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Towards DNS of the Ultimate Regime of Rayleigh--B\'enard Convection

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Towards DNS of the Ultimate Regime of
Rayleigh–B´
enard Convection
Richard J.A.M. Stevens, Detlef Lohse, and Roberto Verzicco
1 Introduction
Heat transfer mediated by a fluid is omnipresent in Nature as well as in technical ap-
plications and it is always among the fundamental mechanisms of the phenomena.
The performance of modern computer processors has reached a plateau owing to the
inadequacy of the fluid based cooling systems to get rid of the heat flux which in-
creases with the operating frequency [1]. On much larger spatial scales, circulations
in the atmosphere and oceans are driven by temperature differences whose strength
is key for the evolution of the weather and the stability of regional and global climate
[2].
The core of the problem, which is referred to as natural convection, is relatively
simple since it reduces to determining the strength of the heat flux crossing the
system for given flow conditions. Unfortunately, the governing equations (Navier–
Stokes) are complex and non–linear, thus preventing the possibility to obtain analyti-
cal solutions. On the other hand laboratory experiments, aimed at tackling these phe-
nomena, have to cope with the issue of how to make a setup of size hm=O(cm–m)
dynamically similar to a system of h=O(m–Km) (figure 1). Indeed, this upscaling
problem is common to many experiments in hydrodynamics; in thermal convection
however it is exacerbated since, as we will see in the next section, the most relevant
governing parameter, the Rayleigh number (Ra), depends on the third power of the
leading spatial scale. This implies that in real applications Ra easily attains huge
values while it hardly hits its low–end in laboratory experiments.
Numerical simulations are subjected to similar limitations because of the spa-
tial and temporal resolution requirements that become more severe as the Rayleigh
R.J.A.M. Stevens ·D. Lohse ·R. Verzicco
PoF, UTwente, e-mail: {r.j.a.m.stevens,d.lohse, r.verzicco}@utwente.nl
R. Verzicco
DII, Uniroma2 e-mail: verzicco@uniroma2.it
1
arXiv:2005.06874v1 [physics.flu-dyn] 14 May 2020
2 Richard J.A.M. Stevens, Detlef Lohse, and Roberto Verzicco
Fig. 1 Cartoon of the scaling
problem for a Rayleigh–
B´
enard flow: a model system
of size hmhas to be operated
in dynamic similarity with a
real system of size h.
number increases [3]. Only recently the former have become a viable alternative to
experiments thanks to the continuously growing power of supercomputers.
Indeed, if the dynamics of the system could be expressed by power laws of the
form ARaβ, experiments and numerical simulations, performed at moderate val-
ues of the driving parameters, could be scaled up to determine the response of the
real systems at extreme driving values. Unfortunately, this strategy works only as-
suming that the coefficients of the power law (Aand β) remain constant for every Ra
and this could not be the case for thermal convection [4]. More in detail, Malkus [5]
and Priestley [6], conjectured that all the mean temperature profile variations occur
within the thermal boundary layers at the heated plates while the mean temperature
in the bulk of the flow is essentially constant. Assuming also that the thermal bound-
ary layers are far enough to evolve independently, one immediately obtains β=1/3.
A few years later, however, Kraichnan [7] noticed that as Ra increases, also the flow
strengthens and the viscous boundary layers eventually must become turbulent. In
this case, referred to as ultimate regime, velocity profiles are logarithmic with the
wall normal distance, and it results β=1/2 (times logarithmic corrections) which
yields huge differences with respect to the previous theory.
This last observation and the fact that most of the practical applications evolve in
the range of very high Rayleigh numbers motivate the effort to study turbulent ther-
mal convection in the ultimate regime even if it requires the solution of formidable
difficulties that we will detail in the next section.
2 A trap problem
One of the most appealing features of thermal convection is that the essence of the
phenomenon can be reduced to a very simple model problem in which a fluid layer
of thickness h, kinematic viscosity νand thermal diffusivity κis heated from below
Towards DNS of the Ultimate Regime of Rayleigh–B´
enard Convection 3
.
h
T
Th
cQ
Q
ν κ
c
Q
Q
h
νκ
a
cb
.
d
Th
T
Fig. 2 Cartoon of the ideal Rayleigh–
B´
enard flow.
Fig. 3 Simplified sketch of a real ex-
perimental set–up. Outside of the cell
only an insulating layer of foam has
been reported while all the details
about heating and cooling systems and
thermal shields have been neglected.
and cooled from above with a temperature difference ThTc=. The temperature
field, in a constant gravity field gproduces a flow motion via the thermal expansion,
parameterized by a constant coefficient α: this is the Rayleigh–B´
enard flow. The
heat flux ˙
Qbetween the plates will be a function of the form ˙
Q=f(h,ν,κ,g,α,)
which involves N=7 different quantities whose minimum number of independent
dimensions is K=4. The Buckingham Π–theorem assures that, in non–dimensional
form, the above relation is equivalent to one with only NK=3 parameters that
can be written as Nu =F(Ra,Pr)being Ra =gαh3/(νκ),Pr =ν/κand Nu =
˙
Q/˙
Qdi f f with Qdi f f the diffusive heat flux through the fluid in absence of motion.
The above relation looks very attractive since it depends only on two indepen-
dent parameters Ra and Pr, the latter being determined solely by the fluid properties.
Many efforts have been made to study the function F(·)through laboratory experi-
ments and, in the last two decades, also by numerical simulations as a viable alter-
native. Unfortunately, the practical realization of the RB convection is substantially
different from the theoretical problem and many additional details come into play.
The first point is that while in the ideal flow the fluid layer is laterally unbounded,
in real experiments, by necessity, it must be somehow confined thus introducing a
second length dor equivalently an additional parameter Γ=d/h, the aspect–ratio.
The parameters become more than one if the tank does not have a cylindrical or
square cross section.
Several additional variables are introduced by the physical realization of the ther-
mal boundary conditions; in fact, in the ideal problem, all the heat entering the
fluid through the lower isothermal hot plate leaves the fluid only crossing the up-
per cold plate, without heat leakage across the side boundaries. In the real flow,
the isothermal surfaces are obtained by thick metal plates (a=O(1–5)cm) of
high thermal diffusivity κpl (copper or aluminum) that provide stable temperature
values at the fluid interface for every flow condition. The sidewall, in contrast,
4 Richard J.A.M. Stevens, Detlef Lohse, and Roberto Verzicco
should minimize the heat transfer and it is therefore made of low thermal diffu-
sivity κsw materials (steel or Plexiglas) and with reduced thickness (e=O(1–5)
mm). To further prevent parasite heat currents, insulation foam layers and even
active thermal shields are installed outside of the cell, thus further increasing the
number of input parameters. Within this scenario, the heat flux function looks
like ˙
Q=f0(h,ν,κ,g,α,,d,e,κsw,a,κpl ,b,κf oam ,...)whose variable counting is
N>14 while it results always K=4 thus implying that the non–dimensional coun-
terpart (Nu =F0(Ra,Pr,...)) involves more than NK>10 independent parame-
ters.
Fig. 4 Compilation of exper-
imental and numerical data
for the compensated Nusselt
versus Rayleigh numbers:
the solid black line is the
theory by [4], blue bullets
are the experiments by [8],
purple squares [9], dark red
diamonds [10], red right trian-
gles [11], orange left triangles
[12], yellow up triangles [13],
yellow down triangles [14],
yellow stars [15], black bul-
lets, numerical simulations by
[16], black squares, prelim-
inary numerical simulations
by Stevens (2019), (Personal
Communication).
The hope is that when the function F0(·)is explored, experimentally or numer-
ically, most of the parameters introduced by the experimental technicalities do not
affect significantly the phenomena and the relevant variables reduce to a tractable
number. Unfortunately, some of the recent, and not so recent, experiments have
shown that it is not always the case since dynamically equivalent flows do not yield
identical results. In figure 4 we report a collection of Nusselt numbers taken from
different sources showing some disagreement both at the low– and high–end of
Ra. While the former differences have been attributed mainly to the heat leakage
through the sidewall [17, 18, 19, 20] and some of the latter to the non–perfect ther-
mal sources [21], most of the discrepancies are still unexplained and they are the
subject of intense investigation by many research groups worldwide. The situation
is even more complex if one does not restrict the analysis only to the Nusselt number
since higher order statistics show higher sensitivity to external perturbations.
In this context, numerical experiments can be particularly helpful since they can
be used as ideal tests to isolate the different perturbations of the basic problem and
assess their effect. However, performing direct numerical simulation of Rayleigh-
B´
enard convection implies several non obvious choices and requires huge compu-
tational resources that, for the parameter range of the ultimate regime, are not fully
Towards DNS of the Ultimate Regime of Rayleigh–B´
enard Convection 5
available yet. In the following we will present estimates of the computational costs
and discuss some of the possible open choices.
The first relevant point is whether the simulation should be aimed at the ideal
RB flow or rather has to mimic a laboratory experiment. In the first case the com-
putational domain should be laterally unbounded which can be approximated by
periodic boundary conditions applied to a rectangular domain of horizontal size
d; this configuration results in a horizontally homogeneous flow that benefits from
easy and efficient uniform spatial discretizations and fast converging statistics. On
the other hand, in a real set–up, the boundedness of the fluid layer generally results
in a smaller fluid volume although the kinematic boundary layer at the sidewall has
to be resolved by additional gridpoints.
Fig. 5 Nusselt number versus
domain aspect–ratio Γ=d/h
at Ra =108and Pr =1. The
dashed line for the ‘unifying
theory’ [4] is computed from
experimental data at Γ=1.
Which of the two configurations is more advantageous is not obvious since the
computational efficiency of the fully bounded flow solvers is smaller than those with
homogeneous directions but the former setup involves smaller flow volumes.
Besides computational considerations there are also physical issues since the
presence of lateral boundaries, and even the shape of the container affect the flow
dynamics [22]. In figure 5 we report the dependence of the Nusselt number on the
domain aspect–ratio Γfor rectangular and cylindrical geometries. It is immediately
evident that, for slender domains Nu depends on Γin the opposite way and only for
Γ1 the two geometries yield similar values while for Γ4 the Nusselt number
converges to the asymptotic value for unbounded domains. All the laboratory ex-
periments reported in figure 4 have been performed in low aspect ratio cylindrical
cells and accordingly also the numerical simulations have been run in a cylinder at
Γ=0.5. In contrast, if a horizontally homogeneous flow has to be simulated, in or-
der to get rid of the numerical confinement effect, it must be computed on domains
at least of Γ=4, even if spectra and higher order statistics indicate that Γ=8 or
Γ=16 is needed to eliminate confinement effects [23].
6 Richard J.A.M. Stevens, Detlef Lohse, and Roberto Verzicco
In the following we give an estimate of the computational resources needed for
direct numerical simulation of turbulent RB convection in rectangular and cylindri-
cal geometries by evaluating the number of nodes contained in the relative mesh.
The basic assumption is that the flow can be divided into bulk and boundary layer
regions, the former discretized by a mesh of the same size as the smallest between
the Kolmogorov and Batchelor scales and the latter with the resolution criteria sug-
gested by [3]. We further assume that the rectangular box has a size d×d×hdis-
cretized in Cartesian coordinates while the cylinder has a diameter dand a height h
discretized in polar coordinates.
For the ease of discussion we will restrict to Pr =1 keeping in mind that as the
Prandtl number deviates substantially from unity the simulation becomes even more
demanding either because the velocity field develops finer scales than the tempera-
ture (Pr 1) or vice versa (Pr 1).
For the mean Kolmogorov scale ηwe can easily write η/h(RaNu)1/4that
with a fit Nu =ARaβ(A'0.05 and β=1/3 from the high end of Ra in figure
4) yields a number of nodes per unit length in the bulk Nbu =0.473Ra1/3. For the
resolution of each boundary layer we rely on the correlation derived by [3] which
suggest a number of nodes Nbl 0.35Ra0.15. Within these figures, the total number
of nodes for the rectangular domain reads NCar =Γ2(0.105Ra +0.156Ra0.816).
We proceed along the same lines for the cylindrical domain keeping in mind
that there is an extra boundary layer at the sidewall and that the polar coordinates
have azimuthal isolines that diverge radially. Therefore the resolution requirements
in this direction are dictated by the location farthest from the symmetry axis. Using
the same correlations as above we obtain NCyl =0.5πΓ 2(0.105Ra +0.156Ra0.816 )+
πΓ (0.223Ra0.816 +0.116Ra0.633 ).
It is worth mentioning that these expressions have been obtained by simplifying
assumptions therefore their results should be taken as coarse estimates and not as
precise measures. For example, Aand βhave been assumed constant and equal to
the high–end Ra values of figure 4 and we have used h2δbl h(with δbl the
boundary layer thickness): all these positions concur to an overestimate of the num-
ber of nodes. On the other hand, the correlation Nbl 0.35Ra0.15 of [3] was obtained
for a Prandtl–Blasius laminar boundary layer that is expected to underestimates the
resolution when the ultimate regime sets in and the boundary layers transition to tur-
bulence. At the transitional Rayleigh number, the above factors might compensate
each other and the estimates could give reasonable numbers.
A comparison of the two expressions immediately shows that the leading or-
der term increases at the same rate with Ra and Γalthough the cylindrical mesh
has asymptotically 60% more nodes than the Cartesian counterpart. This is true al-
though, for the same aspect–ratio, the latter has a volume (Γ2h3) which is more than
20% bigger than the former (πΓ 2h3/4).
If now we focus on the onset of the ultimate regime we have to determine the
critical Rayleigh number at which the boundary layer undertakes the transition to
the turbulent state. This is triggered by the large scales of convection that sweep
the plates by the induced winds; according to Reference [24] the boundary layer
Towards DNS of the Ultimate Regime of Rayleigh–B´
enard Convection 7
Fig. 6 Number of nodes
N(in a cylindrical cell of
Γ=0.5) and achievable flow
Rayleigh number Ra versus
the years for direct numerical
simulations of Rayleigh–
B´
enard convection. Black
squares for various data from
the literature, blue bullets for
simulations from our research
group, big red bullet final
goal for the ultimate regime
simulation.
transition occurs for a shear Reynolds number of ReS420 that Grossmann &
Lohse [4] have estimated to happen around RaC1014.
In a rectangular domain with Γ=4 this Rayleigh number implies a mesh with
NCar >1014 nodes that is clearly infeasible in the mid–term future. In a cylindrical
cell of Γ=0.5, however, it results NCyl 1012 nodes that could be achieved within
the next five years (see figure 6). Indeed, we are already running simulations at
Ra =1013 at Γ=0.5 and even Ra =1014 at Γ=0.25 with meshes of the order of
1011 nodes (R. Stevens, Personal Communication) although we expect to tackle the
ultimate regime only by the ‘next generation’ simulations.
We wish to point out that if we compare the numbers coming from the present
formulas with those currently used for the highest Rayleigh number simulations we
find that the former produce a systematic overestimate of the required resolution. For
example, for a cylindrical mesh of aspect–ratio Γ=0.5 at Ra =1013 our prediction
yields a number of nodes NCyl 4×1011 while a simulation on a mesh 4608 ×
1400 ×4480 (NCyl 2.9×1010) yielded the same Nusselt number as another run
on the finer grid 6144 ×1536 ×6144 with NCyl 5.8×1010 nodes (R. Stevens,
Personal Communication).
A possible explanation for this difference is that in our model we have assumed
that in the bulk the mesh has to be as fine as the mean Kolmogorov scale η. How-
ever, looking at the dissipation spectra of turbulence [25] one finds its peak around
10ηthus implying that also a mesh of size 1.5–2ηalready resolves most of the dis-
sipation. In three–dimensions this difference yields a factor 6.25–8 less in the node
counting that is about the mismatch between our prediction and the actual meshes.
For a while we have been working at improving the simulation code [26, 27, 16]
by more efficient implementations of the solution algorithms and of the paralleliza-
8 Richard J.A.M. Stevens, Detlef Lohse, and Roberto Verzicco
tion strategies in order to reduce the time–to–solution. In addition we are also figur-
ing out alternatives to achieve the ultimate regime in more affordable problems.
One possible way is to exploit the analogy between Rayleigh–B´
enard and Taylor–
Couette (TC) flow [28]. The latter is the flow developing in the gap between two
coaxial cylinders rotating at different angular velocities and whose angular momen-
tum flux across the cylinders behaves as the heat flux between the plates in a RB flow
[29]. It turns out, however, that the mechanical forcing of the TC flow is more effi-
cient in producing turbulent boundary layers than the thermal forcing of RB flows
and the ultimate regime can be achieved for smaller values of the driving parame-
ters that are affordable by numerical simulation [30]. In Reference [31], thanks to
the presence of baffled cylinders, that disrupted the logarithmic part of the turbulent
boundary layer profiles, it has been possible to get rid of the logarithmic correction
and obtain a pure 1/2 power law in the analogous of the Nu versus Ra relationship.
Another possibility is to simulate a two–dimensional RB flow that allows, already
now, to tackle Rayleigh numbers >1014 ; indeed in Reference [32] (and successive
developments) simulations have been run up to Ra '5×1014 with the appearance
of a transition already for Ra 1013.
3 Closing remarks
In this contribution we have briefly introduced the problem of turbulent thermal
convection with a particular look at its transition to the ultimate regime and the
resolution requirements needed for the direct numerical simulation of this flow.
Leaving aside all the complications related to the spurious heat currents through
the sidewall and the imperfect character of the thermal sources, already addressed
in some of the referred papers, it appears that a preliminary fundamental question is
whether the simulation should be aimed at replicating an experimental set–up with
a lateral confinement or to mimic the truly Rayleigh–B´
enard flow that is virtually
infinite in the horizontal directions.
We have shown that in the latter case a domain with aspect–ratio no smaller
than Γ=4 is required and this implies, at the estimated critical Rayleigh number
RaC1014, a computational mesh with more than 1014 nodes that is not likely to
be tractable within the next decade. On the other hand, although for a given Ra and
Γcylindrical, laterally confined geometries contain about 60% more nodes than the
rectangular ‘unbounded’ domains, when restricted to the existing, slender cylinders
of the laboratory experiments the number of nodes becomes more feasible. In partic-
ular, for Γ=0.5 and Ra =1014 the present estimate gives a mesh slightly larger that
a trillion of nodes. Even if this number might look impressive, it is ‘only’ one order
of magnitude bigger than the current state–of–the–art simulations and, according to
figure 6, such meshes will become affordable within the next five years or so. It is
also worthwhile mentioning that the present estimates assume a mesh in the bulk of
the flow that is everywhere as fine as the mean Kolmogorov scale ηwhile actual
grid refinement checks performed on Rayleigh–B´
enard turbulence have shown con-
Towards DNS of the Ultimate Regime of Rayleigh–B´
enard Convection 9
verged results already for meshes of size 2η. This implies that in three–dimensional
flows the actual mesh sizes can be about one order of magnitude smaller and this is
fully confirmed by our ongoing simulations.
Needless to say, once the ultimate regime will have been hit by numerical simula-
tions also in three–dimensions, a terra incognita will be entered. Turbulent boundary
layers have more severe resolution requirements than the laminar counterparts and
once the ballistic plumes of Kraichnan [7], which can be thought of as pieces of de-
tached thermal boundary layer, are shot into the bulk also the resolution of that flow
region is likely to become more demanding. Clearly, attempting resolution estimates
beyond the onset of the transition would be even more speculative than those of the
present paper and only with the data of those simulations at hand, further reasonable
projections can be made.
While waiting for adequate computational resources to tackle thermal convec-
tion in the ultimate regime we can nevertheless compute turbulent flows that exhibit
similar dynamics or that can be reduced to a tractable size by simplifying assump-
tions. These include the Taylor–Couette flow, that can be rigorously shown to be
analogous to Rayleigh-B´
enard convection, or two–dimensional thermal convection
that already now can be simulated well beyond Ra =1014 and has indeed shown
evidence of transition to the ultimate state.
Acknowledgements This work is supported by the Twente Max-Planck Cen-
ter and the ERC (European Research Council) Starting Grant no. 804283 Ulti-
mateRB. The authors gratefully acknowledge the Gauss Centre for Supercomputing
e.V. (www.gauss-centre.eu) for funding this project by providing computing
time on the GCS Supercomputer SuperMUC-NG at Leibniz Supercomputing Centre
(www.lrz.de).
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