We present a data-driven method for the early detection of thermoacoustic instabilities. Recurrence quantification analysis is used to calculate characteristic combustion features from short-length time series of dynamic pressure sensor data. Features like recurrence rate are used to train support vector machines to detect the onset of instability a few hundred milliseconds in advance. The performance of the proposed method is investigated on experimental data from a representative LOX/H2 research thrust chamber. In most cases, the method is able to timely predict two types of thermoacoustic instabilities on test data not used for training. The results are compared with state-of-the-art early warning indicators.
Hot-fire tests were performed with a single-injector research combustor featuring a large optical access (255 × 38 mm) for flame imaging. These tests were conducted with the propellant combination of liquid oxygen and compressed natural gas (LOX/CNG) at conditions relevant for main- and upper-stage engines. The large optical access enabled synchronized flame imaging using OH* and CH* radiation wavelengths covering an area of the combustion chamber from the injection plane to shortly before the contraction section of the nozzle for two sets of operating conditions. Combined with temperature, pressure and unsteady pressure measurements, these data provide a high-quality basis for validation of numerical modeling. Flame width and opening angle were extracted from the imaging in order to determine the flame topology. A two dimensional Rayleigh Index was calculated for an acoustically unexcited and excited interval. These Rayleigh Indices are in good agreement with the thermoacoustic state of the chamber.
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