2. Two-point correlations of filtered-velocity fields

As summarized above, certain statistical conditions that an LES subfilter model must satisfy can be derived by analysing the evolution equations for the different order statistics of the fields, such as single-point moments, multi-point moments, and so on (Meneveau Reference Meneveau1994). We review conditions based on two-point statistics as developed by Meneveau (Reference Meneveau1994) and rephrase the results more conveniently in terms of the filtered strain-rate tensor as opposed to the filtered velocity, as was done in Meneveau (Reference Meneveau1994). We also generalize the prior derivations to the case of non-homogeneous flow. Here, we briefly summarize the derivation of the Karman–Howarth equations (the evolution equations for the two-point velocity correlations) for the case of filtered-velocity fields without assuming homogeneity or isotropy, using the two-point method proposed by Hill (Reference Hill2002).

Starting from the Navier–Stokes equations for a divergence-free velocity field $\boldsymbol {u}$ with viscosity $\nu$, the LES equations for the filtered fields $\tilde {\boldsymbol {u}} = \mathcal {F}(\boldsymbol {u})$, where $\mathcal {F}$ is a spatial filtering operation, read as follows:

(2.1)\begin{equation} \frac{\partial \tilde{u}_i}{\partial t} + \tilde{u}_k \frac{\partial \tilde{u}_i}{\partial x_k} = - \frac{\partial \tilde{p}}{\partial x_i} + \nu \frac{\partial^2 \tilde{u}_i}{\partial x^2_k} - \frac{\partial \tau_{ki}}{\partial x_k}, \end{equation}

where

(2.2)\begin{gather} \tau_{ij} = \widetilde{u_i u_j} - \tilde{u}_i \tilde{u}_j - \tfrac13 \left( \widetilde{u_k u_k} - \tilde{u}_k^2 \right) \delta_{ij}, \end{gather}

(2.3)\begin{gather}\tilde{p}=\mathcal{F}(p) + \tfrac13 \left( \widetilde{u_k u_k} - \tilde{u}_k^2 \right) \end{gather}

are the deviatoric part of the SGS stresses and the modified pressure (where $p$ is the fluid pressure divided by the density), respectively.

Defining velocities at two points, $\tilde {u}^{(1)}_i = \tilde {u}_i(\boldsymbol {x}^{(1)})$, $\tilde {u}_i^{(2)} = \tilde {u}_.(\boldsymbol {x}^{(2)})$, and midpoint position (Hill Reference Hill2002) $\boldsymbol {x}^{(0)}=(\boldsymbol {x}^{(2)}+\boldsymbol {x}^{(1)})/2$, where $\boldsymbol {x}^{(2)}=\boldsymbol {x}^{(1)}+\boldsymbol {r}$ and $\boldsymbol {r}$ is the displacement vector between the two points, one can multiply their corresponding evolution equations by each other, sum them, rearrange and perform a statistical averaging operation (details are provided in the supplementary material). The result is the evolution equation for the velocity two-point correlation function $C_{uu}(\boldsymbol {r},\boldsymbol {x}^{(0)})= \left\langle {\tilde{u}_i^{(1)} \tilde{u}_i^{(2)} } \right\rangle$

(2.4)\begin{align} \frac{\partial C_{uu}}{\partial t} &= - T\left(\boldsymbol{r},\boldsymbol{x}^{(0)}\right) + P_0\left(\boldsymbol{r},\boldsymbol{x}^{(0)}\right)+V\left(\boldsymbol{r},\boldsymbol{x}^{(0)}\right) - \frac{\partial }{\partial x^{(0)}_k} \left\langle \tilde{u}^{(2)}_i \tau^{(1)}_{ki} +\tilde{u}^{(1)}_i \tau^{(2)}_{ki}\right\rangle\nonumber\\ &\quad- \left\langle \tau^{(1)}_{ki} \tilde{S}^{(2)}_{ki} + \tau^{(2)}_{ki}\tilde{S}^{(1)}_{ki} \right\rangle, \end{align}

where

(2.5)\begin{equation} \tilde{S}_{ik} = \frac{1}{2} \left( \frac{\partial \tilde{u}_i}{\partial x_k} + \frac{\partial \tilde{u}_k}{\partial x_i} \right) \end{equation}

is the filtered strain rate and

(2.6)\begin{gather} \hspace{-2.5pc}T(\boldsymbol{r},\boldsymbol{x}^{(0)}) = \frac{\partial }{\partial r_k} \left(\left\langle \tilde{u}^{(1)}_i \tilde{u}^{(2)}_i \tilde{u}^{(2)}_k\right\rangle - \left\langle\tilde{u}^{(1)}_i \tilde{u}^{(2)}_i \tilde{u}^{(1)}_k \right\rangle \right)\nonumber\\ \hspace{6pc}- \frac{1}{2} \frac{\partial }{\partial x^{(0)}_k} \left( \left\langle \tilde{u}^{(1)}_i \tilde{u}^{(2)}_i \tilde{u}^{(2)}_k\right\rangle + \left\langle \tilde{u}^{(1)}_i \tilde{u}^{(2)}_i \tilde{u}^{(1)}_k \right\rangle \right), \end{gather}

(2.7)\begin{gather} P_0(\boldsymbol{r},\boldsymbol{x}^{(0)}) = - \frac{\partial }{\partial x^{(0)}_i} \left( \left\langle \tilde{u}^{(1)}_i \tilde{p}^{(2)} \right\rangle + \left\langle\tilde{u}^{(2)}_i \tilde{p}^{(1)}, \right\rangle\right) \end{gather}

and

(2.8)\begin{equation} V(\boldsymbol{r},\boldsymbol{x}^{(0)}) = 2 \nu \frac{\partial^2 C_{uu}}{\partial r_k r_k} + \frac{1}{2} \nu \frac{\partial^2 C_{uu}}{\partial x^{(0)}_k x^{(0)}_k}. \end{equation}

This equation holds for velocity fields $\tilde {u}_i$ obtained from first solving the Navier–Stokes equations and then filtering the results, and also for velocity fields $\tilde {u}^{LES}_i$ arising from solving LES equations in which the SGS stresses are replaced by a subgrid model, i.e. $\tau _{ij}^{LES}$. For LES and filtered Navier–Stokes to yield the same two-point statistical moment evolution of $\left\langle {\tilde{u}_i^{(1)} \tilde{u}_i^{(2)} } \right\rangle$ as well as the two-point third-order moments $\langle \tilde {u}^{(1)}_i \tilde {u}^{(2)}_i \tilde {u}^{(2)}_k\rangle$ requires as a statistically necessary condition (Meneveau Reference Meneveau1994) that

(2.9)\begin{equation} \left\langle \tau^{(1), LES}_{ki} \tilde{S}^{(2)}_{ki} \right\rangle + \left\langle \tau^{(2), LES}_{ki} \tilde{S}^{(1)}_{ki} \right\rangle = \left\langle \tau^{(1)}_{ki} \tilde{S}^{(2)}_{ki} \right\rangle + \left\langle \tau^{(2)}_{ki} \tilde{S}^{(1)}_{ki} \right\rangle, \end{equation}

and

(2.10)\begin{equation} \frac{\partial }{\partial x^{(0)}_k} \left(\left\langle \tilde{u}^{(2), LES}_i \tau^{(1), LES}_{ki} \right\rangle +\left\langle \tilde{u}^{(1), LES}_i \tau^{(2), LES}_{ki}\right\rangle \right) = \frac{\partial }{\partial x^{(0)}_k} \left(\left\langle \tilde{u}^{(2)}_i \tau^{(1)}_{ki} \right\rangle + \left\langle \tilde{u}^{(1)}_i \tau^{(1)}_{ki}\right\rangle \right). \end{equation}

If the flow is spatially homogeneous and isotropic, all derivatives with respect to $\boldsymbol {x}^{(0)}$ vanish and (2.10) is irrelevant while the last term in (2.9) simply becomes $2\langle \tau ^{(1)}_{ki} \tilde {S}^{(2)}_{ki}\rangle$. In addition, in isotropic flow, terms only depend on $|\boldsymbol {r}|$ (also, tensor contractions may be simplified in terms of single components, but these consequences will not be utilized explicitly here since data from DNS will be used for which all components are available). Equation (2.4) then becomes

(2.11)\begin{equation} \frac{1}{2} \frac{\partial C_{uu}(r)}{\partial t} = - \frac{\partial }{\partial r_k} \left\langle \tilde{u}^{(1)}_i \tilde{u}^{(2)}_i \tilde{u}^{(2)}_k\right\rangle + \nu \frac{\partial^2 C_{uu}(r)}{\partial r_k r_k} - \left\langle \tau_{ki}({\boldsymbol{x}}) \tilde{S}_{ki}({\boldsymbol{x}}+{\boldsymbol{r}}) \right\rangle, \end{equation}

so that a necessary condition for LES to correctly predict two-point moments of the resolved field reduces to

(2.12)\begin{equation} \left\langle \tau^{LES}_{ki}({\boldsymbol{x}}) \tilde{S}^{LES}_{ki}({\boldsymbol{x}}+{\boldsymbol{r}}) \right\rangle = \left\langle \tau_{ki}({\boldsymbol{x}}) \tilde{S}_{ki}({\boldsymbol{x}}+{\boldsymbol{r}}) \right\rangle. \end{equation}

For the case of kinetic energy, i.e. for the single-point case ${r} = 0$, the familiar condition is recovered where LES should correctly predict the SGS rate of dissipation, i.e. $\langle \tau _{ki}^{LES} \tilde {S}^{LES}_{ki} \rangle = \langle \tau _{ki} \tilde {S}_{ki} \rangle$. The Fourier transformed version of this expression (involving $\langle \hat {\tau }_{ki}(\boldsymbol {k}) \hat {\tilde {S}}^*_{ki}(\boldsymbol {k})\rangle$, where a hat denotes three-dimensional (3-D) Fourier transform and $\boldsymbol {k}$ is the wavenumber vector) was used by Cerutti, Meneveau & Knio (Reference Cerutti, Meneveau and Knio2000) to measure spectral eddy-viscosity distributions. In the present work we focus attention on physical space descriptions to highlight the strength of spatial correlation at various distances.

The case of channel flow is statistically homogeneous in the two wall-parallel directions but inhomogeneous in the wall-normal direction. And the presence of walls and a mean pressure gradient breaks isotropy. Due to the special importance of mean shear to this flow, it is convenient to separate the resolved flow also into its statistical mean and fluctuating variables according to $\tilde {u}_i = \langle \tilde {u}_i \rangle + \tilde {u}_i'$. In channel flow, taking the $x_1$ direction to be streamwise and $x_2$ to be wall normal, the averaged flow variables do not depend on $x_1$, $x_3$, and $\langle \tilde {u}_2 \rangle = \langle \tilde {u}_3 \rangle = 0$. Taking these facts into consideration and restricting the displacement between the two points in the correlations to be in the horizontal direction (i.e. ${r} = (r_1,0,r_3)$), (2.4) becomes

(2.13)\begin{align} \frac{\partial \left\langle \tilde{u}'^{(1)}_i \tilde{u}'^{(2)}_i \right\rangle}{\partial t} &= - T'\left(\boldsymbol{r},\boldsymbol{x}^{(0)}\right) + P_0'\left(\boldsymbol{r},\boldsymbol{x}^{(0)}\right) + V'\left(\boldsymbol{r},\boldsymbol{x}^{(0)}\right) + \mathcal{P}\left(\boldsymbol{r},\boldsymbol{x}^{(0)}\right)\nonumber\\ &\quad - \frac{\partial }{\partial x^{(0)}_2} \left\langle \tilde{u}'^{(2)}_i \tau'^{(1)}_{2i} + \tilde{u}'^{(1)}_i \tau'^{(2)}_{2i}\right\rangle - \left\langle \tau'^{(1)}_{ki} \tilde{S}'^{(2)}_{ki} + \tau'^{(2)}_{ki} \tilde{S}'^{(1)}_{ki} \right\rangle, \end{align}

where

(2.14)\begin{equation} \mathcal{P}\left(\boldsymbol{r},\boldsymbol{x}^{(0)}\right) = \langle{\tilde{u}'^{(2)}_1 \tilde{u}'^{(1)}_2}\rangle \frac{\partial \langle {\tilde{u}'_1}}{\partial x^{(0)}_2} \rangle \end{equation}

and the primed terms $T'$, $P_0'$ and $V'$ correspond to the previously defined $T$, $P_0$ and $V$ but for the primed fields, respectively. We note that the last two terms in (2.13) may also be combined and re-expressed in terms of the total variables (without decomposing into fluctuating and mean quantities) if the displacement is taken in the horizontal plane, since then $\langle \tilde {S}_{ki} \rangle$ and $\langle \tau _{ki} \rangle$ are the same at locations 1 and 2. Therefore, again we can state that a necessary condition for LES to generate each of the terms involving filtered velocities and mean velocities in the above equation accurately requires that the equality in (2.12) must hold.

3. Stress–strain-rate correlations in isotropic and channel flow data

Having established the relevance of the stress–strain-rate correlation function for understanding interactions between scales in turbulence, we examine such correlations in two canonical datasets and compare the results to the correlations arising from classic local eddy-viscosity models. We first start by describing the datasets, then the way in which we process the data and finally measure and report the aforementioned correlation functions.

The homogeneous and isotropic turbulence data come from a simulation of the Navier–Stokes equations including a forcing term performed on a periodic grid of $1024^3$ grid points using a pseudo-spectral parallel code (Li et al. Reference Li, Perlman, Wan, Yang, Meneveau, Burns, Chen, Szalay and Eyink2008). The viscous term was integrated analytically using an integrating factor, while all other terms were integrated using a second-order Adams–Bashforth scheme. A combination of phase shift and $2\sqrt {2}/3$ truncation was used to de-alias the simulation. The forcing term is such that kinetic energy in modes with wavenumber less than or equal to 2 was kept constant. The Kolmogorov length $\eta \sim (\epsilon ^3/\nu )^{1/4}$, where $\epsilon$ is the mean energy transfer rate and $\nu$ the molecular kinematic viscosity, was approximately half of the grid spacing. The averaged Taylor-scale Reynolds number of the simulation is ${Re}_{\lambda } \sim 433$. The data are available on the public Johns Hopkins Turbulence Database (JHTDB) server (for more information, see Li et al. (Reference Li, Perlman, Wan, Yang, Meneveau, Burns, Chen, Szalay and Eyink2008)). For our analysis, we use data from eleven independent snapshots distributed over approximately five large-eddy turnover times. All two-point correlations were performed along a given Cartesian direction and correlation functions were then averaged over the three Cartesian directions and over the 11 snapshots in time.

The channel flow data come from two different friction Reynolds numbers, ${Re}_{\tau } \sim 1000$ and ${Re}_{\tau } \sim 5200$. Both simulations solve the Navier–Stokes equations in a domain with periodic boundary conditions in two directions (the horizontal directions parallel to the walls) and no-slip boundary conditions in the other direction (the vertical direction). The data are also available at JHTDB. For more detailed information regarding both channel flow datasets, see Graham et al. (Reference Graham2016) and Lee & Moser (Reference Lee and Moser2015), respectively.

We filter the velocity using a top-hat box filter with different filter lengths. Results using different types of filters (Gaussian and spectral) are also presented although the focus will be on results from the most spatially local filter (box filter). For the case of the channel flows, the filtering is performed only in the horizontal directions. The true SGS stresses (their deviatoric part) are calculated using their usual definition

(3.1)\begin{equation} \tau_{ij} = \widetilde{u_i u_j} - \tilde{u}_i \tilde{u}_j - \tfrac{1}{3}( \widetilde{u_k u_k} - \tilde{u}_k \tilde{u}_k) \delta_{ij}, \end{equation}

while the filtered strain rates are calculated using (2.5) and where the derivatives are calculated using a second-order centred finite difference scheme.

When considering predictions from eddy-viscosity models, in order to avoid introducing model-dependent effects from various possible choices of eddy viscosity (e.g. Smagorinsky (Smagorinsky Reference Smagorinsky1963), dynamic Smagorinsky, (Germano et al. Reference Germano, Piomelli, Moin and Cabot1991), Vreman (Vreman et al. Reference Vreman, Geurts and Kuerten1996), WALE (Nicoud & Ducros Reference Nicoud and Ducros1999)), we focus on the simplest version, namely a ‘constant eddy-viscosity’ model. Specifically,

(3.2)\begin{equation} \tau^{LES}_{ij} = -2 \nu_{LES} \, \tilde{S}_{ij}, \end{equation}

with $\nu _{LES}$ a constant. Since all the two-point correlation functions to be shown below are normalized by their value at zero displacement, the value of $\nu _{LES}$ does not affect the results. We recall that models of the eddy viscosity are typically formulated to properly reproduce the dissipation rate (i.e. the one-point correlations between the strain rate and the stresses), while here we are focused on the non-local properties of the filtered strain rate. The comparisons to be presented were also repeated for the traditional Smagorinsky model (i.e. when $\nu _{LES}$ has spatial fluctuations given by $|\tilde {\boldsymbol {S}}|$) but have led to essentially the same results as when using a spatially constant $\nu _{LES}$, and are thus not shown.

In order to evaluate two-point correlation functions from the data, the averaging operation is performed in the three Cartesian spatial directions and over time snapshots when analysing isotropic turbulence data, while for the case of channel flow no averaging is performed in the vertical direction. Then, the two-point correlations between either the true SGS stresses and the filtered strain rates, and the modelled subgrid stress and the strain rates are obtained by averaging the product of the displaced fields and normalizing to unity at $r=0$. In other words, we normalize each correlation function by its own rate of SGS dissipation rate so as to focus on the spatial correlation structure independent of the mean dissipation rate (hence rendering the value of $\nu _{LES}$ irrelevant for our analysis).

In figure 1(a) we show the two-point correlations using both the true SGS stress tensor and the modelled one extracted from the homogeneous isotropic turbulence data using two different filter sizes $\varDelta =31\eta$ and $\varDelta =53\eta$ (the insets show the same plots but in semi-log scale to better visualize the long-distance tails). While close to the origin both the real and the local eddy-viscosity cases behave similarly, the correlation function for the real subfilter stress sustains correlations with the strain rate for longer distances than the case of the local eddy viscosity. A similar result is seen in figure 1(b) where such correlation functions are shown for the $Re_{\tau }=1000$ channel flow at some height in the logarithmic region $y^+ = 1000$, for two points separated along the streamwise direction.

Figure 1.

Two-point correlations between different subgrid stresses and the filtered strain rates in (a) homogeneous isotropic turbulence and (b) channel flow at $Re_{\tau } = 1000$ at $y^+ = 90$. Data are filtered using a top-hat filter at two different filter sizes and in all three directions for the homogeneous isotropic case (a), while only one filter size and horizontal filtering was used for the channel flow case (b), at $\varDelta ^+=49$ (or $\varDelta /h=0.049$).

As we have seen before, if one wishes an LES to provide realistic predictions of filtered-velocity correlation functions (or spectra) down to distances as close as possible to the filtering scale $\varDelta$, a model should be such that the two-point correlation between subfilter stress and strain rate are correctly reproduced during a priori data analysis. Present results demonstrate that local models cannot correctly capture the relatively long tails in these two-point correlations. Correlations between distances $r=\varDelta$ to approximately $5\varDelta$ are underestimated by local models, yielding only approximately 50 % of the real correlation. Interestingly, we recall that it was already observed that the correlations between subgrid stresses and velocity increments also decay faster in LES than in the true cases (Linkmann, Buzzicotti & Biferale Reference Linkmann, Buzzicotti and Biferale2018). It is important to emphasize that, since we are normalizing the correlations by their value at the origin, the dependence on any constant scalar prefactor is eliminated. We have tested that even if the scalar prefactor is position dependent, as it is with various variants of the eddy-viscosity model (e.g. classical Smagorinsky model), the normalized correlations remain very similar to our present results and thus are not presented here.

Figure 2 shows a sample contour plot of $\tau _{11}$ (filled colours) superimposed with a contour plot (lines) of $-\tilde {S}_{11}$ on a horizontal plane at $y^+=90$ of the channel flow data at $Re_{\tau }=1000$. The elongated features of the real stress are apparent, extending over distances far exceeding the filter scale ($\varDelta /h \sim 0.05$). The features of $-\tilde {S}_{11}$ appear more isotropic with less ‘non-locality’ in the $x$-direction compared to $\tau _{11}$. As is well known, pointwise comparisons between instantaneous distributions of real and modelled SGS stresses using variants of eddy-viscosity models typically lead to very low correlation coefficients (typically less than 20 %). But such instantaneous a priori tests have little prognostic power regarding the statistics resulting from LES. Hence, the main focus of this work is on the two-point correlations (statistical a priori tests for which the interpretation is clear, following the discussion in § 2). For the sake of completeness, we also perform pointwise comparisons below, while recalling the known limitations of interpreting such pointwise comparisons.

Figure 2.

Contour plot of streamwise normal deviatoric SGS stress $\tau _{11}$ (filled colours, in simulation units) superimposed with contour lines of $-\tilde {S}_{11}$ on a horizontal plane of the channel flow at $y^+=90$. The filtering scale is $\varDelta ^+=49$ ($\varDelta /h = 0.049$). The maximum contour line of $-\tilde {S}_{11}$ shown is at $4.8$ (in simulation units), all other have an equal spacing of $0.8$, dashed lines indicate negative values.

4. Non-local eddy-viscosity modelling

4.1. Motivation for non-local eddy-viscosity modelling

It is instructive to begin by discussing kinetic energy dissipation. One possible motivation for the classic local eddy-viscosity modelling in LES is provided directly by the condition of matching SGS energy dissipation rates. Specifically, suppose we wish to ensure that energy will be dissipated at some ‘true’ rate $-\langle \tau _{ki} \tilde {S}_{ki} \rangle$. The LES will generate fluctuations including fluctuating filtered strain rates. Thus, one can be sure that its variance, i.e. the quantity $\langle \tilde {S}^{LES}_{ki} \tilde {S}^{LES}_{ki} \rangle$, will be positive. Its magnitude will depend on the fluctuation amplitudes of $\tilde {S}^{LES}_{ki}$ but will not involve any subtle cancellations of oppositely signed values. Hence, if one sets the SGS stresses $\tau _{ki}^{LES}$ (where we are always working with the traceless tensor) proportional to $-\tilde {S}^{LES}_{ki}$, i.e. $\tau _{ki}^{LES} \sim - \tilde {S}^{LES}_{ki}$, one will be guaranteed a mean dissipation rate that will be proportional to the non-zero $\langle \tilde {S}^{LES}_{ki} \tilde {S}^{LES}_{ki} \rangle$ value resulting in LES. The actual value can be controlled by choices of SGS eddy viscosity, as in the standard model $\tau _{ki}^{LES}=-2 \nu _{LES} \tilde {S}^{LES}_{ki}$.

Now, we wish to generalize this statement to the case of ensuring that two-point moment between the subfilter stress and the filtered strain-rate tensor at some particular displaced position $\boldsymbol {r}^{\prime }$ is predicted correctly. A possible way to guarantee that the two-point correlation $\langle \tau ^{LES}_{ki}(\boldsymbol {x}) \tilde {S}^{LES}_{ki}(\boldsymbol {x}+\boldsymbol {r}^{\prime }) \rangle$ is non-zero with its magnitude set by some prefactor, is to select $\tau ^{LES}_{ki}(\boldsymbol {x})$ to be proportional not to the local value of the filtered strain rate, but to $\tilde {S}^{LES}_{ki}(\boldsymbol {x}+\boldsymbol {r}^{\prime })$ at the desired point, i.e. $\tau ^{LES}_{ki}(\boldsymbol {x}) \sim - \tilde {S}^{LES}_{ki}(\boldsymbol {x}+\boldsymbol {r}^{\prime })$. In general we will want to enforce such a condition for all possible $\boldsymbol {r}^{\prime }$, and so a weighted superposition of strain rates at different locations can be envisioned

(4.1)\begin{equation} \tau^{LES}_{ki}(\boldsymbol{x}) = - \int K(\boldsymbol{r}^{\prime}) \, \tilde{S}^{LES}_{ki}(\boldsymbol{x}+\boldsymbol{r}^{\prime}) \,\,d^3 \boldsymbol{r}^{\prime}, \end{equation}

where $K(\boldsymbol {r}^{\prime })$ represents an eddy viscosity appropriate for displacement $\boldsymbol {r}^{\prime }$.

Multiplying (4.1) by $\tilde {S}^{LES}_{ki}(\boldsymbol {x}+\boldsymbol {r})$ and ensemble averaging yields the two-point correlation relevant to correctly predicting two-point velocity correlations. The result, for homogeneous turbulence, can be written as

(4.2)\begin{equation} -\left\langle \tau^{LES}_{ki}(\boldsymbol{x}) \tilde{S}^{LES}_{ki}(\boldsymbol{x}+\boldsymbol{r}) \right\rangle = \int K(\boldsymbol{r}^{\prime}) \, \left\langle \tilde{S}^{LES}_{ki}(\boldsymbol{x}+\boldsymbol{r}) \tilde{S}^{LES}_{ki}(\boldsymbol{x}+\boldsymbol{r}-\boldsymbol{r}^{\prime}) \right\rangle \, d^3 \boldsymbol{r}^{\prime}, \end{equation}

i.e. a convolution between a kernel and the strain-rate two-point correlation function. Assuming that the latter decays as function of displacement differently than the true correlation $\langle \tau ^{d}_{ki}(\boldsymbol {x}) \tilde {S}_{ki}(\boldsymbol {x}+\boldsymbol {r}) \rangle$ (as figure 1 shows occurs in turbulence), then the convolution with a kernel $K(\boldsymbol {r}^{\prime })$ enables one to generate a SGS model that may display improved two-point correlations between stress and strain rate.

Many options for the kernel $K(\boldsymbol {r}^{\prime })$ could be envisioned, and many ways to optimally find it from data can be developed. Here, we propose to explore the applications of fractional calculus since such operators enable compact representations and we may express the model without introducing length scales a priori into the problem (as we shall see later on, effectively we will be introducing modelling length scales anyhow, but at a later stage). In the next section, we set the stage for definitions of fractional gradients that can be applied in three dimensions to gradient vector fields.

4.2. Fractional-gradient-based non-local eddy viscosity

Most applications of fractional derivatives are essentially one-dimensional, e.g. in time to represent memory effects, for spatially one-dimensional problems, or using fractional Laplacians that do not discriminate between different directions. Recent efforts to develop fractional vector calculus and directionally dependent gradient operators include Meerschaert, Mortensen & Wheatcraft (Reference Meerschaert, Mortensen and Wheatcraft2006) and Tarasov (Reference Tarasov2008). Here, we use a multidimensional generalization of the Caputo fractional derivative (Caputo Reference Caputo1967; Samko Reference Samko1993). In one dimension, the Caputo fractional derivative usually takes the form

(4.3)\begin{equation} {}_C D^{\alpha}_{0,x}\, f(x) = \frac{1}{\varGamma(1-\alpha)} \int^x_0 \frac{\dfrac{\textrm{d}f}{\textrm{d}\kern0.7pt x} (x')}{(x-x')^{\alpha}}\,{\textrm{d}\kern0.7pt x}'. \end{equation}

While this non-symmetric definition, where the limits of integration go from 0 (or some other finite limit) to the point of evaluation, is useful for many problems, and has been used in non-local closures of channel flow RANS (Song & Karniadakis Reference Song and Karniadakis2018), it is not applicable to flows in three dimensions, where there may be various important directions. The need to constrain the domain of integration also arises, as in practice integrating over the whole physical domain would be prohibitively expensive. A symmetrized and truncated version of the Caputo derivative may be written as

(4.4)\begin{equation} {}_{symC} D^{\alpha}_{R}\, f(x) = \frac{1}{2\varGamma(1-\alpha)} \int^{x+R}_{x-R} \frac{\dfrac{\textrm{d}f}{\textrm{d}\kern0.7pt x} (x')}{|x-x'|^{\alpha}}\,{\textrm{d}\kern0.7pt x}'. \end{equation}

Next, a definition of a vector gradient is required. Some definitions of fractional gradients resort to just taking a one-dimensional fractional derivative along each dimension (Meerschaert et al. Reference Meerschaert, Mortensen and Wheatcraft2006; Tarasov Reference Tarasov2008). Such a definition would, however, not be useful in general, as the gradient operation would not be invariant under arbitrary rotations of the coordinate system. Instead, we keep the directionally sensitive derivative inside the integral in one direction (as in the Caputo derivative), but integrate in all directions over a ball of radius $R$ Caputo & Fabrizio (Reference Caputo and Fabrizio2015), according to

(4.5)\begin{equation} D^{\alpha}_i u_j (\boldsymbol{r}) = \frac{1}{\varOmega_d \varGamma(1-\alpha)} \int_{|\boldsymbol{r}-\boldsymbol{r}'|\leq R} \frac{\dfrac{\partial u_j}{\partial x_i} (\boldsymbol{r}')}{ |\boldsymbol{r} - \boldsymbol{r}'|^{\alpha+d-1}} d^d \boldsymbol{r}', \end{equation}

where $\varOmega _d$ is the $d$-D solid angle. In three dimensions, this definition becomes

(4.6)\begin{equation} D^{\alpha}_i u_j (\boldsymbol{r}) = \frac{1}{4 {\rm \pi}\varGamma(1-\alpha)} \int_{|\boldsymbol{r}-\boldsymbol{r}'|\leq R} \frac{\dfrac{\partial u_j}{\partial x_i} (\boldsymbol{r}')}{ |\boldsymbol{r} - \boldsymbol{r}'|^{\alpha+2}} d^3 \boldsymbol{r}'. \end{equation}

The result depends also on the radius $R$, which in practice will be chosen ‘large enough’ to capture non-locality and generate results that do not depend strongly on $R$.

It is possible to show by performing integration by parts (following Li & Deng Reference Li and Deng2007) that, as long as the field $u$ has a well-defined second derivative, this definition complies with the following limiting behaviour at $\alpha$ approaching unity from below:

(4.7)\begin{equation} D^{\alpha}_i u_j (\boldsymbol{r}) \xrightarrow[\alpha \to 1^-]{} \frac{\partial u_j}{\partial r_i} (\boldsymbol{r}). \end{equation}

That is to say, traditional gradient operation corresponds to $\alpha = 1$.

In the limit of $\alpha \to 0^+$, we obtain

(4.8)\begin{equation} D^{\alpha}_i u_j (\boldsymbol{r}) \xrightarrow[\alpha \to 0^+]{} \frac{1}{4{\rm \pi}} \int^{\rm \pi}_0 \int^{2{\rm \pi}}_0 \int^R_0 \frac{\partial u_j}{\partial r_i}(\boldsymbol{r} - \tilde{\boldsymbol{r}})\,\textrm{d}\tilde{r} \sin \theta\,\textrm{d}\theta\,\textrm{d}\phi \end{equation}

(when expressed in spherical coordinate system). For example, for a spatially constant velocity gradient within the sphere of radius $R$, the gradient in the limit $\alpha \to 0^+$ becomes

(4.9)\begin{equation} D^{\alpha}_i u_j (\boldsymbol{r}) \xrightarrow[\alpha \to 0^+]{} R \, \frac{\partial u_j}{\partial r_i}, \end{equation}

similar to a velocity increment (structure function) over a distance $R$.

The units of the fractional gradient are velocity divided by (length)$^{\alpha }$. In a turbulent flow with weak mean gradients, and as long as $\alpha > 0$, the definition of the derivative is expected to converge for sufficiently large $R$, as contributions from different directions will mostly cancel. But possible dependencies on $R$ will be examined quantitatively during the analysis since a priori some dependence on $R$ cannot be excluded.

Now that we have defined a fractional-gradient operator, we can also define the symmetric part, i.e. the fractional strain-rate tensor, according to

(4.10)\begin{equation} \tilde{S}^{\alpha}_{ij} = \tfrac{1}{2} \left(D^{\alpha}_i \tilde{u}_j+D^{\alpha}_j \tilde{u}_i \right). \end{equation}

In order to provide qualitative insights regarding the fractional gradient, we apply the gradient operator to filtered-velocity fields from the isotropic turbulence data from DNS described before (at $\varDelta = 31 \eta$). Details on the numerical calculation of the fractional strain rate are presented in appendix A. Sample signals of transverse $\tilde {S}^{\alpha }_{23}$ and normal $\tilde {S}^{\alpha }_{33}$ velocity gradient tensor elements across parts of the computational domain are shown in figure 3. Each of the curves are normalized by their respective standard deviations $\sigma _{\alpha }$. The standard gradient tensor signals ($\alpha = 1$) are shown as the light curve. The signals corresponding to lower values of $\alpha$ display smaller excursions in general, consistent with the idea that they are more non-local. It is interesting to note that even if subtle, the most non-local case ($\alpha =0.2$) still retains significant small-scale structure in the signal (at the filter scale $\varDelta$) even though it is the most non-local case considered.

Figure 3.

Effects of $\alpha$. Representative signal of two components of the fractional strain rate tensor $\tilde {S}_{ij}^{\alpha } = (D^{\alpha }_i \tilde {u}_j+D^{\alpha }_j \tilde {u}_i)/2$ normalized by its respective standard deviation for different fractional orders. Data are from DNS of isotropic turbulence at $Re_{\lambda } \sim 433$; (a) is for $ij=23$ while (b) is for $ij=33$. The curves correspond to $\alpha = 1, 0.7, 0.5$ and 0.2, from light to dark colour, respectively.

In figure 4 we present plots of the same two components of $\tilde {S}^{\alpha }_{ij}$ for a fixed value of $\alpha =0.2$ but calculated using different cutoff radius $R$. As mentioned earlier, the fractional derivative (4.6) is not independent of $R$, but we can expect results from turbulent flow with weak mean gradients to vary less and less as $R$ increases. The strain-rate signals shown in figure 4 are consistent with such behaviour, with relatively small sensitivity to $R$ for $R\geq 5\varDelta$ in these examples. Note that these results are for a small value of $\alpha$ (0.2). For larger values of $\alpha$ the sensitivity to $R$ is less marked.

Figure 4.

Effects of $R$. Two components of the fractional strain-rate tensor $\tilde {S}_{ij}^{\alpha } = (D^{\alpha }_i \tilde {u}_j+D^{\alpha }_j \tilde {u}_i)/2$ for the same fractional order $\alpha =0.2$, but different cutoff radius $R$; (a) is for $ij=23$ while (b) is for $ij=33$. The different curves correspond to $R = 1\varDelta , 3\varDelta , 5\varDelta$ and $7\varDelta$, from light to dark colour, respectively.

For completeness, we also provide a pointwise qualitative comparison in figure 5 showing a sample contour plot of $\tau _{11}$ (filled colours) superimposed with a contour plot (lines) of $-\tilde {S}^{\alpha }_{11}$ on a horizontal plane at $y^+=90$ of the channel flow data at $Re_{\tau }=1000$ for $\alpha =0.2$. Similar to the local case shown in figure 2, the non-local filtered strain rate does not match the true subgrid stresses. In quantitative terms, the one-point correlations for both cases are approximately $12\,\%$.

Figure 5.

(a) Contour plot of streamwise normal deviatoric SGS stress $\tau _{11}$ (filled colours, in simulation units) superimposed with contour lines of $-\tilde {S}^{\alpha }_{11}$ on a horizontal plane of the channel flow at $y^+=90$ for $\alpha =0.2$. The filtering scale is $\varDelta ^+=49$ ($\varDelta /h = 0.049$). The maximum contour line of $-\tilde {S}^{\alpha }_{11}$ shown is at $0.36$ (in simulation units), all other have an equal spacing of $0.06$, dashed lines indicate negative values. Zoomed in views of the region marked with the red square are shown in panels (b,c,d). (b) Zoomed in view of $\tau _{11}$. (c) Zoomed in view of $-\tilde {S}_{11}$. (d) Zoomed in view of $-\tilde {S}^{\alpha }_{11}$.

Based on the fractional strain-rate tensor, we may now define a fractional eddy-viscosity closure for the deviatoric part of the SGS stress, according to

(4.11)\begin{equation} \tau^{\alpha}_{ij} = -2 \nu_{\alpha} \tilde{S}^{\alpha}_{ij}, \end{equation}

where $\nu _{\alpha }$ is the $\alpha$-dependent SGS eddy viscosity, having units of velocity times (length)$^{\alpha }$. The form based on the fractional gradient defined as in (4.6) has the following desirable properties: it is Galilean invariant, it is rotationally invariant and the stress enters as a divergence in the momentum equation so that it obeys a traditional Gauss theorem (i.e. its volume integral only leaves surface fluxes).

For the purposes of data analysis in this paper, similarly to what we showed in the previous section for local models, we will take $\nu _{\alpha }$ to be constant. Practical applications of such models will of course require specification of $\nu _{\alpha }$, which would indirectly involve specification of a length scale (e.g. setting $\nu _{\alpha } \sim \varDelta ^{2\alpha } |\tilde {S}^{\alpha }|$). Notice that non-local models allow for backwards energy transfer even if $\nu _{\alpha }$ is always positive, since $\tilde {S}^{\alpha }_{ij} \tilde {S}_{ij}$ is not strictly positive. Backwards energy transfer should not be expected if $\alpha$ is close to unity since the non-local strain rate will be very similar to the local one. However, some negative energy transfer (backscatter) could occur for lower values of $\alpha$. Therefore, we also perform an analysis of the energy transfer rate in the section below.