Sponsor(s):Chinese Mechanical Engineering Society
24 issues per year
Current Issue: Issue 24, 2019
Journal of Mechanical Engineering, the 1st in the field of mechanical engineering, is supervised by China Association for Science and Technology (CAST) and sponsored by Chinese Mechanical Engineering Society (CMES). The journal aims to become an international academic journal of mechanical engineering. Its scope covers mechanics, manufacturing science and technology, instrument science and technology, materials science and engineering, carrying engineering, renewable energy and engineering thermophysics, fluid transmission and control, deep sea equipment technology, and automation control. The journal is included in CA, JST, Pж(AJ), EI, CSCD.
Wang Buxuan;Lu Yongxiang
Wang Wenbin;Wang Guobiao
Chen Xuedong;Chen Chaozhi
Huang Tian;Luo Jianbin
Journal of Mechanical Engineering,2019,Vol 55,No. 24
In order to improve the aerodynamic performance of the high-speed train, an efficient multi-objective aerodynamic optimization design method is set up to carry out the multi-objective aerodynamic optimization design of the streamlined head. The three-dimensional parametric model of the streamlined head of the high-speed train is set up, and five optimization design variables are extracted. To reduce the optimization time, the optimal Latin hypercube design method is used for the uniform sampling in the optimization design space, and the aerodynamic loads corresponding to each sampling point are obtained through the computational fluid dynamic method. The Kriging surrogate model is used to construct the approximate model between optimization design variables and aerodynamic loads. The load reduction factor of the high-speed train caused by the aerodynamic loads is computed by the multi-body system dynamic method. Then the aerodynamic drag force and load reduction factor are set as optimization objectives and the multi-objective optimization of the high-speed train head is conducted by the multi-objective genetic algorithm NSGA-Ⅱ. The optimization design variables and optimization objectives show the tendency of convergence. The Pareto frontier computed by the Kriging approximate model is close to that computed by the computational fluid dynamics(CFD). After optimization, the aerodynamic drag of the optimized train is reduced by up to 3.27%, and the load reduction factor is reduced by up to 1.44%. As for the optimal head with minimum aerodynamic drag force and the optimal head with minimum load reduction factor, the main difference is the deformation of the central auxiliary control line, with the former concave and the latter convex.