Li Jincheng, Qi Yayun, Wu Pengpeng, Ding Junjun, Ding Haohao
School of Rail Transportation, Shandong Jiao Tong University, Jinan, China.
School of Mechanotronics and Vehicle Engineering, Chongqing Jiao Tong University, Chongqing, China.
Sci Rep. 2025 Jan 2;15(1):531. doi: 10.1038/s41598-024-85016-5.
In order to reduce turnout rail wear, the paper establishes a coupled dynamics model and a turnout rail wear model that consider the true profile of the turnout rail, the vehicle's continuous traction force while passing, and the operational resistance. Comparative analysis of various models for predicting turnout rail wear indicates that the wear energy model is better suited for this purpose. The ideal profile update step for the turnout rail is 0.05 mm, and the adaptive filtering algorithm, tailored to the turnout characteristics, smooths wear distribution effectively while retaining crucial data features. The wear model developed in the paper predicts wear depth that is 89.91% consistent with the measured data. The grinding model introduced in the paper significantly enhances the wheel-rail contact conditions in the turnout, with lateral and vertical vibration accelerations of the vehicle reduced by 52.56% and 30.43%, respectively, during turnout passage. The research offers theoretical support for mitigating rail wear in high-speed turnouts.
为减少道岔钢轨磨损，本文建立了考虑道岔钢轨真实轮廓、车辆通过时的连续牵引力以及运行阻力的耦合动力学模型和道岔钢轨磨损模型。对各种预测道岔钢轨磨损的模型进行对比分析表明，磨损能量模型更适合此目的。道岔钢轨的理想轮廓更新步长为0.05毫米，针对道岔特性定制的自适应滤波算法在保留关键数据特征的同时有效平滑了磨损分布。本文所建立的磨损模型预测的磨损深度与实测数据的一致性达89.91%。本文引入的打磨模型显著改善了道岔处的轮轨接触状况，车辆通过道岔时的横向和垂直振动加速度分别降低了52.56%和30.43%。该研究为减轻高速道岔钢轨磨损提供了理论支持。
