A Data-Driven Method for Ship Motion Forecast
Accurate forecasting of ship motion is of great significance for ensuring maritime operational safety and working efficiency. A data-driven ship motion forecast method is proposed in this paper, aiming at the problems of low generalization of a single forecast model and insufficient forecast accurac...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/2/291 |
| _version_ | 1827343390460608512 |
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| author | Zhiqiang Jiang Yongyan Ma Weijia Li |
| author_facet | Zhiqiang Jiang Yongyan Ma Weijia Li |
| author_sort | Zhiqiang Jiang |
| collection | DOAJ |
| description | Accurate forecasting of ship motion is of great significance for ensuring maritime operational safety and working efficiency. A data-driven ship motion forecast method is proposed in this paper, aiming at the problems of low generalization of a single forecast model and insufficient forecast accuracy under unknown conditions. First, the fluid dynamics simulations of the ship are carried out under multiple node conditions based on overset mesh technology, and the obtained motion data is used for training the Bidirectional Long Short-term Memory network models. One or more pre-trained forecast models would be selected based on the correlation of condition nodes when forecasting ship motion under non-node conditions. The Golden Jackal Optimization Algorithm is used to compute the regression coefficient of each node model in real time, and finally, the dynamic model average is calculated. The results show that the method proposed in this study can accurately forecast the pitch and heave of the KCS ship in 5 s, 10 s, and 15 s of forecast duration. The accuracy of the multi-order forecast model improves more in longer forecast duration tasks compared with the first-order model. When forecasting ship motion under non-node conditions, the method shows stronger model generalization capabilities. |
| first_indexed | 2024-03-07T22:25:35Z |
| format | Article |
| id | doaj.art-4dceead23a3c44cdb9334ee3410a3bd1 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-07T22:25:35Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-4dceead23a3c44cdb9334ee3410a3bd12024-02-23T15:23:13ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-02-0112229110.3390/jmse12020291A Data-Driven Method for Ship Motion ForecastZhiqiang Jiang0Yongyan Ma1Weijia Li2School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Hongshan District, Wuhan 430074, ChinaSchool of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Hongshan District, Wuhan 430074, ChinaSchool of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Hongshan District, Wuhan 430074, ChinaAccurate forecasting of ship motion is of great significance for ensuring maritime operational safety and working efficiency. A data-driven ship motion forecast method is proposed in this paper, aiming at the problems of low generalization of a single forecast model and insufficient forecast accuracy under unknown conditions. First, the fluid dynamics simulations of the ship are carried out under multiple node conditions based on overset mesh technology, and the obtained motion data is used for training the Bidirectional Long Short-term Memory network models. One or more pre-trained forecast models would be selected based on the correlation of condition nodes when forecasting ship motion under non-node conditions. The Golden Jackal Optimization Algorithm is used to compute the regression coefficient of each node model in real time, and finally, the dynamic model average is calculated. The results show that the method proposed in this study can accurately forecast the pitch and heave of the KCS ship in 5 s, 10 s, and 15 s of forecast duration. The accuracy of the multi-order forecast model improves more in longer forecast duration tasks compared with the first-order model. When forecasting ship motion under non-node conditions, the method shows stronger model generalization capabilities.https://www.mdpi.com/2077-1312/12/2/291ship motion forecastCFDBiLSTMGolden Jackal Optimization |
| spellingShingle | Zhiqiang Jiang Yongyan Ma Weijia Li A Data-Driven Method for Ship Motion Forecast Journal of Marine Science and Engineering ship motion forecast CFD BiLSTM Golden Jackal Optimization |
| title | A Data-Driven Method for Ship Motion Forecast |
| title_full | A Data-Driven Method for Ship Motion Forecast |
| title_fullStr | A Data-Driven Method for Ship Motion Forecast |
| title_full_unstemmed | A Data-Driven Method for Ship Motion Forecast |
| title_short | A Data-Driven Method for Ship Motion Forecast |
| title_sort | data driven method for ship motion forecast |
| topic | ship motion forecast CFD BiLSTM Golden Jackal Optimization |
| url | https://www.mdpi.com/2077-1312/12/2/291 |
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