A data driven method for multi-step prediction of ship roll motion in high sea states. (15th May 2023) - Zhang, Dan; Zhou, Xi; Wang, Zi-Hao; Peng, Yan; Xie, Shao-Rong
Abstract: Ship roll motion in high sea states has large amplitudes and nonlinear dynamics, and its prediction is significant for operability, safety, and survivability. This paper presents a novel data-driven methodology to provide a multi-step prediction of ship roll motions 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 dynamic 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. Compared with the single LSTM and CNN methods, the proposed method has better performance in predicting 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. Highlights: A data-driven methodology is proposed to achieve the multi-step prediction of ship roll motion in high sea states. LSTM and CNN are performed in parallel to extract time-dependent and spatio-temporal information from multidimensional inputs. The CFD method is utilized to generate the motion data in sea state 7 irregular long-peaked waves with different wave directions. The superiority of selecting both motion states and wave heights as the feature space is demonstated.