dedicated to the analysis of the onset of the shedding process. The effort is certainly worthwhile, because, in contrast to the case of the circular cylinder, the boundary layers’s separation points are defined and fixed. This allows a better assessment of the vital influence of the boundary layers upon the wake, in a controlled way. This is not the case for the circular cylinder, because, in this instance, the separation points oscillate in relation to a mean position. In the present analysis, the relationship between the onset of shedding Reynolds number, RehK , and the aspect ratio, AR, is obtained. To this end, a wide range of aspect ratios, between 3 and 25, was investigated. The result represented by this relationship is a novelty in the literature. Values of RehK are strongly influenced by the aspect ratio for the case of the short cylinders — for which AR is low. After AR about 9, the curve flattens and the influence of the aspect ratio upon the shedding Reynolds number is very mild. Besides, the paper discusses another very important aspect; the overall stability of the pre-shedding laminar bubble at the base of the body. It is important to stress that the latter study relies on the fact that the boundary layers separation points are fixed. KEYWORDS: Wakes, Onset of shedding, Blunt-trailing-edged body, Numerical analysis.

INTRODUCTION The aim of the present work is to study the wake behind a blunt-trailing-edged body, consisting of an elliptical nose followed by a straight section (Fig. 1). In the present paper, a study of the onset of shedding is reported, whereas in a second paper, the main topological features of the flow are discussed (Ortega et al., 2012). There is a myriad of publications in the literature related to flows about blunt bodies. It is not the aim of the present authors to present here a thorough literature review about this theme. The interested reader can find these information in the following citations. Some of the most comprehensive and authoritative historical reviews are the ones by Williamson (1996) and Dauchy et al. (1997). On the other hand, low Reynolds number data, relative to elliptical elongated cylinders, is rather scarce. Bearman (1965) has conducted, during the 1960’s, a thorough experimental analysis of this geometrical form. Two-dimensional results were presented for a model with and without splitter plates, and the Reynolds number, based on the model chord, was made to vary in the range of 1.4 x 105 to 2.56 x 105 (Bearman, 1965). Several splitter plate lengths were investigated, and studies of base pressure, shedding frequency, hot-wire traverse distributions of mean flow velocity, and r.m.s. of velocity fluctuations, were done. The main focus of the investigation was the understanding of how those factors would influence the formation length, and, at the same time, to assess the possibility of drag reduction. In a sequence, Bearman (1967), following basically the same line of study, investigated the effect of base bleed behind the same model. Now, the Reynolds number, based on the body base height, was made to vary between 1.3 x 104 and 4.1 x 104.

1.Instituto Tecnológico de Aeronáutica – São José dos Campos/SP – Brazil 2.Pontifícia Universidade Católica do Rio Grande do Sul - Porto Alegre/RS – Brazil Author for correspondence: Marcos Aurélio Ortega | Department of Aerodynamics – Division of Aeronautical Engineering – ITA, CEP: 12.228-001 - São José dos Campos/ SP | Brazil | Email: marcos.ts.ortega@gmail.com Received: 04/09/2014 | Accepted: 06/25/2014

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The model investigated had a span of 71.1 cm, a chord of 15.24 cm, and a base height, h, of 2.54 cm. The aspect ratio, AR = c/h, was equal to 6. The frontal half ellipse had semimajor and -minor axes equal to, respectively, 12.70 cm and 1.27 cm. In the words of the author: “The rear 2.54 cm of the model was parallel sided in order that the flow left the surface smoothly at the trailing edge corners”. As pointed out before, this was the main geometric characteristic that attracted our attention. Recently, Park et al. (2006) have investigated a new passive device for drag reduction in a flow about a bluff body with a cross-section like the one in Fig. 1. The aspect ratio was equal to 6.33 and the study involved both experimental and numerical treatments. Another work that has focused on the elongated cylinder shape is the one by Ryan et al. (2005). In this paper, the authors conducted a numerical Floquet analysis of the three-dimensional transition to turbulence in the wake of the body. For the case of the elongated cylinder, the onset-of-shedding process that happens at low values of the Reynolds number, the well known Hopf bifurcation, responsible for the appearance of the von Kármán vortex street, has never been investigated before. This is the main focus of this article. Values of the onset of shedding Reynolds number are numerically obtained by varying the value of the aspect ratio. For low values of the aspect ratio, the influence upon the Reynolds number in strong, but after about AR = 9, the curve flattens and an asymptotic behavior is observed. After that, an exploration on the influence of the boundary layers upon the shedding process was attained. For this purpose, we have “installed” small bumps on the upper and lower surfaces of the body, and have studied how those perturbations affect the conditions of the vortexes emission. This paper is organized as follows. In the section “Computational strategy”, the main aspects of the mathematical model are presented and discussed. A short review of the Hopf bifurcation is given, with the purpose of establishing the physical scenario to be tackled ahead in the study. After that, some data about validation and convergence of the code are discussed. Convergence characteristics are extremely important here because the physics is essentially time dependent. In the section “In search of the onset of shedding Reynolds number”, the core of the article is divided into three sub-sections. In the first one, a strategy of investigation is designed. We have arrived at the rake of grids idea. This idea was tested, first, by dealing with the circular cylinder, taking into account the

flow

chord central point p yc

h q

xc, yc = position of the body in the calculation domain, h = body base height, c = body chord length.

Figure 1. Cross-sectional view of the elongated cylinder and dimensions of computational domain.

vast number of literature data for this geometry. Because the circular cylinder results were consistent, we then applied it to the elongated body. Finally, we made an effort to assess the influence of the boundary layers upon the formation and shedding of the structures at the base of the body. Some conclusions are then presented.

COMPUTATIONAL STRATEGY Firstly, solutions for flow past a circular cylinder from time-dependent simulations of the Navier-Stokes equations on two-dimensional domains are obtained. The immediate objective is to perform code convergence tests. Secondly, the code is applied to the studies of the flow about the elongated cylinder. The code is called Incompact3d and it was originally developed by Lamballais and Silvestrini (2002), and Lamballais et al. (2008); it is a software for incompressible flows of the immersed boundaries type and it uses compact differences for discretization purposes. TIME DEPENDENT SIMULATIONS We consider the flow of an incompressible Newtonian fluid. The physical behavior is mathematically modelled by the Navier-Stokes equations, written here as:

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Direct Numerical Simulation of the Onset of Vortex Shedding for Blunt Elongated Bodies

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(1) and Lamballais, 2004; Lardeau et al., 2002; Laizet et al., 2009). The two-dimensional approach that is used to investigate the (2) problem is appropriate due to the fact that, when the Reynolds number is low, the flow is everywhere laminar but the main characteristics of the great structures are mostly all present where; ρ is the density, is the pressure field, and is (Barkley and Henderson, 1996). Boundary conditions are required at the limits of the the velocity field. The introduction of an external force field is necessary, computational domain. At the entrance plane — left vertical in order to simulate the presence of the body. The equations boundary in Fig. 1 —, velocity components are specified above, if written in rotational form, are stable to aliasing errors according to: ux = U∞= 1 and uy = 0. A white noise might (Kravchenko and Moin, 1997); therefore, in the numerical be added, in order to speed up a process of transition that is eventually being studied. At the upper and lower boundaries, algorithm, the following formulation is preferred: the uniform flow is enforced, i.e., ux = 1 and uy = 0 (Ribeiro, (3) 2002), while along the exit plane, conditions are established according to a simplified convection equation: where; (4) P * (x,t)is the modif ied pressure f ield (equal to 2 (P+ ρ u /2)), and ω(x,t) is the vorticity field (equal to∇×u). The groups of equations above are written in non- where Uconv is made equal to the main structures mean-convection dimensional form: lengths are scaled by a typical length, velocity at the end of each iteration. The very mild degradation lt, — the diameter for the circular cylinder, d, and the base of the flow, imposed by this condition, is confined to a narrow height for the elongated cylinder, h, — and a typical velocity, region close to the boundary, according to Akselvoll and Moin which happens to be, in both instances, the magnitude of the (1996). The last frontier is composed of the body surface. To cope free-stream velocity, U∞. The Reynolds number that results with it, following Goldstein et al. (1993) and Saiki and Biringen from the non-dimensionalisation is given by Re = U∞. lt / υ∞, (1996), a feedback forcing term was introduced in the momentum equation, in order to represent the presence of the solid body. where υ∞, is the free-stream kinematic viscosity. Time-dependent simulations based on these equations in This procedure presents an outstanding advantage because a two dimensions (ω≡0; ∂/∂z≡0) are carried out using a sixth- Cartesian grid can always be used, independently of the body’s order, compact-finite-difference scheme (Lele, 1992). Meshes external geometry. are Cartesian and the presence of the body is simulated by virtue of an immersed-boundary technique (Goldstein HOPF STABILITY ANALYSIS For sufficiently low Reynolds numbers, the topology in et al., 1993). To integrate Eq. (3), a third-order low-storage Runge-Kutta strategy was used (Lamballais, 1996). The core the near wake of a two-dimensional bluff body is that of the of the algorithm solves a Poisson equation for the pressure, laminar twin vortices. After that, and as the Reynolds number which, in the sequel, acts as a projector of the velocity field grows, an instability corresponding to a Hopf bifurcation leads onto a divergence-free space (Lamballais, 1996). Part of the to a changing of regimes. For the case of the circular cylinder, forcing terms in the immersed boundary sub-routine is and for a Reynolds number, somewhere between 40 and 50, advanced implicitly, instead, and the Crank-Nicolson scheme the steady flow field transitions to a laminar two-dimensional is used (Lamballais, 1996). This feature improves the efficiency shedding wake, the von Kármán vortex street — Williamson of the forcing mechanism (Fadlun et al., 2000; Lamballais (1996) reports a ReKd equal to 49; DuŠek et al. (1994) inform and Silvestrini, 2002). All calculations presented here were a slightly different range, 46 - 47, while Sumer and Fredsoe performed in 64-bits precision. Further details of the code, and (1997) give ReKd = 40. The subscript K in the Reynolds number many verification and validation studies, can be assessed in a symbol is an indicative that the value corresponds to the onset of series of former applications, both two- and three-dimensional shedding, while a superscript indicates the definition reference (Ribeiro, 2002; Vitola, 2006; Lamballais et al., 2008; Silvestrini length. To be more specific, one should remind that, at this low J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.249-266, Jul.-Sep., 2014

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range, there are three limiting values of the Reynolds number, which we shall indicate by Rec, ReA, and ReK. The subscripts correspond to “critical state”, “absolute instability” and “onset of shedding”, respectively. For the case of the circular cylinder and for a Reynolds number based on the diameter, Monkewitz (1988) reports the following: Rec ≈ 5, ReA ≈ 25, and ReK d ≈ 47. The critical Reynolds number, Rec, divides the ranges of complete stability (Re < Rec) and convective instability (Rec < Re < ReA). ReA marks the appearance of the first local absolute instability, while ReKd is indicative of the onset of vortexes shedding. See, for example, Huerre and Monkewitz (1985), or Bers (1983), or Drazin and Reid (2004), for an explanation of those stability concepts. In his studies Monkewitz investigated the stability of a family of incompressible bluff-body wakes by means of a linear parallel, that is, local, approach. One of the main results of this investigation is the confirmation that ReK is really larger than ReA, a result first established by Chomaz et al. (1988). In other words, this means that the Karman vortex street is a consequence of self-excited oscillations of the wake, and that shedding is not triggered by the first appearance of an absolute instability in the near wake. This is a necessary condition, but shedding is the “end product” of a saturated state, that is, global instability sets in after a whole region of the near wake has reached the absolute instability state (Monkewitz, 1988; Chomaz et al., 1988; Yang and Zebib, 1989). Monkewitz (1988) suggests that for forms other than circular, the sequence Rec < ReA < ReK is maintained, but with different numerical values. The majority of the studies which deal with the Hopf bifurcation is either experimental or analytical (based on some variation of the linearized Landau equation). There is also a hybrid approach, where the author significantly simplifies the physical scenario: by considering the basic flow as parallel, for example, and completing the study by numerical means (Triantafyllou et al., 1986). The approach to be followed in this work is the DNS, Direct Numerical Simulation, a numerical tool that solves the complete Navier-Stokes equations. The great advantage is to solve the equations in their original form, and, in doing so, to take into account the essential non-linearities of the physical problem. The disadvantage, which is also considerable, corresponds to the computer costs. The main drawback is the following. For a coarse grid, the elapsed time to reach a stable oscillating state is rather short (taking into account that the Reynolds number is already inside the shedding range). This time length is measured from the

very beginning — iteration 1 —, when the field of flow corresponds to the initial state (usually, uniform distributions of parameters along the domain of calculation). The destabilizing interval of time is short, in this instance, because the numerical error is relatively large in view of the coarseness of the grid. The numerical error is the leading factor in the onset of shedding triggering process (this point will be explored further in this paper). On the other hand, if the grid is coarse, the overall accuracy will most probably be poor. Eventually then, the user might be tempted to refine the grid in order to improve accuracy. As a result of this initiative, the destabilizing interval of time will grow accordingly, because a refined grid will “offer” a smaller destabilizing effect. Because the process is truly an assymptotic one, refining the grid further will correspond to an ever increasing computational time (a similar effect can be observed in Figs. 2 and 4 in relation to compiler precision). The way we have adopted to avoid this physical/numerical difficulty is to search for pre-established ranges of Reynolds numbers, which will, at least under certain controllable circumstances, contain the true value of ReK . That is, we will be looking for data in the form of ReK ± Δ Re. In the sequel, the details of this strategy will be given and discussed.

CONVERGENCE TESTS The main concern in this section is to check for approximation errors. For the cyclic flow computations, the results were verified to be mesh independent to a high degree of accuracy. These error checking studies were conducted considering the flow about a circular cylinder. The cyclic flow calculation was submitted to a complete battery of tests. In former works, members of our team (Ribeiro, 2002; Vitola, 2006) have already investigated the “collection” of optimized code parameters — related both to domain geometry and mathematical algorithms. Notwithstanding this, and due to the nature of the present paper, we have decided to further investigate the convergence characteristics of code Incompact3d, by means of a grid-refinement study— following Barkley and Henderson (1996). To this end, we have used a 30d x 24d grid and several resolutions, as shown in Table 1. The Reynolds number was fixed in 300. Here, an enlarged grid is preferred because errors resulting, for example, from small distance of the body to the domain entrance plane and blockage effects, can be better controlled. Results are given in Table 1 showing convergence to three digits for Δ/d ≤ 0.0167.

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Direct Numerical Simulation of the Onset of Vortex Shedding for Blunt Elongated Bodies

Table 1. Grid refinement study of code Incompact3d. Δ/d

< Cd >

C’d

C’l

0.0250

1.4267

0.0589

0.6776

0.0222

1.4147

0.0584

0.6703

0.0200

1.4178

0.0585

0.6718

0.0185

1.4018

0.0578

0.6615

0.0167

1.3927

0.0562

0.6483

0.0156

1.3924

0.0562

0.6482

IN SEARCH OF THE ONSET OF SHEDDING REYNOLDS NUMBER THE STRATEGY FOR THE CIRCULAR CYLINDER The numerical investigation in the range Re ≈ ReK, that is, for values of the Reynolds number in the onset of shedding region, must be done with very much care. As soon as one approaches the exact value of ReK, the final solution gets extremely sensitive to the various parameters that have some influence in the numerical calculation. Basically, every factor has its influence in the final shedding conditions: (i) Grid resolution (probably, the most influential one); (ii) Type of numerical algorithm; (iii) Domain overall sizes (Lx and Ly); (iv) Accuracy — if single or double precision; (v) Time step; (vi) Type of machine: if 32 or 64 bits; (vii) For codes of the immersed boundary type — as the one here employed is —, how smoothly is the perimeter of the body represented. These items bring to the stage the question of the computational error, a factor that helps to trigger the shedding process when it grows up to a certain point. The problem of the blockage comes embedded in item (iii), and is especially related to Ly. Item (iii) also includes the effect of the position of the body in relation to the domain boundaries, especially the entrance plane. The time step has an indirect effect, because as the grid is refined, most probably one will be required to lower it, in order to guarantee convergence. The Reynolds number is evidently a prime influence. Besides all those aspects, there is still to be remembered the fact that the closing-in process to ReK is, in a way, always asymptotic, and, most certainly, the investigation will require running the code for long intervals of time. In the sequence, we will return to some of these points and discuss them further.

253

After some work, we devised what was judged as a good approach to the problem. The core of our strategy corresponded to the creation of a sort of a “grid rake”. This means a collection of grids with different resolutions. The testing of the idea was done considering the flow about a circular cylinder. For this specific case, five meshes were used in the study, corresponding to [nx/d] = [ny/d] = 12, 18, 24, 30 and 36, where d stands for the circular cylinder diameter. The domain of calculation dimensions were adopted as Lx = 19d, Ly = 12d, and xc = 8d (these values are the results of optimizing studies by Ribeiro (2002); for a Reynolds number equal to 300, these studies indicate n/d = 24 as a best value for the number of grid nodes per diameter). The fact is that, the numerical investigation of the onset of shedding neighborhood is difficult, time consuming, and very elusive. Therefore, we concentrated mainly on the grid resolution, and probed other influences more superficially. The main aim of this particular effort is to try to develop a viable and reliable criterion that could be applied to the ReK region, especially now to the elongated cylinder studies. We studied the flow about the circular cylinder in the range of Reynolds number from 40 to 50. Table 2 shows the wake flow state for the various cases investigated. The first column of the table gives the value of the Reynolds number, while the first line indicates the grid resolution in terms of grid points per cylinder diameter. We began by running the case Re = 40 and ended with Re = 50, and the intermediate values are shown in Table 2. Each of these values of the Reynolds number was investigated using the five grid resolutions, as shown in Table 2, [nx/d] = [ny/d] = 12, 18, 24, 30, 36. All cases were run in single precision in a 64-bits machine, and at least from t = 0 up to t = 1000 units of nondimensional time. The dimensionless time step was always the same and equal to 0.004838, which corresponded to a safe value that would guarantee the convergence at the finer grids. For Re = 40, 41 and 42, there was no shedding, and the signal of the anemometers indicated damping (some “numerical” anemometers were strategically placed in certain positions along the grid.) When the Reynolds number was raised to 43 there was shedding for resolution 12/d. The first Reynolds number value for which we obtained vortexes shedding in all grids was Re = 45. One would be then tempted to define 45 as the value of ReKd for the cylinder, but there is a crucial point here. We do believe that it is basically not possible to define an absolute value of the onset of shedding Reynolds number,

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Table 2. Condition of the circular cylinder wake in the onset of shedding range. Red

12/d

18/d

24/d

30/d

36/d

36/d (0.01%)

36/d (0.1%)

–

s: vortexes shedding; d: vortexes damping (Calculations performed in single precision).

at least when one is using an experimental or numerical tool to probe the process. The most one can do is to search for a range of values inside which one can probably guarantee, under certain controlled conditions, that the value of ReKd resides. To clarify this point, let us focus our attention upon the case “43/18d”, in Table 2. In this instance, there was no shedding until t = 1000. On the other hand, how can one be sure that the shedding will not set in for a certain instant of time after that limit? As we have stressed before, the final stability of the laminar bubble system in the wakeside of the cylinder is a characteristic of the system perse. In other words, given the state of the final saturation, the onset of shedding settles in. Therefore, what might be happenning here is that, for t = 1000, we have not yet reached the final saturated state. If this is the case, running for a longer stretch of time would almost inevitably result in shedding due to the mounting of the overall instability, which is ultimately fed by the numerical errors, being introduced in every iteration. Putting it differently, the result in Table 2 for the 43/18 case is conditioned to the length of the pre-defined time interval, equal, in this case, to 1,000 units. To be really sure, one would have to run the case for a much longer time stretch. If the setting 43/18 is physically not a shedding case, the feeding of perturbation at each iteration would never saturate the laminar bubble system, and the machine would run indefinitely without disturbing the damping state. This is why we argue that, for numerical, as well as for experimental investigations, it is not possible to fix an absolute value of the shedding Reynolds number. In this work, we will concentrate on the effort of determining the ranges of ReK.

A careful observation of Table 2 reveals an unexpected result relative to Re = 43. Because the arrangement 43/18 corresponds to a damped flow, with more reason, the combination 43/24 should also correspond to a damped case. In normal conditions, the amplitude of the numerical error distribution along the grid is the main drive for the shed triggering. All combinations in Table 2 were run in a 64-bits machine (Dell Precision 690) with single precision. In any case, the amplitude of the error diminishes as the grid is refined. After some investigation we found out that the 24 points per diameter grid does not match as best as possible, as it should, the cylinder perimeter. That is, for this resolution, some nodes get slightly mispositioned along the perimeter. This “mispositioning” is recognized considering the details of the immersed boundary strategy. In practice what happens is that the flow in the numerical calculation “sees”, in this case, the cylinder as a body with an augmented rugosity in its surface. Therefore, the perturbation increased, since 43 is located in a very critical position in relation to the interval that is being investigated (Re = 40 - 45), i.e., exactly at the centre of the stretch, the case 43/24 came out of the calculation as a shedding case and not as a damped one. Because the points previously stressed are too important, we would like to illustrate them further. A clear evidence of the fact that the error amplitude is decisive is given in Fig. 2. We rerun several 12 points per diameter cases, but now using double precision, and a much longer calculation that reached 10 141.26 units of time. The running of the code, for such long stretches of time, can be looked at as a severe test for the damping characteristic of the flow. Besides, one has to keep

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.249-266, Jul.-Sep., 2014

Direct Numerical Simulation of the Onset of Vortex Shedding for Blunt Elongated Bodies

Re=41

0,5 0,4 0,3 0,2 0,1 0 -0,1 -0,2 -0,3 -0,4 -0,5 0

Re=42

0,5 0,4 0,3 0,2 0,1 0 -0,1 -0,2 -0,3 -0,4 -0,5 0

500

time (a)

750

250

500

time (b)

Re=44

750

1000

0,5 0,4 0,3 0,2 0,1 0 -0,1 -0,2 -0,3 -0,4 -0,5 0

1000

250

500

time (c)

750

1000

Re=45

250

Re=43

0,005 0,004 0,003 0,002 0,001 0 -0,001 -0,002 -0,003 -0,004 -0,005 0

255

250

500

time (d)

750

1000

250

500

time (e)

750

1000

Figure 2. History of the crosswise velocity component, υ, for the circular cylinder and several values of Re. Grid resolution=12/d. Processing in double precision

in mind that we are using here the coarser grid, the one that rises the largest amount of error. The reader can perceive that shedding was damped for Re = 41 and Re = 42, and very much delayed for the other cases. The reason is evidently the fact that the truncation error has decreased when the compiling precision was increased. As a final test following this line, we introduced a white noise with amplitude of 0.0001, with the basic aim of speeding the onset of shedding. The results are shown on Fig. 3, and when compared with Fig. 2, they confirm the expected speeding of the process. When submitted to this level of excitation, the Re = 42 case did not “resist” and the shedding was initiated, only after about 7,500 units of time. Besides, and again from Fig. 3, one can appreciate the fact that the introduction of this level of noise corresponds basically to running the cases in single precision — compare it to Fig. 4. In fact, and the reader should keep this in mind, what is happening here is that for this grid resolution and double precision compiling, the beginning of vortexes emission was delayed, but not eliminated. The introduction of the white

noise simply “pulled” the shedding process back to an earlier time, what evidently spares a lot of computing time. On the other hand this is a clear example of the difficulties associated to the study of transitional physical problems. The last word about a certain case, i.e., if the flow has finally reached the final global response — Kármán vortex shedding, Monkewitz (1988) —, or not, depends upon many factors, and much care must be exercised. Attention was also paid to the other side of Table 2. Flows with Reynolds numbers 42, 43, and 44 were simulated again, but now subjected to higher levels of excitation. This was accomplished by raising the white noise amplitude. Columns “36/d(0.01%)” and “36/d(0.1%)” contain results for the 36-nodes-per-diameter grid and levels of perturbation of 0.01% and 0.1%, respectively. Again we observe that shedding was obtained for some Reynolds numbers that, otherwise, when excitation was smaller, corresponded to damped cases, at least during the length of time of numerical simulation — 1,000 units. Another clear evidence from Table 2 is the very

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.249-266, Jul.-Sep., 2014

Re=41

0,5 0,4 0,3 0,2 0,1 0 -0,1 -0,2 -0,3 -0,4 -0,5 0

Re=42

0,5 0,4 0,3 0,2 0,1 0 -0,1 -0,2 -0,3 -0,4 -0,5 0

0,005 0,004 0,003 0,002 0,001 0 -0,001 -0,002 -0,003 -0,004 -0,005 0

Ortega, M.A., Girardi, R.M. and Silvestrini, J.H.

500

time (a)

750

250

500

time (b)

750

Re=44

0,5 0,4 0,3 0,2 0,1 0 -0,1 -0,2 -0,3 -0,4 -0,5 0

1000

0,5 0,4 0,3 0,2 0,1 0 -0,1 -0,2 -0,3 -0,4 -0,5 0

250

500

time (c)

750

1000

Re=45

250

Re=43

256

250

500

time (d)

750

1000

250

500

time (e)

750

1000

Figure 3. History of the crosswise velocity component, υ, for the circular cylinder and several values of Re. Grid resolution=12/d. Processing in double precision and a 0.0001 amplitude white noise.

important fact that studies of onset of shedding, as well as every investigation on transitional phenomena, being it experimental or numerical, must bring clearly the information relative to the levels of turbulence/perturbation under which the experience was carried out. Otherwise, an isolated figure, in this specific case the value of the Reynolds number, carries the risk of getting almost meaningless. Maybe the only thing that can be assured is that the provided number furnished falls inside a very wide interval generally well established in the literature. If one compares all those results carefully, and takes into account the arguments previously presented, it is possible to state that the “scanning” of the Reynolds number interval by a rake of grids in single precision and a relatively “small” time stretch (about 1,000 units) is a guarantee that the interval is well located, at least, say, to “first order”. The error of the code in single precision (that corresponds, for the present code, to a perturbation level of 0.01% — compare

Figs. 3 and 4) is capable of triggering the shedding process according to the right trends. That is, if we use, for example, double precision, what happens is that the shedding, in most cases, will be delayed, but, most certainly, not eliminated. We believe that, under those circumstances, and restrained by the conditions of the numerical simulation — among which the most influential is the grid refinement —, it is possible to state, after Table 2, that the onset of the shedding Reynolds number is located in the interval ReKd = 43.5 ± 1.5. If one decides to reach a second order level of accuracy in the determination of ReK , then the computational cost will be amplified by many folds. For example, to run a 36-pointsper-diameter case in double precision, and reach 10 ,000 units of time, one would need about 2,100,000 iterations, what, in the case of our Dell Station 690, took about 40 (forty) days of calculation. This is because the largest time step for guaranteeing convergence is equal to 0.00483786, due to the grid spacing. For reasons of our convenience, the investigation

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.249-266, Jul.-Sep., 2014

Direct Numerical Simulation of the Onset of Vortex Shedding for Blunt Elongated Bodies

Re=41

0,005 0,004 0,003 0,002 0,001 0 -0,001 -0,002 -0,003 -0,004 -0,005 0

Re=42

0,5 0,4 0,3 0,2 0,1 0 -0,1 -0,2 -0,3 -0,4 -0,5 0

250

500

750

time (a)

250

500

time (b)

Re=44

750

1000

0,5 0,4 0,3 0,2 0,1 0 -0,1 -0,2 -0,3 -0,4 -0,5 0

250

500

time (c)

750

1000

Re=45

0,5 0,4 0,3 0,2 0,1 0 -0,1 -0,2 -0,3 -0,4 -0,5 0

1000

Re=43

0,005 0,004 0,003 0,002 0,001 0 -0,001 -0,002 -0,003 -0,004 -0,005 0

257

250

500

time

750

1000

(d)

250

500

time (e)

750

1000

Figure 4. History of the crosswise velocity component, υ, for the circular cylinder and several values of Re. Grid resolution=12/d. Processing in single precision.

that is being reported here was performed using the serial version of code Incompact3d. THE ELONGATED CYLINDER ONSET OF SHEDDING STUDY The objective of this section is to apply to the elongated cylinder the technique that was established for the case of the circular cylinder. Before going into the details, it is important to describe how the different aspect ratios were investigated. The immediate idea would be to maintain the body base height fixed and to adapt the chord length to the desired aspect ratio. It is obvious that, in doing so, the domain of calculation total length, Lx, would increase according to the increase of the aspect ratio. Therefore, having in mind the goal of sparing grid nodes, we decided differently: to keep the chord fixed and to diminish the base height. In both cases, however, either keeping the chord length or the base height fixed, one has to care about the similarity of both the overall size of the domain of

calculation and the grid resolution. This is essential in order to obtain results that can be compared on the same basis. Observe Fig. 1. The following rules were then established: (i) The ratio Ly=h was kept constant; this would assure that any blockage effect, whatever its extension, would, in principle, be the same for every configuration and for every code run. (ii) The ratio q=h was also kept constant; therefore, the influence of the exit plane would be felt in the same terms in every running of the code. By keeping constant the ratios Ly=h and q=h, the same similar evolution domain was offered to the wake, irrespective of the actual investigation. (iii) The ratio of grid nodes per base height, nhy/h, where nhy is the number of crosswise grid points, allocated at the body base height, was kept the same from case to case. This figure ultimately defines ny the total number of points along Ly. The rake of grids for this study was composed of three choices whose number of points were: [nhy/h] = 12, 20 and 30. It is more difficult to select the grids and the resolution in

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.249-266, Jul.-Sep., 2014

Ortega, M.A., Girardi, R.M. and Silvestrini, J.H.

the case of the elongated body when compared to the circular cylinder. The difficulties come from the geometric constraints, to guarantee similarity, plus the fact that the total number of grid nodes for each direction has to be a product of the form [2a3b5c], in order to meet the “needs” of the fast Fourier subroutines. Table 3 shows the main grid characteristics of some of the cases. The result of the investigation is plotted in Fig. 5. It is important to draw attention to the fact that each point in this figure is the result of a rather long process. Many Reynolds number values had to be tried, and for each of them, the extension of the time of calculation was about 1,000 units of non-dimensional time, which demanded a large number of iterations (generally, on the order of six digits). Only after that, one can be sure that the onset of shedding for that particular case was attained. A minimum number of at least 100 steady cycles of emission was made to run, before guaranteeing that the case corresponded to a real “onset of shedding” case. The calculations were performed in a 64-bits machine

(Dell Precision 690), and the Fortran compiler was the Intel ifort version running in single precision. In Fig. 5, the vertical

150 125 100

258

75 50 25 0

Figure 5. The dependence of RehK on the elongated cylinder aspect ratio, AR.

Table 3. Data relative to the principal domains and grids that were used in the study of the elongated body onset of shedding. Observe that lengths are given in base heigths. AR

nhy/h

Lx=h

Ly=h

xc/h

q/h

30.0

15.0

451

181

13.5

15.0

30.0

15.0

601

301

13.5

15.0

30.0

15.0

751

451

13.5

15.0

45.0

15.0

481

181

26.5

15.0

45.0

15.0

751

301

26.5

15.0

45.0

15.0

901

451

26.5

15.0

45.0

15.0

541

181

24.0

15.0

45.0

15.0

601

301

24.0

15.0

45.0

15.0

901

451

24.0

15.0

60.0

15.0

541

181

36.0

15.0

60.0

15.0

601

301

36.0

15.0

60.0

15.0

901

451

36.0

15.0

75.0

15.0

601

181

50.0

15.0

75.0

15.0

601

301

50.0

15.0

75.0

15.0

901

451

50.0

15.0

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.249-266, Jul.-Sep., 2014

Direct Numerical Simulation of the Onset of Vortex Shedding for Blunt Elongated Bodies

bar for each point is an indication of the spreading extension of the Reynolds number as function of the grid resolution. For example, for AR = 14, the result is Re hK = 100 ± 2.0. Therefore, taking into account the envelope of restrictions of the numerical prediction — that we have tried to point out during the discussion so far —, it is guaranteed that the true value of RehK falls inside the spreading interval defined by the vertical lines. On the other hand there is a lot of other factors that might influence this spreading range, as we have discussed previously in the case of the cylinder. However, the grid resolution is indeed the main factor. We do believe that these data (especially, Fig. 5) are important, and have not as yet been published in the literature. Two important results stem immediately as one observes Fig. 5. Firstly, the Reynolds number grows almost linearly for small values of the aspect ratio, and after an AR equal to 9 it behaves in a plateau-like fashion, leveling at about 100. Ryan et al. (2005), in the context of a three-dimensional investigation, have already called attention to the increase in the onset-of-shedding Reynolds number with aspect ratio. Secondly, because the functional relationship of Re and AR is practically linear in the range 3 to 6, an extrapolation of this line to values of AR less than 3 would meet a vertical line through AR = 1 in a range of Reynolds numbers between 45 to 50. It is exactly in this region that the circular cylinder critical Reynolds number is located. It is important to remind that the circular cylinder has an aspect ratio equal to 1. In the present two-dimensional study we learn that, for small values of AR, the Reynolds number really increases, but afterwards it levels off to an almost constant value (at least, up to AR = 25). The main reason for this behavior is suggested by Fig. 6. Instant vorticity fields are shown where values are made dimensionless regarding U∞ and h. Following the order (a), (b), (c), and (d), for [n hy/h] = 12, one may appreciate, respectively, snapshots of cases AR = 3, RehK = 66.5, t = 1930.31 (units of non-dimensional time); AR = 6, RehK = 89.5, t = 758.33; AR = 9, RehK = 100.0, t = 1004.09 and AR = 12, RehK = 101.0, t = 1011.41. The flow about the body and especially the boundary layers asymptote to a common pattern, what Ryan et al. (2005) called a “near-universal” boundary layer. Putting it differently, for low values of AR, the boundary layers are still carrying the history of their initial formation at the ogival front part. As the aspect ratio increases, the flow, especially that part near the tip of the body base, just before separation, gradually looses the influence

259

of the ogive and behaves more like a flow along a flat plate. This is apparent for cases (c) and (d), AR = 9 and 12, where the physical scenario of the streams along the walls of the bodies is exactly alike. THE INFLUENCE OF THE BOUNDARY LAYERS In this section, we will investigate the possible influences of the boundary layers “running” above and below the body upon the physical scenario at the body base and near wake. The main interest at this point is to investigate some specific influences of the boundary layers upon the onset of shedding condition. One should pay special attention to this point. The present study is made possible due to the special geometry of the elongated cylinder. The presence of the two flat surfaces that constitute the body, plus the fact that the separation points are fixed, are instrumental. It is not possible to repeat the same numerical experiments with the circular cylinder, because of the everlasting excursions of the separation points during the shedding state. The first aspect to stress is that the boundary layers at the tips of the body base, just before separation and at the onset of shedding state, are always laminar. The reader can find, in Fig. 7, data about the layers thicknesses and shape factor as functions of the aspect ratios. For each point in the figure, the Reynolds number corresponds to RehK , the onset of shedding value. By the value of H, the shape factor, one can see that the boundary layers are laminar. One should remind that, for the laminar boundary layer along a flat plate and without pressure gradient H is equal to 2.59 (Schlichting, 1979). At this point of this research project, we are focused upon the low Reynolds number range, for which the boundary layers and, most of the time also the shear layers, are laminar. One can find in the literature some studies related to the influence of the turbulent boundary layers upon the characteristics of the wake — Sieverding and Heinemann (1990); Rowe et al. (2001) —, but in general the Reynolds numbers are much larger (the works are mostly experimental) and the emphasis lies on the analysis of “macro” parameters, for example, the influence of the boundary layer shape factor upon the shedding frequency, and studies alike. In Fig. 8, for AR = 7 and Reh = 95, we have plotted the boundary layers profiles at the upper and lower tips of the body base, for eleven instants of time of a complete cycle of emission. Precisely in the case of Fig. 8, the cycle starts at t = 288.88 and ends at t = 290.12. Plotted in the figure, there are eleven profiles for the upper tip and

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.249-266, Jul.-Sep., 2014

260

Ortega, M.A., Girardi, R.M. and Silvestrini, J.H.

(a)

(b)

(c)

(d)

Figure 6. Instant vorticity fields at the onset of shedding Re. (a) AR = 3, RehK = 66.5, t = 1930.31, -1.33 ≤ ωz ≤1.33, Δωz = 0.27; (b) AR = 6, RehK = 89.5, t = 758.33, -1.17 ≤ ωz ≤1.17, Δωz = 0.23; (c) AR = 9, RehK = 100.0, t = 1004.09, -1.0 ≤ ωz ≤1.0, Δωz = 0.2; (d) AR = 12, RehK = 101.0, t = 1011.41, -1.0 ≤ ωz ≤ 1.0, Δωz = 0.2.

eleven for the lower one (the cycle was divided in ten equal time intervals). The case is that, the eleven profiles are basically coincident in the scale of the figure, and it seems that there is only one plotted distribution of the horizontal velocity component. What one can learn from this figure is that, in a situation where there is already vortex shedding — let us remind that for AR = 7 the onset of shedding Reynolds number is 94.0 —, there is virtually no variation in the boundary layers profiles during a whole cycle of emission. If we invert the argument, one may verify that, if the profiles do not change with time during a whole cycle, most probably they do not have any influence upon the mechanism of vortex liberation in the formation region. This is another indication that the global instability with the consequent vortexes evolution

and shedding is a specific characteristic of the wake itself, considered as a dynamical system. Because this is a point of paramount importance, we have focused onto it and introduced new numerical experiments. Using the geometries corresponding to AR = 6, 14 and 22, the following extra cases were investigated. The body shapes were slightly changed by introducing two small bumps, one at the upper wall, and another at the lower one. The bumps were made triangular, with heights equal to 1.0dy, 0.5dy and 0.25dy; the bases of the triangles were always equal to 2dx. The position of both bumps were defined at half-base height, one base height and two base heights, upstream of the upper and lower tips of the trailing edge. The 12/h grid and single precision were used again in those experiences. In this part of the investigation, for every situation, we mostly used the

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.249-266, Jul.-Sep., 2014

Direct Numerical Simulation of the Onset of Vortex Shedding for Blunt Elongated Bodies

2.5

St, Thd, Thm, H

2 1.5

1 0.5

0 -0.5 5

Figure 7. Strouhal number, tip boundary layers thicknesses and shape factor at the onset of shedding Re as function of the aspect ratio. The reference length is always the base height, h. St: Strouhal number (diamonds); Thd: displacement thickness (circles); Thm: moment thickness (deltas); H: shape factor (squares).

value of Reh=(ReKh,12 - 1), that is, the onset of shedding Reynolds number for that configuration and the grid resolution equal to 12/h, minus one unity. The main final results are presented in Table 4 and Fig. 10. In Table 4, the crossing of a row and a column corresponds to one case studied; for example, “14” and “h/0.5dy” indicate an AR equal to 14, and both bumps, the upper and the lower ones, positioned one base height upstream of the base tips, being the height of the bumps equal to 0.5dy. Details of the top tip boundary layers are depicted in Fig. 10. In pictures (a), (b), and (c), the reader can observe four lines marked with symbols: squares, circles, deltas and right triangles, corresponding, respectively, to the plain body, bumps at 0.5h/0.5dy, bumps at 1h/1dy, and bumps at 2h/1dy. There are some important aspects about those profiles. The bumps introduce a distortion in the boundary layer profile in the form of a retracting defect, situated approximately between 0.1h and 0.6h from the wall. Figure 10 depicts details of those regions for a better understanding of the point. When plotted together in a normal scale, it is practically impossible to detect those differences in the value of the horizontal velocity component. Even in the extended scale of Fig. 10,

261

it is not possible to differentiate between the plain body and the (0.5h/0.5dy)-bumps cases. The square and circle symbols, and their corresponding lines, are coincident. For the other instances, i.e., bumps at 1h/1dy and 2h/1dy, the defects in the values of the velocity in relation to the plain body distribution, and for a distance of the wall of y/h = 0.25, are 0.035/0.02 (AR = 6), 0.02/0.01 (AR = 14) and 0.015/0.005 (AR = 22), respectively. Hence, the influence of the bumps upon the tips’ boundary layers diminishes as the aspect ratio grows. By “tip” we mean the point that belongs at the same time to the flat plate and to the base of the body. By observing Fig. 9, one can grasp why this is so. As the aspect ratio grows the thickness of the boundary layers along the walls of the body also grows. Therefore, for larger values of AR, the perturbation due to the bumps has to evolve in a region of the boundary layer relatively closer to the wall, and where shear stresses are larger. This is an indication that the perturbation signal will be more dissipated until reaching the base tip. And the dissipation will be larger for larger values of the aspect ratios. This is probably the reason why there was no shedding for AR = 22 (Table 4). The fact that stems from Fig. 10 and Table 4 is that we only had shedding for a value of the Reynolds number equal to (ReKh ,12 - 1) when the perturbation amplitude grew beyond a certain level. Based on these results, one might appreciate a very important aspect of a bluff body wake. Given that the level of disturbances is sufficiently low (in the “linear” range, say), the “command” of the onset of shedding state is dictated by the wake itself. Only after sufficiently raising the level of the amplitude of the perturbation (the “non-linear” range, say), is that it is possible to disturb, from upstream, the overall state of the twin bubbles at the body’s base region. This agrees with many previous studies, e.g., to name a few, Monkewitz (1988), and Yang and Zebib (1989), for the case of the circular cylinder, and Jackson (1987) for an assortment of geometrical forms. See also Mathis et al. (1984) and Provansal et al. (1987), who report important experimental studies about the Karman instability for the circular cylinder. The reader should appreciate how important this result is. In the case of the elongated cylinder, because the points of separation are fixed, one may conclude, apparently with no possible doubt, that the wake is unstable by itself; for the circular cylinder, even if the boundary layer profiles did not vary during a whole cycle, the oscillation of the separation points — which would prevent a definitive affirmation — is

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.249-266, Jul.-Sep., 2014

Ortega, M.A., Girardi, R.M. and Silvestrini, J.H.

1.5

y/n

1.5

y/n

262

0.5

0.2

0.4

0.6

0.8

0.2

0.4

0.6

u/U

(a)

(b)

0.8

Reh = 95; AR = 7.

Figure 8. Comparison of boundary layer profiles along a shedding cycle: (a) upper tip; (b) lower tip.

(a)

(b)

(c)

(d)

Figure 9. Instant vorticity fields at different aspect ratios: (a) AR = 3, ReKh = 66.5, t = 1930.31; (b) AR = 3, Reh = 300, t = 112.28; (c) AR = 12, ReKh = 101.0, t = 1008.39; (d) AR = 12, Reh = 300, t = 35.81. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.249-266, Jul.-Sep., 2014

Direct Numerical Simulation of the Onset of Vortex Shedding for Blunt Elongated Bodies

263

Table 4. Investigation of upstream influences upon the flow about some representative aspect-ratio geometries. The Reynolds number is one unity less than the onset of shedding Reynolds number for each geometry (and a grid resolution equal to 12/h). 0.5h/0.5dy

h/dy

h/0.5dy

2h/dy

Reh

14 22

h,12

s: shedding state; ns: no shedding.

0,26

0,24

0,22 0,20 0,32

y/n

0,28

y/n

0,28

y/n

0,28

0,24

0,22

0,34

0,36

0,38

0,4

0,20 0,26

0,22

0,28

0,3

0,32

0,34

0,20 0,22

0,24

0,26

u/U

(a)

(b)

(c)

0,28

Figure 10. Details of tip boundary layer profiles. (a) AR = 6 and Reh = 87; (b) AR = 14 and Reh = 97; (c) AR = 22 and Reh = 96. The symbols indicate: Squares: no bumps; Circles: bumps at 0.5h, height 0.5h; Deltas: bumps at 1h, height 1.0 dy; Right triangles: bumps at 2h, height 1.0 dy.

still to be considered. The reader should also be aware that we are not trying to state that for the case of the circular cylinder the wake is not unstable by itself, but that it may depend on the oscillation of the separation points. There are sufficient arguments in the literature to sustain the point that, for the circular cylinder, the wake is also self unstable. What we are trying to convey is the fact that the case of the elongated cylinder, for which the separation points are fixed, helps to better clarify and fix the point. For the range of Reynolds number we have focused on, the boundary layers work as “conveyors” of vorticity, feeding the bubbles at the base region. This may be observed in Fig. 11. The instant bubbles are shown for the AR = 14 configuration and for several Reynolds numbers. For each frame we call attention to the value of the vorticity at certain specific points. The points at the separation stations correspond to the

maxima of the vorticity at the tip boundary layer transversal section. The points located inside the bubble correspond to the approximate bubble centre, and the values on top of the upper bubble are located one base height from the vertical wall. As the Reynolds number grows towards the onset of shedding state the overall level of vorticity grows at the base region. It is evident that, with the raising of the vorticity, the length of the bubbles grows and the state of instability saturation is finally reached. The numerical experiments represented in Fig. 5 and the data collected seems to indicate that the boundary layers feed vorticity, but the transition of the wake to another state of flow — the crossing by the point of bifurcation —, is determined by the dynamics of the bubble itself. On the other hand it is evident that much more work is needed, especially stability studies, in order to better support these conclusions.

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(a)

(b)

(c)

(d)

(e)

(f)

Figure 11. Evolution of the vorticity at the base region of the AR = 14 body, with varying Re. The numbers in each plot correspond to the dimensionless value of the vorticity. (a) Reh = 93; (b) Reh = 95; (c) Reh = 96; (d) Reh = 97; (e) Reh = 98; (f) RehK = 100.

CONCLUSIONS The main goal of this research effort is to study the onset of shedding of the wake behind a blunt-trailing-edged body - an elongated cylinder with an elliptical front part. What motivated this study was the objective of obtaining, for this geometry, much of the same data (at least in quality) as there are for the circular cylinder. In this latter case, the reasons for the great interest of the engineering community are obvious, considering the myriad of applications of this geometrical form. On the other hand, the elongated body has a paramount advantage, in terms of easiness of analysis, because the points of separation are fixed. Therefore, with the elimination of the separation points excursions, some investigations and their consequent analyses are facilitated. We do believe that we have succeeded in our endeavor, and

hope that the collection of results presented here will be of help for those individuals who are pursuing studies in this field. For example, the information contained in Fig. 5, and the studies carried out as a result of the boundary layers influence upon the stability of the great structures in the near wake of the body.

ACKNOWLEDGMENTS The authors would like to acknowledge the support provided by the CNPq — Conselho Nacional de Desenvolvimento Científico e Tecnológico — (grant 303184/2007-8), and FAPESP — Fundação de Amparo à Pesquisa do Estado de São Paulo — (grant 2007/00305-5).

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Saiki, E.M. and Biringen, S., 1996, “Numerical simulation of a cylinder in uniform flow: Application of a virtual boundary method”, Journal of Computational Physics, Vol. 123, No 2, pp. 450-465. doi:10.1006/jcph.1996.0036. Silvestrini, J.H. and Lamballais, E., 2004, “Direct numerical simulation of oblique vortex shedding from a cylinder in shear flow”, International Journal of Heat and Fluid Flow, Vol. 25, No 3, pp. 461-470. doi: 10.1016/j.ijheatfluidflow.2004.02.013. Sieverding, C.H. and Heinemann, H., 1990, “The influence of boundary layer state on vortex shedding from at plates and turbine cascades”, Journal of Turbo-machinery, Vol. 112, No 2, pp. 181-187. doi:10.1115/1.2927631. Schlichting, H., 1979, Boundary-Layer Theory. 7th ed. ,McGraw-Hill Series in Mechanical Engineering. New York.

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doi: 10.5028/jatm.v6i3.371

Study of Conservation on Implicit Techniques for Unstructured Finite Volume Navier-Stokes Solvers Carlos Junqueira-Junior1, Leonardo Costa Scalabrin2, Edson Basso3, João Luiz F. Azevedo3

ABSTRACT: The work is a study of conservation on linearization techniques of time-marching schemes for the unstructured finite volume Reynolds-averaged Navier-Stokes formulation. The solver used in this work calculates the numerical flux applying an upwind discretization based on the flux vector splitting scheme. This numerical treatment results in a very large sparse linear system. The direct solution of this full implicit linear system is very expensive and, in most cases, impractical. There are several numerical approaches which are commonly used by the scientific community to treat sparse linear systems, and the point-implicit integration was selected in the present case. However, numerical approaches to solve implicit linear systems can be non-conservative in time, even for formulations which are conservative by construction, as the finite volume techniques. Moreover, there are physical problems which strongly demand conservative schemes in order to achieve the correct numerical solution. The work presents results of numerical simulations to evaluate the conservation of implicit and explicit time-marching methods and discusses numerical requirements that can help avoiding such non-conservation issues. KEYWORDS: Computational fluid dynamics, Time marching methods, Flux vector splitting scheme, Conservative discretization.

INTRODUCTION This work discusses issues associated with the coupling of implicit integration methods, for unstructured finite volume formulations, with the spatial discretization based on flux vector splitting schemes. The material discussed in the work is an extension of the work of Barth (1987). The original paper discussed the approximate local time linearizations of nonlinear terms for the finite difference formulation using Total Variation Diminishing (TVD) (Harten, 1983) and upwind algorithms. Here, the time conservation of the point-implicit integration for unstructured meshes is analyzed and discussed. Finite volume formulations have the tremendously important property of being conservative by construction. However, some time integration approaches may present numerical issues that can destroy this property, at least for unsteady applications or during the process of converging to a steady state. This shortcoming was observed when performing simulations of the flow inside closed systems in which there is no addition or extraction of fluid mass to/from the system. For such systems, the use of the point-implicit schemes discussed in this present paper has led to heat generation and non-conservation of mass in the interior of the computational domain. Results of simulations using explicit and implicit integration methods are presented in the present paper in order to better understand the non conservation issue of the time-marching methods. An analysis of the problem is also performed by a detailed study of the backward Euler method linearization.

1.Instituto Tecnológico de Aeronáutica – São José dos Campos/SP – Brazil 2.EMBRAER S.A.– São José dos Campos/SP – Brazil 3.Instituto de Aeronáutica e Espaço – São José dos Campos/SP – Brazil Author for correspondence: João Luiz F. Azevedo | Instituto de Aeronáutica e Espaço | Praça Eduardo Gomes, 50 – Vila das Acácias | CEP: 12228-901 São José dos Campos/SP – Brazil | Email: joaoluiz.azevedo@gmail.com Received: 04/24/2014 | Accepted: 08/19/2014

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THEORETICAL FORMULATION The formulation used in the present work is based on the Reynolds-averaged Navier-Stokes set of equations, also known by the Computational Fluid Dynamics (CFD) community as the Reynolds Averaged Navier-Stokes (RANS) equations. The RANS equations are obtained by time filtering the Navier-Stokes set of equations. The compressible RANS equations are written in the algebraic vector form as . (1) The conserved variables vector, Q, the inviscid flux vector, Fe, and the viscous flux vector, Fv, are given by

, (2)

presented to close the Navier-Stokes set of equations is known as the equation of state. This equation considers the perfect gas law, and it is written as , (6) in which the mean total energy per unit volume, e, is given by , (7) and ei stands for internal energy, defined as , (8) in which T stands for the mean static temperature and Cv is the specific heat at constant volume. The heat flux from Eq. (5) is obtained from the Fourier law for heat conduction, and it is given by , (9)

, (3)

in which γ is the ratio of specific heats and Pr is the Prandtl number. Typically, for air, it is assumed that γ = 1.4 and Pr = 0.72. Cp is the gas specific heat at constant pressure and μ is the dynamic molecular viscosity coefficient, calculated as a function of the temperature by the Sutherland law equation (Anderson, 1991), written as

, (4)

in which ρ stands for density, for the velocity vector in Cartesian coordinates, p for static pressure, τ for viscous stress tensor, qH for heat flux vector, e for the total energy per unit volume and βi is given by . (5) The îx, îy and îz terms are the Cartesian-coordinate orthonormal vector basis. It is very important to emphasize that field forces, such as gravity, are neglected here. Other equations are necessary in order to close the system of equations given by Eq. (4). These additional equations are called constitutive relations. The first constitutive equation

, (10) where S = 110K , and µ∞ is the dynamic molecular viscosity coefficient of the fluid at temperature T∞. The components of the viscous stress tensor, for a Newtonian fluid, are given by , (11) in which δij stands for the Kronecker delta.

NUMERICAL FORMULATION The numerical formulation applied in this work is briefly presented in this section. The study is performed using the

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RANS equations discretized in the context of a cell-centered finite-volume. The solver used in the present work is Le Michigan Aerothermodynamics Navier-Stokes Solver (LeMANS). The original framework was developed by Scalabrin (2007) at the University of Michigan to simulate hypersonic flows over reentry capsules. It is a three dimensional (3-D) numerical solver that uses the finite volume formulation for unstructured meshes to solve the RANS equations coupled with non-equilibrium chemical reaction equations and the Spalart-Allmaras (SA) turbulence model (Spalart and Allmaras, 1992; 1994). The numerical flux is calculated using an upwind scheme based on the Steger-Warming flux vector splitting (Steger and Warming, 1981). The time integration is performed by point implicit and Runge-Kutta methods. This section describes the numerical formulations and discretizations applied in the present work. FINITE VOLUME FORMULATION The finite volume formulation is a numerical method applied to represent and evaluate partial differential equations. It is applied by the CFD community to find the solution of the RANS, equations. The method is obtained integrating the flow equations for each control volume within a given mesh. The RANS equations, written in the context of a cell-centered finite-volume formulation, are given by . (12) Considering a cell-centered formulation, Vi is a given cell of the given grid. After the integration it is possible to apply the Gauss theorem over the equation above, resulting in: , (13) in which Si is the outward-oriented area vector and it is defined as . (14) Considering the mean value of the conserved variables within the i-th control volume, one can write the first term of Eq. (13) as . (15) The second term of Eq. (13) can be written as the sum of all faces of a cell

269

, (16) in which the k subscript is the index of the cell face, and nf indicates the number of faces of the i-th volume. Finally, the RANS equations discretized with a finite volume approximation are given by . (17) For this formulation, the fluxes are computed at the faces of the control volume, and the conserved variables are computed in the cell. INVISCID FLUX CALCULATION The inviscid fluxes are calculated using a method based on a classical flux vector splitting formulation, the Steger-Warming Scheme (SW) (Steger and Warming, 1981). This method is an upwind scheme which uses the homogeneous property of the inviscid flux vectors to write , (18) where Fen is the normal flux at the k-th face, and A is the Jacobian matrix of the inviscid flux which can be diagonalized by the matrices of its eigen vectors from the left and from the right, L and R A = R Λ L , (19) and Λ is the diagonal matrix of the eigenvalues of the Jacobian matrix. The A matrix can be split into positive and negative parts as A+ = R Λ+ L and A− = R Λ− L .

(20)

The splitting separates the flux into two parts, the downstream and the upstream flux, in relation to the face orientation as: , (21) where the cl and cr subscripts are the cells on the left and right sides of the face. The split eigen values of the Jacobian matrix are given by , (22)

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In order to avoid sudden sign transition, and then discontinous derivative, the split eigen values receive a small number, ε , turning Eq. (22) into . (23) The soften sign transition turns the derivative continous at the transition point. Numerical studies performed in the present work indicated that this flux vector splitting is too dissipative and it can deteriorate the boundary layer profiles (JunqueiraJunior, 2012; Junqueira-Junior et al., 2013; MacCormack and Candler, 1989). Therefore, a pressure switch is implemented to smoothly shift the Steger-Warming scheme into a centered one. Then, the artificial dissipation is controlled and the numerical stability is maintained, as presented in the following formulation: . (24) in which

(25) The switch, w, is given by , (26) where ∇p is a scalar number, a numerical approximation of the pressure gradient. For small ∇p, = (1 − ) = 0.5, the code runs with a centered scheme, and for larger values of ∇p, = 0 and (1 − ) = 1, the code runs with the original Steger-Warming scheme. For Eq. (26) one suggests α = 6, but some problems may require larger values (Scalabrin, 2007). The applied formulation was originally created with interest on studying flows over reentry capsules. For such particular cases, with very strong shock waves, it is very common to find solutions with numerical and non-physical structures such as carbuncles (Ramalho et al., 2011). To avoid such numerical problems, artificial dissipation has to be added to the method. The dissipation was included into the split eigenvalues, Eq. (23), using an ε factor, which is given by:

(27) where dk is the distance of the k-th face, to the nearest wall boundary. d0 is set by the user and must be smaller than the boundary layer thickness and larger than the shock stand-off distance. is the normal vector of the nearest wall, and is the normal vector to the k-th face. Equation (27) applies the term to decrease the value at the faces parallel to the wall inside the boundary layer (Scalabrin, 2007). This artificial dissipation model has shown an important role in the prediction of boundary layer profiles (Junqueira-Junior, 2012; Junqueira-Junior et al., 2013). VISCOUS FLUX CALCULATION The viscous terms are based on derivative of properties on the faces. To build the derivative terms, two volumes are created over the face where the derivative is being calculated. At the center of each new volume, the derivative is calculated using the Green-Gauss theorem. This computation is used to find the derivative at the desired face. A two dimensional (2-D) example is used in this section to better explain the derivative calculation. Consider the two cells, S1 and S2, in Fig. 1. Two new cells, S3 and S4, are created using node points, P1 and P3, and cell centered points, P2 and P4, to calculate the derivative on 1-3 face. The properties at the faces are calculated using simple averages. For example, in Figs. 1 and 2, the properties are given by

(28)

. (29)

Considering ∇Q as a constant over the cell, the equation above yields

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, (30)

Study of Conservation on Implicit Techniques for Unstructured Finite Volume Navier-Stokes Solvers

n12

n12 n23

n12

n12 n23

n41 n13

n31

n13

n31

n41 n34

n34

n23

271

n41 n13

n31

n13

n31

n41 n34

n23

n34

Figure 1. 2-D example of new volume creation.

Figure 2. 2-D example of derivative calculation.

in which ∇Q is the constant cell-centered gradient. Using the derivatives in the S3 and S4 cells, the derivative at faces 1-3 is computed using

In order to simplify the forthcoming equations, the right hand side of Eq. (32) is written as

. (31) (33) The derivative computation for other types of element, 2-D or 3-D, is straightforward. TIME INTEGRATION The explicit second-order Runge-Kutta (Lomax et al., 2001) and the point implicit scheme are the two time-marching methods applied in the present work. The second-order Runge-Kutta integration method used in this work is given by

in which Rcl is the residue of the i-th cell. The implicit integration applied in the present work is based on the backward Euler method, which is given by . (34) One can linearize the residue at time n + 1 as a function of properties at time n.

(32) , (35)

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From the spatial discretization, the inviscid terms can be written as

. (40)

One can write the system as (36) (41) It is a common practice to assume

, (37)

It is, then, possible to write

and then write

, (42) with (38) , (43)

which is not true and different from the real definition of the Jacobian matrix written in Eq. (20). Using the approximate form, Eq. (38) to calculate the inviscid terms may decrease the numerical stability of the method. Hence, in this work, the true Jacobian matrices of the split fluxes, given by Eq. (20), are implemented in order to calculate the implicit operator. The approximate Jacobian matrices, which are calculated using Eqs. (37) and (38), are implemented at the right hand side of the linear system. Issues involving the true Jacobians matrices have a major importance in the context of numerical stability for computational methods (Anderson et al., 1986; Hirsch, 1990; Steger and Warming, 1981). The viscous terms can be written in the same form as

. (44) and . (45) As the code is an unstructured solver, this system of equations results in a sparse block matrix, where each block is a square matrix of size equal to the number of equations to be solved in each control volume. The solution of such system is typically very expensive and, depending on the size of the mesh, it is not even practical. A less expensive implicit method is applied in the present work, the implicit point integration (Gnoffo, 2003; Venkatakrishnan, 1995; Wright, 1997). The main idea of the implicit point integration is to move all the off-diagonal terms to the right hand side and to solve the resulting system iteratively, i.e.,

(39) . (46)

In this work, the viscous Jacobian matrices are represented by B. Hence, the Jacobian matrix splitting is written as

It is assumed that ∆Q n+1.0 = 0 and four iterations are taken in the process as suggested in the literature (Wright, 1997). The given sparse linear system illustrates the point

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is defined as the properties at the left side of the boundary face and the ghost cells are defined as the properties at the right side of the boundary. In order to simplify the implementation, the properties at the left side of the boundary face are rotated to the face coordinates using . (48) The vector of conservative properties, Q, is written as (49) In Eq. (48),

where each ☐ is a 5 × 5 block matrix. The time step is computed by

is the rotation matrix given by,

(50)

, (47) in which CFL is a parameter set to ensure the stability of the time integration method, l is the size of the cell and is the largest wave speed in the cell.

and the vectors define the face-based reference frame. The properties at the ghost cells are set to

(51)

BOUNDARY CONDITIONS The boundary conditions are implemented using ghost cells. The solver creates the ghost cells to hold properties that satisfy the correct flux calculation at the boundaries. The implementation assigns properties that satisfy the Euler boundary conditions to calculate the inviscid fluxes, and properties that satisfy the Navier-Stokes boundary conditions to calculate the viscous fluxes. Therefore, the ghost volumes store two different types of fluxes for the correct computation of the RANS equations. WALL INVISCID BOUNDARY CONDITIONS The ghost cells hold the properties in the same manner to calculate the inviscid fluxes at the wall and at the symmetry boundaries. Mass and energy fluxes should yield zero, and the momentum flux is equal to the pressure flux. This is accomplished by setting the normal velocity component to the boundary face zero. In the present work, the interior domain

One can write in the matrix form as , (52) in which

is the inviscid wall matrix given by

. (53)

Therefore, the boundary condition can be written as , (54)

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in which the R−1 matrix is given by

conditions can limitate substantially the stability of the numerical method in the marching procedure for the solution convergence.

. (55)

BASIC IMPLICIT FORMULATION A simplified form of the implicit equation, Eq. (42), is written in this section in order to detail the implementation of implicit boundary conditions for flux vector splitting schemes,

It returns the properties to the Cartesian coordinate frame. (59) VISCOUS WALL BOUNDARY CONDITION WITH SPECIFIED TEMPERATURE Viscous wall with specified temperature does not have necessarily zero heat conduction at the boundary face. For this boundary condition the user provides the wall temperature, Twall , and the wall pressure is extrapolated from the interior in order to satisfy the zero normal wall pressure gradient condition Schlichting (1978). Hence, (56) One can computate the conservative properties at the wall boundary using the Cartesian components of the wall velocity, and , which are set by the user.

where the repeated k index, in the second term in left hand side of the equation, indicates summation over all the k faces of the control volume. The equation above is written only to present the relation between an internal cell, cl, and a boundary cell, cr, k. In the original formulation, Eq. (42), the cl-th cell has contributions from other faces, which may or may not be boundaries. As discussed in the present work, the ghost cells hold different values for inviscid and viscous calculations. Hence, using the splitting definition, presented in section Inviscid Fluxes Calculation, Eq. (59) can be written as

(60)

(57)

The contributions of the boundary face can be expressed in terms of the internal cell corrections as

(61) Then, the ghost cell conservative properties are calculated using an average between the left and right cells Hence, Eq. (60), can be rewritten as (58) (62)

IMPLICIT BOUNDARY CONDITIONS Implicit boundary conditions are necessary in order to obtain a truly implicit time-marching method. The use of explicit boundary

The viscous Jacobians are calculated using primitive variables, then the corrections are set for the primitive variables and applied directly at the calculation of the viscous Jacobians. The matrix B+k+ already includes the contribution from the boundary. The A+k+ , A−k- , B+k+ and B−k- are presented in the work of Scalabrin (2007).

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INVISCID WALL MATRICES FOR IMPLICIT BOUNDARY CONDITIONS The matrix for an inviscid wall or for a symmetry boundary is the same matrix presented in Eq. (54), k, inv, wall

W . (63)

-1

CONSERVATION ANALYSIS OF IMPLICIT METHODS GENERAL CONSERVATION ANALYSIS After this brief review, the backward Euler time marching method, Eq. (42), is re-written using a simplified notation

The matrices are applied to ∆Q for the implicit boundary condition according to Eq. (61). VISCOUS WALL WITH SPECIFIED TEMPERATURE MATRIX FOR IMPLICIT BOUNDARY CONDITIONS The viscous Jacobians are created using primitive variables. Hence, the implementation of implicit viscous matrices is performed using primitive variables. They are applied directly at the calculation of the Jacobians matrices. The code was originally created for flow simulation with chemical reactions, hence the mass fraction, Y , is present in the primitive variable vector, which is given by

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. (68) The integration is applied on a generic grid, which is a representative 2-D finite volume mesh using nine quad-cells, as illustrated in Fig. 3, in order to present the conservative issues of such linearization. The [M] matrix, for the mesh used here, is represented by

. (64) In the present work, it is always considered that there is only one species in the flow, then, Y = 1. The implicit matrix for wall boundary with specified temperature is derived from the average between the left and right cells,

(69)

, (65) in which z is given property. The velocity components and temperature at the wall are considered constants in time, hence,

. (66)

The formulation can be considered conservative when the sum of Vi ∆Qin terms, for all cells, yields zero, i.e., , (70)

The mass fraction, Y, at the wall, is given by the left cell value, Ywall = Ycl . Therefore, the implicit matrix for wall boundary with specified temperature is given by

or yet . (71)

(67)

Solving the linear system for the converged solution, which means that Rin = 0, one obtains

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(75)

Figure 3. Representative 2-D finite volume mesh.

(72)

It is possible to simplify this equation in order to write , (73) where the [Ci ] matrix is the sum of the inviscid and viscous Jacobian matrices in each column of the [M] matrix. Comparing Eqs. (71) and (73), one can easily state that, in order to preserve the conservative property of the finite volume formulation, the [Ci ] matrices should be zero and ∆t should be the same for all the cells in the domain, i.e., . (74) POINT IMPLICIT CONSERVATION ANALYSIS If one solves the linear system for the same mesh presented in Fig. 3 using the point-implicit integration, after a converged solution is obtained, i.e., R in = 0, one could finally write

One can state that, as long the sub-iterations of the point implicit method have not achieved the convergence for ∆Qip and/or ∆t is not the same for all the cells in the domain, it is not possible to find a general relation for any ∆ Qip and ∆ Qip−1 such that ∑ [Vi ∆Qin ] = 0. Therefore, the conservation property for the point-implicit integration can be achieved only if the [Ci ] matrices are zero, the solution of ∆Qip for the sub-iterations in p is converged, and ∆t is constant over the entire mesh. However, achieving convergence of the point-implicit subiterations can be as expensive as performing the fully implicit integration. Hence, all practical numerical solvers perform a limited number of sub-iterations. The authors, usually, perform up to 10 sub-iterations in p for the point-implicit integration and, then, move on to the next time step. Typically, this is not enough to achieve convergence for ∆Qip, as discussed the forthcoming section of the paper.

RESULTS AND DISCUSSION Results for the so-called “rigid body simulation” problem are presented in this section in order to expose the effects of the time-marching scheme on the mass conservation. The rigid body problem consists of the simulation of the flow contained between two concentric cylinders, in which both walls rotate at the same angular velocity. Therefore, after convergence, the fluid in the domain is rotating at the same angular velocity as if it were a rigid body. Here, the problem is addressed as a 2-D flow. The present work performed simulations using the point-implicit and the Runge-Kutta time marching methods.

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An analysis of the use of a constant CFL number on all mesh cells is also presented here, in comparison to the use of a constant Δt throughout the mesh. The two dimensional geometry and the detailed 360,000 cell mesh, used in the simulations, are presented in Fig. 4. The external and internal cylinders are rotating walls with fixed angular velocity, ω, and fixed temperature, T0. Air with zero velocity and T0 temperature are considered as initial conditions. All simulations are conducted with the same initial and boundary conditions. Each simulation is performed using a different time marching method, as presented in Table 1. Figures 5 and 6 present the total amount of mass (per unit of length in the cylinder axial direction) inside the computational domain, as a function of the iteration number, for the rigid body simulations. It is clear from the figures that the point implicit time marching method can significantly deteriorate the important conservative property of the finite volume formulation. Moreover, both simulations with the point-implicit timemarching scheme presented exactly the same non-conservative behavior. Moreover, for the point-implicit time integration test cases, i.e., test cases 1 and 2, or cases shown in Fig. 5 (a) and (b), there is almost no influence of the selection of constant CFL or of constant ∆t in the time march. In other words, one could state that, for these test cases, the non-conservation effects of using a constant CFL number are far less significant than the effects of using the point implicit integration. Furthermore, it is correct to state that both simulations diverged after some time (not shown in Fig. 5 (a) and (b). In contrast to that behavior, simulations performed using the explicit Runge-Kutta scheme and a constant time step throughout the domain perfectly conserve the mass in the computational domain, as one would expect from a finite volume code. The results for this test case (case 4) are shown in Fig. 6 (b). On the other hand, results in Fig.6 (a) indicate that, even with an explicit scheme, there is no mass conservation if a variable time step, or constant CFL number, is used in the time integration. This is a serious problem since most convergence acceleration procedures typically employed in aerospace CFD codes are based on the use of implicit integration or on variable time stepping, or both. The present results are clearly demonstrating that, for such cases, there is no mass conservation during the transient process of converging to a steady state solution. Moreover, all results presented in Figs. 5 and 6 reinforce the previous analysis performed in this work. To assure the conservative property of a time marching method, the [Ci ]

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0.05

Y 0

-0.05

0.05

0.1

0.025 0.02 0.015 Y

0.016

0.01

0.012

0.005 0

0.014

-0.02

-0.018 -0.016 -0.014 X 0 -0.01

Figure 4. Rigid body geometry and mesh detail.

Table 1. Definition of the test case configurations for the numerical simulations. Case

Time marching scheme

Constant ∆t or CFL

Point implicit

Constant CFL

Point implicit

Constant ∆t

Runge-Kutta

Constant CFL

Runge-Kutta

Constant ∆t

matrix should be zero. This is automatically enforced by the explicit integration schemes by construction. Therefore, for the point implicit integration, the convergence of the sub-iterations has to be achieved in order to obtain a conservative scheme. Furthermore, all the mesh cells have to advance in time using

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Mass

0.029 0.0289 0.0288 0.0287 0.0286 0.0285 0.0284 0.0283 0.0282 0.0281 0.028 0.0279 0 100 200 300 400 500 600 700 800 900 1000 IT Nb

1.2

Mass variation per iteration 2nd ord. Runge-Jutta constant DCF

0.8 0.6 0.4 0.2 0 0

(a)

Mass [Kg]

500 1000 1500 2000 2500 3000 3500 4000 IT Nb

(a)

Mass variation per iteration

Mass 0.029 0.0289 0.0288 0.0287 0.0286 0.0285 0.0284 0.0283 0.0282 0.0281 0.028 0.0279 0 100 200 300 400 500 600 700 800 900 1000 IT Nb

Mass

1 Mass [Kg]

Mass [Kg]

Mass variation per iteration

(b)

0.02826 0.028258 0.028256 0.028254 0.028252 0.02825 0.028248 0.028246 0.028244 0.028242 0.02824 0

Mass variation per iteration 2nd ord. Runge-Jutta constant CFL

1000

2000

3000 IT Nb

4000

Mass

5000

6000

(b)

Figure 5. Effects of implicit time marching scheme on total mass conservation; (a) Constant CFL calculation for point-implicit scheme; (b) Constant ∆t calculation for point-implicit scheme.

Figure 6. Effects of explicit time marching scheme on total mass conservation; (a) Constant CFL calculation for explicit RK scheme; (b) Constant ∆t calculation for explicit RK scheme.

the same ∆t value in order to maintain the conservative property of the finite volume method. This is true even for the explicit time marching methods.

conservative by construction. The present analysis of the numerical formulation and of the computational results obtained has indicated that the time integration can be considered conservative only if the sum of the Jacobian matrices in each column of the linear system matrix is zero and all mesh cells have the same ∆t value. Moreover, very popular approximate methods used in many CFD codes, to solve sparse linear systems, such as the point implicit integration, need to achieve convergence of the sub-iterations in order to be conservative during the transient portion of the simulation. This is a very expensive proposition and it can make such approximate solvers as expensive as those which perform the direct solution of the full implicit linear system. The important conclusion is that care must be exercised in the linearization of time-marching methods for simulations which demand the conservative property. It is important to

CONCLUSIONS A discussion on the issues associated with the coupling of implicit integration methods, for unstructured finite volume formulations, with the spatial discretization based on flux vector splitting schemes, is presented in this work. The linearization of inviscid and viscous Jacobians may result in a non-conservative method during the transient phase of the flow simulation, even for the finite volume formulation, which is supposed to be

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Study of Conservation on Implicit Techniques for Unstructured Finite Volume Navier-Stokes Solvers

be aware of the effects of such issue on the physical problem of interest. Physical problems with incoming and outcoming flow boundary conditions, very common in simulations for aerospace applications, do not necessarily need a conservative scheme during the transient portion of a steady state calculation. The amount of variation in the flow properties, during one time step, is negligible compared to the flux of the same properties that is crossing the open boundaries of the domain. Moverover, for many steady-state external aerodynamic applications, the initial conditions are strictly numerical, in the sense that they cannot be physically realized as implemented in the solver. In other words, they are typically associated to impulsively started flows. For such cases, the aspect of the lack of mass conservation in the entire computational domain, as the solution evolves from a non-physical initial condition to a physically relevant converged steady state condition, is not an issue. On the other hand, the conservative property is absolutely essential for closed systems, in which the total mass, and other properties, must always be

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conserved in order to achieve a physically relevant solution. In these critical cases, approximate numerical methods should not be used in order to solve the full implicit linear systems.

ACKNOWLEDGEMENTS The authors would like to acknowledge Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq, which partially supported the project under the Research Grant No. 309985/2013-7. Partial support for the present research was also provided by Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP, under Research Grants No. 2013/07375 and No. 2013/21535-0. Further partial support was provided by Fundação Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES, through a Ph.D. scholarship to the first author, which is also gratefully acknowledged.

REFERENCES Anderson, J.D.J., 1991, “Fundamentals of Aerodynamics”, McGrawHill International Editions, New York, NY, USA. Anderson, W.K., Thomas, J.L. and Van Leer, B., 1986, “A Comparison of Finite Volume Flux Vector Splittings for the Euler Equations”, AIAA Journal, Vol. 24, No. 6, pp. 1453–1460. Barth, T.J., 1987, “Analysis of Implicit Local Linearization Techniques for Upwind and TVD Algorithms”, Proceedings of the 25th AIAA Aerospace Sciences Meeting, AIAA Paper No. 87-0595, Reno, NV, USA. Gnoffo, P.A., 2003, “Computational Aerothermodynamics in Aeroassist Applications”, Journal of Spacecraft and Rockets, Vol. 40, No. 3, pp. 305–312. doi: 10.2514/2.3957. Harten, A., 1983, “High Resolution Schemes for Hyperbolic Conservation Laws”, Journal of Computational Physics, Vol. 49, No. 3, pp. 357–393. doi: 10.1016/0021-9991(83)90136-5. Hirsch, C., 1990, “Numerical Computation of Internal and External Flows”, Vol. 2, Computational Methods for Inviscid and Viscous Flows, John Wiley and Sons, New York, USA. Junqueira-Junior, C.A., 2012, “A Study on the Extension of an Upwind Parallel Solver for Turbulent Flow Applications” Masters Thesis, Instituto Tecnológico de Aeronáutica, São José dos Campos, SP, Brasil. Junqueira-Junior, C.A., Azevedo, J.L.F., Scalabrin, L.C. and Basso, E., 2013, “A Study of Physical and Numerical Effects of Dissipation on Turbulent Flows Simulations”, Journal of Aerospace Technology and Management, Vol. 5, No. 2, pp. 145–168. doi: 10.5028/jatm.v5i2.179. Lomax, H., Pulliam, T.H. and Zingg, D.W., 2001, “Fundamentals of Computation Fluid Dynamics”, Springer, NY, USA.

MacCormack, R.W. and Candler, G.V., 1989, “The Solution of the NavierStokes Equations Using Gauss-Seidel Line Relaxation”, Computer and Fluids, Vol. 17, No. 1, pp. 135–150. doi: 10.1016/0045-7930(89)90012-1. Ramalho, M.V.C., Azevedo, J.L.F. and Azevedo, J.H., 2011, “Further Investigation into the Origin of the Carbuncle Phenomenon in Aerodynamic Simulations”, 49th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, AIAA Paper No. 2011-1184, Orlando, FL, USA. Scalabrin, L.C., 2007, “Numerical Simulation of Weakly Ionized Hypersonic Flow Over Reentry Capsules”, PhD Thesis, Department of Aerospace Engineering, The University of Michigan, Michigan, USA. Schlichting, H., 1978, “Boundary-Layer Theory”, 7th Edition, McGraw Hill, New York, USA. Spalart, P.R. and Allmaras, S.R., 1992, “A One-Equation Turbulence Model for Aerodynamic Flows”, Proceedings of the 30th AIAA Aerospace Sciences Meeting and Exhibit, AIAA Paper No. 92–0439, Reno, NV, USA. Spalart, P.R. and Allmaras, S.R., 1994, “A One-Equation Turbulence Model for Aerodynamic Flows”, La Recherche Aerospatiale, Vol. 1, pp. 5–21. Steger, J.L. and Warming, R.F., 1981, “Flux Vector Splitting of the Inviscid Gasdynamic Equations with Application to the Finite-Difference Method”, Journal of Computational Physics, Vol. 40, No. 2, pp. 263– 293. doi: 10.1016/0021-9991(81)90210-2. Venkatakrishnan, V., 1995, “Implicit Schemes and Parallel Computing in Unstructured Grid CFD”, Technical Report, ICASE Report No. 95-28, ICASE. Wright, M.J., 1997, “A Family of Data Parallel Relaxation Methods for the Navier-Stokes Equations”, PhD Thesis, University of Minnesota, Minneapolis, MN, USA.

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ABBREVIATIONS 2-D: Two dimensional 3-D: Three dimensional CFD: Computational Fluid Dynamics CFL: Courant-Friedrichs-Lewy number LeMANS: Le Michigan Aerothermodynamics Navier- Stokes Solver RANS: Reynolds Averaged Navier-Stokes SA: Spalart-Allmaras Tubulence Model SW: Sterger-Warming Scheme TVD: Total Variation Diminishing LIST OF SYMBOLS English Characters a: Speed of the sound A: Jacobian matrix of the inviscid flux B: Jacobian matrix of the viscous flux C: Sum of Jacobian matrices Cp: Specific heat at constant pressure Cv: Specific heat at constant volume dk: Distance of k-th face to the nearest wall e: Total energy per unit volume ei: Internal energy Fe: Inviscid flux vector Fv: Viscous flux vector îx , îy , îz: Cartesian-coordinate orthonormal vector basis I: Identity matrix L: Matrix of eigenvectors from the left m: Normal vector to the nearest wall n: Normal vector nf: Number of faces of a given volume p: Static pressure Pr: Prandtl number qH: Heat transfert vector Q: Conserved variable vector R: Matrix of eigenvectors from the right R: Residue : Rotation matrix ℜ: Gas constant S: Outward-oriented area vector S: Constant for the Sutherland law equation t: Time

T: Temperature v = {u, v, w}: Velocity vector in the cartesian coordinate V: Volume V: Primitive variables vector W: Inviscid wall matrix w: Switch of the Steger and Warming scheme Y: Mass fraction Greek Characters α: Switch factor β: Viscous force work and heat transfer term δij: Kronecker delta : Implicit matrix ϵ: Artificial dissipation γ: Ratio of specific heats κ: Thermal conductivity coefficient λ: Eigenvalue of the Jacobian matrix Λ: Diagonal matrix of the eigenvalues of the Jacobian µ: Dynamic molecular viscosity coefficient ν: Kinematic molecular viscosity coefficient ρ: Density τij: Shear-stress tensor Subscripts cl: Cell on the left side of the face cr: Cell on the right side of the face ∞: Free-stream property inv: Inviscid property k: Index of the cell face n: Normal property at a given face wall: Property at the wall visc: Viscous property Superscripts n: Index of iteration in time p: Index of iteration for the point-implicit time integration +: Positive part of a matrix or vector −: Negative part of a matrix or vector rot: Rotated property

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doi: 10.5028/jatm.v6i3.341

A Numerical Study on Smart Material Selection for Flapped and Twisted Morphing Wing Configurations Mauricio Vicente Donadon1, Lorenzo Iannucci2

ABSTRACT: The developments of innovative adaptive structures on Unmanned Aerial Vehicles (UAVs), such as morphing wings, can potentially reduce system complexities by eliminating control surfaces and their auxiliary equipment. This technology has the potential of allowing a UAV to adapt to different mission requirements or to execute a particular mission more effectively by maintaining an optimum airfoil section over a range of speeds for different segments of a mission profile. Studies on a number of smart materials candidates are currently available in the open literature to achieve wing morphing. The material selection depends on several factors including fast dynamic response, low weight, capability to operate over a wide range of flight conditions and low power consumption. This paper presents a review on smart materials technologies for UAV morphing wings. A numerical study in terms of power requirements is also presented for two morphing wing concepts: flapped and twisted wing planforms. The energy calculations for both morphing configurations were based on a two-step procedure. The first step consists of computing the aerodynamic energy using an in-house Vortex-Lattice (VL) based program. Subsequently the pressure field obtained from the first step is then mapped into a finite element mesh and the structural strain energy is calculated. The numerical results indicated that flapped morphing wings have a better aerodynamic performance when compared to twisted wings and different morphing levels can be achieved using lighter smart materials with lower specific energy for this configuration. KEYWORDS: Morphing wings, Smart materials, Wing design.

INTRODUCTION The goal of multi-mission capability in military and civil air vehicle systems has created a need for technologies which allow drastic wing shape changes during flight. Since most current aircraft are fixed-geometry, they represent a design compromise between conflicting performance requirements in mission segments, such as high-speed cruise, low-speed loiter and low turn radius turn maneuver. If a hybrid aircraft is designed to combine several flights profiles, the wing design must maximize the overall efficiency of the anticipated mission. Through morphing, the aerodynamics of the aircraft can be adapted in order to optimize performance in each segment by changing shape features such as the camber of the airfoils and the twist distribution along the wing. Adapting the shape of wings in flight allows an air vehicle to perform multiple, radically different tasks by dynamically varying its flight envelope. The wing can be adapted to different mission segments, such as cruise, loitering and high speed maneuvering by sweeping, twisting and changing its span, area and airfoil shape. Within this context, morphing wing technology is considered to be a key component in next-generation unmanned aeronautical vehicles (UAVs) for military and civil application. The design of UAVs demands a multidisciplinary integration of different engineering areas, including aerodynamics, structural elasticity, control and actuators/sensors dynamics as schematically shown in Fig. 1. The work presented in this paper is part of an ongoing international research program on UAVs between the Department of Aeronautics, at Imperial College

1.Instituto Tecnológico de Aeronáutica – São José dos Campos/SP – Brazil 2.Imperial College London – London – United Kingdom Author for correspondence: Maurício Vicente Donadon | Instituto Tecnológico de Aeronáutica | Praça Eduardo Gomes, 50 – Vila das Acácias | CEP: 12228-901 São José dos Campos/SP – Brazil | Email: donadon@ita.br Received: 02/26/2014 | Accepted:07/25/2014

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London-UK, and the Instituto Tecnológico de AeronáuticaITA, in Brazil. The paper is focused on a review on smart materials technologies for UAV morphing wings. A numerical study in terms of power requirements is also presented for two morphing wing concepts: flapped and twisted wing planforms. The energy calculation for both morphing configurations is based on a two-step procedure. The first step consists of computing the aerodynamic energy using an in-house VortexLattice (VL) based program. Subsequently, the pressure field obtained from the first step is then mapped into a finite element mesh and the structural strain energy is calculated.

REVIEW ON MORPHING WING TECHNOLOGY

Control

Aerodynamics

Morphing wing design

Structural elasticity

Actuator/Sensor dynamics

Figure 1. UAVs design methodology.

MORPHING WING CONCEPTS The different concepts based on smart material technology, currently available in the open literature for UAVs flight control, can be classified into four distinct groups according to the adopted solution strategy (Fontanazza et al., 2006): • Wings with local morphing capabilities; • Wings with global morphing capabilities; • Composite wings with multi-stable structural behaviour; • Wings with variable stiffness structural parts. The solutions adopted for wings with local morphing capabilities rely on the deformation of compliant parts of the wing. Examples of wings with local morphing capabilities were presented by Lim et al. (2005), in which the authors proposed a compliant trailing edge configuration with lightweight piezo-composite actuator (LIPCA), bonded on the upper part of the skin. Kota et al. (2003) proved the effectiveness of novel compliant mechanisms to change the wing chamber of an airfoil to minimise drag without causing flow separation (Fig. 2) In this case, power is required in order to deform both the (compliant) structure and to generate the required aerodynamic forces. Because of the high chord-wise bending stiffness of a typical closed wing section, twisting the whole wing, or part of the wing, would be more effective (Barrett and Brozoski, 1996). The solution adopted for wings with global morphing capabilities implies in deforming the whole wing, such as twisting the wing along its entire span. This solution

Figure 2. Trailing edge control (Flexus Inc.).

has been considered for flight control of fixed wing aircraft, rotorcraft, and missiles. Previous concepts made use of directionally attached piezoelectric actuators (DAP), embedded within the outer skin of high aspect ratio wings (Barrett and Brozoski, 1996). For low aspect ratio wings and missiles fins, designs with integral main spar and active torque plate were considered (Barrett, 1995). Later designs employed a bending element included into the wing, in order to achieve greater deflections, actively pitching the aerodynamic surface.

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A Numerical Study on Smart Material Selection for Flapped and Twisted Morphing Wing Configurations

More recently, Cesnik and Brown (2003) studied a solution with anisotropic piezocomposites (AFCs) distributed along

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efficiency, the use of “active materials” in an “intrinsically adaptive” mode is a requirement.

a high aspect ratio wing. It has been concluded that novel single crystals fibre composites may be capable of providing the required control capability. Composite wings with multistable structural behaviour exploit the possibility of changing the shape of an unsymmetric composite laminate from one stable position to another, supplying a small amount of energy (Schultz, 2005) to promote mode switching. The advantage is that no further power is required to keep the structure at an equilibrium configuration. The main drawback is that few (two or three) stable shapes are possible so that the resulting control system could not be used for manoeuvring over different range of speeds. Wings with variable stiffness structural parts exploit the energy on the fluid (aerodynamic forces) rather than directly using the smart actuators to change the shape of the wing. Griffin and Hopkins (1997) suggested the Variable Stiffness Spar (VSS) concept in order to improve the manoeuvrability of flexible aircrafts (e.g. to counteract aileron reversal). The solution is based on the simultaneous actuation of a control surface and modification of the wing stiffness. In the VSS, a spar made of separated parts, linked with hinges, can be rotated in order to change its ability to react the shear forces (Chen et al., 2000). Another design, the Torsion-Free (TF) wing concept, consists of two closely spaced very stiff spars which carry most of the shear. The stiffness of the other spars is reduced in order to produce a wing with low torsional stiffness. Two VSS, placed along the leading and trailing edge, are used to tune the wing torsional stiffness (Chen et al., 2000). The TF concept was also investigated by Changho et al. (2002), employing variable stiffness Shape Memory Alloys (SMA) spars to increase roll effectiveness. Amprikidis et al. (2005) have recently developed an “adaptive internal structure” to twist a wing, by moving the position of the elastic axis. This can be obtained by rotating two spars or changing their chord-wise position. With this approach, a considerably lower amount of energy is required to twist the wing and keep it in the desired position. For the specific problem of UAV roll control, third and forth concepts seem to be the most promising ones. Smart materials can be employed either to twist the whole wing or to tune its stiffness. For the latter solution, in order to guarantee fast response and high

CANDIDATE MATERIALS FOR SMART MORPHING WINGS Smart materials are able to respond to a stimulus in a useful and reproducible manner (Suleman, 2001). The materials themselves are not “smart”, in the sense that they passively react to an input rather than making decisions or adapting themselves to the environment. A more accurate definition proposed by Kornbluh et al. (2004) classifies them into “Intrinsically adaptive materials” and “Active materials”. Intrinsically adaptive materials are materials subjected to transformations in their molecular or microscopic structure due to a particular external stimulus (usually characterized by a small energy content regarding the deformation energy within the material), resulting in changes in mechanical properties. The SMA and shape memory polymers (SMP) are examples of intrinsically adaptive materials. Active materials act as transducers, converting some forms of energy (typically electrical, magnetic, and thermal) into mechanical energy. Electroactive polymers, piezoelectric ceramics, and magnetostrictive (Terfenol-D) are some examples of active materials. Active materials with high electromechanical coupling can also be used in an “intrinsically adaptive mode”; in this case, they require less power supply, but their performance are more limited. The main advantages in using smart materials rather than conventional pneumatic or hydraulic actuators are the reduced complexity and improved reliability of the system. Similarly, the potential weight saving and the possibility of using active materials as both actuators and sensors within the structure are clear advantages. The ‘best’ materials and concepts to adopt depend on the specific morphing purpose. Since the aim is to change the shape of the wing for flight control, the morphing system should exhibit: • Relatively fast dynamics; • Capability to operate over a wide range of flight conditions; • High reliability; • Capability of repetitive actuations; • Robustness against uncertainties and disturbances (e.g. gusts); • Low power consumption; • Insensitivity to environment variation.

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Hence, the ideal material should respond quickly to the external stimuli, be capable of large and recoverable free strains, transform effectively the input energy into mechanical energy, and not to be affected by fatigue issues. Table 1 reports the main characteristics of the most common smart materials (maximum free strain, maximum stress, deformation energy density, efficiency, and relative speed of response). SMAs and SMPs can undergo large free strains and exhibit large blocking forces, but they have slow response and limited efficiency. Piezoelectric Ceramic (PZT) and single crystal piezoceramics, exhibit a much lower free strain, but they are electrically activated, capable of producing quite high blocking forces, and sensibly more efficient ones too. Electroactive polymers exhibit good properties, although they can produce low blocking stress.

realization and actuation scheme. For the present study, actuation energy of the deformable parts of the wing have been calculated on the basis of the work performed by the aerodynamic forces during the wing morphing in the aerodynamic flow field. The computation of the aerodynamic work has been carried out using an in-house computational program based on the VL method (Donadon and Iannucci, 2006a). The program enables the prediction of lift, pressure distribution, rolling and pitching moment calculations for flapped and twisted wing planforms.

ACTUATION ENERGY REQUIREMENTS ANALYSIS FOR MORPHING AIRFOILS

Wh =

AERODYNAMIC ENERGY COMPUTATION The term aerodynamic energy defined here refers to the total energy induce by the pitching moment acting on the deformable parts of the wing. Thus, the expressions for the aerodynamic energy for flapped and twisted wings can be respectively written as follows

Wt =

One of the most import issues and concerns in smart wing technology have been the actuation energy and power which have to be provided by the vehicle onboard power system. Naturally, a smart wing may almost always require the deformation of some, preferably secondary, wing structure with the actual power requirements heavily dependent on the wing structural

θf

∫0

Øf

∫0

Myh(θ)dθ=

θf

∫ 0 ∑ Γ ∆y x dθ (1) n=1

h n n

Øf

∫ 0 ∑ Γ ∆y x dØ (2)

Myt(Ø)dØ=

n=1

t n n

where Myh(θ) is the pitching moment around the flapping line, Myt(θ) is the pitching moment around the twisting line, θ is the flap tip deflection and ø is the wing tip twisting angle. Δyn is the elemental spanwise length, Mnh is the distance between the elemental leading vortex segment and the flapping line

Table 1. Most common smart materials (Fontanazza et al., 2006). Material

Max. Strain (%)

Max. Stress (MPa)

Elastic energy density (J/g)

Max. Efficiency (%)

Relative speed

Dielectric Polymer Acrylic

215

16.2

3.4

60-80

Medium

Silicone

0.75

Fast

Electrostrictor Polymer P(VDF-TrFE)

0.17

---

Fast

Piezoelectric Ceramic (PZT)

0.2

110

0.013

>90

Fast

Single Crystal (PZN-PT)

1.7

131

0.13

>90

Fast

Polymer (PVDF)

0.10

4.8

0.0013

n/a

Fast

SMA (TiNi)

>200

>15

<10

Slow

SMP

100

<10

Slow

Terfenol-D

0.2

0.0027

Fast

Conducting polymer (Polyanaline)

450

Slow

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285

Xn t

(b)

(a) x

– U – n

αm

– n

– P2

αi x

αi

– P1

x U∞ (c)

Figure 3. (a) reference line for pitching moment calculations for flapped wing, (b) reference line for pitching moments calculations for twisted wing and (c) elemental angle of attack.

and Xnt is the distance between the elemental leading vortex segment and the twisting line, as shown in Fig. 3 (b). N is the total number of panels and Γn is the elemental vortex strength obtained by solving the following linear system of equations, -1

_w _w  {Γn} =  Cm,n -Cm,n tan(Ψn) 4πU∞{αm} (3)   -1

∆cp,n =

cnU∞

(4)

where cn is the elemental chord. The resultant pressure acting on each panel of the wing can be written in terms of the incremental pressure coefficients as follows ρU∞2 ∆P = ∆c (5) n

-1

2Γn

p,n

 _ w __ww _ w   where are -Ctan(Ψ {Γ } {Γ = n}C=m,n -C Cand tan(Ψ )the 4πU )downwash 4πU {α }{α } and sidewash influence n m,n m,n m,n n  n  ∞ m∞ m   respectively,computed  coefficients, according to the Biot-Savart

WING STRUCTURE STRAIN ENERGY The wing structure strain energy is given by

Law (Bertin, 1989). Ψn is the elemental wing dihedral angle, U∞ is the air flow velocity and αm is the elemental angle of attack schematically illustrated in Fig. 3 (c).

U= 1 2

INCREMENTAL PRESSURE COEFFICIENT CALCULATION The incremental pressure coefficient for the n-th panel of the wing is given by (Lamar and Margason, 1971)

∫∫∫{ε} {σ} dV (6) T

where V is the volume occoupied by the structural elements of the wing, {σ} and {ε} are the stress and strain vectors, respectively. By using the finite element method, Eq. (6) can be rewritten in terms of the wing stiffness matrix and nodal displacement vector as follows

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286

U= 1 {δ}T [K] {δ} (7) 2

where U is the total strain energy generated during the wing morphing process in the presence of the aerodynamic pressure field, together with the actuation forces provided by the smart materials to deform the wing.

carried out assuming that the trailing edge was clamped to the remaining part of the wing. The meshes used for the structural and aerodynamic simulations were the same in order to allow a direct pressure mapping between lattices and finite elements. For the flapped wing, the hinging line was positioned at 70% of the chord and the final flap deflection was assumed to be 10°. A convergence study for the pressure field values indicated that a mesh density of 20 elements, spanwise by 10 elements chordwise, gives results within an accuracy of less

NUMERICAL SIMULATIONS Table 2. Wing dimensions.

Thickness (mm)

This subsection presents a numerical study in terms of actuation energy requirements for both flapped and twisted morphing wing configurations. The chosen wing dimensions as well as flight conditions are typical of small UAVs and they are listed in Tables 2 and 3, respectively. A NACA 0012 airfoil section with dimensions shown in Fig. 4 was assumed for both wings. Both wings have flexible trailing edges made of elastomeric skins, starting at 70% of the chord (region indicated by red dashed line in Fig. 4) and extending up to the full-length chord dimension of the wings. The mechanical properties of the elastomeric skins are depicted in Table 4. In order to compute the pressure field as well as the aerodynamic work induced by the airflow, an in-house VL program (Donadon and Iannucci, 2006a) based on the formulation described in the previous sections was used. Full wing models were required for both wing configurations in order to obtain the resultant pressure field. Once the pressure field was determined, the aeroelastic problem was then solved by mapping the differential pressure field into a finite element model of the deformable parts of the wing and the required elastic energy density determined. The finite element models were developed using the rectangular four-node bi-linear Mindlin shell elements (S4R), with reduced integration available in ABAQUS/ Standard finite element code. The structural analyses were

Spanwise length [m]

1.40

Root chord [m]

0.27

Tip chord [m]

0.27

Angle of Attack [Degrees]

3.0

Flap deflection (for the flapped wing) [Degrees]

10.0

Twisting angle at the wing tip (for the twisted wing) [Degrees]

10.0

Table 3. Flight conditions and air properties. U∞ [m/s]

40.0

Altitude [m]

1000

r [kg/m3]

1.117

�

Table 4. Mechanical properties for the elastomeric skin (Donadon and Iannucci, 2006b). E [MPa]

6.90

0.30

ρ [kg/m3]

1080

20 10 0 -10 -20

100

Chord (mm)

Figure 4. NACA 0012 airfoil section.

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150

200

250

A Numerical Study on Smart Material Selection for Flapped and Twisted Morphing Wing Configurations

to 10°. A comparison in terms of aerodynamic energy generated by flapped and twisted wing planforms is presented in Fig. 10. Figure 11 compares the aeroelastic strain field induced in the trailing edge portions of the flapped and twisted morphing wings.

1.6 1.4 1.4 1.2

1.2 1

0.8 0.6

0.8 0.4 0.2 0

0.2

0.1

0.6 0.4

1.6 1.4 1.2 1 0.8 0.6 0.4

Delta Cp

than 1% when compared to finer meshes and, for this reason, this mesh density was used throughout this work. A typical VL mesh for the flapped wing is shown in Fig. 5. Figure 6 shows the numerical results in terms of pressure distribution for the flapped wing. It can be seen that there is a singularity in the pressure distribution around the hinging line, as expected. This singularity is due to the change in the local angle of attack, which increases the vorticity strength in that region, affecting both lift and pressure distributions. The dimensions and flight conditions for the twisted wing were assumed to be the same as those defined for the flapped wing in order to provide a direct comparison between both wings planforms in terms of lift, pressure distribution, aerodynamic energy and required elastic energy density. The twisting line was placed at 70% of the chord extending throughout the wing span length. The VL mesh and the pressure distribution for the twisted wing are shown in Figs. 7 and 8, respectively. It can be seen from Fig. 8 that there is an increase in both lift and pressure distributions towards the tip of the wing due to the change of the local angle of attack in that region. It also can be noticed that the lift generated by the twisted wing is lower than the lift generated by the flapped wing. The pitching moment about the twisting line is also lower than the one obtained for the flapped wing. The higher values of pitching moment for the flapped wing were expected, because in the twisted wing, just part of the trailing edge is deflected whilst in the flapped wing the whole trailing edge is deflected. Figure 9 shows a comparison in terms of lift generation between the flapped and twisted wings for flap deflection and the local wing twisting angles ranging from 0° up

287

1.2

0.8

0.6

0.4

Y(m)

0.2

0.05

0.1

0.15

0.2

0.25

X(m)

Z(m)

Figure 6. Pressure distribution for the flapped wing.

1.4

1.2

0.8

0.6

0.4 Y(m)

0.2 0

Figure 5. Vortex lattice mesh for the flapped wing.

0.2 0.1 X(m)

0.4 Y(m)

0.2 0

0.2 0 0.1 X(m)

Figure 7. Vortex lattice mesh for the twisted wing.

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288

0.9 0.85

1.6 1.4 1.2

0.75

1.2

0.7 CL 0.65

0.8 0.6 0.4 0.2 0

0.1

0.2

0.8

1.4

0.6

0.8

0.55

0.6

0.5 0.45

0.4

Twisted wing 0

Twisting angle (degrees) 1.4

1.6 1.4 1.2 1 0.8 0.6 0.4

Delta Cp

1.3 1.2 1.1 1 CL 0.9

1.2

0.8

0.6

0.4

Y(m)

0.2

0.05

0.1

0.15

0.8

0.25

0.2

0.7 0.6

X(m)

0.5 0.4

Pitching moment (N.m)

1.5 1 0.5 0

10 8 6 4 2 0

4 5 6 7 8 Twisting angle (degrees) (a)

4 5 6 7 Flap angle (degrees) (b)

Figure 9. Lift generation comparison between twisted (a) and flapped (b) wings.

Aerodynamic energy (J)

Pitching moment (N.m)

Figure 8. Pressure distribution for the twisted wing.

3 2 1 0 20 15 10 5 0

Figure 10. Comparison between aerodynamic energies for twisted (a) and flapped wings (b). J. Aerosp. Technol. Manag., SĂŁo JosĂŠ dos Campos, Vol.6, No 3, pp.281-290, Jul.-Sep., 2014

4 6 Flap angle (degrees) (b)

A Numerical Study on Smart Material Selection for Flapped and Twisted Morphing Wing Configurations

289

Table 5. Energy quantities computed for flapped and twisted morphing wing configuration.

LE, Max. Principal SNEG, (fraction = -1.0) (Ave. Crit.: 75%) +1.408e-02 +1.290e-02 +1.140e-02 +1.173e-02 +1.056e-02 +9.385e-02 +8.212e-02 +7.039e-02 +5.865e-02 +4.692e-02 +3.519e-02 +2.346e-02 +1.173e-02 +0.000e-02

Flapped

Twisted

Strain energy (J)

0.0760

0.0250

Aerodynamic energy (J)

15.00

9.00

Elastic energy density (J/g)

0.0060

0.0036

Max. Stress (MPa)

0.100

0.072

Max. Strain (%)

1.40

1.01

CONCLUSIONS (a)

LE, Max. Principal SNEG, (fraction = -1.0) (Ave. Crit.: 75%) +1.009e-02 +9.245e-02 +8.405e-02 +7.564e-02 +6.724e-02 +5.883e-02 +5.043e-02 +4.202e-02 +3.362e-02 +2.521e-02 +1.681e-02 +8.405e-02 +0.000e-02

(b) Figure 11. Aeroelastic strain field induced in the trailing edge portions flapped (a) and twisted morphing wings (b).

Table 5 compares the required elastic energy density for both morphing wing configurations. It can be seen that twisted configuration requires less actuation energy than flapped wing configuration. On the other hand, the aerodynamic performance in terms of lift generation is much better for the flapped morphing configuration. Comparing the required elastic energy density for both wing configurations with the values provided in Table 1, one can see that only materials 2, 3, 4, 5, 7, 8, and 10 are able to provide the amount of elastic energy density required to deform the wings and sustain them against the aerodynamic pressure field. However, only Single Crystal (PZN-PT) can provide a fast response with maximum efficiency for both morphing configurations.

A review on smart materials technologies and concepts for morphing wing structures was presented and discussed in this paper. A formulation based on the Vortex Lattice Method (VLM) was proposed in order to compute the pressure distribution, lift generation and aerodynamic energy for both flapped and twisted morphing wing planforms. The proposed formulation has been implemented into MATLAB software. Numerical simulations were carried out for a typical small UAV, considering two morphing concepts: flapped and twisted wing configurations. The numerical results indicated that the flapped wing generated higher lift when compared to the twisted wing for the same deflection range. However, less aerodynamic power was required to sustain the twisted wing against the aerodynamic loads. These findings indicated that flapped wing configurations have a better aerodynamic performance when compared to the twisted wing, however, there is still a need of further investigation considering global twisting instead of twisting just part of the wing. A better aerodynamic performance means that the deformable parts of the wing can be made of lighter smart materials with lower specific energy, which allows the fabrication of lighter aircrafts with higher performance and less fuel consumption. The preliminary study presented in this paper suggests Single Crystal (PZN-PT) materials as potential candidates for smart morphing wing structures due to its fast response with maximum efficiency for both morphing configurations studied in this work.

ACKNOWLEDGEMENTS The authors acknowledge the financial support received for this work from BAe Systems-UK through FLAVIIR SEEDCORN PROJECT.

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REFERENCES Amprikidis, M., Cooper, J.E., Rogerson, C. and Vio, G., 2005, “On The Use of Adaptive Internal Structures for Wing Shape Control”, 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Austin, TX.

Fontanazza, A., Talling, R., Jackson, M., Dashwood, R., Dye, D.

Barrett, R. and Brozoski, F., 1996, “Adaptive Flight Control Surfaces, Wings, Rotors and Active Aerodynamics”, Proceedings of the SPIE – International Society for Optics and Photonics, San Diego, CA, v 2717.

Griffin, K. E. and Hopkins, M. A., 1997, “Smart Stiffness for Improved

Barrett, R., 1995, “All-Moving Active Aerodynamic Surface Research”, Smart Materials and Structures, Vol. 4, No. 2, pp. 41-44. doi: 10.1088/09641726/4/2/001.

and Iannucci, L., 2006, “Morphing Wing Technologies Research”, Proceedings of the 1st SEAS DTC Technical Conference, Edinburg, Scotland (CD ROM).

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Bertin, J. J., 1989, “Aerodynamics for Engineers”, 2nd Edition, PrenticeHall Inc.

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Cesnik, C. E. S. and Brown, E. L., 2003, “Active warping control of a joined-wing airplane configuration”, 44th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Norfolk, VA.

Kornbluh, R. D., Prahlad, H., Pelrine, R., Stanford, S., Rosenthal, M.

Chen, P. C., Sarhaddi, D., Jha, R., Liu, D. D., Griffin, K. and Yurkovich, R., 2000, “Variable Stiffness Spar Approach for Aircraft Maneuver Enhancement Using ASTROS”, Journal of Aircraft, Vol. 37, No. 5, pp. 865-871. doi: 10.2514/2.2682.

Stiffness and Damping”, Proceedings of the SPIE - International Society

Changho, N., Chattopadhyay, A. and Youdan K., 2002, “Application of Shape Memory Alloy (SMA) Spars for Aircraft Maneuver Enhancement”, Proceedings of the SPIE - International Society for Optics and Photonics, San Diego, CA, v 4701.

Piezo-composite Actuator (LIPCA)”, Smart Materials and Structures, Vol.

Donadon M. V. and Iannucci L., 2006a, “A Vortex Lattice Program to Compute Aerodynamic Loads in Flapped and Twisted Wing Planforms”, Internal Report-Flaviir SeedCorn Project, Department of Aeronautics, Imperial College London, London, UK .

planforms”, NASA TN D-6142.

Donadon M. V. and Iannucci L., 2006b, “Morphing Wing Concepts and Smart Coupons Manufacturing”, Internal Report- Flaviir SeedCorn Project, Department. of Aeronautics, Imperial College London, London, UK.

CA, v 5054.

A. and von Guggenberg, P. A., 2004, “Rubber to Rigid, Clamped to Undamped: Toward Composite Materials with Wide-Range Controllable for Optics and Photonics, San Diego, CA, v 5388. Lim, S. M., Lee, S., Park, H. C., Yoon, K. J. and Goo, N. S., 2005, “Design and Demonstration of a Biomimetic Wing Section Using a Lightweight 14, No. 4, pp. 496-503. doi: 10.1088/0964-1726/14/4/006. Lamar, J. E. and Margason, J. R., 1971, “Vortex-lattice fortran program for estimating subsonic aerodynamic characteristics of complex

Schultz, M. R., 2005, “A New Concept for Active Bistable Twisting Structures”, Proceedings of the SPIE - International Society for Optics and Photonics, San Diego, CA, v 5764. Suleman, A., Ed., 2001, “Smart structures: application and related technologies”, Springer Wien New York, NY.

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doi: 10.5028/jatm.v6i3.333

Small Solid Propellant Launch Vehicle Mixed Design Optimization Approach Fredy Marcell Villanueva1, He Linshu1, Xu Dajun1

ABSTRACT: For a small country with limited research budget and lack of advanced space technology, it is imperative to find new approaches for the development of low-cost launch vehicles (LV), which is, among all possibilities, an interesting option for rapid access to space, focused on integration of acquired components complemented with indigenously developed subsystems. This approach requires the cooperation of developed countries with huge experience and knowledge in LV development and operations. The main objective is to develop a small three stage solid propellant LV capable of delivering a payload of 100 kg to a circular low earth orbit of 600 km altitude, with the first and second stage solid rocket motors (SRM) hypothetically acquired from different countries and the third one designed and produced domestically in accordance with the production and technological capability. This approach provides main advantages such as: reduction in total time to access the space and to master the basic knowledge of launch operations. For this purpose, an integer continuous genetic algorithm global optimization method was selected and implemented, the SRM characteristics of the first and second stage were considered as integer variables, whereas the design variables of the third stage SRM and the trajectory variable were considered as continuous. A multi discipline feasible (MDF) framework was implemented along with the propulsion, aerodynamic, mass and trajectory models. Despite their particular characteristics and constraints, the results show highly acceptable values, and the approach proved to be reliable for conceptual design level. KEYWORDS: Launch vehicle, Mixed design optimization, Solid propellant.

INTRODUCTION The last decade may be characterized by an increased number of small satellites delivered into the low earth orbit (LEO), and this tendency will be dominant in the coming years. Small satellites have a reduced manufacturing cost, and are relatively easy to operate and maintain. Furthermore, the miniaturization of technology makes possible its delivery into space by using small cost effective launch vehicles (LV). Small countries generally have a limited research budget oriented to space technology development, however, nowadays it is possible to deliver a small satellite into orbit with a reasonable budget, considering the cooperation with technologically more advanced countries. This research was focused on finding a way to have rapid access into space and to master the basic knowledge of space development and operations. In such a way, several options had been analyzed, among them the most suitable alternative in terms of economic investment and development time resulted in a small solid propellant LV with mixed design configuration, involving a strong cooperation with different countries. The strategy considered here prioritizes the technology integration over expensive and time consuming new development, this means that complex and advanced devices were acquired and complemented with indigenous manufactured devices using available resources and technology. As a result, a three stage solid propellant LV was configured, where the first and second stage solid rocket motors (SRM) were acquired from different providers, complemented with a locally developed third stage SRM, which was designed and optimized to accomplish the specific mission.

1.School of Astronautics – Beihang University – Beijing – China Author for correspondence: Fredy Marcell Villanueva | Beihang University | School of Astronautics | Haidian District, 37 Xueyuan Road – New Main Building, B923 | Beijing 100191 – China | Email: marcell385@ yahoo.com Received: 02/13/2014 | Accepted: 08/15/2014

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Villanueva, F.M., Linshu, H. and Dajun, X.

In our research, a mixed integer continuous variables genetic algorithm (GA) method has been used in order to optimize the overall configuration of the LV.

CONSIDERED SOLID ROCKET MOTORS The considered SRM are listed in the Table 2 and were selected based on the variety of design characteristics. However, it is possible to add additional parameters such as cost, availability, technology complexity, country of origin among others.

LAUNCH VEHICLE MODEL

PROPULSION ANALYSIS The propulsion analysis has been conducted for all three stages of the LV, using the classical approach presented in Sutton and Biblarz (2001) and He (2004a; 2004b). For the third stage SRM, a detailed analysis was conducted, considering the properties of the propellant. In this analysis, the burning surface is considered constant by introducing a grain geometry shape coefficient, ks , the burning surface of the grain Sb can be calculated as:

LAUNCH VEHICLE DEFINITION A small three stage solid propellant LV in tandem configuration is considered for this research. The mission is to deliver a 100 kg payload to a circular LEO of 600 km of altitude. The payload volume requirements and the instrument module weight were specified beforehand in mission definition analysis and are shown in Table 1.

(1)

Table 1. Launch Vehicle data. Variables

Units

Value

Payload

100

Fairing mass

Instrument module

Payload deployment module

where, Lm is the rocket motor cylindrical length and D m the diameter. . The burning time tb, grain mass mgn, and mass flow rate mgn of the grain are calculated as: (2)

Table 2. Selected solid rocket motors.

Stage1

Stage2

SRM

Grain mass (kg)

SRM mass (kg)

Diameter (m)

Length (m)

Specific impulse (N.s/kg)

Burn time (s)

Mass flow (kg/s)

Thrust (N)

18400

20791

1990

4.80

2364

283.08

669194

15000

16779

1390

7.25

2314

202.70

469054

9950

11281

1390

5.20

2280

160.48

365903

4530

5207

0.98

4.60

2265

64.71

146578

9800

10950

1990

3.62

2805

150.77

422908

5080

5607

1390

3.10

2776

79.38

220345

4138

4412

1390

2.86

2746

63.66

174815

3700

4190

1390

2.40

2754

54.41

149850

3300

3650

1390

2.63

2824

60.00

169440

1760

1949

0.98

1.85

2776

38.26

106212

650

719

0.85

1.50

2849

15.12

43066

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(3) (12) (4) (13) (5) (14) where, u is the burning rate of propellant, ρgn density of the grain, Lgn = Lm + 0.314Dm length of the grain, Dgn = Dm diameter of the grain, λgn fineness ratio of the grain (grain length/diameter), and ηv the grain volumetric loading fraction. The expansion ratio ε, nozzle throat area At, and nozzle exit area Ae are calculated as: (6)

(7)

(8)

where, mLV is the LV gross mass, m01 first stage mass, m02 second stage mass, m03 third stage solid rocket mass, mIM instrument module mass, mPDM payload deployment module mass, mPAY payload mass, and mst the structural mass of the third stage SRM. He (2004a; 2004b) provided a methodology and a detailed calculation of the third stage SRM structural mass. This design consisted in a classical metallic case made of high strength steel, ethylene propylene diene monomer (EPDM) for chamber insulation, and carbon phenolic for the nozzle. AERODYNAMIC ANALYSIS The aerodynamic coefficients were estimated using the Missile DATCOM 1997 digital (Blake, 1998). This software is easy to use and implemented, and accurate enough for the conceptual design phase. Qazi and He (2005) and Villanueva et al. (2013) applied DATCOM in LV aerodynamics analysis. The lift and drag forces were calculated using the following relations:

(9) (15) where, Pc is the chamber pressure, pe exit pressure, Rc = 296 J/(kg.K) gas constant, Tc = 3300 K temperature in the combustion chamber, Pc max = 1.1Pc maximum value of chamber pressure, and k = 1.2 the specific heat ratio of gas. The specific impulse Isp, and the thrust T can be calculated as:

(10)

(11) where, pa is the atmospheric pressure, I spa average specific impulse, g acceleration due to gravity, and Ae the nozzle exit area. MASS ANALYSIS The mass analysis was conducted for the entire LV, and is represented by the following equations:

(16) where, q is the dynamic pressure, D drag force, L lift force, Sref vehicle reference area, CL lift coefficient, and CD the drag coefficient. The aerodynamic coefficients were calculated repeatedly for each LV configuration, the selected Mach ranged from 0 to 8 and the angle of attack from −8 to +1 degrees. TRAJECTORY ANALYSIS The trajectory analysis considers a 3 degree of freedom (3DOF) model, which has been modeled in SIMULINK (Zipfel, 2007; Fleeman, 2001). The previously calculated aerodynamic coefficients, the mass and the propulsion are the input parameters. In order to obtain a quick result, a 2D coordinate system was adopted, the LV flies as a point mass in a non rotating earth model. Figure 1 illustrates the forces acting on a LV and below a set of governing equations of motion (Xiao, 2001). The LV is flying in an inertial reference coordinate system XOY, with

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y Y

(19) The thrust to weight ratio gives an important value to evaluate the liftoff characteristics of the LV:

(20)

The density variation with altitude can be calculated as: h

(21)

The gravity varies with altitude and can be represented as: Re

(22) X

Figure 1. Forces acting on a launch vehicle.

its origin located in the center of the earth. Furthermore, all forces applied to the LV were considered in relation to the body centered velocity coordinate systems xoy as shown in Fig. 1.

(17)

where, V is the velocity, m vehicle mass, θ pitch angle, η trajectory angle, γ flight path angle, φ range angle, h height above ground, α angle of attack, and αprog (t) is the programmed angle of attack. The axial and normal overload coefficients ensure the integrity of the LV in all phases of flight, and were calculated in a body centered velocity coordinate systems (xoy), as follows: (18)

where, ρ0 is the sea level density, Re radius of earth, β density scale height, and μ the earth gravitational parameter. The mission requires to deliver the payload to an altitude hf with a circular orbital insertion velocity Vf:

(23)

TRAJECTORY PHASES The trajectory of the LV can be described as a composition of several phases, as presented by He (2004a), Qazi and He (2005) and Villanueva et al. (2013). For the present research, the trajectory was sectioned in seven phases, as shown in Fig. 2. Each phase has a specific flight characteristic as described next: • Vertical launch phase: This phase starts from the time of ignition of the first stage SRM until the end of vertical flight time tv (tv = t1 in Fig. 3), during this time the LV flies vertically with a flight path angle equal to 90 degrees. • Pitch over phase: During this phase, the LV maneuver with a negative angle of attack until the transonic velocity is reached. In this point, the angle of attack should approaches zero degrees. • Powered first stage phase: This phase lasts until the end of the burning time of the first stage SRM. The angle of attack should be kept at zero during the stage separation process. • Coasting phase 1: The LV flies with no thrust until the second stage ignites. • Powered second stage phase: The duration of this phase starts with the ignition of the second stage SRM and is equal to its burning time.

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• Coasting phase 2: This phase is characterized by a prolonged

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DESIGN OPTIMIZATION PROBLEM

ballistic free flight approaching the target altitude. • Kick phase: This phase starts with the ignition of the third

stage SRM until the insertion altitude at the required orbital velocity and flight path angle. FLIGHT PROGRAM FORMULATION The flight profile defines the performance and loads acting on the LV Consequently, its selection should be integrated in the optimization process. Figure 3 explains the variation of the angle of attack during the pitch over phase (He, 2004a; Xiao, 2001):

OBJECTIVE FUNCTION There can be different objective functions for LV optimization problem, such as minimization of the LV cost, which can be obtained knowing the cost of the first and second stage SRMs and the development cost of the third stage, and also the minimization of the development time, knowing the availability of the first and second stage SRM and the development time of the third stage SRM. However, this analysis considers the minimization of the gross launch mass (mLV). The mathematical description of design objective is as follows:

(24) (27) (25)

(28) (29)

(26)

where, αmax is the maximum angle of attack, am launch maneuver variable, ta time corresponding to maximum angle of attack, t time of flight, and t1 the time of start of pitch over phase, coincident in value with time tv.

(30) where, gj is the inequality constraints, hk the equality constraints, X the set of variables, Xlb the lower bound of variables and Xub the upper bound of variables. DESIGN VARIABLES The design variables are composed from integer (first and second stage SRMs), and continuous third stage SRM and trajectory variables. They are listed in Table 3 and can be represented as:

Kick phase

(31) (32)

Coasting phase 2

Powered second stage phase 0

Coasting phase 1

Powered first stage phase Pich over phase Vertical launch phase

Figure 2. Trajectory phases of launch vehicle.

−−αmax

Figure 3. Pitch over ascent phase of launch vehicle. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.291-300, Jul.-Sep., 2014

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(33)

INTEGER CONTINUOUS OPTIMIZATION APPROACH The particularity of our problem deals with integer and continuous variables simultaneously. The selection of SRM type for first and second stages were considered as integer variables.

Meanwhile, the trajectory and design parameters of the third stage SRM were considered as continuous. Several engineering applications of mixed integer continuous optimization approach were presented by Haupt et al. (2009), Faustino et al. (2006), as well as detailed explanation in Yeniay (2005) and Gantovnik et al. (2005). Garfield and Allen (1995) used integer optimization applied to the configuration of LVs, Johnson (2002) conducted a screening process of booster for hypersonic vehicles, Calabro et al. (2002) presented the optimization of the propulsion for multistage LVs, and Bhatnagar et al. (2012) solved the mass distribution problem under restrictive condition. Hartfield et al. (2004) have shown the application of GA in finding the global optimum in ramjet propulsion. Bayley and Hartfield (2007) used GA for LV multidisciplinary design optimization with emphasis on minimum cost. GA has been effectively applied to solve the problem of liquid propellant based LV (Riddle et al. 2007), as well as

Table 3. Design variables.

Table 4. Design constraints.

(34) DESIGN CONSTRAINTS The selections of constraints were oriented in order to satisfy the mission, to prevent any failure during flight, and to consider the limitation of the third stage manufacturing technology. They are listed in Table 4:

OPTIMIZATION STRATEGY

Variables

Symbol

Units

Stage 1

srm1

Stage 2

srm2

Rocket motor cylindrical length

Lm3

Rocket motor diameter

Dm3

Chamber pressure

Pc3

Nozzle exit pressure

Pe3

Coefficient of grain shape

ks3

Grain burning rate

m/s

Grain density

ρgn3

kg/m3

X10

Vertical flight time

X11

Time to pitch over

X12

Constraints

Value

Units

Orbit insertion velocity

Vf ≥ 7560

m/s

Final altitude

Vf ≥ 600

Axial overload

nx ≤ 14

Normal overload

ny ≤ 2

Maximum dynamic pressure

qmax ≤ 85

kPa

Angle of attack (0.8 ≤ M≤ 1.3)

α=0

deg

Orbit insertion angle

γ = 0 ± 0.2

deg

Rocket motor diameter

Dm1 ≥ Dm2

Rocket motor mass

mSRM1 ≥ mSRM2

Coasting time 1 (between 1st and 2nd stage)

tc1

C10

Total LV length

LLV ≤18

C11

Grain fineness

X13

Coasting time 2 ( between 2nd and 3rd stage)

tc2

λgn3 < 2

C12

Thrust to weight ratio

v3 ≥ 1.8

X14

Maximum angle of attack (absolute)

αmax

deg

C13

Nozzle exit diameter

de3 ≤0.9Dm3

X15

Launch maneuver variable

C14

Burning time

tb3 ≤65

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Small Solid Propellant Launch Vehicle Mixed Design Optimization Approach

solid propellant LVs (Bayley et al. 2008). Rafique et al. (2009) and Goldberg (1989) provides detailed and comprehensive implementation of GA in solving complex problems. OPTIMIZATION METHOD The adopted and implemented GA optimization method is shown in Fig. 4, where a set of input design variables (SRM type, trajectory and third stage), as well as the lower and upper bounds, are passed to the main loop, where an initial population is randomly created. Furthermore, the selection, the crossover and the mutation operations are performed until the stopping criteria is achieved. The constraints

297

were calculated and handled by external penalty function, as presented in Deb (2000) and detailed and explained in Coello (1999) and Kramer (2010). At each routine, the propulsion, mass, aerodynamics and trajectory analysis were performed. The main characteristics of GA are presented in Table 5. OPTIMIZATION FRAMEWORK The optimization framework considered for this research is based on the multi-discipline feasible (MDF) design, which allows an easy and accurate result (Qazi and He L, 2006), as shown in Fig. 5.

Table 5. Genetic algorithm characteristics. Design Variables Multidisciplinary Design Analysis

Characteristics

Generations

200

Population size

100

Stopping criteria

Function tolerance 10e-6

Population type

Double vector

Selection

Stochastic uniform

Crossover

Single point pc = 0.8

Mutation

Uniform pm = 0.2564

Reproduction

Elite count = 2

Function evaluation

2000

Population initialization

Propulsion analysis

Selection

Mass analysis

Crossover Mutation

Aerodynamic analysis Trajectory analysis

Variables

Stopping criteria Yes Optimal Solution

Figure 4. Genetic algorithm optimization approach.

Vehicle Definition • Vehicle Size

• Fairing Propulsion Analysis

Configuration • Stage dimensions

• Vehicle Performance

Optimal Design

Mass Analysis

Design Variables • Thrust • Burning Time

Aerodynamic Analysis • Mach • Altitude • Stage mass

• Lift • Drag Trajectory Analysis

Figure 5. Multidisciplinary design optimization. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.291-300, Jul.-Sep., 2014

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Villanueva, F.M., Linshu, H. and Dajun, X.

OPTIMIZATION RESULT The results show that the considered mixed integercontinuous GA optimization approach successfully reached the objective function. The optimized LV has a total mass of 23,530 kg and a 16.12 m of length. Table 6 shows the optimized value of variables and in Table 7 the main parameters of the LV third stage are listed. The first and second stages SRM design type (SRM 12 and SRM 22), had been optimized and selected from Table 2. Both SRMs have the same diameter but different length. As it is represented in Fig. 6, the shroud design is configured with the same diameter as the third stage, in order to reduce the aerodynamics forces and interferences. The performance characteristics of the LV, shown in Fig. 7, demonstrates the capability of the three stage solid propellant LV to place a small payload into the LEO orbit maintaining its main parameters inside its limit values, furthermore, the overall design configuration facilitates its launch operations.

Table 6. Optimum values of variables. Variables

Lower Bound

Upper Bound

Optimized Value

srm1

srm2

Lm3 (m)

0.80

1.20

0.90

Dm3 (m)

0.80

1.20

0.83

Pc3 (Pa)

70e5

80e5

77.42e5

Pe3 (Pa)

0.05e5

0.15e5

0.133e5

ks3

1.10

1.60

1.14

u3 (m/s)

6.0e-3

8.0e-3

6.71e-3

ρgn3 (kg/m )

1650

1740

1683.1

X10

tv (s)

3.0

6.0

3.01

X11

tm (s)

18.0

25.0

21.57

X12

tc1 (s)

2.0

8.0

4.46

X13

tc2 (s)

360

400

372.61

X14

αmax (deg)

3.0

6.0

5.731

X15

0.28

0.42

0.319

Table 7. Parameters of launch vehicle third stage.

CONCLUSION A small three-stage solid propellant LV was configured and optimized using a mixed integer-continuous GA optimization method. The first and second stages SRM types were considered as integer variables, whereas the third stage SRM and trajectory as continuous. The main advantage of using GA relies on its independency of initial values to start the optimization, and the ability to handle integer variables. The propulsion, mass, aerodynamic and dynamic models were developed and integrated in a MDF framework.

Figure 6. Optimized three stage launch vehicle. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.291-300, Jul.-Sep., 2014

Parameters

Value (Units)

Stage mass

893.6 (kg)

Propellant mass

762.1 (kg)

Stage dry mass

131.2 (kg)

Propellant mass fraction

0.854

Average Thrust

33.82 (kN)

Specific Impulse vac

2702.8 (N.s/kg)

Nozzle expansion ratio

48.01

Thrust to weight ratio

3.69

Burning time

60.9 (s)

500 400

200 100 0

Thrust to weight ratio

Thrust, kN

500 400 300 200 100 0

10 5 0

100 200 300 400 500 600 Time, s

600

100 200 300 400 500 600 Time, s

14 12 10 8 6 4 2 0

100 200 300 400 500 600

100 200 300 400 500 600 Time, s

Dynamic pressure, kPa

Mach

25 20 15 10 5 0

100 200 300 400 500 600 Time, s

90 80 70 60 50 40 30 20 10 0

Angle of attack, deg 0

5 10 15 20 25 30 35 40 Time, s

100 200 300 400 500 600 Time, s

1 0 -1 -2 -3 -4 -5 -6

5 10 15 20 25 30 35 40 Time, s

600 Altitude, km

Velocity, km/s

Flight path angle, deg 0

Time, s

100 90 80 70 60 50 40 30 20 10 0

100 200 300 400 500 600 Time, s

0.05 0 -0.05 -0.1 -0.15 -0.2 -0.25

Time, s

8 7 6 5 4 3 2 1 0

Normal overload

300

14 12 10 8 6 4 2 0 -2

299

Axial overload

600 Gross mass, ton

Altitude, km

Small Solid Propellant Launch Vehicle Mixed Design Optimization Approach

500 400 300 200 100

60 80 100 120 Time, s

500 1000 1500 2000 2500 Range, km

Figure 7. Performance characteristics of launch vehicle. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.291-300, Jul.-Sep., 2014

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An important contribution of this research is the approach in finding the best LV design configuration to rapid access to space with limited research budget, relied mainly on international cooperation and complemented with the indigenous aerospace manufacturing technology capability.

ACKNOWLEDGMENT Fredy Villanueva wishes to thank Beihang University and China Scholarship Council (CSC), for their financial support.

REFERENCES Bayley, D.J. and Hartfield, R.J., 2007, “Design Optimization of a Space Launch Vehicles for Minimum Cost Using a Genetic Algorithm”, AIAA 43rd AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, AIAA 2007-5852. Bayley, D.J., Hartfield, R.J., Burkhalter, J.E. and Jenkins R.M., 2008, “Design Optimization of a Space Launch Vehicle Using a Genetic Algorithm”, Journal of Spacecrafts and Rockets, Vol. 45, No. 4, pp. 733–740. Bhatnagar, P., Rajan, S. and Saxena, D., 2012, “Study on Optimization Problem of Propellant Mass Distribution Under Restrictive Condition in Multistage Rocket”, International Conference on Advances in Computer Applications (ICACA), No. 1, pp. 27-29. Blake, W.B., 1998, “Missile DATCOM: User’s Manual-1997 FORTRAN 90 Revision”, Wright-Patterson AFB, Oklahoma.

Haupt, S.E., Haupt, R.I. and Young, G.S., 2011, “A Mixed Genetic Algorithm Used in Biological and Chemical Defense Applications”, Software and Computing, Vol. 15, pp. 51-59. doi: 10.1007/s00500-009-0516-z. He, L., 2004a, “Launch Vehicle Design”, Beijing University of Aeronautics and Astronautics Press, Beijing. He, L., 2004b, “Solid Ballistic Missile Design”, Beijing University of Aeronautics and Astronautics Press, Beijing. Johnson, D.B., 2002, “Screening Process for Boosters for Hypersonic Vehicles”, AIAA/AAAF 11th International Space Plane and Hypersonic Systems and technologies, AIAA 2002-5218. Kramer, O., 2010, “A Review of Constraint-handling Techniques for Evolution Strategies”, Applied Computational Intelligence and Soft Computing, Vol. 2010, Article ID 185063, pp. 11. doi: 10.1155/2010/185063.

Calabro, M., Dufour, A. and Macaire, A., 2002, “Optimization of the Propulsion of Multistage Solid Rocket Motor Launcher”, Acta Astronautica, Vol. 50, No. 4, pp. 201–208. doi: 10.1016/S00945765(01)00164-3.

Qazi, M. and He, L., 2005, ““Rapid Trajectory Optimization Using Computational Intelligence for Guidance and Conceptual Design of Multistage Space Launch Vehicle”, AIAA Guidance, Navigation, and Control Conference, AIAA 2005-6062.

Coello, C.A., 1999, “A Survey of Constraint Handling Techniques Used with Evolutionary Algorithm”, Technical Report Lania-RI-99-04, Laboratorio Nacional de Informatica Avanzada, Rebamen 80, Xalapa, Veracruz 91090, Mexico.

Qazi, M. and He, L., 2006, “Nearly Orthogonal Sampling and Neural Network Metamodel Driven Conceptual Design of Multistage Space Launch Vehicle”, Journal of Computer Aided Design, Vol. 38, No. 6, pp. 595-607. doi: 10.1016/j.cad.2006.02.001.

Deb, K., 2000, “An Efficient Constraint Handling Method for Genetic Algorithm”, Computer Methods in Applied Mechanics and Engineering, Vol. 186, No. 2-4, pp.311-338. doi: 10.1016/S00457825(99)00389-8.

Rafique, A.F., He, L., Zeeshan, Q., Kamran, A., Nisar, K. and Wang Xiaowei, 2009 “Integrated System Design of Air Launched Small Space Launch Vehicle Using Genetic Algorithm”, 45th AIAA/ASME/ SAE/ASEE Joint Propulsion Conference and Exhibit, AIAA-2009-5506.

Faustino, A.M., Judice, J.J., Ribeiro, I.M. and Neves, A.S., 2006, “An Integer Programming Model for Truss Topology Optimization”, Investigação Operacional, Vol. 26, No. 1, pp. 111-127.

Riddle, D.B., Hartfield, R.J., Burkhalter, J.E. and Jenkins, R.M., 2007, “Genetic Algorithm Optimization of Liquid Propellant Missile Systems”, 45th AIAA Aerospace Sciences Meeting and Exhibit, AIAA 2007-0362.

Fleeman, E.L., 2001, “Tactical Missile Design”, AIAA, Reston.

Sutton, G.P. and Biblarz, O., 2001, “Rocket Propulsion Elements”, 7th edition, Wiley-Interscience, New York.

Gantovnik, V.B., Gurdal, Z., Watson, L.T. and Anderson-Cook, C.M., 2005, “Genetic Algorithm for Mixed Nonlinear Programming Problems Using Separate Constraint Approximations”, 31st AIAA Journal, Vol. 43, No. 8, pp. 1844-1849. Garfield, J.R. and Allen, B.D., 1995, “Screening Studies and Techniques for All-Solids Space Launch Vehicles”, 31st AIAA/ASME/ SAE/ASEE Joint Propulsion Conference and Exhibit. Goldberg, D.E., 1989, “Genetic Algorithms in Search, Optimization, and Machine Learning”, Addison-Wesley. Hartfield, R.J., Jenkins, R.M. and Burkhalter, J.E., 2004, “Ramjet Powered missile design using a genetic algorithm”, 42nd AIAA Aerospace Sciences Meeting and Exhibit, AIAA 2004-0451.

Villanueva, F.M., He, L., Rafique, A.F. and Rahman T., 2013, “Small Launch Vehicle Trajectory Profile Optimization Using Hybrid Algorithm”, International Bhurban Conference on Applied Science and Technology, Pakistan. doi: 10.1109/IBCAST.2013.6512150. Xiao, Y., 2001, “Rocket Ballistics and Dynamics”, Postgraduate Course, Beihang University. Yeniay, O., 2005, “Penalty Function Methods for Constrained Optimization with Genetic Algorithms”, Mathematical and Computational Applications, Vol. 10, No. 1, pp. 45-56. Zipfel, P.H., 2007, “Modelling and Simulation of Aerospace Vehicle Dynamics”, AIAA, Reston.

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doi: 10.5028/jatm.v6i3.330

Relative Motion Guidance, Navigation and Control for Autonomous Orbital Rendezvous Mohamed Okasha1,2, Brett Newman2

ABSTRACT: In this paper, the dynamics of the relative motion problem in a perturbed orbital environment are exploited based on Gauss’ variational equations. The relative coordinate frame (Hill frame) is studied to describe the relative motion. A linear high fidelity model is developed to describe the relative motion. This model takes into account primary gravitational and atmospheric drag perturbations. In addition, this model is used in the design of a control, guidance, and navigation system of a chaser vehicle to approach towards and to depart from a target vehicle in proximity operations. Relative navigation uses an extended Kalman filter based on this relative model to estimate the relative position and velocity of the chaser vehicle with respect to the target vehicle and the chaser attitude and gyros biases. This filter uses the range and angle measurements of the target relative to the chaser from a simulated Light Detection and Ranging (LIDAR) system, along with the star tracker and gyro measurements of the chaser. The corresponding measurement models, process noise matrix and other filter parameters are provided. Numerical simulations are performed to assess the precision of this model with respect to the full nonlinear model.The analyses include the navigations errors, trajectory dispersions, and attitude dispersions. KEYWORDS: Satellite relative motion, Orbital rendezvous.

INTRODUCTION Although significant progress and technical development have been achieved with regards to orbital rendezvous such as International Space Station supply and repair and automated inspection, servicing, and assembly of space systems, there are limitations with the traditional methods that struggle to meet the new demands for orbital rendezvous. Presently, in order to perform such close proximity operations, mission controllers generally require significant cooperation between vehicles and utilize man-in-the-loop to ensure successful maneuvering of both spacecraft. The interest in autonomous rendezvous and proximity operations has increased with the recent demonstration of XSS-11, Demonstration of Autonomous Rendezvous Technology (DART), and Orbital Express. Autonomous rendezvous and proximity operations have also been demonstrated by Japanese EST-VII, and the Russian Progress vehicles. In addition future missions to the ISS will require autonomous rendezvous and proximity operations (Fehse, 2003; Woffinden and Geller, 2007). Many relative motion modeling and control strategies have been designed using the linearized Clohessy-Wiltshire (CW) equations to describe the relative motion between satellites. The CW equations are valid if two conditions are satisfied: • The distance between the chaser and the target is small compared tothe distance between the target and the center of the attracting planet; and • The target orbit is near circular (Clohessy and Wiltshire, 1960). The CW equations do not include any disturbance forces, for example, gravitational perturbations and environmental forces

1.International Islamic University Malaysia – Kuala Lumpur – Malaysia 2.Old Dominion University – Norfolk/VA – United States Author for correspondence: Mohamed Okasha – Assistant Professor – Department of Mechanical Engineering – Faculty of Engineering–International Islamic University Malaysia | Jalan Gombak | P.O. Box 10 50728 Kuala Lumpur – Malaysia | Email: mokasha@iium.edu.my or Ph.D. Alumni from Old Dominion University | Email: mokas001@odu.edu.my Received: 01/28/2014 | Accepted:07/29/2014

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(solar radiation pressure and atmospheric drag). Alternative linear equations that have been used in the literature to model the relative motion are the Tschauner-Hempel (TH) equations (Tschauner and Hempel, 1965).These expressions generalize the CW equations and are similar to them in their derivation and types of applications. Tschauner and Hempel derived theses equations from the viewpoint of rendezvous of a spacecraft with an object in an elliptical orbit. They found complete solutions for elliptical orbits in terms of the eccentric anomaly. This advancement was followed by additional papers which present the complete analytical solution explicit in time, expanding the state transition matrix in terms of eccentricity (Yamanaka and Ankersen, 2002; Carter, 1998; Melton, 2000; Broucke, 2003; Inalhan et al., 2002; Sengupta and Vadali, 2007; Cho and Park, 2009). This form of solution is used to analyze the relative motion between the chaser and the target vehicles in the relative frame of motion more efficiently and rapidly than solving the exact nonlinear differential equations in the inertial coordinate system. The TH equations do not take into account any perturbation forces. These perturbations have a significant effect on the satellite relative motion. Due to the previous limitations of the CW and TH models, this paper proposes an innovative linear model which includes both the perturbation that reflects the Earth’s oblateness effect and atmospheric drag perturbation in the Cartesian coordinates orbital frame with little complication. Especially in low Earth orbits (LEOs), these perturbations have a deep influence on the relative dynamics, and their inclusion in the linear model can sensibly increase the performance of the linear filters, allowing greater insight of satellite relative motion, and providing an opportunity to investigate alternative feedback control strategies for the proximity operations. Unlike the relative translation motion control, the relative rotational control is a traditional feedback control system. During the mission scenarios, the chaser vehicle may need to track the target vehicle to achieve proper docking maneuvers and or visual inspection tasks. The paper uses an extended Kalman filter formulation to estimate the relative motion and chaser attitude using range and angle measurements from a LIDAR system coupled with gyro and star tracker measurements of the chaser (Woffinder and Geller, 2007; Jenkins and Geller, 2007; Junkins et al., 2005; Woffinden, 2004). The Kalman filter basically consists of two main stages. The first stage is the propagation stage, where the states are propagated numerically and it is based on the proposed linear model. The second stage comes

when the measurements from the sensors are available and it is used to update the states of the first stage. The corresponding measurement models, process noise matrix, and other filter parameters are provided. Momentum wheels are assumed for attitude control and thrusters are assumed for translation control. The effects of the navigation filter, pointing algorithms, and control algorithms are included in the analysis. The objective of this paper is as follows: • To develop linearized high fidelity models for relative motion in a perturbed orbit; • To design a navigation filter that can determine the relative position and velocity between target and chaser vehicles as well as orientations and angular rates of the chaserthat support closed-loop proximity attitude control operations and maneuvers; and • To design a control system for the chaser vehicle to either approach or depart fromthe target vehicle in proximity operations in a general perturbed orbit for coupled translation and rotation relative motion. The analysis in the current paper is summarized as follows. First, we present the relative dynamics equation of motion for the chaser with respect to the target in a general perturbed orbit, along with attitude dynamics models.Next, a linear high fidelity relative motion model is developed to describe relative motion in proximity operations based on Gauss’ variational method. Then, the relative navigation and an extended Kalman filter are presented for the relative motion and attitude estimations, along with the relative translational and rotational controller. In the simulation section, the accuracy and performanceof the relative navigation and controller, based on the high fidelity model, are illustrated through different numerical examples and comparisons are made with the truth nonlinear model. Finally, conclusion of the work is presented and suggestions are made for future work.

RELATIVE MOTION MODELS Consider an Earth-centered inertia (ECI) frame, with orthonormal basis {iX, iY, iZ,}. The vectors iX and iY lie in the equatorial plane, with iX coinciding with the line of equinoxes, and iZ passing through the North Pole. Relative motion is conveniently described in a Local-Vertical-Local-Horizontal

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.301-318, Jul.-Sep., 2014

Relative Motion Guidance, Navigation and Control for Autonomous Orbital Rendezvous

(LVLH) frame,which is attached to the target spacecraft, as shown in Fig.1. This frame has basis {iX, iY, iZ,} with iX lying along the radius vector form the Earth’s center to the spacecraft, iZ coinciding with the normal to the plane defined by the position and velocity vectors of the spacecraft, and iY = iZ x iX. The LVLH frame rotates with angular velocityvector ω, and its current orientation with respect to the ECI frame is given by the 3-1-3 direction cosine matrix, comprising right ascension of ascending node Ω, inclination i, perigee argument ω plus true anomaly f, respectively (Fig.2). The angular velocity can also be expressed in terms of orbital elements and their rates. Let the position of the chaser vehicle in the target’s LVLH frame be denoted by ρ=xix+yiy+ziz, where x, y and z denote the components of the position vector along the radial, transverse,

303

and out-of-plane directions, respectively. ρis determined from ρ=Rc-Rt, where Rc and Rt are the chaser and target absolute position vectors. Then, the most general equations modeling relative motion are given by the following: (1) where [fc]LVLH and [ft]LVLH are the external accelerations acting on the chaser and the target, respectively in the LVLH frame of the target vehicle. In Eq. (1), (..) and (.) denote the first and second derivatives with respect to time. It is assumed, in this paper, that the externalaccelerations arise due to two basic groups of accelerations, defined by the following equation: f = fg + fa + fc + fw (2)

iY iZ iZ

Relative Orbit

iX Target

Chaser

Rc iY

iX Figure 1. Relative Motion Coordinates.

Orbital Normal H

Orbital Plane Perigee

The first group of accelerationsis due to gravitational effects, fg, atmospheric drag, fa, and control, fc. Since Earth isn’t perfectly spherical, more accurate gravity models exist, taking into account Earth’s irregular shape. One irregularity that has a significant influence on space missions is the Earth’s bulge at the equator. This phenomenon is captured in the J2 gravity model (Vallado, 2001; Schaub and Junkins, 2003). The second group of accelerations, fw, is considered to be small accelerations, due to the gravity fields of other planets, solar pressure, or venting, which also perturbs the spacecraft’s motion. These small accelerationsare grouped together and modeled as zero mean normally distributed random variables (Woffinden and Geller, 2007).

V p

Equatorial Plane Line of Nodes

iX Figure 2. Orbital Elements. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.301-318, Jul.-Sep., 2014

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In the literature, the most popular methods to model the spacecraft’s orbit are known as Cowell’s method and Gauss’s method (Vallado, 2001; Schaub and Junkins, 2003). The Cowell’s method is basically defined by specifying the position (R) and velocity (V) vectors of the spacecraft in the inertial coordinate frame, while Gauss’ method is defined by an equivalent set of elements called orbital elements (a,e,i,Ω,ω,f) which correspond to the semi-major axis, eccentricity, inclination, right ascension of the ascending node, argument of periapsis, and true anomaly, respectively, as shown in Fig. 2.

Table 1 summarizes the dynamic equations that are used in order to describe all of these methods. In this table, [.]I and [.]LVLH denote that the forces are defined in inertial and LVLH coordinate frames, respectively; ax , ay and az are the components of disturbance accelerations acting on the target in the LVLH reference coordinate frame; s(∙)=sin(∙) and c(∙)=cos(∙); and R are the Earth gravitational constant and the radius of the Earth; the terms R and V refer to the magnitude of the position and velocity vectors, respectively; the quantity H denotes the magnitude of the specific angular momentum vector defined by H=R×V; X, Y and Z are the components of the spacecraft position vector; CD

Table 1. Orbit Model Methods Summary. Method

Dynamic equations

Cowell’s

Gauss’s

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Relative Motion Guidance, Navigation and Control for Autonomous Orbital Rendezvous

is the atmospheric drag coefficient; A denotes the cross sectional area; m is the spacecraft mass; and finally, ρ is the atmospheric density. Exponential atmospheric behavior is used to model Earth’s atmospheric density. This model and its corresponding parameters are defined inVallado (2001). In order to use the generalized relative dynamic model defined by Eq. (1), the angular velocity vector, ω, and the angular acceleration vector, ώ, ofthe LVLH frame with respect to the ECI frame, needs to be determined. Table 2 summarizes the equations that can be used to compute these vectors. These equations are derived based on using either Cowell’s method (position and velocity vectors) or Gauss’s method (orbital elements). In this table, the matrix TILVLH denotes the direction cosine matrix of the LVLH coordinate frame with respect to the ECI coordinate frame. The Euler’s equation of motion is used to describe the attitude dynamics for both target and chaser vehicles, and a quaternion formulation is used for attitude kinematics. The dynamics for both vehicles are given below as (Woffinden and Geller, 2007):

305

(3a) (3b) (3c) (4a) (4b) (4c) where is the quaternion multiplication operator defined by Lear (1985).

(5)

Table 2. LVLH Coordinate Frame Orientation. Dynamic equations

Given Inertial Position and Velocity

Given Orbital Elements

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and the i vehicle gravity gradient torque is defined by

(9)

(6) In Eqs. 3 and 4, the target states include the quaternion, q , that defines the orientation of the target with respect to the inertial frame, and the target’s angular rate, ωt. Similarly the chaser states are qIc and Ic. andare the target and chaser inertia matrices, respectively. The gravity gradient torque, τ(ig), for both vehicles (τ(cg) for the chaser and τ(tg) for the target) is derived from the Earth’spoint mass gravity models. The random disturbances, τ(td) and, τ(tg) are included in the models to account for disturbance torques acting on each vehicle such as drag, solar radiation and other unmodeled disturbances. These unmodeled disturbances are represented as uncorrelated white noise, with mean and variance defined by a trial and error technique outlined by Lear (1985). The control input, τ(cc), is the torque executed by the actuators (momentum wheels) on the chaser spacecraft. It is assumed that the available sensors are the LIDAR for tracking the target and an assembly of a star tracker and gyros for attitude determination. The parameter states for these sensors are modeled as first-order Markov processes with large time constants, causing them to behave like biases. The parameter states include the gyros bias bωc, star camera misalignments Єss, and LIDAR misalignments Єll. The dynamics model associated with these states is given by: t I

The generated torque and impulsive include errors such as noises νc, biases bc, scale factor biases fc, and misalignments Єc. These errors can be modeled also as white noises. The simulation contains gyros, star tracker, and LIDAR sensor models. The models for these measurements are given by: Gyro Model: (10) Star Tracker Model: (11) LIDAR Model: (12)

where: (13)

where, wbω, ws and wl are white noise terms, driving the firstorder Markov processes and τbω, τs and τl are the corresponding time constants. The actuator models used in the simulation include momentum wheels for orientation control and thrusters for translational control. The mathematical model for the actual control torque, generated by the wheels, and the impulsive thrust, by the thrusters, are:

The gyro models include bias bωc, scale factor bias fωc, and angular random walk noise νωc. The starcamera model accounts for the uncertainty in the alignment of the star camera frame Єss with respect to the chaser frame and sensor noise νss. The qcs refers to the fixed orientation of the star camera coordinate frame with respect to the chaser body coordinate frame. The LIDAR model includes angle measurements (azimuth, α, and elevation, β) noises v α, v β and range (ρ) noise, v ρ. The transformation matrix denoted by Tab is the transformation matrix used to transform any vector from coordinate b to coordinate a. The term ilosl is the line of sight vector in the LIDAR coordinate frame (Fig. 3). The transformations TlῙ , TῙS, TŜS , TŜI, and TIT are a series of transformation matrices to transform the line of sight vector from target LVLH coordinate frame to the LIDAR coordinate frame. These transformations include errors from sensor misalignments, noises, and attitude determination errors. The small angle rotations can be written in terms of quaternions as

(8)

(14)

(7a)

(7b) (7c)

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Relative Motion Guidance, Navigation and Control for Autonomous Orbital Rendezvous

or attitude matrices as (15)

where x is the state vector. This model can be used to approximate the time varying state transition matrix by expanding the time invariant exponential matrix solution in a Taylor series to fourth order, as follows:

where θ=θu is a small rotation vector, and θ× operating on vector ω is a cross product matrix defined by the ordinary cross product θ× ω = ω × θ.

In this section, a linear time varying high fidelity model is obtained to describe the relative motion dynamics. This model is derived based on two main assumptions. The first assumption is that the relative distant between the chaser and the target vehicles is much less than the target orbital radius. The second one assumes that the main disturbance accelerations, that affect both vehicles are the gravitational acceleration and the atmospheric drag acceleration. Based on these assumptions, all terms mentioned in the general relative dynamic expression, Eq. (1), are expanded considering only first order terms to obtain the new proposed model. Table 3 summarizes the procedures that have been followed to obtain this model. In this table the linear time varying model reduces to the following form (16)

ilos

α iY Figure 3. Line of Sight Vector.

NAVIGATION CONTROL MODEL ALGORITHMS The main objective of the navigation system is to estimate the target’s relative position, relative velocity and orientation given noisy sensor measurements, imperfect dynamic models, and uncertain initial conditions. The logic behind the navigation filter is to process information collected from sensors and various mathematical models to generate the best possible estimation of the states. Space navigation application of the Kalman filter is presented in this section. The dynamic models for a closed loop GN&C system are shown in Fig. 4. The navigation model uses an extended Kalman filter to estimate the relative position and velocity of the chaser vehicle with respect to the target vehicle, and the approximated analytical state transition matrix solution. Orbital elements of the target are numerically propagated with respect to time using Gauss’s variational equations, with J2 and drag perturbations. These orbital elements are used to compute the transformation matrix of the target vehicle with respect to the inertial frame, as well as to assist in estimating LIDAR measurements. The dynamic models used to propagate the navigation states are: (18a)

iZ Chaser

(17) This matrix is used in the next section as a part of the extended Kaman filter, to propagate the states forward in time and to compute the filter parameters.

LINEAR HIGH FIDELITY RELATIVE MODEL

Target

307

(18b) iX

(18c)

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Table 3. Relative Orbit Model Summary. Model

Equations

Nonlinear

Linear Time Varying

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Relative Motion Guidance, Navigation and Control for Autonomous Orbital Rendezvous

GN&C System Guidance Algorithms

same rate as the vector attitude observations). A discrete propagation is usually sufficient. Discrete propagation can be derived using a power series approach (Crassidis and Junkins, 2004).

Dynamics Control Algorithms Navigation Filter

Actuators

309

Plant Model

(22)

Sensors

where

Figure 4. Closed Loop GN&C System.

(23a)

(18e)

(18f) where (19) The orbit perturbed acceleration term, â, is different form the term used in the truth model in which it does not contain the unmodeled disturbance acceleration term fw. This navigation target model is used only to assist in the process of estimation. The dynamic modelfor the relative navigation states are:

(23b)

The propagation dynamic model for the error parameters is given by

(24)

(20) where ϕLTV is the state transition matrix, and it is defined by Eq. (17) for the relative linear time varying model. The navigation model for the target angular motion is used only to produce a reference attitude trajectory. This trajectory will be tracked by the chaser attitude control system. (21a)

where ϕMarkov is defined as follows:

(25)

(21b)

An extended Kalman filter is derived from the nonlinear models as illustrated in the equations below (Brown and Hawag, 1997).

(21c)

(26a)

For the chaser vehicle, the propagation of the state can be accomplished by using numerical integration techniques. However, in general, the gyros observations are sampled at a high rate (usually higher than or at least equal to the

(26b) Here, the state vector x can represent relative position, velocity, and orientations of the chaser as well as other

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parameters that need to be estimated for the use by other flight algorithms. The time derivatives of the states x· are a function of the states, inputs, time, and additive process noise w. This process noise is used to approximate unmodeled disturbances and other random disturbances to the dynamics. ~ The measurements zk are modeled as a function of the states, time, and measurement noise v k. The process noise and measurement noise are normally distributed with zero mean and covariance Q and Rk respectively. The following steps summarize the Kalman filter equations, that are used to estimate the relative motion states and it is based on minimizing mean square of the error. • Enter prior estimate of x–k and its error covariance P–k and compute the Kalman gain (27a) ~

(29b)

(29c)

The state vector contains θ c instead of q Ic because the quaternion must obey a normalization constraint, which can be violated by the linear measurement updates associated with the filter. The most common approach to overcome this shortfall involves using a multiplicative error quaternion, where after neglecting higher order terms, the four component quaternion can effectively be replaced by a three component error vector θc (Crassidis and Junkins, 2004).Therefore, within first order, the quaternion update is given by:

• Update estimate by measurement zk

(30)

(27b) (27c)

and the discrete attitude error state transition matrix can also be derived using a power series approach to be:

• Compute error covariance for updated estimate

(31)

(27d)

where

• Project ahead

(27e)

(32a)

(27f) The term ϕk is the state transition matrix, and Hk is the measurement partial matrix that represents the sensitivity of the measurements to changes in the states. The state vector of the Kalman filter is defined to be:

(32b)

(28) (32c)

and Kalman filter matrices are given by:

(32d)

(29a)

By following the line steps of Woffinden and Geller (2007), Woffinden (2004) and Lear (1985), the initial error covariance

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Relative Motion Guidance, Navigation and Control for Autonomous Orbital Rendezvous

matrix P–o , which represents how accurate the initial states are known, is given below for the proposed linear relative model, attitude, and error parameters.

(33a)

311

(34c)

Here, σWx, σWy and σWx are the standard deviations for the random unmodeled acceleration disturbances that act on the relative motion during the sample time period ∆t and σV c, σV ω, σVs and σVl are the ω b random process uncertainty noises for gyros, gyro biases, star tracker misalignments, and LIDAR misalignments, respectively. The measurements sensitivity matrices Hk and sensor measurements noise matrices Rk are defined for both star sensor and LIDAR as:

(33b) (35) (33c) (36) Parameters σx, σy and σz denote the standard deviation uncertainties of the relative position components, and σx, σy and σz are for the relative velocity components. The coefficient ε refers to the uncertainty correlation coupling between relative position and velocity components in the LVLH coordinate frame, and it ranges between a positive and a negative one. The standard deviations σw θ, σw ω, σwS and σwl are referring to b b the uncertainties of initial attitude, gyro biases, star tracker misalignments, and LIDAR misalignments, respectively. The discrete process noise matrix components of the relative motion canbe approximated by:

The measurement partials for the azimuth, elevation and range measurements are computed with the help of the LIDAR measurement range vector. Utilizing Eq. (13) and small angle approximations leads to the following equation for the relative range in terms of the navigation states: (37) Using the chain rule, the partial of the range vector with respect to the navigation states can be expressed as (Woffinden and Geller, 2007):

(38)

(34a)

(39)

The measurement geometry can now be computed by taking the advantages of the property that pαl, pβl and ppl are orthogonal to each other and taking the dot product with respect to each of them. (34b)

(40)

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The evaluation of the relative range vector with respect to the navigation states yields

where (49a)

(41) (49b) Now, the LIDAR measurement sensitivity matrix and covariance matrix can be written as:

and

(42)

(50a)

and

(50b) (50c) (43)

When processing star tracker data, a derived measurement is calculated (Woffinden and Geller, 2007). This quantity is effectively the residual to be processed by the filter.

(44)

^ c are the desired orientation and angular velocity, q^cI and ω desc desc respectively, to be tracked by the chaser vehicle. The angular offset and angular rate offset between target and chaser are denoted ^ respectively. The proportional and derivative by δq^e and δω, control gains Kq and Kω are determined based on the desired natural frequency ωθ, damping ratio ζθ of the attitude control system, and the moment of inertia of the chaser spacecraft Ic (Wie, 1998). (51)

The derived star tracker measurement can be written as a function of the navigation states as: (45)

On the other hand, The translation control algorithm computes the required continuous thrust, fc, based on the previous linear model, in order to track the desired trajectory specified by the following guidance algorithm:

Therefore, the measurement sensitivity matrix for the star tracker can be derived to be

(52a)

(46)

(52b)

and the star tracker measurement covariance is (47) For close proximity operations, a propositional-derivative (PD) controller is employed for both the rotational and translational controls. The commanded torques for the chaser spacecraft to match its orientation with the target vehicle are computed as (48)

(52c) The proportional and derivative control gains Kρ and Kρ. are determined based on the desired natural frequency ωρ and damping ratio ζρ of the translational control system. (53) . Variables ρdes and ρdes are, respectively, the desired relative position and relative velocity to be tracked by the chaser vehicle, and it is defined by the guidance algorithms. It is

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Relative Motion Guidance, Navigation and Control for Autonomous Orbital Rendezvous

worth noting that the equivalent continuous velocity increment ∆V, based on the continuous thrust, can be approximated for small to be

313

The key metrics of the analysis fall into three main categories. The first is navigation performance, which is how well the states are estimated by the filter. This metric is measured by the navigation error, the difference between the true states and the filter states. The second is trajectory control performance, which is a measure of how closely the chaser vehicle is able to follow the guidance algorithms. The third is fuel performance, or ∆V fuel usage, and it is computed based on the linear model developed in the previous section. The preceding guidance and navigation algorithms are illustrated now through different examples. Initial conditions for simulation are listed in Tables 4 to 6. A Simulink model is built using the MATLAB software to demonstrate the closed-loop guidance transfer of the chaser in order to approach and to depart from the target vehicle in any orbit, either circular or elliptic, given uncertain initial conditions, noisy measurements, and limited dynamics. This model consists of three main parts, guidance, navigation, and control. The proposed linear time varying model is used in designing the navigation filter and in maneuver targeting of

the guidance system. The target is assumed to be in a passive nadir pointing mode andnot in maneuvering. The chaser uses star tracker data and gyro data to determine attitude and attitude rate. Momentum wheels and PD controller are used to point the chaser LIDAR at the target. The chaser uses LIDAR data to determine the relative position and velocity of the target. Maneuver targeting algorithms, based on PD controller, are used to compute commands in the chaser body frame as to track the desired trajectory. The performance of the navigation system is shown in Figs. 5 to 7. In this case, the thrusters are off and both target and chaser vehicles are initially in the same neighborhood (Table 5). Figure 5 shows the relative position and relative velocity between the vehicles during simulation. Figure 6 depicts how accurately the navigation system can estimate the chaser’s relative position and velocity. Form this figure, the filter is able to converge within few minutes and the relative position and velocity can be accurately estimatedwithin the accuracy of the sensors. The attitude navigation errors and the PD control tracking performance are shown in Fig. 7. As indicated by this figure, the chaser attitude navigation system is able to converge quickly and the chaser attitude PD controller can track the target attitude and angular velocity trajectories. The basic glidelope rendezvous and close proximity operations scenario used to evaluate the performance of the entire closedloop relative position and attitude control system with the navigation filter consists of two main segments: the inbound and theoutbound segments. Each segment of the glideslope is followed by 3 minutes of station keeping. First, the inbound segment: the chaser starts to approach the target form [58-580 0] m behind the target and ends at [0-100 0] m. After 3 minutes

Table 4. Navigation Filter Parameters.

Table 5. Vehicles Orbital Elements.

(54)

SIMULATION EXAMPLES

Parameter

Value

Initial Relative Position and Velocity Uncertainties Process Noise Measurements Noise

Parameter

Target

Chaser

a,km

6723.2576

0.1

i, deg

51.6467

Ω,deg

188. 0147

Simulation Step

0.1 s

ω,deg

174.3022

Measurements Update

1 Hz

f,deg

270.0882

270.0832

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Table 6. Simulation Initial Conditions. Initial Conditions

Parameter

Chaser

Target

Value

Inertia

δϕ,δθ,δψ

[(7.5&-7.5&7.5)]deg

Rotationalnatural frequency

ωθ

1/30 s-1

Rotational damping ratio

ζθ

0.7

Translational natural frequency

ωρ

1/50 s-1

Translational damping ratio

ζρ

0.7

Rotational disturbances

τId

kg-km2/s2

Translational disturbances

km/s2

Drift rate

3 deg/hr/axis

Random walk

0.05 mrad/s1/2

Misalignment

1 mrad/axis

Noise

1 mrad/axis

Measurements

1 Hz

Misalignment

1 mrad/axis

Noise

[1 mrad 1 mrad 0.5 m]

Measurements

1 Hz

Initial Relative Attitude Errors

Control Parameters

Unmodeled Disturbances

Gyro error (3)

Sensors Errors

Star Tracker error (3)

LIDAR error (3)

of station keeping at -100 m behind the target, the chaser starts to depart away from the target and leading to a new location -1000 m behind the target. The chase then stays at rest at that location for another 3 minutes. The results of this scenario are shown in Figs. 8 and 9. In all of these figures, different segments of the glideslope are shown, and the variations of in-plane relative motion of the chaser with respect to target vehicle are presented. Figure 8 shows the relative position and velocity plots of relative motion along with the required in order to achieve this trajectory maneuver, while Fig. 9 shows the error in relative position and velocity between the truth model and the navigation model. In all of the above glideslopes, the overall performance of the rendezvous and proximity operations are satisfactory.

The continuous thrust is calculated using the estimated relative position and velocity, either from the Kalman filter or from the knowledge of initial conditions, not from the true relative position and velocity of the chaser. As such, the chaser is not expected to reach its intended place exactly, but in the neighborhood thereof. Aided by the sensors, the initial estimation errors subside to an optimal level, determined by the ratio of the process noise matrix , and the measurement noise matrix ,earlier defined. Because of the active range and the angle measurements from the LIDAR system, and relatively small measurement errors, the true and the estimated relative position and velocity states are almost indistinguishable, as seen in previous figures during the steady state.

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Relative Motion Guidance, Navigation and Control for Autonomous Orbital Rendezvous

200

ρX ρY ρZ

ρ, m

-200

40 ρX, m

-400 -600 0

10 15 20 Time, min

-40 -640 -630 -620 -610 -600 -590 -580 ρY, m

ρX ρY ρZ

0.05 ρ, m/s

-20

0.1

∆X ∆Y ∆Z

0.5

-0.05 -0.1

∆V, m/s

-800

315

-0.5 0

10 15 20 Time, sec

-1

15 20 Time, sec

Figure 5. Relative Motion Without.

0.2

ρeX ρeY

ρe, m/s

ρeY

0.1

ρeZ 0

ρeZ

-0.1

-5 0

10 15 20 Time, min

-0.2

200

10 20 Time, min

Navigation

Truth ρY, m/s

100 50

Truth

-0.05 -0.1 -0.15

0 -50 -700

0.05

Navigation

150 ρX, m/s

ρeX

-650

ρY, m

-600

-0.2 -700

-550

-650

ρ Y, m

-600

-550

Figure 6. Relative Motion Navigation Performance Without. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.301-318, Jul.-Sep., 2014

Okasha, M. and Newman, B.

316

x 10-2 6

2 0

Chaser Euler Angles Errors, deg

-0.05

-2 -4

δωCX δωCY δωCZ

0.05

10 20 Time, min

δΦC δθC δΨC

0.1 0

-0.1 -0.2 0

10 15 20 Time, min

-0.1

10 20 Time, min

10 Altitude Tracking Errors, deg

δωC, deg/s

Angular Velocity Tracking Errors, deg/s

0.1 δωCX δωCY δωCZ

5 0 -5

-10

δΦ δθ δΨ

10 20 Time, min

Figure 7. Chaser Attitude Navigation and Control Performance.

500

ρX ρY ρZ

40 20 ρX, m

0 ρ, m

-500

-20

-1000

-40 0

10 15 20 Time, min

ρX ρY ρZ

0 ρ, m/s

-60 -1200-1000 -800 -600 -400 -200 ρY, m

0.02

∆VX ∆VY ∆VZ

0.01 ∆V, m/s

-1500

-0.01

-5

-0.02 -0.03

-10 0

10 15 20 Time, sec

-0.04

Figure 8. Relative Motion Inbound-Outbound Glideslope (Summary Scenario). J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.301-318, Jul.-Sep., 2014

10 15 20 Time, sec

Relative Motion Guidance, Navigation and Control for Autonomous Orbital Rendezvous

ρe, m/s

ρeX ρeY ρeZ

0.5 ρe, m

x 10-2 5

-0.5 -1

10 15 20 Time, min

ρY, m/s

ρX, m

-5

-20

10 15 20 Time, min

Navigation Truth

ρeX ρeY ρeZ

-10

317

Navigation Truth

0 -5

-40 -60 -1200-1000 -800 -600 -400 -200 ρY, m

-10 -1200-1000 -800 -600 -400 -200 ρY, m

Figure 9. Relative Motion Navigation and Control Performance (Summary Scenario).

CONCLUSION The results of this study indicate that the proposed linear model is clearly effective at estimating the relative position and velocity and controlling the relative trajectory. In addition, this model is not restricted to a circular orbit but it can be used as well for an eccentric orbit. Furthermore, by using this model, simple guidance algorithms for glideslope are developed to autonomously approach and depart form a target vehicle. The relative navigation in this study is utilizing range, azimuth, and

elevation measurements of the target relative to the chaser froma simulated LIDAR system, along with the star tracker and gyro measurements of the chaser and an extended Kalman filter. The vehicle attitude dynamics, attitude tracking control, attitude determination, and uncertainties like measurement biases and sensor misalignments are considered in this study to fire the thrusters in the right direction and spin the momentum wheels at the proper rate in the chaser coordinate frame. The analyst must consider, in addition, off nominal situations, limitations and operational range of the sensors, and limitations of the actuators. These topics and others will be addressed in the future.

REFERENCES Brown, R.G. and Hawag, P., 1997, “Introduction to Random Signals and Applied Kalman Filtering”, 3rd Edition, John Wiley & Son Inc., United States.

Carter,T.E., 1998, “State Transition Matrices for Terminal Rendezvous Studies: Brief Survey and New Example”, Journal of Guidance, Control, and Dynamics, Vol. 21, No. 1, pp. 148-155.doi: 10.2514/2.4211.

Broucke, R.A., 2003, “Solution of the Elliptic Rendezvous Problem with the Time as Independent Variable”, Journal of Guidance, Control, and Dynamics,Vol. 26, No. 4, pp. 615-621. doi: 10.2514/2.5089.

Cho, H.C. and Park, S.Y., 2009, “Analytical Solution for Fuel-Optimal Reconfiguration in Relative Motion”,Journal of Optimization Theory and Applications, Vol. 141, No.3, pp. 495-512.doi: 10.1007/s10957008-9482-3.

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Clohessy, W.H. and Wiltshire, R.S., 1960, “Terminal Guidance System for Satellite Rendezvous”, Journal of the Aerospace Sciences, Vol. 27, No. 9, pp. 653–678. Crassidis, J.L. and Junkins, J.L., 2004, “Optimal Estimation of Dynamic System”, 1st Edition, CRC Press LLC, United States. Fehse, W., 2003, “Automated Rendezvous and Docking of Spacecraft”, 1st Edition, Cambridge University Press, United Kingdom. Inalhan, G., Tillerson, M., and How, J. P., 2002, “Relative Dynamics and Control of Spacecraft Formation in Eccentric Orbits”, Journal of Guidance, Control, and Dynamics,Vol. 25, No. 1, pp. 48-58. Jenkins, S.C. and Geller, D.K., 2007, “State Estimation and Targeting For Autonomous Rendezvous and Proximity Operations”, AAS 07-316, Proceedings of theAIAA/AASAstrodynamics Specialists Conference, Mackinac Island, MI. Junkins, J.L., Kim, S., Crassidis, L., Cheng, Y., and Fosbury, A.M., 2005,“Kalman Filtering for Relative Spacecraft Attitude and Position Estimation”, AIAA 2005-6087, Proceedings of the AIAA Guidance, Navigation, and Control Conference, San Francisco, California. Lear, W.M., 1985, “Kalman Filtering Techniques”, NASA Johnson Space Center: Mission Planning andAnalysis Division, Houston, TX, JSC-20688. Melton, R.G., 2000, “Time-Explicit Representation of Relative Motion Between Elliptica lOrbits” Journal of Guidance, Control and Dynamics, Vol. 23, No. 4, pp. 604–610.doi: 10.2514/2.4605.

Sengupta, P. and Vadali, S.R., 2007, “Relative Motion and the Geometry of Formations in KeplerianElliptic Orbits with Arbitrary Eccentricity”, Journal of Guidance, Control, and Dynamics,Vol. 30, No. 4, pp. 953-964.doi: 10.2514/1.25941. Schaub, H. and Junkins, J.L., 2003, “Analytical Mechanics of Space Systems”, American Institute of Aeronautics and Astronautics, Reston, Virginia, United States. Tschauner, J. and Hempel, P., 1965, “Rendezvous zueinem in Elliptischer Bahn Umlaufenden Ziel”, ActaAstronautica, Vol. 11, pp. 104-109. Vallado, D.A., 2001, “Fundamentals of Astrodynamics and Applications”, 2nd Edition, Microcosm Press, El Segundo, California. Woffinden, D.C. and Geller, D.K., 2007, “Navigating the Road to Autonomous Orbital Rendezvous”, Journal of Spacecraft and Rockets, Vol. 44, No. 4, 2007, pp. 898–909.doi: 10.2514/1.30734. Woffinden, D.C. and Geller, D.K., 2007, “Relative Angles-Only Navigation and Pose Estimation for Autonomous Orbital Rendezvous”, Journal of Guidance, Control and Dynamics, Vol. 30, No.5, pp. 14551469.doi: 10.2514/1.28216. Woffinden, D.C., 2004, “On-Orbit Satellite Inspection Navigation and Analysis”, M.S. Thesis, Aeronautics and Astronautics Department,Massachusetts Institute of Technology, Cambridge, MA, United States. Wie, B., 1998,“Space Vehicle Dynamics and Control”, American Institute of Aeronautics and Astronautics, Reston, Virginia, United States. Yamanaka,K. and Ankersen,F., 2002, “New State Transition Matrix for Relative Motion on an Arbitrary Elliptical Orbit”Journal of Guidance, Control, and Dynamics, Vol. 25, No. 1, pp. 60–66.

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doi: 10.5028/jatm.v6i3.321

Location Issues of Thin Shell Parts in the Reconfigurable Fixture for Trimming Operation Hu Fuwen1

ABSTRACT: The location of thin shell parts is always a knotty problem during machining, welding, forming, assembling and inspection operations. This paper mainly focuses on the special location issues in the digital and flexible trimming process for aircraft skins fastened by reconfigurable fixture. Firstly, in view of the dynamic change of effective locators, the “X-2-1” location principle was proposed with reference to the “3-2-1” and “N-2-1” location schemes. Secondly, the standard procedure for solving location parameters was summarized in consideration of location admissibility, holding posture, locator layout and so on. Thirdly, a locating experiment was conducted to investigate the positional accuracy of the reconfigurable fixture and the calculation accuracy of location parameters solution. Fourthly, a quantitative evaluation method to evaluate the dynamic stiffness of the fixturing system was put forward. Moreover, the effects of location layout on the dynamic stiffness were analyzed using the finite element simulation system for the trimming process. A noticeable appearance had been observed that the cliff effect of the dynamic stiffness of the flexible fixturing system may be induced due to the dynamic change of effective locators. Finally, some conclusions and discussion on future works were given. KEYWORDS: Thin shell part, Reconfigurable fixture, Location principle, Dynamic stiffness, Trimming.

INTRODUCTION The location of a component is one of the most important aspects to achieve the required accuracy during a machining process. Generally, the proper design of clamping and locating arrangements is a difficult task, considering its workpiece geometry, tool paths, set-up time, production scale, resistance to cutting forces, costs, adaptability and so forth. For a long time, towards fixture design becoming a science rather than an art, and towards achieving automation and intelligence of fixture design activities, many theories, tools and approaches have been introduced by many researchers (Wang et al., 2010; Cecil, 2001; Boyle et al., 2011). However, most part of this research has concentrated on the fixture design for workpieces which can be regarded as rigid bodies. Actually there are also some low rigidity or flexible components in industrial fields, e.g. aircraft skin panels, automotive cover panels, rocket shells and other thin-wall structures. They cannot be seen as rigid bodies due to their inherent flexibility and inevitable deformation caused by external forces or their own weights. Thereby, their location issues have always been a particularly knotty problem. To overcome it, the single purpose and dedicated fixtures are often employed in practical production. Although this approach would be economical for large scale mass manufacturing (e.g. automobile manufacturing), it would lead to long lead time, a large quantity of tooling, and high production costs under small batch manufacturing environments such as the fabrication of aircrafts and rockets. Therefore, the flexible tooling solutions like reconfigurable fixtures or modular fixtures have been increasingly adopted. These flexible tooling can be changed, reconfigured or reused to suit dimension and shape changes of

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compliant parts. In the past decade or so, a great deal of attention has been increasingly directed toward the design, analysis and synthesis of flexible fixturing systems. There are a number of strategies in order to achieve flexibility (Shirinzadeh, 2002), but this paper mainly focused on the reconfigurable fixtures designed for machining operations of thin shell parts. According to available documents, the earliest idea of a reconfigurable fixture for thin shell parts trimming process may date back to 1987 (Douglas and Ozer, 1987). About ten years later, the reconfigurable fixture for sheet metal machining was reduced to practice (Proctor, 1998). Afterwards, several commercially available reconfigurable tooling systems were designed and built, for example, the Pogo flexible tooling system by CNA manufacturing systems (U.S.A.), and the TORRESTOOL universal holding fixture by the M. Torres group (Spain). Application results from Northrop Grumman indicated that the Pogo flexible tooling system can cut down setup time by about two-thirds on trimming and hole drilling operations on more than 100 different fuselage skin parts (Koelsch, 1998). But these commercially available reconfigurable tooling systems for holding compliant parts are extremely expensive (Walczyk and Longtin, 1999). Therefore, many researchers and manufacturers are inclined to develop the reconfigurable fixturing devices independently. A reconfigurable fixturing system was specifically designed for the drilling of sheet metal parts, which included a T-slot base plate, vertical supports and locating pins (Youcef-Toumi et al., 1987). A novel reconfigurable modular system was presented for the fixturing of thin-walled flexible objects, which consists of a base plate, and height-adjustable locators and clamps (Sela et al., 1997). A computer-controlled reconfigurable fixturing device for compliant parts, based on a matrix of individually-stoppable pins lowered by a single rigid platen, has been developed (Walczyk and Longtin, 2000). Zhou et al. (2008) designed a flexible tooling system for large-scale aircraft skins by means of the manipulator’s drivability. For aircraft skins trimming based on the combination of the reconfigurable fixture and 5-axis gantry type milling machine tool, the author and co-workers have been studying the process planning method, the trimming process simulation, as well as the equipment development (Hu and Li, 2011; Hu et al., 2012a; Hu et al., 2012b). Besides, to evaluate the milling forces in the trimming process of aircraft skins, the author presented an integrated approach based on the combination of the mechanistic model of milling forces, 3D finite elements simulation of milling operations, and the

support vector regression methodology (Hu and Li, 2012). Along with the promotion of research, a better systematic understanding of the issue of thin shell components locating in the flexible fixture has been recognized. These would be described as follows. In the next section, the author would introduce the flexible manufacturing technology for aircraft skins based on the reconfigurable discrete dies and the reconfigurable fixture. And the third section mainly focuses on the distinctive location principle that appears in the trimming process of aircraft skins supported by the reconfigurable fixture. Then the fourth section illustrates the location parameters solution method, considering location allowance, holding posture, locator layout, etc. Then a location experiment was presented to investigate the location accuracy.Subsequently a quantitative evaluation method for the dynamic stiffness of the fixturing system was put forward. Finally, some conclusions and discussion on future works are given.

FLEXIBLE MANUFACTURING MODEL OF AIRCRAFT SKINS Aircraft skin panels with the aerodynamic profile are the key components to fabricate fuselage, wings, empennage, and so forth. Generally, skin panels are made of aluminum alloys, however, in the design of new aircrafts, composite panels increasingly tend to be adopted. The accuracy of their shape and dimension and the integrity of their surfaces and edges are extremely stringent. For instance, laser cutting of high-strength aluminum alloys for aerospace applications has been limited, due to its inevitable heat-affected zone (Riveiro et al., 2008). Therefore, the periphery cutting of aircraft skins mainly employs the machining method or the water jet cutting method. In addition to that, skin panels always have low structural rigidity, and complex internal structure and outer shape. These factors make their forming and machining process very difficult. Thereby, the fabrication technology of aircraft skin panels is always a key technology of aircraft manufacturing. TRADITIONAL FABRICATION PROCEDURE OF AIRCRAFT SKINS Traditionally, a typical fabrication process of metal skins is illustrated in Fig. 1. The entire process for any skin part needs

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Location Issues of Thin Shell Parts in the Reconfigurable Fixture for Trimming Operation

Template for trimming and drilling Wood die Upper die Section templates of contour

Forming Lower die

Skin part

Figure 1. A traditional fabrication procedure of aircraft skins.

a set of dies, templates and other tooling. The setup time of tooling fabrication accounts for approximately 60 to 80 per cent of the cycle time of one part. Furthermore, due to the analog data transfer through the whole process, the accumulative error is often relatively large. Additionally, the whole process has to be executed sequentially rather than concurrently. Once a fault (e.g. springback) occurs on any tooling, repair and rework would be necessary. FLEXIBLE MANUFACTURING MODEL OF AIRCRAFT SKINS In order to improve the fabrication process of metal skins, a flexible, digital and tooling-less manufacturing model was presented, namely, to replace the dedicated dies by discrete multi-point dies and to replace the dedicated trimming template by the reconfigurable fixture (Papazian et al., 2002; Li et al., 2009). As illustrated in Fig. 2, the entire process was accomplished jointly with the digital design, digital tooling, numerical control equipments (e.g. stretch forming machine and CNC machine tool) and digital measure technology. Obviously, the good flexibility of this new manufacturing model can adapt to the dimension and shape changes of aircraft skins, and radically change the traditional fabrication model. As proved in an application experiment performed by Papazian et al. (2002), in this new “tool-less” sheet metal fabrication environment, tooling fabrication time would be reduced by a factor of 8 and labor hours would be reduced to 1/3 of the traditional values. In the aspect of the flexible stretch forming process, based on the discrete-die concept, undoubtedly, it was David Hardt at MIT and his research group that had done the pioneering work, including the prototype reconfigurable multi-points tooling development and the closed-loop shape control technology (Hardt et al., 1981; Walczyk and Hardt, 1998; Munro and

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Walczyk, 2007). On the basis of the original MIT work, since 1996 to 2002, a reconfigurable tooling for flexible fabrication project was sponsored by the Defense Advanced Research Projects Agency. And the project participators included MIT, Northrop Grumman Corporation, Cyril Bath Company, etc. As a result of this project, a commercially available reconfigurable multi-point tooling was designed and built by the Cyril Bath Company in 2002. It was the first production reconfigurable tooling, which had a working volume of 1.06 × 1.83 × 0.30 m3 and consists of 2,688 movable pins (Munro and Walczyk, 2007). In China , approximately since 2003, the scholarly research upon the reconfigurable tooling for the stretch forming of aluminum sheet began, and it has been performed chiefly by the Li Mingzhe’s research group of Jilin University (Cui et al., 2008; Zhou et al., 2005; Cai et al., 2009) and Li Dongsheng’s research team of Beihang University (Chen, 2006; Huang et al., 2008; Yu et al., 2011). At 2007, a larger-size (1.216 × 1.824 × 0.305 m3) production reconfigurable tooling was designed and built by the Beijing Aeronautical Manufacturing Technology Research Institute (Zhou, 2007). In short, a persistent and concerted effort by many industry and academic researchers has brought the concept of reconfigurable “discrete-die” tooling to the production environment. The natures of both the reconfigurable multi-point tooling and the reconfigurable fixture are to use discrete points to approximate a surface. However, as another aspect of the flexible manufacturing model of aircraft skins, the flexible trimming process, based on the reconfigurable fixture, seems to have a reverse development history, namely, from reduction-topractice to academic research. Perhaps there is no such difficult problem as the shape control that remedies the forming defects (e.g. dimpling) which occur in the flexible stretch forming process. Nevertheless, as the chief issue in the development and application of the expensive flexible fixture, the location issue has not been quite clear in academic research. The following factors suggest the particularity and complexity of the workpiece location issues in the flexible trimming process. • The end effectors are both locators and positioners. Namely, they not only clamp the thin shell part through the vacuum absorption, but also locate the part consistently to prevent the possible deformation. Moreover, the locating and clamping are synchronous. • When the “flexibility” of multi-point supporting would encounter the “elasticity” of the low-rigidity thin shell part, the location stability would be difficult to assure. Additionally,

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10 2 Shape measurement

Stretch forming over the reconfigurable tooling 1

Design, simulation and analysis

3 5

Shape measurement

Routing and drilling over the reconfigurable fixture

Main contents of step [1] include pin height calculation, stretching trajectory calculation and springback compensation; In step[2] the sheet metal would be formed over the reconfigurable multi-point tooling; Then, the geometrical precision would be measured via laser scanning; In step[4] the error distribution can be obtained after data processing; If the formation precision cannot meet the requirements, step [1]-[4] begin the cycle again; Otherwise, the surface part would be located in the reconfigurable fixture for machining operations; Certainly, before loading the surface part, the location parameter of the reconfigurable fixture would be solved; In step [8] the surface part would be machined over the reconfigurable multi-point tooling; Then, the machining precision would be measured via laser scanning; In step [10] the error distribution can be obtained after data processing; If the machining precision cannot meet the requirements, step [7]-[10] begin the cycle again.

Figure 2. Flexible manufacturing model of aircraft skins.

with the gradual separation of the thin shell part, and with the loading of the periodical milling forces, the process stability would also be difficult to assure. The whole process stability turns out to be a particularly knotty problem. • The main task of location layout is to determine the positions and numbers of vacuum end effectors. Two types of restriction should be taken into account: one is geometric constraint, which ensures there is no interference between any two adjacent actuator rods, between any two adjacent lateral frames, and between the machining cutter and the vacuum end effectors; another one is physical constraint, which mainly considers the clamping and processing

stability. The method for planning feasible location layout should be studied.

LOCATION PRINCIPLES ADEQUATE LOCATION As it is known, a rigid body has six movement degrees of freedom (3 linear and 3 rotary ones) in a three-dimensional space. If a workpiece can be regarded as a rigid body, it is necessary to arrest the DOF (degrees of freedom) (≤ 6), which

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Location Issues of Thin Shell Parts in the Reconfigurable Fixture for Trimming Operation

affects the machining accuracy. However, due to low rigidity and easy deformation, the thin shell part cannot be considered as a rigid body. As illustrated in Fig. 3, if the elastic surface part is supposed to consiste of n mass points, it would be essential to limit all DOF of n mass points to avoid machining errors. Theoretically, a mass point has three DOF (3 linear), and n tend to be the infinity of integers. Therefore, the only way to limit all 3n DOF entirely is “surface location” provided by the dedicated hard surface fixture (Fig. 4). Moreover, for the cutter penetration, trimming paths should be slotted on the surface of the rigid fixture. Also the trimming or drilling operations can be done by manual cutters, if the plate thickness is less than 2mm, otherwise by the numerical control machine tool.

Z Zi

Xi X

Figure 3. Discrete points model of a thin shell part.

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Apparently, when using the reconfigurable fixture to locate the thin shell part (Fig. 2), only the DOF of the mass points close to the vacuum end effectors can be partly restricted. Due to the loaded milling forces and inherent low rigidity, especially in the normal direction, vibration would be unavoidable. In this case, if the required machining accuracy would not be affected, the location status also can be called adequate location. Evidently, adequate location is one of the essential requirements for guaranteeing the machining accuracy. LOCATION SCHEMES Generally, for a rigid part, the basic location concept is to arrest the motion that may affect the machining accuracy. Specifically, for a prismatic part without an existing hole, the “3-2-1” location principle can arise proper arresting of all the motions. As illustrated in Fig. 5, “3” refers to 3 locators (passive fixture elements) on the primary datum surface; and “2” refers to 2 locators on the secondary datum surface; then “1” refers to 1 locator on the tertiary datum surface. However, the 3-2-1 principles can only be applied for prismatic-workpiece fixturing, and the three perpendicular datum planes and their corresponding features must be well defined. For a prismatic part with an existing hole, the most efficient way of locating the workpiece is to apply three supports and a single internal locator to restrict nine movements at once. And for nonprismatic parts, the fixturing techniques are often dependent on the workpiece shape. Moreover, due to the complex nature of workpiece geometry, there are no generalized fixture-design principles for nonprismatic parts.

The tertiary datum plane

The secondary datum plane

– (X)

–ˆ (Y,Z) Y

X The primary datum plane

Figure 4. Manual trimming of aircraft skins on the dedicated tooling.

–ˆ ˆ (Z,X,Y)

Figure 5. “3-2-1” location principle for a prismatic part without an existing hole. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.319-331, Jul.-Sep., 2014

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The basic “3-2-1” location scheme for rigid parts may not be suitable for thin shell and compliant objects, due to their inherent flexibility and low rigidity. In such case, the ‘‘N-2-1’’ locating scheme (Fig. 6) was formally introduced for deformable sheet metal parts, as compared to the widely accepted “3-2-1” principle for rigid bodies, where N supports are required in order to support the metal sheet part in the primary datum direction to avoid excessive deflection (Cai et al., 1996). It must be pointed out that the ‘‘N-2-1’’ locating scheme was presented considering that of when drilling forces or resistance spot welding forces were induced. Therefore, the ‘‘N-2-1’’ location principle is appropriate when the reconfigurable fixture are used for inspection, laser scribing and for the drilling process. Nevertheless, in case of trimming process, the ‘‘N-2-1’’ locating scheme can not reveal the appearance that some locators gradually lose their effectiveness. Both the researches of Walczyk and Longtin (1999) and the initial research of the author (Hu and Li, 2011) neglected this singular detail. As illustrated in Fig. 7, at the beginning of trimming, there are X locators which take action; while with the separation of the desired shape from the blank, there are Xs locators that gradually lose their effectiveness. Until the end, only Xw locators continue to work. In short, the locators are always changing due to the workpiece separation. The author called this particular location scheme “X-2-1” location principle. “X” refers to the locators of dynamic changes. In order to guarantee the stability of the whole trimming process, designing the locators’ initial layout should take the dynamic change into account. Otherwise, the trimming operation may failure due to machining errors or other accidents.

according to the dimension and shape of the surface part. The nature of location parameters solution is to transfer the aircraft skins digital model into the numerical control data, which drive the reconfigurable fixture into quick and accurate reconstruction. Two different solution methods have been presented by the author: one is based on the geometric structures of locators and positioners (Hu et al., 2012a); another is based on the virtual locating and positioning (Hu et al., 2012b). In this paper, the standard procedure of location parameters solution is summarized as shown in Fig. 8. In a previous research by the authors (Hu et al., 2012a; Hu et al., 2012b), the procedures of holding posture design, locators layout design and location parameters solution were discussed in details. Whereas the location admissibility was omitted in previous researches, it would be described emphatically as follows. Location admissibility indicates whether the thin shell part can be located and supported properly according to the configuration of the reconfigurable fixture and the part model. Specifically, the listed aspects should be taken into account: • The dimensions of the thin shell part are beyond the working stroke of the reconfigurable fixture?

X locators at the initial time

LOCATION PARAMETERS SOLUTION

Gradual separation of desired shape

Location parameters refer to the desired positions that vacuum end effectors and reference positioners should reach,

Gradual invalidity of Xs locators

Sustained working of Xw locators

Z Y

– (X)

0 –ˆ (Y,Z) X ~ (∆)

Figure 6. “N-2-1” location principle for deformable metal sheet parts in drilling and welding.

Figure 7. “X-2-1” location principle for thin shell parts during flexible trimming process.

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Location Issues of Thin Shell Parts in the Reconfigurable Fixture for Trimming Operation

• Due to the part dimensions and the cutting trajectory, the

locators that can be arranged are too few? • The local curvature of the thin shell part is beyond the

maximum allowable swing angle of end effectors? The first two aspects are comparatively easy to judge and address, according to the part model and the features of the reconfigurable fixture. As for the third point, when the part surface is adsorbed onto the vacuum suction of end effectors, the centre line of the end effector should be aligned to the normal

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direction ni of the contact center point Pi on the locating surface. Evidently, the center line would always be in the maximum conical angle θmax, when the end effector swings freely to suit the local curvature of the thin shell part. After the holding posture is defined, as illustrated in Fig. 9, transfer the normal vector ni of Pi point in the workpiece coordinate system into the normal vector niL in the end effector coordinate system. Then figure out the angle θ between niL and the axis ZL If θ≤θmax (1)

CAD models of blank and desired parts Dimension limit Lpart, Wpart, Hpart; Machining operations trimming, drilling, etc. Machining allowance and accuracy.

Features extraction

The part dimension is beyound working strokes of the reconfigurable fizture? Location allowance judging

The maximum of locators is less than fout (safe threshold)? The allowable swing angle of end effectors an suit the local curvature of the part?

Holding posture design

Placement Direction; Contact surface (convex or concavel); Relative height in Z direction.

Locators layout design

Stability; Accesibility; Avold probable collisions.

Locators parameters solution First method

Second method

Position values of end effectors

Virtual assembling

Swing angle values of postioners

Measure the location parameters

Output location parameters Figure 8. Location parameters solution procedure.

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the end effectors can be located at this point. Otherwise, the end effectors cannot suit the local curvature of this point. For instance, the curvature of the leading-edge skin components is often great; thereby the free swing holding cup cannot be available. Therefore, the commercially available reconfigurable tooling system would generally provide a quick-change style adapter such as extension arms (Fig. 10). For the automation of location parameters solution, a special location parameters solution software system (Flextrimming

system) has been improved and seamlessly integrated into the CATIA V5 software via the component application architecture (CAA) development kit, a component object model-like interface. The Flextrimming system can, in sequence, perform the location parameters solution procedure interactively: features extraction, locating allowance judging, holding postures design, locators layout and location parameters solution.

LOCATION ACCURACY Zw

xw ow

θmax

XL YL ZF XF

OF YF

Figure 9. Local curvature and the maximum conical angle.

The geometrical accuracy of a machined feature on a workpiece depends on, partially, the fixture’s ability to precisely locate the workpiece, which is in fact related to the locator’s configurations and positional accuracy of each locator. The reconfigurable flexible, developed independently by our research group, maintains a positioning accuracy of 0.1 mm and a repeatability of 0.05 mm. On the other hand, the solution accuracy of location parameters can reach 0.1μm. Namely, the final location accuracy depends on both the hardware and the software systems, and in order to validate the final location accuracy, an aluminum alloy skin part with a thickness of 1.27 mm was selected and located on the reconfigurable flexible fixture (Fig. 11). Then the located workpiece was measured using the API Tracker3 laser tracker (Fig. 12). Comparative analysis between the digital model and the measurement data were performed, and the obtained error contour is shown in Fig. 13. The final errors may be caused by the multiple effects including measurement error, forming errors and location errors. Thereby errors distribution was in the reasonable scope and it can be acceptable in practical productions. Meanwhile it may be observed that there is little deformation caused by the vacuum absorption of end effectors. Though the aircraft skin is very thin and poorly rigid, its local rigidity is enough to make the end effectors swing freely to automatically adjust to the local curvatures change of the thin shell part.

DYNAMIC STIFFNESS OF THE PROCESS SYSTEM

Figure 10. Quick-change style adapter.

EVALUATION OF DYNAMIC STIFFNESS Dynamic stiffness is a measure of the machining system’s ability to dampen the vibrations from input forces, which would

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Location Issues of Thin Shell Parts in the Reconfigurable Fixture for Trimming Operation

directly affect machining accuracy and stability. Theoretically, under the action of machining forces, the whole deformation of the

Figure 11. Locating set-up.

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process system is caused by each individual component including the deformations of machine tool, cutter, fixture and workpiece. In fact, the thin shell part has relatively poorer stiffness, thus the deformation of thin shell part is the largest part of the whole deformation of the process system. Therefore, other deformations are neglected and only the workpiece deformation is considered. As previously mentioned, the surface part can be regarded as the constitution of discrete points. Then the points close to end effectors could be fully restricted and the displacements of other points would vibrate along with the trimming process. Obviously, the closer the point is to the vacuum adsorption area, the less its vibration will be. Conversely, the farther the point is to the cutting path, the greater its vibration will be. In order to evaluate the dynamic stiffness of the whole fixturing system, n points near to the cutting paths was selected evenly from nodes on the elements as reference points (Fig. 14) and the dynamic stiffness is defined as follows, Kt =

Ft = 1 ∆ (Ft ,t) 1 k + 1 k + . . . + 1 k (2) 1t 2t nt

F δi(Ft ,t) = t (3) k it

∆(Ft,t)=δ1(F,t)+δ2(F,t)+ . . . +δn(F,t) (4) Figure 12. Measurement via laser tracker.

0.375 0.317 0.258 0.200 0.142 0.084 0.026 -0.026 -0.084 -0.142 -0.200 -0.258 -0.317 -0.375 Figure 13. Error distribution contour.

Where Ft denotes the external loads, and the subscript t is the trimming time; δi(Ft,t) is the instantaneous displacement of the i-th reference point; and Δ(Ft,t) is the instantaneous displacement of the whole process system.

RF5 RF11

RF7 RF8

RF5

RF12

RF10 RF9 RF1

RF4 RF3

RF2

Figure 14. Reference points to evaluate the dynamic stiffness. J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.319-331, Jul.-Sep., 2014

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EFFECTS OF LOCATION LAYOUT ON DYNAMIC STIFFNESS In order to analyze the effects of location layout on the dynamic stiffness, the finite element simulation was performed using the specially developed system for the flexible trimming process (Hu et al., 2012b).The basic principle is to sequentially “kill” the elements on the trimming paths in order to simulate the separation process of the desired part. Meanwhile, the milling forces are loaded on the elements that would be “killed” in the next step. As regards the milling

forces magnitude, the proposed method (Hu and Li, 2012) was employed. Hereby, the displacements of the reference points can be solved dynamically. The whole procedure is illustrated in Fig. 15. For the part as shown in Fig. 14, to follow the proposed steps (Fig. 8), five different location layouts were designed and the location parameters were listed in Table 1. The reference coordinate system was given in the Fig. 16. Therefore, the relative distance can be distinguished easily by Table 1 and Fig. 16.

Figure 15. Simulation procedure for trimming process. Table 1. Location layout mm. Layout No.

y11

y12

y13

y21

y22

y23

y31

y32

y33

169

579

1112

481

855

175

583

991

481

923

579

1030

141

617

991

447

991

175

549

991

456

1153

107

515

923

141

515

957

481

923

481

923

175

549

923

481

991

447

991

128

661

1112

141

583

991

617

991

549

957

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0.05 Cliff effect

∆(Ft,t)/mm

0.04

Layout 1 Layout 2 Layout 3 Layout 4 Layout 5

0.03 0.02 0.01 0.00 0.

50.

100.

0.020

150. Time/s (a)

200.

250.

300.

350.

150. Time/s (b)

200.

250.

300.

350.

200.

250.

300.

350.

Layout 1 Layout 2 Layout 3 Layout 4 Layout 5

∆(Ft,t)/mm

0.015

0.010

Cliff effect

0.005

0.000 0.

50.

100.

[x1.E-3] 8.0 Layout 1 Layout 2 Layout 3 Layout 4 Layout 5

∆(Ft,t)/mm

6.0 4.0

Cliff effect

2.0 0.0

50.

100.

150. Time/s (c)

Figure 16. Simulation procedure for trimming process. Cutting parameters: (a) n = 10000 r/min, aP= 1.27 mm, fZ = 0.1 mm; (b) n = 10000 r/min, aP= 2.54 mm, fZ = 0.1 mm; (c) n = 10000 r/min, aP= 3.5 mm, fZ = 0.1 mm.

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From the finite element situation results (Fig. 16), it was quite easy to tell that the first and fifth layouts were the best arrangements when compared to the other three layouts. The symbols n , ap and fZ, respectively, denote the speed of spindle, the part thickness and the feed per tooth. The good location layout could maintain the dynamic stiffness along with the trimming time. Especially, with the decreasing of the part thickness, the location layout has an increasing effect on the dynamic stiffness of the whole process system. Another noticeable appearance, as marked in the Fig. 16, should not be ignored, i.e., the cliff effect of the instantaneous displacement of the reference nodes. In the case, the dynamic stiffness of the fixturing system would drop suddenly, due to some locator, becoming valid. At this time, dramatic vibrations may occur and the machining error would be induced. With the decrease of the part thickness, the effect of layout on the dynamic stiffness would increase. However, the effect of layout on the time of the cliff effect occurrence may not be obvious. If the location layout was designed properly, the cliff effect could be restrained within narrow limits, such as the first and fifth layouts. Therefore, when planning the location layout, the cliff effect case should be avoided.

CONCLUSIONS The numerical control trimming process for aircraft skins in the reconfigurable fixture is a digital and flexible fabrication technology. However, due to the multi-point location, low rigidity of thin shell part and the ongoing separation of desired contours, this promising technology becomes a knotty operation to guarantee good workpiece-holding stability and machining accuracy. This paper mainly contributes to the following issues: • The unique location schema, namely the “X-2-1” location principle, was revealed in view of the gradual reduction of

effective locators of the reconfigurable fixture during trimming process. This work may perfect the location principles of low rigidity parts. Meanwhile, the “discrete points” model of thin shell parts was introduced; thereby it would help to figure out the nature of thin shell part location (i.e., to restrict the degrees of freedom of the discrete mass points) and the quantitative evaluation for the dynamic stiffness of the fixturing system (i.e., to calculate the displatments of the referred nodes). • The location parameters solution method was further summarized. Especially, the location admissibility was mainly presented; this work improved the location design procedure. In addition, the location accuracy was discussed through a locating experiment in order to validate the location parameters solution accuracy as well as the positional accuracy of the reconfigurable fixture. • A quantitative evaluation method of the dynamic stiffness of the fixturing system was put forward. This work provided a criterion for the quantitative analysis of location layout. Moreover, the effects of location layout on the dynamic stiffness of the flexible fixturing system were analyzed using the finite element simulation system for the trimming process. Noticeably, the cliff effect of the dynamic stiffness can be observed from the simulation results. This case should be alleviated or avoided through the optimization of location layout.

ACKNOWLEDGEMENTS Hu Fuwen would like to thank the Doctor Foundation Project of North China University of Technology and the Science and Technology Innovation Platform Project of Beijing Municipality (PXM2014_014212_000016) for their financial support, and Li Dongsheng and Xu Honghai for their constructive opinions.

REFERENCES Boyle, I., Rong, Y. and Brown, D.C., 2011, “A Review and Analysis of Current Computer-Aided Fixture Design Approaches”, Robotics and Computer-Integrated Manufacturing, Vol. 27, No. 1, pp. 1-12. doi: 10.1016/j.rcim.2010.05.008.

Cai, W., Hu, S.J. and Yuan, J.X., 1996, “Deformable Sheet Metal Fixturing: Principles, Algorithms, and Simulations”, Journal of Mechanical Design, Vol. 118, No. 3, pp. 318-324. doi: 10.1115/1.2831031.

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Papazian, J.M., Anagnostou, E.L., Christ, R.J., Hoitsma, Jr.D., Ogilvie, P. and Schwarz, R.C., 2002, “Tooling for rapid sheet metal parts production”, 6th Joint FAA/DoD/NASA Conference on Aging Aircraft, San Francisco, CA,USA.

Cecil J., 2001, “Computer-Aided Fixture Design – a Review and Future Trends”, The International Journal of Advanced Manufacturing Technology, Vol. 18, No. 11, pp. 790-793. doi: 10.1007/s001700170004.

Proctor, P., 1998, “Pogo Fixtures Enhance Tooling Flexibility”, Aviation Week & Space Technology, Vol. 149, No. 8, pp. 50-51.

Chen, L., 2006, “Research and Key Techniques on the Establishment of Reconfigurable Flexible Stretch-Formed Die”, Beihang University, Beijing, China. Cui, X., Xu, X., Li, G., Li, M. and Cai, Z., 2008, “Development and Application of the Multi-Point Stretch-Forming Equipment for Aircraft Skins”, China Metal forming Equipment & Manufacturing Technology, Vol. 43, No. 3, pp. 35-37. doi: 10.3969/j.issn.1672-0121.2008.03.011. Douglas, W.A. and Ozer, T., 1987, “Universal Holding Fixture”, U.S.A patents 4684113. Hardt, D.E., Olson, B.A., Allison, B.T. and Pasch, K., 1981, “Sheet Metal Forming With Discrete Die Surfaces”, Ninth North American Manufacturing Research Conference Proceedings, pp. 140–144. Hu, F. and Li D., 2011, “Process Planning and Simulation Strategies for Perimeter Milling of Thin-Walled Flexible Parts Held by Reconfigurable Fixturing System”, IEEE 2011 Third International Conference on Measuring Technology and Mechatronics Automation, Vol. 2, pp. 9 22926. doi: 10.1109/ICMTMA.2011.513. Hu, F. and Li D., 2012, “Modelling and Simulation of Milling Forces Using an Arbitrary Lagrangian–Eulerian Finite Element Method and Support Vector Regression”, Journal of Optimization Theory and Applications, Vol. 153, No. 2, pp. 461-484. doi: 10.1007/s10957-011-9927-y. Hu, F., Li, D., Li, X. and Zhu, M., 2012a, “Locating Simulation for Aircraft Skins NC Trimming Based on Flexible Holding Fixture”, Computer Integrated Manufacturing Systems, Vol. 18, pp. 993-998. Hu, F., Li, D., Li, X. and Zhu, M., 2012b, “Process Planning of Aircraft Skins NC Trimming Based on Reconfigurable Fixture”, Journal of Beijing University of Aeronautics and Astronautics, Vol. 38, pp. 675-680. Huang, L., Li, D. and Luo, H., 2008, “Research on Closed-Loop Shape Control System of Stretch Forming Over Reconfigurable Tooling”, Journal of Plasticity Engineering, Vol. 15, No. 6, pp. 38-42. Koelsch, J.R., 1998, “Hold It”, Machine Shop Guide, Vol. 3, pp. 26-33. Li, D., Luo, H., Wang, L. and Li, X., 2009, “Numerical Forming Technology of the Aircraft Skin”, Journal of Plasticity Engineering, Vol. 16, pp. 61-65. Munro, C. and Walczyk, D., 2007, “Reconfigurable Pin-Type Tooling: a Survey of Prior Art and Reduction to Practice”, Journal of Manufacturing Science and Engineering, Vol. 129, No. 3, pp. 551-565. doi: 10.1115/1.2714577.

Riveiro, A., Quintero, F., Lusquiños, F., Pou, J. and Pérez-Amor, M., 2008, “Laser Cutting of 2024-T3 Aeronautic Aluminum Alloy”, Journal of Laser Applications, Vol. 20, pp. 225-230. doi: 10.2351/1.2995769. Sela, M.N., Gaudry, O., Dombre, E. and Benhabib, B., 1997, “A Reconfigurable Modular Fixturing System for Thin-Walled Flexible Objects”, The International Journal of Advanced Manufacturing Technology, Vo1. 13, No. 9, pp. 611-617. doi: 10.1007/ BF01350819. Shirinzadeh, B., 2002, “Flexible Fixturing for Workpiece Positioning and Constraining”, Assembly Automation,Vol. 22, No. 2, pp. 112-120. doi: 10.1108/01445150210423143. Walczyk, D.F. and Hardt, D.E., 1998, “Design and Analysis of Reconfigurable Discrete Dies for Sheet Metal Forming”, Journal of Manufacturing Systems, Vol. 17, No. 6, pp. 436-454. doi: 10.1016/ S0278-6125(99)80003-X. Walczyk, D.F. and Longtin, R.S., 1999, “Fixturing of Compliant Parts Using a Matrix of Reconfigurable Pins”, Journal of Manufacturing Science and Engineering-transactions of The Asme, Vol. 122, No. 4, pp. 766772. doi: 10.1115/1.1314599. Wang, H., Rong, Y.K., Li, H. and Shaun, P., 2010, “Computer Aided Fixture Design: Recent Research And Trends”, Computer-Aided Design, Vol. 42, No. 12, pp. 1085-1094. doi: 10.1016/j.cad.2010.07.003. Youcef-Toumi, K., Liu, W.S. and Asada H., 1987, “A Computer Integration of Reconfigurable Fixtures and Drilling of Sheet Metal Parts”, Robotics and Factories of the Future ’87, Proceedings of the 2nd International Conference San Diego, California, pp. 751-759. doi: 10.1007/978-3642-73890-6_90. Yu, C., Li, D. and Li, X., 2011, “Application of the Preprocessing System of Stretch Forming Over Multi-Point Tooling in Real Factory Environment”, Advanced Science Letters, Vol. 4, No. 6-7, pp. 2396-2399. doi: 10.1166/ asl.2011.1457. Zhou, C., Cai, Z. and Li, M., 2005, “Stretching Process Based on Multi-Point Die and Its Numerical Simulation”, Journal of Jilin University of Technology (Natural Science Edition), Vol. 35, pp. 287-291. Zhou, F., 2007, “A New Generation Multi-Point Flexible Tooling for Aircraft Skin Stretch Forming”, Aeronautical Manufacturing Technology, Vol. 11, pp. 30-33. Zhou, K., Qian, Q. and Men, Y., 2008, “A Robotized Reconfigurable Fixture”, China patents 101269466.

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doi: 10.5028/jatm.v6i3.316

Extending the Student Qualitative Undertaking Involvement Risk Model Jeremy Straub1

ABSTRACT: The Student Qualitative Undertaking Involvement Risk Model (SQUIRM) was designed to facilitate the determination of the impact of using student (or inexperienced) workers, on a project. The model identifies several prospective categories of risk. It, then, discusses the risk potential and source and provides a limited consideration of how to mitigate this risk. The risk sources considered included those specific to student (inexperienced worker) involvement, standard risks and standard risk sources which are enhanced by the use of student (inexperienced) workers. This paper presents a qualitative assessment framework and begins the process of quantifying the model. The difference between the use of students (in an academic or industrial setting) versus inexperienced workers is also considered. The base model is presented and extended by further tracing the risk sources back, using root cause analysis techniques. The application of the base and extended models to various projects is discussed. Considerations in choosing which model to use for a given application are also presented. The paper concludes by presenting a value model for considering student (inexperienced worker) involvement benefits versus associated risks, and the differences in the risk reward ratio between academic, internship and junior worker scenarios. KEYWORDS: Risk model, Student workers, Small spacecraft, Aerospace education.

INTRODUCTION Student involvement in research and other projects is common at universities around the world. Through internships, part-time work and other mechanisms, students also perform limited work for commercial, governmental and other employers. Despite the prevalence of student involvement in the development of key technologies and their performance of numerous duties, the management literature contains little consideration of the specific risk elements introduced by student workers. Inexperienced workers (including students, interns and junior employees) have particular characteristics that may create new risk sources and alter the likelihood and magnitude of typical risks. An understanding of the impact of using student (and other inexperienced) student workers is particularly important in the case of aerospace projects due to the low defect tolerance, inaccessibility and criticality of many projects. Small spacecraft, for example, are commonly integrated as secondary payloads on rockets carrying other orders-ofmagnitude more expensive hardware. They must meet the same (or perhaps even more stringent) integration standards as the primary payload. Some small spacecraft have also been launched via the International Space Station, necessitating their compliance with human safety standards. Once they are in orbit, they are also on their own, with no practical servicing capability. Design and implementation failures can, thus, cause a spacecraft to fail integration testing and not get launched, to fail subsequent to integration and damage expensive equipment or pose a threat to astronauts or fail on orbit, impairing mission performance. The training and the research provided by these efforts is integral to

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developing new technologies as well as training the next generation of aerospace professionals. Given this, a better understanding of the risks posed by student and inexperienced staff involvement is necessary. In prior work (Straub et al., 2013b), a risk model specifically targeted at students (and to some extent, at all inexperienced workers) was presented, called the Student Qualitative Undertaking Involvement Risk Model (SQUIRM). This paper, while discussing and classifying numerous types of risk, did not (due to space limitations) present an evaluation of the model nor demonstrate its application to any particular scenarios. This paper picks up where the previous work left off. It expands on the prior work in several ways. First, it presents an enhanced model that augments the base SQUIRM framework with root cause analysis, resulting in a more detailed consideration of student status on typical (non-student) risk factors. The use of this model can provide a more robust evaluation of the impact of student participation, as compared to the base model. However, it is not a panacea, and prospective tradeoffs between the use of the two approaches are discussed. Second, it begins the process of quantifying the SQUIRM and extended SQUIRM frameworks, discussing how the models can be used in order to assess risks (considering likelihood, impact and the mitigation techniques employed) on a single project basis or across multiple projects. Third, it presents a value model for evaluating the participation of student (and other inexperienced) workers. This model facilitates the determination of the value proposition of using this type of staff, which can be compared to increased risks and other associated costs. Finally, the differences between types of inexperienced workers are briefly discussed, before concluding.

BACKGROUND This section provides an overview of areas that the current work benefits from a wealth of prior work in. Despite a growing contemporary interest, the tasking of trainee or inexperienced workers to real-world projects is certainly not a new phenomenon. Apprenticeship-style training has been used throughout history (Elbaum, 1989; Snell, 1996). Modern approaches, however, combine formal and experiential techniques. One relevant technique is project-based learning.

In the remainder of this section, the benefits of project-based learning are, first, discussed. Next, prior work, regarding assessment of the value of students to faculty efforts, is briefly considered. Finally, a brief discussion about risk perception is presented. PROJECT-BASED LEARNING With project-based learning (PBL), students are involved in hands-on projects that could be developed specifically for a course or which might feature student involvement in faculty research or other real-world projects. PBL has been shown to be an effective instructional tool at all levels of education: from collegiate graduate-level to primary school level (Brodeur et al., 2002; Hall et al., 2002; Mathers et al., 2012; Mountrakis and Triantakonstantis, 2012; Nordlie and Fevig, 2011; Straub et al., 2013a). It has also been demonstrated across a wide variety of subject disciplines, including project management (Pollard, 2012), psychology (Dahlgren and Dahlgren, 2002), physics (Duch, 1996), computer science (Broman et al., 2012; Correll et al., 2013), mathematics (Roh, 2003), engineering entrepreneurship (Okudan and Rzasa, 2006) and aerospace (Jayaram et al., 2010; Saunders-Smits et al., 2012), computer (Qidwai, 2011), electrical (Bütün, 2005; Ribeiro, 2008) and mechanical (Coller and Scott, 2009; Robson et al., 2012) engineering. In addition to teaching subject-specific skills, PBL projects can teach students how to work with those outside their specific discipline, as is required in the vast majority (Hayne et al., 2012) of workplaces. Gaining a shared prior knowledge base (such as through PBL techniques) can improve team efficiency (Hayne et al., 2012). Workers with interdisciplinary skills are in demand (Sulaiman et al., 2010); PBL also provides students with an opportunity to learn “soft” skills which are required for workplace success (Jackson and Hancock, 2010). PBL has also been shown to have a beneficial impact on student motivation (Doppelt, 2003), self-image and creativity (Ayob et al., 2012) and material retention (Bauerle and Park, 2012). Field-based/realistic-environment PBL has been shown to increase students’ understanding of course materials (Simons et al., 2012). Nagda et al. (1998) show that one type of PBL, research participation, can also improve student retention, particularly for at-risk students. The benefits of PBL to student placement, after graduation, have been demonstrated by Hotaling et al., (2012) and Fasse

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et al., (2012). Gilmore (2013) even argues that techniques such as PBL, for teaching STEM disciplines, are critical to national prosperity. In aerospace engineering and related disciplines, many students are gaining practical experience working on small spacecraft and high altitude ballooning projects. The SQUIRM framework (Straub et al., 2013b) was created, initially, to assess the risks applicable to student involvement in a small spacecraft project; however, it is useful for many applications beyond this. The utility of PBL for teaching aerospace engineering (Straub et al., 2013a; Straub and Whalen, 2013), software development for aerospace applications (Straub et al., in press) and providing other benefits (Swartwout, 2004; Swartwout, 2011) has been demonstrated. CubeSat projects have been demonstrated to be an effective pedagogical approach (Larsen and Nielsen, 2011; Larsen et al., 2013; Straub, 2013). The level of the aforementioned benefits, Zydney et al. (2002a) proffer, increases with the duration of participation. However, not all students reach these higher levels of benefit; while numerous reasons for premature termination of student participation in a research project exist, manifestation of the risk factors discussed in a subsequent section may explain some of the incomplete experiences. VALUE OF STUDENT INVOLVEMENT TO FACULTY RESEARCH If student involvement’s benefit was solely student education, the need to characterize and mitigate risks would be dramatically reduced. The impact of a student/ inexperience-specific or general risk factor’s occurrence can have impact to the student participant’s success; it can also have a pronounced effect on the project as well. While students may gain (possibly even enhanced) benefit from risk actualization, the project stands to suffer. To characterize the magnitude of impact, it is important to consider faculty perceptions of student involvement on research projects. Zydney et al. (2002b) proffer that faculty see students’ participation as valuable, with over half of them indicating that students’ contribution to their work was “important” or “very important”. Thus, the failure of a student to make progress is a risk that may be comparable to causing damage or other types of impact on prior work. While student participation is valuable to faculty, it appears that project completion may be less important to students, as Prince et al. (2007) demonstrated a lack of correlation

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between the research productivity level of faculty and students’ educational benefits. RISK PERCEPTION One reason that student workers may be more riskoccurrence prone is a failure to properly assess risk likelihood and impact. However, despite a significant correlation between youth and inexperience, it is important to note the potentially confounding impact of risk perception. Because of this, there may be a performance difference between younger and older individuals with similar experience levels in a field. A full exploration of the topic of risk perception is far beyond the scope of this paper; however, reviews of areas of this topic are readily available. Botterill and Mazur (2004) provide a general overview of the topic, while Slovic et al. (1982) consider the value of studying it. Boholm (1998) reviews and compares risk perception research over a twenty-year period and Mitchell (1995) considers risk perception and risk reduction in the context of an organization. The crux of the risk perception problem is that younger individuals may fail to appreciate the applicability of risk to them and its impact (Weinstein, 1984). This has been documented across multiple areas, including driving (Deery, 1999), sexual (Levinson et al., 1995) and other “healththreatening” (Cohn et al., 1995) behaviors. Steinberg (2004) attributes the greater risk-taking tolerance of youth to “age differences in psychological factors that influence self-regulation”. Thus, age may confound the experience/ risk correlation, and intensify certain risk factors when both young age and inexperience are the case. Given this, traditional-age undergraduates may have a higher propensity to fail to see how their actions, behaviors or inaction may create risks, or the impact that these risks may have on them or others. Risk perception, however, is not only affected by age. Correlation has been shown with gender (DeJoy, 1992), culture (Rippl, 2002) and other factors (Sjöberg, 2000; Wildavsky and Dake, 1990). The impact of education in correcting risk perceptions has been demonstrated by Ronan and Johnston (2001). Weber and Milliman’s (1997) work suggests that “risk preference” may be a stable aspect of an individual’s personality, highlighting the importance of risk perception on the acceptance or rejection of the risk in a given circumstance. Renn (1998) discusses the importance of risk perception in relation to the management of risks.

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THE STUDENT QUALITATIVE UNDERTAKING INVOLVEMENT RISK MODEL The following subsections, reprinted with minimal modification from Straub et al. (2013b), provide an overview of the risk categories of the SQUIRM framework (which is depicted in Fig.1). First, technical, schedule and other standard risks will be discussed. Then, the risks posed by student worker involvement will be considered. TECHNICAL, SCHEDULE AND OTHER STANDARD RISKS Every project, including those involving students, must deal with numerous possible risk factors. Project managers attempt to control many of these risk factors, assume others, and they are, ultimately, forced to ignore a large set of risks that they have no insight into or control over. Numerous standard risks are well documented in the literature and will not be reviewed in detail here. The impact of student participation on these standard risks is considered. For each risk factor, a brief description of its nature is provided. This is followed by a discussion of how the risk factor is influenced by or may influence student project involvement. TECHNICAL RISK The technical risk category is comprised of the set of risks that could result from a failure of hardware and software or its integration and operations to perform as required to meet project’s objectives. Three aspects are considered: construction/fabrication of assemblies, failure of purchased components and their integration. Construction/Fabrication Construction and fabrication risks are inherent to any manufacturing process. Quality control processes, including those designed to prevent defects as well as those to detect and remediate defects, are generally included to mitigate these risks. In a student project, which generally doesn’t involve mass-production, one is confronted with two primary risks. First, standards-based quality control may be cost-prohibitive to implement. Second, students who lack knowledge and understanding of the characteristics of the product may be poorly equipped to detect and evaluate the significance of errors.

Component Components obtained from suppliers will occasionally be defective, either due to manufacturing or shipping issues. Production processes generally incorporate an acceptance testing procedure or supplier process validation procedure. A student-involved project, generally, suffers from two risk factors regarding components. First, the limited production (in many cases, producing only a single or small quantity of units) precludes the implementation of a standard quality process. Second, student inexperience may result in a failure to properly design acceptance tests or to detect latent issues. Integration The process of combining components together introduces risks due to design and implementation failures. Design failures may result in a system, which, regardless of how well it is assembled, cannot perform the desired task. Implementation issues may result in degraded performance, non-operation, or failure after a period of time operating. Student designers and workers generally have traits that significantly increase the probability of these risks happening. Having an incomplete or largely untested understanding of the design process or specific design elements may result in wholly unworkable designs or designs with latent and hard-to-detect flaws. Limited time and resources will generally result in a comparatively lower level of testing being conducted. The fact that this testing will likely be performed by inexperienced (student) testers further exacerbates the problem. Even if a perfect design is produced, inexperience in the techniques required for construction may result in sub-par construction, component attachment and solder connection issues, and so forth. These may cause the assembly to not work initially or to be prone to failure. SCHEDULE RISK Every project faces the possibility that its schedule will not be met. External factors, such as the unavailability of key components, and internal factors, such as staff absences or equipment failure, may result in delays. When these delays impact the critical path, the project schedule is impaired. Key areas of consideration for projects involving students include schedule estimation error, critical path risks and schedule creep.

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completely understand the process that they are estimating and, thus, omit the time required for overlooked process components. Either of these may result in (possibly dramatic) under estimation. On the other hand, students may be overwhelmed and wildly overestimate, (so as to avoid the pitfalls of underestimation). This is, however, problematic, as it may result in the project’s momentum being lost, if materials, tools or staff for subsequent phases are not available when a previous phase is completed early.

Schedule Estimation Error Estimation error occurs when the time projected for task completion is different than actual task completion. A certain amount of error is to be expected; however, when tasks are consistently taking longer than projected, the project’s schedule is at risk. Estimation error is common, even for experienced estimators. Students, who do not have significant experience, may fail to consider anything other than the best-case scenario. Alternately, they may not

Project completed Succefully

Student-Project Specific Risk Factors Eventuate and are Remediated

Standard and Student-Involving Project Secific Risk Factors Eventuate and are Remediated

Standard Risk Factors Eventuate and are Remediated

No Risk Factors Eventuate

Schedule Risk Occurs

Scheduled Turnover Occurs

Schedule Risk Remediated

Scheduled Turnover Remediated

Cost Risk Occurs

Unscheduled Turnover Occurs

Estimation Error Risk Factors

Critical Path Risk Factors

Estimation Error Risk Factors

Cost Risk Remediated

Unscheduled Turnover Remediated

Cost Creep Risk Factors

Miss-commitment Occurs Technical Risk Remediated

Miss-commitment Remediated

Inexperience Symptoms Occur

Schedule Creep Risk Factors

Integration Risk Factors

Inexperience Symptoms Remediated

Damage and Rework Risk Factors

Technical Risk Occurs

Component Risk Factors

Construction fabrication Risk Factors

Buying Time Occurs

Figure 1. SQUIRM Model Diagram (Straub et al., 2013b). J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.333-352, Jul.-Sep., 2014

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Critical Path Risk Critical path risk is a set of risk factors that impact the chain of tasks, which, in succession, take the longest amount of time. As the project is not complete until all of these tasks are done, anything that elongates the schedule of a task on the critical path (or another task, which becomes a critical path task due to schedule overrun) affects the project’s overall schedule. Critical path risk can be created by factors that are both external and internal to the project. External factors may include impairment to the availability of supplies, unavailability of key equipment at the needed time, changes in laws or regulations and many other factors. Internal factors, however, are the primary area where projects with student involvement differ from conventional projects. Internal issues that may be exacerbated by student involvement include staff availability issues, delays caused by quality failures — and, thus, the need to repair or recreate the improperly produced items —, and delays caused by poor scheduling. Staff availability and quality issues are discussed in other areas of the model. Poor scheduling may be the result of a failure to identify precursor and successor tasks due to failing to identify required task inputs and outputs or, more simply, error in the actual creation of the schedule. Either of these can easily occur when a schedule is produced by an inexperienced scheduler. Schedule Creep Schedule creep is the schedule component of scope creep. Scope creep occurs when changes or documentation issues result in a more robust product being produced than the one called for by planning. The involvement of students, who are generally eager to please and may not understand the impact of accepting changes (or not understand that they are implicitly accepting a change), increases the risk of schedule creep. The fact that most academic projects are run by professors who are trained as researchers – not project managers – and may have limited documentation further exacerbates this risk. COST RISK With tight budgets and long-duration funding cycles, cost overrun is a significant risk to student-involved projects. Cost overruns can lead to reduced deliverable utility and/or quality. If severe enough (and supplemental funding cannot be sourced), they can even lead to project

termination and failure. Risks that must be considered relative to student involvement include estimation error, cost creep, damage and rework costs, and costs associated with meeting schedule requirements. Cost Estimation Error Cost estimation error closely mirrors schedule estimation error. It occurs when the level of cost required to be incurred for a given activity is different from the level forecast. While variation is expected, proper estimation should result in some tasks concluding with small overruns and others being completed under budget. Generally, an allowance for unexpected costs is included in the budget as a separate line item to allow the absorption of additional costs, should the project average out to a slight overrun. As with schedule estimation error, students who may be estimating costs for the first time (or may have limited domain experience, even if they have performed cost estimating before) may be prone to underestimate, due to ignoring complexity or inadvertently omitting various types of costs or specific costs. Cost Creep Cost creep is the cost component of scope creep. Scope creep occurs when changes are accepted without commensurate changes in budget and schedule. Due to student inexperience and other factors, scope creep is likely on student projects. If scope creep occurs, it is likely that cost creep will occur. Damage and Rework Damage and rework costs are incurred when hardware, facilities, supplies or the item being created are damaged due to carelessness, accident, misuse or otherwise. Damage and rework costs are likely on a student-involved project. First, the lack of a production environment designed for the repetitive production of an item means that construction and integration jigs will be setup on the fly. This may result in inadvertent loss of control, dropping, or the application of unwanted torques or pressure to parts or assemblies. Second, the lack of a repetitive production environment means that there is not a set of well-tested task instructions that can be followed. Third, supply and equipment limitations may result in jury-rigging of various jig-elements, making damage more likely. Forth, horseplay or carelessness may result in damage. All of the aforementioned are exacerbated by having young and/or inexperienced individuals working on the project.

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Buying Time Costs can be incurred to resolve schedule issues. For example, a component could be purchased, at additional expense, to return the project to schedule or an external consultant could be hired to expedite a process. Due to this, schedule issues can become cost issues. Given how student involvement can exacerbate schedule risk, it would seem that student involvement would heighten the possibility of transferring schedule overruns to cost in order to hit a key deadline. RISKS POSED BY STUDENT WORKER INVOLVEMENT Several risk factors are impacted so dramatically by student involvement as to deserve separate consideration from their standard counterparts. Each is now discussed in detail. Scheduled Turnover Scheduled turnover has a dramatic impact, but can be planned for. It is attributable to the fact that students only participate in a given effort for a period of time. When this participation ends the student may be unavailable to provide documentation or assistance related to their work on the project. As students become task-experts, if documentation is not stressed, understanding can be lost — or a key component of an integrated system can become unserviceable. Compounding this issue is the fact that many students are not adept in documenting their work and lack an understanding of the need for documentation and what needs to be documented. Mitigation strategies for this risk include knowledge distribution, stressing documentation throughout a project’s lifecycle, and validating the usefulness of documentation, by requiring its use prior to a student-worker’s departure. Unscheduled Turnover Unscheduled turnover is a risk factor present in all types of organizations. As in corporate work environments, medical, personal and other factors may necessitate a worker’s immediate departure from the workplace. Mitigation techniques for this class of risk include duplication (or responsibility distribution) of key roles, wide knowledge distribution, and stressing documentation and documentation validation. Miss-commitment Students’ miss-commitment can be more problematic than the occurrence of turnover. With turnover, the project leader

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has knowledge of the current status of the team member. With miss-commitment, the individual is still present and ostensibly working on their assigned tasks; however, due to conflicting demands for limited time resources (and the academic-trumping of most project duties) the student worker may not have time to make the requisite level of project progress. This is compounded by the cramming-centric work styles learned by many students, which lead to the belief that everything can be ‘made up’ at the last moment. With student miss-commitment, project leaders may not become aware of the issue, until investigating the cause of a key deadline being missed. Mitigation techniques for this class of risks include defining tasks to have demonstrable milestones, creating an environment where challenges are reported instead of obfuscated, and involving multiple individuals in key tasks. Inexperience Inexperience is, of course, a problem that is faced by numerous projects in every sphere. A team member may be new to the workforce, or may lack experience in the specific areas required by a project. However, inexperience is a particular issue in student-centric projects as many students lack practical experience. This translates into misestimating and a lack of experience in problem resolution techniques. This class of risks can be mitigated by training students in the desired behaviors (e.g., how to estimate in a given sphere, how to deal with problems, etc.). This mitigation not only benefits the project, but also prepares the students for workplace entry.

EXTENDING THE MODEL WITH ROOT CAUSE ANALYSIS TECHNIQUES The original SQUIRM model, presented in prior work (Straub et al., 2013b), expanded upon the causal factors for standard risks, which could be exacerbated by student/ inexperienced workers’ involvement. While some discussion of the causality of the student-worker-specific risks was included, these were not incorporated into the formal model. The SQUIRM-Extended Model (SQUIRM-E) adds these causal factors to the model, as shown in Fig. 2. This addition

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is necessary to begin quantitative assessment using the model (which is discussed in a subsequent section). This section begins with a discussion of the value of the use of root cause analysis and then the rest of this section discusses the new elements of the SQUIRM-E model and expands upon the types of risks posed by them and their causes. ROOT CAUSE ANALYSIS The premise of root cause analysis (RCA) is that a better understanding of the underlying factors of an exceptional occurrence (either positive or negative) facilitate a better understanding of how negative occurrences can be avoided in the future and positive occurrences brought about. Significant prior work exists in this area; a high-level overview is provided by Rooney and Heuvel (2004). RCA has been used for process analysis (Weidl et al., 2005), investigating medical error (Iedema et al., 2006), improving patient safety (Neily et al., 2003), as well as in analyzing and improving industrial safety and performance (Carroll et al., 2002). A discussion of several tools for RCA was presented by Doggett (2004). In the context of this work, RCA was used to assess why student-involved projects and student workers could have higher levels of risk actualization than a similar project not incorporating inexperienced workers. In prior work (Straub et al., 2013b), this was applied to seek out causes that were specific to student (and other inexperienced) workers. In this paper, RCA is used to decompose standard risk factors to assess the prospective contribution of inexperience and related factors on these risk areas. RCA is not the only technique that could be used to assess these types of risks. However, it has several benefits. Unlike some other approaches, for example, it uses a bottom-up approach which makes it suitable for projecting risks instead of analyzing actualized risks. This is particularly valuable in the context of non-operations risk analysis, where prior occurrences in a recurring process cannot be analyzed to project future risk factors and their likelihood. With RCA, the individual factors contributing to each type of prospective risk have been identified. These can, then, inform planning (in order to facilitate avoidance and mitigation) as well as be used to arrive at an understanding of the risk level of a project and its areas of particular risk. To perform RCA, prospective sources of the higher-level risk factors previously presented were identified. These are described in greater detail throughout the remainder of this section.

INEXPERIENCE SYMPTOMS OCCUR The risk categories related to inexperience are a lack of attention to detail, lack of self-motivation, uncertainty as to how to perform a task, overconfidence that causes failure and problems with the work environment. These are now discussed. Lack of Attention to Detail Student workers may lack an understanding of the importance of particular details of a task, lack an understanding of the actual details (i.e., what is a correct implementation at a detailed level versus an incorrect one), or may simply fail to pay the level of attention required. This may be exacerbated due to other time commitments (reducing the amount of time that can be devoted to these details and task performance), the level of strain that the student is under (particularly if the student lacks coping mechanisms), and other factors (such as the amount of time available during the semester, etc.). Lack of Self-Motivation Students (particularly lower-level undergraduates) may not yet have developed the skills, habits and work ethic required to self-motivate work when tasks seem unexciting or are in support of a longer-term goal. This may translate into unsatisfactory performance in terms of meeting deadlines, unsatisfactory work product or other deficiencies. It may also trigger or contribute to other risk factors (such as misscommitment if work piles up due to not starting things until there is an imminent due date, etc.). Unsure of How to Perform Task Students may be unsure of how to perform particular tasks or elements of a task. This may translate into delays waiting for clarification, attempts that result in wasted materials and time, obviously defective products or products with latent defects that may impair progress during later phases (e.g., integration, testing). This lack of understanding may decrease motivation, increase frustration and delays may trigger other issues such as miss-commitment. Overconfidence Causes Failure Students may underestimate the difficulty of a task or overestimate their own capabilities. This can have several different symptoms, depending on when it occurs. First, it can cause issues with scheduling and costing. Students may underestimate the amount of time that will be required

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for learning how to perform a task, experimenting to gain understanding and/or correcting less-than-acceptable products. They may also underestimate the amount of waste material that may be consumed by reattempts to fix defects. Second, it can result in unsatisfactory performance in terms of meeting deadlines, unsatisfactory work product or other deficiencies due to the aforementioned scheduling and the reality of performance conflicting, or a lack of understanding of what an acceptable product is, triggering a need for significant rework. This may translate into delays waiting for clarification, attempts that result in wasted materials and time, obviously defective products or products with latent defects which may impair progress during later phases (e.g., integration, testing). These issues may trigger other risk factors such as miss-commitment, decreased motivation and increased frustration. Third, this may result in students responding negatively to feedback, as they think that it is unnecessarily critical (based on their inaccurate assumptions about their own capabilities and what constitutes an acceptable level of performance). This may also increase frustration, decrease motivation and potentially trigger other issues, such as turnover.

best interests (which may consider short and/or long term goals). The inflexibility of the semester system may limit students’ ability to provide notice (even for a paid position), should they decide to transfer between schools or programs. They may also lose interest at the point that they realize that program participation is no longer supporting their goals (framed now in terms of their new school/department). This may result in low or no-notice turnover.

Problem with Work Environment Student workers may lack an understanding of how to cope with difficulties in the workplace environment. For example, they may not understand how to deal with a poor manager (and the, particularly if a student, manager may lack the skills and understanding required to resolve this conflict). They may also lack the skills required to resolve workplace conflict or to collaborate with others in the work environment. This can potentially trigger miss-commitment, if work is left to pile up while issues are being resolved, or if unscheduled turnover occurs.

Student Takes Internship Students may decide to pursue an internship to increase their skills and/or post-graduation employment opportunities. Internships may pay more than on-campus employment and generally offer work experience benefits and prospective employer contact that on-campus employment cannot. Students may begin an internship with little or no notice (as employers may offer internships at the last minute to meet their needs and funding capabilities); in many cases, however, internships can be a planned absence and a student may be able to/decide to return to the project after its completion.

UNSCHEDULED TURNOVER OCCURS Unscheduled turnover can be caused by a student transferring between degree programs or colleges/universities, as a result of miss-commitment, because of a student’s departure from the university, or even by a student taking an internship or a medical, family or other personal problem. Each is now discussed. Student Transfers Program/School In the context of their educational pursuits, students make decisions in light of what they perceive as their own

Turnover due to Miss-commitment Students may miss-commit (reasons for this are discussed subsequently). If this miss-commitment becomes an acute problem, students may terminate their involvement in paid and/or unpaid extracurricular activities in deference to their immediate academic time needs. This may occur with low or no notice or it may simply result in the student failing to show up (without any sort of explanation). Departure from University Students may leave (or be dismissed from) the university for a wide variety of reasons. This may also result in low or no-notice turnover.

Medical/Family/Personal Problem Like any worker, students may suffer from medical family or other personal problems. These may be intensified by students’ lack of coping skills and/or the lack of a need to maintain an income, even in the face of a major medical condition. Notice levels, the potential for students to return to the project upon the resolution of the issue and the duration of the issue will, obviously, vary significantly based on the nature of the issue.

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SCHEDULED TURNOVER OCCURS Scheduled turnover is an expected occurrence at a college or university. It can be caused by student graduation, the end of a paid (e.g., extramurally funded) work period or the end of a course project period. Each is now considered. Graduation Students enroll in a university with their departure planned (unlike a typical work environment where employees may not plan to make a career out of a job, but also look at it as something to pursue for an indeterminate period of time). Graduation, fortunately, will be an occurrence that is known well in advance and can be planned for to ensure proper handover. Students, however may fail to notify project leaders (either due to a presumption that they should be notified by some other means or to avoid less-interesting handover activities) and/or have a declining level of interest (particularly after they have secured a job or admission into another program for graduate studies, etc.), that may reduce the ability to conduct and/or the quality of handover activities. End of Paid Work Period Research grant (or other funding source) work may have a definite cut-off point after which no additional funding is available to continue a position. This creates a known dateof-departure for a student from a project (or a transition from a paid role to continuation on a volunteer basis). This should be known to the investigator (and thus not suffer from the aforementioned failure-to-notify problem) and be able to be planned for. Students may lose interest and/or change their final day if they find an alternate position, as they approach their known final days.

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underestimation of coursework time commitments, an external work commitment commencing or changing, a change in a student’s course load and/or involvement in other university activities. These are now considered. Underestimation of Coursework Students may overcommit to extramural projects or paid on-campus project work, based on an underestimation of the level of time required for their coursework. This may result in delays, turnover or impaired quality. External Work Commitment/Change Students who are working on a project in either a paid or volunteer basis may have jobs outside the project or may seek/take a job based on the benefits it may provide (e.g., work experience, employer contact) or due to their personal financial situation. This may result in low or no-notice changes in project involvement levels, turnover or a decline in product quality. Change in Course Load Students may change the number or selection of courses they are taking during the semester and this may change somewhat from semester to semester. This may result in turnover, delays, or quality impairment. Involvement in Other University Activities Students may decide to pursue other university extracurricular activities in addition to or instead of the project, or the level of involvement required for (or desired in) these activities may change, reducing the students’ level of involvement in the project and/or causing delays, quality problems or turnover.

End of Course Project Period Course projects, like paid work periods, have definite (and known-to-the investigator) end dates. A desire to receive a good final grade, however, may keep students motivated until the end of the period.

DIFFERENCES BETWEEN AND CHOOSING BETWEEN USING SQUIRM AND SQUIRM-E

MISS-COMMITMENT Miss-commitment is to be expected with students who may be unable to gauge the level of work required both from their academic, paid work and extramural pursuits. Miss-commitment, thus, can occur due to students’

With both the SQUIRM framework and its extension presented, the two can now be compared. This section reviews the differences between SQUIRM and its extension, SQUIRM-E. It discusses the benefits of using one versus the other across multiple scenarios.

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DISCUSSION OF THE DIFFERENCES BETWEEN SQUIRM AND SQUIRM-E The f undament al dif ference b etween SQUIRM and SQUIRM-E is the addition, in SQUIRM-E, of the decomposition of standard risk classes in order to also consider risk sources attributable to student and inexperienced workers. This has resulted in two models, each of which is better suited for certain applications (as compared to the other). The remainder of this section considers specific benefits of using one model over the other. It begins by discussing the comparative simplicity presented by SQUIRM, versus SQUIRM-E, and where this simplicity may be valuable. Next, it discusses how SQUIRM-E leans further towards student workers, making SQUIRM more suitable for use or adaptation to non-student, inexperienced workers (or students in contexts where the student status is less relevant). Finally, logistical considerations such as project size and assessor environment familiarity are discussed before a concluding discussion regarding model selection. COMPARATIVE SIMPLICITY The SQUIRM framework, by abstracting the root causes of the student-specific risk types into larger categories, is comparatively easier to work with. This is particularly useful in cases where real numbers for these risk types are unknown and cannot be accurately estimated, or where data has been collected without sufficient granularity for use with the more granular model. Alternately, those estimating without data may prefer the more detailed model, as it allows them to consider the risk, likelihood and impact for specific prospective problems, without having to consider whole categories at one. The use of the SQUIRM-E framework, thus, would correspond to a bottom-up risk identification strategy, while the SQUIRM framework (for student-specific risk types) would correspond to a top-down risk identification and assessment approach. TYPES OF INEXPERIENCED WORKERS While the SQUIRM model contains elements that may be useful for all areas of inexperience, the elaborations in SQUIRM-E have been targeted specifically at student workers (with a particular focus towards student workers working in the context of a university environment). The further that the actual situation diverges from this, the less valuable the

SQUIRM-E elaborations may be. Alternately, one might use these as a starting point, removing (and/or replacing) irrelevant topics and making changes as needed to relevant ones that have an incorrect focus for the scenario under consideration. PROJECT SIZE For smaller projects or projects that are less critical, there may be less need for and resources with which to perform risk management. In these cases, the use of the simpler model (and, in fact, even simplifying the SQUIRM framework to remove the third-level error sources) may be prudent. FAMILIARITY WITH PARTICULARS OF STUDENT WORK ENVIRONMENT Those with greater familiarity with the risks and nature of the student-involved work environment may find less need for the additional granularity of the SQUIRM-E model. However, as some risk types occur infrequently, heuristic models based on past experiences may oversimplify actual risk levels. Alternately, non-university employers that are less familiar with the particulars of student worker risks may desire to use a modified version of the full SQUIRM-E model. This adaptation is discussed in a subsequent section. CHOOSING A MODEL While the two models are not that dissimilar, the selection of a model should be based on the complexity of the project as well as particular needs related to assessing student-statusattributable risk factors. Choosing the incorrect version of the model to use may result in oversimplification, under or overstatement of risks and/or unnecessary work.

EXAMPLES, APPLICATION AND COMPARISON This section presents three examples which are used to aid reader understanding and application of the models, and to compare the SQUIRM and SQUIRM-E frameworks. These examples include a small spacecraft project, a surface rover project and a near-space recovery system development project. Each is now briefly presented, followed by discussion and the presentation of steps for model use.

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ENGINEERING A SPACECRAFT PROTOTYPE A project to build a prototype for a small spacecraft included numerous risk factors, as it was an initial effort for the participants. The project team included students working on it for class credit and several more that were participating as an extracurricular activity. Risk factors for this project included technical factors and personnel factors. There were numerous technical issues that could have presented a problem as the prototype was student designed and fabricated. A printed circuit board ended up being the factor that created a significant schedule impact (and a minor cost impact, which was absorbed by contingency funds). Due to the board not working during testing (after the students had mounted components on the supplierfabricated-from-student-design board), the prototype was not able to be launched on a high altitude balloon before the end of the spring semester and incurred a significant delay, having to wait until the project team returned in the fall. BUILDING A SURFACE ROVER A project to build a rover model, that is in some ways analogous to one that could be used on the moon or on Mars, suffered from significant personnel issues. Students involved in the project, while eager for it to succeed, lacked the knowledge and experience required to bring the project to fruition. When schedule issues occurred (initially with the mechanical design), no strategy was found to rectify them, and the project’s schedule continued to slip, impairing numerous successor tasks. While near-heroic efforts were made to attempt to complete the rover during the final days of the schedule, insufficient time was available for testing. The project suffered a final component failure which it was unable to recover from. TWO ATTEMPTS AT A NEAR SPACE RECOVERY SYSTEM The Near Space Recovery Technology (NSRT) was proposed as a senior design project for two consecutive years. With senior design projects, students self-select into groups based on selecting a topic that they are interested in working on. The goal of the NSRT project was to create a method to control the descent of a high altitude balloon (HAB) payload. Various approaches for doing this were considered by the NSRT team including timing the balloon burst to maximize the amount of time spent at altitudes with favorable wind patterns

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and, possibly, to incorporate a mechanism for controlling the rate of descent through various altitudes (again to maximize the time spent at favorable ones). The teams, for both years of the NSRT project, were static, with no students entering late or leaving the team mid-year. During the first year of the NSRT project, the students worked well together and had natural leaders. The first group was competitively more motivated. However, the goals of the project were overly ambitious and were not able to be completed. During the second year, the team was less enthusiastic, not self-driven and lacked commitment to the project. No natural leaders emerged from this group. The objectives for the second year project were only a subset of those from the first year project; however, in both cases, the team advanced many areas to an approximately 90%-of-completion threshold before the project ended. DISCUSSION The examples discussed are all university projects. They, however, span three categories of participant commitment. The first (spacecraft prototype) was largely a volunteer project, though there were a limited number of students working for class credit. The surface rover project was a mixed-mode project, with a significant number of students participating as volunteers, and a significant number participating for class credit. The third (recovery system) was a project for class credit. All three projects would have been good candidates for the use of the SQUIRM or SQUIRM-E model. The first, due to its scope, duration and complexity would have been able to support the additional overhead of the SQUIRM-E model and benefit from the greater understanding of student/ inexperienced staff-specific on standard risk classes that it provides. The second, due to its shorter duration, smaller size and more informal nature, would have been better suited to the qualitative use of the SQUIRM framework. The third falls between the two prior examples and would have required a more detailed analysis of application-specific tradeoffs in determining what model to use. Particularly in the second year, however, an analysis of the risk factors, which eventuated during the first year (while not conclusive), could have aided the decision making process. The risk models for these projects are, thus, very different. This is suggested by the different types of risks that eventuated. In the first case, high turnover and what eventually became

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an expected high loss rate between intake and ongoing participant numbers as well as turnover due to students feeling pressures from other areas of their academic pursuits, came to define the risk model. The second suffered from conflicts between the commitment levels of the two groups of students (falling into both the miss-commitment group, from an expectation of participation levels from students in the class participant category with regards to the volunteers, and from unscheduled turnover – for different groupspecific reasons – within both groups). Finally, the third suffered largely from technical issues, largely attributable to inexperience and some miss-commitment.

should be estimated (based on historic data, experience or other technique). Finally, any summative assessment should be performed. This may include combining risk data from sub-tasks into task-level assessments (or from tasks into project-level assessment), evaluating student/inexperienced worker participation value and comparing project-level assessments. The foregoing can be performed qualitatively or quantitatively. Quantitative analysis is discussed in greater detail in the subsequent section.

APPLICATION If similar projects were planned in the future, they could use SQUIRM or SQUIRM-E as appropriate (see above), following a five-step approach. First, the nature of the project must be defined. A discussion of this is beyond the scope of this article; however, several common frameworks exist, including those by Wertz et al. (2011) and Fortescue et al. (2011). A simplified version for small high altitude ballooning projects (which could be adapted to other aerospace projects) has also been proposed (Straub and Fevig, 2012). These frameworks incorporate risk analysis in different ways; however, this process — using SQUIRM/SQUIRM-E — should involve the following four steps. Second, areas of student (inexperienced staff) involvement, areas impacted by student involvement, and areas not impacted by or involving students, should be identified. The use of the SQUIRM/SQUIRM-E model is appropriate for the first two areas; the last one should use conventional risk assessment and management techniques. Third, a granularity level of risk assessment must be determined, based on the scale and nature of the project. Risk could be assessed at the whole-project-level or at any logical division level thereunder. The granularity level need not be consistent; thus, areas of higher risk or risk impact could be assessed at higher levels of granularity than less risky or impactful areas. Forth, for each unit of assessment, risk factors should be identified. This will involve application/task-specific brainstorming as well as reviewing the student/inexperienced worker-attributable factors presented by the SQUIRM model. For each factor, a likelihood and impact level

QUANTIFYING THE MODEL While the discussion up to this point has been qualitative, both the SQUIRM and SQUIRM-E models lend themselves to being used with quantitative data, if it is available. Figure 3 demonstrates how the identified risk areas, along with mitigation/response strategies identified using the SQUIRM/ SQUIRM-E model, can be used to assess the weighted (by likelihood of occurrence) risk impact levels for particular risk sources and for the project overall. The overall project risk levels may serve to facilitate comparison between projects (in conjunction with other metrics such as project importance and cost, etc.). RISK ASSESSMENT Risks are assessed both in terms of their likelihood of occurrence and the magnitude of impact that they may have if they eventuate. Risks may be assessed based on probabilities, if sufficient historical data exists or a probabilistic model is known or can be inferred, or they can be categorized (with approximate average probabilities, used to facilitate quantitative comparisons). The impact can, similarly, be quantified in terms of time, resource and cost (which may be combined into a single cost metric), if data is available. Alternately, they can be categorized and an average value used. MITIGATION/RESPONSE ASSESSMENT The risk effect may be altered by the existence (or development) of mitigation and response strategies. Mitigation strategies may reduce likelihood, impact, or both, while response strategies focus solely on reducing

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analysis. Problematically, this data likely varies on an application-specific basis (or general data would need to be validated for application-specific use). While, for small satellites, some relevant data has been collected by Brumbaugh and Lightsey (2013), and they are collecting data (Brumbaugh and Lightsey, 2014) to facilitate a more robust analysis, this doesn’t cover all areas required by this model, nor does it help those attempting to assess risk in other application areas. For areas and applications where this data is not available, it will need to be estimated based on past experience and other available information. The collection of data specific to particular applications is an area for future work.

Risk

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Final Weighted Effect

Project Aggregate Weighted Risk & Impact Figure 3. Quantifying SQUIRM & SQUIRM-E.

impact. The change created by the existence of one or more of these strategies should be considered. Again, actual numbers or classifications and average values can be used for this assessment. COMBINING FOR RESULT The risk effect and mitigation/response change are combined for each risk factor. Then (if multiple risk factors are present), the final weighted effects are combined, to produce an aggregated risk impact value for the project. It is important, when using this approach, that all values use a comparable scale (e.g., combining average and historical cost values should be done carefully to avoid over or understatement of risks). If risk values are being used to compare projects, then the need for a common scale extends to all items being compared. Thus, it is ideal (but often not practical) to use historical data and (inflation and other factor-adjusted) real costs, as this facilitates direct comparison. DATA FOR MODEL PARAMETERS One particular challenge in the use of SQUIRM or SQUIRM-E quantitatively is the collection of the parameters which are required in order to perform the quantitative

The foregoing may lead one to question the value of using inexperienced workers (particularly students) on any project of particular importance. Would the students/junior employees not be better served (and better serve others) by gaining experience through non-impactful learning exercises instead of work on real projects (which could be negatively impacted)? This section considers the value of student (and other inexperienced) workers. Figure 4 presents a diagram of the considerations. COST OF INEXPERIENCED/STUDENT WORKERS The cost of inexperienced and student workers is aptly identified by the SQUIRM and (to a greater extent) SQUIRM-E models. Clearly, each prospective risk may impair a project (if it eventuates) incurring time, productivity impairment (including productivity impairment of other more senior workers that may need to help rectify student/inexperienced worker mistakes), material and goodwill costs. Somewhat (in many cases) offsetting, this is the lower wage levels paid to student/inexperienced staff. Thus, for tasks that these individuals can learn to perform effectively and with minimal (or comparable to more experienced staff) oversight, a cost savings may be enjoyed. The assignment of junior staff to these types of tasks, however, may impair their learning process and prevent them from

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Student/Inexperienced Worker-Attributable Costs

Training Benefits

Value Proposition

Innovation Benefits

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Figure 4. Value Proposition for the Use of Inexperienced and Student Workers.

gaining (or decrease the speed of them gaining) skills that could make them more valuable to their current and future prospective employers. TRAINING BENEFITS The proverbial adage of “killing two birds with one stone” can be used in an attempt to justify the use of student/unskilled workers on real projects. If students/unskilled workers can be productively contributing to a project while also gaining experience, it would seem that two types of benefit are being gained for a single cost. While this may certainly be true in some (perhaps many) cases, the oversimplification of the cost model (i.e., the consideration of a “single” cost) may be inaccurate. Costs may be higher to facilitate the student/ inexperienced worker participation, which should be taken into account in the comparison. DISCONTINUOUS INNOVATION BENEFITS One area where student/inexperienced workers may offer particular benefit is in identifying sources of discontinuous innovation. These workers, who may not fully understand where the proverbial “box” is, may be well-suited to think outside of it. Swartwout (2004; 2011) identifies this, for example, as a key benefit of “university class” small spacecraft programs: the higher level of risk tolerance and the presence of the junior staff make these types of missions well suited to trying innovative ideas and identifying areas for innovation in operations.

DISCUSSION OF THE DIFFERENCES BETWEEN STUDENT VOLUNTEERS, PAID STUDENT WORKERS, INTERNS AND JUNIOR EMPLOYEES It has been stated, previously, that the SQUIRM and SQUIRM-E frameworks can be used to address risks across several different types of junior employees; however, the risk factor impact posed by these different groups are dissimilar. This section begins the process of considering the differences between the multiple types of workers that the SQUIRM/SQUIRM-E models could be applied to (in some cases with limited modifications). The particulars of each worker type are now discussed, this includes: student volunteers, paid student workers, interns and junior employees. STUDENT VOLUNTEERS Student volunteers will (correctly) view their participation as at-will. If they are interested, see benefits being provided and have time, they will continue working on the project. If they lose interest, feel that they are not receiving (or have already received all applicable) benefits or are confronted with other draws on their time, they will stop. Retention of students from semester-to-semester may be difficult, as they may perceive participation as an opt-in activity (like joining a club or taking a class), where a participation decision is made anew each semester. They may fail to realize or understand the impact of their change in participation status on others that have also donated their time to provide benefit to them or the cost of the time committed to their training by paid staff, etc. PAID STUDENT WORKERS Paid student workers may be more committed, as they are receiving another source of benefit (pay) over and above what is received by volunteers. However, in the context of the comparatively large amounts of money that they are paying (or which is being paid on their behalf ) to attend school, they may see little difference between the paid and unpaid positions in terms of any sense of commitment or longer-term responsibility. Pay, thus, may overcome (or assist in rectifying) lack of interest issues, but may not assist with semester-to-semester turnover issues or commitment in the face of other time draws.

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Extending the Student Qualitative Undertaking Involvement Risk Model

INTERNS Interns (in the case of non-university employers) may see a multi-faceted benefit which may cause particular (comparative) commitment. The intern may be earning credit for their participation, getting paid, gaining experience and gaining an opportunity to demonstrate their capabilities to a prospective employer. The foregoing (particularly if the intern sees the employer as a desirable place to seek post-graduation employment) may cause interns to place the internship amongst their highest priorities, overcoming most of the common (controllable) risk factors and creating a particularly high level of diligence. Interns may or may not have ongoing coursework during the internship period (the lack thereof reducing another set of risk factors). As a generally fixed-term period of employment, however, scheduled turnover is expected. JUNIOR EMPLOYEES Junior employees may see performance as critical to their future livelihood; however, this perception may not always be the case (even if it is accurate, it may not be perceived or employment may be perceived as an entitlement). While most will want to set their careers off on a ‘good foot’, others may find the change in structure (more or less control, different control structures and a need to be self-starting) problematic and not know how to function effectively under the changed structure. Employees may also be looking for new positions, if they take a position that is not of their liking simply to ‘pay the bills’ and may lack the professional discipline to continue to perform while in a job they dislike (or which they are not particularly excited about).

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criteria related to student-specific risk types. It has presented an analytical framework for assessing risk factors, relevant to student and inexperienced workers quantitatively, and evaluating the value of the use of a student/inexperienced worker on a given project. A limited extrapolation to non-student workers has been discussed. Future work will involve the enhancement of the quantitative models presented as well as the collection of a data set to begin to characterize these common risk areas for various classes of projects. It will also involve the development of a SQUIRM-E-based model for junior employees that replaces student-specific factors with those more appropriate to junior employees.

ACKNOWLEDGEMENTS Small satellite development work at the University of North Dakota is, or has been, supported by the North Dakota Space Grant Consortium, North Dakota NASA EPSCoR, the University of North Dakota Faculty Research Seed Money Committee, North Dakota EPSCoR (NSF Grant # EPS-814442), the Department of Computer Science, the John D. Odegard School of Aerospace Sciences and the National Aeronautics and Space Administration. The involvement of the numerous students from multiple disciplines in UND small satellite development is gratefully acknowledged. Also, thanks are given to the numerous faculty mentors who have helped make this possible.

CONCLUSIONS AND FUTURE WORK This paper has expanded the SQUIRM framework into a new SQUIRM-E version that adds additional assessment

Thanks is given to John Nordlie, who provided a subset of the information used for the examples. Thanks is also due to all of the faculty, students and staff who participated in the examples presented herein.

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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.

J. Aerosp. Technol. Manag., São José dos Campos, Vol.6, No 3, pp.353-355, Jul.-Sep., 2014

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