Real-time prediction of 6-DOF motions of a turret-moored FPSO in harsh sea state

Abstract

Predicting the motions of a floating offshore structure could be useful for a motion compensation system and could provide useful early warning information for motion-sensitive operations. In this study, a deep learning (DL) model was developed to provide deterministic predictions of 6-DOF motions of a turret-moored floating production storage and offloading unit (FPSO) in a harsh sea state. These predictions rely purely on past time series of the motion itself. Training and test databases were obtained using a scaled model test in a wave basin. The 6-DOF motions were fed into the DL model simultaneously, and the predictive motion series was obtained from corresponding 6 standalone subnets. The prediction extended by approximately 15 s (one wave cycle) into the future with good accuracy for all 6-DOF motions. By concentrating the predictions in order, a 3-hour time series of the motions was reconstructed. Hyperparameter importance was also evaluated for dropout probability, number of RNN layers, number of fully connected layers, and number of neurons in each layer in terms of accuracy and training duration. Without the requirement for wave measurements, the proposed model is closer to real applications in offshore engineering.