Nonequilibrium entropy limiters in lattice Boltzmann methods
Nonequilibrium entropy limiters
in lattice Boltzmann methods 1
R. A. Brownlee, A. N. Gorban ∗, J. Levesley
Department of Mathematics, University of Leicester, Leicester LE1 7RH, UK
Abstract
We construct a system of nonequilibrium entropy limiters for the lattice Boltz-
mann methods (LBM). These limiters erase spurious oscillations without blurring
of shocks, and do not affect smooth solutions. In general, they do the same work
for LBM as flux limiters do for finite differences, finite volumes and finite elements
methods, but for LBM the main idea behind the construction of nonequilibrium en-
tropy limiter schemes is to transform a field of a scalar quantity — nonequilibrium
entropy. There are two families of limiters: (i) based on restriction of nonequilibrium
entropy (entropy “trimming”) and (ii) based on filtering of nonequilibrium entropy
(entropy filtering). The physical properties of LBM provide some additional bene-
fits: the control of entropy production and accurate estimate of introduced artificial
dissipation are possible. The constructed limiters are tested on classical numeri-
cal examples: 1D athermal shock tubes with an initial density ratio 1:2 and the
2D lid-driven cavity for Reynolds numbers Re between 2000 and 7500 on a coarse
100 × 100 grid. All limiter constructions are applicable for both entropic and non-
entropic quasiequilibria.
Key words: lattice Boltzmann method, numerical regularisation, entropy
PACS: 47.11.Qr, 47.20.-k, 47.11.-j, 51.10.+y
1 Introduction
In 1959, S.K. Godunov [17] demonstrated that a (linear) scheme for a PDE
could not, at the same time, be monotone and second order accurate. Hence,
∗ Corresponding author.
Email addresses: r.brownlee@mcs.le.ac.uk (R. A. Brownlee),
a.gorban@mcs.le.ac.uk (A. N. Gorban), j.levesley@mcs.le.ac.uk
(J. Levesley).
1 This work is supported by EPSRC grant number GR/S95572/01.
Preprint submitted to Physica A 24 October 2018
http://arxiv.org/abs/0704.0043v1
we should choose between spurious oscillation in high order non-monotone
schemes and additional dissipation in first order schemes. Flux limiter schemes
are invented to combine high resolution schemes in areas with smooth fields
and first order schemes in areas with sharp gradients.
The idea of flux limiters can be illustrated by computation of the flux F0,1
of the conserved quantity u between a cell marked by 0 and one of two its
neighbour cells marked by ±1:
F0,1 = (1− φ(r))f low0,1 + φ(r)f
0,1 ,
where f low0, 1 , f
0, 1 are low and high resolution scheme fluxes, respectively, r =
(u0 − u−1)/(u1 − u0), and φ(r) ≥ 0 is a flux limiter function. For r close to 1,
the flux limiter function φ(r) should be also close to 1.
Many flux limiter schemes have been invented during the last two decades [43].
No particular limiter works well for all problems, and a choice is usually made
on a trial and error basis.
Below are several examples of flux limiter functions:
φmm(r) = max [0,min (r, 1)] (minmod, [36]);
φos(r) = max [0,min (r, β)] , (1 ≤ β ≤ 2) (Osher, [10]);
φmc(r) = max [0,min (2r, 0.5(1 + r), 2)] (monotonised central [42]);
φsb(r) = max [0,min (2r, 1) ,min (r, 2)] (superbee, [36]);
φsw(r) = max [0,min (βr, 1) , (r, β)] , (1 ≤ β ≤ 2) (Sweby, [40]).
The lattice Boltzmann method has been proposed as a discretization of Boltz-
mann’s kinetic equation and is now in wide use in fluid dynamics and beyond
(for an introduction and review see [38]). Instead of fields of moments M , the
lattice Boltzmann method operates with fields of discrete distributions f . This
allows us to construct very simple limiters that do not depend on slopes or
gradients.
All the limiters we construct are based on the representation of distributions
f in the form:
f = f ∗ + ‖f − f ∗‖
f − f ∗
‖f − f ∗‖
where f ∗ is the correspondent quasiequilibrium (conditional equilibrium) for
given moments M , f − f ∗ is the nonequilibrium “part” of the distribution,
which is represented in the form “norm×direction” and ‖f − f ∗‖ is the norm
of that nonequilibrium component (usually this is the entropic norm). Lim-
iters change the norm of the nonequilibrium component f − f ∗, but do not
touch its direction or the equilibrium. In particular, limiters do not change the
macroscopic variables, because moments for f and f ∗ coincide. All limiters we
use are transformations of the form
f 7→ f ∗ + φ× (f − f ∗) (1)
with φ > 0. If f − f ∗ is too big, then the limiter should decrease its norm.
The outline of the paper is as follows. In Sec. 2 we introduce the notions and
notations from lattice Boltzmann theory we need, in Sec. 3 we elaborate the
idea of entropic limiters in more detail and construct several nonequilibrium
entropy limiters for LBM, in Sec. 4 some numerical experiments are described:
(1) 1D athermal shock tube examples;
(2) steady state vortex centre locations and observation of first Hopf bifur-
cation in 2D lid-driven cavity flow.
Concluding remarks are given in Sec. 5.
2 Background
The essence of lattice Boltzmann methods was formulated by S. Succi in the
following maxim: “Nonlinearity is local, non-locality is linear” 2 . We should
even strengthen this statement. Non-locality (a) is linear; (b) is exactly and
explicitly solvable for all time steps; (c) space discretization is an exact oper-
ation.
The lattice Boltzmann method is a discrete velocity method. The finite set
of velocity vectors {vi} (i = 1, ...m) is selected, and a fluid is described by
associating, with each velocity vi, a single-particle distribution function fi =
fi(x, t) which is evolved by advection and interaction (collision) on a fixed
computational lattice. The values fi are named populations. If we look at all
lattice Boltzmann models, one finds that there are two steps: free flight for
time δt and a local collision operation.
The free flight transformation for continuous space is
fi(x, t+ δt) = fi(x− viδt, t).
After the free flight step the collision step follows:
fi(x) 7→ Fi({fj(x)}), (2)
2 S. Succi, “Lattice Boltzmann at all-scales: from turbulence to DNA transloca-
tion”, Mathematical Modelling Centre Distinguished Lecture, University of Leices-
ter, Leicester UK, 15th November 2006.
or in the vector form
f(x) 7→ F (f(x)).
Here, the collision operator F is the set of functions Fi({fj}) (i = 1, ...m).
Each function Fi depends on all fj (j = 1, ...m): new values of the populations
fi at a point x are known functions of all previous population values at the
same point.
The lattice Boltzmann chain “free flight → collision → free flight → collision
· · · ” can be exactly restricted onto any space lattice which is invariant with
respect to space shifts of the vectors viδt (i = 1, ...m). Indeed, free flight trans-
forms the population values at sites of the lattice into the population values
at sites of the same lattice. The collision operator (2) acts pointwise at each
lattice site separately. Much effort has been applied to answer the questions:
“how does the lattice Boltzmann chain approximate the transport equation for
the moments M?”, and “how does one construct the lattice Boltzmann model
for a given macroscopic transport phenomenon?” (a review is presented in
book [38]).
In our paper we propose a universal construction of limiters for all possible
collision operators, and the detailed construction of Fi({fj}) is not important
for this purpose. The only part of this construction we use is the local equilibria
(sometimes these states are named conditional equilibria, quasiequilibria, or
even simpler, equilibria).
The lattice Boltzmann models should describe the macroscopic dynamic, i.e.,
the dynamic of macroscopic variables. The macroscopic variables Mℓ(x) are
some linear functions of the population values at the same point: Mℓ(x) =
imℓifi(x), or in the vector form, M(x) = m(f(x)). The macroscopic vari-
ables are invariants of collisions:
mℓifi =
mℓiFi({fj}) (or m(f) = m(F (f))).
The standard example of the macroscopic variables are hydrodynamic fields
(density–velocity–energy density): {n, nu, E}(x) := ∑i{1, vi, v2i /2}fi(x). But
this is not an obligatory choice. If we would like to solve, by LBM methods,
the Grad equations [22] or some extended thermodynamic equations [25], we
should extend the list of moments (but, at the same time, we should be ready
to introduce more discrete velocities for a proper description of these extended
moment systems). On the other hand, the athermal lattice Boltzmann models
with a shortened list of macroscopic variables {n, nu} are very popular.
The quasiequilibrium is the positive fixed point of the collision operator for
the given macroscopic variablesM . We assume that this point exists, is unique
and depends smoothly on M . For the quasiequilibrium population vector for
given M we use the notation f ∗M , or simply f
∗, if the correspondent value of
M is obvious. We use Π∗ to denote the equilibration projection operation of
a distribution f into the corresponding quasiequilibrium state:
Π∗(f) = f ∗m(f).
For some of the collision models an entropic description of equilibrium is pos-
sible: an entropy density function S(f) is defined and the quasiequilibrium
point f ∗M is the entropy maximiser for given M [26,39].
As a basic example we shall consider the lattice Bhatnagar–Gross–Krook
(LBGK) model with overrelaxation (see, e.g., [3,12,23,28,38]). The LBGK col-
lision operator is
F (f) := Π∗(f) + (2β − 1)(Π∗(f)− f), (3)
where β ∈ [0, 1]. For β = 0, LBGK collisions do not change f , for β = 1/2
these collisions act as equilibration (this corresponds to the Ehrenfests’ coarse
graining [15] further developed in [14,19,20]), for β = 1, LBGK collisions act
as a point reflection with the center at the quasiequilibrium Π∗(f).
It is shown [8] that under some stability conditions and after an initial period
of relaxation, the simplest LBGK collision with overrelaxation [23,38] provides
second order accurate approximation for the macroscopic transport equation
with viscosity proportional to δt(1− β)/β.
The entropic LBGK (ELBM) method [5,20,26,39] differs in the definition
of (3): for β = 1 it should conserve the entropy, and in general has the following
form:
F (f) := (1− β)f + βf̃ , (4)
where f̃ = (1 − α)f + αΠ∗(f). The number α = α(f) is chosen so that the
constant entropy condition is satisfied: S(f) = S(f̃). For LBGK (3), α = 2. Of
course, for ELBM the entropic definition of quasiequilibrium should be valid.
In the low-viscosity regime, LBGK suffers from numerical instabilities which
readily manifest themselves as local blow-ups and spurious oscillations.
The LBM experiences the same spurious oscillation problems near sharp gra-
dients as high order schemes do. The physical properties of the LBM schemes
allows one to construct new types of limiters: the nonequilibrium entropy lim-
iters. In general, they do the same work for LBM as flux limiters do for finite
differences, finite volumes and finite elements methods, but for LBM the main
idea behind the construction of nonequilibrium entropy limiter schemes is to
limit a scalar quantity — nonequilibrium entropy (and not the vectors or ten-
sors of spatial derivatives, as it is for flux limiters). These limiters introduce
some additional dissipation, but all this dissipation could easily be evaluated
through analysis of nonequilibrium entropy production.
Two examples of such limiters have been recently proposed: the positivity
rule [6,31,41] and the Ehrenfests’ regularisation [7]. The positivity rule just
provides positivity of distributions: if a collision step produces negative popu-
lations, then the positivity rule returns them to the boundary of positivity. In
the Ehrenfests’ regularisation, one selects the k sites with highest nonequilib-
rium entropy (the difference between entropy of the state f and entropy of the
corresponding quasiequilibrium state f ∗ at a given space point) that exceed a
given threshold and equilibrates the state in these sites.
The positivity rule and Ehrenfests’ regularisation provide rare, intense and
localised corrections. It is easy and also computationally cheap to organise
more gentle transformation with smooth shift of highly nonequilibrium states
to quasiequilibrium. The following regularisation transformation distributes
its action smoothly: we can just choose in (1) φ = φ(∆S(f)) with sufficiently
smooth function φ(∆S(f)). Here f is the state at some site, f ∗ is the corre-
sponding quasiequilibrium state, S is entropy, and ∆S(f) := S(f ∗)− S(f).
The next step in the development of the nonequilibrium entropy limiters is in
the usage of local entropy filters. The filter of choice here is the median filter: it
does not erase sharp fronts, and is much more robust than convolution filters.
An important problem is: “how does one create nonequilibrium entropy lim-
iters for LBM with non-entropic quasiequilibria?”. We propose a solution
of this problem based on the nonequilibrium Kullback entropy. For entropic
quasiequilibrium the Kullback entropy approach gives the same entropic lim-
iters. In thermodynamics, Kullback entropy belongs to the family of Massieu–
Planck–Kramers functions (canonical or grandcanonical potentials).
3 Nonequilibrium entropy limiters for LBM
3.1 Positivity rule
There is a simple recipe for positivity preservation [6,31,41]: to substitute
nonpositive I
0 (f)(x) by the closest nonnegative state that belongs to the
straight line
λf(x) + (1− λ)Π∗(f(x))| λ ∈ R
defined by the two points, f(x) and corresponding quasiequilibrium. This op-
eration is to be applied pointwise, at points of the lattice where positivity
is violated. The coefficient λ depends on x too. Let us call this recipe the
positivity rule (Fig. 1). This recipe preserves positivity of populations and
probabilities, but can affect accuracy of approximation. The same rule is nec-
F(f )
Positivity fixation 
Positivity domain 
Fig. 1. Positivity rule in action. The motions stops at the positivity boundary.
essary for ELBM (4) when the positive “mirror state” f̃ with the same entropy
as f does not exists on the straight line (5).
3.2 Ehrenfests’ regularisation
To discuss methods with additional dissipation, the entropic approach is very
convenient. Let entropy S(f) be defined for each population vector f = (fi)
(below we use the same letter S for local in space entropy, and hope that
context will make this notation always clear). We assume that the global
entropy is a sum of local entropies for all sites. The local nonequilibrium
entropy is
∆S(f) := S(f ∗)− S(f), (6)
where f ∗ is the corresponding local quasiequilibrium at the same point.
The Ehrenfests’ regularisation [6,7] provides “entropy trimming”: we moni-
tor local deviation of f from the corresponding quasiequilibrium, and when
∆S(f)(x) exceeds a pre-specified threshold value δ, perform local Ehrenfests’
steps to the corresponding quasiequilibrium: f 7→ f ∗ at those points.
So that the Ehrenfests’ steps are not allowed to degrade the accuracy of LBGK
it is pertinent to select the k sites with highest ∆S > δ. The a posteriori
estimates of added dissipation could easily be performed by analysis of entropy
production in Ehrenfests’ steps. Numerical experiments show (see, e.g., [6,7])
that even a small number of such steps drastically improve stability.
To avoid the change of accuracy order “on average”, the number of sites with
this step should be ≤ O(Nh/L) where N is the total number of sites, h is
the step of the space discretization and L is the macroscopic characteristic
length. But this rough estimate of accuracy in average might be destroyed
by concentration of Ehrenfests’ steps in the most nonequilibrium areas, for
example, in the boundary layer. In that case, instead of the total number of
sites N in O(Nh/L) we should take the number of sites in a specific region.
The effects of concentration could be easily analysed a posteriori.
3.3 Smooth limiters of nonequilibrium entropy
The positivity rule and Ehrenfests’ regularisation provide rare, intense and
localised corrections. Of course, it is easy and also computationally cheap to
organise more gentle transformation with a smooth shift of highly nonequilib-
rium states to quasiequilibrium. The following regularisation transformation
distributes its action smoothly:
f 7→ f ∗ + φ(∆S(f))(f − f ∗). (7)
The choice of function φ is highly ambiguous, for example, φ = 1/(1+α∆Sk)
for some α > 0 and k > 0. There are two significantly different choices: (i)
ensemble-independent φ (i.e., the value of φ depends on local value of ∆S
only) and (ii) ensemble-dependent φ, for example
φ(∆S) =
1 + (∆S/(αE(∆S)))k−1/2
1 + (∆S/(αE(∆S)))k
, (8)
where E(∆S) is the average value of ∆S in the computational area, k ≥ 1,
and α & 1. For small ∆S, φ(∆S) ≈ 1 and for ∆S ≫ αE(∆S), φ(∆S) tends
αE(∆S)/∆S. It is easy to select an ensemble-dependent φ with control
of total additional dissipation.
3.4 Monitoring of total dissipation
For given β, the entropy production in one LBGK step in quadratic approxi-
mation for ∆S is:
δLBGKS ≈ [1− (2β − 1)2]
∆S(x),
where x is the grid point, ∆S(x) is nonequilibrium entropy (6) at point x,
δLBGKS is the total entropy production in a single LBGK step. It would be
desirable if the total entropy production for the limiter δlimS was small relative
to δLBGKS:
δlimS < δ0δLBGKS. (9)
A simple ensemble-dependent limiter (perhaps, the simplest one) for a given
δ0 operates as follows. Let us collect the histogram of the ∆S(x) distribution,
and estimate the distribution density, p(∆S). We have to estimate a value
∆S0 that satisfies the following equation:
p(∆S)(∆S −∆S0) d∆S = δ0[1− (2β − 1)2]
p(∆S)∆S d∆S. (10)
In order not to affect distributions with small expectation of ∆S, we choose
a threshold ∆St = max{∆S0, δ}, where δ is some predefined value (as in
the Ehrenfests’ regularisation). For states at sites with ∆S ≥ ∆St we pro-
vide homothety with quasiequilibrium center f ∗ and coefficient
∆St/∆S (in
quadratic approximation for nonequilibrium entropy):
f(x) 7→ f ∗(x) +
(f(x)− f ∗(x)). (11)
3.5 Median entropy filter
The limiters described above provide pointwise correction of nonequilibrium
entropy at the “most nonequilibrium” points. Due to the pointwise nature,
the technique does not introduce any nonisotropic effects, and provides some
other benefits. But if we involve the local structure, we can correct local non-
monotone irregularities without touching regular fragments. For example, we
can discuss monotone increase or decrease of nonequilibrium entropy as regular
fragments and concentrate our efforts on reduction of “speckle noise” or “salt
and pepper noise”. This approach allows us to use the accessible resource of
entropy change (9) more thriftily.
Among all possible filters, we suggest the median filter. The median is a more
robust average than the mean (or the weighted mean) and so a single very
unrepresentative value in a neighborhood will not affect the median value
significantly. Hence, we suppose that the median entropy filter will work better
than entropy convolution filters.
The median filter considers each site in turn and looks at its nearby neighbours.
It replaces the nonequilibrium entropy value ∆S at the point with the median
of those values ∆Smed, then updates f by the transformation (11) with the
homothety coefficient
∆Smed/∆S. The median, ∆Smed, is calculated by first
sorting all the values from the surrounding neighbourhood into numerical order
and then replacing that being considered with the middle value. For example,
if a point has 3 nearest neighbors including itself, then after sorting we have
3 values ∆S: ∆S1 ≤ ∆S2 ≤ ∆S3. The median value is ∆Smed = ∆S2. For 9
nearest neighbors (including itself) we have after sorting ∆Smed = ∆S5. For
27 nearest neighbors ∆Smed = ∆S14.
We accept only dissipative corrections (those resulting in a decrease of ∆S,
∆Smed < ∆S) because of the second law of thermodynamics. The analogue
of (10) is also useful for acceptance of the most significant corrections.
Median filtering is a common step in image processing [34] for the smoothing
of signals and the suppression of impulse noise with preservation of edges.
3.6 Entropic steps for non-entropic quasiequilibria
Beyond the quadratic approximation for nonequilibrium entropy all the logic of
the above mentioned constructions remain the same. There exists only one sig-
nificant change: instead of a simple homothety (11) with coefficient
∆St/∆S
the transformation (7) should be applied, where the multiplier φ is a solution
of the nonlinear equation
S(f ∗ + φ(f − f ∗)) = S(f ∗)−∆St.
This is essentially the same equation that appears in the definition of ELBM
steps (4).
More differences emerge for LBM with non-entropic quasiequilibria. The main
idea here is to reason that non-entropic quasiequilibria appear only because of
technical reasons, and approximate continuous physical entropic quasiequilib-
ria. This is not an approximation of a density function, but an approximation
of measure, i.e., from the cubature formula:
f(v) ≈
fiδ(v − vi)
ϕ(v)f(v) dv ≈
ϕ(vi)fi.
The discrete populations fi are connected to continuous (and sufficiently
smooth) densities f(v) by cubature weights fi ≈ wif(vi). These weights for
quasiequilibria are found by moment and flux matching conditions [37]. It
is impossible to approximate the BGS entropy
f ln fdv just by discretiza-
tion (to change integration by summation, and continuous distribution f by
discrete fi), because cubature weights appear as additional variables. Never-
theless, the approximate discretization of the Kullback entropy SK [30] does
not change its form:
SK(f) = −
f(v) ln
f ∗(v)
dv ≈ −
fi ln
, (12)
because fi/f
i approximates the ratio of functions f(v)/f
∗(v) and
i fi . . .
gives the integral
f(v) . . .dv approximation. Here, in (12), the state f ∗ is the
quasiequilibrium with the same values of the macroscopic variables as f . More-
over, for given values of the macroscopic variables, SK(f) achieves its maxi-
mum at the point f = f ∗ (both for continuous and for discrete distributions).
The corresponding maximal value is zero. Below, SK is the discrete Kullback
entropy. If the approximate discrete quasiequilibrium f ∗ is non-entropic, we
can use −SK(f) instead of ∆S(f).
For entropic quasiequilibria with perfect entropy the discrete Kullback entropy
gives the same ∆S: −SK(f) = ∆S(f). Let the discrete entropy have the
standard form for an ideal (perfect) mixture [27].
S(f) = −
fi ln
After the classical work of Zeldovich [44], this function is recognised as a
useful instrument for the analysis of kinetic equations (especially in chemical
kinetics [21]). If we define f ∗ as the conditional entropy maximum for given
k mjkfk, then
ln f ∗k =
µjmjk,
where µj(M) are the Lagrange multipliers (or “potentials”). For this entropy
and conditional equilibrium we find
∆S = S(f ∗)− S(f) =
fi ln
, (13)
if f and f ∗ have the same moments, m(f) = m(f ∗). The right hand side
of (13) is −SK(f).
In thermodynamics, the Kullback entropy belongs to the family of Massieu–
Planck–Kramers functions (canonical or grandcanonical potentials). There is
another sense of this quantity: SK is the relative entropy of f with respect to
f ∗ [18,35].
In quadratic approximation,
−SK(f) =
fi ln
(fi − f ∗i )2
3.7 ELBM collisions as a smooth limiter
On the base of numerical tests, the authors of [41] claim that the positivity
rule provides the same results (in the sense of stability and absence/presence
of spurious oscillations) as the ELBM models, but ELBM provides better
accuracy.
For the formal definition of ELBM (4) our tests do not support claims that
ELBM erases spurious oscillations (see below). Similar observation for Burgers
equation was previously published in [4]. We understand this situation in the
following way. The entropic method consists at least of three components:
(1) entropic quasiequilibrium, defined by entropy maximisation;
(2) entropy balanced collisions (4) that have to provide proper entropy bal-
ance;
(3) a method for the solution of the transcendental equation S(f) = S(f̃) to
find α = α(f) in (4).
It appears that the first two items do not affect spurious oscillations at all,
if we solve the equation for α(f) with high accuracy. Additional viscosity
is, potentially, added by explicit analytic formulas for α(f). In order not to
decrease entropy, errors in these formulas always increase dissipation. This
can be interpreted as a hidden transformation of the form (7), where the
coefficients in φ depend also on f ∗.
3.8 Monotonic and double monotonic limiters
Two monotonicity properties are important in the theory of nonequilibrium
entropy limiters:
(1) a limiter should move the distribution to equilibrium: in all cases of (1)
0 ≤ φ ≤ 1. This is the dissipativity condition which means that limiters
never produce negative entropy.
(2) a limiter should not change the order of states on the line: if for two
distributions with the same moments, f and f ′, ∆S(f) > ∆S(f ′) before
the limiter transformation, then the same inequality should hold after the
limiter transformation too. For example, for the limiter (7) it means that
∆S(f ∗ + xφ(∆S(f ∗ + x(f − f ∗))(f − f ∗)) is a monotonically increasing
function of x > 0.
In quadratic approximation,
∆S(f ∗ + x(f − f ∗)) = x2∆S(f),
∆S(f ∗ + xφ(∆S(f ∗ + x(f − f ∗))(f − f ∗)) = x2φ2(x2∆S(f)),
and the second monotonicity condition transforms into the following require-
ment: yφ(y2s) is a monotonically increasing (not decreasing) function of y > 0
for any s > 0.
If a limiter satisfies both monotonicity conditions, we call it “double mono-
tonic”. For example, Ehrenfests’ regularisation satisfies the first monotonicity
condition, but obviously violates the second one. The limiter (8) violates the
first condition for small ∆S, but is dissipative and satisfies the second one in
quadratic approximation for large ∆S. The limiter with φ = 1/(1+α∆Sk) al-
ways satisfies the first monotonicity condition, violates the second if k > 1/2,
and is double monotonic (in quadratic approximation for the second condi-
tion), if 0 < k ≤ 1/2. The threshold limiters (11) are also double monotonic.
Of course, it is not forbidden to use any type of limiters under the local and
global control of dissipation, but double monotonic limiters provide some nat-
ural properties automatically, without additional care.
4 Numerical experiment
To conclude this paper we report some numerical experiments conducted to
demonstrate the performance of some of the proposed nonequilibrium entropy
limiters for LBM from Sec. 3.
4.1 Velocities and quasiequilibria
We will perform simulations using both entropic and non-entropic quasiequi-
libria, but we always work with an athermal LBM model. Whenever we use
non-entropic quasiequilibria we employ Kullback entropy (13).
In 1D, we use a lattice with spacing and time step δt = 1 and a discrete
velocity set {v1, v2, v3} := {0,−1, 1} so that the model consists of static, left-
and right-moving populations only. The subscript i denotes population (not
lattice site number) and f1, f2 and f3 denote the static, left- and right-moving
populations, respectively. The entropy is S = −H , with
H = f1 log(f1/4) + f2 log(f2) + f3 log(f3),
(see, e.g., [27]) and, for this entropy, the local entropic quasiequilibrium state
f ∗ is available explicitly:
f ∗1 =
1 + 3u2
f ∗2 =
(3u− 1) + 2
1 + 3u2
f ∗3 = −
(3u+ 1)− 2
1 + 3u2
where
fi, u :=
vifi. (15)
The standard non-entropic polynomial quasiequilibria [38] are:
f ∗1 =
f ∗2 =
(1− 3u+ 3u2),
f ∗3 =
(1 + 3u+ 3u2).
In 2D, we employ a uniform 9-speed square lattice with discrete velocities
{vi | i = 0, 1, . . . 8}: v0 = 0, vi = (cos((i − 1)π/2), sin((i − 1)π/2)) for i =
1, 2, 3, 4, vi =
2(cos((i − 5)π
), sin((i − 5)π
)) for i = 5, 6, 7, 8. The
numbering f0, f1, . . . , f8 are for the static, east, north, west, south, north-
east, northwest, southwest and southeast-moving populations, respectively.
As usual, the entropic quasiequilibrium state, f ∗, can be uniquely determined
by maximising an entropy functional
S(f) = −
fi log
subject to the constraints of conservation of mass and momentum [2]:
f ∗i = ρWi
1 + 3u2j
2uj +
1 + 3u2j
1− uj
. (17)
Here, the lattice weights, Wi, are given lattice-specific constants: W0 = 4/9,
W1,2,3,4 = 1/9 and W5,6,7,8 = 1/36. Analogously to (15), the macroscopic vari-
ables ρ and u = (u1, u2) are the zeroth and first moments of the distribution
f , respectively. The standard non-entropic polynomial quasiequilibria [38] are:
f ∗i = ρWi
1 + 3viu+
9(viu)
. (18)
4.2 LBGK and ELBM
The governing equations for LBGK are
fi(x+ vi, t+ 1) = f
i (x, t) + (2β − 1)(f ∗i (x, t)−fi(x, t)), (19)
where β = 1/(2ν + 1).
For ELBM (4) the governing equations are:
fi(x+ vi, t+ 1) = (1− β)f ∗i (x, t) + βf̃i(x, t), (20)
with β as above and f̃ = (1−α)f+αf ∗. The parameter, α, is chosen to satisfy
a constant entropy condition. This involves finding the nontrivial root of the
equation
S((1− α)f + αf ∗) = S(f). (21)
To solve (21) numerically we employ a robust routine based on bisection. The
root is solved to an accuracy of 10−15 and we always ensure that the returned
value of α does not lead to a numerical entropy decrease. We stipulate that
if, at some site, no nontrivial root of (21) exists we will employ the positivity
rule instead (Fig. 1).
4.3 Shock tube
The 1D shock tube for a compressible athermal fluid is a standard benchmark
test for hydrodynamic codes. Our computational domain will be the interval
[0, 1] and we discretize this interval with 801 uniformly spaced lattice sites.
We choose the initial density ratio as 1:2 so that for x ≤ 400 we set ρ = 1.0
else we set ρ = 0.5. We will fix the kinematic viscosity of the fluid at ν = 10−9.
4.3.1 Comparison of LBGK and ELBM
In Fig. 2 we compare the shock tube density profile obtained with LBGK
(using entropic quasiequilibria (14)) and ELBM. On the same panel we also
display both the total entropy S(t) :=
x S(x, t) and total nonequilibrium
entropy ∆S(t) :=
x∆S(x, t) time histories. As expected, by construction,
we observe that total entropy is (effectively) constant for ELBM. On the other
hand, LBGK behaves non-entropically for this problem. In both cases we ob-
serve that nonequilibrium entropy grows with time.
As we can see, the choice between the two collision formulas LBGK (19)
or ELBM (20) does not affect spurious oscillation, and reported regularisa-
tion [29] is, perhaps, the result of approximate analytical solution of the equa-
tion (21). Inaccuracy in the solution of (21) can be interpreted as a hidden
nonequilibrium entropy limiter. But it should be mentioned that the entropic
method consists not only of the collision formula, but, what is important, in-
cludes a special choice of quasiequilibrium that could improve stability (see,
e.g., [13]). Indeed, when we compare ELBM with LBGK using either entopic or
standard polynomial quasiequilibria, there appears to be some gain in employ-
ing entropic quasiequilibria (Fig. 3). We observe that the post-shock region
for the LBGK simulations is more oscillatory when polynomial quasiequilibria
are used. In Fig. 3 we have also included a panel with the simulation result-
ing from a much higher viscosity (ν = 3.3333 × 10−2). Here, we observe no
appreciable differences in the results of LBGK and ELBM.
0 0.5 1
0 100 200 300 400
0 100 200 300 400
0 0.5 1
0 100 200 300 400
0 100 200 300 400
Fig. 2. Density and profile of the 1:2 athermal shock tube simulation with ν = 10−9
after 400 time steps using (a) LBGK (19); (b) ELBM (20). In this example, no
negative population are produced by any of the methods so the positivity rule is
redundant. For ELBM in this example, (21) always has a nontrivial root. Total
entropy and nonequilibrium entropy time histories are shown in panels (c), (d) and
(e), (f) for LBGK and ELBM, respectively.
0 0.5 1
0 0.5 1
0 0.5 1
0 0.5 1
0 0.5 1
0 0.5 1
Fig. 3. Density and velocity profile of the 1:2 isothermal shock tube simula-
tion after 400 time steps using (a) LBGK (19) with polynomial quasiequilib-
ria (16) [ν = 3.3333 × 10−2]; (b) LBGK (19) with entropic quasiequilibria (14)
[ν = 3.3333 × 10−2]; (c) ELBM (20) [ν = 3.3333 × 10−2]; (d) LBGK (19) with
polynomial quasiequilibria (16) [ν = 10−9]; (e) LBGK (19) with entropic quasiequi-
libria (14) [ν = 10−9]; (f) ELBM (20) [ν = 10−9].
4.3.2 Nonequilibrium entropy limiters.
Now, we would like to demonstrate just a representative sample of the many
possibilities of limiters suggested in Sec. 3. In each case the limiter is im-
plemented by a post-processing routine immediately following the collision
step (either LBGK (19) or ELBM (20)). Here, we will only consider LBGK
collisions and entropic quasiequilibria (14).
The post-processing step adjusts f by the update formula:
f 7→ f ∗ + φ(∆S)(f − f ∗),
where ∆S is defined by (6) and φ is a limiter function.
For the Ehrenfests’ regularisation one would choose
φ(∆S)(x) =
1, ∆S(x) ≤ δ,
0, otherwise,
where δ is a pre-specified threshold value. Furthermore, it is pertinent to select
just k sites with highest ∆S > δ. This limiter has been previously applied to
the shock tube problem in [6,7,8] and we will not reproduce those results here.
Instead, our first example will be the following smooth limiter:
φ(∆S) =
1 + α∆Sk
. (22)
For this limiter, we will fix k = 1/2 (so that the limiter is double monotonic in
quadratic approximation to entropy) and compare the density profiles for α =
δ/(E(∆S)k), δ = 0.1, 0.01, 0.001. We have also ensured an ensemble-dependent
limiter because of the dependence of α on the average E(∆S). As with Fig. 2,
we accompany each panel with the total entropy and nonequilibrium entropy
histories. Note the different scales for nonequilibrium entropy. Note also that
entropy (necessarily) now grows due to the additional dissipation.
Our next example (Fig. 5) considers the threshold filter (10). In this example
we choose the estimates ∆S0 = 5E(∆S), 10E(∆S), 20E(∆S) and fix the tol-
erance δ = 0 so that the influence of the threshold alone can be studied. Only
entropic adjustments are accepted in the limiter: ∆St ≤ ∆S. As the threshold
increases, nonequilibrium entropy grows faster and spurious begin to appear.
Finally, we test the median filter (Fig. 6). We choose a minimal filter so that
only the nearest neighbours are considered. As with the threshold filter, we
introduce a tolerance δ and we try the values δ = 10−3, 10−4, 10−5. Only
entropic adjustments are accepted in the limiter: ∆Smed ≤ ∆S.
0 0.5 1
0 100 200 300 400
0 100 200 300 400
0 0.5 1
0 100 200 300 400
0 100 200 300 400
0.025
0 0.5 1
0 100 200 300 400
0 100 200 300 400
Fig. 4. Density and profile of the 1:2 athermal shock tube simulation with ν = 10−9
after 400 time steps using LBGK (19) and the smooth limiter (22) with k = 1/2,
α = δ/(E(∆S)k) and (a) δ = 0.1; (b) δ = 0.01 and (c) δ = 0.001. Total entropy and
nonequilibrium entropy time histories for each parameter set {k, α(δ)} are displayed
in the adjacent panels.
We have seen that each of the examples we have considered (Fig. 4, Fig. 5
and Fig. 6) is capable of subduing spurious post-shock oscillations compared
with LBGK (or ELBM) on this problem (cf. Fig. 2). Of course, by limiting
nonequilibrium entropy the result is necessarily an increase in entropy.
From our experiences our recommendation is that the median filter is the
superior choice amongst all the limiters suggested in Sec. 3. The action of the
median filter is found to be both extremely gentle and, at the same time, very
effective.
4.4 Lid-driven cavity
Our second numerical example is the classical 2D lid-driven cavity flow. A
square cavity of side length L is filled with fluid with kinematic viscosity ν
(initially at rest) and driven by the cavity lid moving at a constant velocity
(u0, 0) (from left to right in our geometry).
0 0.5 1
0 100 200 300 400
0 100 200 300 400
0 0.5 1
0 100 200 300 400
0 100 200 300 400
0 0.5 1
0 100 200 300 400
0 100 200 300 400
Fig. 5. Density and profile of the 1:2 athermal shock tube simulation with ν = 10−9
after 400 time steps using LBGK (19) and the threshold limiter (10) with (a)
∆St = 5E(∆S); (b) ∆St = 10E(∆S) and (c) ∆St = 20E(∆S). Total entropy and
nonequilibrium entropy time histories for each threshold ∆St are displayed in the
adjacent panels.
We will simulate the flow on a 100 × 100 grid using LBGK regularised with
the median filter limiter. Unless otherwise stated, we use entropic quasiequilib-
ria (17). The implementation of the filter is as follows: the filter is not applied
to boundary nodes; for nodes which immediately neighbour the boundary the
stencil consists of the 3 nearest neighbours (including itself) closest to the
boundary; for all other nodes the minimal stencil of 9 nearest neighbours is
used.
We have purposefully selected such a coarse grid simulation because it is read-
ily found that, on this problem, unregularised LGBK fails (blows-up) for all
but the most modest Reynolds numbers Re := Lu0/ν.
4.4.1 Steady-state vortex centres
For modest Reynolds number the system settles to a steady state in which the
dominant features are a primary central rotating vortex, with several counter-
rotating secondary vortices located in the bottom-left, bottom-right (and pos-
0 0.5 1
0 100 200 300 400
0 100 200 300 400
0 0.5 1
0 100 200 300 400
0 100 200 300 400
0 0.5 1
0 100 200 300 400
0 100 200 300 400
Fig. 6. Density and profile of the 1:2 athermal shock tube simulation with ν = 10−9
after 400 time steps using LBGK (19) and the minimal median limiter with (a)
δ = 10−5; (b) δ = 10−4 and (c) δ = 10−3. Total entropy and nonequilibrium
entropy time histories for each tolerance δ are displayed in the adjacent panels.
sibly top-left) corners.
Steady state has been extensively investigated in the literature. The study
of Hou et al [24] simulates the flow over a range of Reynolds numbers using
unregularised LBGK on a 256×256 grid. Primary and secondary vortex centre
data is provided. We compare this same statistic for the present median filtered
coarse grid simulation. We will employ the same convergence criteria used
in [24]. Namely, we deem that steady state has been reached by ensuring
that the difference between the maximum value of the stream function for
successive 10, 000 time steps is less that 10−5. The stream function, which is
not a primary variable in the LBM simulation, is obtained from the velocity
data by integration using Simpson’s rule. Vortex centres are characterised as
local extrema of the stream function.
We compare our results with the LBGK simulations in [24] and [41]. To align
ourselves with these studies we specify the following boundary condition: lid
profile is constant; remaining cavity walls are subject to the “bounce-back”
condition [38]. In our simulations, the initial uniform fluid density profile is
ρ = 2.7 and the velocity of the lid is u0 = 1/10 (in lattice units).
Collected in Table 1, for Re = 2000, 5000 and 7500, are the coordinates of
the primary and secondary vortex centres using (a) unregularised LBGK; (b)
LBGK with median filter limiter (δ = 10−3); (c) LBGK with median filter lim-
iter (δ = 10−4), all with non-entropic polynomial quasiequilibria (18). Lines
(d), (e) and (f) are the same but with entropic quasiequilibria (17). The re-
maining lines of Table 1 are as follows: (g) literature data [24] (unregularised
LBGK on a 256×256 grid); (h) literature data [41] (positivity rule); (i) litera-
ture data [41] (ELBM). With the exception of (g), all simulation are conducted
on a 100 × 100 grid. The top-left vortex does not appear at Re = 2000 and
no data was provided for it in [41] at Re = 5000. The unregularised LBGK
Re = 7500 simulation blows-up in finite time and the simulation becomes
meaningless. The y-coordinate of the two lower-vortices at Re = 5000 in (i)
appear anomalously small and were not reproduced by our experiments with
the positivity rule (not shown).
We have conducted two runs of the experiment with the median filter param-
eter δ = 10−3 and δ = 10−4. Despite the increased number of realisations the
vortex centre locations remain effectively unchanged and we detect no signif-
icant variation between the two runs. This demonstrates the gentle nature of
the median filter. At Reynolds Re = 2000 the median filter has no effect at all
on the vortex centres compared with LBGK.
We find no significant differences between the experiments with entropic and
non-entropic polynomial quasiequilibria in this test.
The coordinates of the primary vortex centre for unregularised LBGK at Re =
5000 are already quite inaccurate as LBGK begins to lose stability. Stability
is lost entirely at some critical Reynolds number 5000 < Re ≤ 7500 and the
simulation blows-up.
Furthermore, we have agreement (within grid resolution) with the data given
in [24]. Also compiled in Table 1 is the data from the limiter experiments
conducted in [41] (although not explicitly discussed in the language of limiters
by the authors of that work). In [41] the authors give vortex centre data for
the positivity rule (Fig. 1) and for ELBM (which we interpret as containing a
hidden limiter). In [41] the positivity rule is called FIX-UP.
As Reynolds number increases the flow in the cavity is no longer steady and a
more complicated flow pattern emerges. On the way to a fully developed tur-
bulent flow, the lid-driven cavity flow is known to undergo a series of period
doubling Hopf bifurcations. On our coarse grid, we observe that the coordi-
nates of the primary vortex centre (maximum of the stream function) is a very
robust feature of the flow, with little change between coordinates (no change
in y-coordinates) computed at Re = 5000 and Re = 7500 with the median fil-
ter. On one hand, because of this observation it becomes inconclusive whether
Table 1
Primary and secondary vortex centre coordinates for the lid-driven cavity flow at
Re = 2000, 5000, 7500.
Primary Lower-left Lower-right Top-left
Re x y x y x y x y
2000 (a) 0.5253 0.5455 0.0909 0.1010 0.8384 0.1010 Not applicable
2000 (b) 0.5253 0.5455 0.0909 0.1010 0.8384 0.1010 Not applicable
2000 (c) 0.5253 0.5455 0.0909 0.1010 0.8384 0.1010 Not applicable
2000 (d) 0.5253 0.5455 0.0909 0.1010 0.8384 0.1010 Not applicable
2000 (e) 0.5253 0.5455 0.0909 0.1010 0.8384 0.1010 Not applicable
2000 (f) 0.5253 0.5455 0.0909 0.1010 0.8384 0.1010 Not applicable
2000 (g) 0.5255 0.5490 0.0902 0.1059 0.8471 0.0980 Not applicable
2000 (h) 0.5200 0.5450 0.0900 0.1000 0.8300 0.0950 Not applicable
2000 (i) 0.5200 0.5500 0.0890 0.1000 0.8300 0.1000 Not applicable
5000 (a) 0.5152 0.6061 0.0808 0.1313 0.7980 0.0707 0.0505 0.8990
5000 (b) 0.5152 0.5354 0.0808 0.1313 0.8081 0.0808 0.0606 0.8990
5000 (c) 0.5152 0.5354 0.0808 0.1313 0.8081 0.0808 0.0707 0.8889
5000 (d) 0.5152 0.5960 0.0808 0.1313 0.8081 0.0808 0.0505 0.8990
5000 (e) 0.5152 0.5354 0.0808 0.1313 0.8081 0.0808 0.0606 0.8990
5000 (f) 0.5152 0.5354 0.0808 0.1313 0.8081 0.0808 0.0707 0.8889
5000 (g) 0.5176 0.5373 0.0784 0.1373 0.8078 0.0745 0.0667 0.9059
5000 (h) 0.5150 0.5680 0.0950 0.0100 0.8450 0.0100 Not available
5000 (i) 0.5150 0.5400 0.0780 0.1350 0.8050 0.0750 Not available
7500 (a) — — — — — — — —
7500 (b) 0.5051 0.5354 0.0707 0.1515 0.7879 0.0707 0.0606 0.8990
7500 (c) 0.5051 0.5354 0.0707 0.1515 0.7879 0.0707 0.0707 0.8889
7500 (d) — — — — — — — —
7500 (e) 0.5051 0.5354 0.0707 0.1515 0.7879 0.0707 0.0606 0.8990
7500 (f) 0.5051 0.5354 0.0707 0.1515 0.7879 0.0707 0.0707 0.8889
7500 (g) 0.5176 0.5333 0.0706 0.1529 0.7922 0.0667 0.0706 0.9098
the median limiter is adding too much additional dissipation. On the other
hand, a more studious choice of control criteria may indicate that the first
bifurcation has already occurred by Re = 7500.
4.4.2 First Hopf bifurcation
A survey of available literature reveals that the precise value of Re at which
the first Hopf bifurcation occurs is somewhat contentious, with most current
studies (all of which are for incompressible flow) ranging from around Re =
7400–8500 [9,32,33]. Here, we do not intend to give a precise value because
it is a well observed grid effect that the critical Reynolds number increases
(shifts to the right) with refinement (see, e.g., Fig. 3 in [33]). Rather, we
will be content to localise the first bifurcation and, in doing so, demonstrate
that limiters are capable of regularising without effecting fundamental flow
features.
To localise the first bifurcation we take the following algorithmic approach.
Entropic quasiequilibria are in use. The initial uniform fluid density profile
is ρ = 1.0 and the velocity of the lid is u0 = 1/10 (in lattice units). We
record the unsteady velocity data at a single control point with coordinates
(L/16, 13L/16) and run the simulation for 5000 non-dimensionless time units
(5000L/u0 time steps). Let us denote the final 1% of this signal by (usig, vsig).
We then compute the energy Eu (ℓ2-norm normalised by non-dimensional
signal duration) of the deviation of usig from its mean:
Eu :=
u0|usig|
(usig − usig)
, (23)
where |usig| and usig denote the length and mean of usig, respectively. We
choose this robust statistic instead of attempting to measure signal amplitude
because of numerical noise in the LBM simulation. The source of noise in LBM
is attributed to the existence of an inherently unavoidable neutral stability
direction in the numerical scheme (see, e.g., [8]).
We opt not to employ the “bounce-back” boundary condition used in the pre-
vious steady state study. Instead we will use the diffusive Maxwell boundary
condition (see, e.g., [11]), which was first applied to LBM in [1]. The essence
of the condition is that populations reaching a boundary are reflected, propor-
tional to equilibrium, such that mass-balance (in the bulk) and detail-balance
are achieved. The boundary condition coincides with “bounce-back” in each
corner of the cavity.
To illustrate, immediately following the advection of populations consider the
situation of a wall, aligned with the lattice, moving with velocity uwall and
with outward pointing normal to the wall in the negative y-direction (this is
the situation on the lid of the cavity with uwall = u0). The implementation
of the diffusive Maxwell boundary condition at a boundary site (x, y) on this
wall consists of the update
fi(x, y, t+ 1) = γf
i (uwall), i = 4, 7, 8,
f2(x, y, t) + f5(x, y, t) + f6(x, y, t)
f ∗4 (uwall) + f
7 (uwall) + f
8 (uwall)
Observe that, because density is a linear factor of the quasiequilibria (17),
the density of the wall is inconsequential in the boundary condition and can
therefore be taken as unity for convenience. As is usual, only those populations
pointing in to the fluid at a boundary site are updated. Boundary sites do not
undergo the collisional step that the bulk of the sites are subjected to.
We prefer the diffusive boundary condition over the often preferred “bounce-
back” boundary condition with constant lid profile. This is because we have
experienced difficulty in separating the aforementioned numerical noise from
the genuine signal at a single control point using “bounce-back”. We remark
that the diffusive boundary condition does not prevent unregularised LBGK
from failing at some critical Reynolds number Re > 5000.
Now, we conduct an experiment and record (23) over a range of Reynolds
numbers. In each case the median filter limiter is employed with parameter
δ = 10−3. Since the transition between steady and periodic flow in the lid-
driven cavity is known to belong to the class of standard Hopf bifurcations
we are assured that E2u ∝ Re [16]. Fitting a line of best fit to the resulting
data localises the first bifurcation in the lid-driven cavity flow to Re = 7135
(Fig. 7). This value is within the tolerance of Re = 7402±4% given in [33] for
a 100×100 grid. We also provide a (time averaged) phase space trajectory and
Fourier spectrum for Re = 7375 at the monitoring point (Fig. 8 and Fig. 9)
which clearly indicate that the first bifurcation has been observed.
5 Conclusions
Entropy and thermodynamics are important for stability of the lattice Boltz-
mann methods. It is now clear: after almost 10 years of work since the pub-
lication of [26] proved this statement (the main reviews are [5,28,39]). The
question is now: “how does one utilise, optimally, entropy and thermody-
namic structures in lattice Boltzmann methods?”. In our paper we attempt to
propose a solution (temporary, at least). Our approach is applicable to both
entropic as well as for non-entropic polynomial quasiequilibria.
5750 6000 6250 6500 6750 7000 7250 7500 7750 8000
0.005
0.015
0.025
0.035
0.045
(7135,0)
Fig. 7. Plot of energy squared, E2u (23), as a function of Reynolds number, Re, using
LBGK regularised with the median filter limiter with δ = 10−3 on a 100× 100 grid.
Straight lines are lines of best fit. The intersection of the sloping line with the x-axis
occurs close to Re = 7135.
We have constructed a system of nonequilibrium entropy limiters for the lattice
Boltzmann methods (LBM):
• the positivity rule that provides positivity of distribution;
• the pointwise entropy limiters based on selection and correction of most
nonequilibrium values;
• filters of nonequilibrium entropy, and the median filter as a filter of choice.
All these limiters exploit physical properties of LBM and allow control of total
additional entropy production. In general, they do the same work for LBM as
flux limiters do for finite differences, finite volumes and finite elements meth-
ods, and come into operation when sharp gradients are present. For smoothly
changing waves, the limiters do not operate and the spatial derivatives can be
represented by higher order approximations without introducing non-physical
oscillations. But there are some differences too: for LBM the main idea behind
the construction of nonequilibrium entropy limiter schemes is to limit a scalar
quantity — the nonequilibrium entropy — or to delete the “salt and pepper”
noise from the field of this quantity. We do not touch the vectors or tensors
of spatial derivatives, as it is for flux limiters.
Standard test examples demonstrate that the developed limiters erase spurious
oscillations without blurring of shocks, and do not affect smooth solutions. The
limiters we have tested do not produce a noticeable additional dissipation and
Fig. 8. Velocity components as a function of time for the signal (usig, vsig) at the
monitoring point (L/16, 13L/16) using LBGK regularised with the median filter
limiter with δ = 10−3 on a 100 × 100 grid (Re = 7375). Dots represent simulation
results and the solid line is a 100 step time average of the signal.
allow us to reproduce the first Hopf bifurcation for 2D lid-driven cavity on a
coarse 100× 100 grid. At the same time the simplest median filter deletes the
spurious post-shock oscillations for low viscosity.
Perhaps, it is impossible to find one best nonequilibrium entropy limiter for
all problems. It is a special task to construct the optimal limiters for a specific
classes of problems.
Acknowledgments
Discussion of the preliminary version of this work with S. Succi and par-
ticipants of the lattice Boltzmann workshop held on 15th November 2006
in Leicester (UK) was very important. Author A. N. Gorban is grateful to
S. K. Godunov for the course of numerical methods given many years ago at
Novosibirsk University. This work is supported by Engineering and Physical
Sciences Research Council (EPSRC) grant number GR/S95572/01.
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
Frequency
Fig. 9. Relative amplitude spectrum for the signal usig at the monitoring point
(L/16, 13L/16) using LBGK regularised with the median filter limiter with δ = 10−3
on a 100 × 100 grid (Re = 7375). We measure a dominant frequency of ω = 0.525.
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	Introduction
	Background
	Nonequilibrium entropy limiters for LBM 
	Positivity rule
	Ehrenfests' regularisation
	Smooth limiters of nonequilibrium entropy
	Monitoring of total dissipation
	Median entropy filter
	Entropic steps for non-entropic quasiequilibria
	ELBM collisions as a smooth limiter
	Monotonic and double monotonic limiters
	Numerical experiment
	Velocities and quasiequilibria
	LBGK and ELBM
	Shock tube
	Lid-driven cavity
	Conclusions
	References
