AI-enabled design and optimization of fully coupled wind-wave integrated floating energy system

Objective

Integration with wave energy convertors (WECs) has been a promising solution to lowering the levelized cost of energy of floating offshore wind turbines (FOWTs) by sharing the platform and mooring systems. The synergy effects of wind-wave integrated floating energy systems (IFES) can be achieved to improve the power performance and motion stability, which requires a proper combination of FOWT and WECs. This project aims to construct an optimization design framework for cost-effective, high-efficiency and stabilized wind-wave IFES based on artificial intelligence (AI) techniques by: i) developing a fully coupled modelling methodology for considering the aero-hydro-servo-elastic effects; ii) developing a real-time hybrid testing method overcoming the conflicts between different scaling laws to validate the numerical model; iii) understanding the interaction mechanism between WECs and FOWT under different environmental loads and operating states; iv) developing a novel machine learning model to efficiently predict dynamic responses of IFES by introducing signal processing algorithms into convolutional neural network and bidirectional long-short term memory model with attention mechanism; v) determining Pareto solution sets using improved non-dominated sorting genetic algorithm. The outcome of this research will help to facilitate the development of offshore wind and wave energy resources in deep sea areas. This project will benefit offshore engineering industry by providing a novel machine learning model for dynamic response prediction based on limited wind and wave data input. The research will also promote the multi-disciplinary integration by covering a wide range knowledge areas including aerodynamics, intelligent control, hydrodynamics, structural dynamics, and computer science. The interdisciplinary knowledge and innovative research skills of the postdoctoral fellowship will be significantly improved after carrying out this challenging and meaningful project.