Time series prediction of ship maneuvering motion based on dynamic mode decomposition
Time series prediction of ship maneuvering motion based on dynamic mode decomposition
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DOI：
10.1016/j.oceaneng.2023.115446
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发表时间：
2023-10
影响因子：
5
通讯作者：
Chang-Zhe Chen;Si-Yu Liu;Z. Zou;L. Zou;Jin-Zhou Liu
中科院分区：
文献类型：
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作者：
Chang-Zhe Chen;Si-Yu Liu;Z. Zou;L. Zou;Jin-Zhou Liu
为了揭示船舶操纵运动的动力学特性，实现船舶操纵运动的快速时间序列预测，采用降阶动态模式分解（DMD）算法对Z字形和回转圆操纵运动进行重构和预测。以KVLCC2型油船为例，利用其自航模型试验数据进行了研究。首先，从试验数据中提取所有DMD模态，并根据其对船舶操纵运动动力学系统的贡献选择占主导地位的DMD模态。然后，采用降阶和全阶DMD算法对船舶操纵运动动力学系统进行重构和预报。比较了降阶、全阶DMD算法和高斯过程回归（GPR）算法。根据DMD主模的增长率和频率揭示了动力系统的动力学特性。通过参数研究分析了截断秩和输入样本对预测精度的影响，结果表明截断秩和输入样本对预测精度有重要影响。此外，对不同算法的计算时间进行了比较和分析。
In order to reveal the dynamic characteristics and achieve rapid time series prediction of ship maneuvering motion, a reduced-order dynamic mode decomposition (DMD) algorithm is applied to reconstruct and predict the zig-zag and turning circle maneuvering motions. A case study is conducted for a KVLCC2 tanker using its free-running model test data. First, all DMD modes are extracted from the test data, and the dominant DMD modes are selected according to their contributions to the dynamical systems of ship maneuvering motions. Then, the dynamical systems of ship maneuvering motions are reconstructed and predicted by the reduced-order and full-order DMD algorithms. A comparison between reduced-order, full-order DMD algorithms and Gaussian process regression (GPR) is conducted. The dynamic characteristics of the dynamical systems are revealed according to the growth rates and frequencies of the dominant DMD modes. The effects of the truncation rank and input samples are analyzed by a parametric study, which indicates that the truncation rank and input samples are crucial to the prediction accuracy. Besides, the computational time of the different algorithms is compared and analyzed.