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Toward the Coevolution of Novel Vertical-Axis Wind Turbines

Preen, Richard; Bull, Larry


Dr Richard Preen
Senior Research Fellow in Machine Learning

Lawrence Bull
School Director (Research & Enterprise) and Professor


© 1997-2012 IEEE. The production of 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 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. Initially, a conventional evolutionary algorithm is used to explore the design space of a single wind turbine and later a cooperative coevolutionary algorithm is used to explore the design space of an array of wind turbines. Artificial neural networks are used throughout as surrogate models to assist learning and found to reduce the number of fabrications required to reach a higher aerodynamic efficiency. Unlike other approaches, such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made.


Preen, R., & Bull, L. (2015). Toward the Coevolution of Novel Vertical-Axis Wind Turbines. IEEE Transactions on Evolutionary Computation, 19(2), 284-294.

Journal Article Type Article
Acceptance Date Apr 8, 2014
Online Publication Date Apr 8, 2014
Publication Date Apr 1, 2015
Journal IEEE Transactions on Evolutionary Computation
Print ISSN 1089-778X
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 19
Issue 2
Pages 284-294
Keywords 3-D printers, coevolution, surrogate-assisted evolution, wind turbines, computational modeling, blades, wind turbines, printers, prototypes, fabrication, aerodynamics
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