Application of Surrogate-Based Optimization Techniques to Aerodynamic Design Cases

Abstract

The paper proposes the application of evolutionary-based optimization coupled with physics-based and adaptively-trained surrogate model to the solution of both two- and three-dimensional aerodynamic optimization problems. The shape parameterization approach consists of the Class-Shape Transformation (CST) method with a sufficient degree of Bernstein polynomials to cover a wide range of shapes. The in-house ZEN flow solver is used for RANS aerodynamic solution. Results show that, thanks to the combined usage of surrogate models and smart training, optimal candidates may be located in the design space even with limited computational resources with respect to standard global optimization approaches.