scholarly journals Analysis of wheel/rail interaction for the development of a new tramway wheel profile

Author(s):  
T. Staskiewicz ◽  
B. Firlik
Keyword(s):  
2021 ◽  
Vol 70 ◽  
pp. 1-11
Author(s):  
Fei Liu ◽  
Lin Liang ◽  
Guanghua Xu ◽  
Jianmin Wang ◽  
Chenggang Hou

2019 ◽  
Vol 71 (2) ◽  
pp. 284-294 ◽  
Author(s):  
AiHua Zhu ◽  
Si Yang ◽  
Qiang Li ◽  
JianWei Yang ◽  
Xi Li ◽  
...  

PurposeThe 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.MethodologyBy 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.FindingsThe 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 implicationsThese 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/valueAt 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.


Author(s):  
Carlos Casanueva ◽  
Per-Anders Jönsson ◽  
Sebastian Stichel

Wheel profile evolution has a large influence on track and wheelset related maintenance costs. It influences important parameters such as equivalent conicity or contact point positioning, which will affect the dynamic behavior of the vehicle, in both tangent track and curve negotiation. High axle loads in freight wagons may increase both the wheel wear and the damage caused by vehicles with both new and already worn profiles. A common profile in Europe is the S1002 profile, developed for rail inclination 1/40. In Sweden rail inclination is 1/30, so contact conditions might not be optimal. The presented work uses Archard’s wear law to analyze the profile wear evolution in a two axle freight vehicle with Unitruck running gear on the Swedish network. This wear calculation methodology has been successfully used to predict uniform wear in passenger vehicles. First, the vehicle model has been optimized in order to improve the speed of the wear simulations. Experimental measurements of wheel profiles have been performed in order to validate the simulations. The conclusion is that the wear methodology successfully used to predict uniform wheel wear in passenger vehicles cannot be directly applied for the calculation of wheel profile evolution in high tonnage freight vehicles. The influence of block brakes or switches and crossings cannot be dismissed when calculating uniform wheel wear in these cases.


2002 ◽  
Vol 37 (sup1) ◽  
pp. 502-513 ◽  
Author(s):  
Tomas Jendel ◽  
Mats Berg

Author(s):  
Guilerme A. C. Caldeira ◽  
JoaquimAP Braga ◽  
António R. Andrade

Abstract The present paper provides a method to predict maintenance needs for the railway wheelsets by modeling the wear out affecting the wheelsets during its life cycle using survival analysis. Wear variations of wheel profiles are discretized and modelled through a censored survival approach, which is appropriate for modeling wheel profile degradation using real operation data from the condition monitoring systems that currently exist in railway companies. Several parametric distributions for the wear variations are modeled and the behavior of the selected ones is analyzed and compared with wear trajectories computed by a Monte Carlo simulation procedure. This procedure aims to test the independence of events by adding small fractions of wear to reach larger wear values. The results show that the independence of wear events is not true for all the established events, but it is confirmed for small wear values. Overall, the proposed framework is developed in such a way that the outputs can be used to support predictions in condition-based maintenance models and to optimize the maintenance of wheelsets.


Wear ◽  
2019 ◽  
Vol 438-439 ◽  
pp. 203109 ◽  
Author(s):  
Rong Chen ◽  
Jiayin Chen ◽  
Ping Wang ◽  
Jiasheng Fang ◽  
Jingmang Xu

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