Ship roll motion in high sea state has large amplitude and nonlinear dynamics, and its prediction is significant for the operability, safety and survivability. This paper presents a novel data-driven methodology to provide multi-step prediction of the ship roll motion in high sea states. A hybrid neural network is proposed that combines long short-term memory (LSTM) and convolutional neural network (CNN) in parallel. The motivation is to extract the nonlinear dynamics characteristics and the hydrodynamic memory information through the advantage of CNN and LSTM, respectively. For the feature selection, the time histories of motion states and wave heights are selected to involve sufficient information. Taken a scaled KCS as the study object, the ship motions in sea state 7 irregular long crested waves are simulated and used for the validation. The results show that at least one period of roll motion can be accurately predicted by using the proposed method. Compared with the single LSTM and CNN method, the proposed method has better performance in the prediction of the amplitude of roll angles. Besides, the comparison results also demonstrate that selecting motion states and wave heights as feature space improves the prediction accuracy, verifying the effectiveness of the proposed method.
翻译:高海拔状态的船舶滚动动态具有巨大的振幅和非线性动态,其预测对可操作性、安全性和生存能力意义重大。本文件展示了一种新的数据驱动方法,以提供高海拔状态船舶滚动的多步预测。建议建立一个混合神经网络,将长期短期内存(LSTM)和进化神经网络(CNN)同时并列。其动机是分别利用CNN和LSTM来提取非线性动态特征和流体动力存储信息。关于特征选择,运动状态和波高的时间历史被选定为充分的信息。以一个规模化的 KCSS为研究对象,模拟并使用海流状态7非定期的长帆浪运动进行验证。结果显示,与单一的LSTM和CNN方法相比,至少可以准确预测一个滚动期。与单一的LSTM和CNN方法相比,拟议的方法在预测滚动角度的振度方面表现更好。此外,比较结果还表明,选择运动状态和波高度作为地貌预测的精确度,从而改进了拟议的空间预测方法。