Design Mining Interacting Wind Turbines

Abstract

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

On Design Mining: Coevolution and Surrogate Models

“…Following [10], the pilot study used multi-layered perceptrons (MLPs) [59] for the surrogate modelling. Using the data from that study, we have subsequently shown that MLPs appear a robust approach in comparison to a number of well-known techniques [56]. That is, MLPs appear efficient at capturing the underlying structure of a design space from the relatively small amount of data points a physical sampling process can be expected to generate.…”

“…For example, individuals from the initial population may perform very differently when partnered with the elite individuals from later generations. A windowed approach of using only the most recent P evaluated individuals in each species for training seemed promising in a prior experiment coevolving a pair of VAWT, however was not statistically significant in practice [56]. Here we explore the effect for larger numbers of species where the temporal variance is potentially much higher (SCGA-bw).…”

“…As we have reviewed elsewhere [56], techniques such as EAs have been used to design wind turbine blades using CFD simulations, some in conjunction with surrogate models, e.g., Chen et al [14]. EAs have also been extensively used to optimise the turbine positioning within wind farms, e.g., Mosetti et al [44].…”

“…Design mining [54,55,56] is the use of computational intelligence techniques to iteratively search and model the attribute space of physical objects evaluated directly through rapid prototyping to meet given objectives. It enables the exploitation of novel materials and processes without formal models or complex simulation, whilst harnessing the creativity of both computational and human design methods.…”

Data-Efficient Design Exploration through Surrogate-Assisted Illumination

“…In computationally expensive problems it is common to use surrogate models, that is, approximate models of the objective function based on previously evaluated solutions (Jin, 2005;Forrester and Keane, 2009;Shahriari et al, 2016;Preen and Bull, 2016). These models are refined through iterative evaluation of new solutions based on an acquisition function, which balances exploitation and exploration to improve accuracy in high-fitness regions.…”