Towards the Evolution of Novel Vertical Axis Turbines

1. Towards the Evolution of Novel Vertical Axis Turbines Richard Preen & Larry Bull UWE, Bristol

2. 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.

3. 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]

4. 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.

5. An Example: Wind Turbines

6. 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.

7. 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.

8. Embodied Evolution of VAWT • Turbines of 30mm3 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

9. Single VAWT

10. Single VAWT: Z-axis design

11. 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.

12. 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.

13. Paired VAWT

14. Example

15. 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.