Large Eddy Simulation of premixed and partially premixed combustion
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Large Eddy Simulation (LES) of bluff body stabilized premixed and partially premixed combustion close to the flammability limit is carried out in this thesis. The LES algorithm has no ad-hoc adjustable model parameters and is able to respond automatically to variations in the inflow conditions. Algorithm validation is achieved by comparison with reactive and non-reactive experimental data. In the reactive flow, two scalar closure models, Eddy Break-Up (EBULES) and Linear Eddy Mixing (LEMLES), are used and compared. Over important regions, the flame lies in the Broken Reaction Zone regime. Here, the EBU model assumptions fail. The flame thickness predicted by LEMLES is smaller and the flame is faster to respond to turbulent fluctuations, resulting in a more significant wrinkling of the flame surface. As a result, LEMLES captures better the subtle effects of the flame-turbulence interaction. Three premixed (equivalence ratio = 0.6, 0.65, and 0.75) cases are simulated. For the leaner case, the flame temperature is lower, the heat release is reduced and vorticity is stronger. As a result, the flame in this case is found to be unstable. In the rich case, the flame temperature is higher, and the spreading rate of the wake is increased due to the higher amount of heat release Partially premixed combustion is simulated for cases where the transverse profile of the inflow equivalence ratio is variable. The simulations show that for mixtures leaner in the core the vortical pattern tends towards anti-symmetry and the heat release decreases, resulting also in instability of the flame. For mixtures richer in the core, the flame displays sinusoidal flapping resulting in larger wake spreading. More accurate predictions of flame stability will require the use of detailed chemistry, raising the computational cost of the simulation. To address this issue, a novel algorithm for training Artificial Neural Networks (ANN) for prediction of the chemical source terms has been implemented and tested. Compared to earlier methods, the main advantages of the ANN method are in CPU time and disk space and memory reduction.