Unsteady load control of large wind turbine smart rotor blades using distributed actuations
The aim of the project is to develop of a novel approach of sophisticated load control techniques for future large wind turbine systems by means of distributed fluidic actuations, namely, smart blade systems. In this project, various flow control devices will be tested in wind/water tunnels. The project will lay a foundation for developing a novel active control approach of next generation smart wind turbine blade systems. The proposed research will be multidisciplinary through integration of fundamental fluid dynamics and aerodynamics, control engineering and wind power engineering.
The cumulative global wind power capacity is now almost doubling every 3 years. The increasing wind energy production leads to increasing applications of large wind turbine rotor diameters which will become susceptible to the unpredictable unsteady atmospheric phenomena. Majority of modern large wind turbines run at variable rotation speed combined with the adjustment of the collective pitch angle of the blades to alleviate the impact of unsteady loads. Since the atmospheric characteristics are unlikely to be uniform across the large wind turbine sweep area, it is therefore unlikely that all the rotor blades will be operated under optimized conditions by using collective pitch adjustment. This project aims to develop of a novel approach of smart blade systems. Although there is a lack of understanding of three-dimensional separated flows and their controls using distributed actuations, the potential for load reduction by using smart control concepts for wind turbines has been proven with various approaches.
Home/EU awards (3 years): Provides tuition fee, £1000 per year Training Support Grant and Stipend (£14,296 2016/7 rate).
Overseas awards (3 years): Provides tuition fee, £1000 per year Training Support Grant, but no stipend.
Applicants should have, or expect to achieve, a first class Masters degree in in a relevant discipline.
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FTE Category A staff submitted: 61.00
Research output data provided by the Research Excellence Framework (REF)
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