Title :
Toward the Coevolution of Novel Vertical-Axis Wind Turbines
Author :
Preen, Richard J. ; Bull, Larry
Author_Institution :
Dept. of Comput. Sci. & Creative Technol., Univ. of the West of England, Bristol, UK
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 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. 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 other approaches, such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made.
Keywords :
aerodynamics; evolutionary computation; learning (artificial intelligence); neural nets; power engineering computing; sustainable development; wind power; wind tunnels; wind turbines; aerodynamic efficiency; approximated wind tunnel condition; artificial evolution; artificial neural network; assist learning; cooperative coevolutionary algorithm; renewable energy production; surrogate model; sustainable energy production; vertical-axis wind turbine; Aerodynamics; Blades; Computational modeling; Fabrication; Printers; Prototypes; Wind turbines; 3-D printers; coevolution; surrogate-assisted evolution; wind turbines;
Journal_Title :
Evolutionary Computation, IEEE Transactions on
DOI :
10.1109/TEVC.2014.2316199