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A simple trick to improve the accuracy of PIV/PTV data

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dc.contributor.authorTirelli, Iacopo
dc.contributor.authorIaniro, Andrea
dc.contributor.authorDiscetti, Stefano
dc.contributor.funderEuropean Commissionen
dc.date.accessioned2023-08-29T07:31:59Z
dc.date.available2023-08-29T07:31:59Z
dc.date.issued2023-02-25
dc.description.abstractParticle Image Velocimetry (PIV) estimates velocities through correlations of particle images within interrogation windows, leading to a spatial modulation of the velocity field. Although in principle Particle Tracking Velocimetry (PTV) estimates locally a non-modulated particle displacement, to exploit the scattered data from PTV it is necessary to interpolate these data on a structured grid, which implies a spatial modulation effect that biases the resulting velocity field. This systematic error due to finite spatial resolution inevitably depends on the interrogation window size and on the interparticle spacing. It must be observed that all these operations (cross-correlation, direct interpolation or averaging in windows) induce modulation on both the mean and the fluctuating part. We introduce a simple trick to reduce this systematic error source of PIV/PTV measurements exploiting ensemble statistics. Ensemble Particle Tracking Velocimetry (EPTV) can be leveraged to obtain the high-resolution mean flow by merging the different instantaneous realisations. The mean flow can be estimated with EPTV, and the fluctuating part can be measured from PIV/PTV. The high-resolution mean can then be superposed to the instantaneous fluctuating part to obtain velocity fields with lower systematic error. The methodology is validated against datasets with a progressively increasing level of complexity: two virtual experiments based on direct numerical simulations (DNS) of the wake of a fluidic pinball and a channel flow and the experimental data of a turbulent boundary layer. For all the cases both PTV and PIV are analysed.en
dc.description.sponsorshipThis project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No 949085, project: NEXTFLOW).en
dc.description.statusPublicadoes
dc.identifier.bibliographicCitationExperimental Thermal and Fluid Science (2023), 145, 110872, pp.:1-5en
dc.identifier.doihttps://doi.org/10.1016/j.expthermflusci.2023.110872en
dc.identifier.issn0894-1777
dc.identifier.publicationissue110872
dc.identifier.publicationtitleEXPERIMENTAL THERMAL AND FLUID SCIENCEen
dc.identifier.publicationvolume145
dc.identifier.urihttps://hdl.handle.net/10016/38119en
dc.identifier.uxxiAR/0000033288
dc.language.isoengen
dc.publisherElsevieren
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/949085en
dc.rights© 2023 The Author(s). Published by Elsevier Inc.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaen
dc.rightsThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync- nd/4.0/).en
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/en
dc.subject.ecienciaAeronáuticaes
dc.subject.otherParticle image velocimetryen
dc.subject.otherParticle tracking velocimetryen
dc.subject.otherTurbulence statisticsen
dc.titleA simple trick to improve the accuracy of PIV/PTV dataen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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