A survey of parametric modelling methods for designing the head of a high-speed train

With the continuous increase of the running speed, the head shape of a high-speed train turns out to be the critical factor to boost the speed further. In order to reduce the time required to design the head of a high-speed train and to improve the modelling efficiency, various parametric modelling methods have been widely applied in the optimization design of the head of a high-speed train to obtain an optimal head shape so that the aerodynamic effect acting on the head of a high-speed train can be reduced and more energy can be saved. This paper reviews these parametric modelling methods and classifies them into four categories: two-dimensional, three-dimensional, CATIA-based, and mesh deformation-based parametric modelling methods. Each of the methods is introduced, and the advantages and disadvantages of these methods are identified. The simulation results are presented to demonstrate that the aerodynamic performance of the optimal models constructed by these parametric modelling methods has been improved when compared with the numerical calculation results of the original models or the prototype models of running trains. Since different parametric modelling methods used different original models and optimization methods, few publications could be found which compare the simulation results of the aerodynamic performance among different parametric modelling methods. In spite of this, these parametric modelling methods indicate that more local shape details will lead to more accurate simulation results, and fewer design variables will result in higher computational efficiency. Therefore, the ability of describing more local shape details with fewer design variables could serve as a main specification to assess the performance of various parametric modelling methods. The future research directions may concentrate on how to improve such ability.

Language

  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 01678490
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jul 30 2018 11:12AM