Angular velocimetry for fluid flows: an optical sensor using structured light and machine learning

Abstract

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.

Document Details

Document Type
Pub Defense Publication
Publication Date
Mar 16, 2021
Source ID
10.1364/oe.417210

Entities

People

  • Alexander Q Anderson
  • B. M. Heffernan
  • Elizabeth F Strong
  • G. B. Rieker
  • Juliet T. Gopinath
  • M. P. Brenner
  • Nazanin Hoghooghi

Organizations

  • Air Force Office of Scientific Research
  • Google
  • Harvard University
  • National Science Foundation
  • University of Colorado
  • University of Colorado Boulder

Tags

Readers

  • Fluid Mechanics and Fluid Dynamics.
  • Geodesy
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks