IDEAS home Printed from https://ideas.repec.org/a/taf/tjrtxx/v11y2023i6p811-832.html
   My bibliography  Save this article

Prediction of rail profile evolution on metro curved tracks: wear model and validation

Author

Listed:
  • Bingguang Wen
  • Shenghua Wang
  • Gongquan Tao
  • Jiaxin Li
  • Dexiang Ren
  • Zefeng Wen

Abstract

Rail side wear is typically encountered in metro lines (particularly on sharp curves). A reliable rail wear prediction model to evaluate the evolution of the rail profile should be developed. In this study, a rail wear prediction model for a metro line is established. The vehicle system dynamics model is implemented in the SIMPACK software, which considers the actual operating conditions of the metro line. The results show that using the original Tγ/A wear rate function to calculate rail wear may yield some deviations, which necessitates a modification of the wear model based on the measured results of rail wear. Because the hardness of high and low rails on curved tracks changes after work hardening, different correction coefficients must be adopted for the wear rate function of each rail. The rail wear evolution and worn profiles are consistent with the field measurement results when using a modified wear function.

Suggested Citation

  • Bingguang Wen & Shenghua Wang & Gongquan Tao & Jiaxin Li & Dexiang Ren & Zefeng Wen, 2023. "Prediction of rail profile evolution on metro curved tracks: wear model and validation," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 11(6), pages 811-832, November.
    • Handle: RePEc:taf:tjrtxx:v:11:y:2023:i:6:p:811-832
    DOI: 10.1080/23248378.2022.2113923
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/23248378.2022.2113923
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/23248378.2022.2113923?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tjrtxx:v:11:y:2023:i:6:p:811-832. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tjrt20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.