Railway wheel profile fine-tuning system for profile recommendation

Abstract

This paper develops a wheel profile fine-tuning system (WPFTS) that comprehensively considers the influence of wheel profile on wheel damage, vehicle stability, vehicle safety, and passenger comfort. WPFTS can recommend one or more optimized wheel profiles according to train operators’ needs, e.g., reducing wheel wear, mitigating the development of wheel out-of-roundness (OOR), improving the shape stability of the wheel profile. Specifically, WPFTS includes four modules: (I) a wheel profile generation module based on the rotary-scaling fine-tuning (RSFT) method; (II) a multi-objective generation module consisting of a rigid multi-body dynamics simulation (MBS) model, an analytical model, and a rigid–flexible MBS model, for generating 11 objectives related to wheel damage, vehicle stability, vehicle safety, and passenger comfort; (III) a weight assignment module consisting of an adaptive weight assignment strategy and a manual weight assignment strategy; and (IV) an optimization module based on radial basis function (RBF) and particle swarm optimization (PSO). Finally, three cases are introduced to show how WPTFS recommends a wheel profile according to train operators’ needs. Among them, a wheel profile with high shape stability, a wheel profile for mitigating the development of wheel OOR, and a wheel profile considering hunting stability and derailment safety are developed, respectively.

References

[1.] 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. Railw Eng Sci 2020, 28 2 160-183

[2.] Shen G, Ayasse JB, Chollet H, Pratt I. A unique design method for wheel profiles by considering the contact angle function. Proc Inst Mech Eng Part F J Rail Rapid Transit 2003, 217 1 25-30

[3.] Shevtsov I, Markine V, Esveld C. Optimal design of wheel profile for railway vehicles. Wear 2005, 258 7–8 1022-1030

[4.] Jahed H, Farshi B, Eshraghi MA, Nasr A. A numerical optimization technique for design of wheel profiles. Wear 2008, 264 1–2 1-10

[5.] Polach O. Wheel profile design for target conicity and wide tread wear spreading. Wear 2011, 271 1–2 195-202

[6.] Cui D, Li L, Jin X, Li X. Optimal design of wheel profiles based on weighed wheel/rail gap. Wear 2011, 271 1–2 218-226

[7.] Shevtsov I, Markine V, Esveld C. Design of railway wheel profile taking into account rolling contact fatigue and wear. Wear 2008, 265 9–10 1273-1282

[8.] Spangenberg U, Fröhling RD, Els PS. Long-term wear and rolling contact fatigue behaviour of a conformal wheel profile designed for large radius curves. Veh Syst Dyn 2018, 57 1 44-63

[9.] Persson I, Iwnicki SD. Optimisation of railway profiles using a genetic algorithm. Veh Syst Dyn 2004, 41 517-527

[10.] 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 1-16

[11.] Firlik B, Staśkiewicz T, Jaśkowski W, Wittenbeck L. Optimisation of a tram wheel profile using a biologically inspired algorithm. Wear 2019, 430–431 12-24

[12.] Ye Y, Sun Y, Dongfang S, Shi D, Hecht M. Optimizing wheel profiles and suspensions for railway vehicles operating on specific lines to reduce wheel wear: a case study. Multibody Syst Dyn 2020, 51 91-122

[13.] Polach O, Nicklisch D. Wheel/rail contact geometry parameters in regard to vehicle behaviour and their alteration with wear. Wear 2016, 366–367 200-208

[14.] BS EN 15313 (2016) Railway applications. In-service wheelset operation requirements. In-service and off-vehicle wheelset maintenance

[15.] Iwnick S. Manchester benchmarks for rail vehicle simulation 1998 London Taylor & Francis Group

[16.] DIN EN 13674-1 (2017) Railway applications—Track—Rail—Part 1: Vignole railway rails 46 kg/m and above (includes Amendment A1:2017)

[17.] EN 13715:2006 (2006) Railway applications—wheelsets and bogies—wheels —Wheels tread, CEN Brussels, February 2006

[18.] Lewis R, Dwyer-Joyce RS. Wear mechanisms and transitions in railway wheel steels. Proc Inst Mech Eng Part J J Eng Tribol 2004, 218 6 467-478

[19.] Kabo E, Ekberg A, Torstensson PT, Vernersson T. Rolling contact fatigue prediction for rails and comparisons with test rig results. Proc Inst Mech Eng Part F J Rail Rapid Transit 2010, 224 4 303-317

[20.] Choi H-Y, Lee D-H, Lee J. Optimization of a railway wheel profile to minimize flange wear and surface fatigue. Wear 2013, 300 1–2 225-233

[21.] UIC Code 518 OR (2009) Testing and approval of railway vehicle from the point of view of their dynamic behaviour—safety—track fatigue—running behavior, International Union of Railway

[22.] EN 12299 (2009) Railway applications-ride comfort for passengers-measurements and evaluation. Brussels: CEN

[23.] Polach O. On non-linear methods of bogie stability assessment using computer simulations. Proc Inst Mech Eng Part F J Rail Rapid Transit 2006, 220 1 13-27

[24.] Vuitton J (2018) Nichtlineare Fahrwerkdynamik an der Stabilitätsgrenze. Dissertation, Technische Universität, Berlin

[25.] King F (2009) The Hilbert transform of waveforms and signal processing. In: Hilbert Transforms (Encyclopedia of Mathematics and its Applications, pp 119–181. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511735271.005.

[26.] Polach O (2020) Contact distribution: criteria for assessing and optimizing wheel/rail profile pairing. In: International rail vehicle conference, Dresden, pp 45–47. http://polach.ch/publikationen.php?lang=de

[27.] Wang K. The track of wheel contact points and the calculation of wheel/rail geometric contact parameters. J Southwest Jiaotong Univ 1984, 19 1 88-99(in Chinese)

[28.] Tao G, Wen Z, Jin X, Yang X. Polygonisation of railway wheels: a critical review. Railw Eng Sci 2020, 28 4 317-345

[29.] Zhai W, Jin X, Wen Z, Zhao X. Wear problems of high-speed wheel/rail systems: observations, causes, and countermeasures in China. Appl Mech Rev 2020, 72 6 060801

[30.] Kalousek J, Johnson KL. An investigation of short pitch wheel and rail corrugations on the Vancouver mass transit system. Proc Inst Mech Eng Part F J Rail Rapid Transit 1992, 206 2 127-135

[31.] Morys B. Enlargement of out-of-round wheel profiles on high speed trains. J Sound Vib 1999, 227 5 965-978

[32.] Tao G, Xie C, Wang H, Yang X, Ding C, Wen Z. An investigation into the mechanism of high-order polygonal wear of metro train wheels and its mitigation measures. Veh Syst Dyn 2020

[33.] Peng B, Iwnicki S, Shackleton P, Zhao Y, Cui D (2005) A practical method for simulating the evolution of railway wheel polygonalization. In: Proceedings of the 25th international symposium on dynamics of vehicles on roads and tracks (IAVSD 2017), 2017 August 14–18; Rockhampton, Queensland, Australia, pp 753–758

[34.] Qu S, Zhu B, Zeng J, Dai H, Wu P. Experimental investigation for wheel polygonisation of high-speed trains. Veh Syst Dyn 2020

[35.] Ma C, Gao L, Cui R, Xin T. The initiation mechanism and distribution rule of wheel high-order polygonal wear on high-speed railway. Eng Fail Anal 2021, 119 104937

[36.] Ye Y, Shi D, Krause P, Tian Q, Hecht M. Wheel flat can cause or exacerbate wheel polygonization. Veh Syst Dyn 2019, 58 10 1575-1604

[37.] Ye Y, Shi D, Poveda-Reyes S, Hecht M. Quantification of the influence of rolling stock failures on track deterioration. J Zhejiang Univ Sci A 2020, 21 10 783-798

[38.] Ye Y, Sun Y. Reducing wheel wear from the perspective of rail track layout optimization. Proc Inst Mech Eng Part K J Multi-body Dyn 2020

[39.] Ye Y, Shi D, Krause P, Hecht M. A data-driven method for estimating wheel flat length. Veh Syst Dyn 2019, 58 9 1-19

[40.] Pandey M, Regis RG, Datta R, Bhattacharya B. Surrogate-assisted multi-objective optimization of the dynamic response of a freight wagon fitted with three-piece bogies. Int J Rail Transp 2020

[41.] Schwenker F, Kestler HA, Palm G. Three learning phases for radial-basis-function networks. Neural Netw 2001, 14 4–5 439-458

[42.] Faris H, Aljarah I, Mirjalili S. Evolving radial basis function networks using Moth-Flame Optimizer. Handb Neural Comput 2017

[43.] Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks 4:1942–1948. doi:https://doi.org/10.1109/ICNN.1995.488968

[44.] Cui D, Wang R, Allen P, An B, Li L, Wen Z. Multi-objective optimization of electric multiple unit wheel profile from wheel flange wear viewpoint. Struct Multidiscip Optim 2018, 59 1 279-289

[45.] Roshanian J, Ebrahimi M. Latin hypercube sampling applied to reliability-based multidisciplinary design optimization of a launch vehicle. Aerosp Sci Technol 2013, 28 1 297-304

[46.] Jendel T. Prediction of wheel profile wear-comparisons with field measurements. Wear 2002, 253 1–2 89-99

[47.] Barbarino G (2004) A fast and reliable mathematical model for the prediction of railway wheel wear. Dissertation, Politecnico di Milano

[48.] Ye Y, Sun Y, Shi D, Peng B, Hecht M (2021) A wheel wear prediction model of non-Hertzian wheel-rail contact considering wheelset yaw: comparison between simulated and field test results, Wear. https://doi.org/10.1016/j.wear.2021.203715

[49.] TB/T 449-2016 (2016) Wheel profile for locomotive and car (Chinese Standard in English), Issued by National Railway Administration

[50.] Götz G (2018) Model validation in rail vehicle technology for driving approval. Berlin: Technical University of Berlin

Funding

China Scholarship Council(201707000113)