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dc.contributor.authorIungo, Giacomo V.en_US
dc.contributor.authorSantoni-Ortiz, Christianen_US
dc.contributor.authorAbkar, M.en_US
dc.contributor.authorPorté-Agel, F.en_US
dc.contributor.authorRotea, Mario A.en_US
dc.contributor.authorLeonardi, Stefanoen_US
dc.date.accessioned2016-03-28T18:58:07Z
dc.date.available2016-03-28T18:58:07Z
dc.date.created2015-06
dc.identifier.issn1742-6588en_US
dc.identifier.urihttp://hdl.handle.net/10735.1/4805
dc.description.abstractIn this paper a new paradigm for prediction of wind turbine wakes is proposed, which is based on a reduced order model (ROM) embedded in a Kalman filter. The ROM is evaluated by means of dynamic mode decomposition performed on high fidelity LES numerical simulations of wind turbines operating under different operational regimes. The ROM enables to capture the main physical processes underpinning the downstream evolution and dynamics of wind turbine wakes. The ROM is then embedded within a Kalman filter in order to produce a time-marching algorithm for prediction of wind turbine wake flows. This data-driven algorithm enables data assimilation of new measurements simultaneously to the wake prediction, which leads to an improved accuracy and a dynamic update of the ROM in presence of emerging coherent wake dynamics observed from new available data. Thanks to its low computational cost, this numerical tool is particularly suitable for real-time applications, control and optimization of large wind farms.en_US
dc.language.isoenen_US
dc.publisherInstitute of Physics Publishingen_US
dc.relation.urihttp://dx.doi.org/10.1088/1742-6596/625/1/012009en_US
dc.rightsCC BY 3.0 (Attribution)en_US
dc.rights©2015 IOP Publishingen_US
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en_US
dc.subjectData reductionen_US
dc.subjectKalman filteringen_US
dc.subjectWakes (Aerodynamics)en_US
dc.subjectWind poweren_US
dc.subjectAlgorithmsen_US
dc.subjectDynamic mode decompositionen_US
dc.subjectReal-time applicationsen_US
dc.subjectWind turbinesen_US
dc.titleData-Driven Reduced Order Model for Prediction of Wind Turbine Wakesen_US
dc.type.genrearticleen_US
dc.identifier.bibliographicCitationIungo, G. V., C. Santoni-Ortiz, M. Abkar, F. Porté-Agel, et al. 2015. "Data-driven Reduced Order Model for prediction of wind turbine wakes." Journal of Physics Conference Series 625(1), doi: 10.1088/1742-6596/625/1/012009.en_US
dc.source.journalJournal of Physics Conference Seriesen_US
dc.identifier.volume625en_US
dc.identifier.issue1en_US
dc.contributor.utdAuthorIungo, Giacomo V.
dc.contributor.utdAuthorSantoni-Ortiz, Christian.
dc.contributor.utdAuthorRotea, Mario A.
dc.contributor.utdAuthorLeonardi, Stefano


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Except where otherwise noted, this item's license is described as CC BY 3.0 (Attribution)