Towards the Evolution of Vertical-Axis Wind Turbines using Supershapes

TL;DR

This paper explores using Gielis superformula for evolving vertical-axis wind turbine designs through physical prototypes and approximation-based evaluation, enabling flexible shape exploration and novel, efficient turbines.

Contribution

It introduces a simple generative encoding with Gielis superformula for VAWT design, expanding shape diversity and enabling physical evolution of efficient turbines.

Findings

Successful evolution of diverse 3D VAWT shapes

Ability to produce designs closely matching target shapes

Identification of novel, efficient turbine designs

Abstract

We have recently presented an initial study of evolutionary algorithms used to design vertical-axis wind turbines (VAWTs) wherein candidate prototypes are evaluated under approximated wind tunnel 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 approximated wind tunnel 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.