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Toward the Coevolution of Novel Vertical-Axis Wind Turbines

Preen, Richard; Bull, Larry

Authors

Dr Richard Preen Richard2.Preen@uwe.ac.uk
Senior Research Fellow in Machine Learning

Lawrence Bull Larry.Bull@uwe.ac.uk
School Director (Research & Enterprise) and Professor



Abstract

© 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.

Citation

Preen, R., & Bull, L. (2015). Toward the Coevolution of Novel Vertical-Axis Wind Turbines. IEEE Transactions on Evolutionary Computation, 19(2), 284-294. https://doi.org/10.1109/TEVC.2014.2316199

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
DOI https://doi.org/10.1109/TEVC.2014.2316199
Keywords 3-D printers, coevolution, surrogate-assisted evolution, wind turbines, computational modeling, blades, wind turbines, printers, prototypes, fabrication, aerodynamics
Public URL https://uwe-repository.worktribe.com/output/836650
Publisher URL http://dx.doi.org/10.1109/TEVC.2014.2316199