Dr. Emre Özkaya, SciComp
Robust Aerodynamic Shape Optimization Using Adjoint Assisted Surrogate Modeling
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.