Comparison of surrogate models for the actual global optimization of a 2D turbomachinery flow
Jacques Peter, Meryem Marcelet, Ste´phane Burguburu, Valentino Pediroda · 2007
Abstract: This article adresses the issue of selecting surrogate models suitable for the global optimization of 2D turbomachinery flows. As a first step towards this goal the analysis of a family of flows on a two-parameter design space is presented. Four types of surrogate models are considered: least square polynomials, artificial neural networks (multi-layer perceptron and radial basis function) and Kriging. Discussed is the ability of these surrogate functions to give a satisfactory description of the exact function of interest on the design space, during a global optimization. The number of CFD evaluations for an adequate description of the exact function is presented. Key–Words: Turbomachinery, global optimization, surrogate model 1