2.1. Governing equations

In the present work, the 3-D compressible N–S equations supplemented by the conservative mass-fraction equations for $N-1$ species ($N$ is the total number of species) are adopted to describe the RM flow. This model is commonly referred to as the four-equation model (Thornber, Groom & Youngs Reference Thornber, Groom and Youngs2018) or the mass fraction model (Abgrall & Karni Reference Abgrall and Karni2001). The governing equations in conservation form can be written as

(2.1)$$\begin{gather} \dfrac{\partial\rho}{\partial t} + \boldsymbol{\nabla}\boldsymbol{\cdot}(\rho\boldsymbol{u}) = 0, \end{gather}$$

(2.2)$$\begin{gather}\dfrac{\partial\rho\boldsymbol{u}}{\partial t} + \boldsymbol{\nabla}\boldsymbol{\cdot}(\rho\boldsymbol{u}\otimes\boldsymbol{u}+p\boldsymbol{\delta}) = \boldsymbol{\nabla}\boldsymbol{\cdot}\boldsymbol{\sigma}, \end{gather}$$

(2.3)$$\begin{gather}\dfrac{\partial\rho E}{\partial t} + \boldsymbol{\nabla}\boldsymbol{\cdot}[(\rho E + p)\boldsymbol{u}] = \boldsymbol{\nabla}\boldsymbol{\cdot}(\boldsymbol{\sigma}\boldsymbol{\cdot}\boldsymbol{u} - \boldsymbol{q}_c - \boldsymbol{q}_d), \end{gather}$$

(2.4)$$\begin{gather}\dfrac{\partial\rho Y_l}{\partial t} + \boldsymbol{\nabla}\boldsymbol{\cdot}(\rho Y_l\boldsymbol{u}) ={-}\boldsymbol{\nabla}\boldsymbol{\cdot}\boldsymbol{J}_l \quad (l = 1,\ldots,N-1), \end{gather}$$

where $\rho$, $\boldsymbol {u} = [u,v,w]^t$, $p$ and $E=e+\frac {1}{2}\boldsymbol {u} \boldsymbol {\cdot } \boldsymbol {u}$ refer to the density, velocity vector, pressure and total energy per unit mass of the mixture, respectively; $e$ is the internal energy per unit mass; $\boldsymbol {\otimes }$ and $\boldsymbol {\delta }$ are the tensor product and Kronecker delta, respectively; and $Y_l$ is the mass fraction of species $l$ with $\sum _{1}^{N}Y_l=1$. With the ideal gas hypothesis and the Dalton's law of partial pressures, the equation of state can be given as

(2.5)\begin{equation} p=\sum_{l=1}^{N}p_l=\sum_{l=1}^{N}\rho_l\frac{\mathcal{R}_0}{M_l}T=\rho\mathcal{R}T=(\gamma - 1)\rho e, \end{equation}

where $\mathcal {R}=\mathcal {R}_0/M$ and $\mathcal {R}_0=8.314$ J (mol K)$^{-1}$ are the gas constant and universal gas constant, respectively; $M=(\sum Y_l/M_l)^{-1}$ and $T$ are the molar mass and the temperature of the mixture, respectively; $p_l$, $\rho _l=\rho Y_l$ and $M_l$ are the pressure, the density and the molar mass of species $l$, respectively. With the definition,

(2.6)\begin{equation} c_p - c_v = \mathcal{R} = \sum_{l=1}^{N}\dfrac{Y_l \gamma_l}{\gamma_l-1}\dfrac{\mathcal{R}_0}{M_l}-\sum_{l=1}^{N}\dfrac{Y_l}{\gamma_l-1}\dfrac{\mathcal{R}_0}{M_l}, \end{equation}

the specific heats at constant pressure and constant volume for the mixture are $c_p = \sum Y_l c_{p,l}$ and $c_v = \sum Y_l c_{v,l}$, respectively, where $c_{p,l}=[\gamma _l/(\gamma _l-1)]\mathcal {R}_0/M_l$ and $c_{v,l}=[1/(\gamma _l-1)]\mathcal {R}_0/M_l$. The specific heat ratio of the gas mixture is $\gamma ={c_p}/{c_v}$. For the Newtonian fluid considered in this work, the viscous stress tensor $\boldsymbol {\sigma }$ is

(2.7)\begin{equation} \boldsymbol{\sigma} = 2\mu\boldsymbol{\mathsf{S}}-\dfrac{2}{3}\mu\vartheta\boldsymbol{\delta}, \end{equation}

where the strain-rate tensor $\boldsymbol{\mathsf{S}}=\frac {1}{2}[\boldsymbol {\nabla }\boldsymbol {u}+(\boldsymbol {\nabla }\boldsymbol {u})^{t}]$ and the dilatation $\vartheta =\boldsymbol {\nabla }\boldsymbol {\cdot }\boldsymbol {u}$. The conductive heat flux $\boldsymbol {q}_c$ and the interspecies enthalpy flux $\boldsymbol {q}_d$ are defined as

(2.8)$$\begin{gather} \boldsymbol{q}_c ={-}\kappa\boldsymbol{\nabla} T, \end{gather}$$

(2.9)$$\begin{gather}\boldsymbol{q}_d = \sum_{l=1}^{N}h_l\boldsymbol{J}_l, \end{gather}$$

where the $l$th-species enthalpy per unit mass is $h_l=c_{p,l}T$, and the mass diffusion flux $\boldsymbol {J}_l$ is given by Groom & Thornber (Reference Groom and Thornber2021)

(2.10)\begin{equation} \boldsymbol{J}_l ={-}\rho D_l\boldsymbol{\nabla} Y_l + Y_l\sum_{n=1}^{N}\rho D_n\boldsymbol{\nabla} Y_n. \end{equation}

In the case of a binary mixture, the mass diffusion coefficients $D_1=D_2=D_{12}$, and $\boldsymbol {J}_l$ reduces to the Fick's law,

(2.11)\begin{equation} \boldsymbol{J}_l ={-}\rho D_l\boldsymbol{\nabla} Y_l. \end{equation}

The viscosity ($\mu$) and thermal conductivity ($\kappa$) of the mixture are given by (Poling, Prausnitz & O'Connell Reference Poling, Prausnitz and O'Connell2001)

(2.12)$$\begin{gather} \mu = \dfrac{\displaystyle\sum_{l=1}^{N}\mu_l Y_l/M_l^{1/2}}{\displaystyle\sum_{l=1}^{N}Y_l/M_l^{1/2}}, \end{gather}$$

(2.13)$$\begin{gather}\kappa = \dfrac{\displaystyle\sum_{l=1}^{N}\kappa_l Y_l/M_l^{1/2}}{\displaystyle\sum_{l=1}^{N}Y_l/M_l^{1/2}}, \end{gather}$$

where the $l$th-species viscosity coefficient ($\mu _l$), thermal conductivity coefficient ($\kappa _l$) and the mass diffusion coefficient ($D_{12}$) are calculated according to Tritschler et al. (Reference Tritschler, Olson, Lele, Hickel, Hu and Adams2014a).

2.2. Numerical methods

The simulation of RM turbulence involving both discontinuities (e.g. shock wave and material interface) and complex small-scale regions (e.g. turbulent fluctuations) remains a great challenge today. The major challenge lies in the simultaneous handling of discontinuities and turbulent fluctuations, which poses two contradictory demands to numerical schemes. On the one hand, a certain amount of numerical dissipation should be introduced into numerical scheme to mitigate spurious oscillations at discontinuities. On the other hand, to precisely resolve turbulent structures spanning a wide range of scales, numerical schemes should possess minimal dispersion and dissipation (Hill, Pantano & Pullin Reference Hill, Pantano and Pullin2006; Pirozzoli Reference Pirozzoli2011), namely, good spectral properties. Moreover, when treating multi-component flows in the framework of the fully conservative four-equation model, traditional shock-capturing schemes such as the weighted essentially non-oscillatory (WENO) scheme (Jiang & Shu Reference Jiang and Shu1996) and the WCNS scheme (Deng & Zhang Reference Deng and Zhang2000), which have achieved great success in the simulation of single-fluid flows, would generate noticeable spurious oscillations at the interface across which there is a sudden jump in specific heat ratio (Larrouturou Reference Larrouturou1991; Abgrall Reference Abgrall1996; Nonomura & Fujii Reference Nonomura and Fujii2017).

To efficiently capture shock waves and meanwhile to accurately resolve small-scale turbulent structures, an optimised six-point WCNS with state-of-the-art spectral properties has been developed recently by Zhou et al. (Reference Zhou, Ding, Huang and Luo2023b). The optimisation procedure includes: (i) two free parameters in WCNS are optimised with the approximated dispersion relation (ADR) technique (Pirozzoli Reference Pirozzoli2006) that is able to attain the spectral properties of a nonlinear scheme; (ii) considering nonlinear mechanism has a dramatic influence on the spectral properties, an advanced nonlinear weighting function of Wong & Lele (Reference Wong and Lele2017) is adopted and its key parameter, $C$, is optimised for better spectral properties; (iii) the optimised parameters are adjusted at each grid point according to the flow conditions there to realise adaptive dissipation. Following this optimisation strategy, a new type of WCNS with low dispersion and adaptive dissipation is achieved. Several benchmark test cases are simulated, and the results show that the developed WCNS exhibits state-of-the-art spectral properties and strong robustness. The optimised WCNS is then extended to multi-species flows by combining the double-flux algorithm of Abgrall & Karni (Reference Abgrall and Karni2001), and thus is suitable for simulating the RM turbulence. Note that the double-flux algorithm is not a conservative numerical method. As analysed by Abgrall & Karni (Reference Abgrall and Karni2001), there exist two main sources of conservation error for the double-flux algorithm, which have opposite effects on the solution and can nearly cancel each other out. As a result, it introduces only a little loss of conservation and produces a negligibly weak influence on the motions of shock and interface. In previous works (Ding et al. Reference Ding, Si, Chen, Zhai, Lu and Luo2017, Reference Ding, Liang, Chen, Zhai, Si and Luo2018; Feng et al. Reference Feng, Xu, Zhai and Luo2021; Li et al. Reference Li, Ding, Luo and Zou2022), the double-flux algorithm combined with the fifth-order WENO scheme has exhibited its ability to reproduce experimental results for various RM instability problems. It is also worth noting that the family of low-dissipation WCNSs has gained great success in the simulation of RM turbulence (Wong, Livescu & Lele Reference Wong, Livescu and Lele2019; Wong et al. Reference Wong, Baltzer, Livescu and Lele2022; Zhou et al. Reference Zhou, Ding, Cheng and Luo2023a).

The present work employs this optimised WCNS to discretise the hyperbolic part of the governing equations (2.1)–(2.4). Specifically, in the interpolation step, the characteristic-wise interpolation of primitive variables is adopted. For the flux difference splitting technique, the Harten–Lax–van Leer–Contact (HLLC) approximate Riemann solver is employed to calculate the flux at the midpoint, which is based on the three-wave assumption and suitable for multi-component compressible flows (Toro Reference Toro2019). The wave speeds estimation method of Einfeldt et al. (Reference Einfeldt, Munz, Roe and Sjögreen1991) and the pressure-control technique of Xie et al. (Reference Xie, Zhang, Lai and Li2019) are used for the HLLC. The spatial derivatives of fluxes are calculated by the robust sixth-order midpoint-and-node-to-node difference scheme of Deng et al. (Reference Deng, Jiang, Mao, Liu, Li and Tu2015). For more details about the WCNS scheme, the readers are referred to Zhou et al. (Reference Zhou, Ding, Huang and Luo2023b).

For the parabolic part of the governing equations (2.1)–(2.4), the viscous term, the diffusion term and the heat conduction term are written in a non-conservative form during the discretisation process. This is analogous to the method adopted by Subramaniam, Wong & Lele (Reference Subramaniam, Wong and Lele2019) when isolating the Laplace operators (Nagarajan, Lele & Ferziger Reference Nagarajan, Lele and Ferziger2003; Pirozzoli Reference Pirozzoli2010),

(2.14)$$\begin{gather} \boldsymbol{\nabla}\boldsymbol{\cdot}\boldsymbol{\sigma} = \mu(\nabla^2\boldsymbol{u}+\boldsymbol{\nabla}\vartheta)+2\boldsymbol{\mathsf{S}}\boldsymbol{\cdot}\boldsymbol{\nabla}\mu -\tfrac{2}{3}\mu\boldsymbol{\delta}\boldsymbol{\cdot}\boldsymbol{\nabla}\vartheta-\tfrac{2}{3}\vartheta\boldsymbol{\delta}\boldsymbol{\cdot}\boldsymbol{\nabla}\mu, \end{gather}$$

(2.15)$$\begin{gather}\boldsymbol{\nabla}\boldsymbol{\cdot}(\boldsymbol{\sigma}\boldsymbol{\cdot}\boldsymbol{u}) =\boldsymbol{u}\boldsymbol{\cdot}(\boldsymbol{\nabla}\boldsymbol{\cdot}\boldsymbol{\sigma})+\boldsymbol{\sigma\colon} \boldsymbol{\nabla}\boldsymbol{u}, \end{gather}$$

(2.16)$$\begin{gather}\boldsymbol{\nabla}\boldsymbol{\cdot}\boldsymbol{q}_c ={-}\kappa\nabla^2 T-\boldsymbol{\nabla} T\boldsymbol{\cdot}\boldsymbol{\nabla}\kappa, \end{gather}$$

(2.17)$$\begin{gather}\boldsymbol{\nabla}\boldsymbol{\cdot}\boldsymbol{q}_d = \sum_{l=1}^{N}(\boldsymbol{\nabla} h_l\boldsymbol{\cdot}\boldsymbol{J}_l+h_l\boldsymbol{\nabla}\boldsymbol{\cdot}\boldsymbol{J}_l), \end{gather}$$

(2.18)$$\begin{gather}\boldsymbol{\nabla}\boldsymbol{\cdot}\boldsymbol{J}_l ={-}\rho D_{12}\nabla^2 Y_l + \rho\boldsymbol{\nabla} D_{12}\boldsymbol{\cdot}\boldsymbol{\nabla} Y_l + D_{12}\boldsymbol{\nabla} \rho\boldsymbol{\cdot}\boldsymbol{\nabla} Y_l, \end{gather}$$

where $\boldsymbol {\nabla }\vartheta$ is also written in the isolated form,

(2.19)\begin{equation} \boldsymbol{\nabla}\vartheta = \dfrac{\partial^2 u_i}{\partial x_i^2} + \sum_{k\neq i}\dfrac{\partial^2 u_k}{\partial x_i \partial x_k}. \end{equation}

Note the indicators, $i$ and $k$, repeated in (2.19) do not represent Einstein summation. This approach solves directly the second derivatives instead of solving first derivatives two times, which can effectively improve the accuracy of the viscous damping on the highest resolvable wavenumber of the mesh (Lele Reference Lele1992). In (2.14)–(2.18), the first derivatives are computed with the fourth-order centre-difference scheme, whereas the second derivatives are computed with an optimised fourth-order nonlinear scheme proposed by Li et al. (Reference Li, Yu, Chen and Li2016). The explicit third-order total variation diminishing Runge–Kutta method is adopted for time integration.

At the boundaries of the computational domain, the ghost cell approach is employed. This enables the use of the same stencil and numerical scheme as those used in the interior grids, and also facilitates the communication in the message passing interface (MPI) parallelisation. At the ghost nodes, the conserved variables are assigned based on the known values at the interior nodes according to specific physical boundary conditions (Fu Reference Fu2021; Wu et al. Reference Wu, Wu, Li and Zhang2021). It should be pointed out that for the outflow boundary condition, the standard zero-gradient extrapolation may result in spurious nonphysical reflection of the outgoing waves (Motheau, Almgren & Bell Reference Motheau, Almgren and Bell2017). Numerous non-reflecting boundary conditions (NRBCs) have been developed to address this issue (Manco & de Mendonca Reference Manco and de Mendonca2019). One simple and effective method is the buffer-zone-based NRBC, in which a buffer zone with artificial damping is set adjacent to the boundary of the physical zone. Buffer zone methods are generally classified into two categories: implicit and explicit. In the implicit approach, a damping function related to the location of the computational domain is incorporated into the governing equations, whereas in the explicit approach the damping function is directly applied to the flow solution. The implicit method, which is less sensitive to the time-step size compared with the explicit method (Gill, Fattah & Zhang Reference Gill, Fattah and Zhang2015), is adopted throughout the present simulations, which can be expressed as (Mani Reference Mani2012)

(2.20)\begin{equation} \dfrac{\partial \boldsymbol{Q}}{\partial t} + \dfrac{\boldsymbol{F}-\boldsymbol{F_{\nu}}}{\partial x} + \dfrac{\boldsymbol{G}-\boldsymbol{G_{\nu}}}{\partial y} + \dfrac{\boldsymbol{H}-\boldsymbol{H_{\nu}}}{\partial z} ={-}\sigma_{d}(x)(\boldsymbol{Q}-\boldsymbol{Q}_{target}). \end{equation}

The left-hand side of the equation corresponds to the governing equations (2.1)–(2.4) in vector form. Here, $\boldsymbol {Q}$ is a conserved variable; $\boldsymbol {F}$, $\boldsymbol {G}$ and $\boldsymbol {H}$ are the convective flux components in the $x$, $y$ and $z$ directions, respectively; $\boldsymbol {F}_{\nu }$, $\boldsymbol {G}_{\nu }$ and $\boldsymbol {H}_{\nu }$ are the summation of the viscous, diffusive and heat conduction fluxes in the $x$, $y$ and $z$ directions, respectively. The damping term on the right-hand side of the equation operates solely in the buffer zone, which can facilitate the transition of the flow solution $\boldsymbol {Q}$ to the target solution $\boldsymbol {Q}_{target}$ by using the artificial damping $\sigma _{d}$. In this work, the damping function of Hu, Morfey & Sandham (Reference Hu, Morfey and Sandham2003) is utilised, which is defined as

(2.21)\begin{equation} \sigma_{d}(x_b) = \left[1+\exp\left(\dfrac{1}{x_b-1}+\dfrac{1}{x_b}\right)\right]^{{-}1}. \end{equation}

Here, $x_b=(x-x_{bs})/(x_{be}-x_{bs})$ is the relative coordinate in the buffer zone with $x_{bs}$ being the starting point of the buffer zone and $x_{be}$ being the end point. Within the physical domain, $\sigma _{d}=0$. Inside the buffer zone, the grid-stretching approach of Colonius, Lele & Moin (Reference Colonius, Lele and Moin1993) is adopted to obtain a larger sponge without increasing the computational cost.

2.3. Computational set-up

As shown in figure 1, the physical domain is a cuboid with dimensions of $2L_0\times L_0\times L_0$ ($L_0=10$ mm) in the $x$, $y$ and $z$ directions, respectively. The domain is connected to two buffer zones at the two ends of the $x$ direction, respectively. In each buffer zone, the mesh is deliberately stretched. The computational boundaries parallel to the $x$ direction take the periodic boundary condition, and the boundaries perpendicular to $x$ direction take the outflow boundary condition (i.e. zero-gradient extrapolation). A right-moving planar shock wave with Mach number $Ma=1.5$ is initially set at $x=-L_0/4$ in air. The pre-shock pressure is $p=101325$ Pa and the temperature is $T=298.15$ K. The post-shock flow is given according to the Rankine–Hugoniot relations. A multi-mode interface that separates air and SF$_6$ is initially positioned at $x=0.11L_0$. The pre-shock Atwood number is $At=0.67$, where Atwood number is defined as $At=(\rho _2-\rho _1)/(\rho _2+\rho _1)$ with $\rho _1$ and $\rho _2$ being the densities of air and $\text {SF}_6$, respectively. To prevent the interface from exiting the physical domain, a background velocity $\Delta U=-158.38$ m s$^{-1}$ is prescribed for the flow. This background velocity is equal in value to the velocity jump of the interface imparted by the incident shock impact, which can be calculated with one-dimensional (1-D) gas dynamics theory. In this way, the shocked interface evolves at the centre of the domain. In the present simulation, $\gamma _1=1.4$ and $M_1=28.964$ g mol$^{-1}$ for air, $\gamma _2=1.094$ and $M_2=146.055$ g mol$^{-1}$ for $\text {SF}_6$.

Figure 1.

Schematic of the computational domain.

The multi-mode interface is created with the flexible approach of Groom & Thornber (Reference Groom and Thornber2020), which has a power spectrum of

(2.22) \begin{equation} P(k)=\left\{\begin{array}{@{}ll} Ck^m, & k_{min}< k< k_{max},\\ 0, & \text{otherwise}, \end{array}\right. \end{equation}

where $k=\sqrt {k_y^2+k_z^2}$ is the radial wavenumber. The coefficient $C$ governs the magnitude of the mean standard deviation, whereas the parameter $m$ (where $m\le 0$) determines the slope of the perturbation distribution. The values of $k_{min}$ and $k_{max}$ determine both the range, denoted as ($k_{min},k_{max}$), and the bandwidth, denoted as $R=k_{max}/k_{min}$, of the perturbation. To investigate the influence of initial condition (especial the large- and small-scale perturbations) on the RM turbulence, three cases with different perturbation spectra are set in the present simulations. For cases 1, 2 and 3, their initial perturbation ranges are $k_{n} \in (24,40)$, $k_{n} \in (16, 48)$ and $k_{n} \in (2,64)$, respectively. Here, $k_{n}=(L_0/2{\rm \pi} )k$ denotes the dimensionless radial wavenumber. The initial perturbation bandwidths are $5/3$, $3$ and $32$ for cases 1, 2 and 3, respectively. For all three cases, the total standard deviation of the perturbation is $\sigma _0=0.5\lambda _{min}$ and $m=0$. The interface has an initial diffusion thicknesses of $\lambda _{min}/4$ for each case. The initial density spectra at the centre of the interface for cases 1, 2 and 3 are given in figure 2. These cases exhibit two notable features: first, their initial bandwidths increase progressively from case 1 to case 3; second, the latter case encompasses the perturbation range of the former, extending to both larger and smaller scales. The grid resolution in the physical domain is $1024\times 512\times 512$. A grid sensitivity study is given in the Appendix to show the uncertainty of the simulation results.

Figure 2.

Initial density spectrum at the mixing layer centre.

Achieving a high Reynolds number is an important goal in the study of RM turbulence (Zhou et al. Reference Zhou, Clark, Clark, Gail Glendinning, Aaron Skinner, Huntington, Hurricane, Dimits and Remington2019). Despite the rapid development of high-performance computing, it remains a great challenging to perform direct numerical simulation and large-eddy simulation of RM turbulence with high Reynolds number, except for the implicit large-eddy simulation of the high-Reynolds-number limit (Zhou et al. Reference Zhou2021). The Taylor Reynolds number, $Re_{\lambda _{T}}=\langle u_{r}^{\prime \prime }\rangle _{yz}\langle \lambda _T\rangle _{yz}/\langle \nu \rangle _{yz}$, is examined for each case. Here, $u_{r}^{\prime \prime }$, $\lambda _{T}$ and $\nu$ are the radial velocity fluctuation, Taylor scale and kinematic viscosity, respectively (Zhou et al. Reference Zhou, Ding, Cheng and Luo2023a). Note $\varphi ^{\prime \prime }=\varphi -\langle \varphi \rangle _{F}$ is the fluctuation under Favre average, $\langle \varphi \rangle _{F}=\langle \rho \varphi \rangle _{yz}/\langle \rho \rangle _{yz}$, and $\langle {\cdot }\rangle _{yz}$ refers to the spatial average in the $yz$ plane. It is found that $Re_{\lambda _{T}}$ presents identical values for cases 1 and 2, and a slightly higher value for case 3 due to the deposition of large-scale energy there. For these cases, $Re_{\lambda _{T}}$ decreases gradually from $Re_{\lambda _{T}}\approx 120$ immediately after shock passage to $Re_{\lambda _{T}}\approx 30$ at the end of simulation. This value is at level of Tritschler et al. (Reference Tritschler, Zubel, Hickel and Adams2014b), but lower than the highest value in Groom & Thornber (Reference Groom and Thornber2021). The continuous drop in Reynolds number is ascribed to the absence of energy source after the initial shock–interface interaction.

3.1. Growth of mixing layer

The mixing layer at late stages visualised from both the bubble and spike sides for cases 1 and 3 are shown in figure 3. Due to the presence of secondary instabilities, such as the KH instability, the bubbles with an initially rounded shape develop into numerous small irregular creases, and meanwhile the necks of the spikes undergo elongation and twisting. It is interesting that several faster-growing individual spikes behave like vortex rings, carrying a small amount of heavy fluid out of the mixing layer, which is consistent with the observation of Youngs (Reference Youngs2004). It is found that cases 1 and 2 present more such fast-growing spikes than case 3. The leading fronts of bubbles and spikes, $F_{b(s)}$, which are defined as the streamwise positions that represent 1 % volume fraction of air and SF$_6$ on the iso-surfaces, respectively, can be extracted (Thornber et al. Reference Thornber2017). Figure 4 shows the contours of bubble and spike fronts at early and late stages for cases 1 and 3. As we can see, the bubbles have undergone multiple generations of bubble merging, whereas the spikes develop into distinct vortex rings. The experimental results of Balakumar et al. (Reference Balakumar, Orlicz, Ristorcelli, Balasubramanian, Prestridge and Tomkins2012) and Balasubramanian, Orlicz & Prestridge (Reference Balasubramanian, Orlicz and Prestridge2013) showed that the mixing layer maintains the memory of initial large-scale perturbation even at the latest time of their experiments. To quantify this imprint, the correlation coefficients of bubble and spike fronts with respect to the initial distributions are calculated, which are defined as

(3.3)\begin{equation} I_{b(s)}=\dfrac{\langle F_{b(s),\tau}F_{b(s),0}\rangle -\langle F_{b(s),\tau}\rangle \langle F_{b(s),0}\rangle }{[(\langle F_{b(s),\tau}^2\rangle -\langle F_{b(s),\tau}\rangle ^2)(\langle F_{b(s),0}^2\rangle -\langle F_{b(s),0}\rangle ^2)]^{1/2}}. \end{equation}

Note higher values of $I_{b(s)}$ indicate more retention of the initial perturbation information. As shown in figure 5(a), the memory of the initial perturbations is forgotten faster and more for cases 1 and 2 than case 3, which implies that the large-scale perturbations are more persistent in the RM turbulence. It is also found that spikes retain a slightly stronger imprint about initial condition than bubbles.

Figure 3.

Iso-surface of mass fraction for (a) case 1 at $\tau \approx 43.94$ and (b) case 3 at $\tau \approx 44.20$. Blue ($Y_{\text {SF}_6}=0.01$) and red ($Y_{\text {SF}_6}=0.99$) correspond to spikes and bubbles, respectively.

Figure 4.

Contours of bubble and spike fronts.

Figure 5.

Temporal evolutions of (a) the initial perturbation imprint measured by $I_{b(s)}$ and (b) the mean bubble wavelength $\langle \lambda _b\rangle$ calculated by (3.5).

The mean bubble wavelength can be calculated from figure 4 using the method of Dimonte et al. (Reference Dimonte2004). The first step is to obtain the autocorrelation function of the bubble front,

(3.4)\begin{equation} \eta_b(\kern0.7pt y,z)=\dfrac{\sum_{y^{\prime},z^{\prime}}(F_b(\kern0.7pt y^{\prime},z^{\prime})-\langle F_b\rangle )(F_b(\kern0.7pt y^{\prime}+y,z^{\prime}+z)-\langle F_b\rangle )}{\sum_{y^{\prime},z^{\prime}}(F_b(\kern0.7pt y^{\prime},z^{\prime})-\langle F_b\rangle )^2}. \end{equation}

Next, the mean diameter of the bubbles is estimated by identifying the radial position at which the azimuthally averaged value of $\eta _b$, $\langle \eta _b\rangle (r) = \langle \eta _b(\kern0.7pt y,z)\rangle |_{r=\sqrt {x^2+y^2}}$, is less than 0.3, i.e. $\langle D_b\rangle = r[\langle \eta _b\rangle (r)<0.3]$. The threshold of 0.3 is determined by testing the images containing objects with known diameters (Ramaprabhu, Dimonte & Andrews Reference Ramaprabhu, Dimonte and Andrews2005). Finally, the mean bubble wavelength is calculated with the relationship suggested by Daly (Reference Daly1967),

(3.5)\begin{equation} \langle \lambda_b\rangle \approx \langle D_b\rangle (\rho_h^+ +\rho_l^+)/\rho_h^+, \end{equation}

where $\rho _h^+$ and $\rho _l^+$ are the post-shock densities of the heavy and light gases, respectively. Combining the bubble front snapshot in figure 4 with the bubble wavelength evolution in figure 5(b), a possible physical scenario for the evolution of bubbles is presented as follows. After the impact of the incident shock, the bubble size experiences a rapid increment in all three cases. Later, as the bubbles are laterally squeezed, the bubbles in case 1 shrinks briefly, i.e. reduces in size. The squeezing process can also cause the inversion of the leading and trailing positions of the bubble front. This explains the negative correlation coefficient for case 1, as given in figure 5(a). For case 3, the existence of large-scale perturbations provides more space for small bubbles to expand, and thus the mean bubble size remains invariant for a certain period of time. The bubble size in case 2 falls between these two cases. At late stages, the bubble merging becomes pronounced, leading to the sustained growth of bubble size for all three cases.

The approach described in (3.4) and (3.5) extracts the dominant bubble wavelengths. As a supplement, we introduce Voronoi diagrams of bubble tips (Oron et al. Reference Oron, Arazi, Kartoon, Rikanati, Alon and Shvarts2001) to describe the bubble sizes more finely. Voronoi diagrams are a specific spatial tessellation that divides space into unique cells associated with each particular point (Ferenc & Néda Reference Ferenc and Néda2007; Monchaux, Bourgoin & Cartellier Reference Monchaux, Bourgoin and Cartellier2010). Each Voronoi cell contains the space that is closer to the corresponding particular point than to any other point. The Voronoi analysis has a wide range of applications in the field of fluid mechanics, such as describing the clustering of inertial particle (Monchaux et al. Reference Monchaux, Bourgoin and Cartellier2010; Brandt & Coletti Reference Brandt and Coletti2022) and the distribution of vortices (Osawa et al. Reference Osawa, Minamoto, Shimura and Tanahashi2021; Ding et al. Reference Ding, Ding, Chong, Wu, Xia and Zhong2023). In the RM and RT instabilities, this approach can be used to visualise the distribution of the bubbles (Kartoon et al. Reference Kartoon, Oron, Arazi and Shvarts2003).

Figure 6(a–d) displays the 2-D Voronoi diagrams of bubbles for cases 1 and 3. To ensure closure of all sub-areas at the domain boundaries, ghost bubble tips based on periodicity are introduced (Jayaram et al. Reference Jayaram, Jie, Zhao and Andersson2020). It is seen that large bubbles in figure 4 are partitioned into several smaller bubbles in figure 6(a–d) due to their non-smoothness. Therefore, this approach primarily measures the features of small bubbles. The probability density function (p.d.f.) of the bubble size can be obtained from the Voronoi diagrams, as shown in figure 6(e). The area of each Voronoi cell, $S$, is normalised by the mean value, $\langle S\rangle = S_d/N_p$, where $S_d$ and $N_p$ are the total area of the domain and the number of bubble tips, respectively. In $d$-dimensional space ($d=1,2,3$), the p.d.f. of the normalised Voronoi cell sizes for entities spatially distributed as a random Poisson process is well described by the $\varGamma$ distribution (Ferenc & Néda Reference Ferenc and Néda2007),

(3.6)\begin{equation} p_{\varGamma}(\kern0.7pt y) = \dfrac{[(3d+1)/2]^{(3d+1)/2}}{\varGamma[(3d+1)/2]}y^{(3d-1)/2}\exp (-[(3d+1)/2]y), \end{equation}

where $\varGamma (\kern0.7pt y)$ is the Gamma function. It is found that the distribution of bubble sizes approximately follows a random Poisson process for all three cases. This finding is useful for the bubble merger model (Alon, Shvarts & Mukamel Reference Alon, Shvarts and Mukamel1993; Alon et al. Reference Alon, Hecht, Mukamel and Shvarts1994, Reference Alon, Hecht, Ofer and Shvarts1995). As illustrated in figure 6( f), the standard deviation of the normalised Voronoi areas of the bubble tips, $\sigma _{S/\langle S\rangle }$, is slightly higher than the value of the $\varGamma$ distribution, $\sigma _{\varGamma } = \sqrt {2/(3d+1)}$, particularly for case 3, which indicates the ‘clustering’ of bubbles (Monchaux et al. Reference Monchaux, Bourgoin and Cartellier2010; Tagawa et al. Reference Tagawa, Mercado, Prakash, Calzavarini, Sun and Lohse2012). The increasing trend also reflects the increasing bubble merging. Figure 6(g) shows the results of the second measurement of bubble wavelength,

(3.7)\begin{equation} \langle \lambda_b\rangle \approx \sqrt{\dfrac{\langle S\rangle }{\rm \pi}}, \end{equation}

which is more representative of small bubbles as mentioned earlier. The results indicate that there are many smaller bubbles produced at early times, which is particularly evident in case 1 and relatively weak in case 3. This may be the reason for the early decrease in the bubble wavelength for case 1, as shown in the first measurement in figure 5(b). Upon observing the proximity of the starting point of the increase in figure 6( f,g), it is found that bubble merging derives the growth of bubble size. It is important to note that, although two distinct approaches are employed to measure bubble sizes, aiming to enhance the robustness of our conclusions regarding bubble size evolution, factors such as diffusion, bubble tilting and coverage collectively diminish the effectiveness of these measurements, particularly in the early stages of establishing self-similar order.

Figure 6.

(a–d) Voronoi cells (blue lines) of bubble tips (red dots). Data are from (a,b) case 1 and (c,d) case 3 at (a,c) $\tau \approx 4$ and (b,d) $\tau \approx 44$. Localised diagrams are used for clarity. (e) The p.d.f. of normalised Voronoi areas $S/\langle S\rangle$. Open symbols: $\tau \approx 4$. Solid symbols: $\tau \approx 44$. ( f) Temporal evolution of scaled standard deviation $\sigma _{S/\langle S\rangle }/\sigma _{\varGamma }$. (g) Temporal evolution of the mean bubble wavelength $\langle \lambda _b\rangle$ calculated by (3.7).

For the entire mixing layer, the mixing width can be calculated by integrating the averaged volume fractions (e.g. Thornber et al. Reference Thornber, Drikakis, Youngs and Williams2010),

(3.8)\begin{equation} W = \int 4\langle\, f_1 \rangle_{yz} \langle\, f_2 \rangle_{yz}\,{\rm d}\kern0.7pt x. \end{equation}

Note the evolutions of the mixing width for cases 1 and 3 have been reported in our previous work (Zhou et al. Reference Zhou, Ding, Cheng and Luo2023a). Here, it is found that the mixing width in case 2 is nearly identical to that of case 1. In addition, the growth rate of the mixing width, $\theta$, has a fitted value of 0.220 in case 2 that is nearly identical to 0.211 in case 1, whereas the fitted value of $\theta$ in case 3 is notably higher ($\theta = 0.333$) (Zhou et al. Reference Zhou, Ding, Cheng and Luo2023a). The value of $\theta$ in the broadband case is lower than the model prediction value $0.4$ (Youngs Reference Youngs2004; Soulard et al. Reference Soulard, Guillois, Griffond, Sabelnikov and Simoëns2018). One possible reason is that the total standard deviation of the perturbation is fixed at $\sigma _0 = 0.5\lambda _{min}$ for reaching the turbulent state faster, which violates the early linearity-ensuring (Thornber et al. Reference Thornber, Drikakis, Youngs and Williams2010; Groom & Thornber Reference Groom and Thornber2020). Therefore, simulations in the present work deviate to some extent from the linear assumption adopted by the model. This may be a reason for the discrepancy between the present simulation and the model prediction. In addition, limited bandwidth for the present simulations may also have a certain influence on the value of $\theta$ (Groom & Thornber Reference Groom and Thornber2020). It indicates that the mixing width evolution tends to remain the same if the initial perturbation bandwidth is narrowed towards a high wavenumber, revealing an insensitivity of the RM mixing layer to initial perturbations. This finding is consistent with the theoretical proposal of Elbaz & Shvarts (Reference Elbaz and Shvarts2018) and Soulard et al. (Reference Soulard, Guillois, Griffond, Sabelnikov and Simoëns2018). They found that the evolution of the mixing width is influenced by both the characteristics of large-scale structures and the nonlinear interactions of small-scale structures, and the sole presence of small-scale structures could lead to similar evolution results for cases with narrowband, short-wavelength perturbations. A detailed explanation of the similarity between case 1 and case 2 is given hereinafter from various perspectives.

3.2. Turbulent kinetic energy

For the RM turbulence, the shock–interface interaction is the primary source of kinetic energy. The kinetic energy deposited at the early stage is subsequently transferred to various scales and ultimately dissipated into internal energy by viscosity in an irreversible manner. It has been found that the RM turbulence at the self-similar stage is analogous to the decaying turbulence (Thornber & Zhou Reference Thornber and Zhou2012; Tritschler et al. Reference Tritschler, Zubel, Hickel and Adams2014b), and the growth rate exponent $\theta$ of the mixing width is closely related to the decay rate exponent $n$ of TKE,

(3.9)\begin{equation} n = 2 - 2\theta. \end{equation}

This relationship can be obtained through dimensional analysis (Thornber et al. Reference Thornber, Drikakis, Youngs and Williams2010), $\sqrt {\text {TKE}}\propto \text {d}W/\text {d}t\propto t^{\theta -1}$. The decay rate of the integral TKE (ITKE) can be estimated by utilising the mean TKE in conjunction with the mixing width $W$, i.e. $\text {ITKE}\propto W*\text {TKE}\propto t^{-(n-\theta )}$, where

(3.10a,b)\begin{equation} \text{ITKE} = \int \text{TKE}(x)L_0^2\,\text{d}\kern0.7pt x,\quad \text{TKE}(x)=\left\langle \dfrac{1}{2}\rho u_i^{\prime\prime}u_i^{\prime\prime}\right\rangle_{yz}. \end{equation}

Both ITKE and TKE (shown here with its maximum value) experience an exponential decay that is compatible with (3.9) at late stages, as seen in figure 7. It is also found that, the curves here for case 1 and case 2 almost collapse with each other.

Figure 7.

Temporal evolutions of TKE for (a) case 1, (b) case 2 and (c) case 3. The dash-dotted and dashed lines represent $\propto \tau ^{-(2-2\theta )}$ and $\propto \tau ^{-(2-3\theta )}$, respectively, where $\theta$ is assigned the fitted value.

Figure 8 displays the spatial distribution of the mean TKE along the $x$ axis for the three cases, where the horizontal coordinate is normalised with the mixing width $W$ and the mixing layer centre $x_c$ (Walchli & Thornber Reference Walchli and Thornber2017) that is determined by

(3.11)\begin{equation} \int_{-\infty}^{x_c}\langle\, f_2 \rangle = \int_{x_c}^{\infty}\langle\, f_1 \rangle\,{\rm d}\kern0.7pt x. \end{equation}

It is shown that the peak of the mean TKE does not occur at the centre of the mixing layer, but is biased towards the spike side (i.e. $(x-x_c)/W<0$), similar to the asymmetric results of Groom & Thornber (Reference Groom and Thornber2023). It indicates that turbulent fluctuations are more active in the spike region. As time proceeds, the distribution curves for the three cases deviate. The curves for case 1 and case 2 exhibit greater irregularity with longer and more complex tails on the spike side, whereas the curve for case 3 is closer to the Gaussian curve with a smoother tail. This is consistent with the observation in figure 3 that for cases 1 and 2 there are numerous small vortex rings ejected from the head of fast-growing spikes, and their scaling behaviour differs from the rest region of the mixing layer (Thornber et al. Reference Thornber, Griffond, Bigdelou, Boureima, Ramaprabhu, Schilling and Williams2019). The distribution curves of TKE for the three cases converge when $\tau > 30$, which indicates the reach of a self-similar state.

Figure 8.

The profiles of plane-averaged TKE along the $x$-direction for (a) case 1, (b) case 2 and (c) case 3 at various moments.

3.3. Mixing measures

To quantify the mixing degree within the mixing zone, two mixing metrics are introduced: the molecular mixing fraction $\varTheta$ (Youngs Reference Youngs1991) and the mixing parameter $\varXi$ (Cook & Zhou Reference Cook and Zhou2002; Thornber et al. Reference Thornber, Drikakis, Youngs and Williams2010), which are respectively given by

(3.12)$$\begin{gather} \varTheta = \dfrac{\displaystyle\int \langle\, f_1 f_2 \rangle\,{\rm d}\kern0.7pt x}{\displaystyle\int \langle\, f_1 \rangle \langle\, f_2 \rangle\,{\rm d}\kern0.7pt x}, \end{gather}$$

(3.13)$$\begin{gather}\varXi = \dfrac{\displaystyle\int \langle {\rm min}(f_1,f_2) \rangle\,{\rm d}\kern0.7pt x}{\displaystyle\int {\rm min}(\langle\, f_1 \rangle,\langle\, f_2 \rangle)\,{\rm d}\kern0.7pt x}. \end{gather}$$

As shown in figure 9, for each case, $\varTheta$ and $\varXi$ have similar evolution trends and asymptotic values. The curves for cases 1 and 2 almost overlap, whereas the curve for case 3 has a lower asymptotic value and enters the plateau stage earlier ($\tau \approx 20$ for case 3, $\tau \approx 30$ for cases 1 and 2). The emergence of the plateau stage also indicates the self-similar evolution of the mixing layer. According to the theory of Soulard et al. (Reference Soulard, Guillois, Griffond, Sabelnikov and Simoëns2018), there is a relationship between the molecular mixing fraction $\varTheta$ and the growth rate exponent $\theta$ of the mixing width:

(3.14)\begin{equation} \varTheta_1 = \dfrac{2-3\theta}{2+(n_p-3)\theta}\quad \left(\theta\leq \dfrac{2}{3}\right), \end{equation}

where the coefficient $n_p$ is related to the proportional contribution of large and small scales to the energy and concentration spectra. Equation (3.14) illustrates that $\varTheta$ decreases as $\theta$ increases, namely, the degree of mixing is inversely related to the growth rate of the mixing layer. This finding aligns with the results depicted in figure 9, wherein case 3 with broadband perturbations exhibits a higher mixing width growth rate but a lower mixing degree.

Figure 9.

Temporal evolutions of (a) the molecular mixing fraction, $\varTheta$, and (b) the mixing parameter, $\varXi$.

By applying mathematical analysis of partial differential equations, Doss (Reference Doss2022) examined the mass transport within a decaying turbulence field, which is a specific critical class balanced between wave-like convection and heat-like diffusion. The author then extended this theory to the RM turbulence, obtaining a relationship between the molecular mixing fraction and the diffusion parameter $\mu =n(2C_{\theta }-1)/(2-n)$:

(3.15)\begin{equation} \varTheta_2 =1-\dfrac{\mu_c}{\mu}=\dfrac{2C_{\theta}-1-(2C_{\theta}-1+\mu_c)\theta} {(2C_{\theta}-1)(1-\theta)}, \end{equation}

where $C_{\theta }$ is the Monin constant (Pope Reference Pope1994) and $\mu _c=1$. Although models (3.14) and (3.15) are developed with two different methods and assumptions, they can match if the free parameters of one model are adjusted to the conventions of the other (Doss Reference Doss2022). The connection between $\varTheta$ and $\theta$ for the three cases at the plateau stage is given in figure 10, where the prediction curves of models (3.14) and (3.15) are also provided. It is worth noting that these curves collapse perfectly when the value of $n_p$ in the former model is set to 1.0 and the value of $C_{\theta }$ in the latter model is adjusted to 3/2. At the plateau stage, all numerical results fall within the range between the two models and also show consistent variation trend with the model predictions. This suggests again that the mixing layer grows more slowly, but more efficiently, for cases with narrowband, short-wavelength perturbations. The present finding demonstrates the validity of the theoretical models.

Figure 10.

Variations of the molecular mixing fraction, $\varTheta$, vs the mixing width growth rate, $\theta$. The solid and dash-dotted lines represent the relationship curves obtained by taking $n_{p}$ in (3.14) as that in (3.9) and $1.1$, respectively; the dashed line represents the curve obtained by using $C_{\theta }=3/2$ in (3.15). The symbols denote the values of $\varTheta$ at the plateau stage.

In addition, the mixing level can be assessed by calculating the p.d.f. of $Y_{\text {SF}_6}$ within the mixing zone. For a dataset of physical quantity $\varphi$, dividing its values equally into $N_b$ bins within the range $[\varphi _{min},\varphi _{max}]$, the discrete p.d.f. of $\varphi$ in the $k$th bin is

(3.16)\begin{equation} \text{p.d.f.} = \dfrac{N_k}{(\Delta\varphi)N}, \end{equation}

where $N$ and $N_k$ are the total number of data points within the dataset and the $k$th bin, respectively. Here $\Delta \varphi = {(\varphi _{max} - \varphi _{min})}/{N_b}$ represents the bin spacing, and $\sum _{k=1}^{N_b}\text {p.d.f.}\Delta \varphi =1$. In this work, $N_b=64$ is adopted, and the dataset is taken as the data in the inner mixing zone (IMZ), where the IMZ is defined as the set of cross-flow planes satisfying the following condition (Tritschler et al. Reference Tritschler, Olson, Lele, Hickel, Hu and Adams2014a):

(3.17)\begin{equation} 4\langle Y\rangle_{yz}(1-\langle Y\rangle_{yz})\geq 0.9. \end{equation}

Figure 11 displays the p.d.f.s of the mass fraction of SF$_6$ in the range $0.1\leq Y_{\text {SF}_6}\leq 0.9$ at the post-shock moment ($\tau \approx 0.3$), the early stage ($\tau \approx 4$), the transition stage ($\tau \approx 15$) and the plateau stage ($\tau > 30$) for all three cases. It should be noted that in the p.d.f. curve of $Y_{{SF}_6}$, the mixing degree is higher for a single peak than for double peaks. In addition, for the single peak situation, the mixing is higher when the peak is narrower (i.e. smaller variance) and the tail is thinner (i.e. lower kurtosis). At $\tau \approx 0.3$, the p.d.f.s of $Y_{{SF}_6}$ show a distinct bimodal distribution with two peaks at the boundaries, which indicates that the fluids in the IMZ are in a nearly separated state. At $\tau \approx 4$, the curves for all three cases converge to a state with a single peak, whose position leans toward the heavy fluid side. The presence of tail buckling in the curves suggests the existence of a substantial amount of pure, unmixed fluids at this moment. At the transition stage ($\tau \approx 15$), the two gases in the IMZ have already reached a high degree of mixing, and the p.d.f. curves show a nearly Gaussian distribution around $Y_{{SF}_6}=0.5$. At the plateau stage ($\tau > 30$), the p.d.f. within the IMZ maintains the Gaussian distribution. Afterwards, the peak value decreases and the tail widens, indicating the transfer of partially mixed fluids from the border of mixing zone into the IMZ for further mixing. In addition, the p.d.f.s of SF$_6$ within the IMZ tend to converge to a single curve for each case, which implies a dynamic equilibrium between the expanding mixing zone and the mixing occurring within the mixing layer. It is found that case 3 exhibits a lower level of mixing within the IMZ than cases 1 and 2, which aligns with the aforementioned finding.

Figure 11.

The p.d.f.s of $\text {SF}_6$ mass fraction, $Y_{{SF}_6}$, within the IMZ for (a) case 1, (b) case 2 and (c) case 3.

3.4. Anisotropy and inhomogeneity

The RM turbulence is highly anisotropic and inhomogeneous due to the inherent directionality of both the moving shock wave and the initial perturbations. The evolutions of anisotropy, characterised by the ratio of velocity fluctuations in different directions (Tritschler et al. Reference Tritschler, Olson, Lele, Hickel, Hu and Adams2014a), and inhomogeneity, measured by density self-correlation (DSC) (Besnard et al. Reference Besnard, Harlow, Rauenzahn and Zemach1992), within the mixing zone ($0.01\le \langle Y\rangle _{yz}\le 0.99$) for all three cases are plotted in figure 12. The two quantities are defined, respectively, as

(3.18)$$\begin{gather} a = \dfrac{\lvert u^{\prime\prime} \rvert}{\lvert u^{\prime\prime} \rvert + \lvert v^{\prime\prime} \rvert + \lvert w^{\prime\prime} \rvert}-\frac{1}{3}, \end{gather}$$

(3.19)$$\begin{gather}b ={-}\left\langle \left(\frac{1}{\rho}\right)^{\prime} \rho^{\prime} \right\rangle = \left\langle \frac{1}{\rho} \right\rangle \langle \rho \rangle -1. \end{gather}$$

The value of $a$ ranges from $-1/3$ to $2/3$. The lower and upper limits correspond to the absence of TKE in the streamwise and spanwise directions, respectively. The value of $a=0$ corresponds to an isotropic state. DSC is a non-negative parameter. Its zero value corresponds to homogeneous mixing, and higher DSC values indicate increased spatial inhomogeneity and a lower degree of mixing. At the late stage, the RM turbulence decreases in anisotropy with time. Despite this, the RM turbulence seems hard to reach complete isotropy as suggested by the curve variation tendency (Tritschler et al. Reference Tritschler, Olson, Lele, Hickel, Hu and Adams2014a; Thornber et al. Reference Thornber2017; Sewell et al. Reference Sewell, Ferguson, Krivets and Jacobs2021). The indicators of anisotropy and inhomogeneity reach stable values at the self-similarity stage, but are different among the three cases. Specifically, the degree of anisotropy and inhomogeneity is higher for case 3 than for cases 1 and 2, and the evolution curves for cases 1 and 2 almost overlap. The asymptotic values for both cases 1 and 2 are $\langle a\rangle _{MZ}\approx 0.087$ and $\langle b\rangle _{MZ}\approx 0.077$, whereas the asymptotic values for case 3 are $\langle a\rangle _{MZ}\approx 0.119$ and $\langle b\rangle _{MZ}\approx 0.155$. Since $a$ and $b$ are large-scale metrics, this result is consistent with the fact that case 3 has initial large-scale perturbations.

Figure 12.

Temporal evolutions of (a) the mean anisotropy, $\langle a\rangle _{MZ}$, and (b) DSC, $\langle b\rangle _{MZ}$, within the mixing zone.

4.1. Coarse-grained method

The coarse-graining approach, which has the same form as the modelling of large-eddy simulation of turbulence, was intended primarily to probe the sub-scale physical processes (Germano Reference Germano1992; Aluie Reference Aluie2013). When a field $\boldsymbol {f}(\boldsymbol {x})$ is subjected to low-pass filtering, it retains only the scales with sizes $\geq \ell$. This process can be regarded as an $n-D$ convolution

(4.1)\begin{equation} \bar{\boldsymbol{f}}(\boldsymbol{x})= G_{\ell}\star \boldsymbol{f} = \int \text{d}^n\boldsymbol{r} G_{\ell}(\boldsymbol{x}-\boldsymbol{r}) \boldsymbol{f}(\boldsymbol{r}), \end{equation}

where $G(\boldsymbol {r})$ is the normalised convolution/filtering kernel, i.e. for dimensionless $\boldsymbol {s}$, $\int \text {d}^n\boldsymbol {s} G(\boldsymbol {s})=1$. To achieve filtering, the real function $G(\boldsymbol {r})$ should exhibit a rapid decay as $\boldsymbol {r}$ becomes larger. Here $G_{\ell }(\boldsymbol {r}) \equiv \ell ^{-n}G(\boldsymbol {r}/\ell )$ is the $n$-dimensional; dilation kernel for $G(\boldsymbol {r})$ and its main support is within the region of diameter $\ell$. Thus, (4.1) can be regarded as a local spatial average. After the coarse graining, the field $\boldsymbol {f}(\boldsymbol {x})$ is decomposed into a large-scale ($\gtrsim \ell$) component $\bar {\boldsymbol {f}}_{\ell }$ and a small-scale ($\lesssim \ell$) component $\boldsymbol {f}^{\prime }_{\ell }=\boldsymbol {f}-\bar {\boldsymbol {f}}_{\ell }$. In this work, we adopt the Gaussian filter kernel, which is commonly used in physical space filtering:

(4.2)\begin{equation} G_{\ell}(\boldsymbol{x}-\boldsymbol{r}) = \left(\dfrac{\gamma}{{\rm \pi} \ell^2}\right)^{n/2}\exp \left(-\dfrac{\gamma \lvert \boldsymbol{x-r} \rvert^2}{\ell^2}\right), \end{equation}

where $\gamma$ is a constant that usually takes the value $\gamma =6$, and the standard deviation is $\sigma ={\ell }/{\sqrt {2\gamma }}$. The Gaussian filter kernel remains positive in the physical space, thereby avoiding the occurrence of unphysical negative densities during the filtering process (Wang et al. Reference Wang, Wan, Chen and Chen2018). The proper scale decomposition for variable-density flows is not as straightforward as it is for incompressible flows. A more suitable approach in such cases is the utilisation of Favre filtering (Garnier, Adams & Sagaut Reference Garnier, Adams and Sagaut2009)

(4.3)\begin{equation} \tilde{f}=\overline{\rho f}/\bar{\rho}. \end{equation}

As shown by Aluie (Reference Aluie2013) and Zhao & Aluie (Reference Zhao and Aluie2018), the Favre filtering satisfies the inviscid criterion for arbitrarily large density variations.

Subsequently, the budget for kinetic energy at large scales ($\gtrsim \ell$) can be derived by filtering the momentum equation in the N–S equations

(4.4)$$\begin{gather} \partial_{t}\left(\bar{\rho}\dfrac{\lvert \tilde{\boldsymbol{u}}_{\ell} \rvert^2}{2}\right) + \boldsymbol{\nabla}\boldsymbol{\cdot} \boldsymbol{J}_{\ell} ={-}\varPi_{\ell} - \varPhi_{\ell} - D_{\ell} + \epsilon^{inj}_{\ell}, \end{gather}$$

(4.5)$$\begin{gather}(J_{\ell})_j = \bar{\rho}\frac{\lvert \tilde{\boldsymbol{u}} \rvert^2}{2}\tilde{u}_j + \bar{p}\tilde{u}_j + \tilde{u}_i\bar{\rho}\tilde{\tau}(u_i,u_j) - \tilde{u}_i\bar{\sigma}_{ij}, \end{gather}$$

(4.6)$$\begin{gather}\varPi_{\ell}={-}\bar{\rho}\partial_j\tilde{u}_i\tilde{\tau}(u_i,u_j), \end{gather}$$

(4.7)$$\begin{gather}\varPhi_{\ell} ={-} \bar{p}_{\ell}\boldsymbol{\nabla}\boldsymbol{\cdot}\tilde{\boldsymbol{u}}_{\ell}, \end{gather}$$

(4.8)$$\begin{gather}D_{\ell}=\partial_j\tilde{u}_i\bar{\sigma}_{ij}, \end{gather}$$

(4.9)$$\begin{gather}\epsilon^{\rm inj}_{\ell}=\tilde{u}_i\bar{\rho}\tilde{F}_i, \end{gather}$$

where $\boldsymbol {J}_{\ell }$, $\varPhi _{\ell }$, $D_{\ell }$ and $\epsilon ^{\rm inj}_{\ell }$ are the spatial transports for the kinetic energy, the large-scale pressure dilatation, the viscous dissipation and the kinetic energy injection, respectively. Here $\tilde {\tau }(f,g) \equiv \widetilde {(fg)}_{\ell }-\tilde {f}_{\ell }\tilde {g}_{\ell }$ is the second-order generalised central moment for the fields $f(\boldsymbol {x})$ and $g(\boldsymbol {x})$ (Germano Reference Germano1992). We use $\varPi _{\ell }$ to denote the inter-scale energy flux (also known as SFS energy flux) in the budget (4.4), which is produced by the large-scale strain $\partial _j\tilde {u}_i$ against the small-scale turbulent stress $\bar {\rho }\tilde {\tau }(u_i,u_j)$ (Aluie Reference Aluie2011) and thus referred to as the deformation work. This form of large-scale kinetic energy budget is commonly used in turbulence studies, especially for incompressible flows. Recent studies (Aluie Reference Aluie2011, Reference Aluie2013) found that there is another pathway of energy transfer between scales for compressible flows, which is concealed in the form of (4.4). In fact, the derivation of (4.4) takes $\tilde {u}_i\partial _i\bar {p}$ in the following form

(4.10)\begin{equation} \tilde{u}_i\partial_i\bar{p} = \partial_i(\tilde{u}_i\bar{p})-\bar{p}\partial_i\tilde{u}_i. \end{equation}

Rewritten in another equivalent form, namely

(4.11)\begin{equation} \tilde{u}_i\partial_i\bar{p} = \dfrac{1}{\bar{\rho}}\partial_i\bar{p}\bar{\tau}(\rho,u_i) +\partial_i(\bar{p}\bar{u}_i)-\bar{p}\partial_i\bar{u}_i, \end{equation}

(4.4) can be transformed to

(4.12)\begin{equation} \partial_{t}\left(\bar{\rho}\dfrac{\lvert \tilde{\boldsymbol{u}}_{\ell} \rvert^2}{2}\right) + \boldsymbol{\nabla}\boldsymbol{\cdot} \boldsymbol{J}_{\ell} ={-}\varPi_{\ell} -\varLambda_{\ell} - \varPhi_{\ell} - D_{\ell} + \epsilon^{inj}_{\ell}. \end{equation}

The spatial transfer and pressure-dilatation terms in the above equation become

(4.13)\begin{equation} (J_{\ell})_j = \bar{\rho}\dfrac{\lvert \tilde{\boldsymbol{u}} \rvert^2}{2}\tilde{u}_j + \bar{p}\bar{u}_j + \tilde{u}_i\bar{\rho}\tilde{\tau}(u_i,u_j) - \tilde{u}_i\bar{\sigma}_{ij}, \end{equation}

and

(4.14)\begin{equation} \varPhi_{\ell} ={-} \bar{p}_{\ell}\boldsymbol{\nabla}\boldsymbol{\cdot}\bar{\boldsymbol{u}}_{\ell}, \end{equation}

respectively. The rest of the terms with the same notation as in (4.4) remain unchanged. Another inter-scale energy flux, newly derived in (4.12), is called baropycnal work, which has the form

(4.15)\begin{equation} \varLambda_{\ell} = \dfrac{1}{\bar{\rho}}\partial_j\bar{p}\bar{\tau}(\rho,u_j). \end{equation}

This term arises from the action of large-scale pressure gradient $\partial _j\bar {p}/\bar {\rho }$ on the small-scale turbulent mass flux $\bar {\tau }(\rho,u_j)$, which is present only in flows with density variation. In (4.12), the positive $\varPi _{\ell }$ and $\varLambda _{\ell }$ denote the forward transfer of TKE from large scales ($\gtrsim \ell$) to small scale ($\lesssim \ell$), whereas the negative versions denote the inverse transfer of TKE from small scale ($\lesssim \ell$) to large scale ($\gtrsim \ell$).

4.2. Inter-scale energy fluxes

This subsection focuses on the evolutions of the two inter-scale energy fluxes in budget (4.12) with filter scales of $k_{\ell }=16$, $k_{\ell }=32$ and $k_{\ell }=48$. As illustrated in figure 2, the scale $k_{\ell }=32$ is initially involved for all three cases, and $k_{\ell }=16$ and $k_{\ell }=48$ represent the upper and lower limits of the initial perturbation spectrum in case 2. Temporal evolutions of the two inter-scale energy fluxes averaged in the mixing zone at the filter scales $k_{\ell }=16$, $k_{\ell }=32$, and $k_{\ell }=48$ are plotted in figure 13. It should be noted that the data are plotted in log–log scale. When $\langle \varPi _{\ell }\rangle _{MZ}$ and $\langle \varLambda _{\ell }\rangle _{MZ}$ are non-positive, they hold no meaning and thus are plotted by their absolute values in the figure. In figure 13(a), the deformation work at all three scales exhibits an inverse transfer at the early stage, then shifts to the forward transfer. It indicates that vorticity initially deposited at the interface forms large vortical structures at the early stage and, subsequently, small vortical structures are generated as a result of the deformation process. As shown in figure 13(b), the mean baropycnal work curves for all three cases almost overlap at the early stage and show a forward transfer behaviour. Subsequently, they shift to a brief period of inverse transfer and return eventually to the forward transfer. At the late stage, notable difference among the three cases is observed, which is similar to the evolution of the mixing features (see figures 9 and 12). Both energy fluxes exhibit exponential decay at the self-similar stage for each case. The decay exponent of deformation work shows a larger difference than that of baropycnal work at each scale, whereas its decay exponent on the largest filter scale is comparable to that of the ITKE.

Figure 13.

Temporal evolutions of (a) deformation work, $\varPi _{\ell }$, and (b) baropycnal work, $\varLambda _{\ell }$, at three filter scales within the mixing zone. Absolute values are plotted using lines for positive data and symbols for no-positive data. The red, yellow and blue represent the results for case 1, case 2 and case 3, respectively. All results in the figure have been normalised by $\bar {\rho }^{+}(\Delta U)^3/\bar {\lambda }$.

Due to anisotropy of RM turbulence, it is necessary to conduct a directional examination of the SFS energy fluxes. To this end, the SFS energy fluxes are expressed in the form of directional components, enabling the assessment of anisotropy at various scales within the mixing layer. The mean SFS energy flux fraction is defined as

(4.16) \begin{equation} \left.\begin{array}{c@{}} c_{\varPi_{\ell}^{\xi}} = \dfrac{\langle \varPi_{\ell}^{\xi}\rangle } {\lvert\langle \varPi_{\ell}^{x}\rangle \rvert+\lvert\langle \varPi_{\ell}^{y}\rangle \rvert +\lvert\langle \varPi_{\ell}^{z}\rangle \rvert},\\ c_{\varLambda_{\ell}^{\xi}} = \dfrac{\langle \varLambda_{\ell}^{\xi}\rangle }{\lvert\langle \varLambda_{\ell}^{x}\rangle \rvert+\lvert\langle \varLambda_{\ell}^{y}\rangle \rvert+\lvert\langle \varLambda_{\ell}^{z}\rangle \rvert}, \end{array}\right\} \end{equation}

where $\varPi _{\ell }^{\xi }=-\bar {\rho }\partial _j\tilde {u}_{\xi }\tilde {\tau }(u_{\xi },u_j)$ and $\varLambda ^{\xi }_{\ell }=({1}/{\bar {\rho }})\partial _{\xi }\bar {p}\bar {\tau }(\rho,u_{\xi })$ with $\xi$ being the $x$, $y$ or $z$ direction. Note the signs of the fluxes are incorporated into the definition, and we have $\sum \lvert c_{\varPi _{\ell }^{\xi }(\varLambda _{\ell }^{\xi })}\rvert =1$. Temporal evolutions of the mean SFS energy flux fraction for all three cases are plotted in figure 14. The $y$ and $z$ components are consistent with each other, which indicates the statistical isotropy in the cross-flow direction. The $x$ component is always greater than the other two components. This gives the evidence of anisotropy at all three scales and also emphasises the necessity of considering anisotropy when modelling the RM turbulence. As the filter scale decreases, the anisotropy measure in the SFS energy flux decreases, which is in accordance with the findings of Lombardini, Pullin & Meiron (Reference Lombardini, Pullin and Meiron2012), Mansoor et al. (Reference Mansoor, Dalton, Martinez, Desjardins, Charonko and Prestridge2020) and Mohaghar et al. (Reference Mohaghar, Carter, Musci, Reilly, McFarland and Ranjan2017) regarding the local isotropy of the energy spectrum. The comparison among the three cases shows that the anisotropy measure of the deformation work for case 3 is greater than that of cases 1 and 2 at all three scales, which is consistent with the results in figure 12. Considering the initial narrowband perturbations in cases 1 and 2 are naturally different from the initial broadband perturbation in case 3, the findings here suggest that the anisotropy caused by the scale evolution is greater than that caused by interactions with other (anisotropic) scales. This also implies that symmetry breaking is gradually weakened during the interactions between scales. Unlike the deformation work, the anisotropy measure of the baropycnal work shows no significant difference among the three cases. The inverse transfer of baropycnal work is predominantly observed in the $x$ direction, and case 3 shows a longer period of dominance in this inverse transfer compared with cases 1 and 2.

Figure 14.

Temporal evolutions of the mean SFS energy fluxes (a,c,e) $c_{\varPi _{\ell }^{\xi }}$ and (b,d, f) $c_{\varLambda _{\ell }^{\xi }}$ within the mixing zone. Data are from (a,b) case 1, (c,d) case 2 and (e, f) case 3. The blue, red and yellow lines represent $\xi =x$, $y$ and $z$, respectively.

4.3. Nonlinear model of baropycnal work

The distinct behaviours of the two SFS energy fluxes imply that they might be governed by different physical mechanisms. Previous studies suggested that the deformation work is linked to processes involving vortex stretching and strain self-amplification (Carbone & Bragg Reference Carbone and Bragg2020; Johnson Reference Johnson2020, Reference Johnson2021), whereas the mechanisms of the baropycnal work in variable-density turbulence remain unclear. Inspired by the theoretical work of Borue & Orszag (Reference Borue and Orszag1998) and Eyink (Reference Eyink2006), Lees & Aluie (Reference Lees and Aluie2019) derived a nonlinear model for baropycnal work,

(4.17)\begin{align} \varLambda_{\ell}(\boldsymbol{x}) \approx \varLambda_{m,\ell}(\boldsymbol{x}) & = \frac{1}{3}C_2\ell^2\frac{1}{\bar{\rho}}(\partial_j\bar{p}\partial_k\bar{\rho}\partial_k\bar{u}_j)\nonumber\\ & =\dfrac{1}{3}C_2\ell^2\dfrac{1}{\bar{\rho}}\left[\boldsymbol{\nabla}\bar{p}\boldsymbol{\cdot}\bar{\boldsymbol{\mathsf{S}}} \boldsymbol{\cdot}\boldsymbol{\nabla}\bar{\rho}+\dfrac{1}{2}\bar{\boldsymbol{\omega}}\boldsymbol{\cdot}(\boldsymbol{\nabla}\bar{\rho} \times\boldsymbol{\nabla}\bar{p})\right]\nonumber\\ & = \varLambda_{\text{SR},\ell} + \varLambda_{\text{BC},\ell}. \end{align}

Here, $\varLambda _\text {SR}$ and $\varLambda _{BC}$ represent the strain generation process and the baroclinic vorticity generation process, respectively, which are given as

(4.18)$$\begin{gather} \varLambda_{\text{SR},\ell}=\dfrac{1}{3}C_2\ell^2\dfrac{1}{\bar{\rho}}[\boldsymbol{\nabla}\bar{p}\boldsymbol{\cdot} \bar{\boldsymbol{\mathsf{S}}}\boldsymbol{\cdot}\boldsymbol{\nabla}\bar{\rho}]=\dfrac{1}{3}C_2\ell^2 \dfrac{1}{\bar{\rho}}\partial_j\bar{p}\partial_k\bar{\rho}\bar{S}_{kj}, \end{gather}$$

(4.19)$$\begin{gather}\varLambda_{\text{BC},\ell} = \dfrac{ 1}{3}C_2\ell^2\dfrac{1}{\bar{\rho}}\left[\dfrac{1}{2}\bar{\boldsymbol{\omega}}\boldsymbol{\cdot}(\boldsymbol{\nabla}\bar{\rho} \times\boldsymbol{\nabla}\bar{p})\right]=\dfrac{1}{3}C_2\ell^2\dfrac{1}{\bar{\rho}} \dfrac{1}{2}\partial_j\bar{p}\partial_k\bar{\rho}\epsilon_{kjs}\bar{\omega}_s. \end{gather}$$

In (4.17), $C_{\iota }$ is the $\iota$th-order moment of the filter kernel. For the Gaussian filter kernel adopted here, $C_{\iota }$ is given as

(4.20)\begin{equation} C_{\iota} = \int\text{d}^{3}\boldsymbol{r} G(\boldsymbol{r})\lvert \boldsymbol{r}^{\iota} \rvert = \dfrac{\iota+1}{\sqrt{\rm \pi}}\gamma^{-\frac{\iota}{2}}\varGamma\left(\dfrac{\iota+1}{2}\right), \end{equation}

where $\varGamma (x)$ is the Gamma function, and we have $C_{2}={3}/{2\gamma }$. The nonlinear model (4.17) indicates that the baropycnal work transfers energy between scales through two pathways: one associated with baroclinic generation of vorticity and the other linked to strain generation caused by the pressure and density gradients (both barotropic and baroclinic). This model has recently been successfully applied to a priori test in forced compressible turbulence (Lees & Aluie Reference Lees and Aluie2019). To the best of the authors’ knowledge, this is the first time the performance of this model has been examined in relation to RM turbulence.

Figure 15 shows snapshots of the SFS energy fluxes at the mixing layer centre at $\tau =44$ for case 1. The other two cases have similar results and thus are not shown here. It is evident that the active regions of SFS energy fluxes display a speckled pattern at smaller filter scales and a chunky pattern at larger filter scales. Both forward and inverse transfers of deformation work are noticeably distinguishable at the larger filter scale, whereas the forward transfer region dominates at the smaller filter scale. There are distinct regions with positive and negative baropycnal work on both large and small filter scales. This implies that the inverse transfer of deformation work primarily occurs at large scales, whereas the inverse transfer of baropycnal work is prevalent at both large and small scales. The snapshots of $\varLambda _{m,\ell }$ computed from the simulation data using the nonlinear model (4.17) are also given in figure 15. The predictions of this nonlinear model are very similar to the simulation results, especially at $k_{\ell }=32$ and $k_{\ell }=48$.

Figure 15.

Snapshots of (a,d,g) the deformation work, $\varPi _{\ell }$, (b,e,h) baropycnal work, $\varLambda _{\ell }$, and (c, f,i) the nonlinear model, $\varLambda _{m,\ell }$, for case 1 at $\tau =43.94$ at the central plane of the mixing layer. (a,b,c) $k_{\ell }=16$, (d,e, f) $k_{\ell }=32$ and (g,h,i) $k_{\ell }=48$. The SFS energy fluxes in the figure have been normalised using their variances.

The relevance of the baropycnal work $\varLambda _{\ell }$ and its nonlinear model $\varLambda _{m,\ell }$ can be quantified by calculating their correlation coefficient:

(4.21)\begin{equation} R_c = \dfrac{\langle \varLambda_{m,\ell}\varLambda_{\ell}\rangle -\langle \varLambda_{m,\ell}\rangle \langle \varLambda_{\ell}\rangle }{[(\langle \varLambda_{m,\ell}^2\rangle -\langle \varLambda_{m,\ell}\rangle ^2)(\langle \varLambda_{\ell}^2\rangle -\langle \varLambda_{\ell}\rangle ^2)]^{1/2}}. \end{equation}

Figure 16(a,c,e) displays the temporal evolutions of $R_c$ for all three cases. The correlation between $\varLambda _{\ell }$ and $\varLambda _{m,\ell }$ increases significantly shortly after the shock impact, particularly at the filter scales $k_{\ell }=32$ and $k_{\ell }=48$ ($R_c >0.85$ and $R_c >0.9$, respectively). On the larger filter scale $k_{\ell }=16$, the correlation is relatively lower but still rise persistently ($R_c >0.6$). As we can see, the correlation coefficients for the baropycnal work and its nonlinear model can reach $R_c\gtrsim 0.79$ ($k_{\ell }=16$), $R_c\gtrsim 0.89$ ($k_{\ell }=32$) and $R_c\gtrsim 0.92$ ($k_{\ell }=48$). These values correspond to the slopes of the joint p.d.f. in figure 16(b,d, f) where it approaches 1.0. Specifically, the $R_c$ values are quite similar for all three cases on the two smaller filter scales, and they are higher for case 3 on the largest filter scale. The difference in the correlation among the three cases is mainly caused by the different distances between the filter scale and the spectral peak. As discussed by Aluie et al. (Reference Aluie, Rai, Yin, Lees, Zhao, Griffies, Adcroft and Shang2022), the approximation, $\bar {\tau }(\rho,u_j) \approx \bar {\tau }(\bar {\rho },\bar {u}_j)$, adopted in the deduction of nonlinear model (4.17), may fail if it approaches scales of the spectral peak or larger. It means that the correlation in the inertial range may decrease as the filter scale approaches the spectral peak. This argument aligns with the present finding. Specifically, the spectral peak in case 3 is located at a larger scale (Zhou et al. Reference Zhou, Ding, Cheng and Luo2023a), and the filter scale $k_{\ell }=16$ is further away from the spectral peak in case 3 than in cases 1 and 2, resulting in a higher correlation in case 3. A similar behaviour can also be seen in figure 5 of Aluie et al. (Reference Aluie, Rai, Yin, Lees, Zhao, Griffies, Adcroft and Shang2022).

Figure 16.

(a,c,e) Temporal evolutions of the correlation coefficients between baropycnal work, $\varLambda _{\ell }$, and its nonlinear model, $\varLambda _{m,\ell }$, within the mixing zone. (b,d, f) The joint p.d.f. of $\varLambda _{\ell }$ and $\varLambda _{m,\ell }$ at $\tau \approx 44$. (a,b) case 1, (c,d) case 2 and (e, f) case 3. The red lines in (b,d, f) represent $y=x$. All the contours range from outermost to innermost as $10^{-5} \sim 10^{-1}$.

The strong correlation between the nonlinear model (4.17) and the baropycnal work in simulation indicates that the model can effectively reveal the distribution of baropycnal work in RM turbulence. Hence, it is reasonable to utilise the model to assess the two processes ($\varLambda _{{SR},\ell }$ and $\varLambda _{{BC},\ell }$), of the baropycnal work during the evolution of the mixing layer. For this purpose, we define the strain/baroclinic component fraction as

(4.22)\begin{equation} c_{\varLambda_{{SR}({BC}),\ell}} = \dfrac{\langle \varLambda_{{SR}({BC}),\ell}\rangle }{\lvert\langle \varLambda_{{SR},\ell}\rangle \rvert+\lvert\langle \varLambda_{{BC},\ell}\rangle \rvert}. \end{equation}

Temporal evolutions of $c_{\varLambda _{{SR},\ell }}$ and $c_{\varLambda _{{BC},\ell }}$ for the three cases are plotted in figure 17. Unlike forced compressible turbulence, in which the primary source of baropycnal work contribution arises from the strain component (Lees & Aluie Reference Lees and Aluie2019), in RM turbulence the baroclinic component plays a crucial role. Specifically, before the self-similarity stage, the forward transfer and inverse transfer via baropycnal work in both case 1 and 2 are primarily dominated by the straining component and the baroclinic component, respectively, whereas in case 3, the baroclinic component of baropycnal work almost always exceeds its strain component at this stage. At the self-similarity stage, both components maintain the forward transfer on average, with the strain component exceeding the baroclinic component and comprising a greater proportion of larger filter scales. Due to the deposition at large-scale baroclinic vorticity in case 3, the average fraction of the baroclinic process is larger in case 3 than cases 1 and 2.

Figure 17.

Temporal evolutions of the mean strain (blue lines) and baroclinic (red lines) component fraction of the nonlinear model, $\varLambda _{m,\ell }$, within the mixing zone for (a) case 1, (b) case 2 and (c) case 3.

4.4. Energy fluxes at the spike and bubble regions

Considering the asymmetric development of the spike and bubble structures in the mixing layer (e.g. figures 3 and 4), it is necessary to investigate the differences in the evolution of the SFS energy fluxes at the spike and bubble regions. Figure 18 displays the mean profile of deformation work and baropycnal work along the $x$-axis for all three cases. It should be noted that the majority of the non-zero region corresponds to the region where the normalised mean TKE exceeds about 0.1 in figure 8. Similar to the finding of Thornber & Zhou (Reference Thornber and Zhou2012), the spike side occupies the predominant events due to the larger velocity shear there. The early stage ($\tau \approx 4$) deformation work and baropycnal work show a preference for forward transfer on the spike side and exhibit higher activity on small filter scales. In contrast, on the bubble side, deformation work and baropycnal work tend to exhibit inverse transfer, with higher activity on the larger and smaller filter scale, respectively. The transfer direction of deformation work in the spike and bubble regions aligns with the finding of Liu & Xiao (Reference Liu and Xiao2016), corresponding to the small vortex structures rolling up on the spike side and the enlarged bubbles on the bubble side (see figure 4). It is interesting that the positive profile of the deformation work on the smallest filter scale extends partially to the bubble side, whereas the negative part on larger filter scales extends partially to the spike side. The mean profiles of the baropycnal work at all three filter scales have their zero values located near the centre of the mixing layer, denoted as $x_c$. At the late stage ($\tau \approx 44$), the deformation work predominantly prefers forward transfer in both spike and bubble regions, except for a partial tendency for inverse transfer that persists on the bubble side at the largest filter scale. The baropycnal work still exhibits a preference for forward transfer on the spike side and inverse transfer on the bubble side. In addition, its mean profiles have zero values still located near $x_c$ as at the early time. This indicates a persistent difference in the tendency of baropycnal work transfer between the spike and bubble regions, which is effectively predicted by the nonlinear model.

Figure 18.

Profiles of plane-averaged SFS energy fluxes, $\varPi _{\ell }$ (blue lines), $\varLambda _{\ell }$ (red lines) and $\varLambda _{m,\ell }$ (yellow lines) along the $x$-direction at (a,c,e) $\tau \approx 4$ and (b,d, f) $\tau \approx 44$ for (a,b) case 1, (c,d) case 2 and (e, f) case 3. All results in the figure have undergone the same non-dimensionalisation as in figure 13.

Comparing the three cases, it is found that the distributions of the SFS energy fluxes within the mixing layer are generally similar, except for case 3 that presents a flatter and wider profile peak. The nonlinear model $\varLambda _{m,\ell }$ gives an underestimation of the numerical results at all three filter scales, as depicted in figure 18. This is expectable because discontinuities can have a significant effect on the accuracy of the model, as pointed out by Lees & Aluie (Reference Lees and Aluie2019) and Aluie et al. (Reference Aluie, Rai, Yin, Lees, Zhao, Griffies, Adcroft and Shang2022). The nonlinear model is based on a first-order approximation and may underestimate the results by neglecting higher-order terms in regions where the field is not smooth enough (e.g. near shock waves or interfaces). Further development of the corresponding model should address this issue, as such discontinuities are not rare in variable-density turbulence. Despite this, it does effectively capture the trend of the baropycnal work, which is consistent with our aforementioned finding regarding a high correlation between the two. The relationship between $\langle \varLambda _{\ell }\rangle$ and $\langle \varLambda _{m,\ell }\rangle$ can be largely characterised by the expression, $\langle \varLambda _{\ell }\rangle /\sigma _{\varLambda _{\ell }}=k_{\varLambda,\ell }(\langle \varLambda _{m,\ell }\rangle /\sigma _{\varLambda _{m,\ell }})+b_{\varLambda,\ell }$, where $k_{\varLambda,\ell }$ and $b_{\varLambda,\ell }$ are two scale-dependent constants.

The deformation and baropycnal works can be decomposed into positive and negative components (Wang et al. Reference Wang, Wan, Chen and Chen2018; Zhao et al. Reference Zhao, Liu and Lu2020),

(4.23)$$\begin{gather} \varPi_{\ell}^{+} = \tfrac{1}{2}(\varPi_{\ell}+\lvert\varPi_{\ell}\rvert),\quad \varPi_{\ell}^{-} = \tfrac{1}{2}(\varPi_{\ell}-\lvert\varPi_{\ell}\rvert), \end{gather}$$

(4.24)$$\begin{gather}\varLambda_{\ell}^{+} = \tfrac{1}{2}(\varLambda_{\ell}+\lvert\varLambda_{\ell}\rvert),\quad \varLambda_{\ell}^{-} = \tfrac{1}{2}(\varLambda_{\ell}-\lvert\varLambda_{\ell}\rvert). \end{gather}$$

Temporal evolutions of both the mean positive and negative components of SFS energy fluxes within the spike and bubble regions are plotted in figure 19. For the sake of brevity, the results for $k_{\ell }=16$ and $k_{\ell }=48$ in cases 1 and 3 are given here as representative. The deformation work exhibits notably distinct mean evolution patterns in the spike and bubble regions at larger filter scales, whereas its mean evolution curves in the spike and bubble regions at smaller filter scales can almost collapse. It suggests that the different evolution trends of spikes and bubbles in the mixing layer are primarily pronounced at larger scales. On large filter scales, $\langle \varPi _{\ell }^{+}\rangle$ is greater and less than $-\langle \varPi _{\ell }^{-}\rangle$ in the spike and bubble region, respectively. For the most part, at smaller filter scales, $\langle \varPi _{\ell }^{+}\rangle$ exceeds $-\langle \varPi _{\ell }^{-}\rangle$ in both the spike and bubble regions. This suggests once again that, on average, the deformation work at larger scales exhibits forward and inverse transfers in the spike and bubble regions, respectively, whereas at smaller scales, it exhibits forward transfer during the whole evolution stage of the mixing layer. Unlike the deformation work, the baropycnal work evolves more uniformly on large and small scales. In particular, the values of $\langle \varLambda _{\ell }^{+}\rangle$ and $-\langle \varLambda _{\ell }^{-}\rangle$ in the spike region closely match those of $-\langle \varLambda _{\ell }^{-}\rangle$ and $\langle \varLambda _{\ell }^{+}\rangle$ in the bubble region, respectively. This indicates a degree of antisymmetry in the baropycnal work between the spike and bubble regions. As depicted in figure 19( f,h), the negative components exceed positive components in both the spike and bubble regions around $\tau =10$ before reaching the self-similar state. At this moment, there is no such antisymmetry for baropycnal work in the spike and bubble regions, and thus a brief period of inverse transfer is observed in figure 13.

Figure 19.

Temporal evolutions of the mean positive (lines with circle) and negative (line with cross) components of (a–d) deformation work and (e–h) baropycnal work at (a,b,e, f) $k_{\ell }=16$ and (c,d,g,h) $k_{\ell }=48$: (a,c,e,g) case 1 and (b,d, f,h) case 3. All results in the figure have undergone the same non-dimensionalisation as in figure 13.

4.5. Influence of strain and rotation effect

As mentioned previously, variable-density turbulence involves two SFS energy fluxes, $\varPi _{\ell }$ and $\varLambda _{\ell }$. The former is linked to vortex stretching and strain self-amplification, whereas the latter is related to baroclinic and straining processes. Hence, it is suitable to examine the evolution of both fluxes with respect to the strain and rotation characteristics of the flow. To accomplish this, we introduce a filtered strain-enstrophy angle (Boratav, Elghobashi & Zhong Reference Boratav, Elghobashi and Zhong1998; Aslangil, Livescu & Banerjee Reference Aslangil, Livescu and Banerjee2020),

(4.25)\begin{equation} \varPsi_{\ell}=\tan ^{{-}1}\dfrac{\tilde{S}_{ij}\tilde{S}_{ij}}{\tilde{W}_{ij}\tilde{W}_{ij}}, \end{equation}

where $\tilde {S}_{ij}=\frac {1}{2}(\tilde {A}_{ij}+\tilde {A}_{ji})$, $W_{ij}=\frac {1}{2}(\tilde {A}_{ij}-\tilde {A}_{ji})$ and $\tilde {A}_{ij}=\partial _j \tilde {u}_i$ are the strain-rate tensor, the rotation-rate tensor and the velocity gradient tensor on the filter scale, respectively. According to this definition, in regions where $\varPsi _{\ell }>{\rm \pi} /4$, strain dominates over rotation, whereas in regions where $\varPsi _{\ell }<{\rm \pi} /4$, rotation dominates over strain.

The p.d.f.s of $\varPsi _{\ell }$ within the mixing layer are shown in figure 20. At the early stage ($\tau \approx 0.34$), the peaks of the p.d.f.s are concentrated at $\varPsi _{\ell }={\rm \pi} /2$, which indicates that the flow is largely dominated by the strain effect at this stage. Subsequently, the peak of the p.d.f.s at $\varPsi _{\ell }={\rm \pi} /2$ decreases gradually and its $\varPsi _{\ell }<{\rm \pi} /4$ part increases. It indicates that more and more flow regions are affected by the rotation effect, although the strain effect remains predominantly dominant. The comparison among the three cases reveals that, at the largest filter scale, the p.d.f. of case 3 in the $\varPsi _{\ell }<{\rm \pi} /4$ region exceeds that of cases 1 and 2, whereas the reverse holds true at the smallest filter scale. This aligns with the density spectra distribution depicted in figure 2, which suggests that there is a greater deposition of large-scale vorticity in case 3 than in cases 1 and 2. In contrast, there is a greater deposition of small-scale vorticity in cases 1 and 2 than case 3. It is interesting that the p.d.f.s of $\varPsi _{\ell }$ exhibit a consistent asymptotic shape during the self-similarity stage at each filter scale for all three cases. Moreover, this asymptotic shape is in agreement with the results of numerical simulation of buoyancy-driven homogeneous variable-density turbulence (Aslangil et al. Reference Aslangil, Livescu and Banerjee2020).

Figure 20.

The p.d.f.s of the filtered strain-enstrophy angle, $\varPsi _{\ell }$. Data are from case 1 at (a) $\tau =0.25$, (d) $\tau =3.97$ and (g) $\tau =43.94$; case 2 at (b) $\tau =0.27$, (e) $\tau =4.21$ and (h) $\tau =43.67$; case 3 at (c) $\tau =0.34$, ( f) $\tau =4.10$ and (i) $\tau =44.20$, respectively.

To illustrate the strain and rotation effects on the SFS energy fluxes, figure 21 gives the conditional averaging of the deformation work and baropycnal work with respect to the filter strain-enstrophy angle. It is generally found that the two SFS energy fluxes exhibit different performances. Specifically, the $\langle \varPi _{\ell }\Vert \varPsi _{\ell }\rangle$ flux displays a clear peak in the area where $\varPsi _{\ell }>{\rm \pi} /4$, which indicates that the deformation work is stronger in the strain-dominated region and this trend is more evident on smaller scales. There is no noticeable peak for $\langle \varLambda _{\ell }\Vert \varPsi _{\ell }\rangle$ in cases 1 and 2, which indicates the weak preference of baropycnal work between the strain and rotation effect. This reflects the fact that baropycnal work involves two pathways: the strain and baroclinic processes. Slight peaks are noticeable in the $\varPsi _{\ell }<{\rm \pi} /4$ area of $\langle \varLambda _{\ell }\Vert \varPsi _{\ell }\rangle$ for case 3, which may be ascribed to the deposition of large-scale vorticity in case 3. Conditional averaging of the positive and negative components of both deformation and baropycnal works is also given in figure 21. The deformation work exhibits a greater positive component in the strain-dominated region. In contrast, the negative component diverges between large and small filter scales. Specifically, on the largest filter scale, the negative component bulges out near $\varPsi _{\ell }={\rm \pi} /2$, whereas on the two smaller filter scales, the negative components bulge out near $\varPsi _{\ell }=0$. The dependence of $\langle \varLambda _{\ell }^{\pm }\Vert \varPsi _{\ell }\rangle$ on $\varPsi _{\ell }$ is not significant, which aligns with $\langle \varLambda _{\ell }\Vert \varPsi _{\ell }\rangle$.

Figure 21.

The conditional averaging of (a,c,e) the deformation work, $\langle \varPi _{\ell }\Vert \varPsi _{\ell }\rangle$, and (b,d, f) the baropycnal work, $\langle \varLambda _{\ell }\Vert \varPsi _{\ell }\rangle$, vs the filtered strain-enstrophy angle, $\varPsi _{\ell }$. Data are from (a,b) case 1 at $\tau =43.94$, (c,d) case 2 at $\tau =43.67$ and (e, f) case 3 at $\tau =44.20$. The yellow, red and blue lines represent the SFS energy fluxed itself, its positive and negative components, respectively. All results in the figure have undergone the same non-dimensionalisation as in figure 13.

Here we present a preliminary analysis of how inter-scale energy transfer responds to various fluid motions and mixing layer regions. The unique features of the two SFS energy fluxes identified in this study appear to be associated with the diverse and intricate vortical structures (Kokkinakis, Drikakis & Youngs Reference Kokkinakis, Drikakis and Youngs2020) in the spike and bubble regions, as well as the complicated strain fields that surround them (Liu & Xiao Reference Liu and Xiao2016). However, further in-depth work is required to clarify the correlation, which would be a very meaningful addition to the RM instability community.

The connection between features of flow regions and inter-scale energy transfer could offer valuable insights for future modelling of compressible variable-density turbulence and also for the examination of existing models. It is essential to ensure that the models can respond accurately to these distinct regions. The uncovered commonalities, such as the preference of SFS energy fluxes for certain flow motions, may assist in modelling the dynamics of the inertial range. In addition, for further optimisation of the model of baropycnal work, higher-order terms near interfaces should be addressed appropriately. It should be noted that the power-law profile of the perturbation can significantly affect the evolution of the mixing layer, particularly integral quantities, and further affects the temporal laws (e.g. the decay rate of TKE) and asymptotic values (e.g. the molecular mixing fraction) that are closely related to $\theta$. Therefore, further studies with various spectrum slopes that can better represent the surface roughness measured from ICF capsules (Barnes et al. Reference Barnes2002) are necessary. This has attracted some attention recently (Groom & Thornber Reference Groom and Thornber2020, Reference Groom and Thornber2023; Soulard & Griffond Reference Soulard and Griffond2022). Although the present study considers only a constant power spectrum, the results regarding inter-scale energy transfer show relatively weak sensitivity to large-scale perturbations. This provides a certain confidence in the applicability of the present finding to situations with different spectrum slopes.