论文标题
含粒子流中的聚类形成是连续的相变
Cluster formation in particle-laden flows is a continuous phase transition
论文作者
论文摘要
研究充满颗粒的流量对于理解多种物理过程，例如云，病原体传播和污染物分散体等多种物理过程至关重要。在这种流中形成了不同的聚类模式，具有不同的惯性颗粒（以Stokes Number st为特征）。我们第一次使用复杂的网络研究了这种流动中的时空动力学。我们在2D Taylor-Green流中模拟粒子，并表明网络测量了局部和全局聚类特性的特征。当粒子从随机分布的初始条件中聚集成特定的模式时，我们通过连续的相变观察到了派生网络中巨型组件的出现。此外，通过ST <0.25的功率定律和在0.25至1的ST的指数函数确定相关时间与Stokes数字有关。我们的发现为载有颗粒流中的聚类现象提供了新的见解。
Studying particle-laden flows is essential to understand diverse physical processes such as rain formation in clouds, pathogen transmission, and pollutant dispersal. Distinct clustering patterns are formed in such flows with particles of different inertia (characterized by Stokes number St). For the first time, we use complex networks to study the spatiotemporal dynamics in such flows. We simulate particles in a 2D Taylor-Green flow and show that the network measures characterize both the local and global clustering properties. As particles cluster into specific patterns from a randomly distributed initial condition, we observe an emergence of a giant component in the derived network through a continuous phase transition. Further, the phase transition time is identified to be related to the Stokes number through a power law for St < 0.25 and an exponential function for St in the range 0.25 to 1. Our findings provide novel insights into the clustering phenomena in particle-laden flows.
