Simulation Of Wheel Wear Of Metro Vehicle Considering Stochastic Parameters

In order to meet the increasing passenger flow,the operating speed of metro train and the vehicle load are continuously increased,and the wheel–rail relationship gradually deteriorates,which leads to abnormal wear of metro wheels.Compared with field test and experimental research,using the wheel wear prediction model to study the metro wheel wear has the advantages of less workload,low cost,and short cycle.Numerical simulation of wheel wear is a very complex process,and the model parameters have a certain influence on the simulation results.In the existing research,the model parameters are mostly set to the design values,however,the actual parameters will change during the construction and operation of the track.Therefore,it is necessary to consider the effect of stochastic parameters on wheel wear prediction.Based on the established metro vehicle wheel wear prediction model,this thesis investigates the influence of stochastic changes of track parameters on wheel wear prediction,and compares the differences in wheel wear prediction of different loading methods of vehicle load and different track modeling methods.The main work and conclusions of this thesis are as follows.(1)Based on the multi-body dynamics software SIMPACK,a metro wheel wear prediction model is established,which includes a vehicle dynamic model,a local contact model based on Hertzian theory and FASTSIM algorithm,a wear model based on the Tγ/A-wear rate function,and a smoothing and updating strategy.After the Tγ/A-wear rate function is corrected by the measured wheel wear,the predicted results of the wheel wear model are consistent with the measurement results,which verifies the accuracy of the wheel wear prediction model.(2)The normal distribution covering various standard deviations and the method of statistical actual proportion are used to simulate the distribution of different track stochastic parameters including rail cant,track gauge,curved track superelevation,and coefficient of friction at wheel–rail interface.The influence of different track parameters wtih different dispersion degree on metro wheel wear prediction is investigated.With an increase of the dispersion degree of the rail cant(1: X,X ranging from 10 to 70),two wear peaks gradually appear in the wheel flange on the outer rail side,the wear peak value near the flange is larger,the wheel wear on the inner rail side is closer to the field site of the tread,and the wear depth gradually decreases.It should be considered in the simulation of wheel wear when the standard deviation of the distribution of rail cant exceeds 10.The stochastic change of the track gauge(ranging between 1430 mm and 1440 mm)has little effect on the wheel flange wear depth,the maximum tread wear depth will decrease with the increase of the degree of dispersion of the gauge,and the wear distribution range slightly increases,the random change of gauge has little effect on the wheel wear simulation results of the entire metro line.The wheel wear simulation results obtained by using the three different superelevation setting methods have no significant difference.For the metro line investigated in this work,the different superelevation setting methods has little effect on wheel wear prediction.The maximum wheel flange wear depth will decrease with the increase of the dispersion degree of coefficient of friction at wheel–rail interface.Under the same coefficient of friction(ranging between 0.2 and 0.6)of the outer and inner rails,it should be considered in the wheel wear simulation when the standard deviation of the distribution of friction of coefficient is greater than 0.075.The stochastic variation of friction of coefficient(ranging between 0.1 and 0.3)has little effect on wheel wear prediction when the outer rail is lubricated.(3)Based on the obtained real-time passenger flow data,a piecewise function of vehicle load varying with operating time is established.The difference of wheel wear prediction in different loading methods(constant load and variable load)of vehicle loadis compared.The results show that in the first 25,000 km of operation mileage,the wheel wear range and wear depth obtained by the simulation of the variable vehicle load condition and the constant vehicle load condition are basically the same.With the increase of the operation mileage,the maximum wheel flange wear depth obtained by the simulation of the variable load condition is greater than that of the constant load condition,but the difference doesn’t change greatly with the increase of the operation mileage.After running for 30,000 km,the maximum wheel flange wear depth and wheel wear area for the variable load condition are 14.8% and4.4%larger on average than that in the constant load condition,respectively.The calculation efficiency of the constant load condition is 17.8 times that of the variable load condition.It is suggested to use the constant load method when the vehicle load is set.(4)The differences of wheel wear prediction results,modeling complexity,and the calculation efficiency using different track modeling methods and different wear update limits are compared.The results show that compared with the line equivalent method,the entire line simulation method is more complex,but can better simulate the actual track.There is little difference between the wear prediction results of the two track modeling methods,and both of them are in a good agreement with the measurement results.The calculated efficiency of the line equivalent model is 3.2 times that of the entire line simulation model.It is suggested to use the line equivalent method,which can give the consideration for calculation accuracy and efficiency.The wear update limit has a great influence on the prediction results of the wheel flange wear depth and the flange thickness.Thus,it is suggested to set it to about 0.1 mm.