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dc.contributor.authorVlasenko, Aen
dc.contributor.authorSteele, ECCen
dc.contributor.authorNimmo-Smith, WAMen
dc.date.accessioned2015-11-13T22:00:03Z
dc.date.accessioned2015-11-13T22:01:23Z
dc.date.available2015-11-13T22:00:03Z
dc.date.available2015-11-13T22:01:23Z
dc.date.issued2015-06-01en
dc.identifier.issn0957-0233en
dc.identifier.urihttp://hdl.handle.net/10026.1/3810
dc.description.abstract

© 2015 IOP Publishing Ltd. The gaps and noise present in particle image velocimetry (PIV) and particle tracking velocimetry (PTV) measurements affect the accuracy of the data collected. Existing algorithms developed for the restoration of such data are only applicable to experimental measurements collected under well-prepared laboratory conditions (i.e. where the pattern of the velocity flow field is known), and the distribution, size and type of gaps and noise may be controlled by the laboratory set-up. However, in many cases, such as PIV and PTV measurements of arbitrarily turbid coastal waters, the arrangement of such conditions is not possible. When the size of gaps or the level of noise in these experimental measurements become too large, their successful restoration with existing algorithms becomes questionable. Here, we outline a new physics-enabled flow restoration algorithm (PEFRA), specially designed for the restoration of such velocity data. Implemented as a 'black box' algorithm, where no user-background in fluid dynamics is necessary, the physical structure of the flow in gappy or noisy data is able to be restored in accordance with its hydrodynamical basis. The use of this is not dependent on types of flow, types of gaps or noise in measurements. The algorithm will operate on any data time-series containing a sequence of velocity flow fields recorded by PIV or PTV. Tests with numerical flow fields established that this method is able to successfully restore corrupted PIV and PTV measurements with different levels of sparsity and noise. This assessment of the algorithm performance is extended with an example application to in situ submersible 3D-PTV measurements collected in the bottom boundary layer of the coastal ocean, where the naturally-occurring plankton and suspended sediments used as tracers causes an increase in the noise level that, without such denoising, will contaminate the measurements.

en
dc.language.isoenen
dc.relation.replaceshttp://hdl.handle.net/10026.1/3809
dc.relation.replaces10026.1/3809
dc.titleA physics-enabled flow restoration algorithm for sparse PIV and PTV measurementsen
dc.typeJournal Article
plymouth.issue6en
plymouth.volume26en
plymouth.publication-statusPublisheden
plymouth.journalMeasurement Science and Technologyen
dc.identifier.doi10.1088/0957-0233/26/6/065301en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Biological and Marine Sciences
plymouth.organisational-group/Plymouth/PRIMaRE Publications
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA07 Earth Systems and Environmental Sciences
plymouth.organisational-group/Plymouth/Research Groups
plymouth.organisational-group/Plymouth/Research Groups/Marine Institute
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dc.identifier.eissn1361-6501en
dc.rights.embargoperiod12 monthsen
rioxxterms.versionofrecord10.1088/0957-0233/26/6/065301en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/under-embargo-all-rights-reserveden
rioxxterms.typeJournal Article/Reviewen


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