Advanced Space Vehicle Design Taking into Account Multidisciplinary Couplings and Mixed Epistemic/Aleatory Uncertainties
| Autor: | Nam H. Kim, Raphael T. Haftka, Rodolphe Le Riche, Nathaniel B. Price, Loïc Brevault, Mathieu Balesdent, Nicolas Bérend, Sebastien Defoort |
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| Přispěvatelé: | ONERA - The French Aerospace Lab [Palaiseau], ONERA-Université Paris Saclay (COmUE), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT), Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Institut Henri Fayol (FAYOL-ENSMSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Département Génie mathématique et industriel (FAYOL-ENSMSE), Ecole Nationale Supérieure des Mines de St Etienne-Institut Henri Fayol, Centre National de la Recherche Scientifique (CNRS), University of Florida [Gainesville] (UF), Ecole Nationale Supérieure des Mines de St Etienne-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Institut Henri Fayol |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Structure (mathematical logic)
020301 aerospace & aeronautics Engineering INCERTITUDES Process (engineering) business.industry Multidisciplinary design optimization 0211 other engineering and technologies 02 engineering and technology MDO Multidisciplinary Design Optimization LANCEURS [PHYS.PHYS.PHYS-SPACE-PH]Physics [physics]/Physics [physics]/Space Physics [physics.space-ph] Consistency (database systems) [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF] 0203 mechanical engineering Multidisciplinary approach Systems engineering Launch vehicle design Systems design Aleatory/epistemic uncertainties Uncertainty quantification business Space vehicle 021106 design practice & management |
| Zdroj: | Space Engineering: Modeling and Optimization with Case Studies Space Engineering: Modeling and Optimization with Case Studies, 114, p. 1-48, 2016, Modeling and Optimization with Case Studies, Springer Optimization and Its Applications, 978-3-319-41506-2. ⟨10.1007/978-3-319-41508-6_1⟩ Springer Optimization and Its Applications ISBN: 9783319415062 |
| Popis: | International audience; Space vehicle design is a complex process involving numerous disciplines such as aerodynamics, structure, propulsion and trajectory. These disciplines are tightly coupled and may involve antagonistic objectives that require the use of specific methodologies in order to assess trade-offs between the disciplines and to obtain the global optimal configuration. Generally, there are two ways to handle the system design. On the one hand, the design may be considered from a disciplinary point of view (a.k.a. Disciplinary Design Optimization): the designer of each discipline has to design its subsystem (e.g. engine) taking the interactions between its discipline and the others (interdisciplinary couplings) into account. On the other hand, the design may also be considered as a whole: the design team addresses the global architecture of the space vehicle, taking all the disciplinary design variables and constraints into account at the same time. This methodology is known as Multidisciplinary Design Optimization (MDO) and requires specific mathematical tools to handle the interdisciplinary coupling consistency. In the first part of this chapter, we present the main classical techniques to efficiently tackle the interdisciplinary coupling satisfaction problem. In particular, an MDO decomposition strategy based on the “Stage-Wise decomposition for Optimal Rocket Design” formulation is described. This method allows the design process to be decentralized according to the different subsystems (e.g. launch vehicle stages) and reduces the computational cost compared to classical MDO methods. In the first part of this chapter, we present the main classical techniques to efficiently tackle the interdisciplinary coupling satisfaction problem. In particular, an MDO decomposition strategy based on the "Stage-Wise decomposition for Optimal Rocket Design" formulation is described. This method allows the design process to be decentralized according to the different subsystems (e.g. launch vehicle stages) and reduces the computational cost compared to classical MDO methods. |
| Databáze: | OpenAIRE |
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