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

Mingyang Zhang; Ghalib Taimuri; Jinfen Zhang; Spyros Hirdaris

doi:10.1177/14750902231157852

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

Mingyang Zhang, Ghalib Taimuri, Jinfen Zhang, Spyros Hirdaris

Wuhan University of Technology

Tutkimustuotos: Lehtiartikkeli › Article › Scientific › vertaisarvioitu

17 Sitaatiot (Scopus)

9 Lataukset (Pure)

Abstrakti

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.

Alkuperäiskieli	Englanti
Sivut	887-905
Sivumäärä	19
Julkaisu	Proceedings of the Institution of Mechanical Engineers. Part M: Journal of Engineering for the Maritime Environment
Vuosikerta	237
Numero	4
Varhainen verkossa julkaisun päivämäärä	11 maalisk. 2023
DOI - pysyväislinkit	https://doi.org/10.1177/14750902231157852
Tila	Julkaistu - marrask. 2023
OKM-julkaisutyyppi	A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

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10.1177/14750902231157852Lisenssi: CC BY-NC

zhang-et-al-2023-a-deep-learning-method-for-the-prediction-of-6-dof-ship-motions-in-real-conditionsFinal published version, 12,1 MBLisenssi: CC BY-NC

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FLARE: FLooding Accident REsponse
Hirdaris, S., Zhang, M. & Matusiak, J.
31/05/2019 → 30/11/2022
Projekti: EU: Framework programmes funding

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

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.",

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

author = "Mingyang Zhang and Ghalib Taimuri and Jinfen Zhang and Spyros Hirdaris",

note = "| openaire: EC/H2020/814753/EU//FLARE",

year = "2023",

month = nov,

doi = "10.1177/14750902231157852",

language = "English",

volume = "237",

pages = "887--905",

journal = "Proceedings of the Institution of Mechanical Engineers. Part M: Journal of Engineering for the Maritime Environment",

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A deep learning method for the prediction of 6-DoF ship motions in real conditions. / Zhang, Mingyang; Taimuri, Ghalib; Zhang, Jinfen et al.
julkaisussa: Proceedings of the Institution of Mechanical Engineers. Part M: Journal of Engineering for the Maritime Environment, Vuosikerta 237, Nro 4, 11.2023, s. 887-905.

Tutkimustuotos: Lehtiartikkeli › Article › Scientific › vertaisarvioitu

TY - JOUR

T1 - A deep learning method for the prediction of 6-DoF ship motions in real conditions

AU - Zhang, Mingyang

AU - Taimuri, Ghalib

AU - Zhang, Jinfen

AU - Hirdaris, Spyros

N1 - | openaire: EC/H2020/814753/EU//FLARE

PY - 2023/11

Y1 - 2023/11

N2 - 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.

AB - 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.

KW - Ship motions

KW - Big data

KW - Fluid-Structure Interactions

KW - Deep learning

KW - Gulf of Finland

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U2 - 10.1177/14750902231157852

DO - 10.1177/14750902231157852

M3 - Article

SN - 1475-0902

VL - 237

SP - 887

EP - 905

JO - Proceedings of the Institution of Mechanical Engineers. Part M: Journal of Engineering for the Maritime Environment

JF - Proceedings of the Institution of Mechanical Engineers. Part M: Journal of Engineering for the Maritime Environment

IS - 4

ER -

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

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FLARE: FLooding Accident REsponse

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