Kinetic and Related Models
c American Institute of Mathematical Sciences
Volume 11, Number 6, December 2018
doi:10.3934/krm.2018058
pp. 1475–1501
LINEAR BOLTZMANN DYNAMICS IN A STRIP WITH LARGE
REFLECTIVE OBSTACLES: STATIONARY STATE
AND RESIDENCE TIME
Alessandro Ciallella∗ and Emilio N. M. Cirillo
Dipartimento di Scienze di Base e Applicate per l’Ingegneria
Sapienza Università di Roma
via A. Scarpa 16, I – 00161, Roma, Italy
(Communicated by Laurent Desvillettes)
Abstract. The presence of obstacles modifies the way in which particles
diffuse. In cells, for instance, it is observed that, due to the presence of
macromolecules playing the role of obstacles, the mean-square displacement
of biomolecules scales as a power law with exponent smaller than one. On the
other hand, different situations in grain and pedestrian dynamics in which the
presence of an obstacle accelerates the dynamics are known. We focus on the
time, called the residence time, needed by particles to cross a strip assuming
that the dynamics inside the strip follows the linear Boltzmann dynamics. We
find that the residence time is not monotonic with respect to the size and the
location of the obstacles, since the obstacle can force those particles that eventually cross the strip to spend a smaller time in the strip itself. We focus on
the case of a rectangular strip with two open sides and two reflective sides and
we consider reflective obstacles into the strip. We prove that the stationary
state of the linear Boltzmann dynamics, in the diffusive regime, converges to
the solution of the Laplace equation with Dirichlet boundary conditions on the
open sides and homogeneous Neumann boundary conditions on the other sides
and on the obstacle boundaries.
1. Introduction. Particle based studies of agent behavior can reproduce realistic
scenarios. Many different real situations, such as grains discharging from a silo,
traffic flow, and pedestrian dynamics, can be studied via particle based modeling.
The focus, in this paper, is on the effect of obstacles on particle flows [15].
It is well known that the mean square distance traveled by particles undergoing
Brownian motion is proportional to time. However, many experimental measures
of molecular diffusion in cells show a sublinear behavior. This phenomenon, called
anomalous diffusion, is in some cases explained as an effect of the presence of
macromolecules playing the role of obstacles for diffusing smaller molecules [16, 26,
31, 32]
While in the case of anomalous diffusion the obstacles induce a sort of slowing
down of the dynamics, on the contrary in many other different contexts it has been
noticed and exploited the fact that the presence of an obstacle can surprisingly
2010 Mathematics Subject Classification. Primary: 82B21, 76P05, 82B40; Secondary: 60J60.
Key words and phrases. Residence time, linear Boltzmann, Kinetic Theory, Monte Carlo methods, obstacles, diffusion.
∗ Corresponding author: Alessandro Ciallella.
1475
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ALESSANDRO CIALLELLA AND EMILIO N. M. CIRILLO
accelerate the dynamics improving, in particular, the flux rate of particles passing
through a bottleneck.
In granular flows, for instance when grains discharge from a silo, it happens that
the out-coming flow can be dramatically reduced due to clogging at the exit. In
[33] it was proposed that this phenomenon is caused by the formation of arches. In
the case of spherical grains it has been demonstrated that the presence of clogging
in a three–dimensional silo critically depends on the ratio between the outlet size
and the diameter of the particles [34]. A solution that is implemented to improve
the granular flow is to place an obstacle above the silo exit [2, 35] which prevents
arches to be formed or to become stable.
A similar phenomenon is observed in pedestrian flows (see, e.g., [4, 21, 23, 25] for
reviews of models and related problems). In the case of pedestrian exiting a room
under panic clogging at the door can be reduced by means of suitably positioned
obstacles, see [1, Section 6.3] and [22, 24]. It has also been noticed that a correct
positioning can reduce injuries under panicked escape from a room thanks to the
so called “waiting–room” effect [17]: pedestrians slow down and accumulate close
to the obstacle so that the exit is decongested. We mention, here, that also the
possibility of clustering far from the exit due to individual cooperation has been
object of study in [10, 13, 14].
The a priori unexpected phenomena discussed above are a sort of inversion of
the Braess’ paradox [5, 27] stating that adding a road link to a road network can
cause cars to take longer to cross the network. Indeed, in the examples discussed
above it seems that adding barriers results in a decrease of the time that particles
need to cross a region of space.
This is precisely the issue we address in this paper. Inspired by [11, 12], we consider particles entering an horizontal strip through the left boundary and eventually
exiting through the right one [20]. We compute the typical time needed to cross the
strip, called the residence time, and analyze its dependence on the size and position
of a reflecting obstacle positioned inside the strip [7, 8]. Surprisingly, we find not
monotonic behaviors of the residence time as a function of the side lengths of the
obstacle and the coordinate of its center [9]. In particular, for suitably choices of
the obstacles the residence time in presence of the barrier is smaller than the one
measured for the empty strip. In other words, our results show that placing a suitable obstacle in the strip allows to select those particles that cross the strip in a
shorter time. We also observe that the same obstacle, placed in different position,
can either increase or decrease the residence time.
Inside the strip we consider particle moving according to the Markov process
solving the linear Boltzmann equation. We stress that this is the first study of
residence times by means of the methods of Kinetic Theory. In this case we calculate
the residence time by directly simulating the motion of single particles by a Monte
Carlo method. This dynamics should be consistent with the Lorentz process at
appropriate regimes. The Lorentz gas model is a system of non–interacting particles
moving in a region where static small disks are distributed according to a Poisson
probability measure. This is a classical model for finite velocity random motions.
Particles perform uniform linear motion up to the contact with a disk where they
are elastically reflected.
We study the system in a diffusive regime and we show that there exists a unique
stationary solution which converges to the solution of a Laplace problem with mixed
LINEAR BOLTZMANN DYNAMICS IN PRESENCE OF OBSTACLES
1477
boundary conditions: Dirichlet boundary conditions on the vertical sides and homogeneous Neumann on the rest. We prove the convergence and check it numerically.
Moreover, by constructing numerically the stationary profiles we qualitatively verify
that the overall flux in presence of obstacles is decreasing, as expected by physical
intuition. This holds even in those cases in which the residence time is smaller with
respect to the empty strip case.
In Section 2 we introduce the model under investigation and we state our main
theoretical results. In Section 3 we propose a Monte Carlo algorithm that we use
to construct the stationary state of the linear Boltzmann model and we discuss the
stationary profile in presence of large fixed obstacles. In Section 4 we discuss our
results on residence time. Finally, Section 5 contains the proofs of the results we
state in Section 2.
2. Model and results. We consider a system of light particles moving in the
two–dimensional space. We choose as the domain a subset Ω of the finite strip
(0, L1 ) × (0, L2 ) ⊂ R2 . This strip has two open boundaries, that we think as the left
side ∂ΩL = {0} × (0, L2 ) and the right side ∂ΩR = {L1 } × (0, L2 ). The strip is in
contact on the left side and on the right side with two mass reservoirs at equilibrium
with particle mass densities ρL and ρR , respectively. Particles traveling into Ω are
instead specularly reflected upon colliding with the upper side (0, L1 ) × {L2 } and
lower side (0, L1 ) × {0} of the strip.
We consider the case in which large fixed obstacles are placed in the strip so
that the domain Ω is a connected set. These obstacles are convex sets with smooth
reflective boundaries. We consider a generic configuration of a finite number of
obstacles with positive mutual distance and positive distance from the sides of the
strip. In the sequel we will call ∂ΩE the union of the obstacle boundaries and the
upper and lower sides of the strip (see Figure 1). Therefore, when a particle reaches
∂ΩE it experiences a specular reflection.
Ω
ρR
ρL
∂ΩL
∂ΩR
∂ΩE
Powered by TCPDF (www.tcpdf.org)
Figure 1. Domain Ω: strip with large fixed obstacles, where ∂ΩL
and ∂ΩR are the vertical open boundaries and ∂ΩE are reflective
boundaries.
The linear Boltzmann equation is a kinetic linear equation, combining free transport and scattering off of a medium. This equation consists of two terms: a free
transport term and a collision operator L.
Let us consider the phase space Ω × S 1 , where S 1 := {v ∈ R2 : |v| = 1}. We will
consider the operator L with elastic collision kernel. The equation for (x, v) ∈ Ω×S 1
and positive times t reads
Powered by TCPDF (www.tcpdf.org)
(∂t + v · ∇x )g(x, v, t) = ηε Lg(x, v, t),
x ∈ Ω, v ∈ S 1 , t ≥ 0
(1)
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ALESSANDRO CIALLELLA AND EMILIO N. M. CIRILLO
where, by the elastic collision rule v 0 = v − 2(n · v)n, the operator L is defined for
any f ∈ L1 (S 1 ) as
Z 1
[f (v 0 ) − f (v)] dδ.
(2)
Lf (v) = λ
−1
Here n = n(δ) is the outward pointing normal to a circular scatterer of radius 1
at the point of collision among the particle with velocity v and the scatterer. So
δ = sin α if α is the angle of incidence between v and n that has δ as impact
parameter (see Figure 2); λ > 0 is a fixed parameter.
v~′
~
n
α
α
1
~v
δ
Powered by TCPDF (www.tcpdf.org)
Figure 2. Elastic collision with a scatterers: impact parameter δ
and angle of incidence α.
We denote by gε the solution of the equation corresponding to the value of ηε ,
that is a positive parameter that we let go to +∞ as ε goes to 0+ . The choice of
the kernel and the related parameters will be discussed at the end of this Section.
The equation describes the evolution of the density of particles, moving of linear
motion and having random collisions, against a circular scatterer, that preserve the
energy. The time between two consecutive jumps in the velocities is distributed
with exponential law with mean value (ληε )−1 . Since both random collisions and
hits against the elastic boundaries preserve the energy, the modulus of the velocity
of a particle moving in Ω is constant, so we consider it to be equal to one.
On the elastic boundary ∂ΩE we impose reflective boundary condition and on
the open boundary ∂ΩL ∪ ∂ΩR we set Dirichlet condition:
(
g(x, v 0 , t) = g(x, v, t)
x ∈ ∂ΩE , v · n < 0, t ≥ 0
(3)
g(x, v, t) = fB (x, v)
x ∈ ∂ΩL ∪ ∂ΩR , v · n > 0, t ≥ 0,
Powered by TCPDF (www.tcpdf.org)
where fB is defined in 4 below and v 0 is given by the elastic collision rule v 0 = v −
2(n · v)n. Here we denote by n = n(x) the inward pointing normal on the boundary
∂Ω of the domain. We consider as initial datum the function f0 (x, v) ∈ L∞ (Ω × S 1 )
and we define fB (not depending on t) as
(
ρL /2π
x ∈ ∂ΩL , v · n > 0
fB (x, v) :=
(4)
ρR /2π
x ∈ ∂ΩR , v · n > 0,
LINEAR BOLTZMANN DYNAMICS IN PRESENCE OF OBSTACLES
1479
where 1/2π is the density of the uniform distribution on S 1 .
We are interested in the study of the stationary problem associated to 1-3:
S
S
x ∈ Ω, v ∈ S 1
v · ∇x g (x, v) = ηε Lg
S
0
S
(5)
g (x, v ) = g (x, v)
x ∈ ∂ΩE , v · n < 0
S
g (x, v) = fB (x, v)
x ∈ ∂ΩL ∪ ∂ΩR , v · n > 0.
We want to investigate the behavior of the solution gεS of 5 and prove its convergence to the stationary solution of the diffusion problem in Ω with mixed boundary
conditions given by
∆ρ(x) = 0
x∈Ω
ρ(x) = ρ
x
∈ ∂ΩL
L
(6)
ρ(x) = ρR
x ∈ ∂ΩR
∂n ρ(x) = 0
x ∈ ∂ΩE .
Theorem 2.1. If ε > 0 is sufficiently small there exists a unique stationary solution
gεS ∈ L∞ (Ω × S 1 ) of 5.
Theorem 2.2. The stationary solution gεS of 5 verifies
gεS → ρ
(7)
as ε → 0, where ρ(x) is the solution to the problem 6. The convergence is in
L∞ (Ω × S 1 ).
The choice of the elastic collision kernel for the operator L defined in 2 is due
to the physical model we have in mind. We are considering a particle moving
with initial velocity v ∈ S 1 and hitting an hard circular scatterer whose position
is random. The random impact parameter δ chosen uniformly in [−1, 1] allows to
individuate this collision. In a similar way we could let the particle move following
the Lorentz process, that is moving freely in a region where static small disks of
radius ε are distributed according to a Poisson probability measure and elastically
colliding with those disks. In this case for suitable choices of the mean value of
the Poisson distribution in terms of ε and ηε it could be possible to prove that the
diffusive limit for the linear Boltzmann equation and the Lorentz process in a (small
disks) low density limit are asymptotically equivalent in the limit ε → 0 (see [3] for
the case of an infinite 2D slab with open boundary).
3. Numerical convergence of stationary linear Boltzmann. We investigate,
here, the stationary solution of the linear Boltzmann equation from a numerical
point of view. Our algorithm directly simulates the motion of single particles following the linear Boltzmann equation. In the simulations we exploit the interpretation of the linear Boltzmann equation as the equation describing a stochastic jump
process in the velocities and we directly simulate the motion of single particles.
We will show that the numerical stationary solution that we construct is close
to the solution of the associated Laplace problem 6 if the scale parameter ε is
small enough, that is to say if the average time tm between two consecutive hits is
sufficiently small. This time will be called in the sequel mean flight time.
We will construct the solution of the Laplace problem 6 in our geometry by using
the COMSOL Multiphysics software.
We proceed in the following way: we consider particles entering in Ω from the
reservoirs. A particle starts its trajectory from the left boundary ∂ΩL or from the
1480
ALESSANDRO CIALLELLA AND EMILIO N. M. CIRILLO
right boundary ∂ΩR , where the mass density is ρL and ρR respectively. Therefore
the number of particles we let enter from each side is chosen to be proportional to ρL
and ρR . In other words, we select the starting side of the particle with probability
ρL /(ρL + ρR ) (left side) and ρR /(ρL + ρR ) (right side) respectively. Then we draw
uniformly the position x in ∂ΩL or ∂ΩR and the velocity v in S 1 with v · n(x) > 0,
n(x) inward-pointing normal.
Once the particle started, it moves with uniform linear motion until it hits a
scatterers or the elastic boundary ∂ΩE . We pick th , the time until the hit with a
scatterers, following the exponential law of mean tm , with tm a fixed parameter.
The particle travels with velocity v for a time th . If during this time it hits the
elastic boundary ∂ΩE , its velocity changes performing an elastic collision. At time
th we simulate an hit with a scatterers by picking an impact parameter δ uniformly
in [−1, 1] and changing the velocity from v to v 0 = v − 2(v · n)n, where δ = sin α
and n = n(δ) is the outward pointing normal to the scatterers such that the angle
of incidence between n and v is α (see Figure 2).
We proceed as before by letting the particle move until it leaves Ω by reaching
again the open boundary ∂ΩL ∪ ∂ΩR . Then the particle exits from the system and
we are ready to simulate another particle. We simulate a number N of particles.
The random number generator we use in our simulation is the Mersenne Twister
[29, 30].
We want to construct the stationary solution of equation 1-3. Note that we can
simulate particles one by one since in the considered model particles are not interacting. Moreover, being the stationary state not dependent on the initial datum f0 ,
we consider in this algorithm only particles starting from the reservoirs. We assume
that in the stationary state the density of particles in a region is proportional to
the total time spent by all particles in that region. Moreover, due to isotropy, there
is no preferential direction for the velocity, so the stationary gεS is not dependent
on v.
We divide the space Ω in equal small square cells. For every particle we calculate
the time that it spends in every cell. Then we calculate the total time that particles
spend in each cell. For tm going to zero and N very big, in every infinitesimal
region of Ω the stationary density has to be proportional to the total time spent
by particles in that region. Considering sufficiently small cells, for tm small enough
and a number of particles simulated N big enough, the total time spent in the cell
we calculate is proportional to our numerical stationary solution.
We construct with our algorithm a grid of sojourn times in the cells. The last
step we have to do is to normalize it. It is sufficient to multiply by a constant,
obtained by imposing the correct value of gεS in a point (e.g., the boundary datum).
We call our numerical solution htm (x). So we fix a cell in contact with the reservoir
where we calculated a sojourn time tc and consider the value fB of the stationary
solution. We choose as multiplication constant c = fB (x)/tc . So the simulated
solution htm (x) is constructed by multiplying the sojourn time in each cell for this
constant c.
In the sequel we will show that for tm sufficiently small and N big enough, the
simulated solution htm (x) well approximates the solution ρ(x) of the associated
Laplace problem.
All the simulations we are going to discuss in this Section are performed with
N = 5 · 107 particles.
LINEAR BOLTZMANN DYNAMICS IN PRESENCE OF OBSTACLES
1481
Let us preliminary consider the case of Ω = (0, 4) × (0, 1) in absence of obstacles.
We fix mass densities at the reservoirs ρL = 1 and ρR = 0.5. In this first case there
is no dependence on the vertical coordinate in the solution of the Laplace problem
6. Indeed we know that the problem has analytic solution ρ(x1 , x2 ) = 1 − x1 /8,
where we are denoting by (x1 , x2 ) ∈ R × R the spatial coordinates x in Ω. We
divide the domain in 200 × 50 equal square cells and we consider simulations with
different values of tm , to understand which values of the mean time tm provide a
good approximation of the solution we are looking for.
In Figures 3 and 4 we show that the choice of tm of the order of 10−2 is suitable
for our purpose. Indeed in Figures 3 we compare the simulated solution htm with
the analytic solution for the values tm = 2 · 10−1 , tm = 10−1 , tm = 2 · 10−2 . We
see in a 3D plot and in a 2D plot, obtained from the previous one by averaging on
the x2 variable, that htm becomes closer to ρ(x1 ) when tm decreases. So in Figures
4 we fix the parameter tm = 10−2 and we verify that htm is close to the function
ρ(x1 ) showing the relative error |htm − ρ|/ρ.
1
stationary density
stationary density
0.9
1
0.9
0.8
0.7
0.6
0.5
1
0
1
0.5
x1 2
3
0.8
0.7
0.6
0.5
x2
0
4 0
1
2
3
4
x1
Figure 3. Plot of the simulated solutions htm in a 3D plot and in
a 2D plot constructed by averaging on the x2 variable: in dark gray
tm = 2 · 10−1 , in gray tm = 10−1 , in light gray tm = 2 · 10−2 . In
black (grid and dashed line) the analytic solution ρ of the associated
Laplace problem.
1
0.016
0.012
x2
0.01
0.5
0.008
0.006
0.004
relative error
0.014
0.002
0
0
0
1
2
3
4
x1
Figure 4. Simulation parameter tm = 10−2 : relative error |htm − ρ|/ρ.
We consider now the more interesting case with presence of obstacles in the strip.
Our domain Ω is the strip (0, 4) × (0, 1) minus the obstacles. We fix again mass
densities at the reservoirs ρL = 1 and ρR = 0.5. We fix again tm = 10−2 , since we
have shown that in the empty case this choice for the exponential clock allows to
1482
ALESSANDRO CIALLELLA AND EMILIO N. M. CIRILLO
construct a numerical solution htm that is close to the analytical solution ρ(x1 , x2 )
of the associated Laplace problem 6. We propose different situations for the domain
Ω and we show in Figures 5 - 7 that in each case the simulation algorithm works
correctly. We compare our numerical solution with the solution ρ(x1 , x2 ) of the
associated Laplace problem 6. We show the plots of the htm and ρ and the map of
the relative error |htm − ρ|/ρ as in Figure 4 .
The first case we consider is the presence of a big squared obstacle with side
8 · 10−1 , in different positions into the strip. In Figure 5 the results on two different
positions are presented.
0.014
0.01
0.008
0.5
0.006
0.004
1
0
1
0.5
x1
2
3
0.002
0
x2
0
0
1
4 0
2
3
4
x1
1
0.012
0.008
0.5
0.006
0.004
relative error
0.01
1
0.9
0.8
0.7
0.6
0.5
x2
stationary density
relative error
0.012
x2
stationary density
1
1
0.9
0.8
0.7
0.6
0.5
0.002
1
0
1
0.5
x1 2
3
4 0
x2
0
0
0
1
2
3
4
x1
Figure 5. Simulation parameter tm = 10−2 : on the left in gray the
numerical solution htm and in black the solution ρ of the associated
Laplace problem; on the right the relative error |htm − ρ|/ρ. Into
the strip there is a square obstacle with side 8 · 10−1 .
Another interesting case is the presence of a very thin and tall obstacle placed
vertically inside the strip. We show it in Figure 6, by picking a thin obstacle of
height of 8 · 10−1 .
The last case we want to present is the presence in the strip of two obstacles.
In Figure 7 we consider two different situations: in the first we set in the strip two
squared obstacles with sides 6 · 10−1 long, in the second we place into the strip two
rectangular obstacles of sides 4 · 10−1 and 7 · 10−1 .
Note that due to the presence of obstacles the solutions are not independent of
the vertical coordinate x2 anymore as it was in the empty strip case. However, we
can notice that before and beyond the obstacles in the x1 direction the stationary
states are closer to a flat state than in the empty case, with a steeper slope in the
tight channels at sides of the obstacles. The total stationary mass flux through any
vertical line {x1 } × (0, 1) ∩ Ω does not depend on x1 . Indeed, this should follow from
the Fick’s law, that we expect to be valid also in presence of obstacles (in absence
LINEAR BOLTZMANN DYNAMICS IN PRESENCE OF OBSTACLES
1483
0.014
0.01
0.008
0.5
0.006
0.004
0.002
1
0
1
0.5
x1
2
3
x2
relative error
0.012
x2
stationary density
1
1
0.9
0.8
0.7
0.6
0.5
0
0
0
4 0
1
2
3
4
x1
Figure 6. Simulation parameter tm = 10−2 : on the left in gray the
numerical solution htm and in black the solution ρ of the associated
Laplace problem; on the right the relative error |htm − ρ|/ρ. In the
strip is placed a very thin obstacle with height of 0.8.
0.016
0.012
0.01
0.5
0.008
0.006
0.004
1
1
0.5
x1 2
3
0.002
0
x2
0
0
1
4 0
2
3
4
x1
x2
stationary density
1
1
0.9
0.8
0.7
0.6
0.5
1
0
1
0.5
x1 2
3
4 0
x2
0.018
0.016
0.014
0.012
0.01
0.008
0.006
0.004
0.002
0
0.5
0
0
1
2
3
relative error
0
relative error
0.014
x2
stationary density
1
1
0.9
0.8
0.7
0.6
0.5
4
x1
Figure 7. Simulation parameter tm = 10−2 : on the left in gray the
numerical solution htm and in black the solution ρ of the associated
Laplace problem; on the right the relative error |htm − ρ|/ρ. In
the first line we show the case of two squared obstacles with side
6 · 10−1 , in the second one a couple of rectangular obstacles, taller
and thinner than the squares.
of obstacle, being the limiting problem one–dimensional, the Fick’s law holds as
shown in [3]), together with the divergence theorem and the fact that the boundary
conditions are homogeneous on ∂ΩE . The Fick’s law would tell us that, in presence
of obstacles, the total flux on the lines {x1 = a} ∩ Ω is smaller than in the empty
case, as it is possible to see focusing on the vertical lines before the obstacles. In
this sense, and opposite to what happen in the case of the study of the residence
time, we find on the flux the intuitive result we expected.
1484
ALESSANDRO CIALLELLA AND EMILIO N. M. CIRILLO
4. Residence time. We consider the domain (0, L1 )×(0, L2 ) with the same boundary conditions as in Section 2, namely, reflecting horizontal boundaries and open
vertical boundaries. As before Ω denotes a subset of this domain obtained by placing large fixed reflecting obstacles. Particles in Ω move according to the Markov
process solving the linear Boltzmann equation and described in detail in Section 3.
In Section 3 we investigated the stationary state of the system and we demonstrated that, provided the mean flight time tm is sufficiently small, the stationary
state is very well approximated by the solution of the Laplace problem 6 even in
presence of obstacles. We have also noted that, due to the presence of obstacles, the
total flux crossing the strip is smaller with respect to the one measured in absence
of obstacles. This implies that if we consider a fixed number of particles entering
the strip throught the left boundary, the number of them exiting through the right
boundary decreses when an obstacle is inserted. In our simulations we remark that
the ratio between the number of particles exiting through the left boundary in presence of an obstacle and in the empty strip case does not depend very much on the
geometry of the obstacle and, in the worst case we considered, it is approximatively
equal to 1/5. Detailed data for the different cases we studied are reported in the
figure captions of this section.
In this section, on the other hand, we focus on those particles that do the entire
trip, that is to say they enter through the left boundary and eventually exit the
strip through the right one. Limiting our numerical computation to these particles,
we measure the average time needed to cross the strip, also called the residence
time and discuss its dependence on the size and on the position of a large fixed
obstacle placed in the strip. The surprising result is that the residence time is not
monotonic with respect to the obstacle parameters, such as position and size. More
precisely, we show that obstacles can increase or decrease the residence time with
respect to the empty strip case depending on their side lengths and on their position.
Moreover, in some cases, by varying only one of these parameters a transition from
the increasing effect to the decreasing effect is observed.
In some cases we observe that the residence time measured in presence of an
obstacle is smaller than the one measured for the empty strip. In other words, we
find that the obstacle is able to select those particles that cross the strip in a smaller
time. More precisely, particles that succeed to cross the strip do it faster than they
would in absence of obstacles.
We now discuss the different cases we considered. All numerical details are in
the figure captions. The statistical error is not represented in the pictures since
it is negligible and it could not be appreciated in the graphs. In each figure we
draw a graph reporting the numerical data and a schematic picture illustrating the
performed experiment. We first describe our result and at the end of this section
we propose a possible interpretation.
In Figure 8 we report the residence time as a function of the obstacle height.
The obstacle is placed at the center of the strip and its width is very small on
the left and larger on the right. We notice that in the case of a thin barrier, the
residence time increases with the height of the obstacle. On the other hand, for a
wider obstacle, we do not find this a priori intuitive result, but we observe a not
monotonic dependence of the residence time on the obstacle height. In particular,
it is interesting to remark that if the obstacle height is chosen smaller that 0.65 the
residence time is smaller than the one measured for the empty strip. This effect is
even stronger if the width of the obstacle is increased (Figure 9).
LINEAR BOLTZMANN DYNAMICS IN PRESENCE OF OBSTACLES
420
400
395
410
390
400
residence time
residence time
1485
390
380
370
385
380
375
370
365
360
360
355
350
350
0
0.2
0.4
0.6
0.8
1
0
0.2
obstacle height
0.6
0.8
1
Ω
Ω
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0.4
obstacle height
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Figure 8. Residence time vs. height of a centered rectangular obstacle with fixed width 4 · 10−2 (on the left) and 4 · 10−1 (on the
right). Simulation parameters: L1 = 4, L2 = 1, tm = 2 · 10−2 , total number of inserted particles 108 , the total number of particles
exiting through the right boundary varies from 5.3 · 105 to 3.6 · 105
(on the left) and from 5.3 · 105 to 2.1 · 105 (on the right) depending
on the obstacle height. The solid lines represent the value of the
residence time measured for the empty strip (no obstacle).
In the left panel of Figure 10 we report the residence time as a function of the
obstacle width. The obstacle is placed at the center of the strip and its height is
fixed to 0.8. When the barrier is thin the residence time is larger than the one
measured in the empty strip case. But, when the width is increased, the residence
time decreases and at about 0.7 it becomes smaller than the empty case value. The
minimum is reached at about 2.3, then the residence time starts to increase and
when the width of the obstacles equals that of the strip the residence time becomes
equal to the empty strip value. This last fact is rather obvious, indeed, in this case
the strip reduces to two independent channels having the same width of the original
strip.
In the right panel of Figure 10 a centered square obstacle is considered. We
note that the residence time happens to be a monotonic decreasing function of the
obstacle side length.
In Figure 11 we show that, and this is really surprising, the residence time is not
monotonic even as a function of the position of the center of the obstacle. In the
left panel a squared obstacle of side length 0.8 is considered, whereas in the right
panel a thin rectangular obstacle 0.04 × 0.8 is placed in the strip. In both cases the
residence time is not monotonic and attains its minimum value when the obstacle is
placed in the center of the strip. It is worth noting, that in the case on the left when
the position of the center lays between 1.5 and 2.5 the residence time in presence
of the obstacles is smaller than the corresponding value for the empty strip.
ALESSANDRO CIALLELLA AND EMILIO N. M. CIRILLO
365
370
360
360
residence time
residence time
1486
355
350
345
340
350
340
330
320
310
335
300
0
0.2
0.4
0.6
0.8
1
0
0.2
obstacle height
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0.6
0.8
1
Ω
Ω
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0.4
obstacle height
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Figure 9. Residence time vs. height of a centered rectangular obstacle with fixed width 8 · 10−1 (on the left) and 12 · 10−1 (on the
right). Simulation parameters: L1 = 4, L2 = 1, tm = 2 · 10−2 , total number of inserted particles 108 , the total number of particles
exiting through the right boundary varies from 5.2 · 105 to 1.4 · 105
(on the left) and from 5.2 · 105 to 1.1 · 105 (on the right) depending
on the obstacle height. The solid lines represent the value of the
residence time measured for the empty strip (no obstacle).
Summarizing, the numerical experiments reported in Figures 8–11 show that
the residence time strongly depends on the obstacle geometry and position. In
particular it is seen that large centered obstacles favor the selection of particles
crossing the strip faster than in the empty strip case.
A possible interpretation of these results can be given. The strip (0, L1 ) × (0, L2 )
is partitioned in the three rectangles L (the part on the left of the obstacles), R
(the part on the right of the obstacles), and C = (0, L1 ) × (0, L2 ) \ (L ∪ R). The
phenomenon we reported above can be explained as a consequence of two competing
effects: the total time spent by a particle in the channels between the obstacle and
the horizontal boundaries is smaller with respect to the time typically spent in C
in the empty strip case because of the volume reduction due to the presence of the
obstacle. On the other hand the times spent in L and in R are larger if compared to
the times spent there by a particle in the empty strip case, due to the fact that it is
more difficult to leave these regions and enter in the channels flanking the obstacle.
The increase or the decrease of the residence time compared to the empty strip case
depends on which of the two effects dominates the particle dynamics.
In Figure 12 we consider the geometry in the right panel of Figure 8. We compute
the average time spent by particles in small squared cells (0.02 × 0.02). This local
residence time in presence of the obstacle is larger than the one measured in the
empty strip, indeed the gray surface in the picture is always above the black one.
But, if the total residence time spent in the regions L, C, and R is computed, one
discovers that the time spent in the region C decreases in presence of the obstacle,
400
365
380
360
360
355
residence time
residence time
LINEAR BOLTZMANN DYNAMICS IN PRESENCE OF OBSTACLES
340
320
300
280
1487
350
345
340
335
260
330
0
0.8
1.6
2.4
3.2
4
0
0.2
obstacle width
0.4
0.6
0.8
1
obstacle side
Ω
Ω
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Figure 10. Residence time vs. width of a centered rectangular
obstacle with fixed height 0.8 (on the left) and vs. the side length of
a centered squared obstacle (on the right). Simulation parameters:
L1 = 4, L2 = 1, tm = 2·10−2 , total number of inserted particles 108 ,
the total number of particles exiting through the right boundary
varies from 4.2 · 105 to 1.1 · 105 (on the left) and from 5.3 · 105
to 1.3 · 105 (on the right) depending on the obstacle width. The
solid lines represent the value of the residence time measured for
the empty strip (no obstacle).
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whereas the time spent in L and R increases. Note that the local residence time in
the cells belonging to the channels in C is larger with respect to the empty strip
case, but the total time in C is smaller due to the fact that the available volume in
C is decreased by the presence of the obstacle. Hence, the result in the right panel
in Figure 8 can be explained as follows: if the height of the obstacle is smaller than
0.6 the effect in C dominates the one in L and R so that the total residence time
decreases. On the other hand, when the height is larger than 0.6 the increase of the
residence time in L and R dominates its decrease in C, so that the total residence
time increases.
The Figure 13, referring to the geometry in the left panel in Figure 10, and the
Figure 14, referring to the geometry in the left panel in Figure 11, can be discussed
similarly. We just note that in Figures 12 and 13 the circles and triangles, which
correspond to the residence time in L and R, coincide due to the symmetry of the
system. Indeed, in both cases the center of the obstacle is at the center of the strip.
5. Proof of results. We prove Theorems 2.1 and 2.2. We firstly construct the
solution of the linear Boltzmann problem in form of a Dyson series. Then we are
able to prove the existence and uniqueness of the associated stationary problem.
To do this we exploit the diffusive limit of the linear Boltzmann equation in a L∞
setting and in a bigger domain containing Ω, by means of the Hilbert expansion
method (see [3, 6, 18]). The stationary solution is constructed in the form of a
1488
ALESSANDRO CIALLELLA AND EMILIO N. M. CIRILLO
500
480
480
460
residence time
residence time
460
440
420
400
380
360
440
420
400
380
340
360
320
0.7
1.4
2.1
2.8
3.5
0.7
obstacle position
Ω
C
1.4
2.1
2.8
3.5
obstacle position
Ω
C
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Figure 11. Residence time vs. position of the center of the obstacle. The obstacle is a square of side length 0.8 on the left and
a rectangle of side lengths 0.04 and 0.8 on the right. Simulation
parameters: L1 = 4, L2 = 1, tm = 2 · 10−2 , total number of inserted particles 108 , the total number of particles exiting through
the right boundary is stable at the order of 2.6 · 105 (on the left)
and of 4 · 105 (on the right) not depending on the obstacle position.
The solid lines represent the value of the residence time measured
for the empty strip (no obstacle).
5
4
3
2
1
0
200
residence time
local residence time
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120
80
40
1
x2
160
0.5
0 0
0
2
1
x1
3
4
0
0.2
0.4
0.6
0.8
obstacle height
Figure 12. As in the right panel in Figure 8. In the left panel the
height of the obstacle is equal to 0.8. Left panel: the mean time
spent by particles crossing the strip in each point of the strip (0.02×
0.02 cells have been considered) for the empty strip case (black) and
in presence of the obstacle (gray). Right panel: residence time in
regions L (circles), C (squares), and R (triangles) in presence of the
obstacle (gray) and for the empty strip case (black).
1
300
240
180
120
60
1
x2
1489
360
10
8
6
4
2
0
residence time
local residence time
LINEAR BOLTZMANN DYNAMICS IN PRESENCE OF OBSTACLES
0
0.5
0 0
1
2
3
0
4
1
2
3
4
obstacle width
x1
Figure 13. As in Figure 12 for the geometry in the left panel in
Figure 10. In the left panel the width of the obstacle is 2.28.
400
4
residence time
local residence time
500
6
2
0
200
100
1
x2
300
0.5
0
0 0
1
2
3
4
0.8
x1
1.6
2.4
3.2
obstacle position
Figure 14. As in Figure 12 for the geometry in the left panel in
Figure 11. In the left panel the position of the center of the obstacle
is 0.8.
Neumann series to avoid the exchange of the limits t → ∞, ε → 0, following the
idea of [3]. Eventually we prove the convergence of the stationary state to the
solution of the mixed Laplace problem. This also requires the Hilbert expansion
method. The auxiliary results stated are proved after the main theorems.
Let us consider the problem 1-3 with the datum gε (x, v, 0) = f0 (x, v) ∈ L∞ (Ω ×
1
S ). We can express the operator defined in 2 as Lf (v) = 2λ(K − I)f (v) , where
Z
1 1
dδ f (v 0 )
(8)
(Kf )(v) =
2 −1
and I is the identity. Therefore the equation 1 can be written as
∂t f + (v · ∇x + 2ηε λI)f = 2ηε λKf.
(9)
We want to exploit the Duhamel’s principle and express the solution as a series
expansion. We consider the semigroup generated by A = (v · ∇x + 2ηε λI). We
recall that in the whole plane R2 this semigroup acts as e−tA f (x, v) = e−2ληε t f (x −
vt, v), while the semigroup generated only by the transport term v · ∇x would be
e−t v·∇ f (x, v) = f (x − vt, v).
We want to consider the semigroup generated by A on our domain Ω initial
datum f0 and boundary conditions 3. Recall that ∂ΩE is a specular reflective
1490
ALESSANDRO CIALLELLA AND EMILIO N. M. CIRILLO
boundary while on ∂ΩL ∪ ∂ΩR the system is in contact with reservoirs with particle
densities fB (x, v). Since the equation describes the evolution of a particle moving
in the space with velocity of modulus one, having random collisions with impact
parameter δ, for any sequence of collision times and impact parameters ti , δi we can
construct the backward trajectory of a particle as long as it stays in Ω. Indeed the
backward trajectory for a particle in (x, v) at time t starts by letting the particle
move with velocity −v. For a time t − t1 it does not hit any scatterers, but if
the particle reaches the elastic boundary ∂ΩE during this time, the velocity −v
becomes −v 0 following the elastic collision rule −v 0 = −(v − 2(n · v)n), where n is
the inward pointing normal to Ω. After a time t−t1 the particle performs a collision
with impact parameter δ1 that produces the velocity −v1 . Then again the particles
travels for a time t1 − t2 elastically colliding if touching the boundary ∂ΩE and so
on until it reaches a reservoir or it has traveled for a total time t.
In the same way, given the sequence x, v, t1 , . . ., tm , δ1 , . . ., δm , we define
the flow Φ−t
m (x, v, t1 , . . . , tm , δ1 , . . . , δm ) as the backward trajectory starting from x
with velocity v and having m transition in velocity obtained after a time t − t1 , . . .,
ti − ti+1 , . . ., tm (i = 1, . . . , m − 1) by a scattering with an hard disk with impact
parameter respectively δi (i = 1, . . . , m). We impose that the trajectories described
by this flow Φ−t (x, v) make a change of velocity from v to v 0 = v − 2(n · v)n any
time the elastic boundary ∂ΩE is reached.
We define the function τ = τ (x, v, t, t1 , . . . , tm , δ1 , . . . , δm ) that represents the
time when the particle that is in (x, v) at time t leaves a reservoirs and it enters
into the strip. So if the backward trajectory having collision times and parameters
t1 , . . . , tm , δ1 , . . . , δm reaches the boundary ∂ΩL ∪ ∂ΩR in the time interval [0, t],
then it happens after a backward time t − τ . If the trajectory Φ−s (x, v) never hits
the boundary ∂ΩL ∪ ∂ΩR for any time s ∈ [0, t] we set τ = 0.
We are now able to write the solution gε (x, v, t) using the Duhamel’s principle.
The semigroup generated by A on our domain has a transport term that we can
express thanks to the flow Φ−t (x, v), and the transported datum is fB or f0 depending on the case the backward trajectory touches a reservoir in the time interval
[0, t] on not. We use the function τ to distinguish these two cases. We consider
the collision operator 2ληε K as the source term for the linear problem 9. So we
construct the following expression for gε
gε (x, v, t) = e−2ληε t f0 (Φ−t
0 (x, v))χ(τ = 0)
−(t−τ )
(x, v))χ(τ > 0)
+ e−2ληε (t−τ ) fB (Φ0
Z t
−(t−t1 )
(x, v), t1 )χ(τ < t1 ) dt1 .
+ ληε
e−2ληε (t−t1 ) 2Kgε (Φ0
(10)
0
The notation χ represents the characteristic function.
The meaning of 10 is clear: we separate the contribution given to gε from trajectories transporting the initial datum f0 , having no collisions with scatterers and
never hitting a reservoirs in the time interval [0, t]; the contribution from trajectories transporting the initial datum fB exiting from a reservoirs at time τ and than
moving in Ω until the time t without colliding any scatterers; finally the last term
is the contribution due to trajectories having the last collision with a scatterers at
time t1 and never touching the reservoirs in the time interval [t1 , t].
We iterate the procedure by using 10 again for the gε in the last integral and
from 8 we find:
LINEAR BOLTZMANN DYNAMICS IN PRESENCE OF OBSTACLES
1491
−(t−τ )
−2ληε (t−τ )
gε (x, v, t) = e−2ληε t f0 (Φ−t
fB (Φ0
(x, v))χ(τ > 0)
0 (x, v))χ(τ = 0) + e
Z 1
Z t
dδ1 e−2ληε t1 f0 (Φ−t
+ ληε
dt1 e−2ληε (t−t1 )
1 (x, v, t1 , δ1 ))χ(τ = 0)
0
+e
−2ληε (t1 −τ )
t1
Z
−1
−(t−τ )
fB (Φ1
(x, v, t1 , δ1 ))χ(τ
> 0)χ(τ < t1 )
dt2 e−2ληε (t1 −t2 )
+ ληε
0
Z
1
×
−1
−(t−t2 )
dδ2 gε (Φ1
(x, v, t1 , δ1 ), t2 )χ(τ < t2 ) .
(11)
By successive iterations we write the series expansion for the density of particles
gε (x, v, t) as
gε (x, v, t) = e−2ληε t f0 (Φ−t
0 (x, v))χ(τ = 0)
Z t
Z
X
−2ληε t
m
+
e
(ληε )
dt1 . . .
0
m≥1
Z
1
Z
1
Z
1
tm−1
dtm
0
dδm χ(τ = 0)f0 (Φ−t
m (x, v, t1 , . . . , tm , δ1 , . . . , δm ))
−1
−(t−τ )
e−2ληε (t−τ ) fB (Φ0
(x, v))χ(τ > 0)
Z
Z
t
tm−1
X
(ληε )m
dt1 . . .
dtm
0
0
m≥1
×
dδ1 . . .
−1
+
+
Z
1
×
dδm χ(τ < tm )χ(τ > 0)e−2ληε (t−τ )
dδ1 . . .
−1
−1
)
× fB (Φ−(t−τ
(x, v, t1 , . . . , tm , δ1 , . . . , δm ))
m
= gεin (x, v, t) + gεout (x, v, t).
(12)
+∞
Note that the series are clearly convergent in L .
In 12 the terms with χ(τ = 0) define gεin that represents the contributions to
gε due to trajectories that stay in Ω for every time in [0, t] while the terms with
χ(τ > 0) define gεout that collects the contributions due to trajectories leaving a
mass reservoir at time τ > 0.
Note that gεout solves the problem 1-3 with initial datum f0 = 0. We will use the
shorthand notation Φ−s (x, v) instead of Φ−s
m (x, v, t1 , . . . , tm , δ1 , . . . , δm ) where it is
clear by the context to which sequence of collisions we refer. Moreover, the terms
with zero collision will be included in the series as the m = 0 terms.
We denote by Sε acting on any h ∈ L∞ (Ω×S 1 ) the Markov semigroup associated
to the gεin term in 12 for an initial datum h, namely
Z t
Z tm−1
X
(Sε (t)h)(x, v) =
e−2ληε t (ληε )m
dt1 . . .
dtm
0
m≥0
Z
1
×
(13)
1
dδ1 . . .
−1
so that in 12 gεin (t) = Sε (t)f0 .
0
Z
−t
dδm χ(τ = 0)h(Φ (x, v)),
−1
1492
ALESSANDRO CIALLELLA AND EMILIO N. M. CIRILLO
Proposition 1. There exists ε0 > 0 such that for any ε < ε0 and for any h ∈
L∞ (Ω × S 1 ) it holds
kSε (ηε )hk∞ ≤ αkhk∞ ,
α < 1.
(14)
Note that in the estimate 14 we are considering t = ηε and the estimate is saying
that there is a strictly positive probability for a backward trajectory to exit from
Ω in a time of the order of ηε .
Proof of Theorem 2.1. From 12 the stationary solution gεS of the problem 5 verifies
gεS = gεout (t0 ) + Sε (t0 )gεS ,
for every t0 > 0. We can formally write it by iterating the previous one in the form
of the Neumann series
X
gεS =
(Sε (t0 ))N gεout (t0 ).
(15)
N ≥0
In order to verify the existence and uniqueness of gεS we show that the 15 converges. Indeed from Proposition 1 and 15, chosen t0 = ηε
X
X
kgεS k∞ ≤
k(Sε (ηε ))N gεout k∞ ≤
αN kgεout (ηε )k∞
N ≥0
N >0
1
1
kg out k∞ ≤
max{ρL , ρR }.
≤
1−α ε
1−α
As a consequence the Neumann series 15 converges in L∞ and identifies a single
element in L∞ . Choosing an arbitrary t0 bigger than ηε of the same order of ηε and
thanks to the semigroup property of Sε it follows that gεS does not depend on the
time t0 . So there exists a unique stationary solution gεS ∈ L∞ (Ω × S 1 ) satisfying
5.
In order to prove Theorem 2.2 we need some properties of the linear Boltzmann
operator L defined in 2. We summarize them in the next lemma.
Lemma 5.1. Let L be the operator defined in 2, then L is a selfadjoint operator
on L2 (S 1 ) and has the form L = 2λ(K − I) where K is a selfadjoint and compact
operator (in L2 (S 1 )). Moreover, K is positive and its spectrum is contained in
[0, 1]. The value 0 is the only accumulation point for theRspectrum and 1 is a simple
eigenvalue. So it holds that {KerL}⊥ = {h ∈ L2 (S 1 ) : RS 1 dv h(v) = 0} and there
exists C > 0 such that for any h ∈ L∞ (S 1 ) that verifies S 1 dv h(v) = 0 we have
kL−1 hk∞ ≤ Ckhk∞ .
(16)
Proof of Lemma 5.1. The existence and the estimate of norm of L−1 are discussed
in Lemma 4.1 from Section 4.1 of [3]. The compactness of the operator K and the
spectral property of L are discussed in [18].
Proof of Theorem 2.2. The proof makes use of the Hilbert expansion (see e.g. [3,
6, 18]). Assume that gεS has the following form
k
+∞
X
1
gεS (x, v) = g (0) (x) +
g (k) (x, v),
ηε
k=1
LINEAR BOLTZMANN DYNAMICS IN PRESENCE OF OBSTACLES
1493
where g (k) are not depending on ηε . We require g (0) to satisfy the same Dirichlet
boundary conditions as the whole solution gεS on ∂ΩL ∪ ∂ΩR :
(
g (0) (x) = ρL
x ∈ ∂ΩL
(17)
(0)
g (x) = ρR
x ∈ ∂ΩR .
By imposing that gεS solves 5 and by comparing terms of the same order we get
the following chain of equations:
v · ∇x g (k) = Lg (k+1) ,
k ≥ 0,
where we used that Lg (0) (x) = 0 since g (0) is independent of v. The first two
equations read
(i) v · ∇x g (0) (x) = Lg (1) (x, v),
(ii) v · ∇x g (1) (x, v) = Lg (2) (x, v).
Let us consider the first one. By the Fredholm alternative, this equation has a
solution if and only if the left hand side belongs to (KerL)⊥ . We recall that the
null space of L is constituted by the constant functions (with respect to v), so we
can solve equationR(i) if and only if the left hand side belongs to (KerL)⊥ = {h ∈
L2 (S 1 ) such that S 1 dv h(v) = 0} (see Lemma 5.1). Since v · ∇x g (0) (x) is an odd
function of v, it belongs to (KerL)⊥ . So we can invert the operator L and set
g (1) (x, v) = L−1 (v · ∇x g (0) (x)) + ζ (1) (x),
(18)
where ζ (1) (x) ∈ Ker L and L−1 (v · ∇x g 0 ) is an odd function of v since L−1 preserves
the parity, namely it maps odd (even) function of v in odd (even) functions (see
[18]).
We integrate
equation (ii) with respect to the uniform measure on S 1 . We can
R
notice that S 1 dv v · ∇x ζ (1) (x) = 0 (ζ (1) depends only on x, so the function in the
R
integral is odd in the velocity) and S 1 dv Lg (2) = 0 (since operator L preserves
mass), so by 18 we obtain
Z
1
dv v · ∇x (L−1 (v · ∇x g (0) (x))) = 0.
(19)
2π
S1
By expanding the scalar product and using the linearity of L−1 we get
−
2
X
Di,j ∂xi ∂xj g (0) (x) = 0.
(20)
i,j=1
R
1
dv vi (−L−1 )vj and we observe that Dij = 0
We define the 2×2 matrix Di,j = 2π
S1
if i 6= j as follows by the change vi → −vi while D11 = D22 = D > 0 thanks to the
isotropy and the spectral property of the operator (see [18]). Hence D is given by
the formula 33
Z
1
D=
dv v · (−L)−1 v,
4π S 1
and the integrated equation (ii) becomes
Z
1
dv v · ∇x (L−1 (v · ∇x g (0) (x))) = 0 ⇔ −D∆x g (0) (x) = 0.
(21)
2π
S1
We require gεS (x, v) to satisfy the reflective boundary condition gεS (x, v 0 ) =
on ∂ΩE . By imposing it on the first term g (1) (x, v) = g (1) (x, v 0 ) for every
gεS (x, v)
1494
ALESSANDRO CIALLELLA AND EMILIO N. M. CIRILLO
x ∈ ∂ΩE , v · n < 0, from 18 we obtain
L(−1) (v · ∇x g (0) ) + ζ (1) (x) = L(−1) (v 0 · ∇x g (0) ) + ζ (1) (x).
(22)
By means of the elastic collision rule v 0 = v − 2(v · n)n, the linearity of L−1 allow
us to write
L(−1) ((v − 2(v · n)n) · ∇x g (0) ) = L(−1) (v · ∇x g (0) ) − 2(n · ∇x g (0) )L(−1) (v · n).
Left and
are the same if and only if (n · ∇x g (0) )L(−1) (v · n) = 0.
R right members in 22(−1)
Since S 1 dv v·n = 0 we get L
(v·n) 6= 0, so the only possibility is (n·∇x g (0) ) = 0.
(0)
Therefore g (x) has to satisfy the Neumann boundary conditions ∂n g (0) (x) = 0,
for all x ∈ ∂ΩE .
From the previous one, 21 and 17 we have shown that the term g (0) (x) solves the
problem
∆x g (0) (x) = 0
x∈Ω
g (0) (x) = ρ
x ∈ ∂ΩL
L
(23)
(0)
g
(x)
=
ρ
x ∈ ∂ΩR
R
∂n g (0) (x) = 0
x ∈ ∂ΩE .
We can deal with this mixed problem following the method of [28], Chapt. II.
Furthermore, regularity Rresults guarantee g 0 ∈ C ∞ (Ω) (see [19], Chapt. 6).
Since 19 shows that S 1 dv v · ∇x g (1) = 0, we can invert L in equation (ii) to
obtain
g (2) (x, v) = L−1 (v · ∇x L−1 (v · ∇x g (0) (x))) + L−1 (v · ∇x ζ (1) (x)) + ζ (2) (x),
(24)
where ζ (2) belongs to the kernel of L.
Now, integrating the third equation v · ∇x g (2) (x) = Lg (3) (x, v) with respect to
the uniform measure on S 1 , we find thanks to 24
Z
dv v · ∇x (L−1 (v · ∇x L−1 (v · ∇x g (0) (x))))+
S1
Z
Z
(25)
−1
(1)
(2)
+
dv v · ∇x (L (v · ∇x ζ (x))) +
dv v · ∇x (ζ (x)) = 0.
S1
S1
The last integral is null because of the independence of ζ (2) (x) from v. The first
integral is null because the function in the integral is an odd function of the velocity
thanks to the fact that the operator L−1 preserves the parity. The 25 becomes
Z
dv v · ∇x (L−1 (v · ∇x ζ (1) (x))) = −D∆x ζ (1) (x) = 0
(26)
S1
Since there are no restriction on the choice of the boundary condition, we impose
the Dirichlet data ζ (1) (x) = 0 on the boundary ∂ΩL ∪ ∂ΩR . So that by the previous
and 26 we find ζ (1) (x) ≡ 0 and hence g (1) (x, v) = L−1 (v · ∇x g (0) (x)).
Because of the 21 the first term of the right hand side of equation 24 is null too.
So 24 reduces to g (2) (x, v) = ζ (2) (x).
Moreover from the third equation we get, by inverting L,
g (3) (x, v) = L−1 (v · ∇x g (2) (x, v)) + ζ (3) (x) = L−1 (v · ∇x ζ (2) (x)) + ζ (3) (x),
with ζ (3) (x) belonging to KerL.
LINEAR BOLTZMANN DYNAMICS IN PRESENCE OF OBSTACLES
1495
ByR integrating on S 1 the fourthRequation v · ∇x g (3) = Lg (4) and by exploiting
that S 1 dv Lg 4 (x, v) = 0 and that S 1 dv v · ∇x ζ (3) (x) = 0 we find
Z
dv v · ∇x (L−1 (v · ∇x ζ (2) (x))) = −D∆x ζ (2) (x) = 0.
(27)
S1
We choose zero boundary condition at the reservoirs, namely ζ (2) (x) = 0 on ∂ΩL ∪
∂ΩR , so we find ζ (2) (x) ≡ 0. Then g (2) (x, v) ≡ 0.
We can now write the expansion for gεS as
gεS = g (0) +
1
1 (1)
g + R ηε .
ηε
ηε
(28)
The remainder Rηε satisfies
v · ∇x Rηε = ηε LRηε .
(29)
We required g (0) to satisfy the same boundary conditions as the whole solution
at contact with the reservoirs, namely on ∂ΩL ∪ ∂ΩR , so the boundary conditions
for Rηε read
(
Rηε (x, v) = −L−1 (v · ∇x g (0) (x))
x ∈ ∂ΩL ∪ ∂ΩR , v · n(x) > 0,
(30)
Rηε (x, v 0 ) = Rηε (x, v)
x ∈ ∂ Ω̃E , v · n(x) < 0.
Note that the problem 29-30 has the form of 5. From Theorem 2.1 we know that
it admits a unique solution in L∞ .
From the 28, thanks to the the fact that both g (1) and Rηε are bounded in L∞
norm, we conclude that gεS → g (0) .
In order to prove Proposition 1 we follow the strategy of the proof of Proposition
3.1 in [3]. Here we have the additional difficulty of the specular reflective boundaries
of horizontal sides of the strip and the presence of the obstacles in Ω. In the proof
are exploited the diffusive limit of the linear Boltzmann equation in a L∞ setting
and in a bigger domain containing Ω as stated in Proposition 2 below and the
properties of L summarized in Lemma 5.1.
We construct the extended domain Λ as the infinite strip constructed by removing
the left and right sides of Ω and keeping the upper and lower elastic boundaries at
x2 = 0 and x2 = L2 and the obstacles into Ω (see Figure 15). We call ∂ΛE the
union of upper and lower sides of Λ with the obstacles boundaries.
Λ
∂ΛE
Powered by TCPDF (www.tcpdf.org)
Figure 15. Domain Λ: infinite strip with big fixed obstacles: the
whole boundaries of Λ is a specular reflective boundary.
Powered by TCPDF (www.tcpdf.org)
1496
ALESSANDRO CIALLELLA AND EMILIO N. M. CIRILLO
We introduce hε : Λ × S 1 × [0, T ] → R+ the solution of the following rescaled
linear Boltzmann equation
2
x∈Λ
(∂t + ηε v · ∇x )hε = ηε Lhε
0
(31)
hε (x, v , t) = hε (x, v, t)
x ∈ ∂ΛE , v · n < 0, t ≥ 0
hε (x, v, 0) = ρ0 (x)
x ∈ Λ,
where ρ0 (x) is a smooth function of the only variable x (local equilibrium).
Proposition 2. Let hε be the solution of 31, with an initial datum ρ0 ∈ C ∞ (Λ)
such that there exists M > 0 with ρ0 (x) = 0 if |x| > M and ∂n ρ0 (x) = 0 for
x ∈ ∂ΛE . Then, as ε → 0, hε converges to the solution of the heat equation
x∈Λ
∂t ρ − D∆ρ = 0
(32)
ρ(x, 0) = ρ0 (x)
x∈Λ
∂n ρ(x, t) = 0
x ∈ ∂ΛE , t ≥ 0,
where the diffusion coefficient D is given by the formula
Z
1
D=
dv v · (−L)−1 v.
4π S 1
(33)
The convergence is in L∞ ([0, T ]; L∞ (Λ × S 1 )).
Proof of Proposition 1. The semigroup Sε defined in 13 can be equivalently written
as extended to functions belonging to L∞ (Λ × S 1 ), namely
Z t
Z m−1
X
−2ληε t
m
(Sε (t)f )(x, v) =χΩ (x)
e
(ληε )
dt1 . . .
dtm
0
m≥0
Z
1
Z
dδ1 . . .
−1
0
(34)
1
−t
−t
dδm χ(τ = 0)f (Φ (x, v))χΩ (Φ (x)),
−1
for any f ∈ L∞ (Λ × S 1 ), where χΩ is the characteristic function of Ω and Φ−t (x) is
the first component (the position) of Φ−t (x, v), the backward flux individuated by
x, v, t1 , . . ., tm , δ1 , . . ., δm . The addition of χΩ (Φ−t (x)) guarantees together with
χ(τ = 0) that the dynamics stay internal to Ω. Moreover, the following estimate
holds
Z t
Z m−1
X
Sε (t)f ≤kf k∞
e−2ληε t (ληε )m
dt1 . . .
dtm
0
m≥0
1
Z
×
0
1
Z
dδm χΩ (Φ−t (x)).
dδ1 . . .
−1
−1
We construct χδΩ ∈ C ∞ (Λ), a mollified version of χΩ , χδΩ ≥ χΩ , χδΩ ≤ 1 and
Ω ⊂ supp(χδΩ ) ⊂ (−δ, L1 + δ) × [0, L2 ]. So we can write
Z t
Z m−1
X
Sε (t)f ≤kf k∞
e−2ληε t (ληε )m
dt1 . . .
dtm
0
m≥0
Z
1
×
dδ1 . . .
−1
0
Z
1
dδm χδΩ (Φ−t (x)).
−1
(35)
LINEAR BOLTZMANN DYNAMICS IN PRESENCE OF OBSTACLES
Note that the series in 35 defines a function F which solves
x∈Λ
(∂t + v · ∇x )F (x, v, t) = ηε LF (x, v, t)
F (x, v 0 , t) = F (x, v, t)
x ∈ ∂ΛE , v · n < 0, t ≥ 0
F (x, v, 0) = χδΩ (x)
x ∈ Λ.
1497
(36)
Defining Gε (x, v, t) as F (x, v, ηε t), Gε solves 31 with initial datum ρ0 = χδΩ . Thanks
to Proposition 2 we know that at time t = 1
kGε (1) − ρδ (1)k∞ ≤ ω(ε)
where ρδ solves 32 with initial datum χδΩ and ω(ε) denotes a positive function
vanishing with ε. Moreover, we can notice that the function ρδ is the solution of
a diffusion equation with initial datum 0 ≤ χδΩ ≤ 1 with support in a bounded
subset of the infinite strip Λ. By the strong maximum principle we know that for
the positive time t = 1, it holds that ρδ (x, 1) < 1. Therefore for ε small enough
kSε (ηε )f k∞ ≤kf k∞ kSε (ηε )χδΩ k∞ ≤ kf k∞ (kGε (1) − ρδ (1)k∞ + kρδ (1)k∞ )
≤kf k∞ (ω(ε) + kρδ (1)k∞ ) < αkf k∞ , α < 1,
(37)
where we have used 35 for t = ηε .
Proof of Proposition 2. Let hε : Λ × S 1 × [0, T ] the solution of 31. We use the
Hilbert expansion technique to prove that hε converges to the solution of the heat
equation 32. We search hε of the form
k
+∞
X
1
hε (x, v, t) = h(0) (x, t) +
h(k) (x, v, t),
ηε
k=1
with coefficient h(k) not depending on ηε . By imposing that hε solves 31 and
comparing terms of the same order we find the identity Lh(0) (x, t) = 0 and the
chain of equations
v · ∇x h(0) =Lh(1)
∂t h(k) + v · h(k+1) =Lh(k+2)
for k ≥ 0.
We impose that h(0) satisfy the same initial condition of the whole solution hε ,
namely
h(0) (x, 0) = ρ0 (x).
Let us start from the first equation (i) v · ∇x h(0) = Lh(1) . Thanks to the Fredholm
alternative and by proceeding as in the proof of Theorem 2.2, we can solve equation
(i) if and only if the left hand side belongs to
Z
(KerL)⊥ = {h ∈ L2 (S 1 ) such that
dv h(v) = 0}.
S1
Since v · ∇x h(0) (x) is an odd function of v, it belongs to (KerL)⊥ . So we can invert
the operator L finding
h(1) (x, v, t) = L−1 (v · ∇x h(0) (x, t)) + ζ (1) (x, t).
(38)
where ζ (x, t) is a function to be determined in the kernel of L. Recall that L−1
preserves the parity.
(1)
1498
ALESSANDRO CIALLELLA AND EMILIO N. M. CIRILLO
We integrate the second equation (ii) ∂t h(0) + v · ∇x h(1) = Lh(2) with respect
to the uniform measure
on the sphereR S 1 . Thanks to the equation 38 and the
R
observations that S 1 dv Lh(2) = 0 and S 1 dv v · ∇x ζ (1) (x, t) = 0, it holds
Z
1
dv ∂t h(0) (x, t) + v · ∇x (L−1 v · ∇x h(0) (x, t)) = 0.
(39)
2π S 1
R
1
As in the proof of Theorem 2.2 defining Di,j = 2π
dv vi (−L−1 )vj , we find that
S1
the diffusion coefficient D is given by the formula 33
Z
1
dv v · (−L)−1 v
D=
4π S 1
so that the heat equation for h(0) is
∂t h(0) − D∆x h(0) = 0.
(40)
0
hε (x, v) has to satisfy the reflective boundary condition hε (x, v , t) = hε (x, v, t)
on ∂ΛE . By imposing it on the first term h(1) (x, v, t) = h(1) (x, v 0 , t) for every
x ∈ ∂ΩE , v · n < 0, we obtain proceeding in the same way of the proof of Theorem
2.2 that h(0) (x, t) has to satisfy the Neumann boundary conditions ∂n h(0) (x, t) = 0,
for all x ∈ ∂ΛE .
We have so shown that the term h(0) (x, t) solves the problem
(0)
(0)
x∈Λ
∂t h − ∆x h = 0
(0)
(41)
h (x, 0) = ρ0 (x)
x∈Λ
∂n h(0) (x, t) = 0
x ∈ ∂ΛE .
In particular h(0) (t) ∈ L∞ (Λ × S 1 ) for any t ≥ 0.
The equation 40 allow us to verify that when integrating the equation (ii) the
left hand side vanishes. It implies that we can invert operator L finding
h(2) (x, v, t) = L−1 (∂t h(0) (x, t) + v · ∇x (L−1 (v · ∇x h(0) (x, t)))
(42)
+ v · ∇x ζ (1) (x, t)) + ζ (2) (x, t),
where ζ (2) (x, t) is a function in Ker L.
Next equation is (iii) ∂t h(1) + v · ∇x h(2) = Lh(3) . When integrating it with
respect to the uniform measure on S 1 , we exploit the fact that the operator L−1
preserves the parity. So, substituting h(1) and h(2) with their expressions given by
38 and 42, the only terms surviving give the equation for ζ (1)
∂t ζ (1) (x, t) − D∆x ζ (1) (x, t) = 0.
(43)
(1)
Since there are no restrictions on the choice of the initial condition for ζ , we fix
ζ (1) (x, 0) = 0. So ζ (1) (x, t) ≡ 0 for any (x, t) and the expression for h(1) reduces to
h(1) (x, v, t) = L−1 (v · ∇x h(0) (x, t)).
By the Lemma 5.1 and the smoothness of h(0) we have
sup kh(1) (t)k∞ ≤ C sup k∇x h(0) (t)k∞ < +∞.
t∈[0,T ]
t∈[0,T ]
In the same way, by Lemma 5.1 and smoothness of h(0) it follows that the
(2)
first term in the expression of h(2) , i.e. h1 = L−1 (∂t h(0) (x, t) + v · ∇x (L−1 (v ·
(0)
∞
∞
1
∇x h (x, t)))), is in L ([0, T ]; L (Λ × S )), as well as its spatial derivatives.
LINEAR BOLTZMANN DYNAMICS IN PRESENCE OF OBSTACLES
1499
Observe now that the left hand side of equation (iii) has null integral on S 1 due
to 43. By inverting L we obtain the formula for h(3)
h(3) (x, v, t) = L−1 (∂t h(1) + v · ∇x h(2) (x, v, t)) + ζ (3) (x, t)
(2)
= L−1 (∂t L−1 (v · ∇x h(0) (x, t)) + v · ∇x (h1 (x, v, t)
+ζ
(2)
(x, t))) + ζ
(3)
(44)
(x, t),
where ζ (3) ∈ Ker L. We integrate now the equation (iv) ∂t h(2) + v · ∇x h(3) = Lh(4)
with respect to the uniform measure on S 1 . We find the equation for ζ (2) (x, t)
∂t ζ (2) − D∆x ζ (2) = S(x, t),
(45)
where the source S(x, t) is given by
Z
1
dv v · ∇x L−1 (∂t L−1 (v · ∇x h(0) (x, t)))
S(x, t) = −
2π S 1
Z
1
(2)
−
dv v · ∇x L−1 (v · ∇x h1 (x, v, t))).
2π S 1
We consider as initial datum ζ (2) (x, 0) = 0, so we have ζ (2) ∈ L∞ ([0, T ]; L∞ (Λ))
and its spatial derivative as well, since S ∈ L∞ ([0, T ]; L∞ (Λ)).
We write the the expansion truncated at order ηε−2 for the solution:
hε (x, v, t) = h(0) (x, t) +
1 (1)
1
1
h (x, v, t) + 2 h(2) (x, v, t) + Rηε (x, v, t).
ηε
ηε
ηε
(46)
We have shown that h(i) (t) ∈ L∞ (Λ × S 1 ) for i = 0, 1, 2. Now we have to prove
that even,the remainder Rηε is in L∞ .
The remainder Rηε satisfies the equation
(∂t + ηε v · ∇x )Rηε = ηε2 LRηε − Tηε
(47)
with initial condition
Rηε (x, v, 0) = −h(1) (x, v, 0) −
1 (2)
h (x, v, 0)
ηε
and boundary conditions
Rηε (x, v 0 , t) = Rηε (x, v, t)
x ∈ ∂ΛE , v · n < 0.
The term Tηε on the left hand side of 47 is Tηε = ∂t h(1) + η1ε ∂t h(2) + v · ∇x h(2) . So
Tηε ∈ L∞ ([0, T ]; L∞ (Λ × S 1 )) and thanks to the smoothness hypothesis on ρ0 also
the initial datum Rηε (x, v, 0) belongs to L∞ .
By denoting by Sηε (t) the semigroup associated to the generator ηε (v · ∇x − ηε L)
with reflective boundary conditions on ∂ΛE , the equation 47 becomes
Z
t
ds Sηε (t − s)Tηε (s).
Rηε (t) = Sηε (t)Rηε (0) +
0
1500
ALESSANDRO CIALLELLA AND EMILIO N. M. CIRILLO
By means of the series expansion found in 35, the solution can be written in the
following way:
Z ηε t
Z m−1
X
2
dt1 . . .
dtm
Rηε (x, v, t) =
e−2ληε t (ληε )m
0
m≥0
Z
1
×
Z
t
ds
0
X
1
dδm Rηε (0)(Φ−ηε t (x, v))
dδ1 . . .
−1
−1
+
0
Z
2
e−2ληε (t−s) (ληε )m
Z
1
Z
dδ1 . . .
−1
ηε (t−s)
Z
dt1 . . .
0
m≥0
×
Z
m−1
dtm
0
1
dδm Tηε (0)(Φ−ηε (t−s) (x, v), s).
−1
Therefore we can estimate
sup kRηε (t)k∞ ≤ kRηε (0)k∞ + T sup kTηε (t)k∞ ≤ C < +∞.
t∈[0,T ]
t∈[0,T ]
So the remainder is uniformly bounded too. Hence from the estimates and 46 it
follows that hε converges to h(0) in L∞ for ηε → ∞.
Acknowledgments. We thank Daniele Andreucci for many useful discussions on
mixed boundary problems and for having suggested the reference [28]. We are
grateful to Mario Pulvirenti for many illuminating discussions on the topic of the
paper and for having suggested the idea of studying the residence time problem in
the framework of Kinetic Theory.
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Received for publication August 2017.
E-mail address: alessandro.ciallella@uniroma1.it
E-mail address: emilio.cirillo@uniroma1.it