ABSTRACT Accurately estimating the coefficient of adhesion (CoA) is important to the safety, maintenance, operation and wheel–rail dynamics of railway systems. Studies have reported the influence of various parameters on the CoA and developed subsequent models, but parameter contribution or model estimation accuracy has not been systematically examined. Therefore, this study aims to quantitatively compare the accuracy of Heuristic, Polach and Dynamic estimation models with statistical measures and to investigate the contribution of various parameters on the CoA. Experimental CoA data was collected using a twin-disc rig at 57 and 85 km/h. The percentage root-mean-square error (PRMSE) of each model was then calculated for the rolling, sliding and overall contact regimes at each speed and multiple regression analysis (MRA) was applied to examine the contribution of each parameter. The Dynamic model was found to most accurately estimate the CoA in the sliding contact regime at both speeds due to its ability to capture dynamic variance. The Polach model also provided accurate estimations, but could not capture dynamic variance at the sliding contact regime. Using MRA, the CoA was found to be most dependent on sliding velocity. Overall, this work contributes to increase the prediction accuracy of wheel–rail contact dynamics.

中文翻译：

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纵向粘附模型的比较和主要粘附参数的识别

摘要 准确估计黏附系数 (CoA) 对铁路系统的安全、维护、运行和轮轨动力学非常重要。研究报告了各种参数对 CoA 的影响并开发了后续模型，但尚未系统地检查参数贡献或模型估计精度。因此，本研究旨在定量比较启发式、Polach 和动态估计模型与统计测量的准确性，并研究各种参数对 CoA 的贡献。使用双盘钻机以 57 和 85 公里/小时的速度收集实验 CoA 数据。然后计算每个模型的均方根误差百分比 (PRMSE)，用于滚动，每个速度下的滑动和整体接触方式和多元回归分析（MRA）被应用于检查每个参数的贡献。发现动态模型能够最准确地估计两种速度下滑动接触状态下的 CoA，因为它能够捕获动态变化。Polach 模型也提供了准确的估计，但无法捕获滑动接触状态下的动态方差。使用 MRA，发现 CoA 最依赖于滑动速度。总的来说，这项工作有助于提高轮轨接触动力学的预测精度。但无法捕获滑动接触状态下的动态变化。使用 MRA，发现 CoA 最依赖于滑动速度。总的来说，这项工作有助于提高轮轨接触动力学的预测精度。但无法捕获滑动接触状态下的动态变化。使用 MRA，发现 CoA 最依赖于滑动速度。总的来说，这项工作有助于提高轮轨接触动力学的预测精度。