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A Framework of LSTM Neural Network Model in Multi-Time Scale Real-Time Prediction of Ship Motions in Head Waves
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Zhan-yang CHEN1, 2, Zheng-yong ZHAN3, Shao-ping CHANG3, Shao-feng XU3, Xing-yun LIU1
Journal of Ship Mechanics | 2024, 28(12) : 1803 - 1819
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Journal of Ship Mechanics | 2024, 28(12): 1803-1819
• Hydrodynamics •
A Framework of LSTM Neural Network Model in Multi-Time Scale Real-Time Prediction of Ship Motions in Head Waves
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Zhan-yang CHEN1, 2, Zheng-yong ZHAN3, Shao-ping CHANG3, Shao-feng XU3, Xing-yun LIU1
Affiliations
  • 1.Department of Ocean Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China
  • 2.State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Dalian University of Technology, Dalian 116024, China
  • 3.Flight Control System Department, AVIC Xi'an Flight Automatic Control Research Institute, Xi'an 710065, China

    • CHEN Zhan-yang (1984-), male, Ph.D., associate professor, corresponding author, E-mail: .

Published: 2024-12-20 doi: 10.3969/j.issn.1007-7294.2024.12.001
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Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations. Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive activities. However, the obvious memory effect of ship motion time series brings certain difficulty to rapid and accurate prediction. Therefore, a real-time framework based on the Long-Short Term Memory (LSTM) neural network model is proposed to predict ship motions in regular and irregular head waves. A 15000 TEU container ship model is employed to illustrate the proposed framework. The numerical implementation and the real-time ship motion prediction in irregular head waves corresponding to the different time scales are carried out based on the container ship model. The related experimental data were employed to verify the numerical simulation results. The results show that the proposed method is more robust than the classical extreme short-term prediction method based on potential flow theory in the prediction of nonlinear ship motions.

deep learning  /  LSTM  /  ship motion  /  real-time prediction  /  irregular waves
Zhan-yang CHEN, Zheng-yong ZHAN, Shao-ping CHANG, Shao-feng XU, Xing-yun LIU. A Framework of LSTM Neural Network Model in Multi-Time Scale Real-Time Prediction of Ship Motions in Head Waves[J]. Journal of Ship Mechanics, 2024 , 28 (12) : 1803 -1819 . DOI: 10.3969/j.issn.1007-7294.2024.12.001
Funding
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  • Shandong Provincial Natural Science Foundation(ZR2024ME139)
  • Aeronautical Science Foundation of China(2024M074189001)
  • The Open Fund of State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Dalian University of Technology(GZ23112)
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Year 2024 volume 28 Issue 12
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132
61
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Article Info
doi: 10.3969/j.issn.1007-7294.2024.12.001
  • Receive Date：2024-06-21
  • Online Date：2026-03-26
  • Published：2024-12-20
Article Data
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History
  • Received：2024-06-21
Funding
Shandong Provincial Natural Science Foundation(ZR2024ME139)
Aeronautical Science Foundation of China(2024M074189001)
The Open Fund of State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Dalian University of Technology(GZ23112)
Affiliations
    1.Department of Ocean Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China
    2.State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Dalian University of Technology, Dalian 116024, China
    3.Flight Control System Department, AVIC Xi'an Flight Automatic Control Research Institute, Xi'an 710065, China

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Corresponding author, E-mail:
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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