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Wheel-rail wear characteristics of a modern tram passing curves with small radius

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The wheel-rail wear was predicted for modern trams with independently rotating wheel-set running on a groove-shaped rail. The tram vehicle-track coupled dynamics model, considering the wheel-rail multi-point contact, was developed using software UM to evaluate the dynamic responses of the vehicle and track. Hertz contact theory, the FastSim algorithm, and Archard material wear model were used to solve the normal contact stress, tangential contact stress, and wheel-ail wear, respectively. The wheel and rail profiles were updated when the wear depth reached to 0.1 mm. In calculation, the wheel and rail wear was calculated and analyzed separately. The results of groove rail and independently rotating wheel set wear predicted by this model coincided with relevant literature, which proved the effectiveness of this model. The wheel and rail wear characteristics of modern trams passing through curved tracks with small radii and the influence of wheel-rail friction coefficient on wheel and rail wear were calculated and analyzed using this model. The results show that the wheel and rail wear on the circular curve and the rear transition curve is more intense. The wheel wear is the most intense on circular curve while the rail wear is on the rear transition curve. The wear of inner and outer rails and wheels of wheel-set one increases with the increase of wheel-rail friction coefficient while the wear of inner and outer wheels of wheel set three have no obvious change. The wear of the inner and outer rails is the most intense at the gauge corner position while is the lightest at the guard rail. The wear of wheel set one is more intense than that of wheel set three and the inner wheel wear is more intense than that of the outer wheel. The present analysis contributes to improve the life of tram wheel and rail and reduce the frequency of wheel-rail maintenance during operation.
Rocznik
Strony
67--90
Opis fizyczny
Bibliogr. 24 poz., rys., tab.,wykr.
Twórcy
  • Rail Transit and Underground Space Research Institute, Shanghai Urban Construction Design and Research Institute (Group) Co., Ltd, Shanghai, China
autor
  • Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Tongji University, Shanghai, China
autor
  • Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Tongji University, Shanghai, China
autor
  • Rail Transit and Underground Space Research Institute, Shanghai Urban Construction Design and Research Institute (Group) Co., Ltd, Shanghai, China
autor
  • Rail Transit and Underground Space Research Institute, Shanghai Urban Construction Design and Research Institute (Group) Co., Ltd, Shanghai, China
autor
  • Rail Transit and Underground Space Research Institute, Shanghai Urban Construction Design and Research Institute (Group) Co., Ltd, Shanghai, China
Bibliografia
  • 1. Zeng, Z.P., Huang, X.D., Wang, J.D., Liu, F.S., Wang, W.D., & Shuaibu, A.A. (2021). Wheel-rail stochastic dynamics and rail wear analysis of small radius curved sections of a tram line based on generalized probability density evolution. Proceedings of the Institution of Mechanical Engineers Part F-Journal of Rail and Rapid Transit, 235(5), 543-558. https://doi.org/10.1177/0954409720947533
  • 2. Suda, Y., & Michitsuji, Y. (2023). Improved curving performance using unconventional wheelset guidance design and wheel-rail interface-present and future solutions. Vehicle System Dynamics, 2199937. https://doi.org/10.1080/00423114.2023.2199937.
  • 3. Chi, M.R., Zhang, W.H., Zeng, J., Dai, H.Y., & Wu, P.B. (2008). New flexible coupled radial bogie with independently rotating wheels. Chinese Journal of Mechanical Engineering, 44(3), 9-15. https://doi.org/10.3901/JME.2008.03.009.
  • 4. Zhu, A.H., Yang, S., Li, Q., Yang, J.W., Fu, C.Z., Zhang, J., & Yao, D.C. (2019). Research on prediction of metro wheel wear based on integrated data-model-driven approach. IEEE Access, 7, 178153-178166. https://doi.org/10.1109/ACCESS.2019.2950391.
  • 5. Pradhan, S., Samantaray, A.K., & Bhattacharyya, R. (2019). Multistep wear evolution simulation method for the prediction of rail wheel wear and vehicle dynamic performance. Simulation, 95(5), 441-459. https://doi.org/10.1177/0037549718785023.
  • 6. Wang, M.Q., Jia, S.X., Chen, E.L., Yang, S.P., Liu, P.F., & Qi, Z. (2022a). Research and application of neural network for tread wear prediction and optimization. Mechanical Systems and Signal Processing, 162, 108070. https://doi.org/10.1016/j.ymssp.2021.108070.
  • 7. Li, Y.Y., Ren, Z.R., Enblom, R., Stichel, S., & Li, G.D. (2020). Wheel wear prediction on a high-speed train in China. Vehicle System Dynamics, 58(12), 1839-1858. https://doi.org/10.1080/00423114.2019.1650941.
  • 8. Chudzikiewicz, A., & Korzeb, J. (2018). Simulation study of wheels wear in low-floor tram with independently rotating wheels. Arch Appl Mech, 88, 175-192. https://doi.org/10.1007/s00419-017-1301-6.
  • 9. Wang, L., Xu, H., Yuan, H., Zhao, W.J., & Chen, X.A. (2015). Optimizing the re-profiling strategy of metro wheels based on a data-driven wear model. European Journal of Operational Research, 242, 975-986. https://doi.org/10.1016/j.ejor.2014.10.033
  • 10. Zeng, Y.C., Song, D.L., Zhang, W.H., Zhou, B., Xie, M.Y., & Tang, X. (2020). A new physics-based data-driven guideline for wear modelling and prediction of train wheels. Wear, 456-457, 203355. https://doi.org/10.1016/j.wear.2020.203355.
  • 11. Wang, S.W., Guo, H., Zhang, S.Y., Barton, D., & Brooks, P. (2022b). Analysis and prediction of double-carriage train wheel wear based on SIMPACK and neural networks. Advances in Mechanical Engineering, 14(3), 1-12. https://doi.org/10.1177/16878132221078491.
  • 12. Shebani, A., & Iwnicki, S. (2018). Prediction of wheel and rail wear under different contact conditions using artificial neural networks. Wear, 406-407, 173-184. https://doi.org/10.1016/j.wear.2018.01.007
  • 13. Wang, S.W., Yan, H., & Liu, C.X. (2018). Analysis and prediction of high-speed train wheel wear based on SIMPACK and back propagation neural networks. Expert Systems, 38, e12417. https://doi.org/10.1111/exsy.12417.
  • 14. Cai, H., Wang, Y.L., Song, C.W., Wang, T.T., & Shen, Y. (2022). Prediction of surface subsidence based on PSO-BP neural network. Journal of Physics: Conference Series, 2400, 012046. https://doi.org/10.1088/1742-6596/2400/1/012046.
  • 15. Su, K.X., Zhang, J.W., Zhang, J.W., Yan, T., & Mei, G.M. (2023). Optimisation of current collection quality of high-speed pantograph-catenary system using the combination of artificial neural network and genetic algorithm. Vehicle System Dynamics, 61(1), 260-285. https://doi.org/10.1080/00423114.2022.2045029.
  • 16. Zhao, W., Wang, C.Y., Zhang, J., & Du, L.M. (2011). Research on matching of railway wheel and rail profiles. Journal of the China Railway Society, 33(02), 34-37. https://doi.org/10.3969/j.issn.1001-8360.2011.02.006.
  • 17. Liu, S.Y., Lei, Z.Y., & Yao, X. (2024). Rail wear characteristics of small curve section in modern Tram. Traffic & Transportation, 40(02), 57-61. https://doi.org/1671-3400(2024)02-0057-05 18. Łuczak, B., Firlik, B., Staśkiewicz, T., & Sumelka, W. (2020). Numerical algorithm for predicting wheel flange wear in trams - Validation in a curved track. Proc IMechE Part F: J Rail and Rapid Transit, 234(10), 1156-1169. https://doi.org/10.1177/0954409719882807.
  • 19. Zhong, X.B., Tao, G.A., Luo, Y.Y., & Yi, X.L. (2016). Optimization method for independently rotating wheel profiles. Journal of Sichuan University of Science & Engineering (Natural Science Edition), 29(03), 33-38. https://doi.org/10.11863/j.suse.2016.03.08.
  • 20. Yang, X.W., Liu, S.T., & Hu, Y.H. (2022). Optimal design of profile of groove shaped rail of modern trams. Journal of Tongji University (Natural Science), 50(06), 891-898. https://doi.org/10.11908/j.issn.0253-374x.21392
  • 21. Wojciechowski, Ł., Gapinski, B., Firlik, B., & Mathia, T.G. (2020). Characteristics of tram wheel wear: Focus on mechanism identification and surface topography. Tribology International, 150, 106365. https://doi.org/10.1016/j.triboint.2020.106365.
  • 22. Zehetbauer, F., Edelmann, J., & Plöchl, M. (2024). Influences of tram characteristics on wheel polygonal wear evolution. Engineering Failure Analysis, 157, 107528. https://doi.org/10.1016/j.eng-failanal.2023.107528
  • 23. Yang, X.W., Liu, X.S., Shen, J.G., & Meng, W. (2019). Effect of rail cant on groove-shaped rail wear in modern tram line. Journal of Tongji University (Natural Science), 47(04), 528-534. https://doi.org/10.11908/j.issn.0253-374x.2019.04.011
  • 24. Ding, J.J., Yang, Y., Li, F., Huang, Y.H., & Jiang, K. (2017). Research on wheel wear and optimization of low-floor trams based on groove track. Journal of the China Railway Society, 39(7), 54. https://doi.org/10.3969/j.issn.1001-8360.2017.07.008.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-86569c93-23c6-4571-b968-63a59f79bb37
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