2.1 Configuration and flow equations

The problem is mostly modelled as proposed by Flood & Davidson (Reference Flood and Davidson1994). We consider a stably stratified flow in a cavity with an upper free surface where the hot fluid is fed in and with a cold porous lower boundary (representing a solidification front), as sketched in figure 2. The cavity is made of a semicircular lower boundary representing the actual front, two adiabatic sidewalls and is considered infinitely extended in the third direction ($\boldsymbol{e}_{z}$). Since we focus on the convective mechanisms, detailed solidification mechanisms are not modelled. As such, the fluid in the cavity is assumed to remain in a single liquid phase. The fluid is assumed to be Newtonian, incompressible, of density $\unicode[STIX]{x1D70C}_{0}$ at a reference temperature $T_{0}$, viscosity $\unicode[STIX]{x1D708}$, thermal diffusivity $\unicode[STIX]{x1D6FC}$, and thermal expansion coefficient $\unicode[STIX]{x1D6FD}$. Further considering that temperature gradients remain moderate, the fluid’s motion is described under the Boussinesq approximation (Chandrasekhar Reference Chandrasekhar1968) and the dynamical equations take the non-dimensional form

(2.1)$$\begin{eqnarray}\displaystyle & \displaystyle \frac{\unicode[STIX]{x2202}\boldsymbol{u}}{\unicode[STIX]{x2202}t}+(\boldsymbol{u}\boldsymbol{\cdot }\unicode[STIX]{x1D735})\boldsymbol{u}+\unicode[STIX]{x1D735}p=RaPrT\boldsymbol{e}_{y}+Pr\unicode[STIX]{x1D6FB}^{2}\boldsymbol{u}, & \displaystyle\end{eqnarray}$$

(2.2)$$\begin{eqnarray}\displaystyle & \displaystyle \frac{\unicode[STIX]{x2202}T}{\unicode[STIX]{x2202}t}+(\boldsymbol{u}\boldsymbol{\cdot }\unicode[STIX]{x1D735})T=\unicode[STIX]{x1D6FB}^{2}T, & \displaystyle\end{eqnarray}$$

(2.3)$$\begin{eqnarray}\displaystyle & \displaystyle \unicode[STIX]{x1D735}\boldsymbol{\cdot }\boldsymbol{u}=0, & \displaystyle\end{eqnarray}$$

where $\boldsymbol{u}=(u,v,w)$ is the velocity vector, $t$ the time, $p$ the modified pressure including the buoyancy term accounting for the reference temperature (Chandrasekhar Reference Chandrasekhar1968; Tritton Reference Tritton1988) and $\boldsymbol{g}=-g\boldsymbol{e}_{y}$ is the gravitational acceleration. These equations are obtained by normalising lengths by the radius of the semicylindrical pool $R$, velocities by $\unicode[STIX]{x1D6FC}/R$, time by $R^{2}/\unicode[STIX]{x1D6FC}$, pressure by $\unicode[STIX]{x1D70C}_{0}(\unicode[STIX]{x1D6FC}/R)^{2}$ and temperature by $\unicode[STIX]{x0394}T$. Here, $\unicode[STIX]{x0394}T$ is the temperature difference between the hot upper free surface and the cold solidification front at the lower boundary. Note that the term involving the reference temperature is absorbed in the pressure gradient through the modified definition of pressure. The Prandtl number, $Pr=\unicode[STIX]{x1D708}/\unicode[STIX]{x1D6FC}$, is fixed to $0.02$, a value typical of liquid metals in continuous casting processes. The Rayleigh number $Ra$ is defined as

(2.4)$$\begin{eqnarray}Ra=\frac{\unicode[STIX]{x1D6FD}g\unicode[STIX]{x0394}TR^{3}}{\unicode[STIX]{x1D708}\unicode[STIX]{x1D6FC}}.\end{eqnarray}$$

The upper boundary at $y=1$ is modelled as a rigid free surface (standard free-slip boundary condition), where incoming fluid at an imposed temperature $\unicode[STIX]{x0394}T$ is poured with a homogeneous spatial distribution. This is expressed with three boundary conditions:

(2.5a-c)$$\begin{eqnarray}\displaystyle \frac{\unicode[STIX]{x2202}}{\unicode[STIX]{x2202}y}\boldsymbol{u}\times \boldsymbol{e}_{y}=0,\quad \boldsymbol{u}\boldsymbol{\cdot }\boldsymbol{e}_{y}=RePr,\quad T(y=1)=1, & & \displaystyle\end{eqnarray}$$

where the mass flux Reynolds number $Re$ is based on the dimensional feeding velocity $u_{0}$. In a continuous casting process, this velocity would correspond to the casting speed (Flood & Davidson Reference Flood and Davidson1994):

(2.6)$$\begin{eqnarray}Re=\frac{u_{0}R}{\unicode[STIX]{x1D708}}.\end{eqnarray}$$

Thus, $Re=0$ corresponds to zero net mass flux, i.e. no fluid crosses through the boundaries of the semicylindrical pool, while $Re\neq 0$ corresponds to non-zero net mass flux, i.e. the fluid enters and leaves the domain uniformly through the upper and lower surfaces as in Flood & Davidson (Reference Flood and Davidson1994). The incoming mass flux is exactly cancelled by the flux of fluid being solidified and pulled at the solid lower boundary $S$, where solidification imposes the reference temperature:

(2.7a-c)$$\begin{eqnarray}\displaystyle \boldsymbol{u}_{S}\times \boldsymbol{e}_{y}=0,\quad \boldsymbol{u}_{S}\boldsymbol{\cdot }\boldsymbol{e}_{y}=RePr,\quad T(y=0)=0. & & \displaystyle\end{eqnarray}$$

The kinematic condition expresses that the fluid flows vertically downwards through the otherwise no-slip but porous lower boundary. Note that the slight differences between the definitions of the Rayleigh and Reynolds numbers given in the introduction and the ones given in this section reflect the difference between the geometries discussed there and the specific one we are considering in this paper. To ensure consistent boundary conditions for the temperature field at the corners of the domain, short sidewalls of $0.05R$ in height separate the upper and the lower boundaries. Impermeable, no-slip boundary conditions for the velocity field and an insulating boundary condition for the temperature field are imposed at these sidewalls. In a real casting process, these walls represent the mould. Finally, the infinite extension of the domain in the third direction $\boldsymbol{e}_{z}$ is represented by periodic boundary conditions for the velocity and temperature fields.

2.2 Linear stability analysis

The system admits steady base solutions that are invariant along $\boldsymbol{e}_{z}$, similar to those found by Flood & Davidson (Reference Flood and Davidson1994) (see the detailed topology of these solutions in § 4). These may, however, be unstable to three-dimensional perturbations. Hence, we shall detect the corresponding bifurcation by analysing the stability of the base two-dimensional flow to infinitesimal three-dimensional perturbations. As such, velocity, pressure and temperature fields are decomposed into the two-dimensional base flow and an infinitesimal three-dimensional perturbation, as

(2.8)$$\begin{eqnarray}\displaystyle & \displaystyle \boldsymbol{u}(x,y,z,t)=\boldsymbol{U}(x,y)+\boldsymbol{u}^{\prime }(x,y,z,t), & \displaystyle\end{eqnarray}$$

(2.9)$$\begin{eqnarray}\displaystyle & \displaystyle T(x,y,z,t)=\bar{T}(x,y)+T^{\prime }(x,y,z,t), & \displaystyle\end{eqnarray}$$

(2.10)$$\begin{eqnarray}\displaystyle & \displaystyle p(x,y,z,t)=P(x,y)+p^{\prime }(x,y,z,t). & \displaystyle\end{eqnarray}$$

Substituting (2.8)–(2.10) into (2.1)–(2.3) and retaining first-order terms only yields the linearised equations governing the evolution of infinitesimal perturbations:

(2.11)$$\begin{eqnarray}\displaystyle & \displaystyle \frac{\unicode[STIX]{x2202}\boldsymbol{u}^{\prime }}{\unicode[STIX]{x2202}t}+(\boldsymbol{u}^{\prime }\boldsymbol{\cdot }\unicode[STIX]{x1D735})\boldsymbol{U}+(\boldsymbol{U}\boldsymbol{\cdot }\unicode[STIX]{x1D735})\boldsymbol{u}^{\prime }+\unicode[STIX]{x1D735}p^{\prime }=RaPrT^{\prime }\boldsymbol{e}_{y}+Pr\unicode[STIX]{x1D6FB}^{2}\boldsymbol{u}^{\prime }, & \displaystyle\end{eqnarray}$$

(2.12)$$\begin{eqnarray}\displaystyle & \displaystyle \frac{\unicode[STIX]{x2202}T^{\prime }}{\unicode[STIX]{x2202}t}+(\boldsymbol{u}^{\prime }\boldsymbol{\cdot }\unicode[STIX]{x1D735})\bar{T}+\boldsymbol{U}\boldsymbol{\cdot }\unicode[STIX]{x1D735}T^{\prime }=\unicode[STIX]{x1D6FB}^{2}T^{\prime }, & \displaystyle\end{eqnarray}$$

(2.13)$$\begin{eqnarray}\displaystyle & \displaystyle \unicode[STIX]{x1D735}\boldsymbol{\cdot }\boldsymbol{u}^{\prime }=0. & \displaystyle\end{eqnarray}$$

The base flow $(\boldsymbol{U},\bar{T},P)$ satisfies the same boundary conditions as the main variables $(\boldsymbol{u},T,p)$ so that the perturbation variables satisfy the homogeneous counterpart of the boundary conditions associated with the base flow. Since the base flow is invariant along $\boldsymbol{e}_{z}$, the general perturbation may be decomposed into normal Fourier modes as

(2.14)$$\begin{eqnarray}\boldsymbol{q}^{\prime }(x,y,z,t)=\mathop{\sum }_{k=-\infty }^{\infty }\hat{\boldsymbol{q}}(x,y,t)\text{e}^{\text{i}kz},\end{eqnarray}$$

where $\boldsymbol{q}^{\prime }=(\boldsymbol{u}^{\prime },T^{\prime },p^{\prime })$ contains all perturbation fields and $k$ is the wavenumber along the homogeneous direction $\boldsymbol{e}_{z}$. Further, the absence of the third component of the velocity field in the base flow allows a single phase of the complex Fourier mode to be considered, following Barkley & Henderson (Reference Barkley and Henderson1996) and others (Barkley, Gomes & Henderson Reference Barkley, Gomes and Henderson2002; Sapardi et al. Reference Sapardi, Hussam, Pothérat and Sheard2017). The two-dimensionality of the base allows us to reduce the three-dimensional perturbation field to a family of two-dimensional fields parametrised by wavenumber $k$ and computed on the same two-dimensional domain as the base flow.

The LSA equations shall be solved by means of a time-stepper method: defining a linear time evolution operator ${\mathcal{A}}(\unicode[STIX]{x1D70F})$ for the time integration of (2.11)–(2.13) over time interval $\unicode[STIX]{x1D70F}$ as

(2.15)$$\begin{eqnarray}\hat{\boldsymbol{q}}(t+\unicode[STIX]{x1D70F})={\mathcal{A}}(\unicode[STIX]{x1D70F})\hat{\boldsymbol{q}}(t),\end{eqnarray}$$

we solve the eigenvalue problem for operator ${\mathcal{A}}(\unicode[STIX]{x1D70F})$ as

(2.16)$$\begin{eqnarray}{\mathcal{A}}(\unicode[STIX]{x1D70F})\hat{\boldsymbol{q}}_{j}=\unicode[STIX]{x1D707}_{j}\hat{\boldsymbol{q}}_{j}.\end{eqnarray}$$

Here $\hat{\boldsymbol{q}}_{j}$ denotes the eigenvector of ${\mathcal{A}}(\unicode[STIX]{x1D70F})$ corresponding to the complex eigenvalue $\unicode[STIX]{x1D707}_{j}$. The growth rate $\unicode[STIX]{x1D70E}$ and frequency $\unicode[STIX]{x1D714}$ of an eigenmode are related to $\unicode[STIX]{x1D707}$ through

(2.17)$$\begin{eqnarray}\unicode[STIX]{x1D707}=\exp [(\unicode[STIX]{x1D70E}+\text{i}\unicode[STIX]{x1D714})\unicode[STIX]{x1D70F}],\end{eqnarray}$$

where the subscript is ignored for brevity. Further, equation (2.17) yields

(2.18a,b)$$\begin{eqnarray}\unicode[STIX]{x1D70E}=\frac{\ln |\unicode[STIX]{x1D707}|}{\unicode[STIX]{x1D70F}};\quad \unicode[STIX]{x1D714}=\frac{\unicode[STIX]{x1D703}}{\unicode[STIX]{x1D70F}},\end{eqnarray}$$

with $\unicode[STIX]{x1D707}=|\unicode[STIX]{x1D707}|\text{e}^{\text{i}\unicode[STIX]{x1D703}}$. An instability occurs if $\unicode[STIX]{x1D70E}>0$, i.e. $|\unicode[STIX]{x1D707}|>1$, while for $|\unicode[STIX]{x1D707}|<1$ the corresponding eigenmode is stable. The growing eigenmode may be either oscillatory ($\unicode[STIX]{x1D714}\neq 0$) or non-oscillatory ($\unicode[STIX]{x1D714}=0$). The smallest Rayleigh number for which at least one wavenumber $k$ achieves $|\unicode[STIX]{x1D707}|=1$ is the critical Rayleigh number for the onset of instability at a particular value of $Re$, which we shall denote as $Ra_{c}$.

3.1 Numerical set-up

We perform three different types of numerical computations. First, steady two-dimensional solutions obtained using DNS of (2.1)–(2.3) with associated boundary conditions. Two-dimensionality is enforced by setting $\unicode[STIX]{x2202}/\unicode[STIX]{x2202}z=0$ and $w=0$. Second, the LSA of three-dimensional perturbations on the two-dimensional base flow is carried out, by solving the eigenvalue problem set out in § 2.2. This yields the bifurcation points and the structure of the first unstable mode, in the sense of growing $Ra$. Third, three-dimensional DNS are performed in weakly sub- and supercritical regimes for two purposes: (1) to assess the relevance of the LSA results and (2) to find the nature of the bifurcation. The latter is obtained by computing the parameters of the Stuart–Landau model from the three-dimensional DNS data, as proposed by Sheard et al. (Reference Sheard, Thompson and Hourigan2004).

Figure 3.

Details of the mesh with polynomial order $N=1$. The mesh contains 184 quadrilateral elements.

Both the two-dimensional base flow and the evolution of the three-dimensional flow near criticality are obtained by solving (2.1)–(2.3) using spectral-element code Nektar++ (Cantwell et al. Reference Cantwell, Moxey, Comerford, Bolis, Rocco, Mengaldo, Grazia, Yakovlev, Lombard and Ekelschot2015). In the spectral-element approach, the computational domain is partitioned into a mesh of many small subdomains called elements, and the variables are projected within a polynomial basis within each element, as in the finite element method. The specificity of the spectral-element method is that ‘mesh’ refinement is mainly achieved by increasing the order of the polynomial basis ($p$-refinement) and that polynomials are represented at Gauss–Lobatto points which ensure spectral convergence under $p$-refinement. Both two- and three-dimensional DNS are performed on the same spectral-element mesh in the $x\text{-}y$ plane. For the three-dimensional simulations, discretisation in the $\boldsymbol{e}_{z}$ direction relies on a Fourier-based spectral method. The computational domain extends by $2\unicode[STIX]{x03C0}$ along $\boldsymbol{e}_{z}$, or equivalently, the lowest Fourier mode in the spectral discretisation is always $k=1$. Figure 3 shows the detail of the two-dimensional $x\text{-}y$ mesh with polynomial order $N=1$, generated using the GMSH package (Geuzaine & Remacle Reference Geuzaine and Remacle2009). The mesh is composed of 184 quadrilateral elements and is structured in the rectangular part of the domain close to the upper free surface up to thickness $0.05$, and unstructured in the remaining part of the domain. A vertical line along the $y$-axis is imposed to ensure the symmetry of the mesh with respect to that line. At the boundaries, elements are more densely packed than in the bulk, with the ratio between the largest to smallest element’s edge size of four. Time-stepping relies on a third-order implicit–explicit (IMEX) method (Vos et al. Reference Vos, Eskilsson, Bolis, Chun, Kirby and Sherwin2011).

The LSA is conducted with open-source eigenvalue solver DOG (Direct Optimal Growth, Blackburn & Sherwin (Reference Blackburn and Sherwin2004), Pitz, Marxen & Chew (Reference Pitz, Marxen and Chew2017)), based on a time-stepper method with spectral-element discretisation. The linearised equations (2.11)–(2.13) are integrated in time using a third-order backward differentiation scheme (Karniadakis, Israeli & Orszag Reference Karniadakis, Israeli and Orszag1991), with the two-dimensional base flow obtained from the DNS. The leading eigenvalues and eigenmodes are obtained using the iterative process from the method prescribed by Tuckerman & Barkley (Reference Tuckerman and Barkley2000) and Barkley, Blackburn & Sherwin (Reference Barkley, Blackburn and Sherwin2008).

For the two-dimensional DNS, the initial condition is set to $\boldsymbol{u}=0$, $p=0$, and $T=y$. The three-dimensional DNS are initiated with the solution obtained for the two-dimensional base flow replicated along $\boldsymbol{e}_{z}$, with added white noise of standard deviation $0.001$ (as well as $0.01$ and $0.1$ for $Ra<Ra_{c}$ when the bifurcation is subcritical, i.e. $Re=100$ and $Re=110$).

Cubic spline interpolation of the SCIPY package (Virtanen et al. Reference Virtanen, Gommers, Oliphant, Haberland, Reddy, Cournapeau, Burovski, Peterson, Weckesser and Bright2019) is used to estimate the Rayleigh number that first produces $\unicode[STIX]{x1D70E}(k)=0$ at different values of $k$. This gives the curve for Rayleigh number versus $k$, whose minimum yields the estimate of the critical Rayleigh number $Ra_{c}$ and the critical wavenumber $k_{c}$. Again, by cubic spline interpolation, we estimate the value of critical frequency $\unicode[STIX]{x1D714}_{c}$ for the corresponding $k_{c}$. The resulting uncertainty on $Ra_{c}$, $k_{c}$ and $\unicode[STIX]{x1D714}_{c}$ remains within 0.01 %.

3.2 Code validation

We first validated the DNS code and the linear stability solver DOG for two-dimensional Rayleigh–Bénard convection in a box. At the top and bottom plates, a no-slip boundary condition for the velocity field and a conducting boundary condition for the temperature field are imposed. Periodic boundary conditions are applied at the sidewalls for both the fields. DNS were validated by calculating the Nusselt number

(3.1)$$\begin{eqnarray}Nu=\frac{\unicode[STIX]{x1D6FC}{\displaystyle \frac{\unicode[STIX]{x0394}T}{R}}+\langle wT\rangle }{\unicode[STIX]{x1D6FC}{\displaystyle \frac{\unicode[STIX]{x0394}T}{R}}},\end{eqnarray}$$

where $\langle \rangle$ represents the volume average. Here, $Nu$ measures the ratio of the total (convective plus conductive) heat flux to the conductive heat flux. It was computed for $Pr=0.71$ and $Ra=5000$ in a two-dimensional box and the relative error between the Nusselt number computed from our DNS and that of Clever & Busse (Reference Clever and Busse1974) was 0.09 %. For the LSA, we recovered the known values of the critical Rayleigh number ($Ra_{c}=1707.7$) and critical wavenumber ($k_{c}=3.116$) found, for instance, in Chandrasekhar (Reference Chandrasekhar1968), to within an uncertainty of 0.002 %. For both DNS and LSA, a rectangular mesh of 100 structured quadrilateral elements with polynomial order $N=9$ was used.

3.3 Convergence tests

Since the spectral-element discretisation is only used in the $x\text{-}y$ plane, we performed a convergence test on the base two-dimensional flows and the eigenvalue problem based on the two-dimensional domain. This test was performed for each value of $Re$ we considered throughout this work, both on the DNS and on the leading eigenvalue returned by the LSA at $k$ for which $\unicode[STIX]{x1D70E}(k)$ is maximum. Table 1 shows an example for the accuracy of the eigenvalue calculations as a function of the polynomial degree $N$ for $Re=500$, the highest value of $Re$ considered in this work. The leading eigenvalue for $Ra=2\times 10^{6}$ and $k=13$ is real and linearly unstable. We increased $N$ until the eigenvalue converged to a precision of five significant figures, which is $N=14$ for this case. This way, the same level of precision was achieved for all results presented in this work. The time step was kept constant for all three types of numerical calculations so that the maximum local Courant number $C_{max}$ remained below unity and the Courant–Friedrich–Levy condition was strictly satisfied everywhere in the domain, at all time. For instance, at $Re=500$, $Ra=2\times 10^{6}$ and $N=16$, the time step is $10^{-6}$, which yields a maximum Courant number of $C_{\text{max}}=0.05$.

Table 1.

Dependence of leading eigenvalues on the polynomial order $N$. Leading eigenvalues computed on the mesh at $Re=500$, $Ra=2\times 10^{6}$ and $k=13$ are provided. The relative error is to the case of the highest polynomial order ($N=16$).

Furthermore, some supercritical cases (for example, $Re=200$ and $Ra=10^{6}$) are unstable to time-periodic, two-dimensional perturbations. In these cases, a DNS of the base flow over the entire two-dimensional domain does not converge to the base steady solution. Since the perturbation breaks a symmetry with respect to the $x=0$ plane, we found the steady state by performing a DNS on the $x\leqslant 0$ half of the domain, implementing symmetry boundary conditions for the velocity (Neumann boundary condition on $u$, Dirichlet boundary condition on $v$) and the temperature (Neumann boundary condition) at $x=0$. The solution over the full domain was then built by symmetry (Mao & Blackburn Reference Mao and Blackburn2014). We checked that the relative error in the magnitude of the most dominant mode for $Re=200$, $Ra=10^{5}$ and $k=7$ obtained from LSA on base flows calculated on the full domain and the half-domain was less than 0.01 %. Thus, the use of half-domain calculation for the base flow is justified and also computationally cheaper.

For the three-dimensional DNS, the dependence on the number of Fourier modes $N_{f}$ was tested by computing the total kinetic energy of the three-dimensional flow over the entire domain. The relative error in the kinetic energy of the supercritical steady state at $Re=200$ and $Ra=1.1\times 10^{5}$ between computations at $N_{f}=32$ and $N_{f}=64$ was 0.01 %. Similar results were obtained for other values of $Re$, and thus 32 Fourier modes were employed for all cases. Note that the three-dimensional simulations are performed only up to $Re=200$, as the simulations for higher values of $Re$ become prohibitively expensive. A summary of all cases investigated is provided in table 5.

4.1 Dynamics of base flow with zero net mass flux ($Re=0$)

To perform a LSA, we first need to obtain the two-dimensional base flow. We first focus on the behaviour of two-dimensional base flow with zero net mass flux. Figure 4 displays the density plots of the temperature field and the velocity vector field $\boldsymbol{u}$ in the $x\text{-}y$ plane for $Re=0$ and $Ra=1$. Despite a ‘stable’ stratification (i.e. the light fluid is mostly on the top of the heavy one), we observe prominent convective rolls. This motion is driven by the misalignment of the pressure gradient and the temperature gradient, which generates a baroclinic imbalance. This effect is best seen by considering an equilibrium state and the small displacement of a fluid particle along an isothermal line. In a Rayleigh–Bénard configuration, isobars and isotherms are aligned, so no change in either buoyancy or pressure forces on the fluid particle occurs as a result of the displacement. If, on the other hand, isobars and isotherms are not aligned, as in the present configuration (and if the isobars are reasonably parallel), the displacement results in a variation of pressure forces in the direction normal to the isobar. Since the displacement takes place along an isotherm, the excess or deficit in pressure forces is not compensated by a variation in buoyancy forces and a motion must take place for viscous forces to restore equilibrium. Near the semicircular boundary of the domain, the temperature gradient is radial, whereas the pressure gradient (dominated by buoyancy) is tilted. While these directions coincide near the centre of the domain (around $x=0$), the angle they form increases to a maximum near the sidewalls. At these locations, the imbalance is maximum and drives a strong downward jet along the circular boundary. The rolls form as a result of the left and right jets meeting at where the flow returns. This form of baroclinic imbalance drives a non-zero base flow, even at arbitrary low-temperature gradients. It is noteworthy that this mechanism is a simpler form of the classical baroclinic imbalance in models incorporating an explicit temperature dependence of the density. In these models, the imbalance appears explicitly in the dimensional vorticity equation, where the curl of the pressure term yields a vorticity source term proportional to the baroclinic vector $1/\unicode[STIX]{x1D70C}(T)^{2}\unicode[STIX]{x1D735}\unicode[STIX]{x1D70C}(T)\times \unicode[STIX]{x1D735}p$ (Vallis Reference Vallis2006), and where the density gradient is $\unicode[STIX]{x1D735}\unicode[STIX]{x1D70C}(T)=-\unicode[STIX]{x1D735}T/\unicode[STIX]{x1D6FD}$ in dimensional variables. Again, in Rayleigh–Bénard configurations the gradients of vorticity and pressure are aligned, so the baroclinic vector is zero. In the semicircular cavity, by contrast, the baroclinic vector is maximum near the corner and drives a jet along the wall in exactly the same way as in the model we are using.

Figure 4.

Two-dimensional base flow at $Re=0$ and $Ra=1$. The pressure gradient ($\unicode[STIX]{x1D735}p$) and the temperature gradient ($\unicode[STIX]{x1D735}T$) are indicated by red and white arrows, respectively. These gradients form an angle at the upper corners, which results in a baroclinic imbalance. In models with explicit dependence of the density on the temperature, the density gradient is related to the temperature gradient as $\unicode[STIX]{x1D735}\unicode[STIX]{x1D70C}=-\unicode[STIX]{x1D6FD}^{-1}\unicode[STIX]{x1D735}T$ in dimensional variables. Note that in this example, the modified pressure is predominantly determined by the buoyancy, as it includes the buoyancy term corresponding to the reference temperature.

Figure 5.

Two-dimensional base flow with zero net mass flux ($Re=0$) at (a) $Ra=10^{4}$; (b) $Ra=10^{5}$; (c) $Ra=10^{6}$; and (d) $Ra=10^{7}$. Colours represent the magnitude of the velocity field. Panels (e and f) display Lissajous graphs in the $(u,v)$ plane for $Ra=10^{6}$ and $Ra=10^{7}$ measured at $(x,y)=(-0.5,0.6)$, respectively exhibiting periodic and chaotic states.

Interestingly, if the curvature of the lower boundary was continuously decreased to zero, the angle between the temperature and pressure gradient would progressively decrease to zero, too. In the limit of a flat lower boundary, the Rayleigh–Bénard configuration would be recovered in a channel of height determined by the sidewalls, even though these would be pushed to infinity. However, the stratification would be stable and since the baroclinic imbalance (and also the baroclinic vector) would cancel out in this limit, the base flow would be still and stable, with a linear temperature gradient in the bulk.

We now gradually increase the Rayleigh number from $Ra=10^{4}$ to $Ra=10^{7}$ with $Re=0$. As expected, the velocity field strengthens at the wall with the increase in Rayleigh number, since the intensity of the temperature gradient in the baroclinic vector increases (see figure 5a–d). While the two-dimensional solution remains steady for $Ra=10^{4}$ and $Ra=10^{5}$, it becomes time-periodic for $Ra=10^{6}$ and chaotic for $Ra=10^{7}$. The periodic and chaotic nature of the solutions for $Ra=10^{6}$ and $Ra=10^{7}$ are illustrated by the Lissajous graphs of the two components of velocities in figures 5(e) and 5(f).

4.2 Base solution with non-zero mass flux ($Re>0$)

One of the key ingredients in the problem we consider is the through-flow. To illustrate its effect on the base flow, we computed the two-dimensional base flows for a range of Reynolds numbers from 0 to 200, at a fixed value of $Ra$ of $10^{4}$ (for the purpose of seeking the critical Rayleigh number for the linear stability, the base flow will have to be recalculated every time either $Ra$ or $Re$ is changed). Figure 6 shows the streamlines of the velocity field and the density plots of the temperature field for the two-dimensional base flow. At $Re=0$, the flow consists of the pair of primary vortices and the pair of secondary vortices which are located at the bottom of the cavity, as discussed in the previous section. At low values of $Re$ ($Re=25$), the primary vortices are slightly displaced downwards and secondary vortices are suppressed under the combined action of the through-flow and the confinement at the lower boundary. The size of the primary vortices increases as a result, and the convection associated with them is slightly enhanced. However, with a further increase in $Re$, the same mechanism incurs a suppression of the primary vortices from the top and a reduction of their size. At $Re=200$, the primary vortices have completely disappeared. Thus, the through-flow enhances convection at low Reynolds numbers (below 25, in the set of Reynolds numbers we explored), but suppresses it at higher Reynolds numbers.

Figure 6.

Streamlines of the steady two-dimensional base flow and temperature field for $Ra=10^{4}$ at (a) $Re=0$; (b) $Re=25$; (c) $Re=50$; (d) $Re=100$; and (e) $Re=200$.

4.3 Vertical heat flux

The efficiency of the convection is estimated by quantifying the vertical heat flux. For this purpose, we compute the Nusselt number at the inlet of the cavity defined as

(4.1)$$\begin{eqnarray}Nu=\frac{-\left\langle \left({\displaystyle \frac{\unicode[STIX]{x2202}T}{\unicode[STIX]{x2202}y}}\right)_{y=1}\right\rangle _{Convective}+(vT)_{y=1}}{-\left\langle \left({\displaystyle \frac{\unicode[STIX]{x2202}T}{\unicode[STIX]{x2202}y}}\right)_{y=1}\right\rangle _{Conductive}+(vT)_{y=1}},\end{eqnarray}$$

and analyse its variations with the Rayleigh number for several fixed values of $Re$. Here the reference conductive state is chosen as a uniform downward flow at non-dimensional velocity $RePr$, without rolls. This way, the Nusselt number in (4.1) measures the enhancement of heat transfer due to the convective flow inside the cavity and not that due to the average downward fluid motion. The temperature distribution for this reference state is obtained by solving the steady advection-diffusion equation

(4.2)$$\begin{eqnarray}\unicode[STIX]{x1D6FB}^{2}T-(RePr\boldsymbol{e}_{y}\boldsymbol{\cdot }\unicode[STIX]{x1D735})T=0,\end{eqnarray}$$

with the same boundary conditions for the temperature as for the base flow (2.5) and (2.7). The variations of $Nu$ with the Rayleigh number for the two-dimensional base flow are shown in figure 7(a). For $Re=0$, the Nusselt number monotonically increases with $Ra$. By contrast, for $Re\neq 0$, $Nu$ first decreases before reaching a minimum, and it goes below unity for $Re=75$ to $Re=200$. The decrease of $Nu$ below unity occurs as the two rolls form and gain in intensity. As they do so, they redistribute heat laterally in the upper part of the pool. As they grow in size, they do so ever closer to the top boundary and oppose the downward temperature gradient near the corners and hence the heat flux through the upper boundary. When the convective rolls have grown to occupy the entire cavity, however, their shape does not evolve any more as $Ra$ is further increased. In this regime, the heat flux near the corners saturates and the main effect of the rolls is to convey cold fluid upwards near $x=0$. The cooling of the region near the upper boundary that ensues leads $Nu$ to increase again and soon exceed unity. There are relatively few instances where convection reduces rather than enhances heat transfer (i.e. $Nu<1$). It has, for example, been observed in the Rayleigh–Bénard convection of homeotropically aligned nematic liquid crystal (Thomas, Pesch & Ahlers Reference Thomas, Pesch and Ahlers1998).

Figure 7.

Nusselt number $Nu$ calculated at the inlet. Panel (a) shows two-dimensional base flow for various values of $Ra$. The shaded region represents the unsteady case. Panel (b) shows a comparison between $Nu$ for two-dimensional (represented by solid symbols) and three-dimensional (represented by hollow symbols) DNS at the same value of the Rayleigh number. Points are obtained near criticality (hence, for different values of $Re$).

Figure 8.

Growth rates of leading eigenmodes as functions of the wavenumber $k$ for (a) $Re=0$ and $Ra\leqslant 10^{4}$; (b) $Re=100$ and $Ra\leqslant 7\times 10^{4}$; and (c) $Re=200$ and $Ra\leqslant 3\times 10^{5}$. Solid symbols represent real leading eigenvalues, while hollow symbols represent complex-conjugate pairs of non-real leading eigenvalues. Eigenvalue spectra for marginally supercritical cases for (d) $Re=0$, $Ra=7\times 10^{3}$ and $k=6$; (e) $Re=100$, $Ra=4\times 10^{4}$ and $k=4$ and (f) $Re=200$, $Ra=1.1\times 10^{5}$ and $k=7$. Symbols: ●, blue, stable eigenvalues; ▪, red, the first unstable eigenvalue.

5.1 Growth rates and eigenvalue spectra

We now turn to the linear stability of the steady two-dimensional states found in § 4. We start by analysing the dependence of the perturbation growth $\unicode[STIX]{x1D70E}$ on the Rayleigh number $Ra$, wavenumber $k$ and mass flux Reynolds number $Re$. Figure 8(a–c) shows the growth rate $\unicode[STIX]{x1D70E}$ as a function of wavenumber $k$ for $Re=0$, 100 and 200. For a particular value of $Re$, the growth rate $\unicode[STIX]{x1D70E}(k)$ is computed for a range of Rayleigh numbers until supercritical regimes are encountered. Figure 8(d–f) shows the eigenvalue spectra for $Re$ (corresponding to figure 8a–c) near to the onset of instability, in marginally supercritical regime. For $Re=0$, all eigenmodes are oscillatory ($\unicode[STIX]{x1D714}\neq 0$) over the entire range of values of $Ra$ we investigated. At low wavenumber $k\simeq 4$, a local maximum in $\unicode[STIX]{x1D70E}(k)$ is observed. We denote the set of eigenvectors forming this maximum as ‘branch I’. These remain stable for all values of $Ra$ we investigated. A second, absolute maximum exists around $k\simeq 6$ and we shall label the corresponding set of modes as ‘branch II’. The mode associated with this maximum becomes unstable for $Ra=7\times 10^{3}$. The corresponding pair of complex conjugate eigenvalues are shown in figure 8(d) as they cross the unit circle that separates stable from unstable eigenmodes. Branches I and II (as well as branch III discussed later in this section, and which is dominant in this case) are illustrated more extensively in the example of $Re=200$ and $Ra=1.8\times 10^{5}$ in figure 9(b).

Figure 9.

(a) Growth rates of the leading eigenmodes at $Ra=10^{4}$ for $Re=0$, 25, 50, and 75. (b) Growth rates of the eigenmodes associated with branches I, II and III for $Re=200$ and $Ra=1.8\times 10^{5}$. Each curve $\unicode[STIX]{x1D70E}(k)$ in figure 8 corresponds to the absolute maximum growth rate over all three branches for each $k$.

When $Re$ is increased from zero, the growth rate first increases for all $k$ (keeping the same value of $Ra$). Consequently, the critical Rayleigh number $Ra_{c}(Re)$ initially decreases up to $Re=25$ (see figure 10a and table 2). The physical reason can be traced to the structure of the two-dimensional base flow: increasing $Re$ from zero suppresses the secondary vortices near $x=0$. These give way to the main bulk cells which grow in size (see figure 6b). This implies that the effective flow length scale increases and so does the effective Rayleigh number. Consequently, a lower Rayleigh number is sufficient to trigger the instability.

For $Re>25$, the effect is reversed. For all $k$, $\unicode[STIX]{x1D70E}(k)$ decreases, and the corresponding critical Rayleigh number $Ra_{c}(Re)$ increases. This time, the effect originates in the reduction in the size and intensity of the main cells in the base flow due to their suppression by the inflow near the top boundary at $y=1$ (see figure 6c). The decrease of $\unicode[STIX]{x1D70E}(k)$ is, however, not uniform over the spectrum of wavelengths and the maximum of $\unicode[STIX]{x1D70E}(k)$ associated with branch I increases in value compared to that associated with branch II. This effect is best illustrated in figure 9(a), which displays $\unicode[STIX]{x1D70E}(k)$ versus $k$ at a fixed value of Rayleigh number ($Ra=10^{4}$) for $Re=0$, 25, 50 and 75. From $Re=100$ onwards, the most unstable mode becomes associated with branch I. From this point on, $Ra_{c}$ continues to increase with $Re$ but more slowly than for $Re\leqslant 100$.

Up to $Re=110$, all calculated unstable eigenmodes are oscillatory. From $Re=150$, a third branch (III) appears very close to branch I, with the specificity that all associated modes are non-oscillatory (i.e. $\unicode[STIX]{x1D714}=0$). For $Re\geqslant 150$, the mode associated with the maximum growth rate in branch III becomes dominant and the onset of the instability occurs through a non-oscillatory mode. The real eigenvalue associated with the single fastest growing mode is represented in the eigenvalue spectra for $Re=200$ and $Ra=1.1\times 10^{5}$ at $k=7$ in figure 8(f). At this point, the main cells in the base flow have disappeared. Hence, the transition from the oscillatory to the non-oscillatory instability is associated with a fundamental change in the nature of the base flow, as it switches from a recirculation-dominated topology to one dominated by the through-flow. As such, this transition separates a buoyancy-driven regime and a hydrodynamic one.

Figure 10.

(a) Critical Rayleigh number, (b) critical frequency, and (c) critical wavenumber as a function of $Re$. In (a) green (red) regions below (above) the curve represents flow regimes that are linearly stable (unstable) to two or three-dimensional perturbations. Symbols: ●, data obtained from the LSA; ▫, three-dimensional DNS up to $Re=200$ at slightly subcritical values of $Ra$; ▵, three-dimensional DNS up to $Re=200$ at slightly supercritical values of $Ra$.

5.2 Variations of $\unicode[STIX]{x1D714}_{c}$ and $k_{c}$ with $Re$

The variations with $Re$ of the critical frequency $\unicode[STIX]{x1D714}_{c}$ and wavelength $k_{c}$ at the onset of instability reflect the transition between branches I, II and III, whose coexistence is illustrated in figure 9(b). As $Re$ increases, the variations of the frequency and wavelength associated with the maximum of each of the branches differ: for $Re\leqslant 100$, branch II dominates and over this interval, both $\unicode[STIX]{x1D714}_{c}$ and $k_{c}$ follow the non-monotonous variations of $Ra_{c}$ observed in the previous section. The switchover from branch II to branch I observed at $Re=100$ translates into a discontinuity in the variations of both $\unicode[STIX]{x1D714}_{c}$ and $k_{c}$. While $\unicode[STIX]{x1D714}_{c}$ practically doubles between $Re=75$ and $Re=100$, $k_{c}$ drops by half over the same interval (evaluating the exact amplitudes of these discontinuities would require a prohibitively large number of simulations). However, both $\unicode[STIX]{x1D714}_{c}$ and $k_{c}$ subsequently increase over the interval of dominance of branch I. The next discontinuity occurs at $Re=150$, at which point branch III becomes dominant at the expense of branch I. Since branch III modes are non-oscillatory, $\unicode[STIX]{x1D714}_{c}$ drops to zero. At the same time, the $k_{c}$ jumps up to higher values and continues to increase with $Re$ beyond $Re=150$.

Discontinuities in length scale at the onset of instabilities are frequently observed when convection is combined with forces other than buoyancy and viscosity. Usually, the discontinuity appears when a parameter representing the ratio of two of the forces in the system is varied, and it reflects the transition between different unstable modes. In rotating magnetohydrodynamic (MHD) convection, for example, a transition occurs between the thin convective plumes favoured by fast rotation and the large convective rolls, favoured by the Lorentz force. As in the present case, each of these patterns corresponds to a distinct branch of eigenmodes of the stability problem. The transition between them takes place at a critical value of the ratio between these forces. The corresponding change in length scale has been experimentally observed to reach an order of magnitude (Nakagawa Reference Nakagawa1957) in agreement with theoretical predictions (Chandrasekhar Reference Chandrasekhar1968; Aujogue, Pothérat & Sreenivasan Reference Aujogue, Pothérat and Sreenivasan2015). Similar phenomena are also observed in mixed convection in magnetic fields, at the changeover between convection dominated regimes and shear-dominated ones (Vo, Pothérat & Sheard Reference Vo, Pothérat and Sheard2017). While in rotating magnetoconvection, the transition only involves non-oscillatory modes; it only involves oscillatory ones in mixed MHD convection. The transition resembling most the one observed here, between oscillatory and non-oscillatory modes, and with a discontinuity in wavelength, was observed when decreasing the Prandtl number in rotating convection (Clune & Knobloch Reference Clune and Knobloch1993).

Finally, in the range of larger Reynolds numbers, an asymptotic behaviour associated with branch III emerges, where $k_{c}$ scales linearly with $Re$ as $k_{c}=(3.2\pm 0.2)+(0.018\pm 0.001)Re$.

Table 2.

Critical Rayleigh number, corresponding wavenumber along the homogeneous direction and frequency at the onset of instability for $Re$ ranging from zero to $500$.

5.3 DNS near criticality

To assess the relevance of the LSA, we perform DNS of the two-dimensional flow perturbed by white noise as described in § 3.1, for each value of $Re$ up to 200, at slightly subcritical ($r_{c}<0$) and slightly supercritical ($r_{c}>0$) values of $Ra$. Here, $r_{c}=(Ra/Ra_{c})-1$ is the criticality parameter. The data from these DNS, including $Ra$, measured frequency and wavelength, are represented in figure 10. We stress that the primary purpose here is not one of validation of the growth rate obtained by LSA (even though this comes as a by-product), but to answer the question of whether the LSA correctly identifies the mode that ‘naturally’ emerges from an unstable base state. As such, it is essential that the initial condition be unbiased towards a particular mode. This is the reason why white noise, rather than the most unstable mode, is used to perturb the base state in the initial condition.

In all investigated cases, the perturbation was found to decay for $Ra<Ra_{c}$ and the flow to bifurcate away from the base state for $Ra>Ra_{c}$. The subcritical decay rate was extracted from the DNS with an exponential fit to the long-time decay of $w$ measured at a single point in the domain as shown in figure 11 for $Re=200$ and $Ra=10^{5}$. The asymptotic decay rate was always found within 0.3 % of the prediction of the LSA, confirming that the fully nonlinear decay is dominated by the leading mode returned by the linear stability (see table 3 for details).

Further confirmation that the leading mode identified by LSA drives the dynamics near $Ra=Ra_{c}$ is found by comparing critical frequencies and wavelengths, $\unicode[STIX]{x1D714}_{c}$, $k_{c}$. In the DNS, the perturbation is isolated by subtracting the steady two-dimensional base solution from the result of the time-dependent three-dimensional simulation. Again, an agreement with a relative error lower than 2 % is found between the DNS and the LSA.

Figure 11.

Decay rate built on the time history of the $w(t)$ velocity component at $(x,y,z)=(0,0.6,0.5)$ obtained by DNS for $Re=200$ and $Ra=10^{5}$, compared to the growth rate obtained by LSA.

Table 3.

Comparison of the decay rate computed from the LSA and DNS for $r_{c}<0$. The relative error in the computation of growth rate from DNS and LSA is represented by $\unicode[STIX]{x1D716}_{\unicode[STIX]{x1D70E}}$.

Figure 12.

For $Re=0$ and $Ra=7\times 10^{3}$: (a) vorticity perturbation along the homogeneous direction $\unicode[STIX]{x1D701}_{z}^{\prime }$ computed from the LSA at $z=0$ plane for $k=6$; (b) vorticity perturbation along the homogeneous direction $\unicode[STIX]{x1D701}_{z}^{\prime }$ computed from the DNS at $z=0$ plane; (c) density plot for the magnitude of the velocity field and (d) iso-surfaces of the $z$-component of vorticity. A movie representing the travelling wave is available in the supplementary material (see supplementary movie 1, available at https://doi.org/10.1017/jfm.2019.1015).

5.4 Topology and time-dependence of the perturbation near criticality

Figures 12, 13 and 14, respectively, show the velocity magnitudes and vorticity distributions in weakly supercritical cases associated with each of the three instability branches (respectively, for $Re=0$, $Re=100$ and $Re=200$). The time evolution of the perturbation reconstructed from the LSA involves both real and imaginary parts as of the LSA eigenvector

(5.1)$$\begin{eqnarray}\boldsymbol{q}^{\prime }(x,y,z,t)=Re\{\hat{\boldsymbol{q}}(x,y)\text{e}^{\unicode[STIX]{x1D70E}t+\text{i}(kz+\unicode[STIX]{x1D714}t)}\}.\end{eqnarray}$$

The snapshots of the topologies of the perturbation from LSA and DNS presented on the figures are captured at the same phase.

Unsurprisingly, the topologies of the perturbations found in the DNS and LSA precisely agree too. In all cases, the instability originates near the symmetry plane $x=0$, just above the location where the jets driven by the baroclinic imbalance on either side of the cavity meet. The driving mechanism is a destabilisation of the return jet, with a different behaviour depending on the branch the unstable mode belongs to: modes from branch II ($Re<75$) develop into a travelling wave along $\boldsymbol{e}_{z}$. Since the travelling wave is made up of two counter-propagative linear waves (respectively, associated with each of the conjugate eigenvalues), the travelling nature of the wave is determined by the complex amplitude of the unstable modes (Clune & Knobloch Reference Clune and Knobloch1993). These cannot be obtained from LSA but appear in the fully nonlinear DNS (see figure 12 and associated animation). By contrast, modes from branch I, which are the most unstable for $100\leqslant Re\leqslant 110$, always develop into a standing wave with transverse oscillations within the $x\text{-}y$ plane.

Figure 13.

For $Re=100$ and $Ra=4\times 10^{4}$: (a) vorticity perturbation along the homogeneous direction $\unicode[STIX]{x1D701}_{z}^{\prime }$ computed from the LSA at $z=0$ plane for $k=4$; (b) vorticity perturbation along the homogeneous direction $\unicode[STIX]{x1D701}_{z}^{\prime }$ computed from the DNS at $z=0$ plane; (c) density plot for the magnitude of the velocity field and (d) iso-surfaces of the $z$-component of vorticity. A movie representing the standing wave is available in the supplementary material (see supplementary movie 2).

Figure 14.

For $Re=200$ and $Ra=1.1\times 10^{5}$: (a) vorticity perturbation along the homogeneous direction $\unicode[STIX]{x1D701}_{z}^{\prime }$ computed from the LSA at $z=0$ plane for $k=7$; (b) vorticity perturbation along the homogeneous direction $\unicode[STIX]{x1D701}_{z}^{\prime }$ computed from the DNS at $z=0$ plane; (c) density plot for the magnitude of the velocity field and (d) iso-surfaces of the $z$-component of vorticity.

6.1 Stuart–Landau model

We now seek to characterise the bifurcation associated with the instabilities identified in the previous section by means of a truncated Stuart–Landau equation. This model has been widely applied to find the nature of bifurcations in a number of fluid flows, for example, flow past a circular cylinder (Provansal, Mathis & Boyer Reference Provansal, Mathis and Boyer1987; Dušek, Le Gal & Fraunié Reference Dušek, Le Gal and Fraunié1994; Schumm, Berger & Monkewitz Reference Schumm, Berger and Monkewitz1994; Albarède & Provansal Reference Albarède and Provansal1995; Henderson & Barkley Reference Henderson and Barkley1996; Thompson & Le Gal Reference Thompson and Le Gal2004), staggered cylinder (Carmo et al. Reference Carmo, Sherwin, Bearman and Willden2008) and rings (Sheard et al. Reference Sheard, Thompson and Hourigan2004), and the flow confined around a $180^{\circ }$ sharp bend (Sapardi et al. Reference Sapardi, Hussam, Pothérat and Sheard2017; Pothérat & Zhang Reference Pothérat and Zhang2018). The principle traces back to the equation proposed by Landau (Reference Landau1944) to describe the transition to turbulence, and later used by Stuart (Reference Stuart1958, Reference Stuart1960) to understand the behaviour of the plane Poiseuille flow. The Stuart–Landau model describes the growth and saturation of the complex amplitude $A(t)$ of a perturbation near the onset of instability as (Landau & Lifsitz Reference Landau and Lifsitz1987)

(6.1)$$\begin{eqnarray}\frac{\text{d}A}{\text{d}t}=(\unicode[STIX]{x1D70E}+\text{i}\unicode[STIX]{x1D714})A-l(1+\text{i}c)|A|^{2}A+O(A^{5}),\end{eqnarray}$$

where $l\in \mathbb{R}$ reflects the level of nonlinear saturation and $c\in \mathbb{R}$ is the Landau constant. For $l>0$, the first two terms on the right-hand side of (6.1) provide a good description of the evolution of the perturbation, and the saturation occurs through the cubic term. In this case, the bifurcation is supercritical. For $l<0$, the cubic term accelerates the growth of the perturbation, and higher-order terms are needed to saturate the growth. This case corresponds to a subcritical transition. The equations for the time evolution of the (real) amplitude $|A(t)|$ and phase $\unicode[STIX]{x1D719}(t)$ are obtained by substituting $A(t)=|A(t)|\text{e}^{\text{i}\unicode[STIX]{x1D719}(t)}$ into (6.1) and separating the real and imaginary parts:

(6.2)$$\begin{eqnarray}\displaystyle & \displaystyle \frac{\text{d}|A|}{\text{d}t}=\unicode[STIX]{x1D70E}|A|-l|A|^{3}, & \displaystyle\end{eqnarray}$$

(6.3)$$\begin{eqnarray}\displaystyle & \displaystyle \frac{\text{d}\unicode[STIX]{x1D719}}{\text{d}t}=\unicode[STIX]{x1D714}-lc|A|^{2}. & \displaystyle\end{eqnarray}$$

Equation (6.2) is rewritten as

(6.4)$$\begin{eqnarray}\frac{\text{d}\log |A|}{\text{d}t}=\unicode[STIX]{x1D70E}-l|A|^{2}.\end{eqnarray}$$

As noted by Sheard et al. (Reference Sheard, Thompson and Hourigan2004), this form of the Stuart–Landau equation makes it convenient to determine $\unicode[STIX]{x1D70E}$ and $l$ from three-dimensional DNS (see § 6.2) if $A(t)$ is defined as the time-dependent amplitude of one component of the velocity perturbation, for example. The amplitude of the perturbation in the saturated state is readily obtained by setting $\unicode[STIX]{x2202}_{t}=0$ in (6.2):

(6.5)$$\begin{eqnarray}|A_{sat}|=\sqrt{\frac{\unicode[STIX]{x1D70E}}{l}}.\end{eqnarray}$$

Furthermore, if the flow settles down to a time-periodic state with constant amplitude $|A_{sat}|$, $\text{d}\unicode[STIX]{x1D719}/\text{d}t$ becomes the constant angular frequency of oscillation $\unicode[STIX]{x1D714}_{sat}$, and (6.3) yields

(6.6)$$\begin{eqnarray}c=\frac{\unicode[STIX]{x1D714}_{sat}-\unicode[STIX]{x1D714}}{\unicode[STIX]{x1D70E}}.\end{eqnarray}$$

Thus, the Landau constant $c$ can be determined by computing the oscillation frequency of the perturbation in the linear regime and at the saturation.

6.2 Nature of the bifurcations

We shall first determine the nature of the bifurcation by calculating constant $l$ and checking its sign. We follow Sheard et al. (Reference Sheard, Thompson and Hourigan2004) and fit equation (6.4) to the time variation of the envelope of $|A(t)|$ extracted from the signal of the $z$-component of velocity $w(t)$ obtained at a single location, in the neighbourhood of $|A|=0$. This particular choice for $|A(t)|$ and the choice of location itself are guided by the requirement of obtaining a clean enough signal. Other choices are possible (Sheard et al. Reference Sheard, Thompson and Hourigan2004), based on either local or global variables (Pothérat & Zhang Reference Pothérat and Zhang2018). The analysis has been carried out in slightly supercritical regimes ($r_{c}>0$, but small) for all values of $Re$ investigated in this paper up to $200$. Four representative examples are shown on figure 15. In all cases, a small area very close to $|A|=0$ is dominated by numerical noise. The high precision of our DNS, however, keeps this interval small compared to the area where the linear approximation (6.4) remains valid. Fitting of (6.4) in the linear range provides the values of $\unicode[STIX]{x1D70E}$ and $l$. Comparing $\unicode[STIX]{x1D70E}$ to the value returned by the LSA provides not only mutual validation for the linear stability and the DNS, but also an estimate for the precision of the fit. Both values are reported in table 4. The relative discrepancy remains below 6 % except for $Re=0$, where the discrepancy is of 11.6 %.

Figure 15.

Stuart–Landau analysis: variation of $(\text{d}\text{log}|A|/\text{d}t)$ versus $|A|^{2}$ and extrapolation to $|A|=0$ from which coefficients $\unicode[STIX]{x1D70E}$ and $l$ are obtained for (a) $Re=0$, $Ra=7\times 10^{3}$; (b) $Re=50$, $Ra=7\times 10^{3}$; (c) $Re=100$, $Ra=4\times 10^{4}$; and (d) $Re=200$, $Ra=1.1\times 10^{5}$. Here, $|A(t)|$ is obtained from time-series of $w(t)$.

Table 4.

Comparison of the growth rate and frequency computed from the LSA and DNS for $r_{c}>0$. The relative error in the computation of frequency from DNS and LSA is represented by $\unicode[STIX]{x1D716}_{\unicode[STIX]{x1D714}}$.

At the onset of instability, $l$ remains positive for $0\geqslant Re\geqslant 75$, which corresponds to the range of values of $Re$ where the instability sets in through branch II modes. In other words, branch II modes become unstable through a supercritical Hopf bifurcation. For $100\leqslant Re<150$, by contrast, $l<0$ indicates that the modes of branch I destabilise through a subcritical Hopf bifurcation. This may appear as surprising considering the very good agreement between the critical Rayleigh number for the onset of instability found by DNS and LSA. Nevertheless, the small value of $l$ suggests that the system may only be mildly subcritical. It is also possible that the addition of white noise of moderate amplitude may not suffice to drive the growth of subcritical perturbations. Indeed, we verified that further increasing the standard deviation of the added white noise to 0.01 and 0.1 did not alter the results. Whether transition could occur for $Ra<Ra_{c}$ could be answered by analysing whether non-modal perturbations could grow (Schmid & Henningson Reference Schmid and Henningson2001), and whether optimal perturbation of sufficient amplitudes could trigger a subcritical transition to another state, as it does for turbulence in pipes (Pringle, Willis & Kerswell Reference Pringle, Willis and Kerswell2012). For $Re\geqslant 150$, the instability occurs through non-oscillatory modes from branch III; $l$ becomes positive again, indicating that this transition is of supercritical type (supercritical pitchfork). In contrast with bifurcation through branch I, the supercritical nature of the instability of modes from branches II and III is consistent with the excellent agreement between LSA and DNS on the value of $Ra$ for the onset of instability and further establishes the relevance of LSA to determine the stability of the flow in these cases.

Figure 16.

(a) Time history of $w(t)$ velocity component measured at $(x,y,z)=(-0.6,0.6,0.5)$ for $Re=0$ and $Ra=7\times 10^{3}$. (b) Frequency spectra obtained from the time series of $w(t)$, respectively, near the onset and near saturation.

6.3 Saturated states

Finally, we shall characterise the saturated states. The analysis was carried out for each investigated value of $Re$ up to $Re=200$, of which we present three typical cases. As discussed in § 5.3, the three-dimensional DNS were performed for two values of $Ra$ in each case, one slightly subcritical and one slightly supercritical. As an example, figure 16(a) shows the time history of $w$ at a single point of the domain for the weakly supercritical case of $Re=0$ and $Ra=7\times 10^{3}$. The normalised frequency spectrum of the time-series

(6.7)$$\begin{eqnarray}E_{w}(\unicode[STIX]{x1D714})={\textstyle \frac{1}{2}}|{\hat{w}}(\unicode[STIX]{x1D714})|^{2},\end{eqnarray}$$

where ${\hat{w}}(\unicode[STIX]{x1D714})$ is the Fourier transform of $w(t)$, is then calculated over two intervals: one near the onset ($40\leqslant t\leqslant 60$) and one in the saturated regime ($100\leqslant t\leqslant 120$). Both are represented in figure 16(b). The initial and saturated frequencies are nearly identical (with relative error to the numerical precision) and differ by 1.7 % from the frequency returned by the LSA. Given that the discrepancy between the frequencies near the onset and in the saturated state differ by much less than the error between the frequency near the onset predicted by LSA and DNS, we consider them to be identical. In this case, from equation (6.6), the Landau constant $c$ is zero, to the precision of our simulations. Following this approach, non-zero values of $c$ were found for other values of $Re$ when $\unicode[STIX]{x1D714}-\unicode[STIX]{x1D714}_{sat}$ significantly exceeded the discrepancy between LSA and DNS. One such example is shown in figure 17, for $Re=100$, where $c=-7.03\pm 0.01$ and the discrepancy between LSA and DNS frequencies at onset is 0.2 %.

All calculated values of $c$ are reported in table 5. Here again, the three branches identified in § 5 exhibit different behaviours: when the instability arises out of modes in branch II, the Landau constant is zero. A shift in frequency does appear as soon as the instability is due to modes belonging to branch I, leading to a negative Landau constant. Finally, as branch III becomes dominant, DNS confirm that the steady saturated state of the mode predicted by the linear stability is non-oscillatory. In this case, the saturated amplitude predicted by (6.5) matches closely that observed in the DNS, as shown on figure 18.

Figure 17.

(a) Time history of $w(t)$ velocity component measured at $(x,y,z)=(-0.6,0.6,0.5)$ for $Re=100$ and $Ra=4\times 10^{4}$. (b) Frequency spectra obtained from the time series of $w(t)$, respectively, near the onset and near saturation.

6.4 Heat flux in the saturated states

To finish, we compare the Nusselt number $Nu$ in base two-dimensional state and the bifurcated three-dimensional state at the onset of instability for all Reynolds numbers in figure 7(b). The maximum difference between them is 0.2 %. Thus, there is hardly any change in the heat transfer. This can be understood as the boundary conditions impose that the horizontal heat flux must be conserved between the two boundaries with periodic boundary conditions. This precludes any horizontal redirection of the horizontal heat flux. Furthermore, the time and spatially periodic character of the instability implies that a drastic change would have had to take place between the streamlines in the $(x\text{-}y)$ plane of the base flow and those of the bifurcated states for a significant change in $Nu$ to be observed. Nevertheless, figure 7(b) expresses that heat transfer at the onset of the instability decreases with $Re$ because of the suppression of the convection by the through-flow.

Figure 18.

Time history of $w(t)$ velocity component measured at $(x,y,z)=(-0.5,0.6,0.5)$ for $Re=200$ and $Ra=1.1\times 10^{5}$.

Table 5.

Summary of Stuart–Landau analysis of three-dimensional DNS for $Re=0$ to $Re=200$ at $Ra>Ra_{c}$.