Abstract: In order to improve the aerodynamic performance of high-speed trains running in open air, a multi-objective optimization design method of the streamlined head shape of high-speed trains is proposed. The total aerodynamic drag force of the high-speed train and the maximum surface sound power of the head coach are set as the optimization objectives and the automatic multi-objective optimization design of the streamlined head shape is carried out. The parametric model of a new type super high-speed train with three carriages is established and five design variables of the head shape are extracted. The regions of the flow fields around trains are automatically meshed by the script file of ICEM CFD software, and the aerodynamic forces and the surface sound power of noise source of high-speed trains are automatically calculated by the script file of FLUENT software. The design variables are automatically updated by non-dominated sorting genetic algorithm-II (NSGA-II) to achieve the automatic optimization design of the head shape of the super high-speed train. After optimization, the correlations between the optimization design variables and the optimization objectives are analyzed, and the key design variables which influence the optimization objectives are obtained. The results show that the correlations between the optimization design variables and the two optimization objectives are the same, and only the values of correlation coefficients are different. A set of Pareto-optimal head shapes are obtained through the multi-objective optimization design. Compared with the original train, the total aerodynamic drag force of the optimized train is reduced up to 2.91%, and the maximum surface sound power of the head coach is reduced up to 7.47%.

Key words: aerodynamic drag, genetic algorithm, high-speed train, surface sound power, multi-objective optimization