Kriging regression of PIV data using a local error estimate

Kriging regression of PIV data using a local error estimate The objective of the method described in this work is to provide an improved reconstruction of an original flow field from experimental velocity data obtained with particle image velocimetry (PIV) technique, by incorporating the local accuracy of the PIV data. The postprocessing method we propose is Kriging regression using a local error estimate (Kriging LE). In Kriging LE, each velocity vector must be accompanied by an estimated measurement uncertainty. The performance of Kriging LE is first tested on synthetically generated PIV images of a two-dimensional flow of four counter-rotating vortices with various seeding and illumination conditions. Kriging LE is found to increase the accuracy of interpolation to a finer grid dramatically at severe reflection and low seeding conditions. We subsequently apply Kriging LE for spatial regression of stereo-PIV data to reconstruct the three-dimensional wake of a flapping-wing micro air vehicle. By qualitatively comparing the large-scale vortical structures, we show that Kriging LE performs better than cubic spline interpolation. By quantitatively comparing the interpolated vorticity to unused measurement data at intermediate planes, we show that Kriging LE outperforms conventional Kriging as well as cubic spline interpolation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Experiments in Fluids Springer Journals

Kriging regression of PIV data using a local error estimate

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2013 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/s00348-013-1650-z
Publisher site
See Article on Publisher Site

Abstract

The objective of the method described in this work is to provide an improved reconstruction of an original flow field from experimental velocity data obtained with particle image velocimetry (PIV) technique, by incorporating the local accuracy of the PIV data. The postprocessing method we propose is Kriging regression using a local error estimate (Kriging LE). In Kriging LE, each velocity vector must be accompanied by an estimated measurement uncertainty. The performance of Kriging LE is first tested on synthetically generated PIV images of a two-dimensional flow of four counter-rotating vortices with various seeding and illumination conditions. Kriging LE is found to increase the accuracy of interpolation to a finer grid dramatically at severe reflection and low seeding conditions. We subsequently apply Kriging LE for spatial regression of stereo-PIV data to reconstruct the three-dimensional wake of a flapping-wing micro air vehicle. By qualitatively comparing the large-scale vortical structures, we show that Kriging LE performs better than cubic spline interpolation. By quantitatively comparing the interpolated vorticity to unused measurement data at intermediate planes, we show that Kriging LE outperforms conventional Kriging as well as cubic spline interpolation.

Journal

Experiments in FluidsSpringer Journals

Published: Jan 5, 2014

References

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