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

• URL: http://arxiv.org/abs/2105.13102v1
• Date: Thu, 27 May 2021 12:58:15 GMT
• Title: Recurrent-type Neural Networks for Real-time Short-term Prediction of Ship Motions in High Sea State
• Authors: Danny D'Agostino, Andrea Serani, Frederick Stern, Matteo Diez
• Abstract summary: The prediction capability of recurrent-type neural networks is investigated for real-time short-term prediction (nowcasting) of ship motions in high sea state. Time series of incident wave, ship motions, rudder angle, as well as immersion probes, are used as variables for a nowcasting problem.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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