Abstract
Global whipping responses contribute to a significant increase in vertical bending moments (VBM), making their accurate prediction crucial for ship safety. In this study, a long short-term memory (LSTM)-based encoder–decoder model is established to predict the whipping responses under varying sea states. The model is trained on a comprehensive dataset, which includes motion data and VBM history of a cruise ship under various sea conditions. This dataset is established via numerical simulation, ensuring a wide range of scenarios for the model to learn from. The efficacy of the LSTM encoder–decoder model in capturing global whipping responses is initially verified under a single sea condition case. This step confirms the model's ability to accurately predict vertical bending moments under known conditions. Subsequently, the model's performance under unprecedented sea conditions is examined. Given that the distribution of training data significantly influences the model's performance and the data from diverse sea conditions typically exhibit distinct data distribution, a mixed data training strategy is employed during the training process in this scenario. The results indicate that the LSTM encoder–decoder model effectively captures whipping responses. Furthermore, the mixed data training strategy significantly improves the model's prediction accuracy for global whipping responses under unprecedented sea conditions.
