Frontiers of Data and Computing ›› 2023, Vol. 5 ›› Issue (2): 106-118.

CSTR: 32002.14.jfdc.CN10-1649/TP.2023.02.009

doi: 10.11871/jfdc.issn.2096-742X.2023.02.009

• Technology and Application • Previous Articles     Next Articles

Development of High-Performance Parallel CFD Software and Aerodynamic Performance Prediction of High-Speed Trains

ZHANG Xinxin1,2(),LIU Xiazhen1,*(),LIANG Shan1,ZHANG Jian1,2,LU Zhonghua1,2,GAO Lingyun1,2,ZHANG Haoyuan1,2   

  1. 1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2022-03-02 Online:2023-04-20 Published:2023-04-24
  • Contact: LIU Xiazhen;


[Application Background] For aerodynamic performance simulation and aerodynamic shape optimization of high-speed trains in complex scenes, the goal of our work is to improve the simulation accuracy and parallel computing efficiency. [Methods] A high-performance computational fluid dynamics software CCFD v3.0 is independently developed, and the core functional modules such as high-precision NS equation space-discrete algorithm, efficient linear algebra solution, turbulence simulation, and parallel grid processing are developed. It has been ported to two domestic heterogeneous supercomputing platforms and specifically optimized for parallel computing. [Results] The computational results delivered by CCFD v3.0 show that the head shape of the high-speed trains, the bottom resistance and friction are the main sources of resistance of the train in running, accounting for 71% and 23%, respectively. The 89% of the resistance of the trains carriages comes from friction and 11% from form drag. [Conclusions] CCFD v3.0 achieves good parallel acceleration in prediction of the aerodynamic performance of the high-speed trains and analysis of the flow field mechanism.

Key words: high-performance computing, CCFD v3.0, parallel optimization, high-speed train, aerodynamic performance