Sungbae Park1
Continuous combustion systems common in propulsion and power generation applications are susceptible to thermoacoustic instability, which occurs under lean burn conditions close to the flammability limit where most emissions and efficiency benefits are achieved, and near stoichiometry where often high power density can be realized. The instability is undesirable because the accompanying large pressure and heat release rate oscillations lead to high levels of acoustic noise and vibration as well as structural damage. Active control is one approach using which such instabilities can be mitigated. Over the past five to ten years, it has been shown conclusively through several lab-scale studies that active control is highly successful in suppressing the pressure oscillations. This success has set the stage for transition of the technology from laboratories to large-scale applications in propulsion and power generation.
This paper provides some of the building blocks for enabling this transition. The first building block concerns the modeling of hydrodynamics and its interactions with the other components that contribute to combustion dynamics. The second is the impact of active control on emissions even while suppressing the pressure instability. The third is the evaluation of model-based active controllers in realistic combustors with configurations that include swirl, large convective delays and unknown changes in the operating conditions. The above three building blocks are investigated experimentally in three different configurations. The first is a 2D backward facing step combustor, constructed at MIT, with the goal of investigating the flame-vortex interactions and the impact of active control on emissions. The second is a dump combustor, constructed at University of Maryland, so as to reproduce more realistic ramjet conditions. The third is an industrial swirl-stabilized combustor, constructed at University of Cambridge, to mimic realistic industrial gas combustor configurations which typically include large convective time delays, swirl, and on-line changes in the operating conditions. Results obtained from these three configurations show that through an understanding of the underlying physics and reduced-order modeling, one can design an appropriate actuation, sensing and control algorithm, all of which lead to a model-based active control that reduces pressure oscillations to background noise.