Prediction of Chinese-lantern submarine hose tension variation in extreme sea states based on data-driven methods

To predict the variation of Chinese-lantern type submarine hose tension in extreme sea states is of great significance for the monitoring, operation and lifetime assessment of catenary anchor leg mooring (CALM) systems. In this research, the machine learning algorithm in radial basis function (RBF) neural network and the deep learning algorithm in long short-term memory (LSTM) neural network model were used to predict the variation of submarine hose tension respectively, and the prediction performance of the two algorithms on the time-series variation of submarine hose tension was evaluated. During the neural network construction, the six-degree-of-freedom (6-DOF) motion of CALM buoy is taken as the training parameter, while the historical tension variation data is also integrated for the correction of the prediction results. The training data are collected by hydrodynamic coupling simulation of the established CALM system with submarine hoses. Different parameter values selection of the neural network model is investigated to determine the optimal structure of the RBF and LSTM neural network models, including spread, initial learning rate, number of nodes in the hidden layer and batch size. Since the direction of environmental loads has a large effect on the response of the submarine hose, four combinations of wind, wave and currents under 100-year self-existing sea state are considered in the research. Through the case study and sensitivity analysis, it is demonstrated that the non-linear relationship between 6-DOF motion and hose tension is varied for different environmental load directions and LSTM neural network is suitable for predicting the variation of Chinese-lantern type submarine hose tension in CALM systems under different combinations of extreme sea states.