Evaluation of Dynamic Tensions of Single Point Mooring System under Random Waves with Artificial Neural Network

Abstract

Real-time monitoring of the mooring safety of floating structures is of great significance to their production operations. A deep learning model is proposed here, based on the long short-term memory (LSTM) artificial neural network. Firstly, the numerical simulation is carried out with the single-point mooring system of a Floating Production Storage and Offloading (FPSO) as the training data of LSTM. Then the proposed LSTM is performed. Finally, taking the motion of FPSO which is not encountered by LSTM neural network model as input, we predict the mooring line tension with this model. Here, one FPSO in the South China Sea is taken as a research case, hydrodynamic and mooring models are established, and the network structure and hyper-parameters of the LSTM model are determined. The prediction results of the LSTM under different combinations of wind, wave, and current are compared with the calculation results of AQWA software. The model constructed here can well predict the mooring line tension of different combinations of wind, wave and current.