Higham et al 2016.pdf (5.88 MB)
A rapid non-iterative proper orthogonal decomposition based outlier detection and correction for PIV data
journal contribution
posted on 2018-06-19, 12:56 authored by J.E. Higham, W. Brevis, Chris KeylockChris KeylockThe present work proposes a novel method of detection and estimation of outliers in particle image velocimetry measurements by the modification of the temporal coefficients associated with a proper orthogonal decomposition of an experimental time series. Using synthetic outliers applied to two sequences of vector fields, the method is benchmarked against state-of-the-art approaches recently proposed to remove the influence of outliers. Compared with these methods, the proposed approach offers an increase in accuracy and robustness for the detection of outliers and comparable accuracy for their estimation.
Funding
The UK Natural Environment Research Council for the PhD studentship of the first author and Engineering and Physical Sciences Research Council for the second author.
History
School
- Architecture, Building and Civil Engineering
Published in
Measurement Science and TechnologyVolume
27Issue
12Citation
HIGHAM, J.E., BREVIS, W. and KEYLOCK, C.J., 2016. A rapid non-iterative proper orthogonal decomposition based outlier detection and correction for PIV data. Measurement Science and Technology, 27 (12), 125303.Publisher
© IOP PublishingVersion
- VoR (Version of Record)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/Acceptance date
2016-10-05Publication date
2016Notes
This is an Open Access Article. It is published by IOP Publishing under the Creative Commons Attribution 3.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/ISSN
0957-0233eISSN
1361-6501Publisher version
Language
- en