Research Progress of Aerodynamic Multi-Objective Optimization on High-Speed Train Nose Shape  

作　　者：Zhiyuan Dai Tian Li Weihua Zhang Jiye Zhang 

机构地区：[1]State Key Laboratory of Traction Power,Southwest Jiaotong University,Chengdu,610031,China

出　　处：《Computer Modeling in Engineering & Sciences》2023年第11期1461-1489,共29页工程与科学中的计算机建模（英文）

基　　金：supported by the Sichuan Science and Technology Program(2023JDRC0062);National Natural Science Foundation of China(12172308);Project of State Key Laboratory of Traction Power(2023TPL-T05).

摘　　要：The aerodynamic optimization design of high-speed trains(HSTs)is crucial for energy conservation,environmental preservation,operational safety,and speeding up.This study aims to review the current state and progress of the aerodynamic multi-objective optimization of HSTs.First,the study explores the impact of train nose shape parameters on aerodynamic performance.The parameterization methods involved in the aerodynamic multiobjective optimization ofHSTs are summarized and classified as shape-based and disturbance-based parameterizationmethods.Meanwhile,the advantages and limitations of each parameterizationmethod,aswell as the applicable scope,are briefly discussed.In addition,the NSGA-II algorithm,particle swarm optimization algorithm,standard genetic algorithm,and other commonly used multi-objective optimization algorithms and the improvements in the field of aerodynamic optimization for HSTs are summarized.Second,this study investigates the aerodynamic multi-objective optimization technology for HSTs using the surrogate model,focusing on the Kriging surrogate models,neural network,and support vector regression.Moreover,the construction methods of surrogate models are summarized,and the influence of different sample infill criteria on the efficiency ofmulti-objective optimization is analyzed.Meanwhile,advanced aerodynamic optimization methods in the field of aircraft have been briefly introduced to guide research on the aerodynamic optimization of HSTs.Finally,based on the summary of the research progress of the aerodynamicmulti-objective optimization ofHSTs,future research directions are proposed,such as intelligent recognition technology of characteristic parameters,collaborative optimization of multiple operating environments,and sample infill criterion of the surrogate model.

关 键 词：High-speed train multi-objective optimization PARAMETERIZATION optimization algorithm surrogate model sample infill criterion 

分 类 号：U264.34[机械工程—车辆工程]