Publication:
On PIV random error minimization with optimal POD-based low-order reconstruction

dc.affiliation.dptoUC3M. Departamento de Ingeniería Aeroespaciales
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Ingeniería Aeroespaciales
dc.contributor.authorRaiola, Marco
dc.contributor.authorDiscetti, Stefano
dc.contributor.authorIaniro, Andrea
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2021-01-29T11:14:22Z
dc.date.available2021-01-29T11:14:22Z
dc.date.issued2015-04
dc.description.abstractRandom noise removal from particle image velocimetry (PIV) data and spectra is of paramount importance, especially for the computation of derivative quantities and spectra. Data filtering is critical, as a trade-off between filter effectiveness and spatial resolution penalty should be found. In this paper, a filtering method based on proper orthogonal decomposition and low-order reconstruction (LOR) is proposed. The existence of an optimal number of modes based on the minimization of both reconstruction error and signal withdrawal is demonstrated. A criterion to perform the choice of the optimal number of modes is proposed. The method is validated via synthetic and real experiments. As prototype problems, we consider PIV vector fields obtained from channel flow DNS data and from PIV measurement in the wake of a circular cylinder. We determine the optimal number of modes to be used for the LOR in order to minimize the statistical random error. The results highlight a significant reduction in the measurement error. Dynamic velocity range is enhanced, enabling to correctly capture spectral information of small turbulent scales down to the half of the cutoff wavelength of original data. In addition to this, the capability of detecting coherent structures is improved. The robustness of the method is proved, both for low signal-to-noise ratios and for small-sized ensembles. The proposed method can significantly improve the physical insight into the investigation of turbulent flows.en
dc.description.sponsorshipThis work has been partially supported by grant TRA2013-41103-P of the Spanish Ministry of Economy and Competitiveness. This grant includes FEDER funding.en
dc.format.extent16
dc.identifier.bibliographicCitationRaiola, M., Discetti, S., Ianiro, A. (2015). On PIV random error minimization with optimal POD-based low-order reconstruction. Experiments in Fluids, 56(4).en
dc.identifier.doihttps://doi.org/10.1007/s00348-015-1940-8
dc.identifier.issn0723-4864
dc.identifier.publicationfirstpage75 - 1
dc.identifier.publicationissue4
dc.identifier.publicationlastpage75 - 15
dc.identifier.publicationtitleExperiments in Fluidsen
dc.identifier.publicationvolume56
dc.identifier.urihttps://hdl.handle.net/10016/31814
dc.identifier.uxxiAR/0000016660
dc.language.isoeng
dc.publisherSpringer Natureen
dc.relation.projectIDGobierno de España. TRA2013-41103-Pes
dc.rights© Springer-Verlag Berlin Heidelberg 2015en
dc.rights.accessRightsopen access
dc.subject.ecienciaAeronáuticaes
dc.subject.otherProper-Orthogonal-Decompositionen
dc.subject.otherImage deformation methodsen
dc.subject.otherTturbulent channel flowen
dc.subject.otherCoherent structuresen
dc.subject.otherHeat-transferen
dc.subject.otherCylinderen
dc.subject.otherDynamicsen
dc.subject.otherFielden
dc.titleOn PIV random error minimization with optimal POD-based low-order reconstructionen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
piv_EIF_2015_ps.pdf
Size:
2.78 MB
Format:
Adobe Portable Document Format