Large Eddy Simulation

The concept of large eddy simulation (LES) is to solve only the larger scales of turbulent motion while modeling the small scales, assuming that the large scales contain most of the energy and that the small scales show universal character. The separation of the turbulent flow into small and large scales is accomplished by spatially filtering the Navier-Stokes equations. The filtering operation applied to an arbitrary quantity φ can be expressed as:

φ(x¯xx) = Z

D

φ(xxx⁰⁰⁰)G(xxx, xxx⁰⁰⁰; ∆)dxxx⁰⁰⁰. (2.24) The integration is performed over the domain D, and G and ∆ are filter function and its filter width, respectively. The filter width corresponds to the smallest scales of ¯φ(xxx).

Different filter functions G exist, and the choice largely depends on the type of code and problem studied. Typical filter functions are the sharp Fourier cutoff filter, the Gaussian filter or the top-hat filter. The sharp Fourier cutoff filter is typically used in conjunction with a spectral numerical solution method, and the Gaussian filter may be applied to explicitly filter the governing equations [181]. However, most LES codes (including the

code in this work) imply the top-hat filter as a model for the numerical discretization employing finite volume cells or finite difference grids. In this case, the filtering operation is not performed explicitly. The top-hat filter can be expressed as (in one dimension for simplicity):

G(x⁰ − x) =

(1/∆ if |x⁰− x| ≤ ∆/2

0 otherwise. (2.25)

The filter corresponds to a spatial average over the filter volume, also referred to as Reynolds filtering. A useful definition is the Favre filter, because it reduces the number of unclosed terms in the filtered (variable density) governing equations. The Favre filter corresponds to a density-weighted filtering:

φ = ρφ/¯˜ ρ. (2.26)

The decomposition accomplished by filtering can be expressed as (similar to the Reynolds decomposition [181]):

φ = ¯φ + φ⁰, (2.27)

for Reynolds averaging and

φ = ˜φ + φ⁰⁰, (2.28)

for Favre averaging.

The filtering operation is applied to the governing equations and filtered terms in-cluding the density, ρ, are replaced by Favre averages, e.g., ρui = ¯ρ ˜ui. Additionally, commutativity of derivatives and filtering is assumed (only strictly valid for uniform filters [171]). In the case of the continuity equation, this yields:

∂ ¯ρ

In the case of the momentum equations, the following equation is obtained:

∂ρui

Using Favre filtering, the first term (I) of Eq. 2.30 becomes ∂(¯ρ˜ui)/∂t. The second term (II) becomes ∂(¯ρugiuj)/∂xj, which includes the term ugiuj that cannot be obtained from the filtered fields and hence requires modeling. This is usually expressed in the form of a subgrid stress (SGS) tensor τ_ij^SGS (in analogy to the Reynolds stress tensor ):

¯

Term V is not of relevance for the present work, as no volume forces are applied to the filtered governing equations. Hence, it is not further detailed here. Putting all terms together and rearranging yields the final filtered form of the momentum equation:

∂ ¯ρ˜ui

The modeling of τ_ij^SGSis one of the central parts of LES modeling. Often, a Boussinesq ap-proximation or eddy diffusivity approach is employed [175], where the effect of unresolved eddies is treated as an additional viscosity, which implies that energy is only transferred from the resolved to unresolved scales and not the opposite way. In analogy to the stress tensor for the Newtonian fluid, the deviatoric part of the subgrid stress tensor is modeled as:

Usually, the isotropic part of the subgrid stress tensor does not appear explicitly in the equations, but is simply added to the filtered pressure. This is adequate in low-Mach problems, where the pressure Poisson equation (described in Chapter 4) solves for the pressure field required to satisfy continuity. In the case of above described modeling, the pressure equation simply computes a pressure parameter that incorporates the isotropic part of the subgrid stress, i.e., pressure and twice the subgrid kinetic energy (it needs to be explicitly modeled and included in a fully compressible description for high-speed flows [176]):

¯

pmod = ¯p + 1

3τ_kk^SGS = ¯p + 1

3ρ( e¯ u²_k− ˜u²k). (2.35) The calculation of the eddy viscosity, appearing in 2.34 is possible via different SGS models. The first model reported, and still widely used, is the Smagorinsky model [213], which shares similarities with Prandtl’s mixing length hypothesis [181]. The Smagorinsky model relates the eddy viscosity to the resolved characteristic rate of strain. From a dimensional analysis viewpoint the turbulent viscosity can be thought of as a length scale l^∗ multiplied by a velocity scale U^∗, which in turn is the length scale divided by a time scale t^∗. The length scale of the Smagorinsky model is approximated to be proportional to the filter size ∆ and the proportionality factor is expressed by a model constant Cs. The time scale used is the inverse filtered characteristic rate of strain, where the filtered characteristic rate of strain is S =

q

2 ˜SijS˜ij, with ˜Sij = 1/2(∂ ˜ui/xj + ∂ ˜uj/xi). The expression for the turbulent viscosity is then:

νt= l^∗U^∗ = l^∗2t^∗−1 = (Cs∆)²S. (2.36) Due to its robustness and simplicity, and consistent modeling with terms concerning the Lagrangian particle phase described later, this model has been selected for the work in Chapters 5, 6 and 7. However, other, more recent models for the eddy viscosity exist. These are, for example, the WALE model [144] and the Sigma model [145]. These models partially overcome some of the weaknesses of the Smagorinsky model, such as the overprediction of eddy viscosity in situations with pure shear or near solid boundaries.

To this end, the Sigma model by Nicoud et al. [145] bases the time scale for νt on the singular values of the resolved velocity gradient tensor (˜gij = ∂ ˜ui/∂xj) and is constructed in such a way that the inverse of the time scale can go to zero, e.g., in pure shear flows.

The singular values of the resolved velocity gradient tensor can be computed from first calculating the invariants of the matrix Gij = ˜gkig˜kj (tr denoting the trace and det the determinant):

From the invariants, angles can be computed:

α₁ = I1²

This then allows for the computation of the singular values of ˜g_ij: σ1 =

The operator (or equivalently the inverse time scale) is constructed from the singular values such that the desired behavior is obtained (with σ₁ ≥ σ2 ≥ σ3 ≥ 0):

νt= (Cσ∆)²σ3(σ1− σ²)(σ2− σ³)

σ₁² . (2.40)

The Sigma model has been implemented in the LES code and tested on different non-reacting flow configurations by the author of this thesis earlier on [203] and used in parts of the work presented here.

A different way to overcome the weaknesses of the Smagorinsky model is the dynamic procedure or Germano model, which has been developed to obtain the mixing model constant Cs dynamically. Originally, this model was proposed by Germano et al. [56]

and subsequently improved by Lilly [118] and Meneveau et al. [131] and extended to variable density flows by Moin et al. [136]. The model is based on a so-called test filtering operation on top of the regular filtering, here denoted with a hat symbol. The subfilter stresses with respect to the test-filtered equations are (i.e., the stresses from the scales removed by explicit filtering):

Tij = dρuiuj− ρuciρucj

bρ . (2.41)

A central quantity for the dynamic model are the Leonard stresses:

Correspondingly to the Favre filtering on the grid filter level, a Favre filter on the test filter level is introduced, such that: ˆ¯ρ ˇ˜φ = cρ ˜¯φ. Equation 2.42 can be computed from the resolved fields available in the LES. As demonstrated by Germano, Eq. 2.42 can also be expressed by taking Eq. 2.41 and subtracting the test-filtered SGS stresses, which is commonly known as the Germano identity:

Lij = Tij − bτij^SGS. (2.43)

The deviatoric parts of both tensors on the RHS of Eq. 2.43 can be expressed by the same subfilter model, e.g., the Smagorinsky model. The only difference is that the model coefficient appears to the power of 1 instead of 2 and is named C for differentiation:

τ_ij^SGS− 1 In Eq. 2.44, ˇS is the characteristic strain obtained from the (Favre-) test-filtered velocity field ˇ˜u. With the deviatoric part of the filtered strain rate tensor ( ˜S_ij^d = 1/2(∂ ˜ui/∂xj +

∂ ˜uj/∂xi)− 1/3δij∂ ˜uk/∂xk), test filtering the stress tensor τ_ij^SGS yields:

bτij^SGS− 1

3δ_ijbτkk^SGS=−2C∆²ρ¯dS ˜S_ij^d. (2.46) From the deviatoric part of the Germano identity the following equation is obtained:

Lij −1

3δijLkk=−2b¯ρC b∆²SˇSˇ˜_ij^d + 2C∆²ρ¯dS ˜S_ij^d. (2.47) Equation 2.47 can be solved for C. However, this tensorial equation produces six inde-pendent equations to obtain C (in variable density Newtonian flows, where the stress tensor is symmetric but its trace does not add up to zero). Germano et al. [56] and Moin et al. [136] proposed to contract this equation with ˜Sij or other tensors (e.g., Eq. 2.42) to obtain a scalar equation. Lilly [118] proposed to use a least-squares approach to minimize the overall error in Eq. 2.47, which shows that Mij is the optimal tensor for contraction (with Mij =−2b¯ρC b∆²SˇSˇ˜_ij^d + 2C∆²ρ¯dS ˜S_ij^d) and produces:

C = Mij(Lij −¹₃δijLkk) MklMkl

. (2.48)

Common to the approaches by Lilly, Germano et al. and Moin et al. is the assumption that C does not change within the test-filtered volume or is unaffected by the test fil-tering operation (hence it does not appear under the hat in Eq. 2.46). Additionally, the

denominator in both approaches can become zero and can generally show highly fluctu-ating behavior leading to unstable simulations. Therefore, numerator and denominator of Eq. 2.48 are usually volume averaged, e.g., over homogeneous directions [56]. A dif-ferent approach has been proposed by Piomelli and Liu [172], where the computation of C involves the value at the last time step and averaging is not necessary any more. This approach is referred to as the localized dynamic model. It is based on the assumption that the model constant appearing in the expression for the test-filtered SGS stresses is known and obtained from an extrapolation of C in time (from past times). This allows for a local least-squares error minimalization without involving a denominator that can go to zero, except for situations where the numerator also vanishes. Following Piomelli and Liu, rearrangement of Eq. 2.47 first yields (without taking C out of the test filtering operation):

−2Cα^ij = Lij −1

3δijLkk− 2 dC^∗βij, (2.49) where C^∗ is the estimate of C and assumed to be known for the purpose of finding C.

The least-squares method yields αij as the optimal tensor for contraction, such that C can be evaluated as:

C = −1 2

(Lij −¹₃δijLkk− 2 dC^∗βij)αij

αlmαlm

. (2.50)

Although the dynamic model greatly improves the predictability of LES due to eliminat-ing the model constant for the subgrid stresses, some important characteristics have to be kept in mind. Because the same model form is used for τij and Tij, the model requires grid filter as well as test filter to be in the inertial subrange such that the same assumptions about the filtered scales can be made. Hence, in theory, the grid needs to be fine enough such that a ‘proper’ LES would be possible at the test filter level (although it has been reported that good results could be obtained if this is not the case [181]). If the dynamic procedure relies on the Smagorinsky model, it should be kept in mind that some of the deficiencies of the Smagorinsky model have implications on the dynamic procedure. In particular, the Smagorinsky model is unable to yield correct values of subgrid stresses and energy transfer to the small scales at the same time, as demonstrated by only a weak correlation of SGS stresses to strain rate tensor observed in a-priori tests of turbulent flows. This could also explain the highly fluctuating nature of the dynamically obtained modeling constant [181].

Overall, the LES method promises to accurately predict unsteady turbulent flows.

This especially holds for high Reynolds number flows with a broad inertial subrange, where the universality of the smallest eddies can be exploited well. Closely related to this is the advantage of the cost of LES being independent of the Reynolds number (with the exception of wall-bounded flows). An increase of the Reynolds number simply extends the spectrum towards larger wave-numbers, i.e., into the range that is expected to be modeled well by the SGS model. However, in reacting flows the situation is different.

In reacting flows, rate controlling processes (i.e., molecular mixing and reaction) occur on small scales, which are unresolved by the typical LES grid, posing challenges to the modeling of the interaction of turbulence and chemistry, as detailed in the next chapter.