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Scalable multigrid algorithm for fluid dynamic shape optimization

In: High Performance Computing in Science and Engineering '21

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

We investigate a parallel approach for the shape optimization of an obstacle in an incompressible Navier–Stokes flow. For this purpose, we used a self-adapting nonlinear extension equation within the method of mappings, which links a boundary control to a mesh deformation. It is demonstrated how the approach preserves mesh quality and allows for large deformations by controlling nonlinearity in critical regions of the domain. Special focus is given to reference configurations, where the transformation has to remove and create obstacle boundary singularities, which is particularly relevant for the employed multigrid discretizations. As benchmark problems, we demonstrate the aerodynamic drag optimization in 2d and 3d configurations. The efficiency of the algorithm is demonstrated in weak scalability studies performed on the supercomputer HPE Hawk for up to 5,120 cores.

Suggested Citation

  • Jose Pinzon & Martin Siebenborn & Andreas Vogel, 2023. "Scalable multigrid algorithm for fluid dynamic shape optimization," Springer Books, in: Wolfgang E. Nagel & Dietmar H. Kröner & Michael M. Resch (ed.), High Performance Computing in Science and Engineering '21, pages 481-495, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-17937-2_30
    DOI: 10.1007/978-3-031-17937-2_30
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