Towards Exascale CFD Simulations Using the Discontinuous Galerkin Solver FLEXI

Marcel Blind (), Min Gao (), Daniel Kempf (), Patrick Kopper (), Marius Kurz (), Anna Schwarz () and Andrea Beck ()
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Marcel Blind: University of Stuttgart, Institute of Aerodynamics and Gas Dynamics
Min Gao: University of Stuttgart, Institute of Aerodynamics and Gas Dynamics
Daniel Kempf: University of Stuttgart, Institute of Aerodynamics and Gas Dynamics
Patrick Kopper: University of Stuttgart, Institute of Aerodynamics and Gas Dynamics
Marius Kurz: University of Stuttgart, Institute of Aerodynamics and Gas Dynamics
Anna Schwarz: University of Stuttgart, Institute of Aerodynamics and Gas Dynamics
Andrea Beck: University of Stuttgart, Institute of Aerodynamics and Gas Dynamics

A chapter in High Performance Computing in Science and Engineering '23, 2026, pp 207-221 from Springer

Abstract: Abstract Modern high-order discretizations bear considerable potential for the exascale era due to their high fidelity and the local computational load that allows for computational efficiency in massively parallel simulations. To this end, the discontinuous Galerkin (DG) framework FLEXI was selected to demonstrate exascale readiness within the Center of Excellence for Exascale CFD (CEEC) by simulating shock buffet on a three-dimensional wing segment at transsonic flight conditions. This paper summarizes the recent progress made to enable the simulation of this challenging exascale problem. For this, it is first demonstrated that FLEXI scales excellently to over 500000 CPU cores on HAWK at the HLRS. To tackle the considerable resolution requirements near the wall, a novel wall model is proposed that takes compressibility effects into account and yields decent results for the simulation of a NACA 64A-110 airfoil. To address the shocks in the domain, a finite-volume-based shock capturing method was implemented in FLEXI, which is validated here using the simulation of a linear compressor cascade at supersonic flow conditions, where the method is demonstrated to yield efficient, robust and accurate results. Lastly, we present the TensorFlow-Fortran-Binding (TFFB) as an easy-to-use library to deploy trained machine learning models in Fortran solvers such as FLEXI.

Date: 2026
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DOI: 10.1007/978-3-031-91312-9_15

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