Flame Transfer Functions and Dynamics of a Closely Confined Premixed Bluff Body Stabilized Flame With Swirl

Near-Blowoff Dynamics of a Bluff-Body Stabilized Flame

This paper describes an experimental investigation of the dynamics of a near-blowoff, bluff-body stabilized flame. This work is motivated by a number of prior observations showing that near-blowoff flames exhibit enhanced unsteadiness. Laser sheet imaging studies and particle image velocimetry velocity field measurements show that the transient dynamics of these flames occur in two distinct sta...

Part III Swirl Stabilized Turbulent Flame Dynamics

Bifurcation of flame structure in a lean-premixed swirl-stabilized combustor: transition from stable to unstable flame

The present work addresses unsteady flame dynamics in a lean-premixed swirl-stabilized combustor, with attention focused on the transition of flame structure from a stable to an unstable state. It was found that the inlet temperature and equivalence ratio are the two most important variables determining the stability characteristics of the combustor. A slight increase in the inlet mixture tempe...

Simulation of Nitrogen Emissions in a Premixed Hydrogen Flame Stabilized on a Low Swirl Burner

There is considerable interest in developing fuel-flexible, low emissions turbines for power generation. One approach is based on burning a variety of lean premixed fuels with relatively low flame temperatures. Such flames can be stabilized in a low swirl burner configuration, for example, using a variety of fuels such as pure hydrogen and hydrogen-seeded hydrocarbon mixtures. However, many hyd...

Optimal artificial neural networks and tabulation methods for chemistry representation in LES of a bluff-body swirl-stabilized flame

Large-eddy simulations (LES) of the Sydney bluff-body swirl-stabilized methane–hydrogen flame are performed, employing two chemistry representation methods, namely a conventional structured tabulation technique and artificial neural networks (ANNs). A generalized method for the generation of optimal artificial networks (OANNs) has been proposed by Ihme et al. [M. Ihme, A.L. Marsden, H. Pitsch, ...