Abstract
The reduction of wheel-rail wear is a fundamental task in railway engineering that significantly affects the operating performance in the lifecycle. To improve the dynamic response and profile wear evolution performance of wheel-rail interaction, a shape optimization procedure for the railway wheel profile is proposed. First, the geometry modeling method, which ensures the continuity of first-order derivation of the wheel profile, is introduced to generate a large number of candidate profiles, and multibody dynamics simulation is conducted to analyze the dynamics response of the wheel profiles, including wear index, lateral force, lateral acceleration of the frame and derailment coefficient. Then, the Kriging model is constructed to establish the relationship between the design variables and objectives obtained by multibody dynamics simulation, and particle swarm optimization (PSO) is employed to evaluate the optimal parameters for wheel profile that simultaneously considers wheel wear, stability, and lateral force. Finally, the performance of the wheel-rail interaction is evaluated to demonstrate the effectiveness of the proposed method. The numerical simulation result indicates that the optimized wheel profile not only has good performance, including contact state, pressure, and friction at the design stage, but also the physical performance is acceptable after a long-term profile evolution during service, which the maximum wear depth of the optimal wheel profile averagely decreases over 10 % in long-term wear evolution.
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References
D. Cui, R. Wang, P. Allen, B. An, L. Li and Z. Wen, Multiobjective optimization of electric multiple unit wheel profile from wheel flange wear viewpoint, Structural and Multidisciplinary Optimization, 59(1) (2019) 279–289.
D. Chen, G. Sun, L. Xing, Y. Wu and G. Shen, A grinding profile design method for rails in the switch area considering a representative set of wheel profiles. J. of Mechanical Science and Technology, 37 (10) (2023) 4923–4933.
H. Y. Choi, D. H. Lee and J. Lee, Optimization of a railway wheel profile to minimize flange wear and surface fatigue, Wear, 300(1–2) (2013) 225–233.
J. Shi, Y. Gao, X. Long and Y. Wang, Optimizing rail profiles to improve metro vehicle-rail dynamic performance considering worn wheel profiles and curved tracks, Structural and Multidisciplinary Optimization, 63(1) (2021) 419–438.
S. Zakharov, I. Goryacheva, V. Bogdanov, D. Pogorelov, I. Zharov, V. Yazykov, E. Torskaya and S. Soshenkov, Problems with wheel and rail profiles selection and optimization, Wear, 265(9–10) (2008) 1266–1272.
M. Novales, A. Orro and M. R. Bugarín, Use of a genetic algorithm to optimize wheel profile geometry, Proceedings of the Institution of Mechanical Engineers, Part F: J. of Rail and Rapid Transit, 221(4) (2007) 467–476.
D. Cui, H. Wang, L. Li and X. Jin, Optimal design of wheel profiles for high-speed trains, Proceedings of the Institution of Mechanical Engineers, Part F: J. of Rail and Rapid Transit, 229(3) (2015) 248–261.
V. L. Markine, I. Y. Shevtsov and C. Esveld, An inverse shape design method for railway wheel profiles, Structural and Multidisciplinary Optimization, 33(3) (2007) 243–253.
I. Y. Shevtsov, V. L. Markine and C. Esveld, Design of railway wheel profile taking into account rolling contact fatigue and wear, Wear, 265(9–10) (2008) 1273–1282.
H. Jahed, B. Farshi, M. A. Eshraghi and A. A. Nasr, Numerical optimization technique for design of wheel profiles, Wear, 264(1–2) (2008) 1–10.
G. Shen, J. B. Ayasse, H. Chollet and I. Pratt, A unique design method for wheel profiles by considering the contact angle function, Proceedings of the Institution of Mechanical Engineers, Part F: J. of Rail and Rapid Transit, 217(1) (2003) 25–30.
D. Ren, G. Tao, Z. Wen and X. Jin, Wheel profile optimisation for mitigating flange wear on metro wheels and verification through wear prediction, Vehicle System Dynamics, 59(12) (2021) 1894–1915.
O. Polach, Wheel profile design for target conicity and wide tread wear spreading, Wear, 271(1–2) (2011) 195–202.
L. Li, D. Cui and X. Jin, State of arts of the study on railway wheel profile optimization, J. of Southwest Jiaotong University, 44(1) (2009) 810–816.
D. Cui, L. Li, X. Jin and X. Li, Optimal design of wheel profiles based on weighed wheel/rail gap, Wear, 271(1–2) (2011) 218–226.
J. Zhang, Z. Wen and L. Sun, Wheel profile design based on rail profile expansion method, Chinese J. of Mechanical Engineering, 44(3) (2008) 44–49.
Y. Qi, H. Dai, P. Wu, F. Gan and Y. Ye, RSFT-RBF-PSO: a railway wheel profile optimisation procedure and its application to a metro vehicle, Vehicle System Dynamics, 60(10) (2021) 1–21.
F. Lin, S. Zhou, X. Dong, Q. Xiao, H. Zhang, W. Hu and L. Ke, Design method of LM thin flange wheel profile based on NURBS, Vehicle System Dynamics, 59(1) (2021) 17–32.
Y. Ye, J. Vuitton, Y. Sun and M. Hecht, Railway wheel profile fine-tuning system for profile recommendation, Railway Engineering Science, 29(1) (2021) 74–93.
D. Cui, H. Wang, L. Li and X. Jin, Optimal design of wheel profiles for high-speed trains, Proceedings of the Institution of Mechanical Engineers, Part F: J. of Rail and Rapid Transit, 229(3) (2015) 248–261.
Y. Ye, Y. Qi, D. Shi, Y. Sun, Y. Zhou and M. Hecht, Rotary-scaling fine-tuning (RSFT) method for optimizing railway wheel profiles and its application to a locomotive, Railway Engineering Science, 28 (2020) 160–183.
Y. Ye, Y. Sun, S. Dongfang, D. Shi and M. Hecht, Optimizing wheel profiles and suspensions for railway vehicles operating on specific lines to reduce wheel wear: a case study, Multibody System Dynamics, 51 (2021) 91–122.
H. Yamashita, C. Feldmeier, Y. Yamazaki, T. Kato, T. Fujimoto, O. Kondo and H. Sugiyama, Wheel profile optimization procedure to minimize flange wear considering profile wear evolution, Proceedings of the Institution of Mechanical Engineers, Part F: J. of Rail and Rapid Transit, 236(6) (2022) 672–683.
B. Liu, T. X. Mei and S. Bruni, Design and optimisation of wheel–rail profiles for adhesion improvement, Vehicle System Dynamics, 54(3) (2016) 429–444.
W. Ren, L. Li, D. Cui and G. Chen, An improved parallel inverse design method of EMU wheel profile from wheel flange wear viewpoint, Shock and Vibration, 2021 (2021) 1–15.
F. Lin, X. Dong, Y. Wang and C. Ni, Multiobjective optimization of CRH3 EMU wheel profile, Advances in Mechanical Engineering, 7(1) (2015) 284043.
R. Chen, C. Hu, J. Xu, P. Wang, J. Chen and Y. Gao, An innovative and efficient method for reverse design of wheel-rail profiles, Applied Sciences, 8(2) (2018) 239.
B. Echard, N. Gayton and M. Lemaire, AK-MCS: an active learning reliability method combining Kriging and monte carlo simulation, Structural Safety, 33(2) (2011) 145–154.
Y. Feng, L. Xin, J. Hao, N. Ding and F. Wang, Numerical simulation of long-span bridge response under downburst: parameter optimization using a surrogate model, Mathematics, 11 (2023) 1–23.
Y. Gao and X. Wang, An effective warpage optimization method in injection molding based on the Kriging model, The International J. of Advanced Manufacturing Technology, 37(9) (2008) 953–960.
W. Li, R. Yang, Q. Qi, Q. Dong and G. Zhao, A novel structural reliability method based on active Kriging and weighted sampling, J. of Mechanical Science and Technology, 35(6) (2021) 2459–2469.
L. Bo, Z. Zhang, Y. Liu, S. Yang, Y. Wang, Y. Wang and X. Zhang, Research on path planning method of solid backfilling and pushing mechanism based on adaptive genetic particle swarm optimization, Mathematics, 12(3) (2024) 1–27.
A. Onat and P. Voltr, Particle swarm optimization based parametrization of adhesion and creep force models for simulation and modelling of railway vehicle systems with traction, Simulation Modelling Practice and Theory, 99 (2020) 102026.
B. Nautiyal, R. Prakash, V. Vimal, G. Liang and H. Chen, Improved salp swarm algorithm with mutation schemes for solving global optimization and engineering problems, Engineering with Computers, 38 (2022) 3927–3949.
GB/T 5599-2019, Specification for Dynamic Performance Assessment and Testing Verification of Rolling Stock, The Standardization Administration of the People’s Republic of China, China (2019).
S. Akıncıoğlu, Taguchi optimization of multiple performance characteristics in the electrical discharge machining of the TiGr2, Facta Universitatis, Series: Mechanical Engineering, 20(2) (2022) 237–253.
F. Wu, H. Lian, G. Pei, B. Guo and Z. Wang, Design and optimization of the variable-density lattice structure based on load paths, Facta Universitatis, Series: Mechanical Engineering, 21(2) (2023) 273–292.
L. Liu, B. Yi, T. C. Wang, Z. Z. Li, J. Zhang and G. H. Yoon, Investigation on numerical analysis and mechanics experiments for topology optimization of functionally graded lattice structure, Additive Manufacturing, 47 (2021) 1–8.
B. Safaei, E. C. Onyibo, M. Goren, K. Kotrasova, Z. Yang, S. Arman and M. Asmael, Free vibration investigation on RVE of proposed honeycomb sandwich beam and material selection optimization, Facta Universitatis, Series: Mechanical Engineering, 21(1) (2023) 31–50.
Acknowledgments
This work was supported by the National Natural Science Foundation of China, grant number 51975589, Zhejiang Provincial Natural Science Foundation of China, grant number LY21E050008, Ningbo Key research and development Program, grant number 2023Z134, and the first author was funded by China Scholarship Council.
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Bing Yi is an Associate Professor of School of Traffic and Transportation Engineering, Central South University, Changsha, China. He received his Ph.D. in Mechanical Engineering from Zhejiang University. He was a research fellow in the Department of Mechanical Engineering at University of Michigan from 2017 to 2019. His research interests include railway engineering, shape and topology optimization, and virtual reality.
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Liu, L., Yi, B., Shi, X. et al. Kriging-PSO-based shape optimization for railway wheel profile. J Mech Sci Technol 38, 4921–4932 (2024). https://doi.org/10.1007/s12206-024-0827-0
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DOI: https://doi.org/10.1007/s12206-024-0827-0