Refining the parameters of Archard's wear model for calculating wear of wheels applied for 25 t per axle freight wagons on Russian railways

Саидова, А В; Orlova, Anna

doi:10.1080/00423114.2013.874564

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…In the study of the relationship, starting from different aspects, foreign scholars proposed some wear assessment criteria. Domestic scholars often use attrition power, attrition work, Heumann wear factor and Elkins wear index [22][23][24][25]. This paper chooses attrition power as assess rail wear.…”

Section: Rail Wear Assessment Methodsmentioning

confidence: 99%

Influence of heavy haul railway curve parameters on rail wear

Wang

Chen

et al. 2015

Engineering Failure Analysis

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Section: Rail Wear Assessment Methodsmentioning

confidence: 99%

Influence of heavy haul railway curve parameters on rail wear

Wang

Chen

et al. 2015

Engineering Failure Analysis

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“…Scholars from India and Poland made a simulation study of railway wheel wear with the Archard model (Pradhan, et al, 2017;Chudzikiewicz and Korzeb, 2018). Saidova and Orlova (2014) carried out a simulation of Russia wagon wheels with modified the Archard model, and performed the verification through simulating the actual track on an experimental track located in Scherbinka, Moscow. The research literature (Casanueva et al, 2014;Nielsen et al, 2016) studied the influence of switches on the heavy duty vehicle wheel wear for the Sweden tracks.…”

Section: The Archard Modelmentioning

confidence: 99%

Simulation and measurement study of metro wheel wear based on the Archard model

Zhu

Yang

et al. 2019

ILT

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Purpose The purpose of this paper is to study the wear evolution of metro wheels under the conditions of different track sequences, track composition and vehicle load and then to predict wheel wear and to guide its maintenance. Methodology By using the SIMPACK and MATLAB software, numerical simulation analysis of metro wheel wear is carried out based on Hertz theory, the FASTSIM algorithm and the Archard model. First of all, the vehicle dynamics model is established to calculate the motion relationship and external forces of wheel-rail in the SIMPACK software. Then, the normal force of wheel-rail is solved based on Hertz theory, and the tangential force of wheel-rail is calculated based on the FASTSIM algorithm through the MATLAB software. Next, in the MATLAB software, the wheel wear is calculated based on the Archard model, and a new wheel profile is obtained. Finally, the new wheel profile is re-input into the vehicle system dynamics model in the SIMPACK software to carry out cyclic calculation of wear. Findings The results show that the setting order of different curves has an obvious influence on wear when the proportion of the straight track and the curve is fixed. With the increase in running mileage, the severe wear zone is shifted from tread to flange root under the condition of the sequence-type track, but the wheel wear distribution is basically stable for the unit-type track, and their wear growth rates become closer. In the tracks with different straight-curved ratio, the more proportion the curved tracks occupy, the closer the severe wear zone is shifted to flange root. At the same time, an increase in weight of the vehicle load will aggravate the wheel wear, but it will not change the distribution of wheel wear. Compared with the measured data of one city B type metro in China, the numerical simulation results of wheel wear are nearly the same with the measured data. Practical implications These results will be helpful for metro tracks planning and can predict the trend of wheel wear, which has significant importance for the vehicle to do the repair operation. At the same time, the security risks of the vehicle are decreased economically and effectively. Originality/value At present, many scholars have studied the influence of metro tracks on wheel wear, but mainly focused on a straight line or a certain radius curve and neglected the influence of track sequence and track composition. This study is the first to examine the influence of track sequence on metro wheel wear by comparing the sequence-type track and unit-type track. The results show that the track sequence has a great influence on the wear distribution. At the same time, the influence of track composition on wheel wear is studied by comparing different straight-curve ratio tracks; therefore, wheel wear can be predicted integrally under different track conditions.

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“…By simulating actual tracks, scholars have obtained experimental wear data, conducted research on the wear coefficient k in the Jendel model using a statistical method, and developed wear coefficient probability graphs applicable to specific track and wheel materials in recent years [31], [32]. These graphs are more detailed compared to the diagram showing the wear coefficient distribution determined by statistics using the Jendel model.…”

Section: Jendel Wear Modelmentioning

confidence: 99%

Research on Prediction of Metro Wheel Wear Based on Integrated Data-Model-Driven Approach

Zhu

Yang

et al. 2019

IEEE Access

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Existing research on wheel wear prediction uses either data-driven or model-based methods. However, due to the high reliability and limited sample characteristics of metro wheel wear, data-driven methods are not accurate enough and require relatively high data costs, and model-based methods mainly lack verification with measured data and generalization ability. To address the shortcomings of the two types of methods, a new approach combining model-based and data-driven methods is used to predict wheel wear in this paper. First, the least-squares algorithm is used to analyze and calculate the difference between the wear measurement for a specific running mileage and the corresponding simulated wear, with the minimum difference taken as an objective function. By means of optimization algorithms including Genetic Algorithm, Particle Swarm Optimization, Tabu Search and Simulated Annealing, the wear coefficient k in Jendel wear model is optimized, thereby obtaining an optimized Jendel wear model. Later, metro wheel wear for additional running mileage is simulated and predicted through combined application of the vehicle system dynamics, wheel-rail contact, and optimized Jendel wear models. Finally, the paper analyzes the wear prediction results obtained by the integrated data-model-driven approach and compares them with the results of traditional methods and measured data. The results suggest that the integrated data-model-driven approach effectively reduces the uncertainty in selecting the wear coefficient by experience, lowers the experimental data costs, and improves the wear prediction accuracy. Therefore, it is a promising approach to wheel wear prediction. INDEX TERMS Metro wheel, wear prediction, data-model driven, optimization algorithms, Jendel wear model.

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Refining the parameters of Archard's wear model for calculating wear of wheels applied for 25 t per axle freight wagons on Russian railways

Cited by 6 publications

References 4 publications

Influence of heavy haul railway curve parameters on rail wear

Influence of heavy haul railway curve parameters on rail wear

Simulation and measurement study of metro wheel wear based on the Archard model

Research on Prediction of Metro Wheel Wear Based on Integrated Data-Model-Driven Approach

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