Open AccessAngular velocimetry for fluid flows: an optical sensor using structured light and machine learning
Author(s) -
Elizabeth Strong,
Alexander Q. Anderson,
Michael P. Brenner,
Brendan M. Heffernan,
Nazanin Hoghooghi,
Juliet T. Gopinath,
Gregory B. Rieker
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.417210
Subject(s) - velocimetry , optics , turbulence , angular velocity , particle image velocimetry , physics , particle tracking velocimetry , flow (mathematics) , flow velocity , computer science , mechanics , classical mechanics
Most velocimetry approaches for fluid flows measure linear components of the velocity vector; yet, the angular velocity components, particularly at small scales in turbulent flows, also need to be resolved to study energy transfer and other important flow characteristics. Here, we detail an optical sensor approach to determine a component of the angular velocity vector. This approach uses beams of structured light and a machine learning-based analysis. We discuss the methodology to train the machine learning model and test it in experimentally validated simulations. This approach represents an interesting new direction for fluid flow velocimetry which may be extended to sense other flow parameters by selecting different light structures.