The paper describes a method for prediction of large container ship extreme roll angles occurring during sailing in harsh weather. Rolling is coupled with other ship motions and exhibits highly nonlinear behavior. Risk of losing containers due to a large roll is primary concern for ship transport. Because of non-stationarity and complicated nonlinearities of both waves and ship motions, it is a considerable challenge to model such a phenomenon. In case of extreme motions, the role of nonlinearities dramatically increases, activating effects of second and higher order. Moreover, laboratory tests may also be questioned because of the scaling and the sea state choice. Therefore, data measured on actual ships during their voyages in harsh weather provide a unique insight into statistics of ship motions. The aim of this work is to benchmark state of art method, which makes it possible to extract the necessary information about the extreme response from onboard measured time histories. The method proposed in this paper opens up the possibility to predict simply and efficiently both short- and long-term extreme response statistics.

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