Literature DB >> 31633074

Log-layer mismatch and modeling of the fluctuating wall stress in wall-modeled large-eddy simulations.

Xiang I A Yang1, George Ilhwan Park1, Parviz Moin1.   

Abstract

Log-layer mismatch refers to a chronic problem found in wall-modeled large-eddy simulation (WMLES) or detached-eddy simulation, where the modeled wall-shear stress deviates from the true one by approximately 15%. Many efforts have been made to resolve this mismatch. The often-usedfixes, which are generally ad hoc, include modifying subgridscale stress models, adding a stochastic forcing, and moving the LES-wall-model matching location away from the wall. An analysis motivated by the integral wall-model formalism suggests that log-layer mismatch is resolved by the built-in physics-based temporal filtering. In this work we investigate in detail the effects of local filtering on log-layer mismatch. We show that both local temporal filtering and local wall-parallel filtering resolve log-layer mismatch without moving the LES-wall-model matching location away from the wall. Additionally, we look into the momentum balance in the near-wall region to provide an alternative explanation of how LLM occurs, which does not necessarily rely on the numerical-error argument. While filtering resolves log-layer mismatch, the quality of the wall-shear stress fluctuations predicted by WMLES does not improve with our remedy. The wall-shear stress fluctuations are highly underpredicted due to the implied use of LES filtering. However, good agreement can be found when the WMLES data are compared to the direct numerical simulation data filtered at the corresponding WMLES resolutions.

Year:  2017        PMID: 31633074      PMCID: PMC6800687          DOI: 10.1103/PhysRevFluids.2.104601

Source DB:  PubMed          Journal:  Phys Rev Fluids            Impact factor:   2.537


  1 in total

1.  Predictive model for wall-bounded turbulent flow.

Authors:  I Marusic; R Mathis; N Hutchins
Journal:  Science       Date:  2010-07-09       Impact factor: 47.728

  1 in total
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1.  Scientific multi-agent reinforcement learning for wall-models of turbulent flows.

Authors:  H Jane Bae; Petros Koumoutsakos
Journal:  Nat Commun       Date:  2022-03-17       Impact factor: 14.919

  1 in total

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