Journal cover
Journal topic
**Nonlinear Processes in Geophysics**
An interactive open-access journal of the European Geosciences Union

Journal topic

- About
- Editorial board
- Articles
- Special issues
- Highlight articles
- Manuscript tracking
- Book reviews
- Subscribe to alerts
- Peer review
- For authors
- For reviewers
- EGU publications
- Imprint
- Data protection

- About
- Editorial board
- Articles
- Special issues
- Highlight articles
- Manuscript tracking
- Book reviews
- Subscribe to alerts
- Peer review
- For authors
- For reviewers
- EGU publications
- Imprint
- Data protection

**Research article**
29 Jun 2018

**Research article** | 29 Jun 2018

Stratified Kelvin–Helmholtz turbulence

- School of Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, Oklahoma 74078, USA

- School of Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, Oklahoma 74078, USA

Abstract

Back to toptop
We study scaling laws of stratified shear flows by performing
high-resolution numerical simulations of inviscid compressible turbulence
induced by Kelvin–Helmholtz instability. An implicit large eddy simulation
approach is adapted to solve our conservation laws for both two-dimensional
(with a spatial resolution of 16 384^{2}) and three-dimensional (with a
spatial resolution of 512^{3}) configurations utilizing different
compressibility characteristics such as shocks. For three-dimensional
turbulence, we find that both the kinetic energy and density-weighted energy
spectra follow the classical Kolmogorov ${k}^{-\mathrm{5}/\mathrm{3}}$ inertial scaling. This
phenomenon is observed due to the fact that the power density spectrum of
three-dimensional turbulence yields the same ${k}^{-\mathrm{5}/\mathrm{3}}$ scaling. However, we
demonstrate that there is a significant difference between these two spectra
in two-dimensional turbulence since the power density spectrum yields a
${k}^{-\mathrm{5}/\mathrm{3}}$ scaling. This difference may be assumed to be a reason for the
${k}^{-\mathrm{7}/\mathrm{3}}$ scaling observed in the two-dimensional density-weight kinetic
every spectra for high compressibility as compared to the *k*^{−3} scaling
traditionally assumed with incompressible flows. Further inquiries are made
to validate the statistical behavior of the various configurations studied
through the use of the Helmholtz decomposition of both the kinetic velocity
and density-weighted velocity fields. We observe that the scaling results are
invariant with respect to the compressibility parameter when the
density-weighted definition is used. Our two-dimensional results also confirm
that a large inertial range of the solenoidal component with the *k*^{−3}
scaling can be obtained when we simulate with a lower compressibility
parameter; however, the compressive spectrum converges to *k*^{−2} for a
larger compressibility parameter.

Download & links

How to cite

Back to top
top
How to cite.

San, O. and Maulik, R.: Stratified Kelvin–Helmholtz turbulence of compressible shear flows, Nonlin. Processes Geophys., 25, 457-476, https://doi.org/10.5194/npg-25-457-2018, 2018.

1 Introduction

Back to toptop
Turbulence is a highly nonlinear multiscale phenomenon which is ubiquitous in nature. It poses some of the most challenging problems in classical physics as well as in computational mathematics. Understanding the nature of compressible turbulence is of paramount importance. Highly compressible turbulence plays an important role in star formation control in dense molecular clouds (Padoan and Nordlund, 2002; Mac Low and Klessen, 2004; Mac Low et al., 1998) and is responsible for important design considerations in many engineering applications. Therefore, there have been several investigations into its statistical behavior. Kida and Orszag (1990) studied the mechanics of energy transfer and distribution and examined small-scale spectra in compressible turbulence with root mean square Mach numbers up to 0.9. Theoretical laws have also been advanced for the statistical behavior of turbulence quantities under the influence of compressibility effects (Shivamoggi, 1992; Lele, 1994; Shivamoggi, 2011; Wang et al., 2013). Kritsuk et al. (2007) utilized an adaptive mesh refinement (AMR) algorithm along with a piecewise parabolic approach for numerical dissipation to obtain scaling tendencies at high Mach number values for both kinetic energy and density-weighted kinetic energy, and density power spectra. In addition, structure functions of different orders were also studied and compared to the limiting case of incompressibility. Aluie (2013) provided a theoretical justification of the presence of an inertial scale which is devoid of any effects of molecular viscosity for supersonic turbulence similar to the classical Richardson–Kolmogorov cascade in homogeneous isotropic incompressible turbulence (Kolmogorov, 1941; Vassilicos, 2015). Magnetic effects on the statistical behavior of supersonic turbulence have also been studied keenly due to implications for astrophysical processes such as in Banerjee and Galtier (2013), where two-point correlation function relations were studied.

Scaling laws incorporating magnetic effects in hydrodynamic turbulence have also been proposed, for instance in Iroshnikov–Kraichnan theory (Iroshnikov, 1964; Kraichnan, 1965), where arguments similar to those used in Kolmogorov theory are used to explain statistical properties of small-scale components in velocity and magnetic fields. Extensions to account for the rather tenuous assumption of isotropy in compressible magnetohydrodynamics (MHD) have also been studied by Goldreich and Sridhar (1997). A generalization of the Iroshnikov–Kraichnan and Goldreich–Sridhar spectra to compressible magnetohydrodynamics has been presented by Shivamoggi (2008), where it is also shown to merge with the MHD shockwave spectrum in the limit of infinite compressibility (Kadomtsev and Petviashvili, 1973). A recent review which examines both hydrodynamic and magnetohydrodynamic implementations of supersonic compressible turbulence on statistical quantities can be found in Falceta-Gonçalves et al. (2014). In this work, we follow the vast majority of investigations (Shivamoggi, 1992; Ottaviani, 1992; Domaradzki and Carati, 2007; Falkovich et al., 2010; Kuznetsov and Sereshchenko, 2015; Shivamoggi, 2015; Sun, 2016; Westernacher-Schneider et al., 2015; Qiu et al., 2016; Bershadskii, 2016; Sun, 2017; Westernacher-Schneider and Lehner, 2017) by utilizing the phenomenological description of turbulence in Fourier space as well as the utilization of two-point velocity structure functions for the statistical examination of our high-fidelity numerical simulations. One of our goals is to investigate scaling laws using a computational framework with moderately high resolutions. We note that several modified energy spectra and anisotropic behaviors have been recently discussed within the context of the Rayleigh–Taylor and Richtmyer–Meshkov instability-induced flows (Zhou, 2017a, b). In terms of reference scaling behavior, we shall be comparing our numerical results of the stratified shear layer turbulence simulations against the theories under the assumption of isentropic flow by solving the Euler equations triggered by stratified shear layers in a periodic box domain.

In this work, we shall examine the stratified compressible turbulence that emerges from a classical Kelvin–Helmholtz instability (KHI) formulation. Similar problems have been studied extensively for their incompressible versions (Hopfinger, 1987; Werne and Fritts, 1999; Peltier and Caulfield, 2003; Boffetta and Mazzino, 2017). In this work, both two- and three-dimensional versions of stratification will be examined for their effects on scaling. It must be noted here that two-dimensional turbulence may be assumed to be an appropriate framework for many geophysical applications which exhibit extremely high aspect ratios and, indeed, incompressible two-dimensional turbulence forms the cornerstone of geostrophic turbulence theory (Boffetta and Ecke, 2012; Shivamoggi, 1998). Astrophysical considerations have also been explored in Biskamp and Schwarz (2001), where the effects of a magnetohydrodynamic coupling have also been examined on scaling behavior. Our focus shall primarily rest on a comparison of numerically obtained behavior of the density power spectrum, the averaged kinetic energy spectrum and the density-weighted kinetic energy spectrum along with second- and third-order velocity structure functions with their theoretical predictions. Some reference scaling laws (in the incompressible limit) we shall be using for comparison are the classical Kolmogorov scaling (Kolmogorov, 1941) for isotropic three-dimensional (3-D) turbulence and Kraichnan scaling (Kraichnan, 1967) for two-dimensional (2-D) isotropic turbulence.

A common strategy for the numerical examination of the statistics of highly compressible turbulence is the use of the Eulerian hydrodynamic conservation laws implemented through an implicit large eddy simulation (ILES) methodology (Passot et al., 1988; Blaisdell et al., 1993). This is because it is commonly accepted that an ILES formulation of the Euler equations provides a good estimation for the Navier–Stokes equations in the limit of infinite Reynolds numbers (Bos and Bertoglio, 2006; Zhou et al., 2014; Sytine et al., 2000). However, two conditions must be enforced in order to satisfy the aforementioned assumption. Firstly, vorticity must be introduced via either boundary and/or initial conditions since the Euler equations are incapable of generating vorticity from irrotational flows. Secondly, an artificial viscosity must be incorporated into the simulation mechanism to mimic the preservation of dissipative behavior of the Navier–Stokes equations in the inviscid limit (Moura et al., 2017). The ILES mechanism is a suitable approach for artificial dissipation through the use of numerical truncation errors and is our simulation algorithm of choice for the high-fidelity numerical experiments in this investigation.

The question we attempt to address through this work is related to the
difference between purely averaged kinetic energy spectra scaling and
density-weighted spectra scaling for both two- and three-dimensional
compressible turbulence. Our observations suggest a different “packaging”
of density in the spectral space for the two-dimensional turbulence case.
This is proven conclusively by comparing the differences in density power
spectrum behavior for both two- and three-dimensional configurations. It is
proposed that the density power spectrum (or in other words the packaging of
density at different wavenumbers) may be a reason that causes a variation in
the *k*^{−3} scaling of the density-weighted kinetic energy cascade with
changing compressibility (higher compressibilities are observed to show
${k}^{-\mathrm{7}/\mathrm{3}}$ scaling) for two-dimensional turbulence as against the constant
${k}^{-\mathrm{5}/\mathrm{3}}$ cascade in three-dimensional turbulence. Our results are also
validated through the use of the second-order structure function behavior
with varying compressibility. High-fidelity simulation data are generated by
utilizing 512^{3} and 16 384^{2} degrees of freedom for the three- and
two-dimensional cases, respectively. We demonstrate that there is no
difference in energy spectrum scalings between kinematic and density-weighted
velocities in three-dimensional simulations since both the power density and
velocity spectra scale with the ${k}^{-\mathrm{5}/\mathrm{3}}$ scaling. However, we have
demonstrated that the difference becomes pronounced in two-dimensional
simulations because the power density spectrum scales with ${k}^{-\mathrm{5}/\mathrm{3}}$, which
is different than the scaling of the kinetic energy spectrum. Furthermore, we
have decomposed both the kinetic velocity and density-weighted velocity
fields into compressive (curl-free) and solenoidal (divergence-free)
components in order to study the effects of compressibility in our two- and
three-dimensional setups. Ultimately, it is our aim to link these analyses to
nonlinear processes exhibiting very high aspect ratios for astrophysical,
heliophysical and plasma physics applications.

2 Compressible turbulence

Back to toptop
The governing laws utilized for our numerical experiments are given by the Euler equations which may be expressed in their dimensionless differential form as

$$\begin{array}{}\text{(1)}& {\displaystyle}& {\displaystyle \frac{\partial \mathit{\rho}}{\partial t}}+\mathrm{\nabla}\cdot \left(\mathit{\rho}\mathit{u}\right)=\mathrm{0},\text{(2)}& {\displaystyle}& {\displaystyle \frac{\partial \left(\mathit{\rho}\mathit{u}\right)}{\partial t}}+\mathrm{\nabla}\cdot (\mathit{\rho}\mathit{u}\otimes \mathit{u}+p\mathit{I})=\mathrm{0},\text{(3)}& {\displaystyle}& {\displaystyle \frac{\partial \left(\mathit{\rho}E\right)}{\partial t}}+\mathrm{\nabla}\cdot (\mathit{\rho}E\mathit{u}+p\mathit{u})=\mathrm{0},\end{array}$$

where *ρ* is the fluid density, $\mathit{u}=\mathit{\{}u,v{\mathit{\}}}^{T}\in {\mathbb{R}}^{\mathrm{2}}$ and $\mathit{u}=\mathit{\{}u,v,w{\mathit{\}}}^{T}\in {\mathbb{R}}^{\mathrm{3}}$ are the flow velocity in a Cartesian co-ordinate system, *p*
is the static pressure, and *E* is the total energy per unit mass. Assuming a
perfect gas with a ratio of specific heats *γ*, the pressure can be
determined by an equation of state which closes our coupled governing
equations given by

$$\begin{array}{}\text{(4)}& p=\mathit{\rho}(\mathit{\gamma}-\mathrm{1})\left(E-{\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}(\mathit{u}\cdot \mathit{u})\right),\end{array}$$

where we have set $\mathit{\gamma}=\mathrm{7}/\mathrm{5}$ in our study. Note that the assumption of the classical equation of state for relating the pressure and total energy of the flow ensures the interaction of solely acoustic and vortical modes (Shivamoggi, 1992). Our computational domain also exhibits periodic boundary conditions in all directions.

The stratified Kelvin–Helmholtz instability (KHI) test case is a famous problem which manifests itself when there is a velocity difference at the interface between two fluids of different densities (Thomson, 1871). It can commonly be observed through experimental observation and numerical simulation, and it is also visible in many natural phenomena, for example in situations with wind flow over bodies of water causing wave formation and in the planet Jupiter's atmosphere between atmospheric bands moving at different speeds (Hwang et al., 2012). The study of this instability in a benchmark formulation reveals key information about the transition to turbulence for two fluids moving at different speeds. For these practical applications, it is common to choose a double shear layer problem to simulate the formation of KHI in a periodic two-dimensional computational setting with unit side length. This stratified shear layer instability problem is used to demonstrate the evolution of linear perturbations into a transition to nonlinear two-dimensional hydrodynamic turbulence. The instability triggers small-scale vortical structures at the sharp density interface initially, which eventually transitions through nonlinear interactions to a completely turbulent field.

A two-dimensional implementation of the dual-shear layer KHI problem is devised through our aforementioned unstable perturbed compressible shear layer. This may be implemented through our computational domain which is a square of unit side length with the following initial conditions:

$$\begin{array}{}\text{(5)}& {\displaystyle}& {\displaystyle}\mathit{\rho}(x,y)=\left\{\begin{array}{ll}\mathrm{1.0},& \text{if}\left|y\right|\ge \mathrm{0.25}\\ \mathrm{2.0},& \text{if}\left|y\right|\mathrm{0.25}\end{array}\right.\text{(6)}& {\displaystyle}& {\displaystyle}u(x,y)=\left\{\begin{array}{ll}\mathit{\alpha},& \text{if}\left|y\right|\ge \mathrm{0.25}\\ -\mathit{\alpha},& \text{if}\left|y\right|\mathrm{0.25}\end{array}\right.\text{(7)}& {\displaystyle}& {\displaystyle}v(x,y)=\mathit{\lambda}\mathrm{sin}(\mathrm{2}\mathit{\pi}nx/L),\text{(8)}& {\displaystyle}& {\displaystyle}p(x,y)=\mathrm{2.5}.\end{array}$$

We can observe that the vertical component of the velocity is perturbed using
a single-mode sine wave (*n*=2, *L*=1) with an amplitude *λ*=0.01.
Our two-dimensional numerical experiments are solved to a final dimensionless
time of *t*=5. We clarify that the ℝ^{2} simulation domain for all
experiments is set in $(x,y)\in [-\mathrm{0.5},\mathrm{0.5}]\times [-\mathrm{0.5},\mathrm{0.5}]$ with *N*^{2}=16 384^{2} degrees of freedom. Figure 1 represents a
schematic expressing the initial conditions of our two-dimensional
simulation. We remark that in this study we perform implicit large eddy
simulation (ILES) simulations by using a finite-volume framework. Our
numerical scheme utilizes the fifth-order accurate, weighted essential
non-oscillatory (WENO) reconstructions equipped with Roe's approximate
Riemann solver (Roe, 1981) at the cell interfaces. It is well
known that the utilization of the artificial dissipation mechanism of ILES
schemes (from the numerical viscosity of upwind biased state reconstructions)
mimics the physical viscosity of the Navier–Stokes equations in the limit of
infinite Reynolds numbers. We utilize a parallel approach for the
computational solution of our governing laws implemented in the OpenMPI
framework. Details about the implementation and the computational performance
of our solver may be found in Maulik and San (2017), additionally
showing weak and strong scaling tests. Our three-dimensional simulations
employ a similar approach.

Figure 2 describes snapshots in time of the density field for this
two-dimensional compressible turbulence test case when *α*=1.0. One can
notice a transition to turbulence once an initial instability has developed.
The shearing velocity magnitude given by *α* controls the
compressibility which is apparent from comparisons with Figs. 3
and 4 where smaller values lead to formation of much smoother
structures and consequently lead to shock-free fields in the incompressible
limit. Evidence from Fig. 4 also shows a delay in the onset of
turbulence due to a reduced shearing velocity. Table 1 also
demonstrates the mean and maximum Mach number values at the final
computational time *t*=5. It is clear that the case for *α*=0.25
corresponds to a perfectly subsonic regime with lower compressibility (i.e.,
the mean Mach number of *M*=0.15).

Figure 5 demonstrates the time evolution characteristics of
the 2-D KHI problem. On the left, we illustrate the time series of the
domain-integrated velocity amplitude (i.e., the root mean square values of
the kinetic velocity) normalized with its initial condition with each
*α* value. It is clear that the KHI instability starts earlier for
larger *α* values. We also demonstrate the evolution of the compensated
kinetic energy spectrum on the right for *α*=1.0. Similar statistical
trends are observed at each time. Therefore, we will only focus on the
results at the final time *t*=5 in our statistical analysis presented in the
next section.

While two-dimensional compressible turbulence investigations are valuable for insight into the physical processes of systems which exhibit extreme aspect ratios (Boffetta and Ecke, 2012), it is well known that the process of energy transfer between scales is fundamentally different when compared to that of three-dimensional flows (Clercx and van Heijst, 2017). Isotropic, homogeneous, incompressible three-dimensional turbulence is characterized by the famous Kolmogorov–Richardson cascade of energy where the largest vortices continuously inject energy into an inertial cascade which terminates in the Kolmogorov length scale (Kolmogorov, 1941) where viscous effects dissipate this energy. This is particularly applicable for engineering flows, where it has been established that turbulence “decays” in the absence of forcing due to viscous dissipation. In contrast, two-dimensional turbulence exhibits the presence of an inverse energy cascade (given by Kraichnan–Batchelor–Leith theories; Kraichnan, 1967; Leith, 1971; Batchelor, 1969) where energy from the smallest scales is transferred to the largest scales. This has implications for the restoration of local isotropy (since large-scale structures created by the inverse energy cascade affect the amount of enstrophy in the field and thus affect the energy dissipation rate). In the presence of periodic boundary conditions (a subject of future investigations), these newly created large-scale structures may lead to significant alteration in scaling laws.

Our computational domain for the three-dimensional turbulence case is
analogous to that of the two-dimensional domain. We utilize a domain given by
a ℝ^{3} set in $(x,y,z)\in [-\mathrm{0.5},\mathrm{0.5}]\times [-\mathrm{0.5},\mathrm{0.5}]\times [-\mathrm{0.5},\mathrm{0.5}]$ with *N*^{3}=512^{3} degrees of freedom. Our initial conditions
are given by

$$\begin{array}{}\text{(9)}& {\displaystyle}& {\displaystyle}\mathit{\rho}(x,y,z)=\left\{\begin{array}{ll}\mathrm{1.0},& \text{if}\left|y\right|\ge \mathrm{0.25}\\ \mathrm{2.0},& \text{if}\left|y\right|\mathrm{0.25}\end{array}\right.\text{(10)}& {\displaystyle}& {\displaystyle}u(x,y,z)=\left\{\begin{array}{ll}\mathit{\alpha},& \text{if}\left|y\right|\ge \mathrm{0.25}\\ -\mathit{\alpha},& \text{if}\left|y\right|\mathrm{0.25}\end{array}\right.\text{(11)}& {\displaystyle}& {\displaystyle}v(x,y,z)=\mathit{\lambda}\mathrm{sin}(\mathrm{2}\mathit{\pi}nx/L),\text{(12)}& {\displaystyle}& {\displaystyle}w(x,y,z)=\mathit{\lambda}\mathrm{sin}(\mathrm{2}\mathit{\pi}nz/L),\text{(13)}& {\displaystyle}& {\displaystyle}p(x,y,z)=\mathrm{2.5},\end{array}$$

and periodic boundary conditions in all directions. We keep our parameters
*n*, *L* and *λ* similar to those used in the two-dimensional case and
utilize *N*^{3}=512^{3} degrees of freedom for the simulation of our
computational domain.

Figure 6 shows the density field at times *t*=1 and *t*=5 for a
shearing velocity magnitude of *α*=1.0. One can observe how the solution
domain has transitioned almost entirely to a turbulent field for this case as
against the very visible stratification observed in lower compressibility
simulations given by *α*=0.5 and *α*=0.25 shown in
Figs. 7 and 8, respectively. Our aim is to quantify the
effect of the shearing velocity on the compressibility and scaling laws of
these co-designed two- and three-dimensional configurations. Similar to the
two-dimensional case, we have plotted the time evolution of the
domain-integrated velocity in Fig. 9 between *t*=0 and *t*=5.
The decay rates in three-dimensional simulations are substantially higher
than those obtained in two-dimensional simulations. This can be attributed to
the use of a lesser number of grid points sampled in each direction. However,
the energy spectrum trend is similar and yields a ${k}^{-\mathrm{5}/\mathrm{3}}$ spectrum at each
time. In the following section, we thus present a systematic analysis based
on data obtained at *t*=5.

3 Turbulence statistics and scaling exponents

Back to toptop
The first statistical measure we investigate is given by the classical kinetic energy spectra. To obtain these spectra, we start with an expression for the spatial kinetic energy in wavenumber space given by (Kida et al., 1990)

$$\begin{array}{}\text{(14)}& {\displaystyle}\mathit{E}(\mathbf{k},t)={\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}\left|\widehat{\mathbf{u}}\right(\mathbf{k},t){|}^{\mathrm{2}},\end{array}$$

where $\widehat{\mathbf{u}}(\mathbf{k},t)$ is the Fourier transform of the velocity vector in the wavenumber space. Equation (14) can also be rewritten in terms of velocity components (assuming a two-dimensional Cartesian domain) as

$$\begin{array}{}\text{(15)}& {\displaystyle}\mathit{E}(\mathbf{k},t)={\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}\left(\left|\widehat{u}\right(\mathbf{k},t){|}^{\mathrm{2}}+|\widehat{v}(\mathbf{k},t){|}^{\mathrm{2}}\right),\end{array}$$

where we compute velocity components $\widehat{u}(\mathbf{k},t)$ and $\widehat{v}(\mathbf{k},t)$ using a fast Fourier transform algorithm (Press et al., 1996). Finally, the spectra can be calculated by integrating over a unit bandwidth (i.e., angle-averaged) in the following manner:

$$\begin{array}{}\text{(16)}& {\displaystyle}E(k,t)=\sum _{k-\frac{\mathrm{1}}{\mathrm{2}}\le \left|{\mathbf{k}}^{\prime}\right|<k+\frac{\mathrm{1}}{\mathrm{2}}}\mathit{E}({\mathbf{k}}^{\prime},t),\end{array}$$

where $k=\left|\mathbf{k}\right|=\sqrt{{k}_{x}^{\mathrm{2}}+{k}_{y}^{\mathrm{2}}}$ in ℝ^{2}. Extensions to three dimensions are
straightforward.

The kinetic energy spectrum is generally utilized for characterizing the energy content of scales in incompressible turbulent flows and does not take the localized scale content of the density into consideration. To include these density effects, following Lele (1994) and Kritsuk et al. (2007), we define an energy spectrum built on density-weighted velocity $\mathit{\omega}=\sqrt{\mathit{\rho}}\mathit{u}$, i.e., through using

$$\begin{array}{}\text{(17)}& {\displaystyle}\mathit{E}(\mathbf{k},t)={\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}\left|\widehat{\mathit{\omega}}\right(\mathbf{k},t){|}^{\mathrm{2}},\end{array}$$

where we can apply the same angle-averaged rule given by Eq. (16) to obtain one-dimensional spectra.

Figure 10 describes the spherical-averaged energy spectra for the
three-dimensional test case. Note here that the spherical average implies
that the local energy content in the Fourier domain is integrated over a
spherical shell of radius *k* in three dimensions. One can observe a scaling
behavior that corresponds to classical Kolmogorov theory in the infinite
Reynolds number limit (i.e., an inertial range with ${k}^{-\mathrm{5}/\mathrm{3}}$ scaling) for
both purely kinetic energy spectra and density-weighted kinetic energy
spectra. The finer dissipative scales are seen to display a *k*^{−6} scaling
behavior for both these statistical quantities as well. We have also plotted
the compensated energy spectra, which illustrate the scaling laws more
quantitatively following the horizontal lines.

The data presented in Fig. 10 have been obtained by performing a
three-dimensional fast Fourier transform (FFT) procedure. From a practical
implementation point of view, we perform a slightly different approach to
compute energy spectra. The main advantage of this procedure is that it is
naturally suited to any parallel computing architecture. For an analogy with
the two-dimensional test cases, we present transversely averaged energy
spectra in Fig. 11 wherein the circular averaging of the energy in
the Fourier domain is carried out over different two-dimensional *z* planes
which are then spatially averaged over the depth of the domain. Similar
trends to the spherical averaging spectral scaling are observed for this
case. However, we note that the obtained spectra are less noisy when using a
direct three-dimensional FFT procedure. This can be interpreted by the
quasi-homogeneity of the flow after the onset of turbulence.

We investigate the performance of the same metrics for the two-dimensional
test case and obtain scaling behavior as seen in Fig. 12 where a
*k*^{−3} scaling behavior is obtained in accordance with the direct cascade
of enstrophy espoused by Kraichnan–Batchelor–Leith (KBL) theory for the
inertial range, especially for the lower compressibility ratio. A higher
magnitude of *α* is seen to yield a more flattened spectrum towards
${k}^{-\mathrm{7}/\mathrm{3}}$ scaling and also delay the formation of the *k*^{−6} cascade in
the dissipation range. Figure 12 also shows the spectral scaling
obtained from the density-weighted kinetic energy spectra where scaling
behavior corresponding to ${k}^{-\mathrm{7}/\mathrm{3}}$ is seen for all *α* values. This
suggests that the two-dimensional configuration of the test case is affected
by the packaging of density content at different scales. The dissipation zone
shows a similar behavior using this metric where a delay in scaling with
*k*^{−6} is obtained by an increase in the magnitude of *α*. We can
conclude that the density-weighted spectrum becomes a more universal
representation for various degrees of compressibility.

Figure 13 shows the effect of the parameter *α* on the
compressibility of the two-dimensional turbulence case through the use of
compensated energy spectra where the distance from the origin in the Fourier
space (in other words *k*) is used to weight instantaneous energy content. We
only present the compensated energy distribution in the first quadrant of the
Fourier space. At *α*=1.0 one can observe a distinct loss of isotropy in
the energy content of the solution field (in spectral space) which
corresponds to an enhanced compressibility. In comparison, *α*=0.5 and
*α*=0.25 display a behavior which is rather isotropic in nature,
indicating weak compressibility.

To demonstrate the effect of density more clearly, we present the difference
spectra for the 2-D KHI turbulence in Fig. 14. Here, we compute
the spectrum of the difference between the velocity ** u** and the
normalized density-weighted velocity $\sqrt{\mathit{\rho}}\mathit{u}/\langle \sqrt{\mathit{\rho}}\rangle $, where $\sqrt{\mathit{\rho}}$ refers to the spatial average of the
square root of density. The results show a clear inertial range with the
${k}^{-\mathrm{5}/\mathrm{3}}$ scaling. This is a manifestation of the density effect in 2-D KHI
turbulence.

To study the effect of compressibility in more detail we perform the
Helmholtz decomposition to compute energy spectra from the curl-free and
divergence-free components of the velocity field. This decomposition has been
extensively used in turbulence studies (i.e., see
Sagaut and Cambon, 2008; Jagannathan and Donzis, 2016; Wang et al., 2017; Falkovich and Kritsuk, 2017; Wang et al., 2018).
In our present work, we investigate the behavior of energy spectra using both
the kinematic velocity and density-weighted velocity fields in 2-D and 3-D
KHI turbulence problems. Let ** v** be a vector field in ∈ℝ

$$\begin{array}{}\text{(18)}& {\displaystyle}\mathit{v}=\mathrm{\nabla}\mathit{\varphi}+\mathrm{\nabla}\times \mathit{A},\end{array}$$

which can be rewritten as

$$\begin{array}{}\text{(19)}& {\displaystyle}\mathit{v}={\mathit{v}}^{\mathrm{c}}+{\mathit{v}}^{\mathrm{s}},\end{array}$$

where *v*^{c}=∇*ϕ* is the compressive (curl-free)
component since the curl of a gradient of any scalar field *ϕ* is zero,
and ${\mathit{v}}^{\mathrm{s}}=\mathrm{\nabla}\times \mathit{A}$ is the solenoidal
(divergence-free) component since the divergence of a curl of any vector
field ** A** is zero. Taking the divergence of Eq. (18) yields
the following Poisson equation:

$$\begin{array}{}\text{(20)}& {\displaystyle}\mathrm{\nabla}\cdot \mathit{v}={\mathrm{\nabla}}^{\mathrm{2}}\mathit{\varphi},\end{array}$$

which can be solved for *ϕ* efficiently using an FFT procedure since
** v** is provided as a quantity of interest that we would like to
decompose into two parts. Once

$$\begin{array}{}\text{(21)}& {\displaystyle}& {\displaystyle}{\mathit{v}}^{\mathrm{c}}=\mathrm{\nabla}\mathit{\varphi},\text{(22)}& {\displaystyle}& {\displaystyle}{\mathit{v}}^{\mathrm{s}}=\mathit{v}-{\mathit{v}}^{\mathrm{c}}.\end{array}$$

We note that there would be infinitely many candidates for the compressive
component since the multiplication of *ϕ* by any arbitrary constant after
solving the Poisson equation would still yield a curl-free velocity field.
However, the energy spectrum scaling behaviors would remain identical for
each realization.

Figure 15 presents the compensated energy spectra for the
3-D KHI problem using both definitions of the velocity vector field (i.e.,
the kinematic velocity and the density-weighted velocity). We have obtained a
${k}^{-\mathrm{5}/\mathrm{3}}$ dominant scaling for the solenoidal component in both definitions.
However, the compressive component demonstrates an anomalous spectrum
especially when we use the kinetic velocity definition. This anomaly can also
be linked to the results of the pressure power spectra that we present in the
next section. Figure 16 presents the same analysis for the
case of 2-D KHI turbulence. Both compressive and solenoidal components scale
with the ${k}^{-\mathrm{5}/\mathrm{3}}$ slope for the density-weighted velocity field. However,
there is a clear difference for the results with various values of *α*
when we look at the Helmholtz decomposition of the kinetic velocity field.
The solenoidal inertial range scaling becomes *k*^{−3} for lower *α*
values, which is consistent with Kraichnan theory. However, the scaling
steepens and gets closer to *k*^{−2} for increasing *α*, which is also
consistent with the Kadomtsev–Petviashvili spectrum for acoustic turbulence.

Observations on the density power spectrum have played an important role in
astrophysics applications (Armstrong et al., 1981). Although it has been
established that the density power spectrum has an inertial scaling of
${k}^{-\mathrm{5}/\mathrm{3}}$ (Shaikh and Zank, 2010; Donzis and Jagannathan, 2013), similar to the
Kolmogorov energy spectrum, Bayly et al. (1992) demonstrated that it
depends on the flow regime as well as the initial conditions by considering a
three-dimensional weakly compressible hydrodynamic turbulence setup. By
studying weakly compressible two-dimensional flows,
Terakado and Hattori (2014) showed that the density spectrum scales between
*k*^{−1} and *k*^{−5} for nonuniform and uniform entropy cases, respectively.
They presented a great discussion for state-of-the-art computations and
scaling law observations for the density power spectrum.

In order to quantify the effect of the scale content of density alone, we devise a power spectrum that reflects the average packaging of density over different scales at any given time in the simulation. This may be given by the following expression:

$$\begin{array}{}\text{(23)}& {\displaystyle}\mathbf{\Gamma}(\mathbf{k},t)={\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}\left|\widehat{\mathit{\rho}}\right(\mathbf{k},t){|}^{\mathrm{2}},\end{array}$$

followed by angle averaging which leads to

$$\begin{array}{}\text{(24)}& {\displaystyle}\mathrm{\Gamma}(k,t)=\sum _{k-\frac{\mathrm{1}}{\mathrm{2}}\le \left|{\mathbf{k}}^{\prime}\right|<k+\frac{\mathrm{1}}{\mathrm{2}}}\mathbf{\Gamma}({\mathbf{k}}^{\prime},t).\end{array}$$

Observations regarding the difference in scaling behavior of the kinetic
energy and density-weighted kinetic energy spectra give us a cause to compare
the scaling behavior of the density power spectra for both our two- and
three-dimensional test cases. Figure 17 shows the density power
spectra for the three-dimensional turbulence test case where it can be seen
that a five-thirds law is followed for the arrangement of density content in
the solution field. A dissipation range scaling of *k*^{−6} can also be
observed. It can be seen that the variation of parameter *α* does not
seem to affect scaling behavior appreciably. Figure 18 shows a
similar examination for the two-dimensional test case where a considerable
difference in scaling behavior is observed. The imposition of two-dimensional
turbulence leads to a considerable alteration in the scaling behavior of the
density power spectrum with a ${k}^{-\mathrm{5}/\mathrm{3}}$ scaling observed in the inertial
range and a *k*^{−3} scaling in the dissipation range. In fact, this
packaging of density consequently affects the density-weighted kinetic energy
spectra described in Fig. 12. The intercomparison of the two- and
three-dimensional statistical quantities suggests that the density power
spectrum (i.e., the arrangement of density at different wavenumbers) plays an
important role with increased compressibility of any simulation wherein the
${k}^{-\mathrm{5}/\mathrm{3}}$ scaling causes a deviation from *k*^{−3} scaling associated with
two-dimensional incompressibility to ${k}^{-\mathrm{7}/\mathrm{3}}$ scaling for *α*=1.0 for
the same test case. In contrast, the ${k}^{-\mathrm{5}/\mathrm{3}}$ density power spectrum of
three-dimensional turbulence causes no variation in scaling behavior with
increased compressibility and also causes similar scaling behaviors for both
averaged kinetic energy spectra as well as averaged density-weighted kinetic
energy spectra as seen in Fig. 10. This is one of the central
conclusions of this investigation.

Similar to the density power spectrum defined in Eq. (23), the pressure power spectrum can be computed as

$$\begin{array}{}\text{(25)}& {\displaystyle}\mathbf{\Pi}(\mathbf{k},t)={\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}\left|\widehat{p}\right(\mathbf{k},t){|}^{\mathrm{2}},\end{array}$$

and its angle-averaged form reads as

$$\begin{array}{}\text{(26)}& {\displaystyle}\mathrm{\Pi}(k,t)=\sum _{k-\frac{\mathrm{1}}{\mathrm{2}}\le \left|{\mathbf{k}}^{\prime}\right|<k+\frac{\mathrm{1}}{\mathrm{2}}}\mathbf{\Pi}({\mathbf{k}}^{\prime},t).\end{array}$$

As discussed in Lesieur et al. (1999), the pressure spectrum can be expressed
by Π(*k*)∝*k**E*(*k*)^{2} by considering dimensional arguments. Indeed,
this yields a pressure spectra scaling of ${k}^{-\mathrm{7}/\mathrm{3}}$ for the Kolmogorov
regime and a pressure spectra scaling of *k*^{−5} for the Kraichnan regime.
Figures 19 and 20 demonstrate the pressure power
spectra for the 3-D and 2-D KHI problems, respectively. In the 3-D case, it
is clear that our results are consistent with the theoretical estimate of
${k}^{-\mathrm{7}/\mathrm{3}}$ scaling for all values of the compressibility parameter *α*.
However, in 2-D turbulence we only observe *k*^{−5} scaling for smaller
scales (i.e., higher wavenumbers). Particularly for weaker compressibility,
given by the *α*=0.25 case, the *k*^{−5} scaling starts earlier.
Figure 20 clearly illustrates that the pressure power spectrum
inertial scaling becomes ${k}^{-\mathrm{5}/\mathrm{3}}$ for stronger compressibility. These
results indicate that the pressure power spectrum can be a useful tool for
characterizing two-dimensional compressible turbulence.

Statistical inferences about the nature of compressible turbulence may also be drawn through the use of velocity structure functions which also show scaling tendencies according to the physics of the solution field (Moin and Yaglom, 1975). A velocity structure function may be expressed as (Babiano et al., 1985; Boffetta and Ecke, 2012; Iyer et al., 2017)

$$\begin{array}{}\text{(27)}& {\displaystyle}{S}^{p}\left(r\right)=\langle \left(\mathit{u}\right(\mathit{x}+\mathit{r})-\mathit{u}(\mathit{x}){)}^{p}\rangle ,\end{array}$$

where the ensemble averaging is taken over all positions ** x** and all
orientations of

We utilize the high-fidelity data of the previously described numerical
experiments for two- and three-dimensional turbulence to obtain structure
function statistics at time *t*=5. Figure 21 shows the
second-order velocity structure function for the longitudinal and transverse
directions for the 3-D test case. One can observe a steadily increasing
alignment with *r*^{2∕3} with a decreasing value of *α*, implying weaker
compressibility. It is worth mentioning here that Kolmogorov theory dictates
a cascade given by *p*∕3. Similar trends are observed for both longitudinal
and transverse directions, suggesting that a certain degree of isotropy now
characterizes the system. For ranges of *r* below 10^{−2}, it is observed
that both longitudinal and transverse structure functions scale according to
*r*^{2} for the second-order structure function.

We undertake a similar statistical examination for our two-dimensional test
case where second-order longitudinal and transverse structure functions are
given by Fig. 22, where it is observed that at low *r*, a scaling
corresponding to *r*^{2} is observed. This is in accordance with findings in
Grossmann and Mertens (1992). At larger values of *r*, the *r*^{2} scaling
transitions to a *r*^{4∕3} scaling at relatively higher compressibility
(i.e., *α*=1.0) and *r* scaling at *α*=0.25. Eventually, it is
expected that an *r*^{2∕3} behavior must emerge with perfect
incompressibility. The aforementioned observations hold true for both
longitudinal and transverse second-order structure functions and are
consistent with the definition of $S\left(r\right)\propto {r}^{n-\mathrm{1}}$. It can be observed
that the velocity structure functions for three-dimensional simulations
generally obey the prediction of the Kolmogorov theory (for lower values of
*α* indicating weak compressibility) as against their two-dimensional
counterparts.

4 Conclusions

Back to toptop
In this investigation, data from high-fidelity numerical experiments are
utilized to study scaling behavior for statistical quantities such as spectra
and structure functions. We study two test cases given by the
Kelvin–Helmholtz instability problem in two and three dimensions to study
spectral scaling laws for compressible shear layer turbulence. Our spectra
are given by the averaged kinetic energy magnitude and the averaged
density-weighted kinetic energy magnitude, and it is observed that while both
quantities exhibit similar trends in three dimensions, the density-weighted
kinetic energy spectra show varying scaling tendencies in two dimensions.
This is demonstrated by a flattening of the density-weighted energy spectra,
expected to exhibit *k*^{−3} scaling in the incompressible limit, to
${k}^{-\mathrm{7}/\mathrm{3}}$ scaling for higher compressibility. Variations are also seen in
the scaling of the dissipation range. This prompts us to investigate the
density power spectrum and the pressure power spectrum for both two- and
three-dimensional cases, and it is observed that two distinct inertial and
dissipation range behaviors can be observed. For the density power spectrum,
both the three-dimensional and two-dimensional cases show a five-thirds
scaling behavior in the inertial range with a *k*^{−6} scaling in the
dissipation range. This basically demonstrates that the scaling laws for both
kinetic energy and power density spectra coincide with each other only for
three-dimensional flows. The pressure power spectrum analysis also
demonstrates that the results are less invariant to variations in the
compressibility parameter for the two-dimensional KHI problem. The scaling
behavior exhibited by the density and pressure power spectra for the
two-dimensional test, combined with the trends observed in the energy
spectrum and structure function analyses, indicates that nonlinear processes
exhibiting extreme aspect ratios may have a fundamentally different set of
nonlinear interactions as compared to moderate aspect ratios (which may be
classified as three-dimensional). Incorporating the effect of boundary
conditions, which inevitably leads to large-scale anisotropy into the scaling
tendencies exhibited here, would account for further interesting deviations
from three-dimensional counterparts. This remains a topic of focus for future
investigation.

Data availability

Back to toptop
Data availability.

All synthetic data generated or analyzed during this study are included in the published article.

Author contributions

Back to toptop
Author contributions.

Omer San conceived the presented study and performed the computations. Romit Maulik helped in writing the paper. Both authors discussed the results and contributed to the final manuscript.

Competing interests

Back to toptop
Competing interests.

The authors declare that they have no conflict of interest.

Acknowledgements

Back to toptop
Acknowledgements.

The authors are very grateful to the editor and four anonymous referees for
their useful comments and suggestions published on the NPGD website that
helped us improve the presentation of this paper. The helpful comments from
Bhimsen Shivamoggi (University of Central Florida) and Bohua Sun
(Cape Peninsula University of Technology) are also appreciated. All numerical
experiments have been performed using the resources of the Oklahoma State
University High Performance Computing (OSU-HPCC) facilities.

Edited by: Ioulia Tchiguirinskaia

Reviewed by: four anonymous referees

References

Back to toptop
Aluie, H.: Scale decomposition in compressible turbulence, Physica D, 247, 54–65, 2013. a

Aris, R.: Vectors, tensors and the basic equations of fluid mechanics, Dover Publications, Inc., New York, USA, 2012. a

Armstrong, J., Cordes, J., and Rickett, B.: Density power spectrum in the local interstellar medium, Nature, 291, 561–564, 1981. a

Arneodo, A., Baudet, C., Belin, F., Benzi, R., Castaing, B., Chabaud, B., Chavarria, R., Ciliberto, S., Camussi, R., and Chilla, F.: Structure functions in turbulence, in various flow configurations, at Reynolds number between 30 and 5000, using extended self-similarity, Europhys. Lett., 34, 411–416, 1996. a

Babiano, A., Claude, B., and Sadourny, R.: Structure functions and dispersion laws in two-dimensional turbulence, J. Atmos. Sci., 42, 941–949, 1985. a, b, c

Banerjee, S. and Galtier, S.: Exact relation with two-point correlation functions and phenomenological approach for compressible magnetohydrodynamic turbulence, Phys. Rev. E, 87, 013019, https://doi.org/10.1103/PhysRevE.87.013019, 2013. a

Batchelor, G. K.: Computation of the Energy Spectrum in Homogeneous Two-Dimensional Turbulence, Phys. Fluids, 12, II–233, 1969. a

Bayly, B., Levermore, C., and Passot, T.: Density variations in weakly compressible flows, Phys. Fluids, 4, 945–954, 1992. a

Bershadskii, A.: Distributed chaos and inertial ranges in turbulence, arXiv preprint arXiv:1609.01617, available at: https://arxiv.org/abs/1609.01617 (last access: 27 June 2018), 2016. a

Biskamp, D. and Schwarz, E.: On two-dimensional magnetohydrodynamic turbulence, Phys. Plasmas, 8, 3282–3292, 2001. a

Blaisdell, G. A., Mansour, N. N., and Reynolds, W. C.: Compressibility effects on the growth and structure of homogeneous turbulent shear flow, J. Fluid Mech., 256, 443–485, 1993. a

Boffetta, G. and Ecke, R. E.: Two-dimensional turbulence, Annu Rev. Fluid Mech., 44, 427–451, 2012. a, b, c

Boffetta, G. and Mazzino, A.: Incompressible Rayleigh-Taylor Turbulence, Annu. Rev. Fluid. Mech., 49, 119–143, 2017. a

Bos, W. J. and Bertoglio, J. P.: Dynamics of spectrally truncated inviscid turbulence, Phys. Fluids, 18, 071701, https://doi.org/10.1063/1.2219766, 2006. a

Clercx, H. J. H. and van Heijst, G. J. F.: Dissipation of coherent structures in confined two-dimensional turbulence, Phys. Fluids, 29, 111103, https://doi.org/10.1063/1.4993488, 2017. a

Domaradzki, J. A. and Carati, D.: An analysis of the energy transfer and the locality of nonlinear interactions in turbulence, Phys. Fluids, 19, 085112, https://doi.org/10.1063/1.2772248, 2007. a

Donzis, D. A. and Jagannathan, S.: Fluctuations of thermodynamic variables in stationary compressible turbulence, J. Fluid Mech., 733, 221–244, 2013. a

Falceta-Gonçalves, D., Kowal, G., Falgarone, E., and Chian, A. C.-L.: Turbulence in the interstellar medium, Nonlin. Processes Geophys., 21, 587–604, https://doi.org/10.5194/npg-21-587-2014, 2014. a

Falkovich, G. and Kritsuk, A. G.: How vortices and shocks provide for a flux loop in two-dimensional compressible turbulence, Phys. Rev. Fluids, 2, 092603, https://doi.org/10.1103/PhysRevFluids.2.092603, 2017. a

Falkovich, G., Fouxon, I., and Oz, Y.: New relations for correlation functions in Navier–Stokes turbulence, J. Fluid Mech., 644, 465–472, 2010. a

Goldreich, P. and Sridhar, S.: Magnetohydrodynamic turbulence revisited, Astrophys. J., 485, 680–688, 1997. a

Grossmann, S. and Mertens, P.: Structure functions in two-dimensional turbulence, Z. Phys. B. Con. Mat., 88, 105–116, 1992. a

Hopfinger, E. J.: Turbulence in stratified fluids: A review, J. Geophys. Res.-Oceans, 92, 5287–5303, 1987. a

Hwang, K., Goldstein, M. L., Kuznetsova, M. M., Wang, Y., Viñas, A. F., and Sibeck, D. G.: The first in situ observation of Kelvin-Helmholtz waves at high-latitude magnetopause during strongly dawnward interplanetary magnetic field conditions, J. Geophys. Res.-Space, 117, A08233, https://doi.org/10.1029/2011JA017256, 2012. a

Iroshnikov, P. S.: Turbulence of a conducting fluid in a strong magnetic field, Sov. Astron., 7, 566–571, 1964. a

Iyer, K. P., Sreenivasan, K. R., and Yeung, P. K.: Reynolds number scaling of velocity increments in isotropic turbulence, Phys. Rev. E, 95, 021101, https://doi.org/10.1103/PhysRevE.95.021101, 2017. a

Jagannathan, S. and Donzis, D. A.: Reynolds and Mach number scaling in solenoidally-forced compressible turbulence using high-resolution direct numerical simulations, J. Fluid Mech., 789, 669–707, 2016. a

Kadomtsev, B. B. and Petviashvili, V. I.: Acoustic turbulence, Sov. Phys. Dokl., 18, 115–115, 1973. a

Kida, S. and Orszag, S. A.: Energy and spectral dynamics in forced compressible turbulence, J. Sci. Comput., 5, 85–125, 1990. a

Kida, S., Murakami, Y., Ohkitani, K., and Yamada, M.: Energy and flatness spectra in a forced turbulence, J. Phys. Soc. Jpn, 59, 4323–4330, 1990. a

Kolmogorov, A. N.: The local structure of turbulence in incompressible viscous fluid for very large Reynolds numbers, in: Dokl. Akad. Nauk SSSR+, 30, 299–303, 1941. a, b, c

Kraichnan, R. H.: Inertial-range spectrum of hydromagnetic turbulence, Phys. Fluids, 8, 1385–1387, 1965. a

Kraichnan, R. H.: Inertial ranges in two-dimensional turbulence, Phys. Fluids, 10, 1417–1423, 1967. a, b

Kritsuk, A. G., Norman, M. L., Padoan, P., and Wagner, R.: The statistics of supersonic isothermal turbulence, Astrophys. J., 665, 416–431, 2007. a, b, c

Kuznetsov, E. A. and Sereshchenko, E. V.: Anisotropic characteristics of the Kraichnan direct cascade in two-dimensional hydrodynamic turbulence, JETP Lett+, 102, 760–765, 2015. a

Leith, C. E.: Atmospheric predictability and two-dimensional turbulence, J. Atmos. Sci., 28, 145–161, 1971. a

Lele, S. K.: Compressibility effects on turbulence, Annu. Rev. Fluid Mech., 26, 211–254, 1994. a, b

Lesieur, M., Ossia, S., and Metais, O.: Infrared pressure spectra in two-and three-dimensional isotropic incompressible turbulence, Phys. Fluids, 11, 1535–1543, 1999. a

Mac Low, M. and Klessen, R. S.: Control of star formation by supersonic turbulence, Rev. Mod. Phys., 76, 125–194, 2004. a

Mac Low, M., Klessen, R. S., Burkert, A., and Smith, M. D.: Kinetic energy decay rates of supersonic and super-Alfvénic turbulence in star-forming clouds, Phys. Rev. Lett., 80, 2754, https://doi.org/10.1103/PhysRevLett.80.2754, 1998. a

Maulik, R. and San, O.: Resolution and energy dissipation characteristics of implicit LES and explicit filtering models for compressible turbulence, Fluids, 2, 14, https://doi.org/10.3390/fluids2020014, 2017. a

Moin, A. S. and Yaglom, A. M.: Statistical Fluid Mechanics: Mechanics of Turbulence, vol. 2, edited by: Lumley, J., M.I.T. Press, Cambridge, Massachusetts, USA, 1975. a

Moura, R. C., Mengaldo, G., Peiró, J., and Sherwin, S. J.: On the eddy-resolving capability of high-order discontinuous Galerkin approaches to implicit LES/under-resolved DNS of Euler turbulence, J. Comput. Phys., 330, 615–623, 2017. a

Ottaviani, M.: Scaling laws of test particle transport in two-dimensional turbulence, Europhys. Lett., 20, 111–116, 1992. a

Padoan, P. and Nordlund, Å.: The stellar initial mass function from turbulent fragmentation, Astrophys. J., 576, 870–879, 2002. a

Passot, T., Pouquet, A., and Woodward, P.: The plausibility of Kolmogorov-type spectra in molecular clouds, Astron. Astrophys., 197, 228–234, 1988. a

Peltier, W. R. and Caulfield, C. P.: Mixing efficiency in stratified shear flows, Annu. Rev. Fluid Mech., 35, 135–167, 2003. a

Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P.: Numerical recipes in Fortran 90, Cambridge University Press, Cambridge, UK, 1996. a

Qiu, X., Ding, L., Huang, Y., Chen, M., Lu, Z., Liu, Y., and Zhou, Q.: Intermittency measurement in two-dimensional bacterial turbulence, Phys. Rev. E, 93, 062226, https://doi.org/10.1103/PhysRevE.93.062226, 2016. a

Roe, P. L.: Approximate Riemann solvers, parameter vectors, and difference schemes, J. Comput. Phys., 43, 357–372, 1981. a

Sagaut, P. and Cambon, C.: Homogeneous turbulence dynamics, vol. 10, Springer, Cham, Switzerland, 2008. a

Shaikh, D. and Zank, G.: The turbulent density spectrum in the solar wind plasma, Mon. Not. R. Astron. Soc., 402, 362–370, 2010. a

Shivamoggi, B. K.: Spectral laws for the compressible isotropic turbulence, Phys. Lett. A, 166, 243–248, 1992. a, b, c

Shivamoggi, B. K.: Spatial intermittency in the classical two-dimensional and geostrophic turbulence, Ann. Phys.-New York, 270, 263–291, 1998. a

Shivamoggi, B. K.: Magnetohydrodynamic turbulence: Generalized formulation and extension to compressible cases, Ann. Phys.-New York, 323, 1295–1303, 2008. a

Shivamoggi, B. K.: Compressible turbulence: Multi-fractal scaling in the transition to the dissipative regime, Physica A, 390, 1534–1538, 2011. a

Shivamoggi, B. K.: Singularities in fully developed turbulence, Phys. Lett. A, 379, 1887–1892, 2015. a

Sun, B.: The temporal scaling laws of compressible turbulence, Mod. Phys. Lett. B, 30, 1650297, https://doi.org/10.1142/S0217984916502973, 2016. a

Sun, B.: Scaling laws of compressible turbulence, Appl. Math. Mech., 38, 765–778, 2017. a

Sytine, I. V., Porter, D. H., Woodward, P. R., Hodson, S. W., and Winkler, K.: Convergence tests for the piecewise parabolic method and Navier-Stokes solutions for homogeneous compressible turbulence, J. Comput. Phys., 158, 225–238, 2000. a

Terakado, D. and Hattori, Y.: Density distribution in two-dimensional weakly compressible turbulence, Phys. Fluids, 26, 085105, https://doi.org/10.1063/1.4892460, 2014. a

Thomson, W.: Hydrokinetic solutions and observations, Philos. Mag., 42, 362–377, 1871. a

Vassilicos, J. C.: Dissipation in turbulent flows, Annu. Rev. Fluid Mech., 47, 95–114, 2015. a

Wang, J., Yang, Y., Shi, Y., Xiao, Z., He, X. T., and Chen, S.: Cascade of kinetic energy in three-dimensional compressible turbulence, Phys. Rev. Lett., 110, 214505, https://doi.org/10.1103/PhysRevLett.110.214505, 2013. a

Wang, J., Gotoh, T., and Watanabe, T.: Scaling and intermittency in compressible isotropic turbulence, Phys. Rev. Fluids, 2, 053401, https://doi.org/10.1103/PhysRevFluids.2.053401, 2017. a

Wang, J., Wan, M., Chen, S., Xie, C., and Chen, S.: Effect of shock waves on the statistics and scaling in compressible isotropic turbulence, Phys. Rev. E, 97, 043108, https://doi.org/10.1103/PhysRevE.97.043108, 2018. a

Werne, J. and Fritts, D. C.: Stratified shear turbulence: Evolution and statistics, Geophys. Res. Lett., 26, 439–442, 1999. a

Westernacher-Schneider, J. R. and Lehner, L.: Numerical measurements of scaling relations in two-dimensional conformal fluid turbulence, J. High Energy Phys., 2017, 27, https://doi.org/10.1007/JHEP08(2017)027, 2017. a

Westernacher-Schneider, J. R., Lehner, L., and Oz, Y.: Scaling relations in two-dimensional relativistic hydrodynamic turbulence, J. High. Energy Phys., 2015, 1–31, 2015. a

Zhou, Y.: Rayleigh–Taylor and Richtmyer–Meshkov instability induced flow, turbulence, and mixing. I, Phys. Rep., 723–725, 1–136, 2017a. a

Zhou, Y.: Rayleigh–Taylor and Richtmyer–Meshkov instability induced flow, turbulence, and mixing. II, Phys. Rep., 723–725, 1–160, 2017b. a

Zhou, Y., Grinstein, F. F., Wachtor, A. J., and Haines, B. M.: Estimating the effective Reynolds number in implicit large-eddy simulation, Phys. Rev. E, 89, 013303, https://doi.org/10.1103/PhysRevE.89.013303, 2014. a

Short summary

We study the scaling laws of stratified shear flows by performing high-resolution numerical simulations of inviscid compressible turbulence induced by Kelvin–Helmholtz instability.

We study the scaling laws of stratified shear flows by performing high-resolution numerical...

Nonlinear Processes in Geophysics

An interactive open-access journal of the European Geosciences Union