Scalability of OpenFOAM Density-Based Solver with Runge–Kutta Temporal Discretization Scheme

Compressible density-based solvers are widely used in OpenFOAM, and the parallel scalability of these solvers is crucial for large-scale simulations. In this paper, we report our experiences with the scalability of OpenFOAM’s native rhoCentralFoam solver, and by making a small number of modification...

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Main Authors: Sibo Li, Roberto Paoli, Michael D’Mello
Format: Article
Language:English
Published: Hindawi Limited 2020-01-01
Series:Scientific Programming
Online Access:http://dx.doi.org/10.1155/2020/9083620
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spelling doaj-cb363ac3071649bc93ea79b43422d86f2021-07-02T12:31:57ZengHindawi LimitedScientific Programming1058-92441875-919X2020-01-01202010.1155/2020/90836209083620Scalability of OpenFOAM Density-Based Solver with Runge–Kutta Temporal Discretization SchemeSibo Li0Roberto Paoli1Michael D’Mello2Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, USADepartment of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, USAIntel Corporation, Schaumburg, IL, USACompressible density-based solvers are widely used in OpenFOAM, and the parallel scalability of these solvers is crucial for large-scale simulations. In this paper, we report our experiences with the scalability of OpenFOAM’s native rhoCentralFoam solver, and by making a small number of modifications to it, we show the degree to which the scalability of the solver can be improved. The main modification made is to replace the first-order accurate Euler scheme in rhoCentralFoam with a third-order accurate, four-stage Runge-Kutta or RK4 scheme for the time integration. The scaling test we used is the transonic flow over the ONERA M6 wing. This is a common validation test for compressible flows solvers in aerospace and other engineering applications. Numerical experiments show that our modified solver, referred to as rhoCentralRK4Foam, for the same spatial discretization, achieves as much as a 123.2% improvement in scalability over the rhoCentralFoam solver. As expected, the better time resolution of the Runge–Kutta scheme makes it more suitable for unsteady problems such as the Taylor–Green vortex decay where the new solver showed a 50% decrease in the overall time-to-solution compared to rhoCentralFoam to get to the final solution with the same numerical accuracy. Finally, the improved scalability can be traced to the improvement of the computation to communication ratio obtained by substituting the RK4 scheme in place of the Euler scheme. All numerical tests were conducted on a Cray XC40 parallel system, Theta, at Argonne National Laboratory.http://dx.doi.org/10.1155/2020/9083620
collection DOAJ
language English
format Article
sources DOAJ
author Sibo Li
Roberto Paoli
Michael D’Mello
spellingShingle Sibo Li
Roberto Paoli
Michael D’Mello
Scalability of OpenFOAM Density-Based Solver with Runge–Kutta Temporal Discretization Scheme
Scientific Programming
author_facet Sibo Li
Roberto Paoli
Michael D’Mello
author_sort Sibo Li
title Scalability of OpenFOAM Density-Based Solver with Runge–Kutta Temporal Discretization Scheme
title_short Scalability of OpenFOAM Density-Based Solver with Runge–Kutta Temporal Discretization Scheme
title_full Scalability of OpenFOAM Density-Based Solver with Runge–Kutta Temporal Discretization Scheme
title_fullStr Scalability of OpenFOAM Density-Based Solver with Runge–Kutta Temporal Discretization Scheme
title_full_unstemmed Scalability of OpenFOAM Density-Based Solver with Runge–Kutta Temporal Discretization Scheme
title_sort scalability of openfoam density-based solver with runge–kutta temporal discretization scheme
publisher Hindawi Limited
series Scientific Programming
issn 1058-9244
1875-919X
publishDate 2020-01-01
description Compressible density-based solvers are widely used in OpenFOAM, and the parallel scalability of these solvers is crucial for large-scale simulations. In this paper, we report our experiences with the scalability of OpenFOAM’s native rhoCentralFoam solver, and by making a small number of modifications to it, we show the degree to which the scalability of the solver can be improved. The main modification made is to replace the first-order accurate Euler scheme in rhoCentralFoam with a third-order accurate, four-stage Runge-Kutta or RK4 scheme for the time integration. The scaling test we used is the transonic flow over the ONERA M6 wing. This is a common validation test for compressible flows solvers in aerospace and other engineering applications. Numerical experiments show that our modified solver, referred to as rhoCentralRK4Foam, for the same spatial discretization, achieves as much as a 123.2% improvement in scalability over the rhoCentralFoam solver. As expected, the better time resolution of the Runge–Kutta scheme makes it more suitable for unsteady problems such as the Taylor–Green vortex decay where the new solver showed a 50% decrease in the overall time-to-solution compared to rhoCentralFoam to get to the final solution with the same numerical accuracy. Finally, the improved scalability can be traced to the improvement of the computation to communication ratio obtained by substituting the RK4 scheme in place of the Euler scheme. All numerical tests were conducted on a Cray XC40 parallel system, Theta, at Argonne National Laboratory.
url http://dx.doi.org/10.1155/2020/9083620
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