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 NO 2 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, '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.
This study describes the development of a new binary encoded genetic algorithm to determine a subset of species and their associated reactions that best represent the full starting point reaction mechanism in modeling a low-pressure stoichiometric 12.5% CH 4 /25% O 2 /62.5% Ar, burner-stabilized premixed flame. The number of species in the subset chosen is kept fixed and is specified at the start of the procedure. The genetic algorithm chooses better and better mechanisms on the basis of an objective function which measures how well the new reduced mechanisms predict a set of species' profiles simulated by the full mechanism. To verify the validity of our approach, a full enumeration was performed on a reduced problem, and it was reassuring to find that the genetic algorithm was able to find the optimum solution to this reduced problem in very few generations. A second step was to take the reduced reaction mechanism and to use a second real encoded genetic algorithm to determine the optimal reaction rate parameters that best model an experimental set of premixed flame species' profiles. This second step not only improved the reduced mechanism's ability to model the experimental profiles but also provided a remarkable improvement over the mechanism developed from step 1 in modeling combustion processes outside those used during the mechanism's development.
The structure of a rich burner stabilised kerosene/O2/N2 flame is predicted using a detailed chemical kinetic mechanism where the kerosene is represented by a mixture of n-decane and toluene. The chemical reaction mechanism, consisting of 440 reactions between 84 species, is capable of predicting the experimentally determined flame structure of Douté et al. (1995) with good success using the measured temperature profile as input. Sensitivity and reaction rate analyses are carried out to identify the most significant reactions and based on this the reaction mechanism was reduced to one with only 165 reactions without any loss of accuracy. Burning velocities of kerosene-air mixtures were also determined over an extensive range of equivalence ratios at atmospheric pressure. The initial temperature of the mixture was also varied and burning velocities were found to increase with increasing temperature. Burning velocities calculated using both the detailed and reduced mechanisms were essentially identical.
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.
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