\documentstyle[graphicx,multicol,epsf,aps]{revtex}
\begin{document}
\title{Multiscaling in Inelastic Collisions}
\author{E.~Ben-Naim$\dag$ and P.~L.~Krapivsky$\ddag$}
\address{$\dag$Theoretical Division and Center for Nonlinear Studies,
Los Alamos National Laboratory, Los Alamos, NM 87545}
\address{$\ddag$Center for Polymer Studies
and Department of Physics, Boston University, Boston, MA 02215}
\maketitle
\begin{abstract}
We study relaxation properties of two-body collisions on the
mean-field level. We show that this process exhibits multiscaling
asymptotic behavior as the underlying distribution is characterized
by an infinite set of nontrivial exponents. These nonequilibrium
relaxation characteristics are found to be closely related to the
steady state properties of the system.
\noindent{PACS numbers: 05.40.+j, 05.20.Dd, 02.50.Ey}
\end{abstract}
\begin{multicols}{2}
Our understanding of the statistical mechanics of nonequilibrium systems
remains incomplete, in sharp contrast with their equilibrium
counterpart. The rich phenomenology associated with dynamics of far from
equilibrium interacting particle systems exposes the lack of a unifying
theoretical framework. Simple tractable microscopic models can therefore
help us gain insight and better the description of nonequilibrium
dynamics.
In this study, we focus on the nonequilibrium relaxation of an infinite
particle system interacting via two body collisions. We find that a
hierarchy of scales underlies the relaxation. In particular, we devise
an extremely simple system which exhibits multiscaling on the mean-field
level while in finite dimensions simple scaling behavior is restored.
Furthermore, we show that this behavior extends to a broader class of
collision processes.
In the mean-field framework, the spatial structure is ignored.
Therefore, we consider an infinite number of identical particles which
are characterized by a single parameter, their velocity $v$. Two-body
collisions are realized by choosing two particles at random and changing
their velocities according to $(u_1,u_2)\to (v_1,v_2)$ with
\begin{eqnarray}
\label{rule}
\pmatrix{v_1\cr v_2\cr}=\pmatrix{\gamma &1-\gamma\cr
1-\gamma&\gamma\cr}\pmatrix{u_1\cr u_2\cr}.
\end{eqnarray}
In other words, the post-collision velocities are given by a linear
combination of the pre-collision velocities. Both the total momentum
($u_1+u_2=v_1+v_2$) and the total number of particles are conserved by
this process. In fact, the collision rule (\ref{rule}) is the most
general linear combination which obeys momentum conservation and
Galilean invariance, i.e., invariance under velocity translation $v\to
v-v_0$.
Our motivation for studying this problem is inelastic collisions in
granular gases \cite{pkh,my,bm}. Therefore, we restrict our attention
to dissipative collisions, $0<\gamma<1$. While the two problems
involve different collision rates, they share the same trivial final
state where all velocities vanish, $P(v,t)\to \delta(v)$ when
$t\to\infty$ (without loss of generality, the average velocity was set
to zero by invoking the transformation $v\to v-\langle v\rangle$). We
chose to describe this work in slightly more general terms since
closely related dynamics were used in different contexts including
voting systems\cite{melzak,ff}, asset exchange processes\cite{sps},
combinatorial processes\cite{d}, and headway distances in traffic
flows\cite{kg}. We will show that multiscaling characterizes
fluctuations in these problems as well.
Velocity fluctuations may be obtained via the probability distribution
function $P(v,t)$ which obeys the following master equation
\begin{eqnarray}
\label{BE}
{\partial P(v,t)\over\partial t}&=&\int_{-\infty}^\infty
\int_{-\infty}^\infty du_1\, du_2\, P(u_1,t)P(u_2,t)\\\nonumber
&\times &\left[\delta(v-\gamma u_1-(1-\gamma)u_2)
-\delta(v-u_2)\right].
\end{eqnarray}
This Boltzmann equation with a velocity independent collision rate is
termed the Maxwell model in kinetic theory \cite{ernst}. The
$\delta-$functions on the right-hand side reflect the collision rule
(\ref{rule}) and guarantee conservation of the number of particles,
$\int dv P(v,t)=1$, and the total momentum $\int dv\, v P(v,t)=0$.
Eq.~(\ref{BE}) can be simplified by eliminating one of the
integrations
\begin{equation}
\label{BE1}
{\partial P(v,t)\over\partial t}+P(v,t)={1\over 1-\gamma}
\int_{-\infty}^\infty du P(u,t)P\left({v-\gamma u\over 1-\gamma},t\right).
\end{equation}
Further simplification is achieved via the Fourier transform
$\hat P(k,t)=\int dv\, e^{ikv}\,P(v,t)$ which obeys
\begin{equation}
\label{BEF}
{\partial \over\partial t}\,\hat P(k,t)+\hat P(k,t)=
\hat P[\gamma k,t]\,\hat P[(1-\gamma)k,t].
\end{equation}
Although the integration is eliminated, this compact equation is
challenging as the nonlinear term is nonlocal.
Velocity fluctuations can be quantified using the moments of the velocity
distribution, \hbox{$M_n(t)=\int dv\, v^n P(v,t)$}. The moments obey a
closed and recursive set of the ordinary differential equations. The
corresponding equations can be derived by inserting the expansion
$\hat P(k,t)=\sum_n {(ik)^n\over n!} M_n(t)$ into Eq.~(\ref{BEF}) or
directly from Eq.~(\ref{BE}). The first few moments evolve according to
$\dot M_0=\dot M_1=0$, and
\begin{eqnarray}
\dot M_2&=&-a_2M_2,\nonumber\\
\dot M_3&=&-a_3M_3,\\
\dot M_4&=&-a_4M_4+a_{24}M_2^2,\nonumber
\end{eqnarray}
with the coefficients
\begin{equation}
\label{an}
a_n\equiv a_n(\gamma)=1-(1-\gamma)^n-\gamma^n,
\end{equation}
and $a_{24}=6\gamma^2(1-\gamma)^2$. Integrating these
rate equations yields $M_0=1$, $M_1=0$ and
\begin{eqnarray}
M_2(t)&=&M_2(0)e^{-a_2t}\nonumber\\
M_3(t)&=&M_3(0)e^{-a_3t}\\
M_4(t)&=&\left[M_4(0)+3M^2_2(0)\right]e^{-a_4t}-3M_2^2(t).\nonumber
\end{eqnarray}
The asymptotic behavior of the first few moments suggests that
knowledge of the RMS fluctuation $v^*\equiv M_2^{1/2}$ is not
sufficient to characterize higher order moments since $M_3^{1/3}/v^*,
M_4^{1/4}/v^*\to\infty$ as $t\to\infty$.
This observation extends to higher order moments as
well. In general, the moments evolve according to
\begin{equation}
\dot M_n+a_nM_n
=\sum_{m=2}^{n-2}{n\choose m}\gamma^{m}(1-\gamma)^{n-m}M_{m}M_{n-m}.
\end{equation}
Note that for \hbox{$0<\gamma<1$}, the coefficients $a_n$ satisfy
\hbox{$a_{n}0$} for
\hbox{$0<\gamma< 1/2$} with \hbox{$b_n\equiv
b_n(\gamma)=(1-\gamma)^{n-1}-\gamma^{n-1}$}. One can verify that the
$b_n$'s decrease monotonically with increasing $n$, $b_n\geq b_{n+1}$
for $n\geq 2$, therefore proving the desired inequality. Since
moments decay exponentially, this inequality shows that the right hand
side in the above equation is negligible asymptotically. Thus, the
leading asymptotic behavior for all $n>0$ is $M_n\sim \exp(-a_n t)$.
Since the $a_n$'s increase monotonically, $a_nM_{n+1}$.
Furthermore, in terms of the second moment one has
\begin{equation}
\label{multi}
M_{n}\propto M_2^{\alpha_n}, \qquad
\alpha_n={1-(1-\gamma)^n-\gamma^n
\over 1-(1-\gamma)^2-\gamma^2}.
\end{equation}
While the prefactors depend on the details of the initial
distribution, the scaling exponents are universal. Therefore, the
velocity distribution does not follow a naive scaling form $P(v,t)\sim
{1\over v^*} P({v\over v^*})$. Such a distribution would imply the
linear exponents $\alpha_n=\alpha^*_n=n/2$. Instead, the actual
behavior is given by Eq.~(\ref{multi}) with the exponents $\alpha_n$
reflecting a multiscaling asymptotic behavior with a nontrivial
(non-linear) dependence on the index $n$. For instance, the high
order exponents saturate, $\alpha_n\to a_2^{-1}$ for $n\to\infty$,
instead of diverging. One may quantify the deviation from ordinary
scaling via a properly normalized set of indices
$\beta_n=\alpha_n/\alpha_n^*$ defined from $M_n^{1/n}\sim
(v^*)^{\beta_n}$. By evaluating the $\gamma=1/2$ case where
multiscaling is most pronounced, a bound can be obtained for these
indices: $7/8,31/48\leq \beta_n\leq 1$ for $n=4,6$ respectively.
Furthermore, $\beta_n\to 1-{2n-3\over 2}\gamma$ when $\gamma\to 0$
indicating that the deviation from ordinary scaling vanishes for
weakly inelastic collisions. Thus, the multiscaling behavior can be
quite subtle \cite{bk1}.
The above shows that a hierarchy of scales underlies fluctuations in
the velocity. In parallel, a hierarchy of diverging time scales
characterizes velocity fluctuations
\begin{equation}
M_n^{1/n}\sim \exp(-t/\tau_n), \qquad \tau_n={n\over a_n}.
\end{equation}
These time scales diverge for large $n$ according to $\tau_n\simeq n$.
Large moments reflect the large velocity tail of a distribution.
Indeed, the distribution of extremely large velocities is dominated by
persistent particles which experienced no collisions up time $t$. The
probability for such events decays exponentially with time \hbox{$P(v,t)\sim
P(v,0)\exp(-t)$} for $v\gg 1$ (alternatively, this behavior emerges
from Eq.~(\ref{BE1}) since the gain term is negligible for the tail
and hence $\dot P+P=0$). This decay is consistent with the large
order moment decay $M_n\sim \exp(-t)$ when $n\to\infty$.
\begin{figure}
\narrowtext
%\centerline{\epsfxsize=7.5cm\epsfbox{fig1.eps}}
\centerline{\includegraphics[width=7.5cm]{fig1}}
\caption{Development of a singularity for a compact initial distribution.
Shown is the probability distribution obtained by simulating the
collision process of Eq.~(\ref{rule}) with $\gamma=1/2$. The data
represents an average over 200 independent realization in a system
with $10^7$ particles, starting from a uniform distribution in the
range $[-1,1]$.}
\end{figure}
Although the leading asymptotic behavior of the moments was
established, understanding the entire distribution $P(v,t)$ remains a
challenge. Simulations of the $\gamma=1/2$ process reveal an
interesting structure for compact distributions. Starting from a
uniform velocity distribution, $P_0(v)=1/2$ for $-10$ and develops an infinite (countable) set of singularities whose
locations depend on the arithmetic nature of $\gamma$ (e.g., it is
very different for rational and irrational $\gamma$'s). On the other
hand, unbounded distributions do not develop such singularities, and
therefore, the loss of analyticity is not necessarily responsible for
the multiscaling behavior.
Asymptotically, our system reaches a trivial steady state
$P(v,t=\infty)=\delta(v)$. To examine the relation between dynamics and
statics, a non-trivial steady state can be generated by considering the
driven version of the collision process\cite{wm,sbcm,ve}. External
forcing balances dissipation due to collisions and therefore results in
a nontrivial nonequilibrium steady state. Specifically, we assume that
in addition to changes due to collisions, velocities may also change due
to an external forcing: \hbox{${dv_j\over dt}|_{\rm heat}=\xi_j$}. We
assume standard uncorrelated white noise \hbox{$\langle\xi_i(t)
\xi_j(t')\rangle=2D\delta_{ij}\delta(t-t')$} with a zero average
$\langle \xi_j\rangle=0$. The left hand side of the master equation
(\ref{BE1}) should therefore be modified by the diffusion term
\begin{eqnarray}
\label{BEFP}
{\partial P(v,t)\over\partial t}\to
{\partial P(v,t)\over\partial t}-D{\partial^2 P(v,t)\over\partial v^2}.
\end{eqnarray}
Of course, the addition of the diffusive term does not alter
conservation of the total particle number and the total momentum, and
one can safely work in a reference frame moving with the center of
mass velocity.
We restrict our attention to the steady state. The Fourier transform
$\hat P_\infty(k)\equiv\hat P(k,t=\infty)$ satisfies
\begin{equation}
\label{FP}
(1+Dk^2)\hat P_\infty(k)=
\hat P_\infty[\gamma k]\,\hat P_\infty[(1-\gamma)k].
\end{equation}
The solution to this functional equation which obeys the conservation
laws $\hat P_\infty(0)=1$ and $\langle v\rangle=\hat P_\infty'(0)=0$ is
found recursively
\begin{equation}
\label{FPsol}
\hat P_\infty(k)=\prod_{i=0}^\infty\prod_{j=0}^i
\left[1+\gamma^{2j}(1-\gamma)^{2(i-j)}Dk^2\right]^{-{i\choose j}}.
\end{equation}
To simplify this double product we take the logarithm
and transform it as follows
\begin{eqnarray}
\ln \hat P_\infty(k)
&=&-\sum_{i=0}^\infty\sum_{j=0}^i {i\choose j}\,
\ln \left[1+\gamma^{2j}(1-\gamma)^{2(i-j)}Dk^2\right]\nonumber\\
&=&\sum_{i=0}^\infty\sum_{j=0}^i {i\choose j}\sum_{n=1}^\infty
{(-Dk^2)^n\gamma^{2jn}(1-\gamma)^{2(i-j)n}\over n}\nonumber\\
&=&\sum_{n=1}^\infty {(-Dk^2)^n\over n}
\sum_{i=0}^\infty\sum_{j=0}^i {i\choose j}\,
\gamma^{2nj}(1-\gamma)^{2n(i-j)}\nonumber\\
&=&\sum_{n=1}^\infty {(-Dk^2)^n\over n}\sum_{i=0}^\infty
\left[\gamma^{2n}+(1-\gamma)^{2n}\right]^i.
\end{eqnarray}
The second identity follows from the series expansion $\ln
(1+q)=-\sum_{n\geq 1}n^{-1}(-q)^n$, and the forth from the binomial
identity $\sum_{j=0}^i {i\choose j}p^jq^{i-j}=(p+q)^i$. Finally, using the
geometric series $(1-x)^{-1}=\sum_{n\geq 0} x^n$, the Fourier transform
at the steady state is found
\begin{equation}
\label{pinf}
\hat P_\infty(k)=\exp\left\{\sum_{n=1}^\infty
{(-Dk^2)^n\over n a_{2n}(\gamma)}\right\},
\end{equation}
with $a_n(\gamma)$ given by Eq.~(\ref{an}). The $n$th cumulant of
the steady state distribution $\kappa_n$ can be readily found from $\ln
\hat P_\infty(k)=\sum_m {(ik)^m\over m!}\kappa_m$. Therefore, the odd
cumulants vanish while the even cumulants are simply proportional to the time
scales characterizing the exponential relaxation of the corresponding
moments:
\begin{equation}
\kappa_{2n}={(2n-1)!\over n}D^n\tau_{2n}.
\end{equation}
Of course, the moments can be constructed from these
cumulants. Interestingly, a direct correspondence between the steady
state characteristics and the nonequilibrium relaxation time scales is
established via the cumulants of the probability distribution.
None of the (even) cumulants vanish, thereby reflecting significant
deviations from a Gaussian distribution. Nevertheless, for
sufficiently large velocities, one may concentrate on the small wave
number behavior. Using the inverse Fourier transform of (\ref{pinf})
one finds the tail of the distribution
\begin{equation}
P_\infty(v)\simeq {1\over \sqrt{2\pi v_0^2}}\,
\exp\left(-{v^2\over 2v_0^2}\right) \qquad v\gg v_0,
\end{equation}
with $v_0^2=2D/a_2$. This in particular implies the large moment
behavior $M_{2n}\to (2n-1)!!\,v_0^{2n}$ as $n\to\infty$. The Gaussian
tail is different than the $\exp(-{\rm const.}\times v^{3/2})$
behavior obtained when the collision rate is proportional to the
velocity difference \cite{ve}. In contrast, large velocities in
one-dimensional inelastic gases are suppressed according to
$\exp(-{\rm const.}\times v^3)$ \cite{bcdr}.
Finally, we briefly discuss a few generalizations and extensions of the
basic model. Note that Eq.~(\ref{multi}) extends to energy-generating
collisions as well ($\gamma<0$ or $\gamma>1$), despite the fact that the
limiting distribution is no longer a $\delta$-function. Next, relaxing
Galilean invariance, the most general momentum conserving collision rule
is
\begin{eqnarray}
\label{rule1}
\pmatrix{v_1\cr v_2\cr}=\pmatrix{\gamma_1 &1-\gamma_2\cr
1-\gamma_1&\gamma_2\cr}\pmatrix{u_1\cr u_2\cr}.
\end{eqnarray}
Following the same steps that led to (\ref{multi}) shows that when
$\gamma_1,\gamma_2\neq 0,1$ and when $M_1=0$ this process also exhibits
multiscaling with the exponents $\alpha_n=a_n/a_2$, where
$a_n(\gamma_1,\gamma_2)={1\over 2}[a_n(\gamma_1)+a_n(\gamma_2)]$. When
$\gamma_1=1-\gamma_2=\gamma$ one recovers the model introduced by Melzak
\cite{melzak}, and when $\gamma_1=\gamma_2=\gamma$ one recovers
inelastic collisions. Since $a_n(\gamma)=a_n(1-\gamma)$ both models
have identical multiscaling exponents. Furthermore, a multiscaling
behavior with the very same exponents $\alpha_n(\gamma)$ is also found
for the process $(u_1,u_2)\to (u_1-\gamma u_1,v_1+\gamma u_1)$
investigated in the context of asset distributions \cite{sps} and
headway distributions in traffic flows \cite{kg}.
One can also consider stochastic rather than deterministic collision
processes by assuming that the collision (\ref{rule1}) occurs with
probability density $\sigma_1(\gamma_1,\gamma_2)$. Our findings extend
to this model: the multiscaling exponents are given by the general
expression $\alpha_n=a_n/a_2$ with \hbox{$a_n=\int d\gamma_1 \int
d\gamma_2 \sigma(\gamma_1,\gamma_2) a_n(\gamma_1,\gamma_2 )$}. In
particular, for completely random inelastic collisions, i.e.,
$\sigma\equiv 1$ and $\gamma_1=\gamma_2=\gamma$, one finds
$a_n={n-1\over n+1}$ and hence $\alpha_n=3{n-1\over n+1}$.
So far, we discussed only two-body interactions.
We therefore consider
$N$-body interactions where a collision is symbolized by
$(u_1,\ldots,u_N)\to (v_1,\ldots,v_N)$. We consider a generalization
of the $\gamma={1\over 2}$ two-body case where the post-collision
velocities are all equal. Momentum conservation implies $v_i=\bar
u=N^{-1}\sum u_i$. The master equation is a straightforward
generalization of the two-body case and we merely quote the moment
equations
\begin{equation}
\dot M_n+a_nM_n=
N^{-n}\sum_{n_i\neq 1} {n\choose {n_1\ldots n_N}} M_{n_1}\cdots M_{n_N}
\end{equation}
with $a_n=1-N^{1-n}$. Using a generalization of the aforementioned
inequality \hbox{$a_{m_1+\cdots+m_k}> a_{m_1}+...+a_{m_k}$} when all
$m_j>1$, we find that the right-hand side of the above equation
remains asymptotically negligible. Therefore,
\hbox{$M_n\sim e^{-a_n t}$} and
\begin{equation}
M_n\sim M_2^{\alpha_n},\qquad
\alpha_n={1-N^{1-n}\over 1-N^{-1}}.
\end{equation}
Thus, this $N$-body ``averaging'' process exhibits multiscaling
asymptotic behavior as well.
Thus far, we investigated a mean field model. When particles reside
on a $d$-dimensional lattice and only nearest neighbors interact, the
above dynamics is equivalent to a diffusion process \cite{bk}. As a
result, the underlying correlation length is diffusive, $L(t)\sim
t^{1/2}$. Within this correlation length the velocities are ``well
mixed'' and momentum conservation therefore implies that $v\sim
L^{-d/2}\sim t^{-d/4}$. Indeed, the infinite dimension limit is
consistent with the above exponential decay. Furthermore, an exact
solution for moments of arbitrary order is possible \cite{bk}. We do
not detail it here and simply quote that ordinary scaling is restored
$M_n\sim t^{-n/4}$, i.e. $\alpha_n=\alpha_n^*=n/2$. Thus, spatial
correlations counter the mechanism responsible for multiscaling.
In summary, we have investigated inelastic collision processes on the
mean-field level. We have shown that such systems are characterized by
multiscaling, or equivalently by an infinite hierarchy of diverging
time scales. Multiscaling holds for several generalizations of the
basic model including stochastic collision models and even processes
which do not obey Galilean invariance. In this latter case, however,
multiscaling is restricted to situations with zero total momentum.
This perhaps explains why multiscaling asymptotic behavior was
overlooked in previous studies \cite{melzak,sps}. Another explanation
is that this behavior may be difficult to detect from numerical
simulations. Indeed, in other problems such as multidimensional
fragmentation \cite{bk1}, and in fluid turbulence, low order moments
deviate only slightly from the normal scaling expectation.
Interestingly, although similarity solutions can be found for the
master equation, multiscaling implies that they are unphysical. For
example, Eq.~(\ref{BEF}) admits an exact Bobylev-Krook-Wu \cite{ernst}
scaling solution, $\hat P(k,t)=(1+K)e^{-K}$, where
$K=Ak\,e^{-\gamma(1-\gamma)t}$ with an arbitrary constant $A$. In the
present case, however, this BKW solution corresponds to the
pathological initial condition $P_0(v)=\delta(v-iA)+iA\delta'(v-iA)$.
There are a number of extensions of this work which are worth
pursuing. We have started with a kinetic theory of a 1D granular gas
with a velocity independent collision rate. Within such a framework,
it is sensible to approximate the collision rate with the RMS velocity
fluctuation. This leads to the algebraic decay $M_n\sim
t^{-2\alpha_n}$ with $\alpha_n$ given by Eq.~(\ref{multi}) and in
particular, Haff's cooling law $T=M_2\sim t^{-2}$ is recovered
\cite{pkh}. Our numerical studies indicate that when velocity
dependent collision rates are implemented, ordinary scaling behavior
is restored. One may also use this model as an approximation for
inelastic collisions in higher dimensions as well, following the
Maxwell approximation in kinetic theory \cite{ernst,bk2}.
\smallskip
This research was supported by the DOE (W-7405-ENG-36), NSF
(DMR9632059), and ARO (DAAH04-96-1-0114).
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\end{multicols}
\end{document}