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Towards the evolution of novel vertical-axis wind turbines

Preen, Richard; Bull, Larry

Authors

Richard Preen Richard2.Preen@uwe.ac.uk
Research Fellow - Deep Evolutionary Learning

Lawrence Bull Larry.Bull@uwe.ac.uk
AHOD Research and Scholarship and Prof



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Presentation Conference Type Conference Paper (unpublished)
Start Date Sep 9, 2013
Publication Date Sep 1, 2013
Peer Reviewed Peer Reviewed
Pages 74-81
APA6 Citation Preen, R., & Bull, L. (2013, September). Towards the evolution of novel vertical-axis wind turbines. Paper presented at 13th UK Workshop on Computational Intelligence, UKCI 2013
Keywords aerodynamic efficiency, approximated wind tunnel conditions, artificial evolution, artificial neural network, surrogate model, vertical-axis wind turbines
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
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