Toward the Coevolution of Novel Vertical-Axis Wind Turbines

TL;DR

This paper explores the use of artificial evolution and neural networks to design and optimize vertical-axis wind turbines physically, aiming to improve renewable energy generation without relying on traditional simulation models.

Contribution

It introduces a novel coevolutionary approach combined with surrogate neural networks for physical turbine design, bypassing complex mathematical modeling.

Findings

Neural networks reduced fabrication needs for efficient turbines

Coevolutionary algorithms optimized turbine array configurations

Physical prototypes validated the evolutionary design process

Abstract

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 still 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 fan generated wind 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 in other approaches, such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made.