With the strong driving forces aiming at the global net-zero GHG target by 2050, offshore renewable energy research, development, and deployment, in terms of ocean energy generation, conversion, storage, transportation and consumption, have becomes more and more pressing issues. The design and optimization of the renewable energy devices for example, wind and tidal turbines have been evolved to a multiple objective optimization including the coupled strength and hydrodynamics optimization process and may even include many other objectives simultaneously. The other objectives could be environmental impact related, in terms of noise and vibration, that would be harmful to flora and fauna. These multiple objective design and optimization processes could be much efficiently and effectively conducted by applying digital twins to perform real-time monitoring and artificial intelligence technology for the design and optimisation. The objectives of this PhD research including: a sound understanding of the design and optimization process; its underlying physics along with its theory and implementation; a successful development of a parametric database by using either numerical tools or experimental testing or both, that is necessary to build a neural network; and then the establishment of a systematic theorical framework, procedure and tool(s) for the AI-assisted turbine design and optimization, and decision-making processes.
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