5 research outputs found

    Cluster persistence in one-dimensional diffusion--limited cluster--cluster aggregation

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    The persistence probability, PC(t)P_C(t), of a cluster to remain unaggregated is studied in cluster-cluster aggregation, when the diffusion coefficient of a cluster depends on its size ss as D(s)sγD(s) \sim s^\gamma. In the mean-field the problem maps to the survival of three annihilating random walkers with time-dependent noise correlations. For γ0\gamma \ge 0 the motion of persistent clusters becomes asymptotically irrelevant and the mean-field theory provides a correct description. For γ<0\gamma < 0 the spatial fluctuations remain relevant and the persistence probability is overestimated by the random walk theory. The decay of persistence determines the small size tail of the cluster size distribution. For 0<γ<20 < \gamma < 2 the distribution is flat and, surprisingly, independent of γ\gamma.Comment: 11 pages, 6 figures, RevTeX4, submitted to Phys. Rev.

    Coarsening of Sand Ripples in Mass Transfer Models with Extinction

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    Coarsening of sand ripples is studied in a one-dimensional stochastic model, where neighboring ripples exchange mass with algebraic rates, Γ(m)mγ\Gamma(m) \sim m^\gamma, and ripples of zero mass are removed from the system. For γ<0\gamma < 0 ripples vanish through rare fluctuations and the average ripples mass grows as \avem(t) \sim -\gamma^{-1} \ln (t). Temporal correlations decay as t1/2t^{-1/2} or t2/3t^{-2/3} depending on the symmetry of the mass transfer, and asymptotically the system is characterized by a product measure. The stationary ripple mass distribution is obtained exactly. For γ>0\gamma > 0 ripple evolution is linearly unstable, and the noise in the dynamics is irrelevant. For γ=1\gamma = 1 the problem is solved on the mean field level, but the mean-field theory does not adequately describe the full behavior of the coarsening. In particular, it fails to account for the numerically observed universality with respect to the initial ripple size distribution. The results are not restricted to sand ripple evolution since the model can be mapped to zero range processes, urn models, exclusion processes, and cluster-cluster aggregation.Comment: 10 pages, 8 figures, RevTeX4, submitted to Phys. Rev.

    Persistence properties of a system of coagulating and annihilating random walkers

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    We study a d-dimensional system of diffusing particles that on contact either annihilate with probability 1/(q-1) or coagulate with probability (q-2)/(q-1). In 1-dimension, the system models the zero temperature Glauber dynamics of domain walls in the q-state Potts model. We calculate P(m,t), the probability that a randomly chosen lattice site contains a particle whose ancestors have undergone exactly (m-1) coagulations. Using perturbative renormalization group analysis for d < 2, we show that, if the number of coagulations m is much less than the typical number M(t), then P(m,t) ~ m^(z/d) t^(-theta), with theta=d Q + Q(Q-1/2) epsilon + O(epsilon^2), z=(2Q-1) epsilon + (2 Q-1) (Q-1)(1/2+A Q) epsilon^2 +O(epsilon^3), where Q=(q-1)/q, epsilon =2-d and A =-0.006. M(t) is shown to scale as t^(d/2-delta), where delta = d (1 -Q)+(Q-1)(Q-1/2) epsilon+ O(epsilon^2). In two dimensions, we show that P(m,t) ~ ln(t)^(Q(3-2Q)) ln(m)^((2Q-1)^2) t^(-2Q) for m << t^(2 Q-1). The 1-dimensional results corresponding to epsilon=1 are compared with results from Monte Carlo simulations.Comment: 12 pages, revtex, 5 figure

    Kang-Redner Anomaly in Cluster-Cluster Aggregation

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    The large time, small mass, asymptotic behavior of the average mass distribution \pb is studied in a dd-dimensional system of diffusing aggregating particles for 1d21\leq d \leq 2. By means of both a renormalization group computation as well as a direct re-summation of leading terms in the small reaction-rate expansion of the average mass distribution, it is shown that \pb \sim \frac{1}{t^d} (\frac{m^{1/d}}{\sqrt{t}})^{e_{KR}} for mtd/2m \ll t^{d/2}, where eKR=ϵ+O(ϵ2)e_{KR}=\epsilon +O(\epsilon ^2) and ϵ=2d\epsilon =2-d. In two dimensions, it is shown that \pb \sim \frac{\ln(m) \ln(t)}{t^2} for mt/ln(t) m \ll t/ \ln(t). Numerical simulations in two dimensions supporting the analytical results are also presented.Comment: 11 pages, 6 figures, Revtex
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