IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v38y2024i5d10.1007_s11269-024-03742-x.html
   My bibliography  Save this article

Study on Multi-Time Scale Hydrodynamic Model Based on Local Time Stepping Scheme and GPUs and its Application in Urban Inundation

Author

Listed:
  • Junhui Wang

    (Tianjin University)

  • Shaowu Li

    (Tianjin University)

  • Jingming Hou

    (Xiʼan University of Technology)

  • Ye Liu

    (Tianjin University)

  • Wenli Hu

    (Tianjin University)

  • Xueli Shi

    (Tianjin University)

  • Jiaohang Yao

    (Tianjin University)

Abstract

In view of the frequent occurrence of urban inundation, this paper establishes a multi-time scale non-uniform grid model that integrates a non-uniform grid division approach, local time stepping scheme and Graphics Processing Unit (GPU) parallel technology to improve computational efficiency. The Harten-Lax-vanLeer-Contact (HLLC) approximate Riemann solution is adopted to estimate interface fluxes, the Monotonie Upwind Scheme for Conservation Laws (MUSUL) and Runge–Kutta methods are adopted to achieve second-order accuracy in time and space. Each grid uses the time step allowed locally to update variables. The results show that the proposed model has certain accuracy and greatly improves the calculation efficiency. When the threshold for grid density is 30%-45% of the average topographic gradient change, the accuracy and the efficiency are optimized in simulation of urban inundation. At this time, the average error of inundation area is about 6.05%, and the simulated speed of the proposed model is about 4.52 times that of the traditional uniform grid model. Therefore, the proposed model has a certain promotion value in large-scale shallow water flow simulations.

Suggested Citation

  • Junhui Wang & Shaowu Li & Jingming Hou & Ye Liu & Wenli Hu & Xueli Shi & Jiaohang Yao, 2024. "Study on Multi-Time Scale Hydrodynamic Model Based on Local Time Stepping Scheme and GPUs and its Application in Urban Inundation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(5), pages 1615-1637, March.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:5:d:10.1007_s11269-024-03742-x
    DOI: 10.1007/s11269-024-03742-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-024-03742-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-024-03742-x?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.

    References listed on IDEAS

    as
    1. Mohammad Taghi Sattari & Anca Avram & Halit Apaydin & Oliviu Matei, 2023. "Evaluation of Feature Selection Methods in Estimation of Precipitation Based on Deep Learning Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(15), pages 5871-5891, December.
    2. Jingyi Gao & Osamu Murao & Xuanda Pei & Yitong Dong, 2022. "Identifying Evacuation Needs and Resources Based on Volunteered Geographic Information: A Case of the Rainstorm in July 2021, Zhengzhou, China," IJERPH, MDPI, vol. 19(23), pages 1-21, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      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:spr:waterr:v:38:y:2024:i:5:d:10.1007_s11269-024-03742-x. 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.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

      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.