Hybrid Algorithm for Development of Reduced Skeletal Kinetic Mechanisms with Specific Species Targeting

A novel algorithm is introduced to reduce the number of detailed kinetic mechanisms. The algorithm uniquely employs a classification of species and a defined parameter that quantitatively identifies the contribution of each species under a combustion condition of interest with specific species targeting. It also incorporates sensitivity analysis, the directed relation graph (DRG) method, and dynamic refinement. The proposed procedure is applied to the GRI-3.0 mechanism with atmospheric pollutants (CO, CO2, NO, and NO2) targeted in a lean methane-air flame at high pressures. The performance of the reduced mechanism is assessed, and good accuracy with considerable computational cost reduction is achieved. Investigated properties by the perfectly stirred reactor (PSR) model incorporated adiabatic flame temperature and the concentrations of targeted pollutants versus equivalence ratio (0.5-1.2), pressure (1-14 bar), and residence time (0.001-1 s) variations. The maximum prediction errors of temperature and greenhouse gas (GHG) mole fractions' profiles were less than 1%, while NOx versus residence time showed errors of 11.7 and 4%, respectively. Additionally, the flame speed yielded a maximum deviation of less than 2%. The computational cost in a 2D (axisymmetric) simulation revealed a 61% reduction. It is shown that the introduced algorithm is effective and can be applied to large mechanisms aiming for particular species predictions under specified operating conditions with lower computational costs.