Patrick Mischke, TU Kaiserslautern
Implementation of a POD Based Surrogate Model for Aerodynamic Shape Optimization
Computational Fluid Dynamics (CFD) simulations are often used with aerodynamic shape optimization in mind. There exist several approaches to perform optimization, for example employing one shot methods or using adjoint computations by utilizing algorithmic differentiation techniques while running the flow solver. However, it may sometimes be preferred to inspect the flow fields across the whole design space, for instance if those strategies seem to operate too local for the problem on hand. This issue can be targeted by the use of surrogate models. A surrogate model predicts the flow field for a given design input in a computationally cheaper way, but also with less accuracy than the full fluid dynamics simulation. The surrogate model uses a set of training data to find reasonable model parameters for future design inputs. The goal of my bachelor thesis, that I present in this talk, was the implementation of an surrogate model for CFD using Proper Orthogonal Decomposition (POD) and Kriging regression. The challenges encountered are discussed, and the NACA 0012 airfoil at transonic flow conditions is presented as test case. The characteristic shock front and the high number of design parameters describing the airfoil geometry of its flow field lead to a rather expensive training process for the surrogate model. However, the discussed approach may still be valuable for other geometries or as foundation for surrogate models using other techniques.