dc.contributor.author choi, ju-hyuck dc.contributor.author Jensen, Jørgen Juncher dc.contributor.author Nielsen, Ulrik Dam dc.date.accessioned 2022-06-29T11:10:19Z dc.date.available 2022-06-29T11:10:19Z dc.date.created 2020-12-17T20:35:28Z dc.date.issued 2021 dc.identifier.isbn 978-981-15-4671-6 dc.identifier.uri https://hdl.handle.net/11250/3001543 dc.description.abstract Extreme value statistics can often be based on the assumption that exceedance events of a high threshold level are statistically independent and identically distributed (i.i.d. process), which further implies the Poisson assumption to be valid. This makes it possible to express the extreme response statistics through the mean up-crossing rate. For non-linear processes, analytic expressions of the mean up-crossing rate do not in general exist. Reliable statistics of mean up-crossing rate based on the brute-force approach, e.g. Monte Carlo simulation (MCS) require long time domain simulations considering a number of different ensemble input. The associated computations can be very time consuming especially when a detailed physical (e.g. hydrodynamic) model is applied. The First Order Reliability Method (FORM) has previously been found efficient for estimation of extreme value prediction of stationary stochastic time domain processes, However, if the non-linearity in a response is significant, the accuracy of the FORM linearized mean up-crossing rate can be limited. en_US The present work attempts to improve the extreme value prediction for non-linear parametric roll motions of ships based on applications of the FORM approach and suggests a model for the mean up-crossing rate for strong non-linear response, validated by comparing with MCS results. dc.language.iso eng en_US dc.publisher Springer en_US dc.relation.ispartof Practical Design of Ships and Other Floating Structures. Proceedings of the 14th International Symposium, PRADS 2019, September 22-26, 2019, Yokohama, Japan- Volume II dc.title Estimation of Extreme Roll Motion Using the First Order Reliability Method en_US dc.type Chapter en_US dc.description.version acceptedVersion en_US dc.rights.holder This is the authors' accepted manuscript to an article published by Springer. en_US dc.source.pagenumber 682-690 en_US dc.identifier.cristin 1861312 cristin.ispublished true cristin.fulltext postprint cristin.qualitycode 1
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