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Wheel wear prediction on a high-speed train in China
KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering. Beijing Jiaotong Univ, Sch Mech Elect & Control Engn, Beijing, Peoples R China.
Beijing Jiaotong Univ, Sch Mech Elect & Control Engn, Beijing, Peoples R China..
KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering.ORCID iD: 0000-0002-3447-6686
KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering.ORCID iD: 0000-0002-8237-5847
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2019 (English)In: Vehicle System Dynamics, ISSN 0042-3114, E-ISSN 1744-5159Article in journal (Refereed) Published
Abstract [en]

The number of operating high-speed trains in China is around 2800 today and 179,200 wheels are under maintenance in one reprofiling period. To help researchers to understand the evolution of the wheel profile and improve the reprofiling strategy of the wheels, this study predicts the development of wheel profiles on a high-speed train as function of mileage and compare simulated worn wheel profiles with measured ones. The methodology includes transient multi-body dynamic simulation, wheel-rail contact calculation and wear calculation with Archard's model. Calibrated by analysing measurements of worn S1002CN profiles and performing parameters sensitivity study in the wear model, the model is then used to predict the development of a recently designed wheel profile, called S1002CN-RF. The simulation results for S1002CN and S1002CN-RF show that the predicted wheel profiles coincide with the measured ones. Wear prediction of another high-speed wheel profile (LMA) validates that the vehicle performance with respect to wear could be further improved compared to using S1002CN or S1002CN-RF. Finally, the influence of track alignment and operating speed is investigated. The wear increases with the speed increasing up to 300 km/h, but stays almost constant with a further speed increase from 300 to 400 km/h.

Place, publisher, year, edition, pages
Taylor & Francis, 2019.
Keywords [en]
High-speed train, wheel profile, wear prediction, dynamic simulation, wheel-rail contact
National Category
Vehicle Engineering
Identifiers
URN: urn:nbn:se:kth:diva-257451DOI: 10.1080/00423114.2019.1650941ISI: 000480205300001Scopus ID: 2-s2.0-85070488946OAI: oai:DiVA.org:kth-257451DiVA, id: diva2:1348469
Note

QC 20190904

Available from: 2019-09-04 Created: 2019-09-04 Last updated: 2019-10-04Bibliographically approved

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Enblom, RogerStichel, Sebastian

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