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dc.contributor.authorSclavounos, Paul D
dc.contributor.authorZhang, Yu
dc.contributor.authorMa, Yu
dc.contributor.authorLarson, David F
dc.date.accessioned2021-10-27T20:29:47Z
dc.date.available2021-10-27T20:29:47Z
dc.date.issued2019
dc.identifier.urihttps://hdl.handle.net/1721.1/135881
dc.description.abstract© 2019 by ASME. The development of an analytical model for the prediction of the stochastic nonlinear wave loads on the support structure of bottom mounted and floating offshore wind turbines is presented. Explicit expressions are derived for the time-domain nonlinear exciting forces in a sea state with significant wave height comparable to the diameter of the support structure based on the fluid impulse theory (FIT). The method is validated against experimental measurements with good agreement. The higher order moments of the nonlinear load are evaluated from simulated force records and the derivation of analytical expressions for the nonlinear load statistics for their efficient use in design is addressed. The identification of the inertia and drag coefficients of a generalized nonlinear wave load model trained against experiments using support vector machine learning algorithms is discussed.
dc.language.isoen
dc.publisherASME International
dc.relation.isversionof10.1115/1.4042264
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
dc.sourceASME
dc.titleOFFSHORE WIND TURBINE NONLINEAR WAVE LOADS AND THEIR STATISTICS
dc.typeArticle
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Center
dc.relation.journalJournal of Offshore Mechanics and Arctic Engineering
dc.eprint.versionFinal published version
dc.type.urihttp://purl.org/eprint/type/JournalArticle
eprint.statushttp://purl.org/eprint/status/PeerReviewed
dc.date.updated2020-08-05T13:37:18Z
dspace.orderedauthorsSclavounos, PD; Zhang, Y; Ma, Y; Larson, DF
dspace.date.submission2020-08-05T13:37:22Z
mit.journal.volume141
mit.journal.issue3
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Needed


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