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极低温下陷阱模型中的反常扩散、局域化、老化及亚老化效应。

Anomalous diffusion, localization, aging, and subaging effects in trap models at very low temperature.

作者信息

Monthus Cécile

机构信息

Service de Physique Théorique, Unité de Recherche associée au CNRS, DSM/CEA Saclay, 91191 Gif-sur-Yvette, France.

出版信息

Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Sep;68(3 Pt 2):036114. doi: 10.1103/PhysRevE.68.036114. Epub 2003 Sep 19.

DOI:10.1103/PhysRevE.68.036114
PMID:14524839
Abstract

We study in detail the dynamics of the one-dimensional symmetric trap model via a real-space renormalization procedure which becomes exact in the limit of zero temperature. In this limit, the diffusion front in each sample consists of two delta peaks, which are completely out of equilibrium with each other. The statistics of the positions and weights of these delta peaks over the samples allows to obtain explicit results for all observables in the limit T-->0. We first compute disorder averages of one-time observables, such as the diffusion front, the thermal width, the localization parameters, the two-particle correlation function, and the generating function of thermal cumulants of the position. We then study aging and subaging effects: our approach reproduces very simply the two different aging exponents and yields explicit forms for scaling functions of the various two-time correlations. We also extend the real-space renormalization group method to include systematic corrections to the previous zero temperature procedure via a series expansion in T. We then consider the generalized trap model with parameter alpha in [0,1] and obtain that the large scale effective model at low temperature does not depend on alpha in any dimension, so that the only observables sensitive to alpha are those that measure the "local persistence," such as the probability to remain exactly in the same trap during a time interval. Finally, we extend our approach at a scaling level for the trap model in d=2 and obtain the two relevant time scales for aging properties.

摘要

我们通过实空间重整化程序详细研究了一维对称陷阱模型的动力学,该程序在零温度极限下变得精确。在此极限下,每个样本中的扩散前沿由两个狄拉克峰组成,它们彼此完全处于非平衡状态。这些狄拉克峰在样本上的位置和权重统计允许我们在(T\rightarrow0)的极限下获得所有可观测量的明确结果。我们首先计算一次性可观测量的无序平均值,例如扩散前沿、热宽度、局域化参数、两粒子关联函数以及位置的热累积量的生成函数。然后我们研究老化和亚老化效应:我们的方法非常简单地重现了两个不同的老化指数,并给出了各种两时间关联的标度函数的明确形式。我们还扩展了实空间重整化群方法,通过在(T)中的级数展开来包括对先前零温度程序的系统修正。然后我们考虑参数(\alpha\in[0,1])的广义陷阱模型,并得出低温下的大尺度有效模型在任何维度上都不依赖于(\alpha),因此唯一对(\alpha)敏感的可观测量是那些测量“局部持续性”的量,例如在一个时间间隔内恰好停留在同一个陷阱中的概率。最后,我们在标度水平上扩展了我们对二维陷阱模型的方法,并获得了老化性质的两个相关时间尺度。

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