ORCID
- Martyn Hann: 0000-0003-3965-9331
- Deborah Greaves: 0000-0003-3906-9630
Document Type
Article
Abstract
Accurate prediction of ocean waves plays an essential role in many ocean engineering applications, such as the control of wave energy converters and floating wind turbines. However, existing studies on phase-resolved wave prediction using machine learning mainly focus on two-dimensional wave data, while ocean waves are usually three-dimensional. In this work, we investigate, for the first time, the phase-resolved real-time prediction of three-dimensional waves using machine learning methods. Specifically, the wave prediction is modeled as a supervised learning task aiming at learning mapping relationships between the input historical wave data and the output future wave elevations. Four frequently-used machine learning methods are employed to tackle this task and a novel Dual-Branch Network (DBNet) is proposed for performance improvement. A group of wave basin experiments with nine directional wave spectra under three sea states are first conducted to collect the data of 3D waves. Then the wave data are used for verifying the effectiveness of the machine learning methods. The results demonstrate that the upstream wave data measured by the gauge array can be used for control-oriented wave forecasting with a forecasting horizon of more than 20 s, where the directional information provided by the upstream gauge array is vital for accurately predicting the downstream wave elevations. In addition, further investigations show that by using only local wave data (which can be easily obtained), the very short-term phase-resolved prediction (less than 5 s) can be achieved.
DOI Link
Publication Date
2023-10-15
Publication Title
Applied Energy
Volume
348
ISSN
0306-2619
Acceptance Date
2023-06-25
Deposit Date
2023-07-29
Embargo Period
2024-07-10
Funding
This work has received funding from the UK Engineering and Physical Sciences Research Council (grant number: EP/S000747/1). The authors acknowledge the Scientific Computing Research Technology Platform (SCRTP) at the University of Warwick for providing High-Performance Computing resources. Daming Wang’s contribution to this work is part-funded by the EU funded MaRINET 2 project (grant agreement ID:731084) and the School of Engineering, Computing and Mathematics at the University of Plymouth .
Keywords
3D waves, Convolutional neural network, Machine learning, Multilayer perceptron, Phased-resolved wave forecasting, Wave tank experiments
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Li, R., Zhang, J., Zhao, X., Wang, D., Hann, M., & Greaves, D. (2023) 'Phase-resolved real-time forecasting of three-dimensional ocean waves via machine learning and wave tank experiments', Applied Energy, 348. Available at: 10.1016/j.apenergy.2023.121529
