Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
The grinding of curved rails is a crucial aspect of subway maintenance and repair. It effectively reduces wear. This paper proposes a multi-objective optimization design method for grinding profiles in curved sections. First, the Dynamic Time Regularisation (DTW) algorithm selects representative grinding profiles as the initial population. Then, the optimal design region is determined through wear characterization analysis. Mathematical expressions of wheel profiles are selected as design variables to build a parametric model. Next, the predictive model that considers the evolution of wheel wear is incorporated into the multi-objective function. The objective function's adaptive weight adjustment coefficient factors are introduced to establish the multi-objective optimization model for wheel profiles. The Latin hypercubic sampling method establishes the RBF agent model for simulation calculation. The optimization design of wheel profiles is carried out using the TS-NSGA-II multi-objective algorithm. Finally, a comparative verification analysis is conducted to assess the profiles before and after optimization. This analysis includes three key aspects: wheel wear evolution analysis, wheel-rail static contact analysis, and vehicle dynamics performance analysis.
Czasopismo
Rocznik
Tom
Strony
art. no. 191472
Opis fizyczny
Bibliogr. 29 poz., rys., tab., wykr.
Twórcy
autor
- East China Jiaotong University, China
- Jiangxi Industrial Polytechnic College, China
autor
- East China Jiaotong University, China
- Pingxiang University, China
autor
- East China Jiaotong University, China
Bibliografia
- 1. Lin Q, Guo J, Wang H, Wang W,Liu Q. Optimal design of rail grinding patterns based on a rail grinding target profile. Proc Inst Mech Eng Part F J Rail Rapid Transi 2018; 232(2): 560-571, https://doi.org/10.1177/0954409716679447.
- 2. Li D, Wei K, Chen R, Xu Y. Analysis of Heavy Hual Railway Wear on Wheel/Rail Contact. Geometry ICTE 2015; 64-69, https://doi.org/10.1061/9780784479384.009.
- 3. Wang P, Gao L, Xin T, Cai X, Xiao H. Study on the numerical optimization of rail profiles for heavy haul railways. Proc Inst Mech Eng Part F J Rail Rapid Transit 2017; 231(6): 649-665, https://doi.org/10.1177/0954409716635685.
- 4. Zeng, W, Yang Y, Qiu W, Xie H, Xie S. Optimization of the target profile for asymmetrical rail grinding in sharp-radius curves for high-speed railways. Adv Mech Eng 2017; 9: 1687814016687196, https://doi.org/10.1177/1687814016687196.
- 5. Liang X, Zheng M. Estimation of rail vertical profile using an h-infinity based optimization with learning. ASME/IEEE Joint Rail Conference. American Society of Mechanical Engineers, 2019; 58523: V001T01A007, https://doi.org/10.1115/JRC2019-1266.
- 6. Huang K,Wu J, Yang X, Gao Z, Liu F, Zhu Y. Discrete Train Speed Profile Optimization for Urban Rail Transit: A Data-Driven Model and Integrated Algorithms Based on Machine Learning. Journal of Advanced Transportation 2019; 2019, https://doi.org/10.1155/2019/7258986.
- 7. Ye Y, Qi Y, Shi D, Sun Y, Zhou Y, Hecht M. Rotary-scaling Fine-tuning (RSFT) Method for Optimizing Railway Wheel Profiles and Its Application to A Locomotive. Railway Engineering Science 2020; 28: 160-183, https://doi.org/10.1007/s40534-020-00212-z.
- 8. Jiang H, Gao L. Optimizing The Rail Profile for High-Speed Railways Based on Artificial Neural Network and Genetic Algorithm Coupled Method. Sustainability 2020; 12(2): 658, https://doi.org/10.3390/su12020658.
- 9. de Paula Pacheco PA, Endlich CS, Vieira KLS, Reis T, dos Santos GFM, dos Santos Júnior AA. Optimization of Heavy Haul Railway Wheel Profile Based on Rolling Contact Fatigue and Wear Performance. Wear 2023; 522: 204704, https://doi.org/10.1016/j.wear.2023.204704.
- 10. Qi Y, Dai H, Gan F, Sang H. Optimization of Rail Profile Design for High-speed Lines Based on Gaussian Function Correction Method. Proc Inst Mech Eng Part F J Rail Rapid Transit 2023; 237(9): 1119-1129, https://doi.org/10.1177/09544097231152564.
- 11. Lin F, Zou L, Yang Y, Shi Z. Design method of worn rail grinding profile based on Frechet distance method. Proc Inst Mech Eng Part F J Rail Rapid Transit 2022; 236(8): 936-949, https://doi.org/10.1177/09544097211049650.
- 12. Shi J, Gao Y, Long X, Wang Y. Optimizing rail profiles to improve metro vehicle-rail dynamic performance considering worn wheel profiles and curved tracks. Structural and Multidisciplinary Optimization 2021; 63: 419-438. https://doi.org/10.1007/s00158-020-02680-7.
- 13. Zhai W, Gao J, Liu P, Wang K. Reducing rail side wear on heavyhaul railway curves based on wheel–rail dynamic interaction. Veh Sys Dyn 2014; 52(sup1): 440-454, https://doi.org/10.1080/00423114.2014.906633.
- 14. Mao X, Shen G. An inverse design method for rail grinding profiles. Veh Sys Dyn 2017; 55(7): 1029-1044, https://doi.org/10.1080/00423114.2014.906633.
- 15. Wang J, Chen S, Li X, Wu Y. Optimal rail profile design for a curved segment of a heavy haul railway using a response surface approach. Proc Inst Mech Eng Part F J Rail Rapid Transit 2016; 230(6): 1496-1508, https://doi.org/10.1177/0954409715602513.
- 16. Choi H, Lee D, Song C, Lee J. Optimization of rail profile to reduce wear on curved track. International Journal of Precision Engineering and Manufacturing 2013; 14: 619-625, https://doi.org/10.1007/s12541-013-0083-1.
- 17. Xu K, Feng Z, Wu H, Xu D, Li F, Shao C. Optimal profile design for rail grinding based on wheel–rail contact, stability, and wear development in high-speed electric multiple units. Proc Inst Mech Eng Part F J Rail Rapid Transit 2020; 234(6): 666-677, https://doi.org/10.1177/0954409719854576.
- 18. Liu B, Mei T, Bruni S. Design and optimization of wheel–rail profiles for adhesion improvement. Veh Sys Dyn 2016; 54(3): 429-444, https://doi.org/10.1080/00423114.2015.1137958.
- 19. Qi Y, Dai H, Wu P, Gan F, Ye Y. RSFT-RBF-PSO: a railway wheel profile optimization procedure and its application to a metro vehicle. Veh Sys Dyn 2021; 60(10): 3398-3418, https://doi.org/10.1080/00423114.2021.1955135.
- 20. Ding J, Lewis R, Beagles A, Wang J. Application of grinding to reduce rail side wear in straight track. Wear 2018; 402: 71-79, https://doi.org/10.1016/j.wear.2018.02.001.
- 21. Ren D, Tao G, Wen Z, Jin X. Wheel profile optimization for mitigating flange wear on metro wheels and verification through wear prediction. Veh Syst Dyn 2020; 59(12): 1894-1915, https://doi.org/10.1080/00423114.2020.1798472.
- 22. Ye Y, Vuitton J, Sun Y, Hecht M. Railway wheel profile Fine-Tuning system for profile recommendation. Railway Engineering Science 2021; 29: 74-93, https://doi.org/10.1007/s40534-021-00234-1.
- 23. Lin F, Zhou S, Dong X, Xiao Q, Zhang H, Hu W. Design method of LM thin flange wheel profile based on NURBS. Veh Syst Dyn 2021; 59(1): 17-32, https://doi.org/10.1080/00423114.2019.1657908.
- 24. Han T, Peng Q, Zhu Z, Shen Y, Huang H, Abid N. A pattern representation of stock time series based on DTW. Physica A: Statistical Mechanics and its Applications 2020; 550: 124161, https://doi.org/10.1016/j.physa.2020.124161.
- 25. Braghin F, Lewis R, Dwyer-Joyce R, Bruni S. A mathematical model to predict railway wheel profile evolution due to wear. Wear 2006; 261(11-12): 1253-1264, https://doi.org/10.1016/j.wear.2006.03.025.
- 26. Li J, Tao G, Liu X, Liang H, Wen Z. Comparative analysis of metro wheel wear prediction based on two trackmodeling methods. Engineering Mechanics 2022; 39(6): 226-235, https://doi.org/10.6052/j.issn.1000-4750.2021.02.0117.
- 27. Dong J, Li Y, Wang M. Fast multi-objective antenna optimization based on RBF neural network surrogate model optimized by improved PSO algorithm. Applied Sciences 2019; 9(13): 2589, https://doi.org/10.3390/app9132589.
- 28. VERMA S, PANT M, SNASEL V. A comprehensive review on NSGA-Ⅱ for multi-objective combinatorial optimization problems. IEEE access 2021; 9: 57757-57791, https://doi.org/10.1109/ACCESS.2021.3070634.
- 29. GB5599-2019. Specification for dynamic performance evaluation and test appraisal of railway vehicles. Ministry of Railways of the People’s Republic of China, 2019.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1fa4c7c3-1e16-4635-bd85-4ae23e2cd2bb
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