Towards the Evolution of Novel Vertical-Axis Wind Turbines

TL;DR

This paper explores using artificial evolution and neural networks to design efficient vertical-axis wind turbines physically tested under wind tunnel conditions, reducing costs without relying on traditional simulations.

Contribution

Introduces a novel approach combining artificial evolution and neural networks for physical design of wind turbines, avoiding mathematical models and assumptions.

Findings

Neural network surrogate reduces prototype fabrication

Physical prototypes show improved aerodynamic efficiency

Cost-effective design process achieved

Abstract

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 approximated wind tunnel conditions. An artificial neural network is used as a surrogate model to assist learning and found to reduce the number of fabrications required to reach a higher aerodynamic efficiency, resulting in an important cost reduction. Unlike in other approaches, such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made.