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Proceedings of IDETC/CIE 2006
ASME 2006 International Design Engineering Technical Conferences &
Computers and Information in Engineering Conference
September 10-13, 2006, Philadelphia, Pennsylvania, USA
DETC2006-99560
Developing Multiple Diverse Potential Designs for Heat Transfer Utilizing Graph
Based Evolutionary AlgorithmsDavid J. Muth Jr.
Department of Mechanical Engineering
Iowa State University
Ames, Iowa 50011
dmuthjr@iastate.edu
Daniel A. Ashlock
Department of Mathematics and Statistics
University of Guelph
Guelph, Ontario N1G 2R4
dashlock@uoguelph.ca
ABSTRACT
This paper examines the use of graph based evolutionary
algorithms (GBEAs) to find multiple acceptable solutions for
heat transfer in engineering systems during the optimization
process. GBEAs are a type of evolutionary algorithm (EA) in
which a topology, or geography, is imposed on an evolving
population of solutions. The rates at which solutions can spread
within the population are controlled by the choice of topology.
As in nature geography can be used to develop and sustain
diversity within the solution population. Altering the choice of
graph can create a more or less diverse population of potential
solutions. The choice of graph can also affect the convergence
rate for the EA and the number of mating events required for
convergence. The engineering system examined in this paper is
a biomass fueled cookstove used in developing nations for
household cooking. In this cookstove wood is combusted in a
small combustion chamber and the resulting hot gases are
utilized to heat the stove's cooking surface. The spatial
temperature profile of the cooking surface is determined by a
series of baffles that direct the flow of hot gases. The
optimization goal is to find baffle configurations that provide an
even temperature distribution on the cooking surface. Often in
engineering, the goal of optimization is not to find the single
optimum solution but rather to identify a number of goodDouglas S. McCorkle
Department of Mechanical Engineering
Iowa State University
Ames, Iowa 50011
mccdo@iastate.edu
Kenneth M. Bryden
Department of Mechanical Engineering
Iowa State University
Ames, Iowa 50011
kmbryden@iastate.edu
solutions that can be used as a starting point for detailed
engineering design. Because of this a key aspect of evolutionary
optimization is the diversity of the solutions found. The key
conclusion in this paper is that GBEA's can be used to create
multiple good solutions needed to support engineering design.
INTRODUCTION
Often optimization of a single analysis-based model is only
one step in the engineering design process. Complete system
design requires satisfying multiple constraints. These arise from
several sources. These include 1) multiple models are often
needed to support engineering design and 2) some criteria are
implicit constraints that cannot be included in an analysis model
(e.g. style, consumer usage). These constraints impact design as
much or more than the design properties that can be explicitly
optimized. For example, a successful design must be viable
economically. These financial concerns are coupled with
manufacturing and resource availability factors. The design
process relies on finding a solution that satisfies all of these
constraints. A particular concern within one area of analysis can
often lead to unacceptable limitations on other criteria. For
example, physical analysis of a system may require using a
specific material in construction that is not currently available
or economically viable. In these cases design is a process of1
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Jr., David J. Muth. Developing Multiple Diverse Potential Designs for Heat Transfer Utilizing Graph Based Evolutionary Algorithms, article, September 1, 2006; [Idaho Falls, Idaho]. (https://digital.library.unt.edu/ark:/67531/metadc888019/m1/2/: accessed March 18, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT Libraries Government Documents Department.