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dc.contributor.authorIungo, Giacomo V.en_US
dc.contributor.authorViola, F.en_US
dc.contributor.authorCiri, Umbertoen_US
dc.contributor.authorRotea, Mario A.en_US
dc.contributor.authorLeonardi, Stefanoen_US
dc.contributor.otherSorensen J.N.en_US
dc.contributor.otherIvanell S.en_US
dc.contributor.otherBarney A.en_US
dc.date.accessioned2016-03-28T19:01:34Z
dc.date.available2016-03-28T19:01:34Z
dc.date.created2015-06
dc.identifier.issn1742-6588en_US
dc.identifier.urihttp://hdl.handle.net/10735.1/4807
dc.description.abstractIn the wind energy industry there is a growing need for real-time predictions of wind turbine wake flows in order to optimize power plant control and inhibit detrimental wake interactions. To this aim, a data-driven RANS approach is proposed in order to achieve very low computational costs and adequate accuracy through the data assimilation procedure. The RANS simulations are implemented with a classical Boussinesq hypothesis and a mixing length turbulence closure model, which is calibrated through the available data. High-fidelity LES simulations of a utility-scale wind turbine operating with different tip speed ratios are used as database. It is shown that the mixing length model for the RANS simulations can be calibrated accurately through the Reynolds stress of the axial and radial velocity components, and the gradient of the axial velocity in the radial direction. It is found that the mixing length is roughly invariant in the very near wake, then it increases linearly with the downstream distance in the diffusive region. The variation rate of the mixing length in the downstream direction is proposed as a criterion to detect the transition between near wake and transition region of a wind turbine wake. Finally, RANS simulations were performed with the calibrated mixing length model, and a good agreement with the LES simulations is observed.en_US
dc.language.isoenen_US
dc.publisherInstitute of Physics Publishingen_US
dc.relation.urihttp://dx.doi.org/10.1088/1742-6596/625/1/012025en_US
dc.rightsCC BY 3.0 (Attribution)en_US
dc.rights©2015 IOP Scienceen_US
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en_US
dc.subjectCalibrationen_US
dc.subjectNavier-Stokes equationsen_US
dc.subjectPlant shutdownsen_US
dc.subjectReynolds numberen_US
dc.subjectWind poweren_US
dc.subjectWind turbinesen_US
dc.subjectBoussinesq hypothesisen_US
dc.subjectTurbulence--Mathematical modelsen_US
dc.subjectPower plants--controlen_US
dc.subjectWind poweren_US
dc.subjectWakes (Aerodynamics)en_US
dc.titleData-Driven RANS for Simulations of Large Wind Farmsen_US
dc.type.genrearticleen_US
dc.identifier.bibliographicCitationIungo, G. V., F. Viola, U. Ciri, M. A. Rotea, et al. 2015. "Data-driven RANS for simulations of large wind farms." Journal of Physics Conference Series 625(1), doi: 10.1088/1742-6596/625/1/012025.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.utdAuthorCiri, Umberto
dc.contributor.utdAuthorRotea, Mario A.
dc.contributor.utdAuthorLeonardi, Stefano
dc.contributor.ORCID0000-0002-0990-8133 (Iungo, GV)en_US
dc.contributor.ORCID0000-0002-9809-7191 (Leonardi, S)en_US


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