Version 1
: Received: 8 March 2024 / Approved: 8 March 2024 / Online: 8 March 2024 (05:25:00 CET)
How to cite:
Zhao, Y.; Zhao, J.; Lu, S. Very Short-Term Prediction of Ship Motion Using Deep Operator Networks. Preprints2024, 2024030493. https://doi.org/10.20944/preprints202403.0493.v1
Zhao, Y.; Zhao, J.; Lu, S. Very Short-Term Prediction of Ship Motion Using Deep Operator Networks. Preprints 2024, 2024030493. https://doi.org/10.20944/preprints202403.0493.v1
Zhao, Y.; Zhao, J.; Lu, S. Very Short-Term Prediction of Ship Motion Using Deep Operator Networks. Preprints2024, 2024030493. https://doi.org/10.20944/preprints202403.0493.v1
APA Style
Zhao, Y., Zhao, J., & Lu, S. (2024). Very Short-Term Prediction of Ship Motion Using Deep Operator Networks. Preprints. https://doi.org/10.20944/preprints202403.0493.v1
Chicago/Turabian Style
Zhao, Y., Jinxiu Zhao and Sinan Lu. 2024 "Very Short-Term Prediction of Ship Motion Using Deep Operator Networks" Preprints. https://doi.org/10.20944/preprints202403.0493.v1
Abstract
The intense motion of a ship can greatly impacts the comfort of crew members and the safety of the vessel. Therefore, accurately estimating and predicting ship attitudes has become an important issue. This paper introduces the latest development in functional deep learning model called DeepOnet. It takes wave height as input and ship motion as output, using a cause-to-result prediction approach. The modeling data used in this study is sourced from publicly available experimental data from the Iowa Institute of Hydraulic Research. Firstly, parameters system tuning was conducted for the neural network's hyperparameters to determine the appropriate combination of parameters. Secondly, the DeepOnet model for wave height and multi-degree-of-freedom motion was established, and the influence of increasing time steps on prediction accuracy was examined. Finally, a comparison was made between the DeepOnet model and the classical time series model LSTM. It was found that the DeepOnet model had a 10-fold improvement in accuracy for roll and heave attitudes. Moreover, as the forecast duration increased, the advantage of DeepOnet showed a trend of strengthening. As a functional prediction model, DeepOnet provides a new promising tool for very short-term prediction of ship motion.
Keywords
DeepOnet; Very short-term prediction; hyperparameters tuning; functional prediction model; LSTM
Subject
Engineering, Marine Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
