Constrained Single-Point Aerodynamic Shape Optimization of the DPW-W1 Wing Through Evolutionary Programming and Support Vector Machines

Abstract

The application of surrogate-based methods to the constrained optimization of aerodynamic shapes is nowadays a very active research field due to the potential of these methods to reduce the number of actual computational fluid dynamics simulation runs, and therefore drastically speed-up the design process. However, their feasibility when handling a large number of design parameters, which in fact is the case in industrial configurations, remains unclear and needs further efforts, as demonstrated by recent research on design space reduction techniques and adaptive sampling strategies. This paper presents the results of applying surrogate-based optimization to the three-dimensional, constrained aerodynamic shape design of the DPW-W1 wing, involving both inviscid and viscous transonic flow. The wing geometry is parameterized by a control box with 36 design variables and the applied approach is based on the use of Support Vector Machines (SVMs) as the surrogate model for estimating the objective function, in combination with an Evolutionary Algorithm (EA) and an adaptive sampling technique focused on optimization, called the Intelligent Estimation Search with Sequential Learning (IES-SL).