Design Mining Interacting Wind Turbines
This is an incremental improvement for optimizing wind turbine designs using physical prototyping and evolutionary algorithms.
This paper extends prior work on surrogate-assisted evolutionary algorithms for designing vertical-axis wind turbines by comparing alternative surrogate modeling techniques and exploring temporal windowing of training data, enhanced local search, and coevolution collaboration schemes.
An initial study of surrogate-assisted evolutionary algorithms used to design vertical-axis wind turbines wherein candidate prototypes are evaluated under fan generated wind conditions after being physically instantiated by a 3D printer has recently been presented. Unlike other approaches, such as computational fluid dynamics simulations, no mathematical formulations were used and no model assumptions were made. This paper extends that work by exploring alternative surrogate modelling and evolutionary techniques. The accuracy of various modelling algorithms used to estimate the fitness of evaluated individuals from the initial experiments is compared. The effect of temporally windowing surrogate model training samples is explored. A surrogate-assisted approach based on an enhanced local search is introduced; and alternative coevolution collaboration schemes are examined.