Dissertation
Profile wear prediction of railroad wheels and its integration with profile optimization considering wear and surface damage evolution
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Autumn 2019
DOI: 10.17077/etd.005193
Abstract
Wear and rolling contact fatigue (RCF) of railroad wheels are known to be the main causes of profile degradation, which has a significant impact on not only the railroad safety, but also on the maintenance and operation costs. Slight changes in the profile geometry due to wear lead to substantial changes in the contact geometry characteristics between the wheel and rail, thereby altering the vehicle performance as well as the derailment safety. Furthermore, RCF is known to be strongly correlated with profile wear evolution. Large amounts of wear create a grinding effect that slows down the surface crack propagation. Thus, a balanced mitigation of profile wear and RCF damage is critically important from the maintenance and operation perspectives. Although many wheel profile optimization procedures have been proposed to mitigate wheel profile wear and RCF damages, optimization is conducted for only the initial profile and the history-dependent contact geometry characteristics due to profile wear evolution are neglected. Therefore, this thesis proposes a new wheel profile optimization procedure considering the wear and RCF damage evolutions in order to address the limitations of the existing wheel profile optimization approaches and to effectively mitigate wheel profile degradations.
To this end, a wear simulation capability that can be integrated with multibody railroad vehicle dynamics simulation algorithms is developed first by considering multi-point wheel-rail contact exhibited for worn profiles. It is validated against experimentally measured profiles obtained by the two-roller and scaled wheel wear tests. It is shown that worn profile shape as well as the magnitude of the wear-induced material loss predicted by the simulation agree well with the test results for the two-roller test condition. On the other hand, in the scaled wheel wear test, a severe flange contact leads to much higher wear rate than that obtained using the two-roller test, while the wear rate for the tread contact adheres to that of the two-roller test. Thus, using the flange wear rate identified in this study, it is shown that the flange wear is predicted accurately using the proposed numerical procedure. The wear prediction capability is further validated against full-scale vehicle test data. For this purpose, a detailed multibody freight vehicle model is developed, and then the wear rates are estimated using the measured material loss data as well as the wheel/rail contact data obtained by the simulation model. The worn wheel profiles predicted by the proposed wear simulation capability agree well with the test data, and the wear prediction capability is justified for use in vehicle maintenance as well as profile optimization.
Finally, a new wheel profile optimization procedure considering the wear and RCF damage evolution is developed by incorporating the proposed wear simulation capability in the profile optimization framework, and the comparative performance evaluation of the optimized profiles is presented. The wear optimized profiles using the proposed optimization procedure allow for the wear reduction to be maintained over an extended traveling distance, while the existing Tγ-based approach utilizing only initial profile for optimization does not make the same claim and only limited improvement for wear mitigation is achieved. This demonstrates the importance of considering wear evolution (i.e., history-dependent contact geometry characteristics) in the profile optimization for wear mitigation. Furthermore, to achieve a balanced mitigation of the profile wear and RCF damage, the objective function is defined by a sum of the normalized wear and damage with a weighting parameter. It is shown that this approach allows for lowering the wear-induced material loss as well as the damage accumulation simultaneously and effectively over an extended traveling distance.
Details
- Title: Subtitle
- Profile wear prediction of railroad wheels and its integration with profile optimization considering wear and surface damage evolution
- Creators
- Christofer Feldmeier
- Contributors
- Hiroyuki Sugiyama (Advisor)Jia Lu (Committee Member)Salam F. Rahmatalla (Committee Member)Shaoping Xiao (Committee Member)Hiroki Yamashita (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Mechanical Engineering
- Date degree season
- Autumn 2019
- DOI
- 10.17077/etd.005193
- Publisher
- University of Iowa
- Number of pages
- xi, 99 pages
- Copyright
- Copyright 2019 Christofer Feldmeier
- Comment
- This thesis has been optimized for improved web viewing. If you require the original version, contact the University Archives at the University of Iowa: https://www.lib.uiowa.edu/sc/contact/
- Language
- English
- Description illustrations
- color illustrations
- Description bibliographic
- Includes bibliographical references (pages 96-99).
- Public Abstract (ETD)
Wear and rolling contact fatigue (RCF) of railroad wheels are known to be the main causes of profile degradation, which has a significant impact on not only the railroad safety, but also on the maintenance and operation costs. Slight changes in the profile geometry due to wear lead to substantial changes in the contact geometry characteristics between the wheel and rail, thereby altering the vehicle performance as well as the derailment safety. Furthermore, large amounts of wear create a grinding effect that slows down the surface crack propagation. Thus, a balanced mitigation of profile wear and RCF damage is critically important from the maintenance and operation perspectives. Although many wheel profile optimization procedures have been proposed to mitigate wheel profile wear and RCF damages, optimization is conducted for only the initial profile and the history-dependent contact geometry characteristics due to profile wear evolution are neglected. Therefore, this thesis proposes a new wheel profile optimization procedure considering the wear and RCF damage evolutions in order to address the limitations of the existing wheel profile optimization approaches and to effectively mitigate wheel profile degradations.
To this end, a wear simulation capability that can be integrated with multibody railroad vehicle dynamics simulation algorithms is developed by considering multi-point wheel-rail contact exhibited for worn profiles. It is rigorously validated against the two-roller and scaled wheel wear test data first and then the full-scale vehicle test data for use in vehicle maintenance as well as profile optimization. A new wheel profile optimization procedure considering the wear and RCF damage evolution is developed by incorporating the proposed wear simulation capability in the profile optimization framework. It is shown that the proposed approach allows for lowering the wear-induced material loss as well as the damage accumulation simultaneously and effectively over an extended traveling distance.
- Academic Unit
- Mechanical Engineering
- Record Identifier
- 9983779697902771
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