Assessing the fast non-Hertzian methods based on the simulation of wheel-rail rolling contact and wear distribution

被引:21
|
作者
An, Boyang [1 ,2 ]
Ma, Daolin [3 ]
Wang, Ping [1 ,2 ]
Zhou, Jiayi [1 ,2 ]
Chen, Rong [1 ,2 ]
Xu, Jingmang [1 ,2 ]
Cui, Dabin [4 ]
机构
[1] Southwest Jiaotong Univ, MOE Key Lab High Speed Railway Engn, Chengdu, Peoples R China
[2] Southwest Jiaotong Univ, Sch Civil Engn, Chengdu, Peoples R China
[3] MIT, Dept Mech Engn, Boston, MA USA
[4] Southwest Jiaotong Univ, Sch Mech Engn, Chengdu, Peoples R China
基金
中国国家自然科学基金;
关键词
Contact mechanics; tribology; wear; non-Hertzian method; MODEL; PREDICTION; VALIDATION; MECHANICS;
D O I
10.1177/0954409719848592
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
This paper aims at assessing several fast non-Hertzian methods, coupled with two wear models, based on the wheel-rail rolling contact and wear prediction. Four contact models, namely Kik-Piotrowski's method, Linder's method, Ayasse-Chollet's STRIPES algorithm and Sichani's ANALYN algorithm are employed for comparing the normal contact. For their tangential modelling, two tangential algorithms, i.e. FASTSIM and FaStrip, are used. Two commonly used wear models, namely the Archard (extended at the KTH Royal Institute of Technology) and USFD (developed by the University of Sheffield based on T-gamma approach), are further utilized for wear distribution computation. All results predicted by the fast non-Hertzian methods are evaluated against the results of Kalker's CONTACT code using penetration as the input. Since the two wear models adopt different expressions for calculating the wear performance, the attention of this paper is on assessing which one is more suitable for the fast non-Hertzian methods to utilize. The comparison shows that the combination of the USFD wear model with any of the fast non-Hertzian methods agrees better with CONTACT+USFD. In general, ANALYN+FaStrip is the best solution for the simulation of the wheel-rail rolling contact, while STRIPES+FASTSIM can provide better accuracy for the maximum wear depth prediction using the USFD wear model.
引用
收藏
页码:524 / 537
页数:14
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