Spurious PIV vector detection and correction using a penalized least-squares method with adaptive order differentials

Spurious PIV vector detection and correction using a penalized least-squares method with adaptive... Spurious vectors (also called “outliers”) in particle image velocimetry (PIV) experiments can be classified into two categories according to their space distribution characteristics: scattered and clustered outliers. Most of the currently used validation and correction methods treat these two kinds of outliers together without discrimination. In this paper, we propose a new technique based on a penalized least-squares (PLS) method, which allows automatic classification of flows with different types of outliers. PIV vector fields containing scattered outliers are detected and corrected using higher-order differentials, while lower-order differentials are used for the flows with clustered outliers. The order of differentials is determined adaptively by generalized cross-validation and outlier classification. A simple calculation method of eigenvalues of different orders is also developed to expedite computation speed. The performance of the proposed method is demonstrated with four different velocity fields, and the results show that it works better than conventional methods, especially when the number of outliers is large. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Experiments in Fluids Springer Journals

Spurious PIV vector detection and correction using a penalized least-squares method with adaptive order differentials

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 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/s00348-017-2350-x
Publisher site
See Article on Publisher Site

Abstract

Spurious vectors (also called “outliers”) in particle image velocimetry (PIV) experiments can be classified into two categories according to their space distribution characteristics: scattered and clustered outliers. Most of the currently used validation and correction methods treat these two kinds of outliers together without discrimination. In this paper, we propose a new technique based on a penalized least-squares (PLS) method, which allows automatic classification of flows with different types of outliers. PIV vector fields containing scattered outliers are detected and corrected using higher-order differentials, while lower-order differentials are used for the flows with clustered outliers. The order of differentials is determined adaptively by generalized cross-validation and outlier classification. A simple calculation method of eigenvalues of different orders is also developed to expedite computation speed. The performance of the proposed method is demonstrated with four different velocity fields, and the results show that it works better than conventional methods, especially when the number of outliers is large.

Journal

Experiments in FluidsSpringer Journals

Published: Jun 5, 2017

References

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