14 research outputs found
Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection
Inexact information aided, low-cost, distributed genetic algorithms for aerodynamic shape optimization
Low-cost genetic optimization based on inexact pre-evaluations and the sensitivity analysis of design parameters
Hierarchical distributed metamodel-assisted evolutionary algorithms in shape optimization
Ordinary Kriging Surrogates in Aerodynamics
International audienceThis chapter describes the methodology used to construct Kriging-based surrogate models and their possible application to uncertainty quantification and robust shape optimization. More particularly, a two-dimensional RAE 2822 airfoil at transonic speed is considered for which the shape of the baseline profile is altered by localized bumps of small amplitudes. The flow around the airfoil is then subjected to important changes compared to the baseline configuration. The aim of the surrogate is to assess their influence on the aerodynamic performance of the profile as quantified by its lift-to-drag ratio. An optimization analysis is subsequently carried out in order to extract the local extrema of this performance measure. Assigning some uncertainty to the bump amplitudes, it also reveals that the global maximum identified by a high-quality surrogate is not necessarily the most robust one. This example constitutes an interesting benchmark for testing uncertainty quantification and robust optimization strategies