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International Journal of Energy for a Clean Environment
SJR: 0.195 SNIP: 0.435 CiteScore™: 0.74

ISSN 印刷: 2150-3621
ISSN オンライン: 2150-363X

International Journal of Energy for a Clean Environment

Formerly Known as Clean Air: International Journal on Energy for a Clean Environment

DOI: 10.1615/InterJEnerCleanEnv.v8.i1.10
pages 1-24

OPTIMIZATION OF A SURROGATE REDUCED AVIATION FUEL-AIR REACTION MECHANISM USING A GENETIC ALGORITHM

Lionel Elliott
Department of Applied Mathematical Studies, The University of Leeds, Leeds LS2 9JT, West Yorkshire, England.
Derek B. Ingham
Centre for CFD, Department of Applied Mathematical Studies, The University of Leeds, Leeds, LS2 9JT, UK; Energy-2050, Faculty of Engineering, University of Sheffield, Sheffield, S10 2TN, UK
Adrian G. Kyne
Energy and Resources Research Institute, University of Leeds, Leeds LS2 9JT, UK
Nicolae Severian Mera
Department of Applied Mathematics, Energy and Resources Research Institute, University of Leeds, Leeds LS2 9JT, UK
Mohamed Pourkashanian
Department of Fuel and Energy, Energy and Resources Research Institute / Centre for Computational Fluid Dynamics, University of Leeds, Leeds LS2 9JT, UK
C. W. Wilson
Department of Mechanical Engineering, University of Sheffield, Sheffield S1 3JD, UK

要約

This study investigates reducing an existing detailed aviation fuel-air reaction mechanism based on identifying the most important reactions using a rate of production analysis. A genetic algorithm (GA) approach is then used to determine new reaction rate parameters (As, βs, and Eas in the Arrhenius expressions) that recover the mechanism's ability to predict experimentally determined species and ignition delay profiles. The GA does not blindly produce mechanisms that reproduce any given profile; instead, the reaction rate coefficients are confined to lie within physically realistic bounds associated with the National Institute of Standards and Technology database. This leads to mechanisms that can be applied to combustion problems that lie outside those used in the optimization process. A new multiobjective function has been developed that allows the GA to fit both ignition delay and premix flame data. This allows the substantial amount of existing experimental data to be incorporated into the mechanism validation procedure, thus improving the predictive capabilities of the mechanisms that are being developed. This study provides a reduced aviation fuel-air reaction scheme whose overall performance in predicting experimental major species profiles and ignition delay times is comparable to that of the original detailed starting mechanism.