衰减湍流中惯性粒子的聚类演化
Evolution of inertial particle clustering in decaying turbulence
AI总结：
该研究通过直接数值模拟发现，衰减湍流中惯性粒子的优先聚集不仅依赖瞬时斯托克斯数，还受流动历史影响，由瞬时流动状态与初始状态共同决定。
AI中文摘要：
湍流中的惯性粒子动力学通常基于瞬时粒子斯托克斯数来解释，这一观点得到了统计平稳均匀各向同性湍流研究的支持。但在自由衰减湍流中，柯尔莫哥洛夫时间尺度会连续演化，即使粒子属性保持不变，有效斯托克斯数St也会发生变化。优先聚集是否仅由瞬时St唯一决定，还是依赖于流动的先前演化，这一问题在很大程度上尚未得到探索。我们采用单向耦合极限下重质点粒子的直接数值模拟，研究均匀各向同性湍流衰减过程中的惯性粒子聚类。通过三维沃罗诺伊 tessellation（沃罗诺伊镶嵌）对聚类进行量化，研究的粒子群体要么从统计平稳的聚类状态演化而来，要么从初始随机分布演化而来。我们同时考虑常规受迫均匀各向同性湍流和用物理信息神经网络重构的速度场。结果表明，优先聚集不能仅用瞬时St来描述：尽管粒子滑移速度遵循经典稳态对瞬时St的依赖关系，但聚类统计在整个衰减过程中仍保留着可测量的历史依赖性。现有的稳态标度律仍然适用于团簇大小演化，但仅通过依赖历史的 prefactors（ prefactors 可译为“ prefactors 因子”，此处保留英文原名）实现。从不同初始条件演化而来的粒子分布会迅速收敛到相似的大尺度空间结构，表明载体流决定了优先聚集的几何形状，而更精细的聚类统计则保留了先前演化的痕迹。这些结果表明，非平稳湍流中的优先聚集由瞬时流动状态及其初始状态共同决定。
英文摘要：
Inertial particle dynamics in turbulence are commonly interpreted in terms of the instantaneous particle Stokes number, as supported by studies in statistically stationary homogeneous isotropic turbulence. But in freely decaying turbulence the Kolmogorov time scale evolves continuously, causing the effective Stokes number, St, to vary even though particle properties remain unchanged. Whether preferential concentration remains uniquely determined by this instantaneous St or depends on the flow's previous evolution has remained largely unexplored. We investigate inertial-particle clustering during the decay of homogeneous isotropic turbulence using direct numerical simulations of heavy point particles in the one-way coupling limit. Clustering is quantified through 3D Voronoï tessellations for populations evolving either from statistically stationary clustered states or initially random distributions. We consider both conventionally forced homogeneous isotropic turbulence and velocity fields reconstructed using physics-informed neural networks. We show that preferential concentration cannot be described solely by the instantaneous St. While particle slip velocity follows the classical steady-state dependence on the instantaneous St, clustering statistics retain a measurable history dependence throughout decay. Existing stationary scaling laws remain applicable to cluster-size evolution, but only through history-dependent prefactors. Particle distributions evolving from different initial conditions rapidly converge towards similar large-scale spatial organisation, indicating that the carrier flow determines the geometry of preferential concentration while finer clustering statistics preserve imprints of the previous evolution. These results demonstrate that preferential concentration in non-stationary turbulence is governed jointly by the instantaneous flow state and its initial state.
