中文摘要：

为改善高速列车明线运行时的气动性能,建立高速列车流线型头型的多目标优化设计方法,以高速列车的整车气动阻力和头车最大表面声功率为优化目标,对流线型头型进行多目标自动优化设计.建立三车编组某新型超高速列车的参数化模型,提取头型的五个设计变量,采用ICEM CFD软件脚本文件对列车周围流场区域进行网格自动划分,采用FLUENT软件脚本文件对列车气动力和表面气动噪声源声功率进行自动计算,通过第二代非劣排序的遗传算法（Non-dominated sorting genetic algorithm-Ⅱ,NSGA-Ⅱ）对设计变量进行自动更新,实现超高速列车头型的自动优化设计.优化完成后,对优化设计变量与优化目标的相关性进行分析,得到影响优化目标的关键设计变量.结果表明,各优化设计变量与两个优化目标的相关性相同,只是相关系数值不同.经过多目标优化设计,得到一系列的Pareto最优头型;与原型列车相比,优化后列车的整车气动阻力最多减小2.91%,头车最大表面声功率最多减小7.47%.

英文摘要：

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%.