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.

中文翻译：

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旋转比例尺微调（RSFT）方法优化铁路车轮轮廓及其在机车中的应用

现有的多目标车轮轮廓优化方法主要包括三个子模块：（1）车轮轮廓生成；（2）多体动力学仿真；（3）优化算法。对于第一个模块，提出了一种比较保守的旋转比例微调（RSFT）方法，该方法引入了两个设计变量和一个经验公式，以微调传统车轮轮廓，以提高其工程实用性。对于第二个模块，对于在勃兰根堡–吕贝兰线上使用的TRAXX机车，基于克里格代理模型（KSM）建立了表示车轮轮廓与轮轨磨损数之间关系的优化函数。对于第三个模块，提出了一种结合KSM的回归能力和粒子群优化（PSO）迭代计算能力的方法，以快速，可靠地实现车轮轮廓优化的任务。最后，通过RSFT–KSM–PSO方法，我们为在勃兰根堡–吕贝兰线上使用的TRAXX机车提出了两种耐磨车轮轮廓，即S1002-S和S1002-M。S1002-S轮廓将总磨损次数最小化了30％，而S1002-M轮廓通过适当地牺牲了胎面磨损次数，使磨损分布更加均匀，总磨损次数减少了21％。准静态和波动稳定性测试进一步证明，通过RSFT–KSM–PSO方法设计的轮廓对于实际工程应用是有前途的。