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.

Prediction of emissions from combustion systems is a complex problem involving the coupling between the flow field and chemistry. CFD analysis is the most commonly employed approach. However, it has the drawback that a very high computational cost prevents the use of detailed chemistry models. This master thesis, which focuses on NOx emissions, uses a more unconventional method of emissions prediction: Chemical Reactor Network (CRN). The advantage of this method is that, as it does not use fine discretisation, closure models nor fluid dynamics equations, it allows the implementation of detailed chemistry mechanisms.

A CRN is developed first for a single-element Lean Direct Injection (LDI) combustor and then the CRN is adapted for a Multi-Point LDI (MPLDI) combustor. CFD and experimental results are used to set up the CRN. In the base case scenario, in which kerosene is the fuel choice, the NOx emissions predicted are very close to experimental measurements. This is particularly meaningful given the high uncertainties of modelling this highly complex turbulent combustion process.

The developed CRN is also used to predict NOx emissions for the same combustor when the fuel choice is varied. Namely, the alternative fuels considered are kerosene enriched with hydrogen, methane and methane enriched with hydrogen. In comparison to kerosene, higher Lower Heating Values (LHVs) of these fuels lead to lower combustor temperatures for the same power input. Consequently, the thermal NOx pathway is weakened and the NOx mass flow generated is reduced. Nevertheless, these fuels with a higher LHV come with an increased operational risk that must be overcome before their implementation in aviation becomes possible.