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