Prediction of dynamic responses for execution of marine operations using partitioning of multimodal directional wave spectra and machine learning regression models

Abstract

Safe execution of marine operations (MOs) depends on accurate prediction of vessel dynamic responses, which are necessary to help a superintendent make on-board decisions. However, it can be challenging to conduct complex numerical simulations prior to execute a MO, especially at offshore sites characterized by complex wave conditions, i.e. multimodal directional (2D) wave spectra. In this context, this article introduces a methodology for assessing vessel dynamic responses using characteristics of actual 2D wave spectra and regression models from machine learning. First, a state-of-the-art algorithm is used for 2D spectra partitioning and spectral characterization (climate). This allows identifying wave systems that can occur at a study location, and their integral parameters. For that, a data set for the North Atlantic Ocean, spanning 36 years is used. Then, the main wave parameters of each wave system including significant wave height, peak period and peak direction, are used as features to train and test regression models from machine learning tool kits. Accurate predictions of dynamic responses were obtained with a “boosted trees” regression model considering all six long-term wave systems (18 features) detected at the site. These findings are valuable as new tools to help on-board personnel make safe and quick decisions.