Consequences of Symmetries on the Analysis and Construction of Turbulence Models
Since they represent fundamental physical properties in turbulence (conservation laws, wall laws, Kolmogorov energy spectrum, ...), symmetries are used to analyse common turbulence models. A class of symmetry preserving turbulence models is proposed. This class is refined such that the models respec...
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nasplib_isofts_kiev_ua-123456789-1461712025-02-09T20:16:05Z Consequences of Symmetries on the Analysis and Construction of Turbulence Models Razafindralandy, D. Hamdouni, A. Since they represent fundamental physical properties in turbulence (conservation laws, wall laws, Kolmogorov energy spectrum, ...), symmetries are used to analyse common turbulence models. A class of symmetry preserving turbulence models is proposed. This class is refined such that the models respect the second law of thermodynamics. Finally, an example of model belonging to the class is numerically tested. 2006 Article Consequences of Symmetries on the Analysis and Construction of Turbulence Models / D. Razafindralandy, A. Hamdouni // Symmetry, Integrability and Geometry: Methods and Applications. — 2006. — Т. 2. — Бібліогр.: 26 назв. — англ. 1815-0659 2000 Mathematics Subject Classification: 22E70; 35A30; 35Q30; 76D05; 76F65; 76M60 https://nasplib.isofts.kiev.ua/handle/123456789/146171 en Symmetry, Integrability and Geometry: Methods and Applications application/pdf Інститут математики НАН України |
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Since they represent fundamental physical properties in turbulence (conservation laws, wall laws, Kolmogorov energy spectrum, ...), symmetries are used to analyse common turbulence models. A class of symmetry preserving turbulence models is proposed. This class is refined such that the models respect the second law of thermodynamics. Finally, an example of model belonging to the class is numerically tested. |
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Consequences of Symmetries on the Analysis and Construction of Turbulence Models |
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Consequences of Symmetries on the Analysis and Construction of Turbulence Models |
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Consequences of Symmetries on the Analysis and Construction of Turbulence Models |
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Consequences of Symmetries on the Analysis and Construction of Turbulence Models |
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Consequences of Symmetries on the Analysis and Construction of Turbulence Models |
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consequences of symmetries on the analysis and construction of turbulence models |
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Consequences of Symmetries on the Analysis and Construction of Turbulence Models / D. Razafindralandy, A. Hamdouni // Symmetry, Integrability and Geometry: Methods and Applications. — 2006. — Т. 2. — Бібліогр.: 26 назв. — англ. |
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Symmetry, Integrability and Geometry: Methods and Applications |
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Symmetry, Integrability and Geometry: Methods and Applications Vol. 2 (2006), Paper 052, 20 pages
Consequences of Symmetries on the Analysis
and Construction of Turbulence Models
Dina RAZAFINDRALANDY and Aziz HAMDOUNI
LEPTAB, Avenue Michel Crépeau, 17042 La Rochelle Cedex 01, France
E-mail: drazafin@univ-lr.fr, ahamdoun@univ-lr.fr
Received October 28, 2005, in final form May 02, 2006; Published online May 12, 2006
Original article is available at http://www.emis.de/journals/SIGMA/2006/Paper052/
Abstract. Since they represent fundamental physical properties in turbulence (conservation
laws, wall laws, Kolmogorov energy spectrum, . . . ), symmetries are used to analyse common
turbulence models. A class of symmetry preserving turbulence models is proposed. This
class is refined such that the models respect the second law of thermodynamics. Finally, an
example of model belonging to the class is numerically tested.
Key words: turbulence; large-eddy simulation; Lie symmetries; Noether’s theorem; thermo-
dynamics
2000 Mathematics Subject Classification: 22E70; 35A30; 35Q30; 76D05; 76F65; 76M60
1 Introduction
Turbulence is one of the most interesting research fields in mechanics. But with the current per-
formance of computers, a direct simulation of a turbulent flow remains difficult, even impossible
in many cases, due to the high computational cost that it requires. To reduce this computational
cost, use of turbulence models is necessary. At the present time, many turbulence models exist
(see [22]). However, derivation of a very large majority of them does not take into account the
symmetry group of the basic equations, the Navier–Stokes equations.
In turbulence, symmetries play a fundamental role in the description of the physics of the
flow. They reflect existence of conservation laws, via Noether’s theorem. Notice that, even if
Navier–Stokes equations are not directly derived from a Lagrangian, Noether’s theorem can be
applied and conservation laws can be deduced. Indeed, there exists a Lagrangian (which will be
called “bi-Lagrangian” here) from which Navier–Stokes equations, associated to their “adjoint”
equations, can be derived. A way in which this bi-Lagrangian can be calculated is described
by Atherton and Homsy in [1]. The expression of this bi-Lagrangian and the infinitesimal
generators of the associated Euler–Lagrange equations are given in Appendix A. However, the
conservation laws are not studied in this paper.
The importance of symmetries in turbulence is not limited to the derivation of conservation
laws. Ünal also used a symmetry approach to show that Navier–Stokes equations may have
solutions which have the Kolmogorov form of the energy spectrum [24]. Next, symmetries
enabled Oberlack to derive some scaling laws for the velocity and the two point correlations [19].
Some of these scaling laws was reused by Lindgren et al in [15] and are proved to be in good
agreement with experimental data. Next, symmetries allowed Fushchych and Popowych to
obtain analytical solutions of Navier–Stokes equations [8]. The study of self-similar solutions
gives also an information on the behaviour of the flow at a large time [4]. Lastly, we mention
that use of discretisation schemes which are compatible with the symmetries of an equation
reduces the numerical errors [20, 13].
mailto:drazafin@univ-lr.fr
mailto:ahamdoun@univ-lr.fr
http://www.emis.de/journals/SIGMA/2006/Paper052/
2 D. Razfindralandy and A. Hamdouni
Introduction of a turbulence model in Navier–Stokes equations may destroy symmetry pro-
perties of the equations. In this case, physical properties (conservation laws, scaling laws,
spectral properties, large-time behaviour, . . . ) may be lost. In order to represent the flow
correctly, turbulence models should then preserve the symmetries of Navier–Stokes equations.
The first aim of this paper is to show that most of the commonly used subgrid turbulence
models do not have this property. The second goal is to present a new way of deriving models
which are compatible with the symmetries of Navier–Stokes equations and which, unlike many
existing models, conform to the second law of thermodynamics. As it will be shown in appendix,
conformity with this law leads to stability of the model, in the sense of L2.
The paper will be structured as follows. In Section 2, the principle of turbulence modelling,
using the large-eddy simulation approach, will be concisely presented, as well as some common
models. These models will be analysed in Section 3 under the symmetry consideration. In
Section 4, a class of symmetry preserving and thermodynamically consistent models are derived.
One example of a model of the class is numerically tested in Section 5. Some conclusions will
be drawn in Section 6. In Appendix A, it will be shown that Navier–Stokes equations can be
derived from a bi-Lagrangian. At last, in Appendix B, stability of thermodynamically consistent
models is proved.
2 Large-eddy simulation
Consider a three-dimensional incompressible Newtonian fluid, with density ρ and kinematic
viscosity ν. The motion of this fluid is governed by Navier–Stokes equations:
∂u
∂t
+ div(u⊗ u) +
1
ρ
∇p = div T,
div u = 0, (1)
where u = (ui)i=1,2,3 and p are respectively velocity and pressure fields and t the time variable.
T is a tensor such that ρT is the viscous constraint tensor. T can be linked to the strain rate
tensor S = (∇u + T∇u)/2 according to the relation:
T =
∂ψ
∂S
,
ψ being a positive and convex “potential” defined by:
ψ = ν trS2.
Since a direct numerical simulation of a realistic fluid flow requires very significant compu-
tational cost, (1) is not directly resolved. To circumvent the problem, some methods exist. The
most promising one is the large-eddy simulation. It consists in representing only the large scales
of the flow. Small scales are dropped from the simulation; however, their effects on the large
scales are taken into account. This enables to take a much coarser grid.
Mathematically, dropping small scales means applying a low-pass filter. The large or resolved
scales φ of a quantity φ are defined by the convolution:
φ = Gδ ∗ φ,
where Gδ is the filter kernel with a width δ, and the small scales φ′ are defined by
φ′ = φ− φ.
Consequences of Symmetries on the Analysis and Construction of Turbulence Models 3
It is required that the integral ofGδ over R3 is equal to 1, such that a constant remains unchanged
when the filter is applied.
In practice, (u, p) is directly used as an approximation of (u, p). To obtain (u, p), the filter
is applied to (1). If the filter is assumed to commute with the derivative operators (that is not
always the case in a bounded domain), this leads to:
∂u
∂t
+ div(u⊗ u) +
1
ρ
∇p = div(T + Ts),
div u = 0, (2)
where Ts is the subgrid stress tensor defined by Ts = u ⊗ u − u⊗ u which must be model-
led (expressed by a function of the resolved quantities) to close the equations. Currently, an
important number of models exists. Some of the most common ones will be reminded here.
They will be classified in four categories: turbulent viscosity, gradient-type, similarity-type and
Lund–Novikov-type models.
2.1 Turbulent viscosity models
Turbulent viscosity models are models which can be written in the following form:
Td
s = νsS, (3)
where νs is the turbulent viscosity. The superscript (d) represents the deviatoric part of a tensor:
Q 7→ Qd = Q−
1
3
(trQ)Id,
where Id is the identity operator. The deviatoric part has been introduced in order to have the
equality of the traces in (3). In what follows, some examples of turbulent viscosity models are
presented.
• Smagorinsky model (see [22]) is one of the most widely used models. It uses the local
equilibrium hypothesis for the calculation of the turbulent viscosity. It has the following
expression:
Td
s = (CSδ)2|S|S,
where CS ' 0.148 is the Smagorinsky constant, δ the filter width and |S| =
√
2 tr
(
S
2).
• In order to reduce the modelling error of Smagorinsky model, Lilly [14] proposes a dynamic
evaluation of the constant CS by a least-square approach. This leads to the so-called
dynamic model defined by:
Td
s = Cdδ
2|S|S, with Cd =
tr(LM)
trM2
. (4)
In these terms,
L = ũ⊗ ũ− ũ⊗ u, M = δ
2 |̃S|S− δ̃
2
|S̃|S̃,
the tilde represents a test filter whose width is δ̃, with δ̃ > δ.
The last turbulent viscosity model which will be considered is the structure function model.
4 D. Razfindralandy and A. Hamdouni
• Metais and Lesieur [16] make the hypothesis that the turbulent viscosity depends on the
energy at the cutoff. Knowing its relation with the energy density in Fourier space, they
use the second order structure function and propose the structure function model:
Td
s = CSF δ
√
F 2(δ)S, (5)
where F 2 is the spatial average of the filtered structure function:
r 7→ F 2(r) =
∫∫
‖z‖=r
‖u(x)− u(x + z)‖2 dz dx.
The next category of models, which will be reminded, consists of the gradient-type models.
2.2 Gradient-type models
To establish the gradient-type models, the subgrid stress tensor is decomposed as follows:
Ts = u⊗ u− ( u⊗ u + u⊗ u′ + u′ ⊗ u + u′ ⊗ u′ ).
Next, each term between the brackets are written in Fourier space. Then, the Fourier transform
of the filter, which is assumed to be Gaussian, is approximated by an appropriate function.
Finally, the inverse Fourier transform is computed. The models in this category differ by the
way in which the Fourier transform of the filter is approximated.
• If a second order Taylor series expansions according to the filter width δ is used in the
approximation, one has:
Ts = −
δ
2
12
∇u T∇u. (6)
• The gradient model is not dissipative enough and not numerically stable [26, 11]. Thus, it
is generally combined to Smagorinsky model. This gives Taylor model:
Ts = −
δ
2
12
∇u T∇u + Cδ
2|S|S.
• The Taylor approximation of the Fourier transform of the filter tends to accentuate the
small frequencies rather than attenuating them. Instead, a rational approximation can be
used [12, 2]. This gives the following expression of the model:
Ts = −
δ
2
12
(
Id −
δ
2
24
∇2
)−1
[∇u T∇u] + Cδ
2|S|S.
To avoid the inversion of the operator
(
Id − δ
2
24∇
2
)
, Ts is approximated by:
Ts = −
δ
2
12
Gδ ∗ [∇u T∇u] + Cδ
2|S|S. (7)
Gδ is the kernel of the Gaussian filter. The convolution is done numerically. The model (7)
is called the rational model.
Consequences of Symmetries on the Analysis and Construction of Turbulence Models 5
2.3 Similarity-type models
Models of this category are based on the hypothesis that the statistic structure of the small
scales are similar to the statistic structure of the smallest resolved scales. Separation of the
resolved scales is done using a test filter (symbolized by ˜). The largest resolved scales are then
represented by ũ and the smallest ones by u− ũ. From this hypothesis, we deduce the similarity
model:
Ts = ũ⊗ ũ− ũ⊗ u. (8)
From this expression, many other models can be obtained by multiplying by a coefficient, by
filtering again the whole expression or by mixing with a Smagorinsky-type model.
The last models that we will consider are Lund–Novikov-type models.
2.4 Lund–Novikov-type models
• Lund and Novikov include the filtered vorticity tensor W = (∇u − T∇u) in the expres-
sion of the subgrid model. Cayley–Hamilton theorem gives then the Lund–Novikov model
(see [22]):
−Td
s = C1δ
2|S|S + C2δ
2(S2)d + C3δ
2(
W
2)d
+ C4δ
2(SW −WS) + C5δ
2 1
|S|
(
S
2
W − SW
2)
, (9)
where the coefficients Ci depend on the invariants obtained from S and W. The expression
of these coefficients are so complex that they are considered as constants and evaluated
with statistic techniques.
• To reduce the computation cost of the previous model, Kosovic brings a simplification and
proposes the following model:
−Td
s = (Cδ)2
[
2|S|S + C1
(
S
2)d + C2(SW −WS)
]
, (10)
where the constants C, C1 and C2 are calculated using the theory of homogeneous and
isotropic turbulence.
The derivation of these models was done using different hypothesis but did not take into
consideration the symmetries of Navier–Stokes equations which may then be destroyed. So, in
the next section, these models will be analysed by a symmetry approach.
3 Model analysis
The (classical) symmetry groups of Navier–Stokes equations have been investigated for some
decades (see for example [7, 3]). They are generated by the following transformations:
• The time translations: (t,x,u, p) 7→ (t+ a,x,u, p),
• the pressure translations: (t,x,u, p) 7→ (t,x,u, p+ ζ(t)),
• the rotations: (t,x,u, p) 7→ (t,Rx,Ru, p),
• the generalized Galilean transformations:
(t,x,u, p) 7→ (t,x + α(t),u + α̇(t), p− ρx · α̈(t)),
• and the first scaling transformations: (t,x,u, p) 7→ (e2at, eax, e−au, e−2a p).
6 D. Razfindralandy and A. Hamdouni
In these expressions, a is a scalar, ζ (respectively α) a scalar (resp. vectorial) arbitrary
function of t and R a rotation matrix, i.e. RTR = Id and detR = 1. The central dot (·) stands
for R3 scalar product.
If it is considered that ν can change during the transformation (which is then an equivalence
transformation [9]), one has the second scaling transformations:
(t,x,u, p, ν) 7→ (t, eax, eau, e2ap, e2aν),
where a is the parameter.
Navier–Stokes equations admit other known symmetries which do not constitute a one-
parameter symmetry group. They are
• the reflections: (t,x,u, p) 7→ (t,Λx,Λu, p),
which are discrete symmetries, Λ being a diagonal matrix Λ = diag(ι1, ι2, ι3) with ιi =
±1, i = 1, 2, 3,
• and the material indifference: (t,x,u, p) 7→ (t, x̂, û, p̂),
in the limit of a 2D flow in a simply connected domain [5], with
x̂ = R(t) x, û = R(t) u + Ṙ(t) x, p̂ = p− 3ωϕ+
1
2
ω2‖x‖2,
where R(t) is a 2D rotation matrix with angle ωt, ω an arbitrary real constant, ϕ the usual
2D stream function defined by:
u = curl(ϕe3),
e3 the unit vector perpendicular to the plane of the flow and ‖•‖ the Euclidean norm.
We wish to analyse which of the models cited above is compatible with these symmetries.
The set of solutions (u, p) of Navier–Stokes equations (1) is preserved by each of the symmetries.
We then require that the set of solutions (u, p) of the filtered equations (2) is also preserved by
all of these transformations, since (u, p) is expected to be a good approximation of (u, p). More
clearly, if a transformation
T : (t, x,u, p) 7→ (t̂, x̂, û, p̂)
is a symmetry of (1), we require that the model is such that the same transformation, applied
to the filtered quantities:
T : (t, x,u, p) 7→ (t̂, x̂, û, p̂),
is a symmetry of the filtered equations (2). When this condition holds, the model will be said
invariant under the relevant symmetry.
The filtered equations (2) may have other symmetries but with the above requirement, we
may expect to preserve certain properties of Navier–Stokes equations (conservation laws, wall
laws, exact solutions, spectra properties, . . . ) when approximating (u, p) by (u, p).
We will use the hypothesis that test filters do not destroy symmetry properties, i.e. ̂̃φ = ˜̂
φ
for any quantity φ.
For the analysis, the symmetries of (1) will be grouped into four categories:
– translations, containing time translations, pressure translations and the generalized Gali-
lean transformations,
Consequences of Symmetries on the Analysis and Construction of Turbulence Models 7
– rotations and reflections,
– scaling transformations,
– material indifference.
The aim is to search which models are invariant under the symmetries within the considered
category.
3.1 Invariance under translations
Since almost all existing models are autonomous in time and pressure, the filtered equations (2)
remain unchanged when a time or pressure translation is applied. Almost all models are then
invariant under the time and the pressure translations.
The generalized Galilean transformations, applied to the filtered variables, have the following
form:
(t,x,u, p) 7→ (t̂, x̂, û, p̂) = (t,x + α(t),u + α̇(t), p− ρx · α̈(t)) .
All models in Section 2, in which x and u are present only through ∇u are invariant since
∇̂û = ∇û = ∇u,
where ∇̂ = (∂/∂x̂1, ∂/∂x̂2, ∂/∂x̂3).
The remaining models, i.e. the dynamic and the similarity models are also invariant because
˜̂u⊗ û− ˜̂u⊗ ˜̂u = ˜(u + α̇)⊗ (u + α̇)− ˜(u + α̇)⊗ ˜(u + α̇) = ũ⊗ u− ũ⊗ ũ.
3.2 Invariance under rotations and reflections
The rotations and the reflections can be put together in a transformation:
(t,x,u, p) 7→ (t,Υx,Υu, p)
where Υ is a constant rotation or reflection matrix. This transformation, when applied to the
filtered variables, is a symmetry of (2) if and only if
T̂s = Υ Ts
TΥ. (11)
Let us check if the models respect this condition.
• For Smagorinsky model, we have:
∇̂û = [∇(û)]TΥ = [∇(Υu)]TΥ = Υ[∇u]TΥ. (12)
This leads to the objectivity of S:
Ŝ = Υ S TΥ.
And since |Ŝ| = |S|, (11) is verified. Smagorinsky model is then invariant.
• For similarity model (8), one has:
û⊗ û = (Υu)⊗ (Υu) = Υ(u⊗ u)TΥ.
By means of these relations, invariance can easily been deduced.
8 D. Razfindralandy and A. Hamdouni
• The same relations are sufficient to prove invariance of the dynamic model since the trace
remains invariant under a change of orthonormal basis.
• The structure function model (5) is invariant because the function F 2 is not altered under
a rotation or a reflection.
• Relations (12) can be used again to prove invariance of each of the gradient-type models.
• Finally, since
Ŵ = Υ W TΥ,
Lund–Novikov-type models are also invariant.
Any model of Section 2 is then invariant under the rotations and the reflections.
3.3 Invariance under scaling transformations
The two scaling transformations can be gathered in a two-parameter transformation which, when
applied to the filtered variables, have the following expression:
(t,x,u, p, ν) 7→
(
e2at, eabx, eb−au, e2b−2ap, e2bν
)
.
where a and b are the parameters. The first scaling transformations corresponds to the case
b = 0 and the second ones to the case a = 0.
It can be checked that the filtered equations (2) are invariant under the two scaling trans-
formations if and only if
T̂s = e2b−2aTs. (13)
Since Ŝ = e−2aS, this condition is equivalent to:
ν̂s = e2bνs (14)
for a turbulent viscosity model.
• For Smagorinsky model, we have:
ν̂s = CSδ
2|Ŝ| = e−2aCSδ
2|S| = e−2aνs.
Condition (14) is violated. The model is invariant neither under the first nor under the
second scaling transformations. Note that the filter width δ does not vary since it is an
external scale length and has no functional dependence on the variables of the flow.
• The dynamic procedure used in (4) restores the scaling invariance. Indeed, it can be shown
that:
Ĉd = e2b+2aCd,
that implies:
ν̂s = Ĉdδ
2|Ŝ| = e2bCdδ
2|S| = e2bνs.
The dynamic model is then invariant under the two scaling transformation.
Consequences of Symmetries on the Analysis and Construction of Turbulence Models 9
• For the structure function model, we have:
F̂ 2 = eb−aF 2
and then
ν̂s = eb−aνsm,
that proves that the model is not invariant.
• Since
∇̂û = e2a∇u
the gradient model (6) violates (13), Ts varying in the following way:
T̂s = e4aTs.
This also implies that none of the gradient-type models is invariant.
• It is straight forward to prove that the similarity model (8) verifies (13) and is invariant.
• At last, Lund–Novikov-type models are not invariant because they comprise a term similar
to Smagorinsky model.
In fact, none of the models where the external length scale δ appears explicitly is invariant
under the scaling transformations. Note that the dynamic model, which is invariant under these
transformations, can be written in the following form:
Td
s =
tr(LN)
tr(N2)
|S|S,
where
N = |̃S|S− (δ̃/δ)2|S̃|S̃.
It is then the ratio δ̂/δ which is present in the model but neither δ alone nor δ̂ alone.
In summary, the dynamic and the similarity models are the only invariant models under the
scaling transformations. Though, scaling transformations have a particular importance because
it is with these symmetries that Oberlack [18] derived scaling laws and that Ünal [24] proved
the existence of solutions of Navier–Stokes equations having Kolmogorov spectrum.
The last symmetry property of Navier–Stokes equations is the material indifference, in the
limit of 2D flow, in a simply connected domain.
3.4 Material indifference
The material indifference corresponds to a time-dependent plane rotation, with a compensation
in the pressure term. We will not write explicitly the dependence on time of the rotation
matrix R.
• The objectivity of S (see Section 3.2) directly leads to invariance of Smagorinsky model.
10 D. Razfindralandy and A. Hamdouni
• For similarity model (8), we have:
T̂s = RTs
TR + R(ũ⊗ x− ũ⊗ x̃)TR
+ Ṙ(x̃⊗ u− x̃⊗ ũ)TR + Ṙ(x̃⊗ x− x̃⊗ x̃)T Ṙ.
Consequently, if the test filter is such that
(ũ⊗ x− ũ⊗ x̃) = 0, (x̃⊗ u− x̃⊗ ũ) = 0, (x̃⊗ x− x̃⊗ x̃) = 0, (15)
then the similarity model is invariant under the material indifference. All filters do not
have this property. For instance, it can be shown [21] that, for the usual box filter, the
left-hand sides of equations (15) do not vanish and are respectively inO(δ̃), O(δ̃) and O(δ̃
2
).
• Under the same conditions (15) on the test filter, the dynamic model is also invariant.
• The structure function model is invariant if and only if
F̂ 2 = F 2. (16)
Let us calculate F̂ 2. Let uz be the function x 7→ u(x + z). Then
F̂ 2 =
∫
‖z‖=δ
‖(Ru + Ṙx)− (Ruz + Ṙx + Ṙz)‖2 dz
=
∫
‖z‖=δ
‖u− uz − TRṘz‖ dz.
Knowing that TRṘz = ωe3 × z, we get:
F̂ 2 = F 2 + 2πω2δ
3 − 2ω
∫
‖z‖=δ
(u− uz) · (e3 × z) dz.
Condition (16) is violated. So, the structure function model is not invariant under the
material indifference.
• For the gradient model, we have:
∇̂û = R∇u TR + ṘTR, T ∇̂û = R T∇u TR + R T Ṙ. (17)
Let J be the matrix such that ṘTR = −ωJ = −R T Ṙ or, in a component form:
J =
(
0 1
−1 0
)
.
Then,
̂(∇u T∇u) = R(∇u T∇u)TR + ωR∇u TRJ− ωJR T∇u TR + ω2Id.
The commutativity between J and R finally leads to:
T̂s = RTs
TR + ωR(∇uJ− J T∇u) + ω2Id.
This proves that the gradient model is not invariant.
• The other gradient-type models inherit the lack of invariance of the gradient model.
Consequences of Symmetries on the Analysis and Construction of Turbulence Models 11
• It remains the Lund–Novikov-type models. We will begin with Kosovic model (10) since
it is simpler. The first two terms of (10) are unchanged under the transformation. For the
filtered vorticity tensor W, it follows from (17) that:
Ŵ = RW TR− ωJ.
Thus,
Ŝ Ŵ − Ŵ Ŝ = R(S W −W S)TR− ωR(SJ− JS)TR
using again the commutativity between J and R. As for them, S and J are not com-
mutative. In fact, using properties of S, it can be shown that SJ = −JS. This implies
that
Ŝ Ŵ − Ŵ Ŝ = R(S W −W S)TR− 2ωRSJTR. (18)
This shows that Kosovic model is not invariant.
• Lastly, consider Lund–Novikov model (9). We have:
Ŵ
2
= RW
2 TR− ωR(JW + WJ)TR− ω2Id.
Since W is anti-symmetric and the flow is 2D, W is in the form:
W =
(
0 w
−w 0
)
.
A direct calculation leads then to
JW = WJ = −wId
and
Ŵ
2
= RW
2 TR− (2w − ω)ωId.
Let us see now how each term of the model (9) containing W varies under the transfor-
mation.
From the last equation, we deduce the objectivity of (W2)d:
(̂W2)d = R (W2)d TR.
For the fourth term of (9), we already have (18). And for the last term,
Ŝ
2
Ŵ − Ŝ Ŵ
2
= R(S2
W − SW
2)TR− ωRS
2 TRJ− (2w − ω)ωRS TR.
Putting these results together, we have:
T̂d
s = RTd
s
TR− ωδ
2
R
[
2C4SJ− C5
1
|S|
(
S
2
J− (2w − ω)ωS
)]
TR.
We conclude that Lund–Novikov model is not invariant the material indifference. This
ends the analysis.
12 D. Razfindralandy and A. Hamdouni
Translations Rotations, Scaling Material
reflections transformations indifference
Smagorinsky Y Y N Y
Dynamic Y Y Y Y∗
Structure function Y Y N N
Gradient Y Y N N
Taylor Y Y N N
Rational Y Y N N
Similarity Y Y Y Y∗
Lund Y Y N N
Kosovic Y Y N N
Table 1. Results of the model analysis.
Y=invariant, N=not invariant, Y∗=invariant if (15) is verified.
Table 1 summarizes the results of the above analysis. “Y” means that the model is invariant
under all the symmetries of the category, “N” the opposite and “Y∗” that the model is invariant
if the conditions (15) on the test filter is verified. It can be seen on this table that only two models
among the nine, the dynamic and the similarity models, are invariant under the symmetry group
of Navier–Stokes equations. The scaling transformations, which are of a particular importance
(scaling laws, Kolmogorov spectrum, . . . ) are violated by almost all models.
The dynamic and the similarity models have an inconvenience that they necessitate use of a
test filter. Rather constraining conditions, (15), are then needed for these models to preserve
the material indifference. In addition, the dynamic model does not conform to the second law of
thermodynamics since it may induce a negative dissipation. Indeed, ν + νs can take a negative
value. To avoid it, an a posteriori forcing is generally done. It consists of assigning to νs a value
slightly higher than −ν:
νs = −ν (1− ε),
where ε is a positive real number, small against 1. Non-conformity to the second law of thermo-
dynamics may be detrimental for a model because, as it will be shown in Appendix B, consistence
with this law leads to stability of the model.
Considering this lack of invariance of existing models and to non-conformity with thermo-
dynamical principles, we propose in the next section a new way of deriving models which, on
one hand, possess the symmetry group of Navier–Stokes equations and, on the other hand, are
compatible with the second law of thermodynamics.
4 Invariant and thermodynamically consistent models
First, we will build a class of models which possess the symmetries of Navier–Stokes equations
and next refine this class such that the models also satisfy the thermodynamics requirement.
Consequences of Symmetries on the Analysis and Construction of Turbulence Models 13
4.1 Invariance under the symmetries
Suppose that S 6= 0. Let Ts be an analytic function of S:
Ts = A(S). (19)
By this way, invariance under the time, pressure and generalised Galilean translations and under
the reflections is guaranteed. From (19), Cayley–Hamilton theorem and invariance under the
rotations lead to:
Td
s = A(χ, ζ) S +B(χ, ζ) Adjd S, (20)
where χ = trS
2 and ζ = detS are the invariants of S (the third invariant, trS, vanishes), Adj
stands for the operator defined by
(AdjS)S = (detS)Id,
(AdjS is simply the comatrix of S ) and A and B are arbitrary scalar functions. Contrarily to
Lund–Novikov model, these coefficient functions will not be taken constant.
Next, a necessary and sufficient condition for Ts defined by (20) to be invariant under the
second scale transformations is that ν can be factorized:
Td
s = νA0(χ, ζ) S + νB0(χ, ζ) Adjd S.
Lastly, Ts is invariant under the first scaling transformations if
T̂s = e−2aTs.
Rewritten for A0 and B0, this condition becomes:
A0(e−4aχ, e−6aζ) = A0(χ, ζ), B0(e−4aχ, e−6aζ) = e2aB0(χ, ζ).
After differentiating according to a and taking a = 0, it follows:
−4χ
∂A0
∂χ
− 6ζ
∂A0
∂ζ
= 0, −4χ
∂B0
∂χ
− 6ζ
∂B0
∂ζ
= 2B0.
To satisfy these equalities, one can take
A0(χ, ζ) = A1
(
ζ
χ3/2
)
, B0(χ, ζ) =
1
√
χ
B1
(
ζ
χ3/2
)
.
Finally, if v =
ζ
χ3/2
then
Td
s = νA1(v) S + ν
1
√
χ
B1(v) Adjd S. (21)
A subgrid-scale model of class (21) remains then invariant under the symmetry transforma-
tions of Navier–Stokes equations.
In fact several authors were interested in building invariant models for a long time. But
because they did not use Lie theory, they did not consider some symmetries such as the sca-
ling transformations which are particularly important. Three of the few authors who consi-
dered all the above symmetries in the modeling of turbulence are Ünal [25] and Saveliev and
14 D. Razfindralandy and A. Hamdouni
Gorokhovski [23]. The present manner to build invariant models generalises the Ünal’s one in
the sense that it introduces ν and the invariants of S into the models. In addition, Ünal used the
Reynolds averaging approach (RANS) instead of the large-eddy simulation approach (LES) for
the turbulence modelling. Saveliev and Gorokhovski in [23] used the LES approach but derive
their model in a different way than in the present article.
Let us now return to considerations which are more specific to large eddy simulation. We
know that Ts represents the energy exchange between the resolved and the subgrid scales.
Then, it generates certain dissipation. To account for the second law of thermodynamics, we
must ensure that the total dissipation remains positive that is not always verified by models in
the literature. In order to satisfy this condition, we refine class (21).
4.2 Consequences of the second law of thermodynamics
At molecular scale, the viscous constraint is:
T =
∂ψ
∂S
.
The potential ψ = ν trS2 is convex and positive that ensures that the molecular dissipation is
positive:
Φ = tr(TS) ≥ 0.
The tensor Ts can be considered as a subgrid constraint, generating a dissipation
Φs = tr(TsS).
To preserve compatibility with the Navier–Stokes equations, we assume that Ts has the same
form as T:
Ts =
∂ψs
∂S
. (22)
where ψs is a potential depending on the invariants χ and ζ of S. This hypothesis refines
class (21) in the following way.
Since trS = 0, one deduces from (22):
Td
s = 2
∂ψs
∂χ
S +
∂ψs
∂ζ
Adjd S.
Comparing it with (21), one gets:
1
2
νA1(v) =
∂ψs
∂χ
, ν
1
√
χ
B1(v) =
∂ψs
∂ζ
.
This leads to:
∂
∂ζ
(
1
2
A1(v)
)
=
∂
∂χ
(
1
√
χ
B1(v)
)
.
If g is a primitive of B1, a solution of this equation is
A1(v) = 2g(v)− 3vg′(v) and B1(v) = g′(v). (23)
Consequences of Symmetries on the Analysis and Construction of Turbulence Models 15
Then, the hypothesis (22) involves existence of a function g such that:
Td
s = ν
[
2g(v)− 3vg′(v)
]
S + ν
1
√
χ
g′(v) Adjd S. (24)
Now, let ΦT be the total dissipation. We have:
ΦT = tr[(T + Ts)S].
Using (21) et (23), one can show that
ΦT ≥ 0 ⇐⇒ 1 +A1(v) + 3vB1(v) ≥ 0
⇐⇒ 1 + g(v) ≥ 0. (25)
In summary, a model belonging to class (24) with a continuous function g verifying
1 + g ≥ 0, (26)
is a model possessing the symmetry group of Navier–Stokes equations conform to the second law
of thermodynamics. Such a model can take into account the inverse energy cascade, since Φs
can have negative values. In addition, by putting S in a diagonal form, it can be shown that
v belongs to a bounded interval [−v∗, v∗] where v∗ ' 0.136. Consequently, it is not necessary
to satisfy (26) out of this interval. Another important property of such a model is its stability,
in the sense that the L2-norm of the filtered velocity remains bounded. In fact, all models
which are consistent with the second law of thermodynamics are stable. This will be proved in
Appendix B.
In the next section, we show that our approach can lead to numerically efficient results.
A very simple model of class (24) is then chosen and compared to the two most popular models
which are Smagorinsky and the dynamic models (see [22, 14]).
5 Numerical test
We choose a simple linear function for g:
g(v) = Cv.
where the constant C can depend on the filter width and other parameters.
Let d be the ratio:
d =
δ
`
,
where ` is a length scale related to the size of the domain. The introduction of this ratio is also
useful to have the right dimensions. We now take:
C = (Csd)2,
where Cs is a pure constant, set to be equal to Smagorinsky constant, i.e. Cs ' 0.16. Doing so,
condition (25) is verified and one has:
Td
s = ν(Csd)2
(
−
detS
||S||3
S +
1
||S||
Adjd S
)
.
16 D. Razfindralandy and A. Hamdouni
>
L=9m
H
=
3
m
W=3m
U
= 0.455 m/s
inlet height : 0.168 m
outlet height : 0.46 m
inlet velocity : U
in
>
in
>
>
>
1
x
3
x
2
x
Figure 1. Geometry of the ventilated room.
-0.4 -0.2 0 0.2 0.4 0.6
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Velocity u /U
in
Experiments
H
ei
g
h
t
Smagorinsky
Dynamic
Invariant
x
/H
1
2
Figure 2. Mean velocity profiles at x/L = 2/3.
In the present paper, this model will be called “invariant model”. We use this model to
simulate a flow within a ventilated room (Nielsen’s cavity [17]) which interests us particularly
for applications in building field. The results will then be compared to those provided by the
Smagorinsky model and the dynamic model.
The geometry of the room is presented on Fig. 1. For this configuration, we take ` = 1m.
The code used for the resolution was developed by Chen et al and is described in [6]. The
spatial discretization is performed by a finite difference scheme.
Fig. 2 compares the velocity profiles given by Smagorinsky, the dynamic and the invariant
models with experimental data at x1/L = 2/3 and x3/W = 0.5. It can be observed on it that
the invariant model gives a better result than Smagorinsky and dynamic models, without need
of a test filtering. The result is in good agreement with experiments, except near the floor.
Notice that no wall model was used.
6 Conclusion
In this article, we presented a new class of physically compatible subgrid turbulence models. The
main ingredient used is the symmetry group of the Navier–Stokes equations which contains a
fundamental information on the properties of the flow. The second principle of thermodynamics
was also introduced. From a practical point of view, conformity with this principle ensures
stability of the model.
A simple model of the class was tested and encouraging results was obtained. However, the
aim of this test was not to present a complete analysis of the model but to check that the
symmetry approach can lead to good numerical results. Further studies will be done in future
works on the choice of the parameters of the model and on analysis of the numerical results.
The way presented here for deriving symmetry compatible models is a general way. It can be
applied to other equations (non-isothermal fluid, . . . ). Other parameters can also be included.
For example, dependence of the model on the viscosity ν can be replaced by dependence on the
dissipation rate.
A Noether’s theorem to Navier–Stokes equations
Noether’s theorem can be applied to evolution equations which can be derived from a Lagrangian,
i.e. evolution equations which can be expressed in an Euler–Lagrange form:
∂L(r)
∂r
−Div
∂L(r)
∂ṙ
= 0, (27)
Consequences of Symmetries on the Analysis and Construction of Turbulence Models 17
where r is the dependent variable, r = r(y), y = (yi)i the independent variable, L the La-
grangian and Div the operator:
f 7→ Div f =
∑
i
df
dyi
.
From the infinitesimal generators of (27), conservation laws are deduced.
Navier–Stokes equations cannot be directly written in the form (27). However, thanks to an
approach of Atherton and Homsy [1], see also [10], which consists in extending the Lagrangian
notion, it will be shown in this appendix that Noether’s theorem can be applied to Navier–Stokes
equations.
We will say that an evolution equation
F (y, r) = 0 (28)
is derived from a “bi-Lagrangian” if there exists an (non necessarily unique) application
L : (r, s) 7→ L(r, s) ∈ R
such that (28) is equivalent to
∂L(r, s)
∂s
−Div
∂L(r, s)
∂ṡ
= 0.
s is called the adjoint variable and the equation
∂L(r, s)
∂r
−Div
∂L(r, s)
∂ṙ
= 0
is called the adjoint equation of (28). The Noether theorem can then be applied since the
evolution equation, associated to his adjoint, can be written in an Euler–Lagrangian form
∂L(w)
∂w
−Div
∂L(w)
∂ẇ
= 0,
where w = (r, s).
Navier–Stokes equations are derived from a bi-Lagrangian
L((u, p), (v, q)) =
1
2
(
∂u
∂t
· v − u ·
∂v
∂t
)
+
(
q −
1
2
u · v
)
− p div v + ν tr (T∇u · ∇v) .
where v = (vi)i and q are the adjoint variables. The corresponding adjoint equations are
−
∂v
∂t
+ (v · ∇uT − u · ∇v) = ∇q + ν∆v,
div v = 0.
Noether’s theorem can then be applied. The infinitesimal generators of the couple of equations
(Navier–Stokes equations and their adjoint equations) are:
X0 =
∂
∂t
,
Y0 = ζ(t)
∂
∂p
,
18 D. Razfindralandy and A. Hamdouni
Xij = xj
∂
∂xi
− xi
∂
∂xj
+ uj
∂
∂ui
− ui
∂
∂uj
+ vj
∂
∂vi
− vi
∂
∂vj
, i = 1, 2, j > i,
Xi = αi(t)
∂
∂xi
+ α′i(t)
∂
∂ui
− xi α
′′
i (t)
∂
∂p
, i = 1, 2, 3,
Y1 = 2t
∂
∂t
+ xk
∂
∂xk
− uk
∂
∂uk
− 2p
∂
∂p
− q
∂
∂q
,
Y ′
0 = η(t)
∂
∂q
,
X ′
ij = (xjui − xiuj)
∂
∂q
+ xj
∂
∂vi
− xi
∂
∂vj
, i = 1, 2, j > i,
X ′
i =
(
xiσ
′(t)− uiσ(t)
) ∂
∂q
− σ(t)
∂
∂vi
, i = 1, 2, 3,
Y ′
1 = vk
∂
∂vk
+ q
∂
∂q
,
where ζ, the αi’s, η and σ are arbitrary scalar functions.
Conservation laws for Navier–Stokes equations can be deduced from these infinitesimal gene-
rators. However, that requires non-trivial calculations and is not done in this paper.
In the last section, we will prove that a model which is consistent with the second law of
thermodynamics, i.e. such that the total dissipation remains positive, is stable.
B Stability of thermodynamically consistent models
After an eventual change of variables such that u vanishes along the boundary Γ of the domain Ω,
the filtered equations can be written in the following form:
∂u
∂t
+ div(u⊗ u) +
1
ρ
∇p− div(T− Ts) = F ,
div u = 0 (29)
associated to the conditions
u = 0 sur Γ,
u(0,x) = γ(x) on Ω,∫
Ω
p(t,x) dx = 0 ∀ t ∈ [0, tf ].
F is an appropriate function of t and x and tf is the final observation time.
Proposition. Let (u, p) be a regular solution of (29) where Ts is symmetric and verif ies the
condition:
tr[(T− Ts)S] ≥ 0.
Then:
‖u(t,x)‖L2(Ω) ≤ ‖γ(x)‖L2(Ω) +
∫ tf
0
‖F (τ,x)‖L2(Ω) dτ .
This proposition ensures a finite energy when the model conforms to the second law of
thermodynamics.
Consequences of Symmetries on the Analysis and Construction of Turbulence Models 19
Proof. Let (•, •) denote the scalar product of L2(Ω) and (u, p) a regular solution of (29). From
the first equation of (29) and the boundary condition, we have:(
∂u
∂t
,u
)
+ b(u,u,u)−
1
ρ
(p,div u) + (T− Ts,∇u) = (F ,u),
where b is defined by the trilinear form
b(u1,u2,u3) =
(
div(u1 ⊗ u2),u3
)
.
From integrals by parts, the boundary condition and the divergence free condition, it can be
shown that b(u,u,u) = 0 and (p,div u) = 0. Since (T− Ts) is symmetric, it follows that(
∂u
∂t
,u
)
+ (T− Ts,S) = (F ,u).
Consequently
1
2
d
dt
(u,u) + (T− Ts,S) = (F ,u).
Now, using the main hypothesis, we have:
(T− Ts,S) =
∫
Ω
tr[(T− Ts)S] dx ≥ 0
and then
1
2
d
dt
(u,u) ≤ (F ,u).
Consequently,
‖u‖L2(Ω)
d
dt
‖u‖L2(Ω) ≤ (F ,u) ≤ ‖F ‖L2(Ω)‖u‖L2(Ω).
After simplifying by the L2-norm of u and integrating over the time, it follows:
‖u(t,x)‖L2(Ω) ≤ ‖γ(x)‖L2(Ω) +
∫ t
0
‖F (τ,x)‖L2(Ω) dτ
≤ ‖γ(x)‖L2(Ω) +
∫ tf
0
‖F (τ,x)‖L2(Ω) dτ .
This ends the proof of the proposition. �
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1 Introduction
2 Large-eddy simulation
2.1 Turbulent viscosity models
2.2 Gradient-type models
2.3 Similarity-type models
2.4 Lund-Novikov-type models
3 Model analysis
3.1 Invariance under translations
3.2 Invariance under rotations and reflections
3.3 Invariance under scaling transformations
3.4 Material indifference
4 Invariant and thermodynamically consistent models
4.1 Invariance under the symmetries
4.2 Consequences of the second law of thermodynamics
5 Numerical test
6 Conclusion
A Noether's theorem to Navier-Stokes equations
B Stability of thermodynamically consistent models
|