一种基于深度学习的六自由度船舶运动预测方法

IF 1.5 4区工程技术 Q3 ENGINEERING, MARINE

Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment Pub Date : 2023-03-11 DOI:10.1177/14750902231157852

Mingyang Zhang, G. Taimuri, Jin-fen Zhang, S. Hirdaris

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引用次数: 21

摘要

提出了一种基于深度学习的六自由度船舶运动预测方法。自动识别系统(AIS)的大数据流、现铸和测深记录用于提取运动轨迹并理想化环境条件。采用快速流固耦合(FSI)模型生成考虑周围水和船舶控制装置影响的船舶运动。通过学习芬兰湾两个港口之间的船舶航行和水文气象条件对应的数据流，验证了考虑操作条件对船舶动力学影响的变压器神经网络。对船舶在两个港口间的转弯周期和运动动力学进行了预测，结果表明该方法能够捕捉到作业条件对船舶耐波性和操纵性能的影响。

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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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来源期刊

Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment ENGINEERING, MARINE-

CiteScore

3.90

自引率

11.10%

发文量

审稿时长

>12 weeks

期刊介绍： The Journal of Engineering for the Maritime Environment is concerned with the design, production and operation of engineering artefacts for the maritime environment. The journal straddles the traditional boundaries of naval architecture, marine engineering, offshore/ocean engineering, coastal engineering and port engineering.