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2020 | OriginalPaper | Chapter

7. Uncertainty-Based Multidisciplinary Design Optimization (UMDO)

Authors : Loïc Brevault, Mathieu Balesdent

Published in: Aerospace System Analysis and Optimization in Uncertainty

Publisher: Springer International Publishing

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Abstract

This chapter is devoted to the description of the MDO formulations in the presence of uncertainty. In Chapter 1, deterministic MDO formulations have been introduced, highlighting the interest of such methodologies to solve complex and multidisciplinary design problems. Uncertainty-based multidisciplinary design optimization (UMDO) deals with the presence of uncertainty in MDO problems. The understanding of the importance of UMDO is spreading among academia and industry quickly. Nevertheless, in comparison with the deterministic MDO approaches, the UMDO methodologies are still in the early stages of development and numerous challenges have still to be solved. In the last decades, important improvements have been made in this field of research and are presented in this chapter. UMDO problems combine the challenges of deterministic MDO (organization of the design process, control of interdisciplinary couplings, etc.) and the difficulties involved by uncertainty propagation for multi-physics problems. Most of the existing UMDO formulations are built on the uncertainty propagation techniques dedicated to multidisciplinary problems presented in Chapter 6. The algorithms used to solve these UMDO problems are not discussed in this chapter, but all the presented optimization techniques in Chapter 5 may be used to solve UMDO problems.

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previous chapter Uncertainty Propagation for Multidisciplinary Problems

next chapter Multi-Fidelity for MDO Using Gaussian Processes

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Title: Uncertainty-Based Multidisciplinary Design Optimization (UMDO)
Authors: Loïc Brevault
Mathieu Balesdent
Publisher: Springer International Publishing
Book: Aerospace System Analysis and Optimization in Uncertainty
Print ISBN: 978-3-030-39125-6

Electronic ISBN: 978-3-030-39126-3

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-39126-3_7

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