Samant Omstavan, Alageshan Jaya Kumar, Sharma Sarveshwar, Kuley Animesh
Centre for Fusion, Space and Astrophysics, University of Warwick, Coventry, CV4 7AL, UK.
Department of Physics, Indian Institute of Science, Bangalore, 560012, India.
Sci Rep. 2021 May 17;11(1):10456. doi: 10.1038/s41598-021-89953-3.
Inertial particles advected by a background flow can show complex structures. We consider inertial particles in a 2D Taylor-Green (TG) flow and characterize particle dynamics as a function of the particle's Stokes number using dynamic mode decomposition (DMD) method from particle image velocimetry (PIV) like-data. We observe the formation of caustic structures and analyze them using DMD to (a) determine the Stokes number of the particles, and (b) estimate the particle Stokes number composition. Our analysis in this idealized flow will provide useful insight to analyze inertial particles in more complex or turbulent flows. We propose that the DMD technique can be used to perform similar analysis on an experimental system.
由背景流平流输送的惯性粒子会呈现出复杂的结构。我们考虑二维泰勒 - 格林（TG）流中的惯性粒子，并使用来自粒子图像测速（PIV）类数据的动态模态分解（DMD）方法，将粒子动力学表征为粒子斯托克斯数的函数。我们观察到焦散结构的形成，并使用DMD对其进行分析，以（a）确定粒子的斯托克斯数，以及（b）估计粒子斯托克斯数的组成。我们在这种理想化流动中的分析将为分析更复杂或湍流流动中的惯性粒子提供有用的见解。我们提出DMD技术可用于在实验系统上进行类似的分析。
