Breeding a better stove: the use of computational fluid dynamics and genetic algorithms to optimise a wood burning stove for Eritrea

Burnham-Slipper, Hugh (2009) Breeding a better stove: the use of computational fluid dynamics and genetic algorithms to optimise a wood burning stove for Eritrea. PhD thesis, University of Nottingham.

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Improved cooking stoves can bring significant benefits to women and children in rural African situations, due to reduced fuel consumption and improved indoor air quality. This investigation focuses on the use of Computational Fluid Dynamics (CFD) and Genetic Algorithms (GAs) to optimise a stove for Eritrea.

Initial work focussed on developing a model of wood combustion in a fixed bed. An experimental investigation was carried out on regular wood cribs to determine the burn rate and temperature field above a wood fire. The experimental data was used to develop a numerical model using CFD software Fluent 6.2 and user-defined functions for the fixed bed of fuel. The model assumed that pyrolysis was limited by heat transfer through the fuel, and that char combustion was limited by oxygen diffusion to the fuel surface. Simulation results yielded a mean and maximum error of 16% and 42% respectively in fuel burn rate.

In the second phase of the investigation, the numerical model of wood combustion was used as part of a larger CFD model to capture the behaviour of a complete stove. The model was compared with experimental data for rocket type stoves with different geometries. The model correctly identified the trends of fuel burn rate and heat transfer in the experimental data, though agreement with experimental values was poor and the model exhibited significant errors when altering stove height and diameter.

In the final phase of the investigation, the stove model was used in conjunction with a genetic algorithm to optimise the stove shape. Two methods of genetic coding were investigated. The resulting stove is expected to half fuel consumption compared to the classic mogogo stove, though this remains to be experimentally verified.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Clifford, M.J.
Pickering, S.
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA 357 Fluid mechanics
Faculties/Schools: UK Campuses > Faculty of Engineering > Department of Mechanical, Materials and Manufacturing Engineering
Item ID: 10669
Depositing User: EP, Services
Date Deposited: 09 Feb 2009 11:29
Last Modified: 14 Oct 2017 16:16

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