Richard Preen & Larry Bull UWE, Bristol

Introduction Evolutionary computing has been applied widely. Over 70 examples of “human competitive” performance have been noted [Koza, 2010]. Most of this work has included simulation or models. When simulations are costly, surrogate models can be used. Data mining techniques are used to create approximations of the function space from sample points gathered from the simulator/model.

Embodied Evolution 1 Some hard problems are difficult to model or simulate in a useful way. Small amount of work using simulated evolutionary design directly on a task: Jet nozzle [Rechenberg, 1971] Mobile robotics [Nolfi, 1992] Electronic circuits [Thompson, 1998] Unconventional computing [Harding & Miller, 2004] Chemical systems [Theis et al., 2007]

Embodied Evolution 2 Explore use of surrogate models in conjunction with direct solution evaluation only for complex tasks. No best-guess simulator or model used. Combine with emerging rapid-fabrication (3D printing) technology. Potential for truly unexpected results in a wide range of domains. Many issues, of course: time, noise in evaluations, representations, kinds of surrogates, etc.

An Example: Wind Turbines

Wind Energy “In theory, small-scale wind energy has the potential to generate 41.3 TWh of electricity and save 17.8 MtCO2 in the UK annually” [Carbon Trust, 2008]. Wind flow is rarely constant and consistent, rather it is usually veering and turbulent, and the influences of nearby obstacles can significantly alter wind flow patterns. Vertical axis wind turbines (VAWT) represent a very effective approach to harnessing wind power in many situations – especially urban areas. In comparison to the more common horizontal axis wind turbine (HAWT), VAWT can also be easier to manufacture, may scale more easily, and are typically inherently light- weight with little or no noise pollution.

A Simple Representation Confined design space to a four-blade Savonius VAWT with alterations in blade shape (profile and twist) possible. Genome [5,8,2,4] defines offsets from central spindle.

Embodied Evolution of VAWT Turbines of 30mm 3 volume Population size 20 Tournament selection Tip speed used as fitness measure Direct evaluations for first three generations (60 fabs) MLP surrogate model of fitness MLP trained for 1000 epochs per generation Best and random individual fabricated per generation

Single VAWT

Single VAWT: Z-axis design

Beyond Modelling Evolutionary computing has previously been used to design both HAWT and VAWT via CFD models. Essentially impossible for turbine arrays where interactions are considered. Dabiri et al. have recently highlighted how the spacing constraints of HAWT arrays often do not apply for VAWT, and even that performance can be increased by exploitation of inter-turbine flow effects. View wind farm as an energy-capture ecosystem and coevolve heterogeneous, interacting VAWT.

Embodied Coevolution Two “species” of turbine. Two populations and surrogate models (L, R). Each evaluated with best individual from other population. Evolve alternately. Seed with 10 best from single VAWT experiments. Allow counter-rotation. All other features the same as before.

Paired VAWT

Example

Results Speciation seen – L and R physically different. Homogeneous pairing of either species not as effective as the heterogeneous case. Counter-rotation not seen in fittest solutions here. Approach potentially unaffected by array size increase. CFD modelling impossible.