# Large eddy simulation (LES)

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- | Large eddy simulation (LES) is a popular technique for simulating turbulent flows. An implication of [[Kolmogorov]]'s (1941) theory of self similarity is that the large eddies of the flow are dependant on the geometry while the smaller scales more [[universal]]. This feature allows one to explicitly solve for the large eddies in a calculation and implicitly account for the small eddies by using a [[subgrid-scale model]] (SGS model). | + | ==Introduction== |

+ | Large eddy simulation (LES) is a popular technique for simulating turbulent flows. An implication of [[Kolmogorov]]'s (1941) theory of self similarity is that the large eddies of the flow are dependant on the geometry while the smaller scales more [[universal]]. This feature allows one to explicitly solve for the large eddies in a calculation and implicitly account for the small eddies by using a [[#Subgrid-scale models|subgrid-scale model]] (SGS model). | ||

- | Mathematically, one may think of separating the velocity field into a resolved and sub-grid part. The resolved part of the field represent the "large" eddies, while the subgrid part of the velocity represent the "small scales" whose effect on the resolved field is included through the subgrid-scale model. Formally, one may think of filtering as the convolution of a function with a [[LES filters|filtering kernel]]. However, most practical (and commercial) | + | Mathematically, one may think of separating the velocity field into a resolved and sub-grid part. The resolved part of the field represent the "large" eddies, while the subgrid part of the velocity represent the "small scales" whose effect on the resolved field is included through the subgrid-scale model. Formally, one may think of filtering as the convolution of a function with a [[LES filters|filtering kernel]] <math>G</math>: |

+ | |||

+ | :<math> | ||

+ | \bar{u}_i(\vec{x}) = \int G(\vec{x}-\vec{\xi}) u(\vec{\xi})d\vec{\xi},</math> | ||

+ | |||

+ | resulting in | ||

+ | |||

+ | :<math>u_i = \bar{u}_i + u'_i,</math> | ||

+ | |||

+ | where <math>\bar{u}_i</math> is the resolvable scale part and <math>u'_i</math> is the subgrid-scale part. However, most practical (and commercial) implementations of LES use the grid itself as the filter (the [[LES filters#Box filter|box filter]]) and perform no explicit filtering. More information about the theory and application of filters is found in the [[LES filters|LES filters article]]. | ||

This page is mainly focused on LES of incompressible flows. For compressible flows, see [[Favre averaged Navier-Stokes equations]]. | This page is mainly focused on LES of incompressible flows. For compressible flows, see [[Favre averaged Navier-Stokes equations]]. | ||

- | + | The filtered equations are developed from the incompressible [[Navier-Stokes equations]] of motion: | |

+ | |||

:<math> | :<math> | ||

- | \frac{\partial{u_i}}{\partial t} + u_j \frac{\partial u_i}{\partial x_j} = -\frac{1}{\rho} \frac{\partial p}{\partial x_i} + \nu \ | + | \frac{\partial{u_i}}{\partial t} + u_j \frac{\partial u_i}{\partial x_j} = -\frac{1}{\rho} \frac{\partial p}{\partial x_i} + \frac{\partial}{\partial x_j}\left(\nu \frac{\partial u_i}{\partial x_j}\right). |

</math> | </math> | ||

- | + | Substituting in the decomposition <math>u_i = \bar{u}_i + u'_i</math> and <math>p = \bar{p} + p'</math> and then filtering the resulting equation gives the equations of motion for the resolved field: | |

:<math> | :<math> | ||

- | + | \frac{\partial{\bar{u}_i}}{\partial t} + \bar{u}_j \frac{\partial \bar{u}_i}{\partial x_j} = -\frac{1}{\rho} \frac{\partial \bar{p}}{\partial x_i} | |

- | \frac{\partial{\bar{ | + | + \frac{\partial}{\partial x_j}\left(\nu \frac{\partial \bar{u}_i}{\partial x_j}\right) |

- | + \frac{\partial \tau_{ij}}{\partial x_j} | + | + \frac {1}{\rho}\frac{\partial \tau_{ij}}{\partial x_j}. |

</math> | </math> | ||

- | + | We have assumed that the filtering operation and the differentiation operation commute, which is not generally the case. It is thought that the errors associated with this assumption are usually small, though filters that commute with differentiation have been developed ("ref?"). The extra term <math> \frac{\partial \tau_{ij}}{\partial x_j} </math> arises from the non-linear advection terms, due to the fact that | |

+ | |||

:<math> | :<math> | ||

\overline{ u_j \frac{\partial u_i}{\partial x_j} } \ne | \overline{ u_j \frac{\partial u_i}{\partial x_j} } \ne | ||

- | \bar{ | + | \bar{u}_j \frac{\partial \bar{u}_i}{\partial x_j} |

</math> | </math> | ||

and hence | and hence | ||

:<math> | :<math> | ||

- | \tau_{ij} = \bar{ | + | \tau_{ij} = \bar{u}_i \bar{u}_j - \overline{u_i u_j} |

</math> | </math> | ||

- | Subgrid-scale turbulence models usually employ the [[Boussinesq hypothesis]], and seek to calculate (the deviatoric part of) the SGS stress using: <br> | + | Similar equations can be derived for the subgrid-scale field (i.e. the residual field). |

+ | |||

+ | Subgrid-scale turbulence models usually employ the [[Boussinesq eddy viscosity assumption|Boussinesq hypothesis]], and seek to calculate (the deviatoric part of) the SGS stress using: <br> | ||

:<math> | :<math> | ||

- | \tau _{ij} - \frac{1}{3}\tau _{kk} \delta _{ij} = - 2\ | + | \tau _{ij} - \frac{1}{3}\tau _{kk} \delta _{ij} = - 2\mu_t \bar S_{ij} |

</math> | </math> | ||

Line 42: | Line 56: | ||

</math> | </math> | ||

<br> | <br> | ||

- | and <math> \ | + | and <math> \nu_t </math> is the subgrid-scale turbulent viscosity. Substituting into the filtered Navier-Stokes equations, we then have |

+ | |||

+ | :<math> | ||

+ | \frac{\partial{\bar{u}_i}}{\partial t} + \bar{u}_j \frac{\partial \bar{u}_i}{\partial x_j} = -\frac{1}{\rho} \frac{\partial \bar{p}}{\partial x_i} + \frac{\partial}{\partial x_j}\left([\nu+\nu_t]\frac{\partial\bar{u}_i}{\partial x_j}\right), | ||

+ | </math> | ||

+ | |||

+ | where we have used the incompressibility constraint to simplify the equation and the pressure is now modified to include the trace term <math>\tau _{kk} \delta _{ij}/3</math>. | ||

== Subgrid-scale models == | == Subgrid-scale models == | ||

- | *[[Smagorinsky-Lilly model|Smagorinsky | + | *[[Smagorinsky-Lilly model|Smagorinsky model]] (Smagorinsky, 1963) |

*[[Dynamic subgrid-scale model|Algebraic Dynamic model]] (Germano, et. al., 1991) | *[[Dynamic subgrid-scale model|Algebraic Dynamic model]] (Germano, et. al., 1991) | ||

+ | *[[Dynamic global-coefficient subgrid-scale model|Dynamic Global-Coefficient model]] (You & Moin, 2007) | ||

*[[Kinetic energy subgrid-scale model|Localized Dynamic model]] (Kim & Menon, 1993) | *[[Kinetic energy subgrid-scale model|Localized Dynamic model]] (Kim & Menon, 1993) | ||

*[[Wall-adapting local eddy-viscosity (WALE) model|WALE (Wall-Adapting Local Eddy-viscosity) model]] (Nicoud and Ducros, 1999) | *[[Wall-adapting local eddy-viscosity (WALE) model|WALE (Wall-Adapting Local Eddy-viscosity) model]] (Nicoud and Ducros, 1999) | ||

*[[RNG-LES model]] | *[[RNG-LES model]] | ||

+ | *[[Structural modeling]] | ||

== References == | == References == | ||

- | * | + | *{{reference-paper|author=Germano, M., Piomelli, U., Moin, P. and Cabot, W. H.|year=1991|title=A dynamic sub-grid scale eddy viscosity model|rest=Physics of Fluids, A(3): pp 1760-1765, 1991}} |

- | + | *{{reference-paper|author=You, D. and Moin, P.|year=2007|title=A dynamic global-coefficient subgrid-scale eddy-viscosity model for large-eddy simulation in complex geometries|rest=Physics of Fluids, 19(6): 065110, 2007}} | |

- | * | + | *{{reference-paper|author=Kim, W and Menon, S.|year=1995|title=A new dynamic one-equation subgrid-scale model for large eddy simulation|rest=In 33rd Aerospace Sciences Meeting and Exhibit, Reno, NV, 1995}} |

- | * | + | *{{reference-paper|author=Nicoud, F. and Ducros, F.|year=1999|title=Subgrid-scale modelling based on the square of the velocity gradient tensor|rest=Flow, Turbulence and Combustion, 62: pp- 183-200, 1999}} |

+ | *{{reference-paper|author=Smagorinsky, J|year=1963|title=General circulation experiments with the primitive equations, i. the basic experiment. Monthly Weather Review|rest=91: pp 99-164, 1963}} | ||

+ | |||

+ | [[Category:Turbulence models]] |

## Latest revision as of 21:50, 24 June 2013

## Introduction

Large eddy simulation (LES) is a popular technique for simulating turbulent flows. An implication of Kolmogorov's (1941) theory of self similarity is that the large eddies of the flow are dependant on the geometry while the smaller scales more universal. This feature allows one to explicitly solve for the large eddies in a calculation and implicitly account for the small eddies by using a subgrid-scale model (SGS model).

Mathematically, one may think of separating the velocity field into a resolved and sub-grid part. The resolved part of the field represent the "large" eddies, while the subgrid part of the velocity represent the "small scales" whose effect on the resolved field is included through the subgrid-scale model. Formally, one may think of filtering as the convolution of a function with a filtering kernel :

resulting in

where is the resolvable scale part and is the subgrid-scale part. However, most practical (and commercial) implementations of LES use the grid itself as the filter (the box filter) and perform no explicit filtering. More information about the theory and application of filters is found in the LES filters article.

This page is mainly focused on LES of incompressible flows. For compressible flows, see Favre averaged Navier-Stokes equations.

The filtered equations are developed from the incompressible Navier-Stokes equations of motion:

Substituting in the decomposition and and then filtering the resulting equation gives the equations of motion for the resolved field:

We have assumed that the filtering operation and the differentiation operation commute, which is not generally the case. It is thought that the errors associated with this assumption are usually small, though filters that commute with differentiation have been developed ("ref?"). The extra term arises from the non-linear advection terms, due to the fact that

and hence

Similar equations can be derived for the subgrid-scale field (i.e. the residual field).

Subgrid-scale turbulence models usually employ the Boussinesq hypothesis, and seek to calculate (the deviatoric part of) the SGS stress using:

where is the rate-of-strain tensor for the resolved scale defined by

and is the subgrid-scale turbulent viscosity. Substituting into the filtered Navier-Stokes equations, we then have

where we have used the incompressibility constraint to simplify the equation and the pressure is now modified to include the trace term .

## Subgrid-scale models

- Smagorinsky model (Smagorinsky, 1963)
- Algebraic Dynamic model (Germano, et. al., 1991)
- Dynamic Global-Coefficient model (You & Moin, 2007)
- Localized Dynamic model (Kim & Menon, 1993)
- WALE (Wall-Adapting Local Eddy-viscosity) model (Nicoud and Ducros, 1999)
- RNG-LES model
- Structural modeling

## References

**Germano, M., Piomelli, U., Moin, P. and Cabot, W. H. (1991)**, "A dynamic sub-grid scale eddy viscosity model", Physics of Fluids, A(3): pp 1760-1765, 1991.**You, D. and Moin, P. (2007)**, "A dynamic global-coefficient subgrid-scale eddy-viscosity model for large-eddy simulation in complex geometries", Physics of Fluids, 19(6): 065110, 2007.**Kim, W and Menon, S. (1995)**, "A new dynamic one-equation subgrid-scale model for large eddy simulation", In 33rd Aerospace Sciences Meeting and Exhibit, Reno, NV, 1995.**Nicoud, F. and Ducros, F. (1999)**, "Subgrid-scale modelling based on the square of the velocity gradient tensor", Flow, Turbulence and Combustion, 62: pp- 183-200, 1999.**Smagorinsky, J (1963)**, "General circulation experiments with the primitive equations, i. the basic experiment. Monthly Weather Review", 91: pp 99-164, 1963.