Wave-dependent predictability of floating offshore wind turbine responses: A BiLSTM study based on fully coupled CFD simulations
Authors:
- Rizwan HaiderRizwan Haider
- Wei ShiWei Shi
- Zaibin LinZLZaibin Lin
- Tien Anh TranTien Anh Tran
- Ji WuJWJi Wu
- Xin LiXLXin Li
Abstract
No abstract availableFunding Information
- Key Technologies Research and Development Program (2022YFB4201300)
- Royal Society (IEC\\NSFC\\ 223281)
- National Natural Science Foundation of China (52371268)
- Engineering and Physical Sciences Research Council (EP/Y016297/1)
This publication has 23 references indexed in Scilit:
- A comprehensive survey of deep learning for time series forecasting: architectural diversity and open challengesArtificial Intelligence Review, 2025
- Coupled analysis of floating offshore wind turbines with new mooring systems by CFD methodOcean Engineering, 2024
- Deep learning based short-term motion prediction of floating wind turbine under shutdown conditionApplied Ocean Research, 2024
- Time series prediction based on LSTM neural network for top tension response of umbilical cablesMarine Structures, 2023
- Short-term motion prediction of floating offshore wind turbine based on muti-input LSTM neural networkOcean Engineering, 2023
- Predicting heave and surge motions of a semi-submersible with neural networksApplied Ocean Research, 2021
- A novel approach for motion predictions of a semi-submersible platform with neural networkJournal of Marine Science and Technology, 2020
- The input vector space optimization for LSTM deep learning model in real-time prediction of ship motionsOcean Engineering, 2020
- On the sensitivity and uncertainty of wave energy conversion with an artificial neural-network-based controllerOcean Engineering, 2019
- Ice forces acting on towed ship in level ice with straight drift. Part I: Analysis of model test dataInternational Journal of Naval Architecture and Ocean Engineering, 2018