Machine Learning Enhancement of Manoeuvring Prediction for Ship Digital Twin using Full-scale Recordings

Digital Twins have much attention in the shipping industry, attempting to support all phases of a vessel’s life cycle. With several tools appearing in Digital Twin software suites, high-quality manoeuvring and performance prediction remain cornerstones. Propulsion efficiency is in focus while in service. Simulator-based training is in focus to ensure safety of manoeuvring in confined waters and harbours. Prediction of ships’ velocity and turn rate are essential for correct look and feel during training, but phenomena like dynamic inflow to propellers, bank and shallow water effects limit simulators’ accuracy, and master mariners often comment that simulations could be in better agreement with actual behaviours of their vessel. This paper focuses on digital twin enhancements to better match reality. Using data logged during in-service operation, we first consider a system identification perspective, employing a first-principles model structure. Showing that a complete firstprinciples model is not identifiable under the excitation met in service, we employ a Recurrent Neural Network to predict deviations between measured velocities and the model output. The outcome is a hybrid of a first-principles model with a machine learning generic approximator add-on. The paper demonstrates significant improvements in prediction accuracy of both in-harbour manoeuvring and shallow water passage conditions.