1. 1 Breeding a Better Stove the use of Genetic Algorithms and Computational Fluid Dynamics to Improve Stove Design H Burnham-Slipper MJ Clifford SJ Pickering

2. Introduction Appropriate and Inappropriate Stoves Experimental Work Computer Modelling CFD Genetic Algorithm Results Conclusions Outline

3. Global problem – half the world cooks on wood burning stoves Indigenous stoves can be inefficient, dangerous, smoky, hazardous to health Introduced stoves can be unpopular Our approach is to combine local indigenous knowledge and preferences with advanced computer modelling techniques to develop an improved stove for use in Eritrea Introduction / Motivation

4. Appropriate and Inappropriate Stoves Classic Eritrean mogogo – smoky, inefficient, but free

5. Eritrea Research and Training Center Mogogo - $40 Appropriate and Inappropriate Stoves

6. MIRT – improved efficiency, but developed in Ethiopia. “The stove of our enemies” Appropriate and Inappropriate Stoves

7. Aprovecho design – improved efficiency, but heavy use of material and poor thermal distribution Appropriate and Inappropriate Stoves

8. CleanCook alcohol stove – unfamiliar technology and materials. Unsuitable for cooking injera Appropriate and Inappropriate Stoves

9. 9 Experimental Set-up Experimental aim: –mass-rate data –temperature data Apparatus: –regular wood cribs –mass balance –K-type thermocouples –extractor hood –a tiny bit of fire- lighter

10. 10 Experimental Results

11. 11 Numerical Model Formulation Assume: –char combustion limited by diffusion of oxygen through species boundary layer –volatile release limited by conduction of heat through char layer –volatiles burn in air, limited by turbulent mixing

12. 12 Numerical Model Formulation Fluent 6.2 CFD code: –buoyancy-driven flow –k-ε turbulence model –species transport –DO radiation model –UDF fuel model –lumpiness function

13. 13 Numerical Model Results Fluent 6.2 CFD code: –burn-rate agrees with experimental –temperature & velocity fields agree with experiment & literature

14. 14 Stove Modelling

15. 15 Stove Characterisation Aprovecho rocket, HBS rocket, mogogo, 3-stone fire with & without grate

16. Stoves have evolved over hundreds (maybe thousands) of years There may be good reasons why stoves are the way they are A genetic algorithm can speed up the natural evolution of stove design Genetic Algorithm

17. Take two stoves Allow the stoves to mate Define ten children (new stoves) using randomly selected genes from parent stoves Test the efficiency of the new stoves using CFD Discard all but the best two stoves Repeat (The method can be adapted to include genetic abnormalities / random mutations)

18. Genetic Algorithm

19. Genetic Algorithm Progress

20. Genetic Algorithm Result

21. Conclusions and Further Work Engineers need to take many factors into account when designing stoves Respecting local stoves and building on indigenous knowledge is vital if a new design is to be successful Combining genetic algorithms and CFD represents a novel approach to stove design, mimicking the natural evolution of stoves It remains to be seen if the new design can be manufactured and tested, we also have a lot of field work to do