IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v440y2023ics0096300322007354.html
   My bibliography  Save this article

Novel mass-based multigrid relaxation schemes for the Stokes equations

Author

Listed:
  • He, Yunhui

Abstract

In this work, we propose three novel block-structured multigrid relaxation schemes based on distributive relaxation, Braess–Sarazin relaxation, and Uzawa relaxation, for solving the Stokes equations discretized by the marker-and-cell scheme. The mass matrix obtained from the bilinear finite element method is directly used to approximate the inverse of scalar Laplacian operator in the relaxation schemes. Using local Fourier analysis, we theoretically derive optimal smoothing factors for the resulting three relaxation schemes. Specifically, mass-based distributive relaxation, mass-based Braess–Sarazin relaxation, and mass-based σ-Uzawa relaxation have optimal smoothing factor 13, 13 and 13, respectively. These smoothing factors are smaller than those in our earlier work [1], where weighted Jacobi iteration is used for inventing Laplacian involved in the Stokes equations. The mass-based relaxation schemes do not cost more than the original ones using the Jacobi iteration. Another superiority of mass-based relaxation is that there is no need to compute the inverse of a matrix. These new relaxation schemes are appealing.

Suggested Citation

  • He, Yunhui, 2023. "Novel mass-based multigrid relaxation schemes for the Stokes equations," Applied Mathematics and Computation, Elsevier, vol. 440(C).
  • Handle: RePEc:eee:apmaco:v:440:y:2023:i:c:s0096300322007354
    DOI: 10.1016/j.amc.2022.127665
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300322007354
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2022.127665?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.

    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:eee:apmaco:v:440:y:2023:i:c:s0096300322007354. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.