Wheel Profile Optimization of Speed-up Freight Train Based on Multi-population Genetic Algorithm

Abstract

The geometric shape of the wheel tread is mathematically expressed, and geometric parameters affecting the shape of the wheel were extracted as design variables. The vehicle dynamics simulation model was established based on the vehicle suspension parameters and track conditions of the actual operation, and the comprehensive dynamic parameters of the vehicle were taken as the design objectives. The matching performance of the wheel equivalent conicity with the vehicle and track parameters was discussed, and the best equivalent conicity was determined as the constraint condition of the optimization problem; a numerical calculation program is written to solve the optimization model based on a multi-population genetic algorithm. The results show that the algorithm has a fast calculation speed and good convergence. Compared with the LM profile, the two optimized profiles effectively reduce the wheelset acceleration and improve the lateral stability of the bogie and vehicle stability during straight running. Due to the optimized profile increases the equivalent conicity under larger lateral displacement of the wheelset, the lateral wheel-rail force, derailment coefficient, wheel load reduction rate, and wear index are reduced when the train passes through the curve line. This paper provides a feasible way to ensure the speed-up operation of a freight train.