A deep learning method for the prediction of 6-DoF ship motions in real conditions

This paper presents a deep learning method for the prediction of ship motions in 6 Degrees of Freedom (DoF). Big data streams of Automatic Identification System (AIS), now-cast, and bathymetry records are used to extract motion trajectories and idealise environmental conditions. A rapid Fluid-Structure Interaction (FSI) model is used to generate ship motions that account for the influence of surrounding water and ship-controlling devices. A transformer neural network that accounts for the influence of operational conditions on ship dynamics is validated by learning the data streams corresponding to ship voyages and hydro-meteorological conditions between two ports in the Gulf of Finland. Predictions for a ship turning circle and motion dynamics between these two ports show that the proposed method can capture the influence of operational conditions on seakeeping and manoeuvring.

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| openaire: EC/H2020/814753/EU//FLARE

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Ship motions, Big data, Fluid-Structure Interactions, Deep learning, Gulf of Finland

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10.1177/14750902231157852

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Zhang, M, Taimuri, G, Zhang, J & Hirdaris, S 2023, 'A deep learning method for the prediction of 6-DoF ship motions in real conditions', Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, vol. 237, no. 4, pp. 887-905. https://doi.org/10.1177/14750902231157852

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https://urn.fi/URN:NBN:fi:aalto-202303152391

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[article-cris] Insinööritieteiden korkeakoulu / ENG

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A deep learning method for the prediction of 6-DoF ship motions in real conditions

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