Recurrent-type Neural Networks for Real-time Short-term Prediction of Ship Motions in High Sea State

2021/05/27 by Danny D’Agostino, D'Agostino, Danny, Andrea Serani +5
Engineering · #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Machine Fault Diagnosis Techniques #Machine Learning (cs.LG) #Ship Hydrodynamics and Maneuverability

paper · pdf · doi:10.48550/arxiv.2105.13102

openalex publication_date 2021/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The prediction capability of recurrent-type neural networks is investigated for real-time short-term prediction (nowcasting) of ship motions in high sea state. Specifically, the performance of recurrent neural networks, long-short term memory, and gated recurrent units models are assessed and compared using a data set coming from computational fluid dynamics simulations of a self-propelled destroyer-type vessel in stern-quartering sea state 7. Time series of incident wave, ship motions, rudder angle, as well as immersion probes, are used as variables for a nowcasting problem. The objective is to obtain about 20 s ahead prediction. Overall, the three methods provide promising and comparable results.

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