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

Pietro Marco Congedo 1 Gianluca Geraci 2 Rémi Abgrall 1 Gianluca Iaccarino 3
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 Mechanical Engineering Dept
3 Mechanical Engineering Department [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
Type de document :
Chapitre d'ouvrage
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〉
Liste complète des métadonnées
https://hal.inria.fr/hal-01091941
Contributeur : Pietro Marco Congedo <>
Soumis le : dimanche 7 décembre 2014 - 22:26:28
Dernière modification le : jeudi 11 janvier 2018 - 06:22:35

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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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