A deep learning method for the prediction of 6-DoF ship motions in real conditions-Reference-Cited by-同舟云学术

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

Published:2023-03-11 Issue:4 Volume:237 Page:887-905
ISSN:1475-0902
Container-title:Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment
language:en
Short-container-title:Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment

Author:

Zhang Mingyang¹^ORCID,Taimuri Ghalib¹^ORCID,Zhang Jinfen²^ORCID,Hirdaris Spyros¹

Affiliation:

1. Department of Mechanical Engineering, Marine Technology Group, Aalto University, Espoo, Finland

2. National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan, Hubei, China

Abstract

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.

Publisher

SAGE Publications

Subject

Mechanical Engineering,Ocean Engineering

Link

http://journals.sagepub.com/doi/pdf/10.1177/14750902231157852

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