Scalable multigrid algorithm for fluid dynamic shape optimization

Jose Pinzon (), Martin Siebenborn () and Andreas Vogel ()
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Jose Pinzon: University Hamburg, Department of Mathematics
Martin Siebenborn: University Hamburg, Department of Mathematics
Andreas Vogel: Ruhr University Bochum, High Performance Computing

A chapter in High Performance Computing in Science and Engineering '21, 2023, pp 481-495 from Springer

Abstract: 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-17937-2_30

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DOI: 10.1007/978-3-031-17937-2_30

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