A Review of 3D Particle Tracking and Flow Diagnostics Using Digital Holography

TL;DR

This review comprehensively summarizes digital holography techniques for 3D particle tracking and flow diagnostics, highlighting hardware configurations, processing methods, and future prospects including machine learning applications across various scientific fields.

Contribution

It provides an in-depth overview of DH principles, hardware setups, processing approaches, and discusses future directions with emphasis on machine learning integration.

Findings

Different hardware configurations have unique advantages and limitations.

Machine learning enhances accuracy in DH-based particle tracking.

DH techniques are applicable across diverse scientific and industrial fields.

Abstract

Advanced three-dimensional (3D) tracking methods are essential for studying particle dynamics across a wide range of complex systems, including multiphase flows, environmental and atmospheric sciences, colloidal science, biological and medical research, and industrial manufacturing processes. This review provides a comprehensive summary of 3D particle tracking and flow diagnostics using Digital Holography (DH). We begin by introducing the principles of DH, accompanied by a detailed discussion on numerical reconstruction. The review then explores various hardware setups used in DH, including inline, off-axis, and dual or multiple-view configurations, outlining their advantages and limitations. We also delve into different hologram processing methods, categorized into traditional multi-step, inverse, and machine learning-based approaches, providing in-depth insights into their applications for 3D particle tracking and flow diagnostics across multiple studies. The review concludes with a discussion on future prospects, emphasizing the significant role of machine learning in enabling accurate DH-based particle tracking and flow diagnostic techniques across diverse fields, such as manufacturing, environmental monitoring, and biological sciences.