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The Optimisation of Reaction Rate Parameters for Chemical Kinetic Modelling Using Genetic Algorithms

[+] Author Affiliations
L. Elliott, D. B. Ingham, A. G. Kyne, N. S. Mera, M. Pourkashanian

University of Leeds, Leeds, UK

C. W. Wilson

QinetiQ Group PLC, Farnborough, Hampshire, UK

Paper No. GT2002-30092, pp. 563-572; 10 pages
  • ASME Turbo Expo 2002: Power for Land, Sea, and Air
  • Volume 1: Turbo Expo 2002
  • Amsterdam, The Netherlands, June 3–6, 2002
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-3606-1 | eISBN: 0-7918-3601-0
  • Copyright © 2002 by ASME


It is well recognised that many important combustion phenomena are kinetically controlled. Whether it be the burning velocity of a premixed flame, the formation of pollutants in an exhaust stack or the conversion of NO to NO2 in a gas turbine combustor, it is important that a detailed chemical kinetic approach be undertaken in order to fully understand the chemical processes taking place. This study uses a genetic algorithm to determine new reaction rate parameters (A’s, β’s and Ea ’s in the Arrhenius expressions) for the combustion of both a hydrogen/air and methane/air mixture in a perfectly stirred reactor. In both cases, output species profiles obtained from an original set of rate constants are reproduced by a new different set obtained using a genetic algorithm inversion process. The new set of rate constants lie between predefined boundaries (±25% of the original values) which in future work can be extended to represent the uncertainty associated with experimental findings. In addition, this powerful technique may be used in developing reaction mechanisms whose newly optimised rate constants reproduce all the experimental data available, enabling a greater confidence in their predictive capabilities. The results of this study therefore demonstrate that the genetic algorithm inversion process promises the ability to assess combustion behaviour for fuels where the reaction rate coefficients are not known with any confidence and, subsequently, accurately predict emission characteristics, stable species concentrations and flame characterisation. Such predictive capabilities will be of paramount importance within the gas turbine industry.

Copyright © 2002 by ASME



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