On the use of a multigrid-reduction-in-time algorithm for multiscale convergence of turbulence simulations

Journal Article · 06 May 2023 · Computers and Fluids

DOI: https://doi.org/10.1016/j.compfluid.2023.105910 · OSTI ID:2007265

Guzik, Stephen M.  ^[1]; Christopher, Joshua  ^[1]; Walters, Sean ^[1]; Gao, Xinfeng ^[1]; Schroder, Jacob B. ^[2]; Falgout, Robert D.  ^[3]

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1. Colorado State Univ., Fort Collins, CO (United States)
2. Univ. of New Mexico, Albuquerque, NM (United States)
3. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Simulations of turbulent flow present challenges in terms of accuracy and affordability on modern highly-parallel computer architectures. A multigrid-reduction-in-time algorithm is used to provide a framework for separately evolving different scales of turbulence and for parallelizing the temporal domain, thereby increasing the concurrency. It is hypothesized that the space–time locality of the small scales of turbulence can be used to circumvent difficulties in applying temporal multigrid to flows dominated by inertial physics. For algorithms that fall well short of spectral accuracy (fourth-order is used in this work) attention must be paid to the accuracy of features on scales transferred between multigrid levels. Numerical experiments were performed using implicit large-eddy simulation. Results from applying the approach to an infinite-Reynolds number Taylor–Green flow and a double-shear flow at a Reynolds number of 11650 provide strong evidence that the approach has merit. The multigrid-reduction-in-time framework can be used to parallelize the temporal domain of a high-Reynolds-number turbulent flow and permit independent convergence of different scales. Establishing this foundation allows for future research in reducing the wall-clock time to solve turbulent flows while retaining the same accuracy as sequential solvers. In conclusion, current performance results from parallelizing the temporal domain are not competitive with those from sequential-in-time methods.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC52-07NA27344; B643713; B647699
• OSTI ID: 2007265
• Report Number(s): LLNL-JRNL-827388; 1042482
• Journal Information: Computers and Fluids, Vol. 261; ISSN 0045-7930
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English