Wheel-rail contact and friction models: A review of recent advances

Fang, Chong; Jaafar, Sulaiman A; Zhou, Wei; Yan, Hongkai; Meng, Xianghui

doi:10.1177/09544097231156730

Cited by 10 publications

(2 citation statements)

References 134 publications

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“…For rail vehicles, the destruction of the adhesion between the wheel and rail is the basic reason of wheel slip/idling [4]. One of the reasons for the traction and braking forces of wheelsets is the presence of contact and friction between the wheel and rail [5].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Based Speed Control with Creep Rate Constraints for Autonomous Driving of Mining Electric Locomotives

Li,

Zhu,

2024

Applied Sciences

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The working environment of mining electric locomotives is wet and muddy coal mine roadway. Due to low friction between the wheel and rail and insufficient utilization of creep rate, there may be idling or slipping between the wheels and rails of mining electric locomotives. Therefore, it is necessary to control the creep rate within a reasonable range. In this paper, the autonomous control algorithm for mining electric locomotives based on improved ε-greedy is theoretically proven to be convergent and effective firstly. Secondly, after analyzing the contact state between the wheel and rail under wet and slippery road conditions, it is concluded that the value of creep rate is an important factor affecting the autonomous driving of mining electric locomotives. Therefore, the autonomous control method for mining electric locomotives based on creep control is proposed in this paper. Finally, the effectiveness of the proposed method is verified through simulation. The problem of wheel slipping and idling caused by insufficient friction of mining electric locomotives in coal mining environments is effectively suppressed. Autonomous operation of vehicles with optimal driving efficiency can be achieved through quantitative control and utilization of the creep rate between wheels and rails.

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Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Based Speed Control with Creep Rate Constraints for Autonomous Driving of Mining Electric Locomotives

Li,

Zhu,

2024

Applied Sciences

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“…Moreover, severe wear easily leads the rail into a rolling contact fatigue zone, which further aggravates the wheel-rail response and poses a potential threat to the vehicle operational safety (Silva et al , 2023). In addition, the railroad system is in the open line environment, wheel-rail contact will inevitably be affected by the pollution of the third medium and the weather conditions (Khan et al , 2022; Ma et al , 2021; Fang et al , 2023). Therefore, the friction coefficient is often in a non-stationary state.…”

Section: Introductionmentioning

confidence: 99%

Influence of wheel profile wear coupled with friction coefficient on the dynamic response of subway vehicles

2023

ILT

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Purpose The purpose of this study is to match appropriate friction coefficients for subway operational vehicles, considering the dynamic variations of wheel profile wear. Design/methodology/approach This study combines experimental testing and numerical simulation to investigate the influence of wheel profile wear coupled with the friction coefficient on the vehicle dynamic response. Findings For the test route in this paper, it is recommended to control the friction coefficient on straight sections between 0.25 and 0.3, and on curved sections between 0.2 and 0.3. This satisfies the required adhesion coefficient for normal train traction and braking, while also ensuring the straight running performance and curve negotiation performance of the vehicle. Practical implications Reasonable friction coefficient ranges are proposed for straight and curved track lines to improve the operational safety and economy of the vehicles. Moreover, this study can provide a theoretical basis and reference direction for developing anti-wear measures for rail vehicles operating on fixed routes. Originality/value Considering the wear characteristics of operating vehicles and the dynamic changes in the wear profile, this paper explores the adaptability of different degrees of wheel wear profiles to different friction coefficients. Based on the response characteristics of vehicle dynamics, reasonable lubrication recommendations are proposed for this operating vehicle.

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DEM modelling of surface indentations caused by granular materials: application to wheel–rail sanding

Suhr,

Skipper,

Lewis

et al. 2024

Comp. Part. Mech.

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The presented surface indentation model is one step towards building a DEM model for wheel–rail sanding. In railways, so-called low-adhesion conditions can cause problems in traction and braking, and sanding is used to overcome this problem. Sand grains are blasted towards wheel–rail contact, fracture repeatedly as they enter the nip and are drawn into the contact and then increase adhesion. Research on this topic has mostly been experimental, but focussed on adhesion enhancement measurement. Thus, physical mechanisms increasing the adhesion are not well understood. Previous works involved experiments and DEM modelling of single sand grain crushing tests under realistic wheel–rail contact pressures of 900 MPa, focusing on sand fragment spread and formation of clusters of solidified fragments. In the experiments, indents in the compressing steel plates were also observed, which are also observed on wheel and rail surfaces in railway operation. These are now modelled by adapting an existing surface indentation model from literature to the case of surface indentations caused by granular materials. Two test cases are studied, and experimental spherical indentation tests for model parametrisation are presented. In a proof of concept, the mentioned single sand grain crushing tests under 900 MPa pressure are simulated including the surface indentation model. This work contributes to DEM modelling of wheel–rail sanding, which is believed to be a good approach to deepen the understanding of adhesion increasing mechanisms under sanded conditions.

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Wheel-rail contact and friction models: A review of recent advances

Cited by 10 publications

References 134 publications

Reinforcement Learning Based Speed Control with Creep Rate Constraints for Autonomous Driving of Mining Electric Locomotives

Reinforcement Learning Based Speed Control with Creep Rate Constraints for Autonomous Driving of Mining Electric Locomotives

Influence of wheel profile wear coupled with friction coefficient on the dynamic response of subway vehicles

DEM modelling of surface indentations caused by granular materials: application to wheel–rail sanding

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