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Multi-objective Design Optimization Using High-Order Statistics for CFD Applications

Pietro Marco Congedo 1 Gianluca Geraci 2 Rémi Abgrall 1 Gianluca Iaccarino 2
1 BACCHUS - Parallel tools for Numerical Algorithms and Resolution of essentially Hyperbolic problems
Inria Bordeaux - Sud-Ouest, UB - Université de Bordeaux, CNRS - Centre National de la Recherche Scientifique : UMR5800
2 Department of Mechanical Engineering [Stanford]
Abstract : This work illustrates a practical and efficient method for performing multi-objective optimization using high-order statistics. It is based on a Polynomial Chaos framework, and evolutionary algorithms. In particular, the interest of considering high-order statistics for reducing the number of uncertainties is studied. The feasibility of the proposed method is proved on a Computational Fluid-Dynamics (CFD) real-case application.
Keywords : Robust optimization High-order statistics Dimension reduction Genetic algorithms
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https://hal.inria.fr/hal-01091941
Contributor : Pietro Marco Congedo <>
Submitted on : Sunday, December 7, 2014 - 10:26:28 PM
Last modification on : Saturday, February 27, 2021 - 6:08:02 PM

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Pietro Marco Congedo, Gianluca Geraci, Rémi Abgrall, Gianluca Iaccarino. Multi-objective Design Optimization Using High-Order Statistics for CFD Applications. David Greiner; Blas Galván; Jacques Périaux; Nicolas Gauger; Kyriakos Giannakoglou; Gabriel Winter. Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences, 36, Springer International Publishing, pp.111-126, 2014, ⟨10.1007/978-3-319-11541-2_7⟩. ⟨hal-01091941⟩

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