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Breeding a Better Stove

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

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Breeding a Better Stove

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

  2. Outline IntroductionAppropriate and Inappropriate StovesExperimental WorkComputer Modelling CFD Genetic AlgorithmResultsConclusions

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

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

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

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

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

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

  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. Experimental Results

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

  14. Stove Modelling

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

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

  17. Genetic Algorithm 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

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