Abstract: In order to improve the aerodynamic performance of the high-speed train, an efficient multi-objective aerodynamic optimization design method is set up to carry out the multi-objective aerodynamic optimization design of the streamlined head. The three-dimensional parametric model of the streamlined head of the high-speed train is set up, and five optimization design variables are extracted. To reduce the optimization time, the optimal Latin hypercube design method is used for the uniform sampling in the optimization design space, and the aerodynamic loads corresponding to each sampling point are obtained through the computational fluid dynamic method. The Kriging surrogate model is used to construct the approximate model between optimization design variables and aerodynamic loads. The load reduction factor of the high-speed train caused by the aerodynamic loads is computed by the multi-body system dynamic method. Then the aerodynamic drag force and load reduction factor are set as optimization objectives and the multi-objective optimization of the high-speed train head is conducted by the multi-objective genetic algorithm NSGA-II. The optimization design variables and optimization objectives show the tendency of convergence. The Pareto frontier computed by the Kriging approximate model is close to that computed by the computational fluid dynamics (CFD). After optimization, the aerodynamic drag of the optimized train is reduced by up to 3.27%, and the load reduction factor is reduced by up to 1.44%. As for the optimal head with minimum aerodynamic drag force and the optimal head with minimum load reduction factor, the main difference is the deformation of the central auxiliary control line, with the former concave and the latter convex.

Key words: high-speed train, aerodynamic performance, multi-objective optimization, Kriging surrogate model, Pareto frontier