Real-time prediction of wave-induced hull girder loads for a large container ship based on the recurrent neural network model and error correction strategy
2
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
Real-time acquisition of wave-induced hull girder loads of a sailing ship will help the captain make reasonable decisions, which is of great significance for improving the safety of the ship’s navigation. This paper investigates the real-time prediction method of hull girder loads based on the Recurrent Neural Network (RNN) model and error correction strategy. Firstly, taking the vertical bending moment, horizontal bending moment, and torsional moment at the mid-ship position of a large container ship as examples, corresponding neural network prediction models are established through parameter influence analysis. Secondly, various sea state conditions are used to verify the feasibility of established network prediction models to predict the hull girder loads in real-time. The VBM prediction model performs better than the TM prediction model and HBM prediction model, and the errors of the TM prediction model and HBM prediction model are slightly larger in some cases. Lastly, an improved prediction model based on an error correction strategy is proposed to improve the prediction accuracy of the neural network prediction model, and the adequate performance of the error correction strategy is discussed.
Abstract
Real-time acquisition of wave-induced hull girder loads of a sailing ship will help the captain make reasonable decisions, which is of great significance for improving the safety of the ship’s navigation. This paper investigates the real-time prediction method of hull girder loads based on the Recurrent Neural Network (RNN) model and error correction strategy. Firstly, taking the vertical bending moment, horizontal bending moment, and torsional moment at the mid-ship position of a large container ship as examples, corresponding neural network prediction models are established through parameter influence analysis. Secondly, various sea state conditions are used to verify the feasibility of established network prediction models to predict the hull girder loads in real-time. The VBM prediction model performs better than the TM prediction model and HBM prediction model, and the errors of the TM prediction model and HBM prediction model are slightly larger in some cases. Lastly, an improved prediction model based on an error correction strategy is proposed to improve the prediction accuracy of the neural network prediction model, and the adequate performance of the error correction strategy is discussed.
참고문헌 (18)
- Almallah, I., "Global load determination of high-speed wave-piercing catamarans using finite element method and linear least squares applied to sea trial strain measurements", J. Mar. Sci. Technol, 25(3), pp.901~913 (2020)
- Aravanis, G. I., "Damage detection in ship hull structures under operational variability through strain sensing", Ocean Eng, pp.286~ (2023)
- Chen, X., "Estimation of directional wave spectra and hull structural responses based on measured hull data on 14,000 TEU large container ships", Mar. Struct, pp.80~ (2021)
- Fan, G. J., "Short-term motion prediction of a semisubmersible by combining LSTM neural network and different signal decomposition methods", Ocean Eng, pp.267~ (2023)
- Hao, L. Z., "Recurrent neural networks for nonparametric modeling of ship maneuvering motion", Int. J. Nav. Archit. Ocean Eng, 14 (2022)
- JinHyeok, K., "Prediction of the superiority of the hydrodynamic performance of hull forms using deep learning", Int. J. Nav. Archit. Ocean Eng, 14, pp.1~15 (2022)
- J¨orges, C., "Prediction and reconstruction of ocean wave heights based on bathymetric data using LSTM neural networks", Ocean Eng, pp.232~ (2021)
- Juan, N. P., "Review of the application of artificial neural networks in ocean engineering", Ocean Eng, pp.259~ (2022)
- Lee, J. B., "Prediction of ship power based on variation in deep feed-forward neural network", Int. J. Nav. Archit. Ocean Eng, 13, pp.641~649 (2021)
- Lee, J. B., "In-plane and out-of-plane bending moments and local stresses in mooring chain links using machine learning technique", Int. J. Nav. Archit. Ocean Eng, 13, pp.848~857 (2021)
- Li, X. H., "A data-driven methodology for predicting residual strength of subsea pipeline with double corrosion defects", Ocean Eng, pp.279~ (2023)
- Moreira, L., "Neural network model for estimation of hull bending moment and shear force of ships in waves", Ocean Eng, pp.206~ (2020)
- Qiao, D. S., "Realtime prediction of dynamic mooring lines responses with LSTM neural network model", Ocean Eng, pp.219~ (2021)
- Su, Y. M., "Real-time prediction of large-scale ship model vertical acceleration based on recurrent neural network", J. Mar. Sci. Eng, 8(10) (2020)
- Tayyar, G. T., "Overall hull girder nonlinear strength monitoring based on inclinometer sensor data", Int. J. Nav. Archit. Ocean Eng, 12, pp.902~909 (2020)
- Xie, Y. J., "Deep gated recurrent unit networks for timedomain long-term fatigue analysis of mooring lines considering wave directionality", Ocean Eng, pp.284~ (2023)
- Xu, J. Z., "Estimation of Wave-Induced Ship Hull Bending Moment from Ship Motion Measurements", ~ (2000)
- Zhang, M., "Indirect monitoring of distributed ice loads on a steel gate in a cold region", Cold Reg. Sci. Technol, 151, pp.267~287 (2018)