IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-031-46870-4_27.html
   My bibliography  Save this book chapter

Geometric Constrained Scalable Algorithm for PDE-Constrained Shape Optimization

In: High Performance Computing in Science and Engineering '22

Author

Listed:
  • Jose Pinzon

    (University Hamburg, Department of Mathematics)

  • Martin Siebenborn

    (University Hamburg, Department of Mathematics)

  • Andreas Vogel

    (Ruhr University Bochum, High Performance Computing)

Abstract

In this project, the parallel performance of shape optimization schemes is investigated using varying core counts for several levels of refinement. Weak scalability results are presented for up to 49 152 cores on the national supercomputer HAWK, and the effects on performance related to a high number of cores-per-node is discussed. The algorithm developed during the project is benchmarked in an optimal design problem oriented towards fluid dynamics applications. This work is concerned with situations where it is necessary to retain certain geometric properties, such as volume and barycenter. Therefore, the optimization process solves a PDE-constrained optimization problem that incorporates the imposed geometric constraints in the descent directions. It is done by solving the corresponding minimization problem in appropriate Banach spaces. 2d and 3d results are presented where the domain needs a high number of elements to be properly defined. The system of equations consists of a very large number of degrees of freedom, thus it can only be solved efficiently via the most modern distributed-memory systems, such as HAWK at HLRS.

Suggested Citation

  • Jose Pinzon & Martin Siebenborn & Andreas Vogel, 2024. "Geometric Constrained Scalable Algorithm for PDE-Constrained Shape Optimization," Springer Books, in: Wolfgang E. Nagel & Dietmar H. Kröner & Michael M. Resch (ed.), High Performance Computing in Science and Engineering '22, pages 415-428, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-46870-4_27
    DOI: 10.1007/978-3-031-46870-4_27
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    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:spr:sprchp:978-3-031-46870-4_27. 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: 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.