A numerical study of turbulent combustion systems was pursued to examine different computational modeling techniques, namely computational fluid dynamics (CFD) and chemical reactor network (CRN) methods. Both methods have been studied and analyzed as individual techniques as well as a coupled approach to pursue better understandings of the mechanisms and interactions between turbulent flow and mixing, ignition behavior and pollutant formation. A thorough analysis and comparison of both turbulence models and chemistry representation methods was executed and simulations were compared and validated with experimental works. An extensive study of turbulence modeling methods, and the optimization of modeling techniques including turbulence intensity and computational domain size have been conducted. The final CFD model has demonstrated good predictive performance for different turbulent bluff-body flames. The NOx formation and the effects of fuel mixtures indicated that the addition of hydrogen to the fuel and non-flammable diluents like CO2 and H2O contribute to the reduction of NOx. The second part of the study focused on developing chemical models and methods that include the detailed gaseous reaction mechanism of GRI-Mech 3.0 but cost less computational time. A new chemical reactor network has been created based on the CFD results of combustion characteristics and flow fields. The proposed CRN has been validated with the temperature and species emission for different bluff-body flames and has shown the capability of being applied to general bluff-body systems. Specifically, the rate of production of NOx and the sensitivity analysis based on the CRN results helped to summarize the reduced reaction mechanism, which not only provided a promising method to generate representative reactions from hundreds of species and reactions in gaseous mechanism but also presented valuable information of the combustion mechanisms and NOx formation. Finally, the proposed reduced reaction mechanism from the sensitivity analysis was applied to the CFD simulations, which created a fully coupled process between CFD and CRN, and the results from the reduced reaction mechanism have shown good predictions compared with the probability density function method. / Ph. D.