In the field of combustion systems, methods and tools are under development to fulfill the need for fast and accurate prediction of emissions such as NO and CO. CFD-CRN is a hybrid approach that utilizes a combination of computational fluid dynamics (CFD) and a chemical reactor network (CRN) to simulate the flow field and chemical kinetics of a combustion system in detail. This thesis describes the research into the effect of applying the energy equation to update the temperature whilst solving the CRN. The objective is to quantify the effect of the common assumption that the temperature generated by CFD is sufficiently accurate and can be kept fixed to reduce the nonlinearity of the system of equations to be solved in the CRN. This research uses and modifies the computational tool AGNES, that was developed at the Delft University of Technology and is able to automatically cluster, solve and visualise the results of a CRN based on results form CFD. The test cases used are the Sandia Flame D, which is a piloted methane-air jet flame with a Reynolds number of 22400, and the Verissimo et al. test case, which is a flameless combustion burner. The CRN was clustered with the zones and tolerances set by Monaghan et al. The results were validated using experimental data of temperature and species mass fractions and the sensitivity of the clustering method was studied.
The results of CFD-CRN simulations were used to evaluate the effect of updating the temperature using the energy equation. The results show that solving the energy equation leads to a progressive overprediction of temperature in the far field of the computational domain, which consequently results in the overprediction of NO and CO concentrations. A potential cause for the overprediction is identified as the heat transfer in the form of conduction and radiation not being accounted for in the solving of the CRN. Unless the diffusion of heat is accurately modeled, the application of the energy equation is not recommended.