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Developing Multiple Diverse Potential Designs for Heat Transfer Utilizing Graph Based Evolutionary Algorithms

[+] Author Affiliations
David J. Muth, Jr., Douglas S. McCorkle, Kenneth M. Bryden

Iowa State University, Ames, IA

Daniel A. Ashlock

University of Guelph, Guelph, ON Canada

Paper No. DETC2006-99560, pp. 325-332; 8 pages
  • ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3: 26th Computers and Information in Engineering Conference
  • Philadelphia, Pennsylvania, USA, September 10–13, 2006
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 0-7918-4257-8 | eISBN: 0-7918-3784-X
  • Copyright © 2006 by ASME


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 good 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.

Copyright © 2006 by ASME



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