Modelling of track wear damage due to changes in friction conditions: A comparison between AC and DC electric drive locomotives

Liu, Sheng; Tian, Ye; Daniel, William J.T.; Meehan, Paul A.

doi:10.1016/j.wear.2016.05.023

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…To meet the challenges posed by such a varied wheel-track interface environment, some researchers also focused on the creep control system to relieve wheel/rail wear under low adhesion track intervals. Such as, these works [21][22][23] showed that creep/traction control has a crucial influence on wear during acceleration under low speed. Tian presented a locomotive traction controller based on a proportionalintegral and sliding mode controller with fuzzy logic creep reference generator, and results showed that a fuzzy logic-based sliding mode controller can achieve higher traction force with lower creepage at high-speed operation.…”

Section: Introductionmentioning

confidence: 99%

Wheel wear prediction for high speed railway vehicles during acceleration

Sang

Zeng

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part F:

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Wheel wear prediction for high-speed railway vehicles during the acceleration process is of great importance for the improvement of operating characteristics and the development of friction management strategy near the railway station, especially in complex climatic environments. This work analyzes and predicts the wheel wear evolution of high-speed motor cars during acceleration. Firstly, detailed dynamic models of the high-speed motor car are developed, in which a comprehensive gear transmission system is also adopted to capture the accurate dynamic responses of the motor car. Secondly, the length of the simulated track interval with different friction coefficients is set according to the Gaussian distribution function to consider the influence of the friction coefficient. Moreover, a Proportional-Integral(PI) creep controller is also introduced to meet the challenge due to varying wheel-rail contact conditions, and a wheel wear prediction model based on the aforementioned submodels is established to obtain the wheel wear evolution. Finally, simulation results of dynamic responses from the vehicle dynamic model and wheel wear prediction procedure are compared with the measured field data. The results show that the time-varying meshing stiffness from the gear transmission system will aggravate the interaction between wheel and rail and deteriorate the wheel wear. When the high-speed motor car passes through a poor adhesion zone in the acceleration, it is easier to trigger the creep controller at the low-speed period while it will not occur at the high-speed period.

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Section: Introductionmentioning

confidence: 99%

Wheel wear prediction for high speed railway vehicles during acceleration

Sang

Zeng

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part F:

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“…The influences of train dynamics on locomotive wheel wear and RCF are also reflected by the interaction between train dynamics and traction control systems. 17–19 It is easy to understand that, under different in-train forces, different traction control commands are needed.…”

Section: Introductionmentioning

confidence: 99%

Parallel co-simulation of locomotive wheel wear and rolling contact fatigue in a heavy haul train operational environment

Spiryagin

Sun

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part F:

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Locomotive wheel wear and rolling contact fatigue simulations that consider both train dynamics and detailed traction control systems have not been reported. This paper developed a parallel co-simulation method to link an in-house longitudinal train dynamics simulator to a commercial software package named GENSYS. An advanced longitudinal train dynamics model, a traction control system model and a wheel–rail contact model were then incorporated into the simulation. Three wear calculation models (T-gamma model, USFD model and Archard model) and two rolling contact fatigue calculation models (T-gamma-based rolling contact fatigue model and shakedown-based rolling contact fatigue model) were implemented. A train with the configuration of 1 locomotive +54 wagons +1 locomotive +54 wagons was simulated. This paper shows that the simulation method is successful and can be used for such more detailed locomotive wheel wear and rolling contact fatigue calculations. Wear and rolling contact fatigue calculation results show that the wear numbers that were calculated using the T-gamma wear model and damage indexes that were calculated using the T-gamma-based rolling contact fatigue model were similar between the leading and remote locomotives. However, wear rates that were calculated using the USFD wear model, wear volumes that were calculated using the Archard model and fatigue indexes that were calculated using the shakedown-based rolling contact fatigue model have evident differences between the leading and remote locomotives. Maximum differences in these results were about 12, 18 and 34%, respectively.

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