Panamax cargo-vessel excessive-roll dynamics based on novel deconvolution method

Abstract

This study presents a state-of-the-art extreme-value-prediction methodology based on deconvolution that can be utilized in marine, offshore, and naval-engineering applications. First, a measured gust-windspeed dataset is utilized to illustrate the accuracy of the deconvolution method. Second, a real-time roll dynamics raw dataset measured onboard an operating loaded TEU2800 container vessel is analyzed, and the vessel motion data are measured during numerous trans-Atlantic crossings. The risk of container loss owing to excessive rolling motion is a key issue in cargo vessel transportation. The complex nonlinear and nonstationary characteristics of incoming waves and the associated cargo vessel movements render it challenging to accurately forecast excessive vessel roll angles. When a loaded cargo vessel sails through a harsh stormy environment, higher-order dynamic motion effects become evident and the effect of nonlinearities may increase significantly. Meanwhile, laboratory testing are affected by the wave parameters and similarity ratios used. Consequently, raw/unfiltered motion data obtained from cargo vessels traversing in adverse weather conditions provide valuable insights into cargo vessel reliability. Parametric extrapolations based on certain functional classes are typically employed to extrapolate and fit probability distributions estimated from the underlying dataset. This investigation aims to present an alternative nonparametric extrapolation methodology based on the intrinsic properties of the raw underlying dataset without introducing any assumptions regarding the extrapolation functional class.

This novel extrapolation deconvolution method is suitable for contemporary marine-engineering and design applications, as well as serves as an alternative to existing reliability methods. The prediction accuracy of the deconvolution methodology is demonstrated by comparing it with a modified four-parameter Weibull-type extrapolation technique. Compared with its counterpart sub-asymptotic statistical methods, such as the modified Weibull-type fit, peaks over the threshold, and generalized Pareto, the advocated deconvolution method is superior in term of its extrapolation numerical stability.