|dc.description.abstract||Combustor blowout is a very serious concern in modern land-based and aircraft engine combustors. The ability to sense blowout precursors can provide significant payoffs in engine reliability and life. The objective of this work is to characterize the blowout phenomenon and develop a sensing methodology which can detect and assess the proximity of a combustor to blowout by monitoring its acoustic signature, thus providing early warning before the actual blowout of the combustor. The first part of the work examines the blowout phenomenon in a piloted jet burner. As blowout was approached, the flame detached from one side of the burner and showed increased flame tip fluctuations, resulting in an increase in low frequency acoustics. Work was then focused on swirling combustion systems. Close to blowout, localized extinction/re-ignition events were observed, which manifested as bursts in the acoustic signal. These events increased in number and duration as the combustor approached blowout, resulting an increase in low frequency acoustics. A variety of spectral, wavelet and thresholding based approaches were developed to detect precursors to blowout.
The third part of the study focused on a bluff body burner. It characterized the underlying flame dynamics near blowout in greater detail and related it to the observed acoustic emissions. Vorticity was found to play a significant role in the flame dynamics. The flame passed through two distinct stages prior to blowout. The first was associated with momentary strain levels that exceed the flames extinction strain rate, leading to flame holes. The second was due to large scale alteration of the fluid dynamics in the bluff body wake, leading to violent flapping of the flame front and even larger straining of the flame. This led to low frequency acoustic oscillations, of the order of von Karman vortex shedding. This manifested as an abrupt increase in combustion noise spectra at 40-100 Hz very close to blowout. Finally, work was also done to improve the robustness of lean blowout detection by developing integration techniques that combined data from acoustic and optical sensors.||en_US