An efficient parallel algorithm for DNS of buoyancy-driven turbulent flows
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作者:
Yi-zhao Zhang
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机构:Shanghai University,Shanghai Key Laboratory of Mechanics in Energy Engineering, Shanghai Institute of Applied Mathematics and Mechanics, School of Mechanics and Engineering Science
Yi-zhao Zhang
Shu-ning Xia
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机构:Shanghai University,Shanghai Key Laboratory of Mechanics in Energy Engineering, Shanghai Institute of Applied Mathematics and Mechanics, School of Mechanics and Engineering Science
Shu-ning Xia
Yu-hong Dong
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机构:Shanghai University,Shanghai Key Laboratory of Mechanics in Energy Engineering, Shanghai Institute of Applied Mathematics and Mechanics, School of Mechanics and Engineering Science
Yu-hong Dong
Bo-fu Wang
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机构:Shanghai University,Shanghai Key Laboratory of Mechanics in Energy Engineering, Shanghai Institute of Applied Mathematics and Mechanics, School of Mechanics and Engineering Science
Bo-fu Wang
Quan Zhou
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机构:Shanghai University,Shanghai Key Laboratory of Mechanics in Energy Engineering, Shanghai Institute of Applied Mathematics and Mechanics, School of Mechanics and Engineering Science
Quan Zhou
机构:
[1] Shanghai University,Shanghai Key Laboratory of Mechanics in Energy Engineering, Shanghai Institute of Applied Mathematics and Mechanics, School of Mechanics and Engineering Science
Direct numerical simulation (DNS);
parallel algorithm;
turbulent flows;
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摘要:
In this paper, an explicit low-storage simplified M — stage Runge-Kutta (SRK) scheme for high Reynolds-number incompressible flows is presented. In the SRK scheme, the Poisson equation is solved only once in the final substage of each time step. By taking advantage of the SRK scheme and the advanced hybrid MPI+MPI model, we have developed an efficient parallel solver for buoyancy-driven turbulent flow. The spatial and temporal accuracies of the solver are validated with Taylor-Green vortex flow. Both the RK and SRK schemes are implemented for the simulation of turbulent Rayleigh-Bénard convection as well as Rayleigh-Taylor flow. The results show that the SRK scheme can save approximately 20% of the computation time.
机构:
UPMC Univ Paris 06, Sorbonne Univ, CNRS, UMR 7190,Inst Jean Le Rond dAlembert, F-75005 Paris, FranceUniv Lyon, CNRS, Ecole Cent Lyon, LMFA,INSA,UCBL, Ecully, France