Optimization of rail profile design for high-speed lines based on Gaussian function correction method

Abstract

As high-speed trains operate at a higher speed, the problem of rail wear is more serious. In this paper, a new Gaussian function correction (GFC) method is proposed to design the new rail profile, two parameters are introduced to control the removal area. Then a high-speed train vehicle dynamic model is established, the Kriging surrogate model (KSM) is used to reduce the number of simulations and the Non dominated sorting genetic algorithm-II (NSGA-II) algorithm is used to optimize the rail profile. Finally, the dynamic characteristics and wheel/rail wear evolution of the optimized profile are analyzed. The results show that the dynamic performance of the optimized rail profile has been improved. The maximum wear depth of the optimized rail profile is reduced by 15.63% when passing a total weight of 16 Mt. The wheel wear depth of S1002CN profile contact with CHN60OPT is reduced by 4.8%. The proposed GFC method can quickly generate a new rail profile and has good engineering significance for rail grinding. The GFC-KSM-NSGA- II method can be used to optimize the rail profiles for high-speed lines, and it can further guide the operation and maintenance.