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

CONGEDO, Pietro Marco
Parallel tools for Numerical Algorithms and Resolution of essentially Hyperbolic problems [BACCHUS]
GERACI, Gianluca
Department of Mechanical Engineering [Stanford]
ABGRALL, Rémi
Parallel tools for Numerical Algorithms and Resolution of essentially Hyperbolic problems [BACCHUS]
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Langue
en
Chapitre d'ouvrage
Ce document a été publié dans
Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences, Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences. 2014-11-15, vol. 36, p. 111-126
Springer International Publishing
Résumé en anglais
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 ...Lire la suite >
Mots clés en anglais
High-order statistics
Dimension reduction
Genetic algorithms
Robust optimization
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https://oskar-bordeaux.fr/handle/20.500.12278/198939
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