High-order accurate entropy-stable discontinuous collocated Galerkin methods with the summation-by-parts property for compressible CFD frameworks: Scalable SSDC algorithms and flow solver

This work reports on the performances of a fully-discrete hp-adaptive entropy stable discontinuous collocated Galerkin method for the compressible Naiver-Stokes equations. The resulting code framework is denoted by SSDC, the first S for entropy, the second for stable, and DC for discontinuous collocated. The method is endowed with the summation-by-parts property, allows for arbitrary spatial and temporal order, and is implemented in an unstructured high performance solver. The considered class of fully-discrete algorithms are systematically designed with mimetic and structure preserving properties that allow the transfer of continuous proofs to the fully discrete setting. Our goal is to provide numerical evidence of the adequacy and maturity of these high-order methods as potential base schemes for the next generation of unstructured computational fluid dynamics tools. We provide a series of test cases of increased difficulty, ranging from non-smooth to turbulent flows, in order to evaluate the numerical performance of the algorithms. Results on weak and strong scaling of the distributed memory implementation demonstrate that the parallel SSDC solver can scale efficiently over 100,000 processes. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

This study presents a fully-discrete hp-adaptive entropy stable discontinuous collocated Galerkin method for the compressible Navier-Stokes equations, utilizing the SSDC framework. The method demonstrates high-order numerical performance and systematic design, showcasing its potential as a base scheme for future unstructured computational fluid dynamics tools. Results indicate efficient scaling of the parallel SSDC solver over 100,000 processes.