Assessment of random dynamic behavior for EMUs high-speed train based on Monte Carlo simulation基于蒙特卡罗模拟的电力动车组高速列车随机动力行为评估
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Momhur A, Zhao YX, Gebre A, 2024. Assessment of random dynamic behavior for EMUs high-speed train based on Monte Carlo simulation. Probabilistic Engineering Mechanics, 77: 103663.DOI: 10.1016/j.probengmech.2024.103663
本文提出了一种新的统计方法，以获得不规则线路激励下含独立不确定参数的动力响应。该方法结合了三维车轨耦合动力学模型和不确定参数。此外，本文还提出了一种处理动态指标的新方法，包括脱轨系数、垂直/横向轮轨力、垂直/横向车体加速度和轮对减载率。通过将模拟 (确定性) 结果与现场测量数据对比，验证了模型的有效性，并在有限数据下显示出良好的一致性。研究结果表明，当动力系统中存在不确定性参数时，会引发较大的振动效应。采用总拟合效果、车辆安全一致性以及尾部拟合效果选择最佳方法。因此基于此方法，在有限数据条件下对数正态分布和最大极值分布可能是关于动力安全性的合适分布假设。关键词: 多体动力学, 随机行为, 不确定性参数, 统计方法, 最大极值A novel statistical method was developed to obtain a dynamic response with irregular line excitations and independent uncertain parameters. The proposed approach combines a three-dimensional vehicle-track coupling dynamics model and uncertainty parameters. Moreover, a new method is used to treat the dynamic indices: derailment coefficient, vertical/lateral wheel/rail force, vertical/lateral car body acceleration, and wheel reduction ratio. The model is validated by comparing simulations (deterministic) results with field measurements, which provide excellent agreement with limited data. According to the findings, the results reveal that the high vibration effect arises when the uncertainty parameter in the dynamic system exists. The total fit effects, the consistency of the vehicle safety, and the tail fit effects are determined for selecting the best method. Therefore, regarding the approach, the lognormal and extreme maximum distribution values may be the appropriate assumed distribution for dynamic safety under limited data.
Keywords: Multi-body dynamics; Random behavior; Uncertainty parameters; Statistical method; Extreme maximum.![]()
图 1: CRH2A 型高速列车随机动力学行为计算方法Fig. 1. Methodology of the random dynamic behavior of CRH2A-type high-speed train
图 2: 不确定性传播样本结果作为多体动力学输入示意图Fig. 2. A diagram that shows how uncertainty propagation sample results are input into multi-body dynamics
Fig. 3. Present research work random dynamic model
图 4: 车辆系统动力学视图: (a) 侧视图; (b) 俯视图; (c) 正视图; (d) 坐标Fig. 4. Schematic diagram of vehicle system dynamics view: (a) Side; (b) Top; (c) Front; (d) Coordinates
Fig. 5. Depicts the relationship between the wheel and rail contact force
图 6: 中国铁路轨道谱的纵向与横向轨道不平顺样本: (a) 纵向; (b) 横向Fig. 6. Irregularity samples of vertical and lateral rails managed from the Chinese rail track spectrum: (a) Vertical; (b) Lateral
图 7: 确定性与可测量结果的对比方法: (a) 车体垂向加速度; (b) 车体垂向功率谱密度; (c) 车体横向加速度; (d) 车体横向功率谱密度Fig. 7. Methods for comparing deterministic and measurable results: (a) Vertical car body acceleration; (b) Vertical car body PSD; (c) Lateral car body acceleration; (d) Lateral car body PSD
图 8: 时域概率密度函数: (a) 垂向力; (b) 垂向力云图; (c) 横向力; (d) 横向力云图; (e) 车轮减速比; (f) 车轮减速比云图Fig. 8. Probability density function against time domain: (a) Vertical force; (b) Contour plot of vertical force; (c) Lateral force; (d) Contour plot of lateral force; (e) Wheel reduction ratio; (f) Contour plot of wheel reduction ratio
图 9: 动力学指标频谱分析: (a) 垂向轮轨力; (b) 横向轮轨力; (c) 垂向车体加速度; (d) 横向车体加速度Fig. 9. Spectral analysis of dynamics indices: (a) Vertical wheel/rail force; (b) Lateral wheel/rail force; (c) Vertical car body acceleration; (d) Lateral car body acceleration
图 10: 随机与概率密度拟合: (a) 垂向 Sperling 指标; (b) 确定性垂向稳定性; (c) 非确定性垂向稳定性Fig. 10. Stochastic and probability density fitting: (a) Vertical sperling; (b) Deterministic vertical stability; (c) Nondeterministic vertical stability (case 8)
图 11: 随机与概率密度拟合: (a) 横向 Sperling 指标; (b) 确定性横向稳定性; (c) 非确定性横向稳定性Fig. 11. Stochastic and probability density fitting: (a) Lateral speriling; (b) Deterministic lateral stability; (c) Nondeterministic vertical stability (case 8)
图 12: 基于概率密度函数的确定性工况 7 与 8 对比: (a,c,e) 垂向车体加速度概率密度函数; (b,d,f) 横向车体加速度概率密度函数Fig. 12. Comparisons among deterministic cases 7 and 8 based on probability density functions: (a,c,e) Vertical car body acceleration PDF; (b,d,f) Lateral car body acceleration PDF
图 13: 确定性工况 7 与 8 对比: (a,c,e) 垂向车体加速度功率谱密度; (b,d,f) 横向车体加速度功率谱密度Fig. 13. Comparisons between deterministic, case 7, and case 8: (a,c,e) Vertical car body acceleration PSD; (b,d,f) Lateral car body acceleration PSD
图 14: 样本 1 数据的七类分布统计图: (a-c) 脱轨系数; (d-f) 车轮减速比Fig. 14. Statistical diagrams of seven distributions for data of Sample 1 (Sim-1): (a-c) Derailment coefficient; (d-f) Wheel reduction ratio
图 15: 样本 1 数据的七类分布统计图: (a-c) 垂向轮轨力; (d-f) 横向轮轨力Fig. 15. Statistical diagrams of seven distributions for data of Sample 1 (Sim-1): (a-c) Vertical wheel/rail force; (d-f) Lateral wheel/rail force
图 16: 样本 1 数据的七类分布统计图: (a-c) 垂向车体加速度; (d-f) 横向车体加速度Fig. 16. Statistical diagrams of seven distributions for data of Sample 1 (Sim-1): (a-c) Vertical car body acceleration; (d-f) Lateral car body acceleration
作者信息 | Authors
Awel Momhur, 通讯作者 (Corresp.)西南交通大学 (Southwest Jiao Tong University) 机械工程学院Email: awel.mohammedseid@aait.edu.et
西南交通大学 (Southwest Jiao Tong University) 机械工程学院
埃塞俄比亚亚的斯亚贝巴大学 (Addis Ababa University) 土木与环境工程学院
律梦泽 M.Z. Lyu | 编辑 (Ed)
P.D. Spanos | 审校 (Rev)
陈建兵 J.B. Chen | 审校 (Rev)
彭勇波 Y.B. Peng | 审校 (Rev)
