Dynamic subgrid-scale model
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== Introduction == | == Introduction == | ||
| - | The limitations of the [[Smagorinsky-Lilly model|Smagorinsky model]] have lead to the formulation of more general subgrid-scale models. Perhaps the best known of these newer models is the dynamic subgrid-scale | + | The limitations of the [[Smagorinsky-Lilly model|Smagorinsky model]] have lead to the formulation of more general subgrid-scale models. Perhaps the best known of these newer models is the dynamic subgrid-scale (DSGS) model of [[#References|Germano et al (1991)]]. The DSGS model may be viewed as a modification of the Smagorinsky model, as the dynamic model allows the Smagorinsky constant <math>C_S</math> to vary in space and time. <math>C_S</math> is calculated locally in each timestep based upon two filterings of the flow variables, which we will denote by superscript <math>r</math> and superscript <math>t</math>. These filters are called the grid filter and the test filter, respectively, and the test filter width is assumed to be larger the grid filter width. |
| - | will denote by superscript <math>r</math> and superscript <math>t</math>. These filters are called the grid filter and the test filter, respectively, and the test filter width is assumed to be larger the grid filter width. | + | |
== Original model == | == Original model == | ||
Latest revision as of 19:56, 8 May 2007
Contents |
Introduction
The limitations of the Smagorinsky model have lead to the formulation of more general subgrid-scale models. Perhaps the best known of these newer models is the dynamic subgrid-scale (DSGS) model of Germano et al (1991). The DSGS model may be viewed as a modification of the Smagorinsky model, as the dynamic model allows the Smagorinsky constant 
to vary in space and time. is calculated locally in each timestep based upon two filterings of the flow variables, which we will denote by superscript 
and superscript 
. These filters are called the grid filter and the test filter, respectively, and the test filter width is assumed to be larger the grid filter width.
Original model
Filtering with the grid filter results in the normal LES equations, with 
given by
Filtering again with the test filter results in a similar set of equations, but with a different subgrid-scale stress term, given by
where the superscript 
indicates grid filtering followed by test filtering. The two subgrid-scale stress terms are related by the Germano identity:
where
is the resolved turbulent stress. The Germano identity is used to calculate dynamic local values for by applying the Smagorinsky model to both 
and . The anisotropic part of 
is the represented as
where
may now be computed as
In practice, DSM requires stabilization. Often, this has been done by averaging
in a homogeneous direction. In cases where this is not possible, local averaging has been used in place of an average in a homogenous direction.
Alternate solution (Lilly)
Lilly (1991) proposed a least squares procedure that is generally preferred to the original calculation of :
Stabilization must also be employed here as well.
References
- Germano, M., Piomelli, U., Moin, P. and Cabot, W. H. (1991), "A Dynamic Subgrid-Scale Eddy Viscosity Model", Physics of Fluids A, Vol. 3, No. 7, pp. 1760-1765.
- Lilly, D. K. (1991), "A Proposed Modification of the Germano Subgrid-Scale Closure Method", Physics of Fluids A, Vol. 4, No. 3, pp. 633-635.
