Study on dynamic tension estimation for the underwater soft yoke mooring system with LSTM-AM neural network

• Main Authors: Ma, Gang, Jin, Conglin, Wang, Hong Wei, Li, Peng, Kang, Hooi Siang
• Format: Article
• Published: Elsevier Ltd 2023
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://eprints.utm.my/106055/; http://dx.doi.org/10.1016/j.oceaneng.2022.113287

It is significant to estimate the mooring tension for the safety, operation and maintenance of single point mooring system of floating production, storage and offloading unit (FPSO). In this study, a neural network model named long-short term memory combined with attention mechanism (LSTM-AM) is adopted to estimate the mooring legs dynamic tension of an underwater soft yoke mooring system in time domain. The mooring legs tension and FPSO motions are set as the training features in the LSTM-AM neural network, together with the corresponding first-order and second-order central moments. The training data collection is implemented with the numerical hydrodynamic coupled analysis of FPSO and an underwater soft yoke mooring system. Different variables are studied to determine the optimal structure of the LSTM-AM neural network model, including the time window, the layer numbers, the neural units and the optimizer. Through cases studied, it is proved that the LSTM-AM neural network model is suitable to estimate the mooring legs tension of FPSO and the underwater soft yoke mooring system in different sea states.