Multi-objective optimization for the aerodynamic noise of the high-speed train in the near and far field based on the improved NSGA-II algorithm

被引:9
|
作者
Yuan, Chun Yan [1 ]
Li, Ming Qing [2 ]
机构
[1] Changan Univ, Dept Civil Engn, Xian 710064, Shaanxi, Peoples R China
[2] Changan Univ, Dept Mech Engn, Xian 710064, Shaanxi, Peoples R China
关键词
high-speed train; train head; aerodynamic noises; large eddy simulation; boundary element method; vortex shape; LARGE-EDDY SIMULATION; DIPOLE SOUND SOURCE; NUMERICAL-ANALYSIS; PREDICTION; DRAG;
D O I
10.21595/jve.2017.18526
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
With the increased running speed of trains, the aerodynamic noise of trains becomes increasingly obvious. Reducing aerodynamic noises has become one of keys to controlling the noise of high-speed trains. This paper conducted a numerical simulation on the aerodynamic noise of head of the high-speed train. Firstly, this paper established a mathematical-physical model for the three-dimensional turbulent flow field of a high-speed train, adopted standard k-epsilon equation turbulent model and broadband noise source model to compute the aerodynamic noise sources of the high-speed train and applied three-dimensional transient large eddy simulation (LES) to compute the external unsteady flow field of the high-speed train after obtaining noise sources. Based on the unsteady flow field, then this paper applied FW-H equations to compute the far-field aerodynamic noise of the high-speed train. After obtaining the unsteady fluctuation pressure on the surface of the train, this paper computed the radiation characteristics of aerodynamic noises around the high-speed train based on the boundary element method (BEM). Researched results showed: The main aerodynamic noise sources of the high-speed train were at the nose tip of head train; fluid separation and recombination were main reasons for the aerodynamic noise of the highspeed train; vortexes in the position of head train were striped and horseshoe-shaped or hairpin vortexes were mainly in the area of tail train; in addition, vortexes were symmetrically distributed along the longitudinal symmetry plane of train; dipole noises were mainly distributed in the area of head train, whose main energy was decreased with the increased frequency; the quadrupole noise of aerodynamic noises of the high-speed train was mainly distributed in the wake flow area of tail train; when the high-speed train ran at the speed of 300 km/ h, the maximum sound pressure level of far-field observation points was 76.8 dB; additionally, aerodynamic noises in the far field were mainly a broadband noise, whose main energy was within the frequency range of 1250 Hz to 3150 Hz. Finally, the improved NSGA-II algorithm was used to conduct a multi-objective optimization for the head shape. The aerodynamic drag of the high-speed train could be most reduced by 6.74 %, and the dipole aerodynamic noise source could be most reduced by 8.34 dB. The improved NSGA-II algorithm has an obvious effect on the multi-objective optimization of the head shape.
引用
收藏
页码:4759 / 4782
页数:24
相关论文
共 50 条
  • [21] Multi-objective combustion optimization for boiler based on BP-improved NSGA-II
    xu W.
    Huang Y.
    Cao G.
    Chen B.
    Jin B.
    Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition), 2022, 52 (05): : 943 - 952
  • [22] Multi-objective optimization of impingement cooling of concave wall based on NSGA-II algorithm
    Zhao H.
    Song S.
    Wang Z.
    Hangkong Dongli Xuebao/Journal of Aerospace Power, 2024, 39 (06):
  • [23] Multi-Objective Optimization of Laser Cladding Parameters Based on RSM and NSGA-II Algorithm
    Wang Yanyan
    Li Jiahao
    Shu Linsen
    Su Chengming
    LASER & OPTOELECTRONICS PROGRESS, 2022, 59 (07)
  • [24] Multi-Objective Optimization of Construction Project Management Based on NSGA-II Algorithm Improvement
    Yang, Yong
    Men, Jinrui
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2025, 16 (01) : 432 - 444
  • [25] Multi-objective process parameter optimization for winding process based on NSGA-II algorithm
    Han, Yuze
    Liu, Yanpeng
    Ren, Zhongjie
    Ren, Mingfa
    Fuhe Cailiao Xuebao/Acta Materiae Compositae Sinica, 2024, 41 (10): : 5622 - 5633
  • [26] A Memory-Based NSGA-II Algorithm for Dynamic Multi-objective Optimization Problems
    Sahmoud, Shaaban
    Topcuoglu, Haluk Rahmi
    APPLICATIONS OF EVOLUTIONARY COMPUTATION, EVOAPPLICATIONS 2016, PT II, 2016, 9598 : 296 - 310
  • [27] Multi-objective Optimization Design of an AFFMPM Machine based on SVM and NSGA-II Algorithm
    Wang, Shuai
    Lin, Mingyao
    Chan, C. C.
    2024 IEEE 21ST BIENNIAL CONFERENCE ON ELECTROMAGNETIC FIELD COMPUTATION, CEFC 2024, 2024,
  • [28] Multi-objective optimization of vuilleumier cycle heat pump based on NSGA-II algorithm
    Xie, Yingbai
    Zhou, Botao
    Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2017, 38 (07): : 1807 - 1813
  • [29] Multi-objective classification based on NSGA-II
    Zhao, Binping
    Xue, Yu
    Xu, Bin
    Ma, Tinghuai
    Liu, Jingfa
    INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS, 2018, 9 (06) : 539 - 546
  • [30] Research on Multi-Objective Process Parameter Optimization Method in Hard Turning Based on an Improved NSGA-II Algorithm
    Zhang, Zhengrui
    Wu, Fei
    Wu, Aonan
    PROCESSES, 2024, 12 (05)