Optimization of vortex generators for drag reduction of a high-speed train

Paper in proceeding, 2008

Shape optimization of passive flow control devices called vortex generators on a high-speed train is presented. Three different response surface models are used in the optimization process: polynomial functions, radial basis neural networks (RBNN) and RBNN-enhanced polynomial-based response surfaces. The three approaches produce different results and the combination of RBNN and polynomial functions in the last approach is found to be the best as it enables construction of high order polynomial functions and the model's fit with the data is the best. Drag reduction of approximately 5 % was obtained with the optimal design of vortex generators.