Hybrid physics-data driven spectral forecasts of semisubmersible response

TL;DR

This paper presents a hybrid physics-data driven Bayesian framework for probabilistic vessel motion forecasting, effectively quantifying uncertainty and correcting bias in semisubmersible heave response predictions.

Contribution

It introduces a novel Bayesian approach that accounts for heteroskedasticity and time correlation, enhancing prediction accuracy and uncertainty quantification for offshore vessel motion.

Findings

Effective in predicting vessel heave during resonance periods

Provides reliable uncertainty quantification and bias correction

Improves safety and efficiency in offshore operations

Abstract

A framework for probabilistic forecasting of vessel motion is developed and validated for a semisubmersible operating in long period swell. Bayesian statistical methods are applied to predictions of the heave response from a physics model using numerical wave spectra and measured motion data. Model diagnoses motivate an additional level of complexity required for the error structure in the Bayesian model, specifically to account for heteroskedasticity and time-correlated errors. The hybrid model forecasts were evaluated during periods where the heave resonance and cancellation frequencies were excited. The method is demonstrated to be effective for providing reliable quantification of uncertainty and correcting bias in the raw physics model predictions. This justifies its value for improving the efficiency and safety of offshore operations.