With the speed upgrade of the high-speed train,the aerodynamic drag becomes one of the key factors to restrain the train speed and energy saving.In order to reduce the aerodynamic drag of train head,a new parametric a...With the speed upgrade of the high-speed train,the aerodynamic drag becomes one of the key factors to restrain the train speed and energy saving.In order to reduce the aerodynamic drag of train head,a new parametric approach called local shape function(LSF) was adopted based on the free form surface deformation(FFD) method and a new efficient optimization method based on the response surface method(RSM) of GA-GRNN.The optimization results show that the parametric method can control the large deformation with a few design parameters,and can ensure the deformation zones smoothness and smooth transition of different deformation regions.With the same sample points for training,GA-GRNN performs better than GRNN to get the global optimal solution.As an example,the aerodynamic drag for a simplified shape with head + one carriage + tail train is reduced by 8.7%.The proposed optimization method is efficient for the engineering design of high-speed train.展开更多
基金supported by the National Basic Research Program of China ("973" Project) (Grant No. 2011CB711100)the National Hi-Tech Research and Development Program of China ("863" Project) (Grant No.2009BAQG12A03)Computing Facility for Computational Mechanics,Institute of Mechanics,Chinese Academy of Sciences
文摘With the speed upgrade of the high-speed train,the aerodynamic drag becomes one of the key factors to restrain the train speed and energy saving.In order to reduce the aerodynamic drag of train head,a new parametric approach called local shape function(LSF) was adopted based on the free form surface deformation(FFD) method and a new efficient optimization method based on the response surface method(RSM) of GA-GRNN.The optimization results show that the parametric method can control the large deformation with a few design parameters,and can ensure the deformation zones smoothness and smooth transition of different deformation regions.With the same sample points for training,GA-GRNN performs better than GRNN to get the global optimal solution.As an example,the aerodynamic drag for a simplified shape with head + one carriage + tail train is reduced by 8.7%.The proposed optimization method is efficient for the engineering design of high-speed train.
