Advanced Space Vehicle Design Taking into Account Multidisciplinary Couplings and Mixed Epistemic/Aleatory Uncertainties

Abstract : 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.
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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〉
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Mathieu Balesdent, Loïc Brevault, Nathaniel Price, Sébastien Defoort, Rodolphe Le Riche, et al.. Advanced Space Vehicle Design Taking into Account Multidisciplinary Couplings and Mixed Epistemic/Aleatory Uncertainties. 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〉. 〈hal-01475842〉

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