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dc.contributor.authorGaidai, Oleg
dc.contributor.authorXing, Yihan
dc.contributor.authorWang, Fang
dc.contributor.authorWang, Shuaishuai
dc.contributor.authorYan, Ping
dc.contributor.authorNæss, Arvid
dc.date.accessioned2022-07-12T07:30:45Z
dc.date.available2022-07-12T07:30:45Z
dc.date.created2022-05-11T22:06:59Z
dc.date.issued2022-07
dc.identifier.citationGaidai, O., Xing, Y., Wang, F., Wang, S., Yan, P., & Næss, A. (2022) Improving extreme anchor tension prediction of a 10-MW floating semi-submersible type wind turbine, using highly correlated surge motion record. Frontiers in Mechanical Engineering, 8:888497en_US
dc.identifier.issn2297-3079
dc.identifier.urihttps://hdl.handle.net/11250/3004605
dc.description.abstractExtreme value prediction of the load-effect responses of complex offshore structures such as the floating wind turbine (FWT) is crucial in ultimate limit state (ULS) design. This paper considers two cases to understand the feasibility of the bivariate correction on the extreme load and motion responses of a 10-MW semi-submersible type FWT. The empirical anchor tension force and surge motion used in this study are obtained from the FAST simulation tool (developed by the National Renewable Energy Laboratory) with the load cases stimulated at under-rated, rated and above rated speeds. Then, the bivariate correction method is applied to model FWT extreme response for a 5-years return period prediction with a 95% confidence interval (CI), based on just 2 min short response record. The proposed methodology permits accurate correction of the bivariate extreme value in case of, for example, corrupted measurement sensor data. Based on the proposed novel method’s performance, it is concluded that the bivariate correction method can offer better robust and precise bivariate predictions of coupled surge motion and anchor tension of the FWT.en_US
dc.language.isoengen_US
dc.publisherFrontiers Media S.Aen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectvindturbineren_US
dc.subjectbivariate correction methoden_US
dc.subjectbivariate probability distributionen_US
dc.titleImproving extreme anchor tension prediction of a 10-MW floating semi-submersible type wind turbine, using highly correlated surge motion recorden_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2022 Gaidai, Xing, Wang, Wang, Yan and Naess.en_US
dc.subject.nsiVDP::Teknologi: 500::Maskinfag: 570en_US
dc.source.volume8en_US
dc.source.journalFrontiers in Mechanical Engineeringen_US
dc.identifier.doi10.3389/fmech.2022.888497
dc.identifier.cristin2023727
dc.source.articlenumber888497en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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