Towards the evolution of novel vertical-axis wind turbines

Preen, Richard; Bull, Larry

Abstract

Renewable and sustainable energy is one of the most important challenges currently facing mankind. Wind has made an increasing contribution to the world's energy supply mix, but still remains a long way from reaching its full potential. In this paper, we investigate the use of artificial evolution to design vertical-axis wind turbine prototypes that are physically instantiated and evaluated under approximated wind tunnel conditions. An artificial neural network is used as a surrogate model to assist learning and found to reduce the number of fabrications required to reach a higher aerodynamic efficiency. Unlike in other approaches, such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made.

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: 13th UK Workshop on Computational Intelligence, UKCI 2013
• Start Date: Sep 9, 2013
• End Date: Sep 11, 2013
• Publication Date: Sep 1, 2013
• Peer Reviewed: Peer Reviewed
• Pages: 74-81
• Keywords: aerodynamic efficiency, approximated wind tunnel conditions, artificial evolution, artificial neural network, surrogate model, vertical-axis wind turbines
• Public URL: https://uwe-repository.worktribe.com/output/928641
• Publisher URL: http://dx.doi.org/10.1109/UKCI.2013.6651290
• Additional Information: Title of Conference or Conference Proceedings : 13th UK Workshop on Computational Intelligence, UKCI 2013