3.1. Newton–Kantorovich method

Following Corson (Reference Corson2011), we solve the steady version of the governing equations (2.1)–(2.3) numerically using a Newton–Kantorovich (NK) iterative scheme (Boyd Reference Boyd2000). It is convenient to first introduce a stream function ${\it\psi}$ to describe the fluid velocity, so that $(u,w)=(\partial _{z}{\it\psi},-\partial _{x}{\it\psi})$ . Then the time-independent dimensionless equations can be written as

(3.1) $$\begin{eqnarray}\displaystyle & {\rm\nabla}^{2}{\it\psi}=-\mathit{Ra}\,\partial _{x}{\it\theta}, & \displaystyle\end{eqnarray}$$

(3.2) $$\begin{eqnarray}\displaystyle & \partial _{z}{\it\psi}\partial _{x}{\it\theta}-\partial _{x}{\it\psi}\partial _{z}{\it\theta}=-\partial _{x}{\it\psi}+{\rm\nabla}^{2}{\it\theta}, & \displaystyle\end{eqnarray}$$

${\it\theta}(x,z)=T(x,z)-(1-z)$

${\it\theta}$

${\it\psi}$

$L_{s}$

$x$

$z$

$L_{s}$

(3.3) $$\begin{eqnarray}\left[\begin{array}{@{}c@{}}{\it\theta}\\ {\it\psi}\end{array}\right]=\mathop{\sum }_{n=-N/2}^{N/2}\left[\begin{array}{@{}c@{}}\hat{{\it\theta}}_{n}(z)\\ \hat{{\it\psi}}_{n}(z)\end{array}\right]\text{e}^{\text{i}nk_{s}x}=\mathop{\sum }_{n=-N/2}^{N/2}\mathop{\sum }_{m=0}^{M}\left[\begin{array}{@{}c@{}}a_{mn}\\ b_{mn}\end{array}\right]T_{m}(z)\,\text{e}^{\text{i}nk_{s}x},\end{eqnarray}$$

where $k_{s}=2{\rm\pi}/L_{s}$ is the fundamental wavenumber of the spatially periodic steady solution, $M$ is the vertical truncation mode number, $N$ is the horizontal truncation mode number and $T_{m}(z)$ is the $m$ th Chebyshev polynomial. In each $L_{s}\times 1$ computational domain we seek steady solutions with reflection symmetry about $x=L_{s}/2$ and centrosymmetry within each of the two $L_{s}/2\times 1$ subdomains which contain a single convection cell. These symmetry constraints require

(3.4) $$\begin{eqnarray}a_{mn}\;\text{is real};~b_{mn}\;\text{is imaginary};\quad a_{mn}=b_{mn}=0\quad \text{if }(m+n)\text{ is even}.\end{eqnarray}$$

To employ the NK algorithm, we rearrange (3.1) and (3.2) into the following form:

(3.5) $$\begin{eqnarray}\displaystyle & {\rm\nabla}^{2}{\it\psi}=F^{{\it\psi}}({\it\theta}_{x}), & \displaystyle\end{eqnarray}$$

(3.6) $$\begin{eqnarray}\displaystyle & {\rm\nabla}^{2}{\it\theta}=F^{{\it\theta}}({\it\psi}_{x},{\it\psi}_{z},{\it\theta}_{x},{\it\theta}_{z}), & \displaystyle\end{eqnarray}$$

$i$

${\it\theta}^{i}(x,z)$

${\it\psi}^{i}(x,z)$

${\it\theta}(x,z)$

${\it\psi}(x,z)$

$F^{{\it\psi}}$

$F^{{\it\theta}}$

(3.7) $$\begin{eqnarray}\displaystyle {\rm\nabla}^{2}{\it\psi}=(F^{{\it\psi}})^{i}+(F_{{\it\theta}_{x}}^{{\it\psi}})^{i}[{\it\theta}_{x}-{\it\theta}_{x}^{i}]+O([{\it\theta}_{x}-{\it\theta}_{x}^{i}]^{2}),\end{eqnarray}$$

(3.8) $$\begin{eqnarray}\displaystyle {\rm\nabla}^{2}{\it\theta} & = & \displaystyle (F^{{\it\theta}})^{i}+(F_{{\it\psi}_{x}}^{{\it\theta}})^{i}[{\it\psi}_{x}-{\it\psi}_{x}^{i}]+(F_{{\it\psi}_{z}}^{{\it\theta}})^{i}[{\it\psi}_{z}-{\it\psi}_{z}^{i}]+(F_{{\it\theta}_{x}}^{{\it\theta}})^{i}[{\it\theta}_{x}-{\it\theta}_{x}^{i}]\nonumber\\ \displaystyle & & \displaystyle +\,(F_{{\it\theta}_{z}}^{{\it\theta}})^{i}[{\it\theta}_{z}-{\it\theta}_{z}^{i}]+O([{\it\psi}_{x}-{\it\psi}_{x}^{i}]^{2},\,[{\it\psi}_{z}-{\it\psi}_{z}^{i}]^{2},\,[{\it\theta}_{x}-{\it\theta}_{x}^{i}]^{2},\,[{\it\theta}_{z}-{\it\theta}_{z}^{i}]^{2}),\qquad\end{eqnarray}$$

where, for example, $F_{{\it\psi}_{x}}^{{\it\theta}}$ denotes the Frechet derivative of the function $F^{{\it\theta}}({\it\psi}_{x},{\it\psi}_{z},{\it\theta}_{x},{\it\theta}_{z})$ with respect to ${\it\psi}_{x}$ . After defining the correction terms

(3.9a,b ) $$\begin{eqnarray}{\rm\Delta}^{{\it\psi}}={\it\psi}^{i+1}-{\it\psi}^{i},\quad {\rm\Delta}^{{\it\theta}}={\it\theta}^{i+1}-{\it\theta}^{i}\end{eqnarray}$$

and evaluating the Frechet derivatives, the linear differential equations for the corrections can be expressed as

(3.10) $$\begin{eqnarray}\displaystyle & {\rm\nabla}^{2}{\rm\Delta}^{{\it\psi}}+\mathit{Ra}\,D_{x}{\rm\Delta}^{{\it\theta}}=-\mathit{Ra}\,{\it\theta}_{x}^{i}-{\rm\nabla}^{2}{\it\psi}^{i}, & \displaystyle\end{eqnarray}$$

(3.11) $$\begin{eqnarray}\displaystyle & [-D_{x}+{\it\theta}_{z}^{i}D_{x}-{\it\theta}_{x}^{i}D_{z}]{\rm\Delta}^{{\it\psi}}+[{\rm\nabla}^{2}-{\it\psi}_{z}^{i}D_{x}+{\it\psi}_{x}^{i}D_{z}]{\rm\Delta}^{{\it\theta}}={\it\psi}_{z}^{i}{\it\theta}_{x}^{i}-{\it\psi}_{x}^{i}{\it\theta}_{z}^{i}+{\it\psi}_{x}^{i}-{\rm\nabla}^{2}{\it\theta}^{i},\qquad & \displaystyle\end{eqnarray}$$

$D_{x}$

$D_{z}$

$x$

$z$

(3.12a,b ) $$\begin{eqnarray}{\it\theta}=\hat{{\it\theta}}_{0}(z)+2\mathop{\sum }_{n=1}^{N/2}\hat{{\it\theta}}_{n}(z)\cos (nk_{s}x),\quad {\it\psi}=2\mathop{\sum }_{n=1}^{N/2}-\hat{{\it\phi}}_{n}(z)\sin (nk_{s}x),\end{eqnarray}$$

where $\hat{{\it\phi}}_{n}\equiv \text{Im}\{\hat{{\it\psi}}_{n}\}$ is real. Then, for a given horizontal wavenumber $nk_{s}$ , (3.10) and (3.11) become

(3.13) $$\begin{eqnarray}\displaystyle & -[D_{zz}-(nk_{s})^{2}]{\rm\Delta}^{\hat{{\it\phi}}_{n}}-nk_{s}\,\mathit{Ra}\,{\rm\Delta}^{\hat{{\it\theta}}_{n}}=nk_{s}\,\mathit{Ra}\,\hat{{\it\theta}}_{n}^{i}+[D_{zz}-(nk_{s})^{2}]\hat{{\it\phi}}_{n}^{i}, & \displaystyle\end{eqnarray}$$

(3.14) $$\begin{eqnarray}\displaystyle & [nk_{s}+g_{n}^{i}]{\rm\Delta}^{\hat{{\it\phi}}_{n}}+[D_{zz}-(nk_{s})^{2}+h_{n}^{i}]{\rm\Delta}^{\hat{{\it\theta}}_{n}}=\hat{f}_{n}^{i}, & \displaystyle\end{eqnarray}$$

(3.15a,b ) $$\begin{eqnarray}{\rm\Delta}^{\hat{{\it\phi}}_{n}}=\hat{{\it\phi}}_{n}^{i+1}-\hat{{\it\phi}}_{n}^{i},\quad {\rm\Delta}^{\hat{{\it\theta}}_{n}}=\hat{{\it\theta}}_{n}^{i+1}-\hat{{\it\theta}}_{n}^{i},\end{eqnarray}$$

$D_{zz}$ is the second partial derivative operator with respect to $z$ , $g_{n}^{i}$ and $h_{n}^{i}$ can be obtained by calculating the convolution of the non-constant coefficient terms on the left-hand side of (3.11) for each iterate and $\hat{f}_{n}^{i}$ represents the coefficients of the right-hand side of (3.11) in Fourier space at the $i$ th iterate. We solve (3.13) and (3.14) numerically using a Chebyshev spectral collocation method.

Although the NK method is only locally convergent, the basin of attraction (in the space of initial iterates) can be expanded by updating the variables for each iterate using

(3.16) $$\begin{eqnarray}\left[\begin{array}{@{}c@{}}\hat{{\it\phi}}_{n}\\ \hat{{\it\theta}}_{n}\end{array}\right]^{i+1}=\left[\begin{array}{@{}c@{}}\hat{{\it\phi}}_{n}\\ \hat{{\it\theta}}_{n}\end{array}\right]^{i}+a\left[\begin{array}{@{}c@{}}{\rm\Delta}^{\hat{{\it\phi}}_{n}}\\ {\rm\Delta}^{\hat{{\it\theta}}_{n}}\end{array}\right],\end{eqnarray}$$

where $0\leqslant a\leqslant 1$ . The step-length coefficient $a$ is reduced whenever $F_{res}^{i+1}>bF_{res}^{i}$ , where $F_{res}^{i}$ is the norm of the residual of the steady governing equations at the $i$ th iterate and $b\approx 1$ is an adjustable parameter. The iteration is continued until $F_{res}^{i+1}<10^{-7}$ , and then the spatial resolution is increased until the relative error in $\mathit{Nu}$ is less than $10^{-5}$ .

Computations are performed for a discrete set of $\mathit{Ra}=50\times 10^{(\hat{\jmath }-1)/10}$ from $\mathit{Ra}=50$ to $\mathit{Ra}=31\,548$ and $L_{s}=0.01\times 10^{(\hat{k}-1)/10}$ (for integer $\hat{\jmath }$ and $\hat{k}$ ). For each $\mathit{Ra}$ , we first solve the steady governing equations for a small aspect ratio $L_{s}$ that is slightly greater than $L_{c}$ , and then use that solution as the initial guess for a case with larger $L_{s}$ .

Figure 1.

Temperature and stream function fields at $\mathit{Ra}=9976~(L_{c}=0.063)$ : (a) $L_{s}=0.1$ ; (b) $L_{s}=0.1585$ ; (c) $L_{s}=0.3162$ ; (d) $L_{s}=0.5012$ . The aspect ratio in (c) is close to the mean inter-plume spacing $L_{m}=0.319$ from DNS performed in a domain with $L=5.01$ . At small $L_{s}$ (a,b) the interior streamlines are independent of $z$ . However, as $L_{s}$ is increased (c,d) the interior streamlines become $z$ -dependent.

3.2. Solution structure

The structure of steady convection at large $\mathit{Ra}$ depends on $L_{s}$ . When $L_{s}<L_{c}$ , the only steady solution is the conduction state. As $L_{s}$ is increased, the conduction solution becomes linearly unstable, and two thin thermal boundary layers arise near the upper and lower walls (figure 1). Unlike the unsteady flow observed in DNS, the proto-plumes are absent in the steady solution. Near the walls there exists a boundary layer in the temperature field and a thicker boundary layer in the stream function field. Away from these nested boundary layers, the interior structure for small $L_{s}$ is quite simple: the temperature deviation from the horizontal mean ${\it\theta}^{\prime }=T-\overline{T}$ and the stream function ${\it\psi}$ are almost independent of $z$ , so their $z$ -derivatives are small (figure 1 a,b; figure 2 a,b), and there exists only a single non-zero horizontal Fourier mode (figure 2 a,b). Indeed, this type of steady interior flow is well approximated using the analytical heat-exchanger solution

(3.17) $$\begin{eqnarray}\displaystyle & \displaystyle T(x,z)=\hat{T}\cos (kx)-\frac{k^{2}}{\mathit{Ra}}z+\left(\frac{k^{2}}{2\,\mathit{Ra}}+\frac{1}{2}\right), & \displaystyle\end{eqnarray}$$

(3.18) $$\begin{eqnarray}\displaystyle & w(x)=\mathit{Ra}\,\hat{T}\cos (kx), & \displaystyle\end{eqnarray}$$

(3.19) $$\begin{eqnarray}\displaystyle & u=0 & \displaystyle\end{eqnarray}$$

$\overline{T}$

$\hat{T}$

$\overline{T}$

$\mathit{Ra}$

$L_{s}$

Figure 2.

The $z$ -dependent Fourier components of the temperature field for steady convective states at $\mathit{Ra}=9976$ : (a) $L_{s}=0.1$ ; (b) $L_{s}=0.1585$ ; (c) $L_{s}=0.3162$ ; (d) $L_{s}=0.5012$ . At small $L_{s}$ (a,b) the horizontal-mean temperature in the interior agrees closely with the analytical heat-exchanger solution; in the core, only a single (non-mean) Fourier mode is active and the temperature fluctuations are nearly independent of $z$ . However, as $L_{s}$ is increased (c,d) more Fourier modes arise and the structure of the steady solutions departs from that of the analytical heat-exchanger solution even in the interior.

Perhaps not surprisingly, as $L_{s}$ is increased the steady large- $\mathit{Ra}$ numerical solutions do not retain the heat-exchanger structure in the interior. Instead, the streamlines deviate from the vertical (figure 1 c,d), and multiple horizontal Fourier modes are excited, although the fundamental mode still dominates the flow (figure 2 c,d). Moreover, when $L_{s}$ is sufficiently large, the numerically computed $\overline{T}$ clearly deviates from that predicted by the heat-exchanger solution. In fact, the slope of $\overline{T}$ at the mid-plane $z=0.5$ becomes positive, implying that hotter fluid overlies colder fluid in the interior. We refer to this class of numerically computed steady states as stably stratified core solutions.

To quantify the proximity of a given numerically computed steady solution to the analytical heat-exchanger solution in the interior we introduce the parameter

(3.20) $$\begin{eqnarray}{\it\gamma}=-\frac{\mathit{Ra}}{k_{s}^{2}}\partial _{z}\overline{T}|_{z=0.5}\end{eqnarray}$$

measuring the ratio of the numerically computed mean temperature gradient at the mid-plane to the analytically predicted (constant) vertical temperature gradient. The solutions are identical when ${\it\gamma}=1$ , and so we quantitatively (but arbitrarily) define the steady convective state as a numerical heat-exchanger solution when $0.99\leqslant {\it\gamma}\leqslant 1$ .

Figure 3 shows how the structure of the steady solution changes as $\mathit{Ra}$ and $L_{s}$ are varied. For small $L_{s}>L_{c}$ , we observe that $0.99\leqslant {\it\gamma}\leqslant 1$ , so these solutions belong to the class of numerical heat-exchanger solutions. However, ${\it\gamma}$ decreases appreciably as $L_{s}$ increases, and the steady solutions assume a transitional form for $0\leqslant {\it\gamma}<0.99$ . When $L_{s}$ is sufficiently large, ${\it\gamma}$ changes sign, yielding a family of stably stratified core solutions. It should be noted that the wavelength $L_{h}$ of solutions with ${\it\gamma}=0.99$ (see figure 3 b), which separates the heat-exchanger and non-heat-exchanger solutions, is approximately half the mean inter-plume spacing ( $L_{m}$ in figure 3 b) measured from the DNS of Hewitt et al. (Reference Hewitt, Neufeld and Lister2012).

Figure 3.

(a) Proximity of numerical solutions to the heat-exchanger solution: ${\it\gamma}=-(\mathit{Ra}/k_{s}^{2})\partial _{z}\overline{T}|_{z=0.5}$ versus $L_{s}\mathit{Ra}$ . As $L_{s}$ is increased, ${\it\gamma}$ eventually departs from unity and the interior structure of the numerical solution changes from that of the heat-exchanger solution to that of the stably stratified core solution. (b) Variation with $\mathit{Ra}$ of $L_{h}$ , the wavelength of steady solutions with ${\it\gamma}=0.99$ (i.e. numerical heat-exchanger solutions). Also plotted for comparison are $L_{c}\sim 2{\rm\pi}\mathit{Ra}^{-0.5}$ , the wavelength of the marginal stability boundary, $L_{m}\approx (2{\rm\pi}/0.47)\mathit{Ra}^{-0.4}$ , the mean inter-plume spacing measured from the DNS of Hewitt et al. (Reference Hewitt, Neufeld and Lister2012), and $L_{f}\sim 2\sqrt{{\rm\pi}}\mathit{Ra}^{-0.25}$ , the wavelength of the fastest-growing linear mode. Interestingly, at large $\mathit{Ra}$ , $L_{m}\approx 2L_{h}$ .

3.3. Maximising $\mathit{Nu}$

Of particular physical importance is the relationship between the Nusselt number $\mathit{Nu}$ and the two control parameters $\mathit{Ra}$ and $L$ . Wen et al. (Reference Wen, Chini, Dianati and Doering2013) systematically studied the influence of the domain aspect ratio on the heat transport in turbulent porous medium convection using upper bound theory and DNS. At large $\mathit{Ra}$ the turbulent flow in porous medium convection was found to be self-sustaining with a $\mathit{Ra}$ -dependent lateral scale, identified as a ‘minimal flow unit’, above which the heat transport is effectively independent of $L$ . The dependence of $\mathit{Nu}$ on $\mathit{Ra}$ and $L_{s}$ in steady porous medium convection, however, differs both qualitatively and quantitatively from that characterising turbulent heat transport in a porous layer. Figure 4 shows the variation of $\mathit{Nu}$ with $\mathit{Ra}$ and $L_{s}$ for steady convective states (see also Corson Reference Corson2011). There is no convection when the domain aspect ratio is smaller than the wavelength $L_{c}$ of the marginal stability mode, so $\mathit{Nu}=1$ in this regime. Unlike time-dependent convection, for which the value of $\mathit{Nu}$ asymptotes when the domain aspect ratio exceeds the size of the minimal flow unit, the heat transport in steady convection is maximised along a particular path in ( $\mathit{Ra},L_{s}$ ) parameter space (the solid black curve in figure 4).

Figure 4.

Contour plot of the Nusselt number in $(\mathit{Ra},L_{s})$ parameter space for steady convective states. The solid line ( $L_{max\;Nu}$ ) marks the path along which $\mathit{Nu}$ is maximised. Here, $L_{c}$ , $L_{m}$ and $L_{f}$ are as in figure 3.

To accurately extract the scalings associated with the steady heat-flux-maximising solutions, we employed greater resolution in parameter space for ( $\mathit{Ra},L_{s}$ ) pairs near the ridge along which $\mathit{Nu}$ is maximised. We also continued the computations to $\mathit{Ra}=31\,548$ , as shown in figures 4 and 5. When $\mathit{Nu}$ is maximum and $\mathit{Ra}$ is less than roughly 100, $L_{s}$ scales as $\mathit{Ra}^{-1/4}$ , the scaling for the wavelength $L_{f}$ of the fastest-growing linear mode. As $\mathit{Ra}$ is increased, the ridge of maximum $\mathit{Nu}$ shifts to the right and for $10^{3}<\mathit{Ra}<10^{4}$ , $L_{s}\sim \mathit{Ra}^{-0.52}$ . As is evident in figure 5(a), the heat-flux-maximising solutions at large $\mathit{Ra}$ are, in fact, numerical heat-exchanger states. Figure 5(b) shows the variation of $\mathit{Nu}$ with $\mathit{Ra}$ for high- $\mathit{Ra}$ steady solutions on the ridge (Corson Reference Corson2011), along with results from upper bound analysis and DNS. Both the upper bound calculation and the DNS predict $\mathit{Nu}\sim \mathit{Ra}$ for the unsteady flow. Steady convection at large $\mathit{Ra}$ is thus seen to transport less heat than the realised turbulent flow, with $\mathit{Nu}\sim \mathit{Ra}^{0.6}$ for the equilibrium states (Corson Reference Corson2011). Like the real flow, however, the heat transported by the steady heat-flux-maximising solutions increases substantially as the inter-plume spacing (i.e.  $L_{s}$ ) decreases from $O(1)$ to asymptotically small values; in contrast, the matched asymptotic analysis of Fowler (Reference Fowler1997) suggests that in steady porous medium convection at fixed aspect ratio $\mathit{Nu}\sim \mathit{Ra}^{1/3}=o(\mathit{Ra}^{0.6})$ .

Figure 5.

(a) Rayleigh-number scaling of the inverse wavelength associated with heat-flux-maximising steady convective states (dots). It should be noted that at sufficiently large $\mathit{Ra}$ multiple states yield nearly the same (maximum) heat flux. The dashed line is the best fit curve $1/L_{s}=0.070\mathit{Ra}^{0.52}$ for $\mathit{Ra}\leqslant 12\,559$ . For reference, $1/L_{h}$ and $1/L_{c}$ are also plotted as functions of $\mathit{Ra}$ . (b) Rayleigh-number scaling of the Nusselt number for steady convective solutions (dots). The dashed line is the best fit curve $\mathit{Nu}=0.155\mathit{Ra}^{0.60}+1.213$ . For reference, data from upper bound analysis and various DNS are also shown.

3.4. Statistical structure of the columnar flow in DNS

As described above, the spatial structure of steady convection varies appreciably with $L_{s}$ . A natural question concerns the difference between the structure of the steady convective states and that of the time-averaged flow observed in DNS. As $\mathit{Ra}$ increases, DNS indicate that the interior flow becomes more organised and is dominated by persistent vertical columnar flow across the domain, driven by the chaotic mixing of small proto-plumes at the upper and lower boundaries (Otero et al. Reference Otero, Dontcheva, Johnston, Worthing, Kurganov, Petrova and Doering2004; Hewitt et al. Reference Hewitt, Neufeld and Lister2012). The columnar flow has been modelled using the heat-exchanger solution in Hewitt et al. (Reference Hewitt, Neufeld and Lister2012, Reference Hewitt, Neufeld and Lister2013). As discussed in § 3.2, the heat-exchanger solution is well represented by the interior part of steady solutions only for convective states with sufficiently small aspect ratios; of course, the time-averaged columnar flow in DNS need not satisfy the steady governing equations. To further investigate the differences between the time-averaged columnar flow and the strictly steady convective states, we performed DNS in the high- $\mathit{Ra}$ regime, extending previous results to $\mathit{Ra}\approx 10^{5}$ . The unsteady version of the system (3.1) and (3.2) was solved numerically using a Fourier–Chebyshev-tau pseudospectral algorithm. Temporal discretisation was achieved using the Crank–Nicolson method for the linear terms and a two-step Adams–Bashforth method for the nonlinear terms, yielding second-order accuracy in time. The code was thoroughly validated and gives $\mathit{Nu}$ values quantitatively matching those of previous DNS, as shown in figure 5(b).

Figure 6 shows snapshots of the temperature fields and corresponding long-time-averaged magnitudes of the (complex) Fourier amplitudes of the temperature fluctuations (i.e. deviations from the horizontal mean) as functions of $z$ from our DNS at $\mathit{Ra}=50\,000$ conducted in two different domains. As is evident in figure 6(a), there exist 17 very well organised columnar flows, each consisting of a single rising and descending mega-plume, at the given parameter values. The time-averaged amplitudes of the temperature fluctuations, $\langle |\hat{{\it\theta}}_{n}|\rangle$ , in figure 6(c) reveal that the interior flow is a composite of a few low-wavenumber Fourier modes but is dominated by one mode, features shared by the strictly steady stably stratified core solutions. Indeed, we recall that steady convective states with $L_{s}=L_{m}$ , the mean inter-plume spacing observed in DNS, are stably stratified core solutions (figures 1 c, 2 c and 3 b). Furthermore, at high wavenumber, the Fourier amplitudes $\hat{{\it\theta}}_{n}$ are strongly localised near the upper and lower walls, where they superpose to comprise the small rolls and proto-plumes within the thermal and vorticity boundary layers. Figure 6(b,d) shows corresponding results for DNS performed in a narrower domain, confirming that the flow has a structure similar to that in the larger domain. In particular, the time-averaged interior flow is well represented by only six Fourier modes.

Figure 6.

Snapshots of the temperature fields (a,b) and corresponding time-averaged Fourier amplitudes (c,d) of the temperature fluctuations (i.e. deviations from the horizontal mean) from DNS at $\mathit{Ra}=50\,000$ . In (a,c) $L=2.39$ , while in (b,d) $L=2.39/17$ .

Nevertheless, the DNS indicate that the interior slope of the time- and horizontal-mean temperature field is not positive (figure 7 a); i.e. the core is not stably stratified. Furthermore, although the interior mean temperature profile is roughly linear in $z$ with negative slope for large $\mathit{Ra}$ , this slope is not well predicted by the heat-exchanger solution either: compared with the slope of $\overline{T}$ for the heat-exchanger solution, the mean temperature gradient from the DNS is more negative (figure 7 a). On the other hand, figure 7 does confirm that the mean temperature gradient approaches zero as $\mathit{Ra}$ is increased and that the mean amplitude of the dominant Fourier mode in the interior is almost independent of $\mathit{Ra}$ at large $\mathit{Ra}$ (figure 7 b), consistent with the measurements in Hewitt et al. (Reference Hewitt, Neufeld and Lister2012) and the form of the heat-exchanger solution. In short, the statistical structure of the turbulent columnar flow at large $\mathit{Ra}$ resembles the heat-exchanger solution in the interior, but with a modified mean temperature gradient and more than one Fourier mode to adequately represent the fluctuations.

Figure 7.

Statistical structure of turbulent convection obtained from DNS. (a) Comparison of the long-time- and horizontally averaged temperature profile $\langle \overline{T}\rangle$ from DNS with $\overline{T}$ from the analytical heat-exchanger model. (b) Time-averaged amplitude $A_{d}$ of the dominant Fourier mode (after subtraction of the horizontal mean) at $z=0.5$ . For each $\mathit{Ra}$ , DNS was performed in a domain with $L=10L_{f}$ so that 17 columnar flows were captured. In (a), only half of the mean temperature profile (i.e. for $0\leqslant z\leqslant 0.5$ ) is plotted due to statistical antisymmetry about the mid-plane.

4.1. Floquet theory

To investigate the stability properties of the steady convective states described in the previous section, we next perform a secondary stability analysis using spatial Floquet theory. First, all fields are expressed as the sum of the steady nonlinear base flow (denoted with a subscript ‘ $s$ ’) and a time-varying perturbation,

(4.1) $$\begin{eqnarray}\displaystyle & T(x,z,t)=T_{s}(x,z)+\widetilde{{\it\theta}}(x,z,t), & \displaystyle\end{eqnarray}$$

(4.2) $$\begin{eqnarray}\displaystyle & {\it\psi}(x,z,t)={\it\psi}_{s}(x,z)+\widetilde{{\it\psi}}(x,z,t), & \displaystyle\end{eqnarray}$$

$T_{s}=(1-z)+{\it\theta}_{s}$

(4.3) $$\begin{eqnarray}\displaystyle & {\rm\nabla}^{2}\widetilde{{\it\psi}}=-\mathit{Ra}\,\partial _{x}\widetilde{{\it\theta}}, & \displaystyle\end{eqnarray}$$

(4.4) $$\begin{eqnarray}\displaystyle & \partial _{t}\widetilde{{\it\theta}}={\rm\nabla}^{2}\widetilde{{\it\theta}}-\partial _{x}{\it\theta}_{s}\partial _{z}\widetilde{{\it\psi}}+\partial _{z}{\it\theta}_{s}\partial _{x}\widetilde{{\it\psi}}+\partial _{x}{\it\psi}_{s}\partial _{z}\widetilde{{\it\theta}}-\partial _{z}{\it\psi}_{s}\partial _{x}\widetilde{{\it\theta}}-\partial _{x}\widetilde{{\it\psi}}, & \displaystyle\end{eqnarray}$$

(4.5a−d ) $$\begin{eqnarray}\widetilde{{\it\theta}}(x,0,t)=0,\quad \widetilde{{\it\theta}}(x,1,t)=0,\quad \widetilde{{\it\psi}}(x,0,t)=0,\quad \widetilde{{\it\psi}}(x,1,t)=0\end{eqnarray}$$

and $L$ -periodicity (not generally $L_{s}$ -periodicity) in $x$ . Floquet theory implies that solutions may be sought having the following form:

(4.6) $$\begin{eqnarray}\left[\begin{array}{@{}c@{}}\widetilde{{\it\theta}}\\ \widetilde{{\it\psi}}\end{array}\right]=\text{e}^{\text{i}{\it\beta}k_{s}x}\left\{\mathop{\sum }_{n=-\infty }^{\infty }\left[\begin{array}{@{}c@{}}\hat{\widetilde{{\it\theta}}}_{n}(z)\\ \hat{\widetilde{{\it\psi}}}_{n}(z)\end{array}\right]\text{e}^{\text{i}nk_{s}x}\right\}\text{e}^{{\it\lambda}t}+\text{c.c.},\end{eqnarray}$$

where ${\it\lambda}$ is the temporal growth rate, $\text{i}{\it\beta}k_{s}$ is the Floquet exponent, with the real Floquet parameter ${\it\beta}$ providing the freedom to modify the fundamental horizontal wavenumber of the perturbation (i.e. for ${\it\beta}\neq 0$ , ${\it\beta}k_{s}$ is the wavenumber of the largest horizontal scale), and c.c. denotes complex conjugate. Since (4.6) is invariant under integer shifts in ${\it\beta}$ and under reflections ${\it\beta}\rightarrow -{\it\beta}$ , we can restrict attention to $0\leqslant {\it\beta}\leqslant 0.5$ without loss of generality. Substitution of (4.6) into (4.3) and (4.4) yields

(4.7) $$\begin{eqnarray}\displaystyle & \text{i}(n+{\it\beta})k\,\mathit{Ra}\,\hat{\widetilde{{\it\theta}}}_{n}+[D_{zz}-(n+{\it\beta})^{2}k^{2}]\hat{\widetilde{{\it\psi}}}_{n}=0, & \displaystyle\end{eqnarray}$$

(4.8) $$\begin{eqnarray}\displaystyle & [D_{zz}-(n+{\it\beta})^{2}k^{2}+\widetilde{h}_{n}]\hat{\widetilde{{\it\theta}}}_{n}+[-\text{i}(n+{\it\beta})k+\widetilde{g}_{n}]\hat{\widetilde{{\it\psi}}}_{n}={\it\lambda}\hat{\widetilde{{\it\theta}}}_{n} & \displaystyle\end{eqnarray}$$

$n$

$\widetilde{h}_{n}$

$\widetilde{g}_{n}$

$\hat{\widetilde{{\it\psi}}}_{n}=\text{i}\hat{\widetilde{{\it\phi}}}_{n}$

(4.9) $$\begin{eqnarray}\unicode[STIX]{x1D63C}\boldsymbol{V}={\it\lambda}\unicode[STIX]{x1D63D}\boldsymbol{V},\end{eqnarray}$$

where $\unicode[STIX]{x1D63C}$ and $\unicode[STIX]{x1D63D}$ are real matrices due to the symmetries (3.4) of the base flow, and the vector $\boldsymbol{V}=(\cdots \hat{\widetilde{{\it\theta}}}_{n-1},\hat{\widetilde{{\it\theta}}}_{n},\hat{\widetilde{{\it\theta}}}_{n+1}\cdots \hat{\widetilde{{\it\phi}}}_{n-1},\hat{\widetilde{{\it\phi}}}_{n},\hat{\widetilde{{\it\phi}}}_{n+1}\cdots \!)^{\text{T}}$ . The eigenvalue ${\it\lambda}$ and the corresponding eigenvector $\boldsymbol{V}$ can be obtained by numerically solving this differential eigenvalue problem. It should be noted that in the following sections, the convective rather than diffusive time, ${\it\tau}\equiv t\mathit{Ra}$ , will be utilised, so that the corresponding convective growth rate ${\it\sigma}\equiv {\it\lambda}/\mathit{Ra}$ .

4.2. Secondary stability results

The eigensystem (4.9) is discretised using a Chebyshev collocation method and the infinite-dimensional system is truncated to $0\leqslant m\leqslant M$ vertically and $-N/2\leqslant n\leqslant N/2$ horizontally. Arnoldi iteration is used to solve the resulting algebraic eigenvalue problem to obtain the leading eigenvalues and eigenfunctions. The numerical resolution is increased until the relative error in the eigenvalue with the largest real part, ${\it\sigma}_{m}$ , is less than $10^{-4}$ .

Figure 8 shows the contours of maximum growth rate, $\text{Re}\{{\it\sigma}_{m}\}$ , as a function of ${\it\beta}$ and $L_{s}$ for various $\mathit{Ra}$ . At a given $\mathit{Ra}$ , the steady solution at small $L_{s}$ (specifically, $L_{c}<L_{s}<L_{b}$ , where $L_{b}$ is a stability boundary defined in figures 10 and 11) is marginally stable for ${\it\beta}=0$ – corresponding to disturbances that simply translate the steady base flow in $x$ – although there is insufficient resolution around ${\it\beta}=0$ to observe this clearly in the contour plots. However, the solution is unstable for a range of long-wavelength perturbations ( $0<{\it\beta}\leqslant 0.5$ , see figure 8). The Floquet parameter ${\it\beta}$ of the fastest-growing perturbation is approximately 0.2–0.3, implying that the most unstable disturbance has a wavelength 3–5 times the wavelength of the base flow. At $\mathit{Ra}=1581$ and 3155, the growth rate of the most unstable mode decreases as $L_{s}$ is increased and, in fact, the steady solution becomes linearly stable for a finite range of $L_{s}$ . However, as $L_{s}$ is further increased, the steady state becomes unstable, exhibiting a growth rate that is essentially independent of ${\it\beta}$ . Similar phenomena are observed at larger $\mathit{Ra}$ except that the linearly stable region shrinks nearly to a point at $\mathit{Ra}=5000$ and vanishes completely at larger $\mathit{Ra}$ .

Figure 8.

Contours of the maximum growth rate $\text{Re}\{{\it\sigma}_{m}\}$ as a function of ${\it\beta}$ and $L_{s}$ for various $\mathit{Ra}$ : (a) $\mathit{Ra}=1581$ ; (b) $\mathit{Ra}=3155$ ; (c) $\mathit{Ra}=5000$ ; (d) $\mathit{Ra}=9976$ . At small $L_{s}~(L_{c}<L_{s}<L_{b})$ , the base state is stable within small domains of size $L_{s}\times 1$ (since $L=L_{s}$ for ${\it\beta}=0$ ), but unstable to certain long-wavelength perturbations ( $0<{\it\beta}\leqslant 0.5$ ); at large $L_{s}$ , the base state is unstable even for ${\it\beta}=0$ , and has the same growth rate for different ${\it\beta}$ . It should be noted that the contour plot in ( $d$ ) has been annotated to indicate the parameters corresponding to the bulk (‘B’) and wall (‘W’) mode eigenfunctions displayed in figure 9.

Figure 9.

The fastest-growing 2D temperature eigenfunctions at $\mathit{Ra}=9976$ shown in a domain with aspect ratio $L=5L_{s}$ (where $L_{s}$ is the wavelength of the steady base state): (a) $L_{s}=0.1$ , ${\it\beta}=0.2$ ; (b)  $L_{s}=0.1585$ , ${\it\beta}=0$ ; (c) $L_{s}=0.1585$ , ${\it\beta}=0.2$ . At small $L_{s}$ , a bulk mode controls the instability, and at large $L_{s}$ , a wall mode dominates. It should be noted that at large $L_{s}$ the spatial structure of the fastest-growing (wall) mode is nearly independent of ${\it\beta}$ .

Figure 10.

The leading eigenvalues at $\mathit{Ra}=9976$ : (a) $L_{s}=0.1$ , ${\it\beta}=0.2$ ; (b) $L_{s}=0.1585$ , ${\it\beta}=0$ ; (c) $L_{s}=0.1585$ , ${\it\beta}=0.2$ ; $\text{Re}\,{\it\sigma}~(\text{Im}~{\it\sigma})$ is the real (imaginary) part of ${\it\sigma}$ . In each case, the inset shows a magnification of the region near the origin, with the asterisk denoting the most unstable bulk mode(s). Here, $L_{b}$ is defined such that when $L_{s}<L_{b}$ (as in a) only bulk modes exist. As $L_{s}$ is increased (b,c), an increasing number of wall modes are destabilised. One should note the similarity of the eigenspectra in (b) and (c) for the same (large) $L_{s}$ .

Figure 11.

Schematic identifying distinct steady states and the associated secondary instability regimes at large $\mathit{Ra}$ . DNS are performed for six steady base states with aspect ratios $L_{s1}{-}L_{s6}$ to study the fully nonlinear evolution of the fastest-growing instability modes. Here, $L_{o}$ is the aspect ratio for which the normalised horizontal-mean temperature gradient ${\it\gamma}=0$ .

Figure 9 shows, for $\mathit{Ra}=9976$ , the 2D eigenfunctions corresponding to these two families of secondary instabilities. At small $L_{s}$ , e.g.  $L_{s}=0.1$ , when the growth rate depends on the horizontal wavenumber ${\it\beta}k_{s}$ , the most unstable perturbation (occurring for ${\it\beta}\approx 0.2$ ) is a bulk mode that spans the layer (figure 9 a). However, for larger $L_{s}$ , e.g.  $L_{s}=0.1585$ , the most unstable perturbation for each ${\it\beta}$ has nearly the same growth rate and a very similar spatial structure, which is strongly localised near the upper and lower walls (figure 9 b,c). Figure 10 shows the eigenspectrum for three different ( ${\it\beta},L_{s}$ ) combinations. We note that the bulk mode, as shown in figure 9(a), occurs for certain long-wavelength (e.g.  ${\it\beta}=0.2$ ) disturbances and exhibits comparably small growth rates ( ${\it\sigma}_{m}=O(0.1)$ ). At small $L_{s}$ , the bulk modes control the instability. As $L_{s}$ is increased, however, an increasing number of wall modes become unstable. These wall modes, which are born in a Hopf bifurcation associated with the advection of small-scale disturbances within the thermal boundary layers (see Aidun & Steen (Reference Aidun and Steen1987) and Graham & Steen (Reference Graham and Steen1992, Reference Graham and Steen1994)), ultimately dominate the secondary instability with growth rates 10–50 times larger than those associated with the bulk modes. For sufficiently large $L_{s}$ , the eigenspectra for disturbances with distinct fundamental horizontal wavenumbers are very similar. We conclude this section by contrasting our secondary stability results with those of Hewitt et al. (Reference Hewitt, Neufeld and Lister2013). Not only do we find two modes of instability rather than one, but in the presence of walls the bulk modes are found to have much reduced growth rates: specifically, $O(0.1)$ rather than $O(1)$ .

5.1. $L_{s2}~(\mathit{Ra}=9976)$

For $\mathit{Ra}=9976$ , we first perform DNS to investigate the dynamics ensuing from unstable steady states with $L_{s}=L_{s2}$ , which is within the bulk instability parameter regime. The initial condition comprises 20 replicas of the steady convective state at $L_{s}=0.1$ plus a small-amplitude contribution of the corresponding fastest-growing perturbation at ${\it\beta}=0.2$ . Figure 12 depicts the nonlinear evolution from this initial state to the final turbulent columnar flow. Initially, the dominant horizontal mode number in the interior is $n_{d}=20$ . In accord with the stability analysis in § 4 and as is evident in figure 12(b,c), the base state is unstable to a bulk mode. As the secondary mode grows in amplitude, the pattern coarsens to an unsteady convective state at ${\it\tau}=87.29$ (figure 12 d) with five times the wavelength of the initial steady cellular solution. The resulting mean inter-plume spacing, however, is larger than the final $L_{m}$ . Subsequently, some plumes growing from the upper and lower boundary layers split the wider plumes into narrower ones (figure 12 e). These proto-plumes appear to be generated by a localised instability that closely resembles the wall-mode secondary instability of strictly steady base states having a wavelength greater than $L_{b}$ (figure 12 d). Ultimately, the system converges to a statistically steady turbulent state (figure 12 f). Thus, there exist two stages of evolution: initially, the bulk instability controls the evolution of the flow so that the background plumes merge forming a convective flow with smaller interior wavenumber; then, as the plume spacing becomes too wide, a variant of the boundary instability intensifies so that small plumes generated from the upper and lower walls are able to split the wider plumes into narrower ones until the flow settles into a statistically steady state. We note that the first stage of this process is loosely similar to the instability and predicted nonlinear evolution of the analytical heat-exchanger flow described in Hewitt et al. (Reference Hewitt, Neufeld and Lister2013), in which only the bulk instability mode is found.

Figure 12.

Snapshots of the temperature field from DNS showing the nonlinear evolution of the fastest-growing secondary instability mode for $L_{s2}=0.1$ , $L=20L_{s2}$ , $\mathit{Ra}=9976$ : (a) ${\it\tau}=0$ ; (b) ${\it\tau}=49.38$ ; (c) ${\it\tau}=54.37$ ; (d) ${\it\tau}=87.29$ ; (e) ${\it\tau}=93.28$ ; (f) ${\it\tau}=446.94$ . (g) The time evolution of the dominant horizontal mode number $n_{d}$ at $z=0.5$ (solid line). The dashed line shows the time-average dominant mode number and the circles correspond to the times highlighted in (a–f).

Figure 13.

Snapshots of the temperature field from DNS showing the nonlinear evolution of the fastest-growing secondary instability mode for $L_{s3}=0.1585$ , $L=16L_{s3}$ , $\mathit{Ra}=9976$ : (a) ${\it\tau}=0$ ; (b) ${\it\tau}=2.49$ ; (c) ${\it\tau}=42.90$ ; (d) ${\it\tau}=57.86$ ; (e) ${\it\tau}=82.30$ ; (f) ${\it\tau}=349.17$ . (g) The time evolution of the dominant horizontal mode number $n_{d}$ at $z=0.5$ (solid line). The dashed line shows the time-average dominant mode number and the circles correspond to the times highlighted in (a–f).

5.2. $L_{s3}~(\mathit{Ra}=9976)$

Next, we consider a wider base flow with $L_{s}=L_{s3}=0.1585$ , which also is a member of the numerical heat-exchanger family but which is (most) unstable to wall modes. From the stability analysis, the growth rate and spatial structure of the most unstable perturbation are nearly independent of ${\it\beta}$ (figures 8 d and 9 b,c). The initial condition for the DNS consists of 16 copies of this steady state plus a small-amplitude contribution of the most unstable perturbation at ${\it\beta}=0$ . Figure 13 shows the nonlinear evolution from this initial condition to the final state. By construction, the dominant horizontal mode number in the interior is $n_{d}=16$ at ${\it\tau}=0$ . At early times ( ${\it\tau}=2.49$ , figure 13 b), proto-plumes generated in the upper and lower boundary layers because of the wall instability are continually swept into and thus merge with the mega-plumes in the interior. However, the resulting quasi-time-periodic flow itself evidently is unstable to a variant of the bulk mode (figure 13 c), causing a coarsening of the convective pattern (figure 13 d,e), in rough correspondence with the first stage of the evolution for the base flow with $L_{s}=L_{s2}$ . The plumes generated from the walls then split the wider plumes before the flow reaches a statistically steady state (figure 13 f), similar to the second stage of the evolution for the case for which $L_{s}=L_{s2}$ . Interestingly, the final state is not dominated by a unique horizontal wavenumber in the interior. In short, there exist three evolutionary stages for the scenario with $L_{s}=L_{s3}$ . In the linear instability regime, the wall mode dominates the evolution of the flow so that a series of proto-plumes rising from the walls merge with the background mega-plumes; this process leaves the mean inter-plume spacing unchanged from its initial value $L_{s}$ . However, the modified roughly time-periodic flow appears to be susceptible to a bulk instability, causing the merger of some mega-plumes. Finally, as the inter-plume spacing increases, a wall-like mode is again activated and some of the nascent proto-plumes split the wider plumes, yielding the final turbulent convective state. This scenario suggests that a generalisation of the bulk instability may occur for unsteady flow, and that this instability intensifies as the inter-plume spacing is reduced.

5.3. $L_{s4}$ and $L_{s5}~(\mathit{Ra}=9976)$

Figure 14 shows the time evolution of the dominant horizontal mode number in the interior for the cases $L_{s}=L_{s4}$ and $L_{s}=L_{s5}$ . For $L_{s}=L_{s4}=0.224$ , the steady state belongs to the family of stably stratified core solutions, but the inter-plume spacing is still less than that of the final state. From figure 14(a), it can be ascertained that the evolution for the case $L_{s4}$ is similar to that for the case $L_{s3}$ . In figure 14(b), $L_{s5}=0.316$ is very close to $L_{m}$ so that the base state evolves directly to the final state, with the dominant interior horizontal wavenumber fluctuating within a small range.

Figure 14.

The time evolution of the dominant horizontal mode number $n_{d}$ at $z=0.5$ for the temperature field from DNS at $\mathit{Ra}=9976$ (solid line); the dashed line shows the time-average dominant mode number: (a) $L_{s4}=0.224$ , $L=12L_{s4}$ ; (b) $L_{s5}=0.316$ , $L=10L_{s5}$ .

Figure 15.

Snapshots of the temperature field from DNS showing the nonlinear evolution of the fastest-growing secondary instability mode for $L_{s6}=0.5012$ , $L=10L_{s6}$ , $\mathit{Ra}=9976$ : (a) ${\it\tau}=0$ ; (b) ${\it\tau}=1.50$ ; (c) ${\it\tau}=40.40$ ; (d) ${\it\tau}=573.64$ . (e) The time evolution of the dominant horizontal mode number $n_{d}$ at $z=0.5$ (solid line). The dashed line shows the time-average dominant mode number and the circles correspond to the times highlighted in (a–d).

5.4. $L_{s6}~(\mathit{Ra}=9976)$

Figure 15 shows the evolution for the case $L_{s}=L_{s6}=0.501$ , which is much larger than $L_{m}$ . As demonstrated using secondary stability analysis, this wide-aspect-ratio convective state is strongly unstable to a wall mode. At early times, a series of proto-plumes generated from the upper and lower boundary layers continually feed the background mega-plumes (figure 15 b,c), creating a quasi-periodic flow state. Moreover, for a short time, the dominant interior horizontal wavenumber for this unsteady flow remains constant at $L_{s6}$ , hence the inter-plume spacing remains much larger than $L_{m}$ . However, due to the strong boundary instability some proto-plumes grow and split the wider mega-plumes into narrower ones. This process continues until the flow converges to the final state. Thus, there is only a single stage of evolution for this case: the strong boundary instability dominates the evolution of both the initial steady state and the ensuing unsteady flow with large $L_{s}$ , generating boundary plumes that ultimately split the wide-aspect-ratio interior mega-plumes. This boundary instability becomes much stronger as $L_{s}$ increases.

As noted above, only a fraction of the growing proto-plumes successfully split the wide mega-plumes. Figure 16 shows this plume splitting process. Initially, a small (warm) proto-plume is generated near the lower wall because of the boundary-localised instability (figure 16 a). Shortly thereafter, many smaller proto-plumes generated around its root merge with the primary proto-plume, accelerating its growth (figure 16 b,c). Meanwhile, another (cold) proto-plume is generated near the upper wall and grows downward (figure 16 c,d). The upward growing plume soon reaches the upper wall, forming a mega-plume (figure 16 e). Near the bottom wall, this mega-plume begins to merge with its neighbouring warm mega-plume; however, the growing cold plume disrupts this merging process by splitting the wider root into two narrower roots (figure 16 f). Finally, the cold plume reaches the bottom wall, forming another downwelling mega-plume (figure 16 g). Hence, the wider plume is successfully split into two narrower ones (figure 16 h).

Figure 16.

The splitting process from a wider plume to a narrower one, $L_{s6}=0.5012$ , $L=10L_{s6}$ , $\mathit{Ra}=9976$ : (a) ${\it\tau}=40.4$ ; (b) ${\it\tau}=43.4$ ; (c) ${\it\tau}=47.9$ ; (d) ${\it\tau}=51.4$ ; (e) ${\it\tau}=56.4$ ; (f) ${\it\tau}=59.9$ ; (g) ${\it\tau}=61.9$ ; (h) ${\it\tau}=68.8$ . The solid ellipse marks the growing process for the hot plume and the dashed ellipse marks the growing process for the cold plume. Only the portion of the domain $0\leqslant x\leqslant 0.69$ is shown to highlight the plume splitting process.

Figure 17.

Snapshots of the temperature field from DNS showing the nonlinear evolution of the fastest-growing linear instability mode for any $L_{s1}\leqslant L_{c}$ , $L=10L_{f}~(L_{f}=0.2387)$ , $\mathit{Ra}=50\,000$ : (a) ${\it\tau}=0$ ; (b) ${\it\tau}=6$ ; (c) ${\it\tau}=8.25$ ; (d) ${\it\tau}=9.5$ ; (e) ${\it\tau}=10.5$ ; (f) ${\it\tau}=16.5$ ; (g) ${\it\tau}=28$ ; (h) ${\it\tau}=227$ . (i) The time evolution of the dominant horizontal mode number $n_{d}$ at $z=0.5$ (solid line). The circles correspond to the times highlighted in (a–h).

5.5. $L_{s1}~(\mathit{Ra}=50\,000)$

As described in § 2, the conduction solution is linearly stable in domains of sufficiently small aspect ratio ( $L<L_{c}$ ). This solution is, of course, linearly unstable at larger $L$ . We note that since the wavenumber of the fastest-growing mode $k_{f}=\pm ({\rm\pi}\sqrt{\mathit{Ra}}-{\rm\pi}^{2})^{1/2}$ , the corresponding wavelength $2{\rm\pi}/k_{f}$ is larger than the mean inter-plume spacing measured from DNS at large $\mathit{Ra}$ , e.g. at $\mathit{Ra}=50\,000$ . To illustrate the nonlinear evolution of a non-convective base flow with very small $L_{s}=L_{s1}\leqslant L_{c}$ in the high- $\mathit{Ra}$ regime, the conduction solution plus its most unstable perturbation at $\mathit{Ra}=50\,000$ is used as the initial condition for DNS in a larger domain with $L=10L_{f}$ . From figure 17, we see that at early times the flow evolves in accord with the predictions of linear theory (see (2.9) and figure 17 b,c). However, after a short while, thermal boundary layers form near the upper and lower walls, and in the interior the flow develops a stably stratified structure (with hot/lighter fluid above cold/heavier fluid, figure 17 d). This structure is similar to that of the steady stably stratified core solution at large $L_{s}$ . Since the inter-plume spacing is comparably large, a boundary instability dominates the subsequent evolution, with many small proto-plumes generated near the walls (figure 17 e). Unlike the previous cases in which the proto-plumes merge into the background mega-plumes, the strong stable stratification in the core prevents the warm and cold plumes from penetrating across the domain. Instead, these proto-plumes impact near the mid-plane (figure 17 f). After a series of plume merging and splitting events, the flow evolves to the final state (figure 17 g,h). Figure 17(i) shows the evolution of the dominant interior mode number for this scenario.

Figure 18.

Variation of mean inter-plume spacing with $\mathit{Ra}$ . The points have been computed from the DNS reported here; the solid line marks the fitted mean inter-plume spacing measured from the DNS of Hewitt et al. (Reference Hewitt, Neufeld and Lister2012). The DNS performed in this study were carried out in a domain with aspect ratio $L=10L_{f}$ for $39\,716\leqslant \mathit{Ra}\leqslant 99\,763$ ; a domain with $L=11L_{f}$ was also used for $\mathit{Ra}=39\,716$ . For $\mathit{Ra}\leqslant 19\,905$ , the dominant interior horizontal mode varies (in time) within a small range so that $L_{m}$ can be determined by taking a long-time average of the inter-plume spacing. However, for $\mathit{Ra}\geqslant 39\,716$ , the interior flow becomes very well organised and (apparently) can be statistically steady for a band of wavelengths (the dash-dotted box).

5.6. Statistical inter-plume spacing up to $\mathit{Ra}=O(10^{5})$

From the preceding discussion, we conclude that for large $\mathit{Ra}$ the final mean inter-plume spacing is determined by a balance between (suitably generalised) bulk-mode and wall-mode instabilities. When the inter-plume spacing is small, the bulk mode controls the instability, causing plume merger and coarsening of the convective pattern; when the inter-plume spacing is large, the wall-mode instability dominates, causing small plumes generated from the walls to split the wider plumes into narrower ones. To find the mean inter-plume spacing at which the effects of these two instabilities balance, we performed DNS up to $\mathit{Ra}=O(10^{5})$ and measured $L_{m}$ by taking a long-time average of the inter-plume spacing. Our results are plotted in figure 18. Interestingly, for $\mathit{Ra}\geqslant 39\,716$ , the interior flow becomes very well organised and appears to be metastable within a band of wavelengths. For instance, at $\mathit{Ra}=50\,000$ , given different initial conditions, there can exist 14, 16, 17 or 18 plumes in a domain with $L=10L_{f}$ . These numerical experiments suggest that very long averaging times and very wide domains are required to firmly establish the nonlinear scale selection manifested in turbulent porous medium convection. In particular, our data arguably could be fitted by scaling relations of the form $L_{m}\sim \mathit{Ra}^{{\it\alpha}}$ with ${\it\alpha}\neq -0.4$ , the exponent proposed by Hewitt et al. (Reference Hewitt, Neufeld and Lister2012).