dc.contributor.author | Tran, Duy Tan | |
dc.contributor.author | Robinson, Haakon | |
dc.contributor.author | Rasheed, Adil | |
dc.contributor.author | San, Omer | |
dc.contributor.author | Tabib, Mandar | |
dc.contributor.author | Kvamsdal, Trond | |
dc.date.accessioned | 2021-03-23T09:21:13Z | |
dc.date.available | 2021-03-23T09:21:13Z | |
dc.date.created | 2020-11-05T12:45:39Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 1742-6588 | |
dc.identifier.uri | https://hdl.handle.net/11250/2734989 | |
dc.description.abstract | Atmospheric flows are governed by a broad variety of spatio-temporal scales, thus making real-time numerical modeling of such turbulent flows in complex terrain at high resolution computationally unmanageable. In this paper, we demonstrate a novel approach to address this issue through a combination of fast coarse scale physics based simulator and a family of advanced machine learning algorithm called the Generative Adversarial Networks. The physics-based simulator generates a coarse wind field in a real wind farm and then ESRGANs enhance the result to a much finer resolution. The method outperforms state of the art bicubic interpolation methods commonly utilized for this purpose. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IOP Publishing | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | GANs enabled super-resolution reconstruction of wind field | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.volume | 1669 | en_US |
dc.source.journal | Journal of Physics: Conference Series (JPCS) | en_US |
dc.source.issue | 012029 | en_US |
dc.identifier.doi | 10.1088/1742-6596/1669/1/012029 | |
dc.identifier.cristin | 1845254 | |
dc.relation.project | Norges forskningsråd: 268044 | en_US |
dc.description.localcode | Open access. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltd | en_US |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |