Panamax cargo-vessel excessive-roll dynamics based on novel deconvolution method基于去卷积新方法的巴拿马型货船的过度横摇动力学
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Gaidai O, Ashraf A, Cao Y, Sheng JL, Zhu Y, Li HC, 2024. Panamax cargo-vessel excessive-roll dynamics based on novel deconvolution method. Probabilistic Engineering Mechanics, 77: 103676.DOI: 10.1016/j.probengmech.2024.103676
本研究提出了一类基于去卷积的极值预测新方法，适用于海洋和船舶工程。首先，采用阵风风速观测数据集验证去卷积方法的精度。其次，分析了 TEU2800 载重集装箱船服役中测得的实时横摇动力学原始数据集，其在多次横渡大西洋期间记录了船舶运动数据。过度横摇运动造成的集装箱损失风险是货船运输中的一个关键问题。来浪的复杂非线性和非平稳特性以及相关货船运动使得准确预测过度船舶横摇角存在挑战。当载重货船穿越恶劣风暴环境时，高阶动力运动效应变得明显，非线性效应可能显著增加。同时，实验室测试受到所用波浪参数和相似比的影响。因此，在不利天气条件下航行货船获得的原始/未过滤运动数据提供了有关货船可靠性的宝贵资料。通常采用基于某些函数类参数外推和拟合从基础数据集中估计的概率分布。本研究旨在提出一种替代的非参数外推方法，基于原始基础数据集的内在属性，而不引入任何关于外推函数类的假设。这类外推去卷积新方法适用于现代海洋工程和设计应用，并作为现有可靠性方法的替代方案。通过将去卷积方法与修正的四参数 Weibull 型外推技术对比，展示了其预测精度。与其对应的次渐近统计方法 (如修正的 Weibull 型拟合、阈值超越法和广义 Pareto 法) 相比，所提去卷积方法在外推数值稳定性方面具有优势。关键词: 去卷积, 集装箱船, 跨大西洋航行, 横摇运动, 风险, 运输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.
Keywords: Deconvolution; Container vessel; Trans-Atlantic voyage; Rolling motion; Risk;
Transportation.Fig. 1. Example of TEU loaded container vessel
Fig. 2. Deconvolution scheme
Fig. 3. Measured wind speeds
Fig. 4. Combined onboard-measured cargo-vessel roll angle's joint time series
图 5: 货船横摇角: (a) 线性概率密度函数尾部外推的去卷积概率; (b) 去卷积方法的概率预测及基于四参数 Weibull 技术的验证Fig. 5. Cargo vessel roll angle: (a) Deconvoluted probability with linear PDF tail extrapolation; (b) Probability prediction by deconvolution method, verified using four-parameter Weibull (NG) technique
Fig. 6. Poincare sixth-order plot
作者信息 | Authors
上海海洋大学 (Shanghai Ocean University)
上海海洋大学 (Shanghai Ocean University)
曹宇 Yu Cao, 通讯作者 (Corresp.)上海海洋大学 (Shanghai Ocean University)Email: y_cao@shou.edu.cn
重庆交通大学 (Chongqing Jiao Tong University)
江苏科技大学 (Jiangsu University of Science & Technology)
上海海洋大学 (Shanghai Ocean University)
律梦泽 M.Z. Lyu | 编辑 (Ed)
P.D. Spanos | 审校 (Rev)
陈建兵 J.B. Chen | 审校 (Rev)
彭勇波 Y.B. Peng | 审校 (Rev)
