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arXiv 1007.0759cond-mat.stat-mech
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随机长程相互作用系统的弛豫动力学

Relaxation dynamics of stochastic long-range interacting systems

Shamik Gupta, David Mukamel

摘要:

长程相互作用系统,而放松到平衡,可能会陷入非平衡准定态(QSS)的时间与系统的大小代数发散。这些有趣的非玻尔兹曼态已经观察到确定性哈密顿演化的范式系统,哈密顿平均场(HMF)模型。我们在这里研究的鲁棒性QSS随机过程超出确定性动力学内的微正则系综。为此,我们推广的HMF模型,除了确定性的随机三粒子碰撞动力学。通过分析由此产生的玻尔兹曼方程的相空间密度,我们表明,在随机性的存在下,QSS只发生作为一个交叉现象,在有限的时间内确定的随机过程的强度。特别是,我们认为,平衡的弛豫时间不与系统的大小代数规模。我们提出了一个缩放形式的弛豫时间,这是在广泛的数值模拟的结果非常好的协议。更广泛的有效性,这些结果进行了测试不同的随机HMF模型,涉及微正则蒙特卡罗动态移动。

英文摘要:

Long-range interacting systems, while relaxing towards equilibrium, may get trapped in nonequilibrium quasistationary states (QSS) for a time which diverges algebraically with the system size. These intriguing non-Boltzmann states have been observed under deterministic Hamiltonian evolution of a paradigmatic system, the Hamiltonian Mean-Field (HMF) model. We study here the robustness of QSS with respect to stochastic processes beyond deterministic dynamics within a microcanonical ensemble. To this end, we generalize the HMF model by allowing for stochastic three-particle collision dynamics in addition to the deterministic ones. By analyzing the resulting Boltzmann equation for the phase space density, we demonstrate that in the presence of stochasticity, QSS occur only as a crossover phenomenon over a finite time determined by the strength of the stochastic process. In particular, we argue that the relaxation time to equilibrium does not scale algebraically with the system size. We propose a scaling form for the relaxation time which is in very good agreement with results of extensive numerical simulations. The broader validity of these results is tested on a different stochastic HMF model involving microcanonical Monte Carlo dynamical moves.

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