Inverse estimation of a vertical current velocity profile using motions of an FPSO and artificial neural network

Abstract

This study utilizes artificial neural network (ANN) models to inversely estimate the near-real-time 3D ocean vertical current profile (magnitudes and directions) using floater-mounted motion sensors with (or without) two bi-axial inclinometers on the top portion of a riser. A coupled time-domain dynamics model that takes into account the interaction between the FPSO (Floating Production Storage and Offloading platform), mooring lines, risers, and cables is established to provide inputs and outputs for ANN models. The dynamics simulations are performed using the 2-year-long measured environmental (wind, wave, current) conditions. Then, three ANN models with different combinations of input variables are evaluated, and the process of hyperparameter selection is conducted to increase the prediction accuracy compared to the actually measured current profiles. The root-mean-square errors (RMSEs) and R-squared of current speed, the contour plots of measured and estimated current speed and direction, representative motions of the FPSO, and inclination and azimuthal angles of a riser are systematically presented. The results demonstrate that the proposed ANN model adequately estimates the 3D current profile from FPSO motions while the accuracy especially at deeper depth is improved by considering bi-axial inclinometers on the riser as additional inputs.