Das Moupriya, Green Jason R
Department of Chemistry, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, MA, 02125, USA.
Max-Planck Institute for the Physics of Complex Systems, 01187, Dresden, Germany.
Nat Commun. 2019 May 14;10(1):2155. doi: 10.1038/s41467-019-10040-3.
Fluids cooled to the liquid-vapor critical point develop system-spanning fluctuations in density that transform their visual appearance. Despite a rich phenomenology, however, there is not currently an explanation of the mechanical instability in the molecular motion at this critical point. Here, we couple techniques from nonlinear dynamics and statistical physics to analyze the emergence of this singular state. Numerical simulations and analytical models show how the ordering mechanisms of critical dynamics are measurable through the hierarchy of spatiotemporal Lyapunov vectors. A subset of unstable vectors soften near the critical point, with a marked suppression in their characteristic exponents that reflects a weakened sensitivity to initial conditions. Finite-time fluctuations in these exponents exhibit sharply peaked dynamical timescales and power law signatures of the critical dynamics. Collectively, these results are symptomatic of a critical slowing down of chaos that sits at the root of our statistical understanding of the liquid-vapor critical point.
冷却到液-气临界点的流体在密度上会出现跨越系统的涨落，这改变了它们的视觉外观。然而，尽管有丰富的现象学，但目前还没有对该临界点处分子运动中的力学不稳定性作出解释。在这里，我们将非线性动力学和统计物理的技术结合起来，分析这种奇异状态的出现。数值模拟和解析模型表明，临界动力学的有序机制如何通过时空李雅普诺夫向量的层次结构来测量。一部分不稳定向量在临界点附近变软，其特征指数明显受到抑制，这反映了对初始条件的敏感性减弱。这些指数的有限时间涨落在临界动力学的动力学时间尺度和幂律特征上表现出尖锐的峰值。总体而言，这些结果表明混沌的临界减慢是我们对液-气临界点进行统计理解的根源。
