DEEP LEARNING IN PIV APPLICATIONS

Abstract

In the last decades, the field of fluid dynamics has evolved significantly thanks to the development of experimental and measurement techniques (hot wire, LDA, PTV, PIV), as well as the increasing computational capabilities and the improvement of available software and methods, such as CFD. All these techniques generate enormous volumes of data. Extracting valuable and useful information from them is often a time-consuming task, that could be aided by Deep Learning (DL). Herein, some of the many possible applications of DL, in particular the YOLO algorithm, in the practice of PIV, are considered and suggested.