Methods for panoramic visualization and digital analysis of thermophysical flow fields. A review

TL;DR

This review comprehensively covers modern panoramic visualization and digital analysis methods for thermophysical flow fields, emphasizing recent technological advances, image processing algorithms, and big data challenges in flow research.

Contribution

It provides an extensive overview of the evolution and current state of flow visualization techniques, highlighting recent digital processing and machine learning applications.

Findings

Modern methods enable quantitative flow analysis

Cross-correlation algorithms are central to image processing

Big data and machine learning are increasingly important

Abstract

The paper presents a review of modern methods in the registration, processing, and analysis of dynamic processes in liquids, gases, plasmas, multiphase media, which are realized in researches and technology. The review author observes both the physical fundamentals of flow visualization and the basics of modern technologies for the digital processing of recorded flow images. A brief analysis of the panoramic visualization methods progress history covers a period of one and a half centuries. In the works of the last decade, the focus is on the methods of computer processing, tools, technologies for analyzing and recognizing panoramic thermophysical fields, which make it possible to obtain quantitative information about flows. The review contains an analysis of publications describing the main modern methods of visualization of flows: methods based on the phenomenon of refraction; electroluminescence; digital tracing (particle image velocimetry), visualization of surface flows (PSP and TSP coatings, liquid crystals; oil coatings). Particular attention is paid to methods using cross-correlation image processing algorithms. Those are: digital tracing (PIV), shadow background method (BOS), seedless shadow methods, thermographic PIV, velocity measurement in viscous coatings, micro, tomographic modifications of PIV, etc. The actual problem of digital data analyzing in a panoramic experiment is touched upon - the problem of big data. Examples of machine learning use in the analysis of big data sets of shadow surveys are given. Some examples of numerical simulation data visualization (simulation of experimental flowfields) are considered.