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A dynamic localization model for large-eddy simulation of turbulent flows

Ghosal, Sandip, Lund, Thomas S., Moin, Parviz, Akselvoll, Knut·1995·Journal of Fluid Mechanics

Abstract

In a previous paper, Germano, et al. (1991) proposed a method for computing coefficients of subgrid-scale eddy viscosity models as a function of space and time. This procedure has the distinct advantage of being self-calibrating and requires no a priori specification of model coefficients or the use of wall damping functions. However, the original formulation contained some mathematical inconsistencies that limited the utility of the model. In particular, the applicability of the model was restricted to flows that are statistically homogeneous in at least one direction. These inconsistencies and limitations are discussed and a new formulation that rectifies them is proposed. The new formulation leads to an integral equation whose solution yields the model coefficient as a function of position and time. The method can be applied to general inhomogeneous flows and does not suffer from the mathematical inconsistencies inherent in the previous formulation. The model has been tested in isotropic turbulence and in the flow over a backward-facing step.

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Metadata

Authors
Ghosal, S., Lund, T. S., Moin, P., & Akselvoll, K.
Venue
Journal of Fluid Mechanics
Published
1995-03-10
Volume
286
Pages
229-255
ISSN
  • 0022-1120 (ISSN-L)
  • 1469-7645
OpenAlex
W2171452164
Cited by
791
References
0
FWCI
43.08
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A dynamic localization model for large-eddy simulation of turbulent flows. https://app.cochat.ai/papers/10.1017/s0022112095000711
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Ghosal, S., Lund, T. S., Moin, P., & Akselvoll, K. (1995). A dynamic localization model for large-eddy simulation of turbulent flows. Journal of Fluid Mechanics. https://doi.org/10.1017/s0022112095000711

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.