Micro-Scale Particle Tracking: From Conventional to Data-Driven Methods

作     者：Wang, Haoyu Hong, Liu Chamorro, Leonardo P. 

作者机构：Univ Illinois Dept Mech Sci & Engn Urbana IL 61801 USA Univ Illinois Dept Aerosp Engn Urbana IL 61801 USA Univ Illinois Dept Civil & Environm Engn Urbana IL 61801 USA Univ Illinois Dept Geol Urbana IL 61801 USA 

出 版 物：《MICROMACHINES》 (微型机械)

年 卷 期：2024年第15卷第5期

页      面：629-629页

核心收录：

学科分类：08[工学] 0804[工学-仪器科学与技术] 0805[工学-材料科学与工程（可授工学、理学学位）] 0703[理学-化学] 0802[工学-机械工程] 0702[理学-物理学] 

基　　金：UK Research and Innovation  UKRI  (105409) 

主　　题：micro-scale positioning particle tracking velocimetry fluid mechanics data-driven method deep learning neural networks 

摘      要：Micro-scale positioning techniques have become essential in numerous engineering systems. In the field of fluid mechanics, particle tracking velocimetry (PTV) stands out as a key method for tracking individual particles and reconstructing flow fields. Here, we present an overview of the micro-scale particle tracking methodologies that are predominantly employed for particle detection and flow field reconstruction. It covers various methods, including conventional and data-driven techniques. The advanced techniques, which combine developments in microscopy, photography, image processing, computer vision, and artificial intelligence, are making significant strides and will greatly benefit a wide range of scientific and engineering fields.