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

Abstract

Abstract 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 fine-tuning (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–Rübeland 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–Rübeland 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.

Keywords

• Wheel profile optimization
• Wear reduction
• Rotary-scaling fine-tuning
• Particle swarm optimization
• Kriging surrogate model