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dc.contributor.authorHan, Xu
dc.contributor.authorSævik, Svein
dc.contributor.authorLeira, Bernt Johan
dc.date.accessioned2021-02-10T08:04:17Z
dc.date.available2021-02-10T08:04:17Z
dc.date.created2020-12-21T14:14:50Z
dc.date.issued2020
dc.identifier.isbn978-0-7918-8431-7
dc.identifier.urihttps://hdl.handle.net/11250/2727053
dc.description.abstractIn the recent decade, maritime and energy industries have realized the potential of using operational data in combination with a virtual replication of the real physical asset, termed the digital twin. The digital twin then serves as a platform for data management, asset monitoring, and inspection and maintenance management, featuring an improved basis for cost effective operations and future decision making in terms of e.g. life extension. The present paper deals with application of the digital twin concept in marine operations where it is essential to handle the inherent uncertainties of vessel performance by applying a model that can adapt to the real operating conditions. In this paper a case study is presented for identifying the most sensitive parameters in the vessel hydrodynamic model w.r.t. the vessel motion RAOs. The study also shows that the parametric sensitivity depends on the interesting vessel response parameter, wave direction and loading condition. A digital twin adaptive to various operational conditions may require parametric tuning of the numerical model. It is important to identify the correct parameter(s) for modification. A simplified and idealized case study is also carried out to test the requirements to a successful parameter identification for model tuning.en_US
dc.language.isoengen_US
dc.publisherASMEen_US
dc.relation.ispartofASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering - Volume 1: Offshore Technology
dc.relation.urihttps://asmedigitalcollection.asme.org/OMAE/proceedings/OMAE2020/84317/Virtual,%20Online/1092573
dc.titleA Sensitivity Study of Vessel Hydrodynamic Model Parametersen_US
dc.typeChapteren_US
dc.description.versionpublishedVersionen_US
dc.identifier.doi10.1115/OMAE2020-19039
dc.identifier.cristin1862402
dc.relation.projectNorges forskningsråd: 237929en_US
dc.description.localcodeLocked until 18.6.2021 due to copyright restrictions. Copyright © 2020 by ASMEen_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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