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

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.

Rotary-scaling fine-tuning (RSFT) method for optimizing railway wheel profiles and its application to a locomotive

The existing multi-objective wheel profile optimization methods mainly consist of three sub-modules:(1)wheel profile generation,(2)multi-body dynamics simulation,and(3)an optimization algorithm.For the first module,a comparably conservative rotary-scaling finetuning(RSFT)method,which introduces two design variables and an empirical formula,is proposed to fine-tune the traditional wheel profiles for improving their engineering applicability.For the second module,for the TRAXX locomotives serving on the Blankenburg–Rubeland line,an optimization function representing the relationship between the wheel profile and the wheel–rail wear number is established based on Kriging surrogate model(KSM).For the third module,a method combining the regression capability of KSM with the iterative computing power of particle swarm optimization(PSO)is proposed to quickly and reliably implement the task of optimizing wheel profiles.Finally,with the RSFT–KSM–PSO method,we propose two wear-resistant wheel profiles for the TRAXX locomotives serving on the Blankenburg–Rubeland line,namely S1002-S and S1002-M.The S1002-S profile minimizes the total wear number by 30%,while the S1002-M profile makes the wear distribution more uniform through a proper sacrifice of the tread wear number,and the total wear number is reduced by 21%.The quasi-static and hunting stability tests further demonstrate that the profile designed by the RSFT–KSM–PSO method is promising for practical engineering applications.