Gradient-Based Aerodynamic Robust Optimization Using the Adjoint Method and Gaussian Processes

Abstract

The use of robust design in aerodynamic shape optimization is increasing in popularity in order to come up with configurations less sensitive to operational conditions. However, the addition of uncertainties increases the computational cost as both design and stochastic spaces must be explored. The objective of this work is the development of an efficient framework for gradient-based robust design by using an adjoint formulation and a non-intrusive surrogate-based uncertainty quantification method. At each optimization iteration, the statistic of both the quantity of interest and its gradients are efficiently obtained through Gaussian Processes models. The framework is applied to the aerodynamic shape optimization of a 2D airfoil. With the presented approach it is possible to reduce both the mean and standard deviation of the drag compared to the deterministic optimum configuration. The robust solution is obtained at a reduced run time that is independent of the number of design parameters.