| Summary: | Combustion modeling is performed with Conditional Source-term Estimation (CSE) using both Laminar Flamelet Decomposition (LFD) and Low-dimensional Manifolds. CSE with Laminar Flamelet Decomposition (LFD) is used in the Large Eddy Simulation (LES) context to study the non-premixed Sandia D-flame. The results show that the flame temperature and major species are well predicted with both steady and unsteady flamelet libraries. A mixed library composed of steady and unsteady flamelet solutions is needed to get a good prediction of NO. That the LFD model allows for tuning of the results is found to be significant drawback to this approach. CSE is also used with a Trajectory Generated Low-dimensional Manifold (TGLDM) to simulate the Sandia D-flame. Both GRI-Mech 3.0 and GRI-Mech 2.11 are found to be able to predict the temperature and major species well. However, only GRI-Mech 2.11 gives a good prediction of NO. That GRI-Mech 3.0 failed to give a good prediction of NO is in agreement with the findings of others in the literature. The Stochastic Particle Model (SPM) is used to extend the TGLDM to low temperatures where the original continuum TGLDM failed. A new method for generating a trajectory for the TGLDM by averaging different realizations together is proposed. The new TGLDM is used in simulations of a premixed laminar flame and a perfectly stirred reactor. The results show that the new TGLDM significantly improves the prediction. Finally, a time filter is applied to individual SPM realizations to eliminate the small time scales. These filtered realizations are tabulated into TGLDM which are then used to predict the autoignition delay time of a turbulent methane/air jet in RANS using CSE. The results are compared with shock tube experimental data. The TGLDMs incorporating SPM results are able to predict a certain degree of fluctuations in the autoignition delay time, but the magnitude is smaller than is seen in the experiments. This suggests that fluctuations in the ignition delay are at least in part due to turbulent fluctuations, which might be better predicted with LES. === Science, Faculty of === Mathematics, Department of === Graduate
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