Shape optimization of high-speed trains for improved aerodynamic performance

Abstract

A new procedure for the optimization of aerodynamic properties of trains is presented, where simple response surface (RS) models are used as a basis for optimization instead of a large number of evaluations of the Navier—Stokes solver. The suggested optimization strategy is demonstrated in two flow optimization cases: optimization of the train's front for the crosswind stability and optimization of vortex generators (VGs) for the purpose of drag reduction. Besides finding the global minimum for each aerodynamic objective, a strategy for finding a set of optimal solutions is demonstrated. This is based on the use of genetic algorithms on RS models. The resulting Pareto-optimal solutions are used to explore the extreme designs and find trade-offs between design objectives. For the optimization of VGs, three different RS models are used: polynomial functions, radial basis neural networks (RBNN), and RBNN-enhanced polynomial RSs. 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 the construction of high-order polynomial functions, and the model's fit with the data is the best.

Keywords

aerodynamic shape optimization train aerodynamics response surface multi-objective optimization Pareto-optimal front

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