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Fluctuations and rare events in stochastic aggregation
1. Fluctuations and rare events in stochastic
aggregation
Colm Connaughton
Mathematics Institute and Centre for Complexity Science,
University of Warwick, UK
Collaborators: R. Rajesh (Chennai), R. Tribe (Warwick), O. Zaboronski (Warwick).
IUTAM Syposium - Common aspects of extreme events in
fluids
University College Dublin July 2-6, 2012
./figures/warwickL
http://www.slideshare.net/connaughtonc Stochastic aggregation
2. Introduction to cluster-cluster aggregation (CCA)
Many particles of one
material dispersed in
another.
Transport: diffusive,
advective, ballistic...
Particles stick together on
contact.
Applications: surface and colloid physics, atmospheric
science, biology, cloud physics, astrophysics...
./figures/warwickL
http://www.slideshare.net/connaughtonc Stochastic aggregation
3. Mean-field model: Smoluchowski’s kinetic equation
Cluster size distribution, Nm (t), satisfies the kinetic equation :
Smoluchowski equation :
m
∂Nm (t) 1
= dm1 dm2 K (m1 , m2 )Nm1 Nm2 δ(m − m1 − m2 )
∂t 2 0
∞
− dm1 dm2 K (m, m1 )Nm Nm1 δ(m2 − m − m1 )
0
J
+ δ(m − m0 )
m0
Source of monomers: input mass flux J.
Assumes no correlations.
Very extensive literature on this equation.
./figures/warwickL
http://www.slideshare.net/connaughtonc Stochastic aggregation
4. Mass cascades in CCA with a source and sink
Kernel is often homogeneous:
K (am1 , am2 ) = aβ K (m1 , m2 )
Today we will mostly consider the con-
stant kernel (β = 0):
K (m1 , m2 ) = λ.
K (m1 , m2 ) = 1.
Stationary state for t → ∞, m m0 (White 1967):
√ β+3
Nm = c J m− 2 .
Describes a cascade of mass from source at m0 to sink at large
m. Analogous to the Richardson cascade in turbulence. ./figures/warwickL
http://www.slideshare.net/connaughtonc Stochastic aggregation
5. Stochastic Particle System Model of Aggregation
Consider a lattice in d dimensions with particles of integer
masses. Nt (x, m)=number of particles of mass m on site x at
time t.
Diffusion: Nt (x, m) → Nt (x, m) − 1
Nt (x + n, m) → Nt (x + n, m) + 1
Rate: DNt (x, m)/2d
Aggregation: Nt (x, m1 ) → Nt (x, m1 ) − 1
Nt (x, m2 ) → Nt (x, m2 ) − 1
Nt (x, m1 + m2 ) → Nt (x, m1 + m2 ) + 1
Rate: λNt (x, m1 )Nt (x, m2 )
Injection: Nt (x, m) → Nt (x, m) + 1
Rate: J
./figures/warwickL
http://www.slideshare.net/connaughtonc Stochastic aggregation
6. Path integral representation of correlation functions
Stochastic particle system can be reformulated as a statistical
field theory (Doi, Peliti, Zeldovich, Ovchinnikov, Cardy...)
¯
nm = D[zm , zm ] zm e−Seff [zm ,zm ]
¯ (1)
where
T
Seff = dτ dx ¯ ¯
zm (∂t zm − D∆zm − Jδm,1 ) + H[zm + 1, zm ]
0 m
and
1
¯
H[z , z] = − ¯ ¯ ¯
λm1 ,m2 (zm1 +m2 − zm1 zm2 ) zm1 zm2 .
2 m1 ,m2
./figures/warwickL
http://www.slideshare.net/connaughtonc Stochastic aggregation
7. Diffusive fluctuations in d ≤ 2
Critical dimension for the theory is 2. Below 2-d diffusive
fluctuations cause mean field theory to fail.
Exact solution (Takayasu 1985)for size distribution in 1-d
(constant kernel):
1 4 3
Nm = c J 3 m − 3 cf MF: Nm ∼ m− 2 .
Renormalisation of reaction rate within perturbative RG
framework ( = 2 − d):
d 2d+2
Nm ∼ J d+2 m− d+2
Leading term in RG expansion is exact in d = 1 ( = 1)! ./figures/warwickL
http://www.slideshare.net/connaughtonc Stochastic aggregation
8. Multiscaling and intermittency
For d ≤ 2 higher order mass correlation functions exhibit
multi-scaling with m:
Cn = Nm1 . . . Nmn ∼ m−γn 3
MF: γn = n 2
γ0 = 0.
4
γ1 = 3 in 1-d (exact solution).
γ2 = 3 is exact (analogue of
4
5 -law!).
These are not on a straight line.
RG calculation gives:
γn = 2d+2 n + d+2 n(n−1) + O( 2 )
d+2 2
where = 2 − d.
./figures/warwickL
http://www.slideshare.net/connaughtonc Stochastic aggregation
9. Origin of intermittency: anti-correlation between
clusters
“Large particles are large because they have merged with all of
their neighbours".
Due to recurrence of random
walks in d < 2 particles
encounter their neighbours
infinitely often.
Large particles surrounded by
voids (correlation hole) and are
thus spatially correlated.
Spatial intermittency of the mass distribution in low dimensions
is due to sites having atypically few or atypically many particles.
./figures/warwickL
http://www.slideshare.net/connaughtonc Stochastic aggregation
10. Rare events in stochastic aggregation
Can we calculate probabilities of atypical configurations?
Spatial problem is hard. Start with a simpler 0-d problem:
Initial condition: M particles of size 1. No source.
Particles aggregate at rate λ.
What is annihilation time, τ , when 1 particle remains?
Mean-field equation for total number of particles:
˙ λ
N = − N2 N(0) = M.
2
Solution: N(t) = 2M/(2 + Mλ t) ⇒ τMF = 2/λ for large M.
Two types of rare events in limit M 1:
Fast annihilation: P(N(τ ) = 1) with τ τMF .
Slow annihilation: P(N(τ ) > 1) with τ τMF . ./figures/warwickL
http://www.slideshare.net/connaughtonc Stochastic aggregation
11. Path integral formula for probabilities
Path integral formula for probabilities is similar to before but
includes boundary terms:
¯
P(N(T ) = 1) = D[zm , zm ] e−Seff [zm ,zm ]
¯ (2)
where
T
Seff = dt ¯
zm ∂t zm ¯
+ H[zm , zm ]
0 m
+ ¯ ¯
(zm zm − M ln zm ) δm,1 δ(t) − ln zm δm,M δ(t − T )
m
and
1
¯
H[z , z] = − ¯ ¯ ¯
λm1 ,m2 (zm1 +m2 − zm1 zm2 ) zm1 zm2 .
2 m1 ,m2 ./figures/warwickL
http://www.slideshare.net/connaughtonc Stochastic aggregation
12. Instanton equations for early annihilation
If probability of the rare event of interest is concentrated on a
single trajectory (“instanton") then Seff can be estimated using
the Laplace method. Trajectory satisfies:
δH 1
˙
zm = − = ¯ ¯
λm1 ,m2 (δm,m1 +m2 − zm1 δm,m2 − zm2 δm,m1 ) zm1 zm2
¯m
δz 2m
1 ,m2
˙ δH 1
¯
zm = =− ¯ ¯ ¯
λm1 ,m2 (zm1 +m2 − zm1 zm2 )(zm1 δm,m2 + zm2 δm,m1 )
δzm 2m m2
1,
with boundary conditions:
¯
zm (0) zm (0) = M δm,1 ¯
zm (T ) zm (T ) = 1 δm,M .
Integrals of motion:
E = H(z c , z c ) (’Instanton energy’)
¯
c ¯c
M = m mzm zm (Mass)
Find that:
./figures/warwickL
Seff = −E · t + boundary terms
http://www.slideshare.net/connaughtonc Stochastic aggregation
13. Solution of the instanton equations for constant kernel
Task: find the value of E for which the boundary conditions are
¯
satisfied. For constant kernel, can solve for N = m zm (t)zm (t):
λ
∂t N = − N 2 + E, N(0) = M N(T ) = 1.
2
Solution:
Know E < 0 so let E = − λ p2 .
2
λ 2 M
N(t) = p tan p (t − t0 ) t0 = tan−1 .
2 λp p
π
For M → ∞ and t → 0, we find p ∼ λt .
Obtain
π2
P(N(T ) = 1) ∼ e− 2 λ T for T τMF and M 1.
./figures/warwickL
http://www.slideshare.net/connaughtonc Stochastic aggregation
14. Late annihilation
The opposite regime of late annihilation is not described by
previous instanton equations. For T τMF then
P(N(T ) > 1) ≈ P(N(T ) = 2). Exact equation for ∂t N(t) :
λ
∂t N = − N (N − 1)
2
For late times N(t) = 1 + n(t) with n(t) ∈ {0, 1} and
λ
∂t n = − ( n 2 + n )
2
= −λ n since n2 = n
⇒ n ∼ e−λt
⇒ P(n(t) = 1) ∼ e−λt
Probability of late annihilation
P(N(T ) > 1) ∼ e−λt for T τMF ./figures/warwickL
http://www.slideshare.net/connaughtonc Stochastic aggregation
15. Statistics of mass flux fluctuations
Analogue of the mass flux for toy model: J = M (τ is the
τ
annihilation time). JMF = τM = Mλ . Consider relative size of
MF 2
fluctuations of J above and below JMF .
M π 2 J+
P(J > J+ ) = P(τ < ) ∼ e− 2λM as M → ∞
J+
M − λM
P(J < J− ) = P(τ > ) ∼ e J− as M → ∞
J−
Take J+ = LJMF , J− = JMF . Fluctuations are not symmetric with
L
respect to L:
P(J > JMF L) π2
∼ e−( 4 −1)L
P(J < JMF /L)
Large fluxes are exponentially less probable than small ones.
Reminiscent of Gallavotti-Cohen Fluctuation Relation for
entropy production in dynamical systems. ./figures/warwickL
http://www.slideshare.net/connaughtonc Stochastic aggregation
16. Conclusions
Cluster-cluster aggregation exhibits non-equilibrium
statistical dynamics which are closely analogous to
turbulence with a mass cascade playing the role of the
energy cascade.
CCA with diffusive transport has a critical dimension of 2.
Unlike turbulence, the RG flow for this system has a stable
perturbative fixed point in d < 2 of order = 2 − d.
Mass distribution exhibits spatial intermittency due to
anti-correlation between particles. Multi-scaling exponents
can be calculated by RG.
Probabilities of rare configurations can be calculated using
“instanton" method (at least in 0-d). Instanton equations
are very closely related to mean-field equations.
Fluctuations of the mass flux exhibit a (geometric)
asymmetry which is very reminiscent of various “fluctuation
./figures/warwickL
relations" in other non-equilibrium systems.
http://www.slideshare.net/connaughtonc Stochastic aggregation