Dr. Emre Özkaya, SciComp

Title:
Robust Aerodynamic Shape Optimization Using Adjoint Assisted Surrogate Modeling

Abstract:

In the present work, we present an hybrid optimization framework for robust aerodynamic shape optimization. The suggested method combines a Kriging (also known as Gaussian process regression) based surrogate model with an adaptive sampling strategy assisted by the gradient information obtained from a discrete adjoint solver. In this way, it is possible to incorporate the uncertainties in design variables into the optimization algorithm. The feasibility of the suggested method is demonstrated by a comparative design optimization study using the benchmark test cases of the open source CFD software SU2.