Large Eddy Simulation of Turbulent Premixed and Partially Premixed Combustion

In this thesis, a computational fluid dynamics (CFD) approach is used to study turbulent premixed and partially premixed combustion. The CFD approach is based on large eddy simulation (LES) in which the large-scale structures of the flow are resolved on a grid, leaving only the small-scale structures (subgrid scales) to be modeled. The combustion modelling is based on the flamelet concept in which the scale separation of the flow and the chemistry is assumed. This thesis work is made up of the following parts. First, focusing on the turbulent premixed combustion, the level-set G-equation flamelet model is applied to study high density ratio flames without explicit filter in order to capture the effect of thin reaction zone embedded in the LES subgrid. Conventional fractional step methods are shown to be numerically instable for high density ratio flames. A highly robust numerical method, which is known as the ghost fluid method (GFM), is implemented. The G-equation based flamelet model and the ghost fluid method are evaluated on a lean propane/air premixed flame stabilized by a bluff body. The methods are shown to be able to capture the density ratio effect on the flame dynamics, including the near flame holder wrinkling due to Kelvin Helmholtz (KH) instabilities, the downstream large scale wrinkling due to the lower frequency Bénard/von-Karman (BVK) instability at low density ratio conditions, and the suppression of BVK instability at high density ratio conditions. In LES, spatial filtering of the reaction zone leads to thickening of the reaction zone. It is shown that thickening of the reaction zone can lead to significant under-prediction of the turbulence intensity at high density ratio conditions. The effects of flame thickening are further studied for the cases of the flame/vortex interaction and hydrodynamic instability. Essentially, with thickening of the reaction zones, the development of flame wrinkling and hydrodynamic instability are suppressed. Second, focusing on the partially premixed combustion, a two-scalar flamelet approach for LES is developed. In this approach, the trailing edge of the flame is assumed to be controlled by diffusion of mass and heat and thereby it is modelled using a steady diffusion flamelet model. The stabilization of the flame is due to the propagation of the leading premixed flame front (triple flame) in turbulent flows. The leading premixed flame is modelled using the level-set G-equation. This model is applied to simulate partially premixed flames of various fuels in a conical burner to understand the structure and the dynamics of the turbulent partially premixed flames operating in the flamelet regime and in certain cases with thicker reaction zones and local flame extinction. It is found that in general partially premixed flames are more stable when the level of partial premixing of air to the fuel stream decreases. However, at high Reynolds number conditions, an optimal level of partial premixing is found where the flame is most stable. There are two possible flammable surfaces in the partially premixed flames in the conical burner, where the mixture is in stoichiometric condition. At low Reynolds number flows, the inner flame is observed experimentally whereas it is not possible to stabilize at high Reynolds number flows. Numerical results based on the two-scalar flamelet model correctly predicted the blowoff of the inner flame at high Reynolds number conditions. It is well known that LES results are sensitive to the inflow conditions. The sensitivity of LES results to inflow turbulence and the mean flow profiles is systematically investigated based on the conical burner. It is shown that in the proximity of the burner the onset of the flow instability is not only dependent on the shape of the mean profiles, but also on the anisotropy of the inflow turbulence and the integral length scale of the inflow turbulence. This calls for special care in validation of LES models and more detailed experimental data for the inflow conditions when preparing for the database for model development and validation