Multiobjective genetic algorithm optimization for calculating the reaction rate coefficients for hydrogen combustion

It is well recognized that many important combustion phenomena are kinetically controlled. Whether it be the burning velocity of a premixed flame, the formation of pollutants in different stages of a combustion process, or the conversion of NO to NO2 in a gas turbine engine exhaust, it is important that a detailed chemical kinetic approach be undertaken in order to fully understand the chemical processes taking place. In this study, a multiobjective genetic algorithm approach is developed for determining new reaction rate parameters (A's, beta's, and E-a's in the three-parameter functional form of the Arrhenius expressions) for the combustion of hydrogen/air mixtures. The multiobjective structure of the genetic algorithm employed allows for the incorporation of both perfectly stirred reactor and laminar premixed flame data in the inversion process, thus producing more efficient reaction mechanisms. Various inversion procedures based on reduced sets of data are investigated and tested on hydrogen/air combustion in order to generate efficient inversion schemes for future investigations concerning complex hydrocarbon fuels.