Vertical-axis wind turbines (VAWT) have the potential to revolutionize the wind energy market, thanks to their numerous advantages over traditional horizontal-axis wind turbines (HAWT).
While HAWTs are more commonly used today, VAWTs have a slower rotation speed, making them less noisy and able to achieve greater wind energy density. Additionally, VAWTs require less space for the same output both on- and off-shore and are more wildlife-friendly.
However, the engineering challenge of air flow control has limited the commercialization of VAWTs. Researchers at the School of Engineering Unsteady Flow Diagnostics Lab have identified a solution utilizing sensor technology and machine learning.
In a groundbreaking study published in Nature Communications, researchers discovered optimal pitch profiles for VAWT blades that significantly increase turbine efficiency and reduce vibrations. By employing a genetic learning algorithm, they were able to determine the best pitch for VAWT blades, making them more efficient and robust.
The adoption of VAWTs could play a crucial role in meeting the UN’s 2050 carbon emissions objectives, as Europe strives to increase its wind energy capacity. Despite the social and legislative challenges surrounding wind turbines, the benefits of VAWTs make them a promising alternative for the future of sustainable energy.
With the potential to harness wind power more effectively and efficiently, VAWTs represent a major breakthrough in renewable energy technology. By addressing the engineering issues that have held them back, these innovative turbines could help accelerate the transition to a greener, more sustainable future.
