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Steady Rayleigh–Bénard convection between no-slip boundaries

Baole Wen\aff1\corresp [email protected]    David Goluskin\aff2\corresp [email protected]       Charles R. Doering\aff1,3,4 \aff1Department of Mathematics, University of Michigan, Ann Arbor, MI 48109-1043, USA \aff2Department of Mathematics & Statistics, University of Victoria, Victoria, BC, V8P 5C2, Canada \aff3Department of Physics, University of Michigan, Ann Arbor, MI 48109-1040, USA \aff4Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109-1042, USA
Abstract

The central open question about Rayleigh–Bénard convection—buoyancy-driven flow in a fluid layer heated from below and cooled from above—is how vertical heat flux depends on the imposed temperature gradient in the strongly nonlinear regime where the flows are typically turbulent. The quantitative challenge is to determine how the Nusselt number NuNu depends on the Rayleigh number RaRa in the RaRa\to\infty limit for fluids of fixed finite Prandtl number \Pran\Pran in fixed spatial domains. Laboratory experiments, numerical simulations, and analysis of Rayleigh’s mathematical model have yet to rule out either of the proposed ‘classical’ NuRa1/3Nu\sim Ra^{1/3} or ‘ultimate’ NuRa1/2Nu\sim Ra^{1/2} asymptotic scaling theories. Among the many solutions of the equations of motion at high RaRa are steady convection rolls that are dynamically unstable but share features of the turbulent attractor. We have computed these steady solutions for RaRa up to 101410^{14} with \Pran=1\Pran=1 and various horizontal periods. By choosing the horizontal period of these rolls at each RaRa to maximize NuNu, we find that steady convection rolls achieve classical asymptotic scaling. Moreover, they transport more heat than turbulent convection in experiments or simulations at comparable parameters. If heat transport in turbulent convection continues to be dominated by heat transport in steady rolls as RaRa\to\infty, it cannot achieve the ultimate scaling.

keywords:
convection, coherent structure, heat transport

1 Introduction

Rayleigh–Bénard convection (RBC) is the buoyancy-driven flow in a fluid layer heated from below and cooled from above in the presence of gravity. The emergent convective flow enhances heat flux from the warm bottom boundary to the cool top boundary beyond the conductive flux from diffusion alone. This dimensionless enhancement factor—the ratio of bulk-averaged vertical heat flux from both conduction and convection to the flux from conduction alone—defines the Nusselt number NuNu. In Rayleigh’s mathematical model (Rayleigh, 1916) NuNu depends on several dimensionless quantities characterizing the problem at hand: (i) what we now call the Rayleigh number RaRa, which is proportional to the imposed temperature drop across the layer, (ii) the fluid’s Prandtl number PrPr, which is the ratio of kinematic viscosity to thermal diffusivity, and (iii) details of the spatial domain, often captured by an aspect ratio Γ\Gamma that is a ratio of a horizontal length scale to the vertical layer height.

Convection is coherent at RaRa values not too far above the critical value RacRa_{c} beyond which the conductive no-flow state is linearly unstable. By coherent we mean flows with few scales present; spatial scales might include a horizontal period and the vertical thickness of boundary layers, and temporally the flow may be steady or time-periodic. Meanwhile, convection is turbulent at the large RaRa values pertinent to many engineering and scientific applications. Turbulent flows are complex and contain a range of spatial and temporal scales and, in the present context, have thermal and viscous boundary layers at the top and bottom boundaries from which thermal plumes emerge and mix the bulk. In a given domain it is expected that a scaling of NuNu with respect to both PrPr and RaRa will emerge in the RaRa\to\infty limit (Kadanoff, 2001).

After nearly a century of increasingly sophisticated mathematical analysis, increasingly resolved direct numerical simulations (DNS), and increasingly refined laboratory experiments, two quantitatively distinct conjectures remain in contention for the heat transport scaling law at large RaRa (Chillà & Schumacher, 2012; Doering, 2020). The two conjectures follow from heuristic physical arguments that both seem plausible but give incompatible predictions: the ‘classical’ scaling NuPr0Ra1/3Nu\sim Pr^{0}Ra^{1/3} and the ‘ultimate’ scaling NuPr1/2Ra1/2Nu\sim Pr^{1/2}Ra^{1/2}, with the latter sometimes including logarithmic-in-RaRa modifications.

For RBC between flat, no-slip, isothermal boundaries, rigorous analysis of the governing equations has yielded upper bounds of the form Nu𝒪(Ra1/2)Nu\leq\mathcal{O}(Ra^{1/2}) uniformly in PrPr and Γ\Gamma (Howard, 1963; Doering & Constantin, 1996), but this still allows for either classical or ultimate scaling. Upper bounds that rule out ultimate scaling by being asymptotically smaller than 𝒪(Ra1/2){\cal O}(Ra^{1/2}) have been derived in the limit of infinite \Pran\Pran (Doering et al., 2006; Otto & Seis, 2011; Whitehead & Doering, 2012) and for two-dimensional convection between stress-free boundaries (Whitehead & Doering, 2011). For the no-slip boundaries relevant to experiments, however, it remains an open question whether an upper bound asymptotically smaller than Ra1/2Ra^{1/2} is possible.

In view of the problem’s stubbornness, a new strategy is called for to determine—or at least to bound—NuNu as a function of Ra,PrRa,Pr, and Γ\Gamma. Toward that end we have undertaken an indirect approach consisting of two parts. The first part is to study coherent flows for which one can reasonably hope to determine asymptotic heat transport, and the second part is to investigate how transport by those coherent flows compares with transport by turbulent convection. The simplest coherent flows are steady—i.e., time-independent—solutions of the equations of motion. Many such states exist, although they are generally unstable at large RaRa. We focus on what might be called the simplest type of steady states: two-dimensional convection rolls like the counter-rotating pairs shown in figure 1(aa, bb). In horizontally periodic or infinite domains in two or three dimensions, such rolls bifurcate supercritically from the conductive state in the linear instability identified by Rayleigh (1916). A roll pair of any width-to-height aspect ratio Γ\Gamma admitted by the domain exists for sufficiently large RaRa.

For steady rolls, the dependence of NuNu on the parameters (Γ,Pr,Ra)(\Gamma,Pr,Ra) at asymptotically large RaRa is accessible to computation. As for whether heat transport by steady rolls can be connected to transport by turbulence, there are several reasons for optimism. Relationships between turbulent attractors and the unstable coherent states embedded therein have been established in models of wall-bounded shear flows (Graham & Floryan, 2021), where particular steady states, traveling waves, and time-periodic states have been found that closely reflect turbulent flows in terms of integral quantities as well as particular flow structures. Analogous study of RBC began only recently but indeed suggests that certain steady states capture qualitative aspects of turbulent convection (Waleffe et al., 2015; Sondak et al., 2015; Kooloth et al., 2021; Motoki et al., 2021). Our findings add to this evidence. The desire to understand and perhaps strengthen the mathematical bound Nu𝒪(Ra1/2)Nu\leq\mathcal{O}(Ra^{1/2}) is further motivation for studying unstable states since bounds apply to all solutions of the governing equations regardless of stability. It is an open question whether any solutions can achieve ultimate scaling, let alone turbulent solutions.

Refer to caption
Figure 1: Steady convection rolls at Ra=109Ra=10^{9} and Pr=1Pr=1 with (aa) Γ=2\Gamma=2 and (bb) the value Γ0.235\Gamma^{*}\approx 0.235 that maximizes NuNu at these values of RaRa and PrPr. Color indicates temperature, and streamlines are shown for counterclockwise (solid) and clockwise (dash-dotted) motions. (cc) Dependence of NuNu on the horizontal wavenumber k=2π/Γk=2\pi/\Gamma found by computing steady rolls of various aspect ratios ({\scriptstyle\color[rgb]{0,1,0}\triangle}). Highlighted points are the two Γ\Gamma shown in panels (aa) and (bb) along with the value Γloc0.0614\Gamma^{*}_{loc}\approx 0.0614 that locally maximizes Nu(Γ)Nu(\Gamma). Cubic spline interpolation (   ) is used to find Γ\Gamma^{*} and Γloc\Gamma^{*}_{loc} precisely.

Here we report numerical computations of steady convection rolls for a Pr=1Pr=1 fluid contained between no-slip isothermal top and bottom boundaries. We reach sufficiently large RaRa values to convincingly reveal several asymptotic scalings of NuNu, depending on the horizontal periods of the rolls. These are the first clearly asymptotic scalings found for any type of flow—steady, turbulent, or otherwise—for RBC in the no-slip case. Notably, the largest heat transport among steady rolls of all horizontal periods displays the classical NuRa1/3Nu\sim Ra^{1/3} scaling. We further observe that NuNu for these steady rolls is larger than turbulent NuNu from all laboratory experiments and two- or three-dimensional (2D or 3D) simulations at comparable parameters. This observation supports the conjecture that steady states maximize NuNu among all stable or unstable flows, as was recently verified for a truncated model of RBC (Olson et al., 2021) using methods that are not yet applicable to the full governing equations. If steady-roll transport continues to dominate turbulent transport as RaRa\to\infty, then our finding of classical scaling for steady rolls would rule out ultimate scaling of turbulent convection.

The asymptotic scaling of steady rolls is already known in the case of stress-free velocity conditions at the top and bottom boundaries, which were considered for mathematical convenience in Rayleigh’s original work. In that case NuRa1/3Nu\sim Ra^{1/3} as RaRa\to\infty at fixed PrPr and Γ\Gamma, and the aspect ratio of the roll pair maximizing NuNu at each RaRa and PrPr approaches Γ1.9\Gamma\approx 1.9 (Chini & Cox, 2009; Wen et al., 2020). Recent computations of steady rolls in the no-slip case for pre-asymptotic RaRa values up to 10910^{9} revealed significant differences from the stress-free problem (Waleffe et al., 2015; Sondak et al., 2015). The dependence Nu(Γ)Nu(\Gamma) for no-slip rolls at fixed RaRa and PrPr can have multiple local maxima, as shown in figure 1(cc), and the aspect ratio Γ\Gamma^{*} that globally maximizes Nu(Γ)Nu(\Gamma) approaches zero rather than a constant as RaRa\to\infty. Steady rolls of NuNu-maximizing aspect ratios Γ\Gamma^{*} were reported in Sondak et al. (2015) for Ra[5×106,3×108]Ra\in[5\times 10^{6},3\times 10^{8}] at Pr=1Pr=1, yielding fits of ΓRa0.217\Gamma^{*}\sim Ra^{-0.217} and Nu(Γ)Ra0.31Nu(\Gamma^{*})\sim Ra^{0.31}. This heat transport scaling is faster than with Γ\Gamma fixed: computations in Waleffe et al. (2015) for Ra[5×105,5×106]Ra\in[5\times 10^{5},5\times 10^{6}] at Pr=7Pr=7 with Γ=2\Gamma=2 fixed yield the fit NuRa0.28Nu\sim Ra^{0.28}. These best-fit scaling exponents are, however, not asymptotic.

Steady convection rolls are dynamically unstable at large RaRa and cannot be found by standard time integration, so we employed a purpose-written code that iteratively solves the time-independent equations. We computed rolls with Γ=2\Gamma=2 fixed for Ra2×1010Ra\lesssim 2\times 10^{10} and with the parameter-dependent aspect ratios Γ\Gamma^{*} and Γloc\Gamma^{*}_{loc} (cf. figure 1) that globally and locally maximize Nu(Γ)Nu(\Gamma), respectively, for Ra1014Ra\leq 10^{14}. These RaRa values are evidently large enough to reach asymptotia: the results reported below strongly suggest that fixed-Γ\Gamma rolls asymptotically transport heat like NuRa1/4Nu\sim Ra^{1/4} while the ever-narrowing rolls of aspect ratio Γ\Gamma^{*} achieve the classical NuRa1/3Nu\sim Ra^{1/3} scaling.

2 Computation of steady-convection-roll solutions

Following Rayleigh (1916), we model RBC using the Boussinesq approximation to the Navier–Stokes equations with constant kinematic viscosity ν\nu, thermal diffusivity κ\kappa, and coefficient of thermal expansion α\alpha. We nondimensionalize lengths by the layer height hh, temperatures by the fixed difference Δ\Delta between the boundaries, velocities by the free-fall scale Uf=gαhΔU_{f}=\sqrt{g\alpha h\Delta}, and time by the free-fall time h/Ufh/U_{f}. Calling the horizontal coordinate xx and the vertical coordinate zz, the gravitational acceleration of magnitude gg is in the 𝐳^-\mathbf{\hat{z}} direction. The evolution equations governing the dimensionless velocity vector 𝐮=(u,w)\mathbf{u}=(u,w), temperature TT, and pressure pp are then

tu+𝐮𝐮\displaystyle\partial_{t}u+\mathbf{u}\cdot\nabla\mathbf{u} =p+(Pr/Ra)1/22𝐮+T𝐳^,\displaystyle=-\nabla p+({Pr}/{Ra})^{1/2}\;\nabla^{2}\mathbf{u}+T\mathbf{\hat{z}}, (1a)
𝐮\displaystyle\nabla\cdot\mathbf{u} =0,\displaystyle=0, (1b)
tT+𝐮T\displaystyle\partial_{t}T+\mathbf{u}\cdot\nabla T =(PrRa)1/22T,\displaystyle=(PrRa)^{-1/2}\;\nabla^{2}T, (1c)

where

Ra=gαh3ΔκνandPr=νκ.\displaystyle Ra=\frac{g\alpha h^{3}\Delta}{\kappa\nu}\quad\mbox{and}\quad Pr=\frac{\nu}{\kappa}. (2aa,bb)

The dimensionless spatial domain is (x,z)[0,Γ]×[0,1](x,z)\in[0,\Gamma]\times[0,1], and all variables are horizontally periodic. The top and bottom boundaries are isothermal with T=0T=0 and T=1T=1, respectively, while no-slip conditions require 𝐮\mathbf{u} to vanish on both boundaries. The conductive state (𝐮,T)=(𝟎,1z)(\mathbf{u},T)=(\mathbf{0},1-z) becomes unstable when RaRa increases past the critical value Rac1708Ra_{c}\approx 1708 (Jeffreys, 1928), at which a roll pair with horizontal period Γ2.016\Gamma\approx 2.016 bifurcates supercritically. As RaRa\to\infty the horizontal period of the narrowest marginally stable roll pair decreases as 𝒪(Ra1/4)\mathcal{O}(Ra^{-1/4}), while the horizontal period of the fastest-growing linearly unstable mode decreases more slowly as 𝒪(Ra1/8)\mathcal{O}(Ra^{-1/8}).

In terms of the dimensionless solutions to 1, the Nusselt number is

Nu=1+(PrRa)1/2wT,Nu=1+(PrRa)^{1/2}\,\langle wT\rangle, (3)

where \langle\cdot\rangle denotes an average over the spatial domain and infinite time. For steady states no time average is needed.

To compute rolls at RaRa values large enough to reach the asymptotic regime we developed a numerical scheme by adapting the approach of Wen et al. (2020) and Wen & Chini (2018) to the case of no-slip boundary conditions. In these numerics the temperature is represented using the deviation θ\theta from the conductive profile, meaning T=1z+θT=1-z+\theta, and the velocity is represented using a stream function ψ\psi, where 𝐮=zψ𝐱^xψ𝐳^\mathbf{u}=\partial_{z}\psi\mathbf{\hat{x}}-\partial_{x}\psi\mathbf{\hat{z}} so that the (negative) scalar vorticity is ω=xwzu=2ψ\omega=\partial_{x}w-\partial_{z}u=-\nabla^{2}\psi. In terms of these variables, steady (t=0\partial_{t}=0) solutions of 1 satisfy

zψxωxψzω\displaystyle\partial_{z}\psi\partial_{x}\omega-\partial_{x}\psi\partial_{z}\omega =(Pr/Ra)1/22ω+xθ,\displaystyle=({Pr}/{Ra})^{1/2}\;\nabla^{2}\omega+\partial_{x}\theta, (4a)
2ψ\displaystyle\nabla^{2}\psi =ω,\displaystyle=-\omega, (4b)
zψxθxψzθ\displaystyle\partial_{z}\psi\partial_{x}\theta-\partial_{x}\psi\partial_{z}\theta =xψ+(PrRa)1/22θ\displaystyle=-\partial_{x}\psi+(PrRa)^{-1/2}\;\nabla^{2}\theta (4c)

with fixed-temperature and no-slip boundary conditions,

θ|z=0,1=0,ψ|z=0,1=0,andzψ|z=0,1=0.\displaystyle\theta|_{z=0,1}=0,\qquad\psi|_{z=0,1}=0,\qquad\text{and}\qquad\partial_{z}\psi|_{z=0,1}=0. (5aa,bb,cc)

To compute solutions of the time-independent equations 4 and 5 by an iterative method, we do not need to impose all boundary conditions precisely on each iteration—the conditions need to hold only for the converged solution. Thus we do not impose (5cc) exactly, instead using approximate boundary conditions on ω\omega for equation 4a. These are derived by Taylor expanding ψ\psi about the top and bottom boundaries to find

ψ|z=1δ\displaystyle\psi|_{z=1-\delta} =ψ|z=1zψ|z=1δ+z2ψ|z=1δ22z3ψ|z=1δ36+𝒪(δ4),\displaystyle=\psi|_{z=1}-{\partial_{z}\psi}\big{|}_{z=1}\delta+{\partial^{2}_{z}\psi}\big{|}_{z=1}\frac{\delta^{2}}{2}-{\partial^{3}_{z}\psi}\big{|}_{z=1}\frac{\delta^{3}}{6}+\mathcal{O}(\delta^{4}), (6a)
ψ|z=δ\displaystyle\psi|_{z=\delta} =ψ|z=0+zψ|z=0δ+z2ψ|z=0δ22+z3ψ|z=0δ36+𝒪(δ4),\displaystyle=\psi|_{z=0}+{\partial_{z}\psi}\big{|}_{z=0}\delta+{\partial^{2}_{z}\psi}\big{|}_{z=0}\frac{\delta^{2}}{2}+{\partial^{3}_{z}\psi}\big{|}_{z=0}\frac{\delta^{3}}{6}+\mathcal{O}(\delta^{4}), (6b)

where δ>0\delta>0 is small. Combining equations (4b) with (5bb,cc) and neglecting 𝒪(δ4)\mathcal{O}(\delta^{4}) terms in 6 give the approximate boundary conditions,

zω|z=13δω|z=16δ3ψ|z=1δ=0,zω|z=03δω|z=06δ3ψ|z=δ=0.\displaystyle\partial_{z}\omega|_{z=1}-\frac{3}{\delta}\ \omega|_{z=1}-\frac{6}{\delta^{3}}\ \psi|_{z=1-\delta}=0,\quad-\partial_{z}\omega|_{z=0}-\frac{3}{\delta}\ \omega|_{z=0}-\frac{6}{\delta^{3}}\ \psi|_{z=\delta}=0. (7aa,bb)

In computations we set δ\delta to be the distance between the boundary and the first interior mesh point.

The time-independent equations (4) are solved numerically subject to boundary conditions (5aa,bb) and (7) using a Newton–GMRES (generalized minimal residual) iterative scheme. The spatial discretization is spectral, using a Fourier series in xx and a Chebyshev collocation method in zz (Trefethen, 2000). All of our computations had at least 20 collocation points in the viscous and thermal boundary layers. At RaRa just above the linear instability, iterations starting from the unstable eigenmode converge to the steady rolls we seek. At larger RaRa, already-computed steady rolls from nearby RaRa and Γ\Gamma values were used as the initial iterate. Every 2 to 4 Newton iterations, we change the boundary values of the iterate to match the zψ=0\partial_{z}\psi=0 boundary condition exactly. Prior to convergence this makes the boundary values slightly inconsistent with the governing equations, but the converged solutions satisfy the equations and the no-slip boundary conditions to high precision. Newton iterations were carried out until the Lebesgue L2L^{2}-norm of the residual of the governing steady equations had a relative magnitude less than 101010^{-10}. To accurately locate Γ\Gamma^{*} and Γloc\Gamma^{*}_{loc}, rolls were computed at several nearby Γ\Gamma, and then Nu(Γ)Nu(\Gamma) was interpolated with cubic splines like those in figure 1(cc). Details of computational results, including resolutions used, are included in the supplementary material.

3 Results

We computed steady Pr=1Pr=1 rolls for aspect ratios Γ\Gamma encompassing the three distinguished values indicated by figure 1(cc): the fixed value Γ=2\Gamma=2 and the RaRa-dependent values Γ\Gamma^{*} and Γloc\Gamma^{*}_{loc} that globally and locally maximize NuNu over Γ\Gamma. As previously observed by Sondak et al. (2015), the Nu(Γ)Nu(\Gamma) curve has a single maximum when RaRa is small and develops a second local maximum at smaller Γ\Gamma when RaRa increases past roughly 2×1052\times 10^{5}. The value of NuNu at this second local maximum remains less than the value at the first, so the picture remains as in figure 1(cc) with Γ\Gamma^{*} on the left and Γloc\Gamma^{*}_{loc} on the right, in contrast to the Pr=10Pr=10 and 100 cases (Sondak et al., 2015). For most RaRa values we did not compute rolls over a full sweep through Γ\Gamma as in figure 1(cc), instead searching over Γ\Gamma only as needed to locate Γ\Gamma^{*} and Γloc\Gamma^{*}_{loc}. The rest of this section reports Nusselt number and Reynolds number scalings for the computed steady rolls, and the supplementary material provides tabulated data.

3.1 Asymptotic heat transport

Figure 2 shows the dependence of NuNu on RaRa for steady rolls with aspect ratios Γ=2\Gamma=2, Γ\Gamma^{*}, and Γloc\Gamma^{*}_{loc}. In the top panel Nu1Nu-1 is compensated by Ra1/3Ra^{1/3}, so the horizontal line approached by rolls of the NuNu-maximizing aspect ratios Γ\Gamma^{*} corresponds to classical 1/31/3 scaling. The downward slopes of the data for aspect ratios 2 and Γloc\Gamma^{*}_{loc} correspond to scaling exponents smaller than 1/3. Values of NuNu at Γ\Gamma^{*} computed previously for Ra109Ra\leq 10^{9} (Sondak et al., 2015; Waleffe, 2020) are shown in figure 2 also, and they agree with our computations very precisely—e.g., the Ra=109Ra=10^{9} data point agrees with our value of NuNu to within 0.0008%.

Refer to caption
Figure 2: Top: Compensated plot of Nu1Nu-1 vs. RaRa for steady rolls with Pr=1Pr=1 and aspect ratios of Γ=2\Gamma=2, Γ\Gamma^{*}, and Γloc\Gamma^{*}_{loc}, where the RaRa-dependent values Γ\Gamma^{*} and Γloc\Gamma^{*}_{loc} are where Nu(Γ)Nu(\Gamma) has global and local maxima, respectively (cf. figure 1). Values of NuNu at Γ\Gamma^{*} from Sondak et al. (2015) and Waleffe (2020) are also shown (\star). Scaling fits (   ) over the last decade of each data set yield exponents of 0.330.33, 0.290.29, and 0.250.25. Bottom: Finite difference approximations of the local scaling exponent βn=d(logNu)d(logRa)\beta_{n}=\frac{{\rm d}(\log Nu)}{{\rm d}(\log Ra)}. Exponents of 1/31/3, 2/72/7, and 1/41/4 are shown to guide the eye (     ).

The bottom panel of figure 2 shows the RaRa-dependent local scaling exponent βn=d(logNu)d(logRa)\beta_{n}=\frac{\mathrm{d}(\log Nu)}{\mathrm{d}(\log Ra)} of the NuNuRaRa relation for Γ=2\Gamma=2, Γ\Gamma^{*}, and Γloc\Gamma^{*}_{loc}. This quantity educes small variations not visible in the top panel. In particular, for rolls of aspect ratios Γ\Gamma^{*}, the exponent βn\beta_{n} exhibits a small but rapid change just below Ra=109Ra=10^{9}, beyond which it smoothly approaches the classical 1/31/3 exponent that appears to be the RaRa\to\infty asymptotic behavior. This rapid change seems to coincide with the velocity becoming vertically uniform outside the boundary layers, as reflected in the streamlines of figure 1(bb); further details of the rolls’ structure will be reported elsewhere. Rolls with Γ=2\Gamma=2 fixed undergo a similarly rapid change around Ra2×107Ra\approx 2\times 10^{7} and then approach NuRa1/4Nu\sim Ra^{1/4} scaling that appears to be asymptotic. Rolls of aspect ratio Γloc\Gamma^{*}_{loc} show intermediate NuNu scaling whose best-fit exponent over the last decade of data is 0.29.

Refer to caption
Figure 3: Top: Compensated plot of the fundamental horizontal wavenumber k=2π/Γk=2\pi/\Gamma vs. RaRa for the aspect ratios Γ\Gamma^{*} and Γloc\Gamma^{*}_{loc} that maximize Nu(Γ)Nu(\Gamma) globally and locally, respectively, at Pr=1Pr=1. Scaling fits (   ) to 2π/Γ2\pi/\Gamma^{*} over Ra[1010,1014]Ra\in[10^{10},10^{14}] and 2π/Γloc2\pi/\Gamma^{*}_{loc} over Ra[1013,1014]Ra\in[10^{13},10^{14}] yield exponents of 0.20 and 0.25, respectively. Bottom: Finite difference approximations of the local exponent βk=d(logk)d(logRa)\beta_{k}=\frac{{\rm d}(\log k)}{{\rm d}(\log Ra)}. The values 1/41/4 and 1/51/5 (     ) agree with the scaling fit exponents to two digits.

The top panel of figure 3 shows the RaRa-dependence of the wavenumber k=2π/Γk=2\pi/\Gamma for Γ\Gamma^{*} and Γloc\Gamma^{*}_{loc}, compensated by Ra1/5Ra^{1/5}. The compensated wavenumbers for Γ\Gamma^{*} approach a horizontal line, suggesting that the NuNu-maximizing rolls narrow according to the power law ΓRa1/5\Gamma^{*}\sim Ra^{-1/5}. This narrowing of Γ\Gamma^{*} is slow relative to the case of RBC in a porous medium, where ΓRa1/2\Gamma^{*}\sim Ra^{-1/2} (Wen et al., 2015).

The bottom panel of figure 3 shows the RaRa-dependence of the local scaling exponent βk=d(logk)d(logRa)\beta_{k}=\frac{{\rm d}(\log k)}{{\rm d}(\log Ra)}. For k=2π/Γk=2\pi/\Gamma^{*} the local scaling exponent remains close to 1/51/5 after the transition around Ra=109Ra=10^{9}. For k=2π/Γlock=2\pi/\Gamma^{*}_{loc} the exponent seems to approach 1/41/4, suggesting that Γloc\Gamma^{*}_{loc} has the same Ra1/4Ra^{-1/4} scaling as the narrowest marginally stable mode. Variations in βk\beta_{k} beyond Ra=1012Ra=10^{12} for Γ\Gamma^{*} are evident, but these might be due to numerical imprecision: NuNu depends very weakly on Γ\Gamma around the maximum of Nu(Γ)Nu(\Gamma), as seen in figure 1(cc), so the value of Γ\Gamma^{*} cannot be determined nearly as precisely as the value of Nu(Γ)Nu(\Gamma^{*}).

3.2 Asymptotic kinetic energy

Refer to caption
Figure 4: Top: Compensated plot of ReRe vs. RaRa for steady rolls with Pr=1Pr=1 and aspect ratios Γ=2\Gamma=2, Γ\Gamma^{*}, and Γloc\Gamma^{*}_{loc}. Scaling fits yield ReRa0.50Re\sim Ra^{0.50} for Γ=2\Gamma=2 and ReRa0.40Re\sim Ra^{0.40} for Γloc\Gamma^{*}_{loc} over the last decade of each data set, and ReRa0.47Re\sim Ra^{0.47} for Γ\Gamma^{*} over Ra[1010,1014]Ra\in[10^{10},10^{14}] (   ). Bottom: Finite difference approximations to the local exponent βr=d(logRe)d(logRa)\beta_{r}=\frac{{\rm d}(\log Re)}{{\rm d}(\log Ra)}. Exponents of 1/21/2 and 2/52/5 are shown to guide the eye (     ).

Another emergent quantity central to RBC is the bulk Reynolds number based on root-mean-squared velocity, which in terms of dimensionless solutions to 1 is

Re=(RaPr)1/2|𝐮|21/2.Re=\left(\frac{Ra}{Pr}\right)^{1/2}\langle|{\bf u}|^{2}\rangle^{1/2}. (8)

Figure 4 depicts the dependence of ReRe on RaRa for the steady rolls of aspect ratios Γ=2\Gamma=2, Γ\Gamma^{*}, and Γloc\Gamma^{*}_{loc}. The top panel shows ReRe compensated by Ra1/2Ra^{1/2} while the bottom panel shows the local scaling exponent βr=d(logRe)d(logRa)\beta_{r}=\frac{{\rm d}(\log Re)}{{\rm d}(\log Ra)}. Rolls with the fixed aspect ratio Γ=2\Gamma=2 approach the asymptotic scaling ReRa1/2Re\sim Ra^{1/2} that corresponds to the root-mean-squared velocity being proportional to the free-fall velocity UfU_{f}. For rolls with NuNu-maximizing aspect ratios Γ\Gamma^{*}, the scaling fit over Ra[1010,1014]Ra\in[10^{10},10^{14}] is ReRa0.47Re\sim Ra^{0.47}, which is quite close to the ReRa0.46Re\sim Ra^{0.46} scaling observed in recent 3D direct numerical simulations up to Ra=1015Ra=10^{15} at Pr=1Pr=1 in a slender cylinder with a height 10 times its diameter (Iyer et al., 2020). For the Γloc\Gamma^{*}_{loc} rolls the scaling exponent of ReRe is indistinguishable from 2/52/5. The measured exponents (0.50, 0.47, 0.40) are unchanged if ReRe is defined using the pointwise maximum velocity rather than using the root-mean-squared velocity as in 8. All three aspect ratios result in smaller speeds than steady rolls between stress-free boundaries, where ReRa2/3Re\sim Ra^{2/3} for any fixed PrPr and Γ\Gamma (Wen et al., 2020).

4 Comparison with turbulent convection

To compare heat transport by steady rolls with that by turbulent thermal convection, we compiled Nusselt number data from high-RaRa DNS with Pr=1Pr=1 or 0.7 and laboratory experiments where the estimated PrPr is between 0.7 and 1.3. Figure 5 shows these NuNu values compensated by Ra1/3Ra^{1/3}, along with NuNu values of steady convection rolls at the NuNu-maximizing aspect ratios Γ\Gamma^{*}. Strikingly, heat transport by the NuNu-maximizing 2D steady rolls is larger than transport by turbulent convection in all cases.

Refer to caption
Figure 5: NuNu compensated by Ra1/3Ra^{1/3} for steady rolls of NuNu-maximizing aspect ratios Γ\Gamma^{*} at \Pran=1\Pran=1, along with NuNu from turbulent 2D and 3D DNS and experiments with estimated \Pran[0.7,1.3]\Pran\in[0.7,1.3]. For horizontally periodic domains, 2D DNS with (Γ,\Pran)=(2,1)(\Gamma,\Pran)=(2,1) were done by Johnston & Doering (2009) and Zhu et al. (2018), and 3D DNS with Γ8\Gamma\geq 8 and \Pran=1\Pran=1 were done by Vieweg & Schumacher (2020) and Krug et al. (2020). For DNS in cylinders of diameter-to-height ratio Γc\Gamma_{c}, Iyer et al. (2020) used (Γc,\Pran)=(0.1,1)(\Gamma_{c},\Pran)=(0.1,1) and Scheel & Schumacher (2017) used (Γc,\Pran)=(1,0.7)(\Gamma_{c},\Pran)=(1,0.7). For laboratory experiments in cylinders, where the plotted data is truncated according to \Pran[0.7,1.3]\Pran\in[0.7,1.3], the domains and estimated \Pran\Pran ranges are Γc=0.5\Gamma_{c}=0.5 and \Pran[0.7,1.3]\Pran\in[0.7,1.3] for Chavanne et al. (2001), Γc=4\Gamma_{c}=4 and \Pran[0.7,1.27]\Pran\in[0.7,1.27] for Niemela & Sreenivasan (2006), Γc=0.5\Gamma_{c}=0.5 and \Pran[0.79,0.86]\Pran\in[0.79,0.86] for He et al. (2012), and Γc=1\Gamma_{c}=1 and \Pran[0.95,1.17]\Pran\in[0.95,1.17] for Urban et al. (2014). Experiments used working fluids of low-temperature helium gas (Chavanne et al., 2001; Niemela & Sreenivasan, 2006; Urban et al., 2014) or sulfur hexafluoride (He et al., 2012).

The turbulent data shown in figure 5, as detailed in the figure caption, include DNS in horizontally periodic 2D and 3D domains, wherein 2D steady rolls solve the equations of motion, as well as 3D DNS and laboratory experiments in cylinders that do not admit 2D rolls. Values of NuNu for steady rolls with Γ=2\Gamma=2 fixed are omitted from figure 5 for clarity, but they lie below all turbulent values once RaRa approximately exceeds 2×1092\times 10^{9} (cf. figure 2), and this gap would only widen at larger RaRa if their NuRa1/4Nu\sim Ra^{1/4} scaling persists. The laboratory data sets in figure 5 have unavoidably varying PrPr values that can be hard to estimate, as well as non-Oberbeck–Boussinesq effects (Urban et al., 2011, 2012, 2014). The figure includes only a narrow range of estimated PrPr values in order to avoid significant non-Oberbeck–Boussinesq effects. When data over a wider range of estimated PrPr is included, a few data points from the experiments of Chavanne et al. (2001) lie above the Nu(Γ)Nu(\Gamma^{*}) values of steady rolls, as shown in the supplementary material.

Our finding that steady rolls of NuNu-maximizing aspect ratios apparently display classical NuRa1/3Nu\sim Ra^{1/3} asymptotic scaling does not ineluctably imply anything about turbulent convection. Taking a dynamical systems point of view, however, steady solutions admitted by the domain are fixed points of 1, so they and their unstable manifolds are part of the global attractor. Turbulent trajectories may linger near these fixed points and so inherit some quantitative features (Kooloth et al., 2021), as has been found for unstable coherent states in shear flows (Nagata, 1990; Waleffe, 1998; Wedin & Kerswell, 2004; Gibson et al., 2008; Suri et al., 2020; Graham & Floryan, 2021). Indeed, figure 5 shows scaling similarities between steady and turbulent convection. Further exploration of the global attractor calls for study of 3D steady flows. Recently computed ‘multi-scale’ 3D steady states (Motoki et al., 2021) give larger NuNu values than all 2D rolls at moderate RaRa, but their scaling at large RaRa is unknown. Simpler 3D steady convection patterns remain to be computed as well. Analytically, it is an open challenge to construct approximations of 2D or 3D steady flows that are asymptotically accurate as RaRa\to\infty, as has been done for 2D rolls between stress-free boundaries (Chini & Cox, 2009; Wen et al., 2020). Such constructions could be used to verify that NuRa1/3Nu\sim Ra^{1/3} is indeed the exact asymptotic scaling for the NuNu-maximizing rolls we have computed, as well as to determine the precise ReReRaRa scaling relations for rolls of both NuNu- and ReRe-maximizing aspect ratios.

More generally, figure 5 highlights the absence of reproducible evidence for ultimate NuRa1/2Nu\sim Ra^{1/2} scaling, and it raises the intriguing possibility that steady rolls with NuRa1/3Nu\sim Ra^{1/3} might transport more heat than turbulent convection as RaRa\to\infty. We know of no counterexamples to this hypothesis, including in the case of stress-free boundaries (Wen et al., 2020). Heat transport by solutions of 1 with no-slip isothermal boundaries has been mathematically proved to be limited by Nu𝒪(Ra1/2)Nu\leq\mathcal{O}(Ra^{1/2}) (Howard, 1963; Doering & Constantin, 1996), but it remains unknown whether any solutions attain the ultimate scaling of this upper bound. One avenue for pursuing a stronger mathematical statement is to study two conjectures suggested by our computations: that steady convection maximizes NuNu among all solutions of 1 regardless of their stability or time-dependence, and that steady solutions of 1 are subject to an upper bound of the form Nu𝒪(Ra1/3)Nu\leq\mathcal{O}(Ra^{1/3}). Therefore, although numerically computed flows can never determine RaRa\to\infty scaling definitively, our results suggest a new mathematical approach that may be able to finally resolve the question of asymptotic NuNu scaling in turbulent convection.

Acknowledgements

After this manuscript was written our senior author, Charles Doering, passed away too soon. Beyond his many contributions to the present study, we are forever indebted to him for his mentorship, to say nothing of his many lasting contributions to the field of fluid dynamics. He will be deeply missed by us and many others. We also want to acknowledge helpful discussions about the present work with L.M. Smith, D. Sondak, and F. Waleffe. This work was supported by US National Science Foundation awards (DMS-1515161, DMS-1813003), Canadian NSERC Discovery Grants Program awards (RGPIN-2018-04263, RGPAS-2018-522657, DGECR-2018-00371), and computational resources provided by Advanced Research Computing at the University of Michigan.

Declaration of interests

The authors report no conflict of interest.

Supplementary Material

Numerical solutions

Tables LABEL:tab:Gamma2 to LABEL:tab:GammaLocal give the NuNu, kk, and ReRe values for numerical solutions with Pr=1Pr=1 and Γ=2\Gamma=2, Γ\Gamma^{*}, and Γloc\Gamma^{*}_{loc}, respectively.

Table 1S: Details for numerical solutions with Pr=1Pr=1 and Γ=2\Gamma=2, including the resolution of Fourier modes (NxN_{x}) and Chebyshev collocation points (NzN_{z}).
RaRa PrPr k=2π/Γk=2\pi/\Gamma   Nx×NzN_{x}\times N_{z}    NuNu    ReRe
1013/410^{13/4} 1 π\pi 128 ×\times 65 1.056590 1.617336
1.9×1031.9\times 10^{3} 1 π\pi 128 ×\times 65 1.145807 2.682155
2×1032\times 10^{3} 1 π\pi 128 ×\times 65 1.212037 3.317190
2.25×1032.25\times 10^{3} 1 π\pi 128 ×\times 65 1.355410 4.550975
2.5×1032.5\times 10^{3} 1 π\pi 128 ×\times 65 1.474455 5.537770
2.75×1032.75\times 10^{3} 1 π\pi 128 ×\times 65 1.575599 6.391812
3×1033\times 10^{3} 1 π\pi 128 ×\times 65 1.663162 7.159844
1014/410^{14/4} 1 π\pi 128 ×\times 65 1.714193 7.624400
3.5×1033.5\times 10^{3} 1 π\pi 128 ×\times 65 1.808754 8.526064
4×1034\times 10^{3} 1 π\pi 128 ×\times 65 1.926775 9.740578
4.5×1034.5\times 10^{3} 1 π\pi 128 ×\times 65 2.025985 10.85141
5×1035\times 10^{3} 1 π\pi 128 ×\times 65 2.111714 11.88534
1015/410^{15/4} 1 π\pi 128 ×\times 65 2.204811 13.09152
8×1038\times 10^{3} 1 π\pi 128 ×\times 65 2.476330 17.05494
10410^{4} 1 π\pi 128 ×\times 65 2.648664 20.07400
1017/410^{17/4} 1 π\pi 128 ×\times 65 3.122843 29.50047
1018/410^{18/4} 1 π\pi 128 ×\times 65 3.665041 42.29585
1019/410^{19/4} 1 π\pi 128 ×\times 65 4.287042 59.56858
10510^{5} 1 π\pi 128 ×\times 65 4.994322 82.84462
1021/410^{21/4} 1 π\pi 128 ×\times 65 5.795869 114.2355
1022/410^{22/4} 1 π\pi 128 ×\times 97 6.703915 156.5252
1023/410^{23/4} 1 π\pi 128 ×\times 97 7.732236 213.4031
10610^{6} 1 π\pi 128 ×\times 97 8.896615 289.7982
1025/410^{25/4} 1 π\pi 256 ×\times 97 10.21546 392.2837
1026/410^{26/4} 1 π\pi 256 ×\times 129 11.71065 529.6372
1027/410^{27/4} 1 π\pi 256 ×\times 129 13.40898 713.6005
10710^{7} 1 π\pi 512 ×\times 129 15.34493 959.9367
1.35×1071.35\times 10^{7} 1 π\pi 512 ×\times 129 16.46456 1119.932
1.5×1071.5\times 10^{7} 1 π\pi 512 ×\times 129 16.87881 1182.172
1.6×1071.6\times 10^{7} 1 π\pi 512 ×\times 193 17.13944 1222.005
1.65×1071.65\times 10^{7} 1 π\pi 512 ×\times 193 17.26636 1241.484
1.7×1071.7\times 10^{7} 1 π\pi 512 ×\times 193 17.39216 1260.709
1.736×1071.736\times 10^{7} 1 π\pi 512 ×\times 193 17.48282 1274.414
1.76×1071.76\times 10^{7} 1 π\pi 512 ×\times 193 17.54351 1283.493
1029/410^{29/4} 1 π\pi 512 ×\times 193 17.58987 1290.377
1.786×1071.786\times 10^{7} 1 π\pi 512 ×\times 193 17.60946 1293.277
1.8×1071.8\times 10^{7} 1 π\pi 512 ×\times 193 17.64500 1298.522
1.85×1071.85\times 10^{7} 1 π\pi 512 ×\times 193 17.77160 1317.122
1.9×1071.9\times 10^{7} 1 π\pi 512 ×\times 193 17.89693 1335.501
1.95×1071.95\times 10^{7} 1 π\pi 512 ×\times 193 18.02044 1353.657
2×1072\times 10^{7} 1 π\pi 512 ×\times 193 18.14193 1371.593
2.4×1072.4\times 10^{7} 1 π\pi 512 ×\times 193 19.04221 1507.890
2.8×1072.8\times 10^{7} 1 π\pi 512 ×\times 193 19.83413 1633.418
1030/410^{30/4} 1 π\pi 512 ×\times 193 20.47798 1739.647
4.5×1074.5\times 10^{7} 1 π\pi 512 ×\times 193 22.44036 2087.182
1031/410^{31/4} 1 π\pi 512 ×\times 193 23.76002 2340.822
10810^{8} 1 π\pi 768 ×\times 257 27.50669 3144.931
1033/410^{33/4} 1 π\pi 768 ×\times 257 31.81154 4220.616
2.15×1082.15\times 10^{8} 1 π\pi 768 ×\times 257 33.36657 4649.541
1034/410^{34/4} 1 π\pi 768 ×\times 257 36.75427 5658.648
4.64×1084.64\times 10^{8} 1 π\pi 896 ×\times 321 40.44652 6875.704
1035/410^{35/4} 1 π\pi 896 ×\times 321 42.42917 7580.057
10910^{9} 1 π\pi 1024 ×\times 321 48.94284 10145.79
1037/410^{37/4} 1 π\pi 1024 ×\times 321 56.42926 13571.10
2.15×1092.15\times 10^{9} 1 π\pi 1024 ×\times 321 59.13988 14935.81
2.5×1092.5\times 10^{9} 1 π\pi 1024 ×\times 321 61.38714 16116.94
1038/410^{38/4} 1 π\pi 1024 ×\times 321 65.06338 18145.61
10153/1610^{153/16} 1 π\pi 1024 ×\times 321 67.42466 19511.79
4×1094\times 10^{9} 1 π\pi 1024 ×\times 321 68.96487 20429.24
4.64×1094.64\times 10^{9} 1 π\pi 1024 ×\times 321 71.58241 22019.93
1039/410^{39/4} 1 π\pi 1024 ×\times 321 75.09725 24262.22
101010^{10} 1 π\pi 1024 ×\times 321 86.68318 32430.06
1041/410^{41/4} 1 π\pi 1024 ×\times 321 100.0909 43339.02
2×10102\times 10^{10} 1 π\pi 1024 ×\times 321 103.0738 45995.60
2.15×10102.15\times 10^{10} 1 π\pi 1024 ×\times 321 104.9517 47705.08
Table 2S: Details for numerical solutions with Pr=1Pr=1 and the aspect ratios Γ\Gamma^{*} that globally maximize Nu(Γ)Nu(\Gamma), including the resolution of Fourier modes (NxN_{x}) and Chebyshev collocation points (NzN_{z}).
RaRa PrPr k=2π/Γk=2\pi/\Gamma^{*}   Nx×NzN_{x}\times N_{z}    NuNu    ReRe
1013/410^{13/4} 1 3.116683 128 ×\times 65 1.056697 1.619793
1.9×1031.9\times 10^{3} 1 3.123537 128 ×\times 65 1.145870 2.683859
2×1032\times 10^{3} 1 3.128360 128 ×\times 65 1.212070 3.318462
2.25×1032.25\times 10^{3} 1 3.143491 128 ×\times 65 1.355411 4.550777
2.5×1032.5\times 10^{3} 1 3.161280 128 ×\times 65 1.474516 5.535574
2.75×1032.75\times 10^{3} 1 3.180831 128 ×\times 65 1.575828 6.387203
3×1033\times 10^{3} 1 3.202416 128 ×\times 65 1.663668 7.152347
1014/410^{14/4} 1 3.216383 128 ×\times 65 1.714937 7.614958
3.5×1033.5\times 10^{3} 1 3.247094 128 ×\times 65 1.810118 8.512058
4×1034\times 10^{3} 1 3.292192 128 ×\times 65 1.929322 9.719380
4.5×1034.5\times 10^{3} 1 3.329096 128 ×\times 65 2.029942 10.82473
5×1035\times 10^{3} 1 3.378413 128 ×\times 65 2.117243 11.84892
1015/410^{15/4} 1 3.426419 128 ×\times 65 2.212421 13.04623
8×1038\times 10^{3} 1 3.575467 128 ×\times 65 2.492199 17.05494
10410^{4} 1 3.665236 128 ×\times 65 2.671348 19.99537
1017/410^{17/4} 1 3.880392 128 ×\times 65 3.171063 29.49222
1018/410^{18/4} 1 4.118841 128 ×\times 65 3.757873 42.57254
1019/410^{19/4} 1 4.419469 128 ×\times 89 4.454688 60.44520
10510^{5} 1 4.793529 128 ×\times 89 5.278963 84.69567
1021/410^{21/4} 1 5.242992 128 ×\times 129 6.252782 117.4323
1022/410^{22/4} 1 5.782949 128 ×\times 129 7.404680 161.3367
1023/410^{23/4} 1 6.420133 128 ×\times 129 8.769978 219.8304
10610^{6} 1 7.171170 128 ×\times 129 10.39171 297.1691
1025/410^{25/4} 1 8.051634 128 ×\times 129 12.32188 398.7272
1026/410^{26/4} 1 9.073939 128 ×\times 129 14.62274 531.4357
5×1065\times 10^{6} 1 9.997275 192 ×\times 257 16.76769 665.2339
1027/410^{27/4} 1 10.25059 192 ×\times 257 17.36827 704.3108
10710^{7} 1 11.59439 192 ×\times 257 20.64616 929.1624
1029/410^{29/4} 1 13.12083 192 ×\times 257 24.56044 1221.405
3×1073\times 10^{7} 1 14.68186 256 ×\times 257 28.77198 1562.064
1030/410^{30/4} 1 14.84741 256 ×\times 257 29.23512 1601.174
1031/410^{31/4} 1 16.80072 256 ×\times 257 34.81847 2094.287
10810^{8} 1 18.99815 256 ×\times 321 41.48855 2735.227
1033/410^{33/4} 1 21.45545 256 ×\times 321 49.46027 3569.756
3×1083\times 10^{8} 1 23.89666 256 ×\times 321 58.05030 4550.127
1034/410^{34/4} 1 24.15059 256 ×\times 321 58.99612 4663.393
1035/410^{35/4} 1 26.84021 256 ×\times 321 70.43089 6139.224
6×1086\times 10^{8} 1 27.08270 256 ×\times 321 71.85714 6344.418
6.5×1086.5\times 10^{8} 1 27.31152 256 ×\times 321 73.66207 6619.142
7×1087\times 10^{8} 1 27.35757 256 ×\times 321 75.37932 6911.933
7.5×1087.5\times 10^{8} 1 26.83143 256 ×\times 321 77.02411 7294.891
8×1088\times 10^{8} 1 26.28179 256 ×\times 321 78.61210 7683.031
8.5×1088.5\times 10^{8} 1 26.25586 256 ×\times 321 80.14209 7971.925
9×1089\times 10^{8} 1 26.36825 256 ×\times 321 81.61573 8227.378
9.5×1089.5\times 10^{8} 1 26.54403 256 ×\times 321 83.03697 8462.830
10910^{9} 1 26.73696 256 ×\times 321 84.40976 8687.394
1037/410^{37/4} 1 29.78702 512 ×\times 449 101.5246 11462.12
1038/410^{38/4} 1 33.65968 512 ×\times 449 122.1559 14978.49
1039/410^{39/4} 1 38.04901 512 ×\times 449 146.9986 19554.08
101010^{10} 1 42.83017 512 ×\times 449 176.9293 25585.09
1041/410^{41/4} 1 48.06231 512 ×\times 449 213.0247 33536.66
1042/410^{42/4} 1 53.90331 512 ×\times 449 256.5802 43967.29
1043/410^{43/4} 1 60.50367 512 ×\times 513 309.1454 57597.49
101110^{11} 1 67.95755 512 ×\times 513 372.5844 75402.24
1045/410^{45/4} 1 76.33729 512 ×\times 769 449.1508 98685.64
1046/410^{46/4} 1 85.71701 512 ×\times 897 541.5753 129175.9
1047/410^{47/4} 1 96.12138 512 ×\times 897 653.1727 169237.3
101210^{12} 1 107.6085 512 ×\times 897 787.9764 221995.2
1049/410^{49/4} 1 120.1234 512 ×\times 897 950.9070 291852.5
1050/410^{50/4} 1 133.7508 512 ×\times 897 1147.971 384498.9
1051/410^{51/4} 1 148.8836 512 ×\times 1025 1386.450 506738.9
101310^{13} 1 166.3042 512 ×\times 1025 1675.036 666086.2
1053/410^{53/4} 1 186.3974 512 ×\times 1025 2024.094 873176.4
1054/410^{54/4} 1 209.4395 512 ×\times 1281 2446.172 1142290
1055/410^{55/4} 1 235.2120 512 ×\times 1537 2956.470 1494811
101410^{14} 1 263.0987 512 ×\times 1793 3573.640 1962459
Table 3S: Details for numerical solutions with Pr=1Pr=1 and the aspect ratios Γloc\Gamma^{*}_{loc} that locally maximize Nu(Γ)Nu(\Gamma), including the resolution of Fourier modes (NxN_{x}) and Chebyshev collocation points (NzN_{z}).
RaRa PrPr k=2π/Γlock=2\pi/\Gamma^{*}_{loc}   Nx×NzN_{x}\times N_{z}    NuNu    ReRe
1022/410^{22/4} 1 14.09456 96 ×\times 129 5.864201 82.92705
1023/410^{23/4} 1 16.41959 96 ×\times 129 6.914669 105.6969
10610^{6} 1 18.89401 96 ×\times 129 8.148261 135.6083
1025/410^{25/4} 1 21.66773 96 ×\times 129 9.587445 173.8004
1026/410^{26/4} 1 24.88046 96 ×\times 129 11.26803 221.7384
5×1065\times 10^{6} 1 27.86575 96 ×\times 129 12.81012 267.8677
1027/410^{27/4} 1 28.70377 96 ×\times 129 13.23878 280.9653
10710^{7} 1 33.19466 96 ×\times 129 15.56038 354.6158
1029/410^{29/4} 1 38.29704 96 ×\times 129 18.29931 448.0501
3×1073\times 10^{7} 1 43.50700 96 ×\times 193 21.21050 554.8653
1030/410^{30/4} 1 44.06680 96 ×\times 193 21.52859 566.9565
1031/410^{31/4} 1 50.67686 96 ×\times 193 25.33533 717.1889
10810^{8} 1 58.31124 96 ×\times 193 29.82540 906.1212
1033/410^{33/4} 1 67.11615 128 ×\times 321 35.12519 1143.903
3×1083\times 10^{8} 1 76.25123 128 ×\times 321 40.76618 1413.329
1034/410^{34/4} 1 77.23740 128 ×\times 321 41.38311 1443.747
1035/410^{35/4} 1 88.88239 128 ×\times 321 48.77387 1821.661
10910^{9} 1 102.3071 128 ×\times 321 57.50461 2297.406
1037/410^{37/4} 1 117.7822 128 ×\times 449 67.82060 2896.298
1038/410^{38/4} 1 135.6225 128 ×\times 449 80.01178 3650.131
1039/410^{39/4} 1 156.1963 128 ×\times 449 94.42106 4598.740
101010^{10} 1 179.9312 128 ×\times 449 111.4542 5792.111
1041/410^{41/4} 1 207.3119 128 ×\times 449 131.5910 7293.324
1042/410^{42/4} 1 238.9044 128 ×\times 449 155.3995 9181.462
1043/410^{43/4} 1 275.3612 128 ×\times 449 183.5515 11555.93
101110^{11} 1 317.4310 128 ×\times 449 216.8420 14541.83
1045/410^{45/4} 1 365.9813 128 ×\times 641 256.2116 18296.30
1046/410^{46/4} 1 422.0132 128 ×\times 641 302.7737 23016.84
1047/410^{47/4} 1 486.6804 128 ×\times 641 357.8457 28951.78
101210^{12} 1 561.6657 128 ×\times 641 422.9866 36390.82
1049/410^{49/4} 1 647.4534 128 ×\times 641 500.0423 45793.62
1050/410^{50/4} 1 746.8566 128 ×\times 641 591.1965 57587.15
1051/410^{51/4} 1 861.4431 128 ×\times 769 699.0342 72424.84
101310^{13} 1 994.2189 128 ×\times 769 826.6155 91031.10
1053/410^{53/4} 1 1147.050 128 ×\times 769 977.5620 114458.7
1054/410^{54/4} 1 1323.461 128 ×\times 1025 1156.161 143907.6
1055/410^{55/4} 1 1527.004 128 ×\times 1025 1367.486 180935.2
101410^{14} 1 1762.395 128 ×\times 1025 1617.546 227422.7
Refer to caption
Figure 1S. NuNu compensated by Ra1/3Ra^{1/3} for steady rolls of NuNu-maximizing aspect ratios Γ\Gamma^{*} at \Pran=1\Pran=1, along with NuNu from turbulent 2D and 3D DNS and experiments with estimated \Pran[0.6,2]\Pran\in[0.6,2]. For horizontally periodic domains, 2D DNS with (Γ,\Pran)=(2,1)(\Gamma,\Pran)=(2,1) were done by Johnston & Doering (2009) and Zhu et al. (2018), and 3D DNS with Γ8\Gamma\geq 8 and \Pran=1\Pran=1 were done by Vieweg & Schumacher (2020) and Krug et al. (2020). For DNS in cylinders of diameter-to-height ratio Γc\Gamma_{c}, Iyer et al. (2020) used (Γc,\Pran)=(0.1,1)(\Gamma_{c},\Pran)=(0.1,1) and Scheel & Schumacher (2017) used (Γc,\Pran)=(1,0.7)(\Gamma_{c},\Pran)=(1,0.7). For laboratory experiments in cylinders, where the plotted data is truncated according to \Pran[0.5,2]\Pran\in[0.5,2], the domains and estimated \Pran\Pran ranges are Γc=0.5\Gamma_{c}=0.5 and \Pran[0.6,2]\Pran\in[0.6,2] for Chavanne et al. (2001), Γc=4\Gamma_{c}=4 and \Pran[0.69,1.84]\Pran\in[0.69,1.84] for Niemela & Sreenivasan (2006), Γc=0.5\Gamma_{c}=0.5 and \Pran[0.79,0.86]\Pran\in[0.79,0.86] for He et al. (2012), and Γc=1\Gamma_{c}=1 and \Pran[0.95,1.5]\Pran\in[0.95,1.5] for Urban et al. (2014). Experiments used working fluids of low-temperature helium gas (Chavanne et al., 2001; Niemela & Sreenivasan, 2006; Urban et al., 2014) or sulfur hexafluoride (He et al., 2012).

Comparison with turbulent convection

Figure 1S is nearly identical to figure 5 in the main text, comparing heat transport by NuNu-maximizing steady rolls with transport by turbulent convection, except that more experimental data with Prandtl numbers further from 1 are included. In figure 1S the criterion for inclusion is an estimated Prandtl number of Pr[0.5,2]Pr\in[0.5,2] rather than the range \Pran[0.7,1.3]\Pran\in[0.7,1.3] in figure 5 of the main text. (In fact all of the estimated \Pran\Pran are at least 0.6, so \Pran[0.6,2]\Pran\in[0.6,2] in figure 1S.) The working fluids in the experiments—gaseous helium or sulfur hexaflouride—are used near their critical points, leading to coupling and sensitive variation of material parameters that can be difficult to estimate. Faster variation of PrPr with RaRa is associated with increasing non-Oberbeck–Boussinesq effects as well; see Urban et al. (2011, 2012, 2014) for a discussion of experimental challenges. Data in figure 5 is truncated using the narrower range Pr[0.7,1.3]Pr\in[0.7,1.3] mainly to reduce non-Oberbeck–Boussinesq effects—we expect \Pran\Pran alone to have a more modest effect, even over the wider range [0.5,2][0.5,2].

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