Experimental uncertainties associated with particle image velocimetry (PIV) based vorticity algorithms

Experimental uncertainties associated with particle image velocimetry (PIV) based vorticity...  We have recently used Particle Image Velocimetry (PIV) to study the dynamics of vortex propagation in reacting and non-reacting flows. In order to do so, it became necessary to assess the uncertainty in PIV-based vorticity data. A computer simulation was developed to investigate how uncertainty propagates throughout the post-processing, numerical data smoothing, and vorticity calculating algorithms commonly used in the analysis of PIV data. Results indicate that the average uncertainty in vorticity per interrogation cell (normalized to the average vorticity, and then surface averaged), for a simple vortex, can be reduced to approximately ±4% with appropriate measures. This value was obtained using PIV autocorrelation software, a local regression technique combined with a Gaussian-smoothing filter. Our best experimental results (these areas with no lost or spurious vectors) are consistent with Stoke’s theorem. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Experiments in Fluids Springer Journals

Experimental uncertainties associated with particle image velocimetry (PIV) based vorticity algorithms

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Publisher
Springer-Verlag
Copyright
Copyright © 1999 by Springer-Verlag Berlin Heidelberg
Subject
Engineering; Engineering Fluid Dynamics; Fluid- and Aerodynamics; Engineering Thermodynamics, Heat and Mass Transfer
ISSN
0723-4864
eISSN
1432-1114
D.O.I.
10.1007/s003480050263
Publisher site
See Article on Publisher Site

Abstract

 We have recently used Particle Image Velocimetry (PIV) to study the dynamics of vortex propagation in reacting and non-reacting flows. In order to do so, it became necessary to assess the uncertainty in PIV-based vorticity data. A computer simulation was developed to investigate how uncertainty propagates throughout the post-processing, numerical data smoothing, and vorticity calculating algorithms commonly used in the analysis of PIV data. Results indicate that the average uncertainty in vorticity per interrogation cell (normalized to the average vorticity, and then surface averaged), for a simple vortex, can be reduced to approximately ±4% with appropriate measures. This value was obtained using PIV autocorrelation software, a local regression technique combined with a Gaussian-smoothing filter. Our best experimental results (these areas with no lost or spurious vectors) are consistent with Stoke’s theorem.

Journal

Experiments in FluidsSpringer Journals

Published: Jan 1, 1999

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