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dc.contributor.authorGu, Peter
dc.contributor.authorWalid, Ahmed Ahmed
dc.contributor.authorIskandarani, Yousef
dc.contributor.authorKarimi, Hamid Reza
dc.date.accessioned2013-05-02T08:55:56Z
dc.date.available2013-05-02T08:55:56Z
dc.date.issued2013
dc.identifier.citationGu, P., Walid, A. A., Iskandarani, Y., & Karimi, H. R. (2013). Modeling, simulation and design optimization of a hoisting rig active heave compensation system. International Journal of Machine Learning and Cybernetics, 4(2), 85-98. doi: 10.1007/s13042-012-0076-xno_NO
dc.identifier.issn1868-8071
dc.identifier.urihttp://hdl.handle.net/11250/136942
dc.descriptionPublished version of an article in the journal: International Journal of Machine Learning and Cybernetics. Also available from the publisher at: http://dx.doi.org/10.1007/s13042-012-0076-xno_NO
dc.description.abstractThe objective of this paper is to present an approach in developing a virtual active heave compensation system for a draw-works on a hoisting rig. A virtual system enables quicker overall product development time of a physical system as well as flexibility in optimizing the design parameters. Development of the virtual system started with the modelling of the draw-works and hoisting rig dynamics. Simulations of this model were run in two operational modes while subject to a sinusoidal wave: heave compensation and seabed landing of a payload. The results were analyzed and used for optimization in terms of cost and performance. This lays the groundwork for further testing either through hardware-in-the-loop testing (HIL) or using an actual prototype.no_NO
dc.language.isoengno_NO
dc.publisherSpringerno_NO
dc.subjectactive heave compensation (AHC)no_NO
dc.subjectdraw-worksno_NO
dc.subjecthoisting rigno_NO
dc.subjectmodelingno_NO
dc.subjectsimulationno_NO
dc.titleModeling, simulation and design optimization of a hoisting rig active heave compensation systemno_NO
dc.typeJournal articleno_NO
dc.typePeer reviewedno_NO
dc.subject.nsiVDP::Technology: 500::Materials science and engineering: 520no_NO
dc.source.pagenumber85-98no_NO
dc.source.volume4no_NO
dc.source.journalInternational Journal of Machine Learning and Cyberneticsno_NO
dc.source.issue2no_NO
dc.identifier.doi10.1007/s13042-012-0076-x


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