Smagorinsky SGS model in OpenFOAM

Last Updated: May 4, 2019

The Smagorinsky subgrid scale (SGS) model was developed by Joseph Smagorinsky in the meteorological community in the 1960s. It is based on the eddy viscosity assumption, which postulates a linear relationship between the SGS shear stress and the resolved rate of strain tensor. This model serves as a base for other SGS models.

OpenFOAM Version: OpenFOAM-dev, OpenFOAM-1606+

Implementation in OpenFOAM

The Smagorinsky SGS model is the oldest and best known subgrid scale model. The subgrid scale stress tensor \(\tau_{ij}\) is
\begin{align}
\tau_{ij} &= \overline{u_{i}u_{j}} – \overline{u}_{i}\overline{u}_{j} \tag{1a}\\
&= \frac{1}{3} \tau_{kk} \delta_{ij} + \left( \tau_{ij} \; – \frac{1}{3} \tau_{kk} \delta_{ij} \right) \tag{1b} \label{eq:smg2}\\
&\approx \frac{1}{3} \tau_{kk} \delta_{ij} – 2 \nu_{sgs} dev(\overline{D})_{ij} \tag{1c} \label{eq:smg3}\\
&= \frac{2}{3} k_{sgs} \delta_{ij} – 2 \nu_{sgs} dev(\overline{D})_{ij}, \tag{1d} \label{eq:smg4}
\end{align}
where \(\nu_{sgs}\) is the subgrid scale eddy viscosity and the resolved-scale strain rate tensor \(\overline{D}_{ij}\) is defined as
\begin{equation}
\overline{D}_{ij} = \frac{1}{2} \left( \frac{\partial \overline{u}_{i}}{\partial x_{j}} + \frac{\partial \overline{u}_{j}}{\partial x_{i}}\right), \tag{2} \label{eq:D}
\end{equation}
and the subgrid scale kinetic energy \(k_{sgs}\) is
\begin{equation}
k_{sgs} = \frac{1}{2} \tau_{kk} = \frac{1}{2} \left( \overline{u_{k}u_{k}} – \overline{u}_{k}\overline{u}_{k} \right). \tag{3} \label{eq:ksgs}
\end{equation}
The subgrid scale stress tensor \(\tau_{ij}\) is split into an isotropic part \(\frac{1}{3} \tau_{kk} \delta_{ij}\) and an anisotropic part \(\tau_{ij} \; – \frac{1}{3} \tau_{kk} \delta_{ij}\) in Eq. \eqref{eq:smg2}. I think \(dev(\overline{D})\) is used instead of \(\overline{D}\) because the anisotropic part is a traceless tensor in Eq. \eqref{eq:smg3}.

Assumption 1: Eddy viscosity approximation
In analogy with the molecular viscous stress in laminar flows, the anisotropic part is approximated by relating it to the resolved rate of strain tensor \(\overline{D}_{ij}\) as in Eq. \eqref{eq:smg3}
\begin{equation}
\tau_{ij} \; – \frac{1}{3} \tau_{kk} \delta_{ij} \approx \; – 2 \nu_{sgs} dev(\overline{D})_{ij}. \tag{1c}
\end{equation}
In OpenFOAM, the subgrid scale viscosity is computed as
\begin{equation}
\nu_{sgs} = C_{k} \Delta \sqrt{k_{sgs}}, \tag{4} \label{eq:nusgs}
\end{equation}
where \(C_{k}\) is a model constant whose default value is \(0.094\) and \(\Delta\) is the grid size that defines the subgrid length scale. The method for calculating \(\Delta\) is specified in the turbulenceProperties file and the available options are as follows:

  • cubeRootVol
  • maxDeltaxyz
  • maxDeltaxyzCubeRoot
  • smooth
  • vanDriest
  • Prandtl
  • IDDESDelta

For the Smagorinsky SGS model, the vanDriest option is the first choice.

Our remaining task is to evaluate the distribution of subgrid scale kinetic energy \(k_{sgs}\).

Assumption 2: Local equilibrium
The SGS kinetic energy \(k_{sgs}\) is computed with the assumption of the balance between the subgrid scale energy production and dissipation (local equilibrium)
\begin{equation}
\overline{D} : \tau_{ij} + C_{\epsilon} \frac{k_{sgs}^{1.5}}{\Delta} = 0, \tag{5} \label{eq:equilibrium}
\end{equation}
where the operator : is a double inner product of two second-rank tensors that can be evaluated as the sum of the 9 products of the tensor components. We can compute \(k_{sgs}\) by solving Eq. \eqref{eq:equilibrium} as shown below:
\begin{align}
&\overline{D} : \left( \frac{2}{3} k_{sgs} I -2 \nu_{sgs} dev(\overline{D}) \right) + C_{\epsilon} \frac{k_{sgs}^{1.5}}{\Delta} = 0 \\
\Leftrightarrow &\quad \overline{D} : \left( \frac{2}{3} k_{sgs} I -2 C_{k} \Delta \sqrt{k_{sgs}} dev(\overline{D}) \right) + C_{\epsilon} \frac{k_{sgs}^{1.5}}{\Delta} = 0 \\
\Leftrightarrow &\quad \sqrt{k_{sgs}} \left( \frac{C_{\epsilon}}{\Delta} k_{sgs} + \frac{2}{3} tr(\overline{D}) \sqrt{k_{sgs}} -2 C_{k} \Delta \left( dev(\overline{D}) : \overline{D} \right) \right) = 0 \\
\Leftrightarrow &\quad a k_{sgs} + b \sqrt{k_{sgs}} – c = 0 \\
\Rightarrow &\quad k_{sgs} = \left( \frac{-b + \sqrt{b^2 + 4ac}}{2a} \right)^2, \tag{6} \label{eq:ksgs2}
\end{align}
where
\begin{eqnarray}
\left\{
\begin{array}{l}
a = \frac{C_{\epsilon}}{\Delta} \\
b = \frac{2}{3} tr(\overline{D}) \tag{7} \label{eq:coeffs}\\
c = 2 C_{k} \Delta \left( dev(\overline{D}) : \overline{D} \right).
\end{array}
\right.
\end{eqnarray}

In the case of incompressible flows, Eq. \eqref{eq:coeffs} reduces to
\begin{eqnarray}
\left\{
\begin{array}{l}
b = \frac{2}{3} tr(\overline{D}) = 0 \\
c = 2 C_{k} \Delta \left( dev(\overline{D}) : \overline{D} \right) = C_{k} \Delta \vert \overline{D} \vert^2, \tag{8} \label{eq:coeffsInc}
\end{array}
\right.
\end{eqnarray}
where
\begin{equation}
\vert \overline{D} \vert = \sqrt{2 \overline{D} : \overline{D}}. \tag{9} \label{eq:abs}
\end{equation}
By substituting these relations into Eq. \eqref{eq:ksgs2}, we can get
\begin{equation}
k_{sgs} = \frac{c}{a} = \frac{C_{k} \Delta^2 \vert \overline{D} \vert^2}{C_{\epsilon}}. \tag{10} \label{eq:ksgsInc}
\end{equation}
We can get the following expression for the SGS eddy viscosity in the case of incompressible flows by substituting the above equation into Eq. \eqref{eq:nusgs}
\begin{equation}
\nu_{sgs} = C_{k} \sqrt{\frac{C_{k}}{C_{\epsilon}}} \Delta^2 \vert \overline{D} \vert. \tag{11} \label{eq:nusgsInc}
\end{equation}
By comparing it with the following common expression in the literature
\begin{equation}
\nu_{sgs} = \left( C_{s} \Delta \right)^2 \vert \overline{D} \vert, \tag{12} \label{eq:nusgstext}
\end{equation}
we can get the following relation for the Smagorinsky constant \(C_{s}\):
\begin{equation}
C_{s}^2 = C_{k} \sqrt{\frac{C_{k}}{C_{\epsilon}}}. \tag{13} \label{eq:smgConstInc}
\end{equation}

Features
  • Algebraic eddy viscosity model (0-equation model)
  • The rotation rate is not taken into account in the calculation of \(\nu_{sgs}\)
  • Based on the local equilibrium assumption
  • Appropriate value of the Smagorinsky constant is problem dependent
  • Not to be able to handle transition
  • Not to be able to deal with energy backscatter
  • Van-Driest damping is necessary for the SGS eddy viscosity in the near-wall region

The last three items arise from the fact that \(\nu_{sgs} \ge 0\) in Eq. \eqref{eq:nusgs}.

References

[1] (Free AccessJ. Smagorinsky, General circulation experiments with the primitive equations: I. The basic experiment*. Monthly weather review, 91(3), 99-164, 1963.

Large Eddy Simulation (LES)


Large Eddy Simulation (LES) is one of the most promising methods for computing industry-relevant turbulent flows. It is used to predict unsteady flow behaviors with lower computational cost as compared to Direct Numerical Simulation (DNS).

The essential idea of LES dates back to Joseph Smagorinsky (1963) in meteorology and to James W. Deardorff (1970) in engineering.

Keywords
Inertial subrange, Wall-modeled LES, Wall-resolved LES, Smagorinsky, Taylor microscale

Nomenclature – Abbreviations
  • LES: Large Eddy Simulation
  • ILES: Implicit Large Eddy Simulation
  • MILES: Monotone Integrated Large Eddy Simulation
  • VLES: Very Large Eddy Simulation
  • WMLES: Wall-Modeled Large Eddy Simulation
  • SGS: subgrid-scale
  • WALE: Wall-Adapting Local Eddy-Viscosity
What is Large Eddy Simulation?

Professor Parviz Moin of Stanford University briefly explains what LES is in his interview. I’m surprised that he received his Masters and Ph.D. degrees in Mathematics and Mechanical Engineering from Stanford in the same year (1978)!

The verification of LES simulations is difficult because both the error induced by the SGS model and the numerical discretization error are dependent on the grid resolution. In the following presentation (37:40-42:06), he introduces one method using explicit filtering to distinguish the errors [6]:

If the grid-independent solution of the explicit filtered LES equations fails to accurately predict the filtered DNS flow field, its failure can be solely attributed to the capability of the subgrid stress model employed.

Categorization of LES

LES can be classified in terms of the following points as shown in Fig. 1:

  • How the effect of the SGS stress is modeled
  • How the spatial filtering is applied in the solution procedure.
Fig. 1

In OpenFOAM, LES with the implicit filtering is implemented, in which only the filter width is specified and the filter shape is not. There exists this ambiguity in the definition of the filter. Lund [7] provides a clear description of the implicit filtering in LES:

The nearly universal approach is to simply write down the filtered Navier-Stokes equations together with an assumed model for the subgrid-scale stresses and then apply the desired spatial discretization to this “filtered” system. Although it is rarely mentioned, what one is doing by adopting this procedure is to imagine that the finite support of the computational mesh together with the low-pass characteristics of the discrete differentiating operators act as an effective filter. One then directly associates the computed velocity field with the filtered velocity. This procedure will be referred to as implicit filtering since an explicit filtering operation never appears in the solution procedure.

The explicit filtering methodology aims to damp the error-prone length scales smaller than the filter width that can be generated by the nonlinear convective term. In the case of the implicit filtering, we hope that this error has little effect on the resolved scales.

Subgrid-scale (SGS) model
Fig. 2
SGS models in OpenFOAM
  • Smagorinsky

    Smagorinsky SGS model

  • kEqn

    One equation eddy-viscosity model

  • dynamicLagrangian

    Dynamic SGS model with Lagrangian averaging

  • dynamicKEqn

    Dynamic one equation eddy-viscosity model

  • WALE

    Wall-adapting local eddy-viscosity (WALE) SGS model

  • DeardorffDiffStress

    Differential SGS Stress Equation Model

For the dynamic SGS models, the spatial averaging operations of the coefficients are often performed to stabilize the calculation. The homogeneousDynSmagorinsky model that had been implemented in older versions takes the average of the coefficient in the whole computational domain.

Other SGS models
  • Vreman SGS model [3]

    \begin{equation}
    \nu_{sgs} = c \sqrt{\frac{B_{\beta}}{\alpha_{ij}\alpha_{ij}}}
    \end{equation}

Calculation of filter width in OpenFOAM

The method for calculating the filter width \(\Delta\) is specified in the turbulenceProperties file. The available options in OpenFOAM are as follows:

  • cubeRootVol
  • maxDeltaxyz
  • maxDeltaxyzCubeRoot
  • smooth
  • vanDriest
  • Prandtl
  • IDDESDelta
Grid resolution measures

The choice of the computational grid size has sensible impact on the quality of the LES simulations and there are several techniques to assess the grid resolution in the LES computations [4].

  • Estimations based on prior RANS results
  • Single-grid estimators
  • Multi-grid estimators
Principal use
  • Aeroacoustics
  • Aerodynamics
  • Combustion
Refereces

[1] W. Rodi, J. H. Ferziger, M. Breuer and M. Pourquié, Status of Large Eddy Simulation: Results of a Workshop. Journal of Fluids Engineering, 119(2), 248-262, 1997.
[2] P. Comte, Large eddy simulations and subgrid scale modelling of turbulent shear flows
[3] A. W. Vreman, An eddy-viscosity subgrid-scale model for turbulent shear flow: Algebraic theory and applications. Physics of Fluids, 16(10), 2004.
[4] S. E. Gant, Reliability Issues of LES-Related Approaches in an Industrial Context. Flow, turbulence and combustion, 84(2), 325-335, 2010.
[5] U. Piomelli, Large eddy simulations in 2030 and beyond. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 372(2022), 2014.
[6] S. T. Bose, P. Moin and D. You, Grid-independent large-eddy simulation using explicit filtering. Center for Turbulence Research Annual Research Briefs 2008.
[7] T. S. Lund, The Use of Explicit Filters in Large Eddy Simulation. Computers and Mathematics with Applications 46, 603-616, 2003.

Refereces (Japanese)

[8] N. Fukushima et al., A Scale Self-Recognition Mixed SGS Model in Large Eddy Simulation of Homogeneous Isotropic Turbulence. Nagare, 34, 419-422, 2015.

List of Laboratories
List of Laboratories (Japan)