Large eddy simulation (LES) is a mathematical model for
turbulence
In fluid dynamics, turbulence or turbulent flow is fluid motion characterized by chaotic changes in pressure and flow velocity. It is in contrast to a laminar flow, which occurs when a fluid flows in parallel layers, with no disruption between ...
used in
computational fluid dynamics
Computational fluid dynamics (CFD) is a branch of fluid mechanics that uses numerical analysis and data structures to analyze and solve problems that involve fluid flows. Computers are used to perform the calculations required to simulate ...
. It was initially proposed in 1963 by
Joseph Smagorinsky
Joseph Smagorinsky (29 January 1924 – 21 September 2005) was an American meteorologist and the first director of the National Oceanic and Atmospheric Administration (NOAA)'s Geophysical Fluid Dynamics Laboratory (GFDL).
Early life
Joseph Sma ...
to simulate atmospheric air currents,
and first explored by Deardorff (1970).
LES is currently applied in a wide variety of engineering applications, including
combustion
Combustion, or burning, is a high-temperature exothermic redox chemical reaction between a fuel (the reductant) and an oxidant, usually atmospheric oxygen, that produces oxidized, often gaseous products, in a mixture termed as smoke. Combus ...
,
acoustics,
and simulations of the atmospheric boundary layer.
The simulation of turbulent flows by numerically solving the
Navier–Stokes equations
In physics, the Navier–Stokes equations ( ) are partial differential equations which describe the motion of viscous fluid substances, named after French engineer and physicist Claude-Louis Navier and Anglo-Irish physicist and mathematician Geo ...
requires resolving a very wide range of time and length scales, all of which affect the flow field. Such a resolution can be achieved with
direct numerical simulation
A direct numerical simulation (DNS)Here the origin of the term ''direct numerical simulation'' (see e.g. p. 385 in ) owes to the fact that, at that time, there were considered to be just two principal ways of getting ''theoretical'' results r ...
(DNS), but DNS is computationally expensive, and its cost prohibits simulation of practical engineering systems with complex geometry or flow configurations, such as turbulent jets, pumps, vehicles, and landing gear.
The principal idea behind LES is to reduce the computational cost by ignoring the smallest length scales, which are the most computationally expensive to resolve, via
low-pass filtering of the Navier–Stokes equations. Such a low-pass filtering, which can be viewed as a time- and spatial-averaging, effectively removes small-scale information from the numerical solution. This information is not irrelevant, however, and its effect on the flow field must be modelled, a task which is an active area of research for problems in which small-scales can play an important role, such as near-wall flows,
reacting flows,
and multiphase flows.
Filter definition and properties
An
LES filter can be applied to a spatial and temporal field
and perform a spatial filtering operation, a temporal filtering operation, or both. The filtered field, denoted with a bar, is defined as:
:
where
is the filter convolution kernel. This can also be written as:
:
The filter kernel
has an associated cutoff length scale
and cutoff time scale
. Scales smaller than these are eliminated from
. Using the above filter definition, any field
may be split up into a filtered and sub-filtered (denoted with a prime) portion, as
:
It is important to note that the
large eddy simulation filtering operation does not satisfy the properties of a
Reynolds operator In fluid dynamics and invariant theory, a Reynolds operator is a mathematical operator given by averaging something over a group action, satisfying a set of properties called Reynolds rules. In fluid dynamics Reynolds operators are often encountere ...
.
Filtered governing equations
The governing equations of LES are obtained by filtering the
partial differential equations
In mathematics, a partial differential equation (PDE) is an equation which imposes relations between the various partial derivatives of a multivariable function.
The function is often thought of as an "unknown" to be solved for, similarly to ...
governing the flow field
. There are differences between the incompressible and compressible LES governing equations, which lead to the definition of a new filtering operation.
Incompressible flow
For incompressible flow, the
continuity equation
A continuity equation or transport equation is an equation that describes the transport of some quantity. It is particularly simple and powerful when applied to a conserved quantity, but it can be generalized to apply to any extensive quantity. ...
and Navier–Stokes equations are filtered, yielding the filtered incompressible continuity equation,
:
and the filtered Navier–Stokes equations,
:
where
is the filtered pressure field and
is the rate-of-strain tensor evaluated using the filtered velocity. The
nonlinear
In mathematics and science, a nonlinear system is a system in which the change of the output is not proportional to the change of the input. Nonlinear problems are of interest to engineers, biologists, physicists, mathematicians, and many oth ...
filtered advection term
is the chief cause of difficulty in LES modeling. It requires knowledge of the unfiltered velocity field, which is unknown, so it must be modeled. The analysis that follows illustrates the difficulty caused by the nonlinearity, namely, that it causes interaction between large and small scales, preventing separation of scales.
The filtered advection term can be split up, following Leonard (1975),
as:
:
where
is the residual stress tensor, so that the filtered Navier-Stokes equations become
:
with the residual stress tensor
grouping all unclosed terms. Leonard decomposed this stress tensor as
and provided physical interpretations for each term.
, the Leonard tensor, represents interactions among large scales,
, the Reynolds stress-like term, represents interactions among the sub-filter scales (SFS), and
, the Clark tensor,
represents cross-scale interactions between large and small scales.
Modeling the unclosed term
is the task of sub-grid scale (SGS) models. This is made challenging by the fact that the subgrid stress tensor
must account for interactions among all scales, including filtered scales with unfiltered scales.
The filtered governing equation for a passive scalar
, such as mixture fraction or temperature, can be written as
:
where
is the diffusive flux of
, and
is the sub-filter flux for the scalar
. The filtered diffusive flux
is unclosed, unless a particular form is assumed for it, such as a gradient diffusion model
.
is defined analogously to
,
:
and can similarly be split up into contributions from interactions between various scales. This sub-filter flux also requires a sub-filter model.
Derivation
Using
Einstein notation
In mathematics, especially the usage of linear algebra in Mathematical physics, Einstein notation (also known as the Einstein summation convention or Einstein summation notation) is a notational convention that implies summation over a set of ...
, the Navier–Stokes equations for an incompressible fluid in Cartesian coordinates are
:
:
Filtering the momentum equation results in
:
If we assume that filtering and differentiation commute, then
:
This equation models the changes in time of the filtered variables
. Since the unfiltered variables
are not known, it is impossible to directly calculate
. However, the quantity
is known. A substitution is made:
:
Let
. The resulting set of equations are the LES equations:
:
Compressible governing equations
For the governing equations of compressible flow, each equation, starting with the conservation of mass, is filtered. This gives:
:
which results in an additional sub-filter term. However, it is desirable to avoid having to model the sub-filter scales of the mass conservation equation. For this reason, Favre
proposed a density-weighted filtering operation, called Favre filtering, defined for an arbitrary quantity
as:
:
which, in the limit of incompressibility, becomes the normal filtering operation. This makes the conservation of mass equation:
:
This concept can then be extended to write the Favre-filtered momentum equation for compressible flow. Following Vreman:
:
where
is the shear stress tensor, given for a Newtonian fluid by:
:
and the term
represents a sub-filter viscous contribution from evaluating the viscosity
using the Favre-filtered temperature
. The subgrid stress tensor for the Favre-filtered momentum field is given by
:
By analogy, the Leonard decomposition may also be written for the residual stress tensor for a filtered triple product
. The triple product can be rewritten using the Favre filtering operator as
, which is an unclosed term (it requires knowledge of the fields
and
, when only the fields
and
are known). It can be broken up in a manner analogous to
above, which results in a sub-filter stress tensor
. This sub-filter term can be split up into contributions from three types of interactions: the Leondard tensor
, representing interactions among resolved scales; the Clark tensor
, representing interactions between resolved and unresolved scales; and the Reynolds tensor
, which represents interactions among unresolved scales.
Filtered kinetic energy equation
In addition to the filtered mass and momentum equations, filtering the kinetic energy equation can provide additional insight. The kinetic energy field can be filtered to yield the total filtered kinetic energy:
:
and the total filtered kinetic energy can be decomposed into two terms: the kinetic energy of the filtered velocity field
,
:
and the residual kinetic energy
,
:
such that
.
The conservation equation for
can be obtained by multiplying the filtered momentum transport equation by
to yield:
:
where
is the dissipation of kinetic energy of the filtered velocity field by viscous stress, and
represents the sub-filter scale (SFS) dissipation of kinetic energy.
The terms on the left-hand side represent transport, and the terms on the right-hand side are sink terms that dissipate kinetic energy.
The
SFS dissipation term is of particular interest, since it represents the transfer of energy from large resolved scales to small unresolved scales. On average,
transfers energy from large to small scales. However, instantaneously
can be positive ''or'' negative, meaning it can also act as a source term for
, the kinetic energy of the filtered velocity field. The transfer of energy from unresolved to resolved scales is called backscatter (and likewise the transfer of energy from resolved to unresolved scales is called forward-scatter).
Numerical methods for LES
Large eddy simulation involves the solution to the discrete filtered governing equations using
computational fluid dynamics
Computational fluid dynamics (CFD) is a branch of fluid mechanics that uses numerical analysis and data structures to analyze and solve problems that involve fluid flows. Computers are used to perform the calculations required to simulate ...
. LES resolves scales from the domain size
down to the filter size
, and as such a substantial portion of high wave number turbulent fluctuations must be resolved. This requires either
high-order numerical schemes, or fine grid resolution if low-order numerical schemes are used. Chapter 13 of Pope
addresses the question of how fine a grid resolution
is needed to resolve a filtered velocity field
. Ghosal
found that for low-order discretization schemes, such as those used in finite volume methods, the truncation error can be the same order as the subfilter scale contributions, unless the filter width
is considerably larger than the grid spacing
. While even-order schemes have truncation error, they are non-dissipative,
and because subfilter scale models are dissipative, even-order schemes will not affect the subfilter scale model contributions as strongly as dissipative schemes.
Filter implementation
The filtering operation in large eddy simulation can be implicit or explicit. Implicit filtering recognizes that the subfilter scale model will dissipate in the same manner as many numerical schemes. In this way, the grid, or the numerical discretization scheme, can be assumed to be the LES low-pass filter. While this takes full advantage of the grid resolution, and eliminates the computational cost of calculating a subfilter scale model term, it is difficult to determine the shape of the LES filter that is associated with some numerical issues. Additionally, truncation error can also become an issue.
In explicit filtering, an
LES filter is applied to the discretized Navier–Stokes equations, providing a well-defined filter shape and reducing the truncation error. However, explicit filtering requires a finer grid than implicit filtering, and the computational cost increases with
. Chapter 8 of Sagaut (2006) covers LES numerics in greater detail.
Boundary conditions of large eddy simulations
Inlet boundary conditions affect the accuracy of LES significantly, and the treatment of inlet conditions for LES is a complicated problem. Theoretically, a good boundary condition for LES should contain the following features:
(1) providing accurate information of flow characteristics, i.e. velocity and turbulence;
(2) satisfying the Navier-Stokes equations and other physics;
(3) being easy to implement and adjust to different cases.
Currently, methods of generating inlet conditions for LES are broadly divided into two categories classified by Tabor et al.:
The first method for generating turbulent inlets is to synthesize them according to particular cases, such as Fourier techniques, principle orthogonal decomposition (POD) and vortex methods. The synthesis techniques attempt to construct turbulent field at inlets that have suitable turbulence-like properties and make it easy to specify parameters of the turbulence, such as turbulent kinetic energy and turbulent dissipation rate. In addition, inlet conditions generated by using random numbers are computationally inexpensive. However, one serious drawback exists in the method. The synthesized turbulence does not satisfy the physical structure of fluid flow governed by Navier-Stokes equations.
The second method involves a separate and precursor calculation to generate a turbulent database which can be introduced into the main computation at the inlets. The database (sometimes named as ‘library’) can be generated in a number of ways, such as cyclic domains, pre-prepared library, and internal mapping. However, the method of generating turbulent inflow by precursor simulations requires large calculation capacity.
Researchers examining the application of various types of synthetic and precursor calculations have found that the more realistic the inlet turbulence, the more accurate LES predicts results.
Modeling unresolved scales
To discuss the modeling of unresolved scales, first the unresolved scales must be classified. They fall into two groups: resolved sub-filter scales (SFS), and sub-grid scales(SGS).
The resolved sub-filter scales represent the scales with wave numbers larger than the cutoff wave number
, but whose effects are dampened by the filter. Resolved sub-filter scales only exist when filters non-local in wave-space are used (such as a
box or
Gaussian
Carl Friedrich Gauss (1777–1855) is the eponym of all of the topics listed below.
There are over 100 topics all named after this German mathematician and scientist, all in the fields of mathematics, physics, and astronomy. The English eponym ...
filter). These resolved sub-filter scales must be modeled using filter reconstruction.
Sub-grid scales are any scales that are smaller than the cutoff filter width
. The form of the SGS model depends on the filter implementation. As mentioned in the
Numerical methods for LES
Numerical may refer to:
* Number
* Numerical digit
* Numerical analysis
Numerical analysis is the study of algorithms that use numerical approximation (as opposed to symbolic manipulations) for the problems of mathematical analysis (as distin ...
section, if implicit LES is considered, no SGS model is implemented and the numerical effects of the discretization are assumed to mimic the physics of the unresolved turbulent motions.
Sub-grid scale models
Without a universally valid description of turbulence, empirical information must be utilized when constructing and applying SGS models, supplemented with fundamental physical constraints such as
Galilean invariance
Galilean invariance or Galilean relativity states that the laws of motion are the same in all inertial frames of reference. Galileo Galilei first described this principle in 1632 in his ''Dialogue Concerning the Two Chief World Systems'' using th ...
.
Two classes of SGS models exist; the first class is functional models and the second class is structural models. Some models may be categorized as both.
Functional (eddy–viscosity) models
Functional models are simpler than structural models, focusing only on dissipating energy at a rate that is physically correct. These are based on an artificial eddy viscosity approach, where the effects of turbulence are lumped into a turbulent viscosity. The approach treats dissipation of kinetic energy at sub-grid scales as analogous to molecular diffusion. In this case, the deviatoric part of
is modeled as:
:
where
is the turbulent eddy viscosity and
is the rate-of-strain tensor.
Based on dimensional analysis, the eddy viscosity must have units of
. Most eddy viscosity SGS models model the eddy viscosity as the product of a characteristic length scale and a characteristic velocity scale.
= Smagorinsky–Lilly model
=
The first SGS model developed was the Smagorinsky–Lilly SGS model, which was developed by
Smagorinsky and used in the first LES simulation by Deardorff.
It models the eddy viscosity as:
:
where
is the grid size and
is a constant.
This method assumes that the energy production and dissipation of the small scales are in equilibrium - that is,
.
= The Dynamic Model (Germano et al. and beyond)
=
Germano et al.
[
] identified a number of studies using the Smagorinsky model that each found different values for the Smagorinsky constant
for different flow configurations. In an attempt to formulate a more universal approach to SGS models, Germano et al. proposed a dynamic Smagorinsky model, which utilized two filters: a grid LES filter, denoted
, and a test LES filter, denoted
for any turbulent field
. The test filter is larger in size than the grid filter and adds an additional smoothing of the turbulence field over the already smoothed fields represented by the LES. Applying the test filter to the LES equations (which are obtained by applying the "grid" filter to Navier-Stokes equations) results in a new set of equations that are identical in form but with the SGS stress
replaced by
. Germano al. noted that even though neither
nor
can be computed exactly because of the presence of unresolved scales, there is an exact relation connecting these two tensors. This relation, known as the Germano identity is
Here
can be explicitly evaluated as it involves only the filtered velocities and the operation of test filtering. The significance of the identity is that if one assumes that turbulence is self similar so that the SGS stress at the grid and test levels have the same form
and
, then the Germano identity provides an equation from which the Smagorinsky coefficient
(which is no longer a 'constant') can potentially be determined.
).html" ;"title="nherent in the procedure is the assumption that the coefficient is invariant of scale (see review
)">nherent in the procedure is the assumption that the coefficient is invariant of scale (see review
)
In order to do this, two additional steps were introduced in the original formulation. First, one assumed that even though
was in principle variable, the variation was sufficiently slow that it can be moved out of the filtering operation
. Second, since
was a scalar, the Germano identity was contracted with a second rank tensor (the rate of strain tensor was chosen) to convert it to a scalar equation from which
could be determined.
Lilly
found a less arbitrary and therefore more satisfactory approach for obtaining C from the tensor identity. He noted that the Germano identity required the satisfaction of nine equations at each point in space (of which only five are independent) for a single quantity
. The problem of obtaining
was therefore over-determined. He proposed therefore that
be determined using a least square fit by minimizing the residuals. This results in
:
Here
:
and for brevity
,
Initial attempts to implement the model in LES simulations proved unsuccessful. First, the computed coefficient
was not at all "slowly varying" as assumed and varied as much as any other turbulent field. Secondly,
the computed
could be positive as well as negative. The latter fact in itself should not be regarded as a
shortcoming as a priori tests using filtered DNS fields have shown that the local subgrid dissipation rate
in a turbulent field is almost as likely to be negative as it is positive even though the integral over the fluid domain is always positive representing a net dissipation of energy in the large scales. A slight preponderance of positive values as opposed to strict positivity of the eddy-viscosity results in the observed net dissipation. This so-called "backscatter" of energy from small to large scales indeed corresponds to negative C values in the Smagorinsky model. Nevertheless, the Germano-Lilly formulation was found not to result in stable calculations. An ad hoc measure was adopted by averaging the numerator and denominator over homogeneous directions (where such directions exist in the flow)
:
When the averaging involved a large enough statistical sample that the computed
was positive (or at
least only rarely negative) stable calculations were possible. Simply setting the negative values to zero (a procedure called "clipping") with or without the averaging also resulted in stable calculations.
Meneveau proposed
an averaging over Lagrangian fluid trajectories with an exponentially decaying "memory". This can be applied to problems lacking homogeneous directions and can be stable if the effective time over which the averaging is done is long enough and yet not so long as to smooth out spatial inhomogenieties of interest.
Lilly's modification of the Germano method followed by a statistical averaging or synthetic removal of negative viscosity regions seems ad hoc, even if it could be made to "work". An alternate formulation of the least square minimization procedure known as the "Dynamic Localization Model" (DLM) was suggested by
Ghosal et al.
In this approach one first defines a quantity
:
with the tensors
and
replaced by the appropriate SGS model. This tensor then represents the amount by which the subgrid model fails to respect the Germano identity at each spatial location. In Lilly's approach,
is then pulled out of the hat operator
:
making
an algebraic function of
which is then determined by requiring that
considered as a function of C have the least possible value.
However, since the
thus obtained turns out to be just as variable as any other fluctuating quantity in turbulence, the original assumption of the constancy of
cannot be justified a posteriori. In the DLM approach one avoids this inconsistency by not invoking the step of removing
C from the test filtering operation. Instead, one defines a global error over the entire flow domain by the quantity
:
where the integral ranges over the whole fluid volume. This global error