Towards the evolution of vertical-axis wind turbines using supershapes

Preen, Richard; Bull, Larry

Abstract

© 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.

Citation

Preen, R. J., Preen, R., & Bull, L. (2014). Towards the evolution of vertical-axis wind turbines using supershapes. Evolutionary Intelligence, 7(3), 155-167. https://doi.org/10.1007/s12065-014-0116-4

• 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
• DOI: https://doi.org/10.1007/s12065-014-0116-4
• Keywords: computational geometry, evolutionary algorithms, superformula, 3D printers, wind energy
• Public URL: https://uwe-repository.worktribe.com/output/808620
• Publisher URL: http://dx.doi.org/10.1007/s12065-014-0116-4