STTR Phase I: Active Blade Morphing Control to Improve Efficiency and Reduce Loading for Wind Turbines

Atrevida Science Llc, Charlotte NC

Investigators

Abstract

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is expanding global deployment of wind turbines with increased production not obtainable with today’s fixed blades. An adaptable blade with advanced control capabilities helps solve technical and scientific challenges as wind projects accelerate their move offshore, extending the physics of the larger turbines needed for future wind farms. Developing a high-fidelity modeling tool to design morphing blades capable of boosting energy production, reducing wear and tear, dampening vibration, improving stability, and reducing load effectively achieves two crucial goals. These goals include accelerating the deployment of renewable energy with affordable electricity that is efficiently and economically extracted from wind and improving the loading and stability necessary for the development of floating wind farms capable of installation in challenging water depths and extreme weather. The technology proposed makes large turbines better at capturing wind in more locations to protect against volatile energy prices, generate jobs, and promote greater participation in the global energy transition for developed and developing countries alike. Furthermore, the blade technology resulting from this research provides opportunities for new manufacturing techniques and commercial applications in other industries such as aviation, automotive, and marine renewable energy. This STTR Phase I project proposes to examine a high-fidelity model to support the design and control of an advanced wind turbine blade configuration with an adaptive twist angle distribution (TAD). Conventional control is generally applied to rotor torque to maximize wind capture, and thus production, below the rated wind speed. Above this speed, control shifts to the blade pitch angle to maintain full power. However, limitations in existing designs lead to trade-offs where wind capture or power production is relinquished to reduce loads, mitigate vibration, and improve stability. The actively adaptive TAD provides greater control capabilities and satisfies these objectives without trade-offs. A crucial goal of this research is the means to understand the complex aeroelastic and aerodynamic relationships with respect to the TAD. The technology is a high-fidelity model that simulates these dynamics and the aeroelastic performance in a reasonable amount of time. This model involves the development of a framework combining these dynamics and uses computational tools that leverage data analytics and machine learning. The technical result of the proposed work is the creation of a digital twin that enables effective design and robust control of highly sophisticated blades with adaptive TAD. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.