Abstract
AbstractThe 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.
Funder
Office of Naval Research Global
Publisher
Springer Science and Business Media LLC
Subject
Ocean Engineering,Energy Engineering and Power Technology,Water Science and Technology,Renewable Energy, Sustainability and the Environment
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