|Drag-reduction design on high-speed trains with intelligent optimization algorithm|
|Yang GW(杨国伟); Yao SB(姚拴宝); Guo DL(郭迪龙)
|Source Publication||PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE
|Conference Name||THE SECOND INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE
|Conference Date||8-11 April 2014
|Conference Place||Ajaccio, Corsica, France
|Abstract||In the aerodynamic design processes of Chinese new generation trains of CRH380 series, their shapes were mainly ascertained though the optimized choice from the numerous engineering design projects. In the paper, an aerodynamic optimization charts was constructed for the further drag-reduction design of high-speed trains, which includes the local shape function developed for the parameterization of complex shapes to reduce the design variables largely, the Kriging surrogate model constructed with cross-tests for the reduction of CFD running times, Latin Hypercube Sampling (LHS) with the Max-Min criterion for the sample choices, the grid deformation based on the reduced-point RBF interpolation to improve the deforming efficiency, an modified Ant Colony Optimization (ACO) to pick up the iterative convergence. Then the streamline part of the simplified CRH380A train is taken as the initial optimization shape, and six design variables are selected for the control of the width and height of head-nose and body, the view angle of chauffeur cab and the changeable length of cowcatcher. The volume of train-head is added as optimization constraints. The optimized results indicated that the aerodynamic drag can be reduced with about 5.68%, at the same time, the lift of train-tail reduced as well. The drag-reduction mechanisms are investigated through the comparisons of flow-field and pressure distributions between the initial and optimized train. Finally, the performances between the original complex CRH380A train and the recovered optimized shapes are compared to testify the validation of the developed intelligent optimization method. © Civil-Comp Press, 2014.|
Ant colony optimization
Computational fluid dynamics
Radial basis function networks
High speed train (HST)
Intelligent optimization algorithm
Kriging surrogate model
|Affiliation||Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing, China|
Yang GW,Yao SB,Guo DL. Drag-reduction design on high-speed trains with intelligent optimization algorithm[C],2014.
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