Investigating the Influence of Wheel Wear on Vehicle Dynamic Behavior by Introducing Yaw and Track Irregularity
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Date
2023-02
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Addis Ababa University
Abstract
This research intends to indicate the influence of wheel wear due to yaw and track irregularity on vehicle dynamic behavior demonstrated in terms of waer depth, derailment coefficient and ride index. Software simulation-based and wear data validation was used. A Hertzian contact model was used for the wheel-rail contact while SIMPACK and in house made MATLAB code of Archard wear model were used for multibody modeling and analysis of wear depth of each wheel. Main influencing factors involved in the analysis were curvature radius, speed, yaw, and track irregularities variations which were demonstrated in terms of wear depth and vehicle dynamic behavior expressed in derailment coefficient and ride index. A 1 km track long specified curve radius, speed, and yaw damper values were devised for the analysis process with a wear factor of 10,000 applied for the wear depth calculation.
Based on the analysis, re-profiling analysis was performed till 60,000km with 10,000km interval data extraction and wear depth was found to match close to the values from wear data collected from AALRT. Further case studies also confirmed that 50m radius of curvature resulted the highest wear depth (6.18mm) and higher derailment coefficient (1.01) and unconfortable despite the existence of yaw damper or track irregularity. Yaw at lower running speeds on the other hand didn’t affect much the ride index while it improved the derailment coefficient. But when speed becomes higher and its value increased, wear depth reduced and both ride index and derailment coefficient deteriorated. Finally, track irregularity alone brought only slight disturbance to the derailment coefficient and ride index. But it has significant increase to both values when combined with factors like yaw. As a result, measures shall be taken to minimize the combined effect of yaw and track irregularity.
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Keywords
Vehicle dynamic modeling, Archard wear model, wear depth, derailment coefficient, ride index, re-profiling, wear data.