Prediction of ship maneuvering motion with grey-box modelling incorporating mechanism and data
A grey-box modelling incorporating mechanism and data is presented in this paper to predict the ship manoeuvering motion. The traditional mathematical model in three degrees of freedom is adopted to reveal the known movement mechanism, in which an additional hydrodynamic correction term is developed by Neural Network (NN) based on the data to adaptively estimate the errors induced by the idealised approximation. The KRISO Container Ship (KCS) is taken as the study object and the free-running model tests are carried out to obtain the data for establishing the NN model of hydrodynamic correction terms. The typical manoeuvers (tactical circles, zigzags, Williamson turns) are simulated by the grey-box model and the mathematical model, respectively. The results indicate that the grey-box modelling method proposed in this paper can not only improve the prediction accuracy but also demonstrate a good generalisation performance.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/17445302
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Supplemental Notes:
- © 2023 Informa UK Limited, trading as Taylor & Francis Group. Abstract reprinted with permission of Taylor & Francis.
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Authors:
- Han, Yang
- Hao, Lizhu
- Shi, Chao
- Pan, Ziying
- Gu, Min
- Publication Date: 2024-8
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 1196-1209
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Serial:
- Ships and Offshore Structures
- Volume: 19
- Issue Number: 8
- Publisher: Taylor & Francis
- ISSN: 1744-5302
- Serial URL: http://www.tandfonline.com/tsos20
Subject/Index Terms
- TRT Terms: Hydrodynamics; Maneuvering; Neural networks; Predictive models; Ship motion
- Subject Areas: Data and Information Technology; Hydraulics and Hydrology; Marine Transportation;
Filing Info
- Accession Number: 01931487
- Record Type: Publication
- Files: TRIS
- Created Date: Sep 20 2024 8:50AM