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Towards the evolution of vertical-axis wind turbines using supershapes

Preen, Richard; Bull, Larry


Richard Preen
Senior Research Fellow in Machine Learning

Lawrence Bull
School Director (Research & Enterprise) and Professor


© 2014, Springer-Verlag Berlin Heidelberg. We have recently presented an initial study of evolutionary algorithms used to design vertical-axis wind turbines (VAWTs) wherein candidate prototypes are evaluated under fan generated wind conditions after being physically instantiated by a 3D printer. That is, unlike other approaches such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made. However, the representation used significantly restricted the range of morphologies explored. In this paper, we present initial explorations into the use of a simple generative encoding, known as Gielis superformula, that produces a highly flexible 3D shape representation to design VAWT. First, the target-based evolution of 3D artefacts is investigated and subsequently initial design experiments are performed wherein each VAWT candidate is physically instantiated and evaluated under fan generated wind conditions. It is shown possible to produce very closely matching designs of a number of 3D objects through the evolution of supershapes produced by Gielis superformula. Moreover, it is shown possible to use artificial physical evolution to identify novel and increasingly efficient supershape VAWT designs.


Preen, R., & Bull, L. (2014). Towards the evolution of vertical-axis wind turbines using supershapes. Evolutionary Intelligence, 7(3), 155-167.

Journal Article Type Article
Publication Date Nov 25, 2014
Journal Evolutionary Intelligence
Print ISSN 1864-5909
Electronic ISSN 1864-5917
Publisher Springer (part of Springer Nature)
Peer Reviewed Peer Reviewed
Volume 7
Issue 3
Pages 155-167
Keywords computational geometry, evolutionary algorithms, superformula, 3D printers, wind energy
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