Four-Dimensional Measurement of Thermoacoustic Oscillations

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Thermoacoustic oscillation troubles combustion engineers since the early days of the Apollo program. Today there is an increasing awareness regarding the necessity of a dramatic reduction of pollutant emissions, becoming important to thermal turbomachinery used in ground-based gas turbines and aero-engines aiming towards climate-neutral aviation and sustainable fuels. State-of-the art technology increases the susceptibility to thermoacoustic oscillations in these low-emission machines. In a previous project funded by FWF and DFG, our joint groups in Graz and Dresden proved that a so-called “camera-based laser interferometric vibrometer” (CLIV) can quantitatively record these density and heat release oscillations. But, non-symmetric flames require multidirectional observation in order to enable three-dimensional reconstructions of local density structures, sound production and convection velocities. This need is in conflict with the limited field of view (FOV) of CLIV. The use of multidirectional Background-Oriented-Schlieren method (3D-BOS) could solve this problem, but needs calibration by laser interferometric vibrometry. Additionally, a density tagging velocimetry (DTV) by multiple exposure technique could add information on the flame dynamics and thus the fourth dimension. A most promising numerical approach to solve this four-dimensional ill-posed problem with limited-angle projections and missing data is the application of specially trained, deep neural networks (DNN).HYPOTHESIS: The underlying hypothesis of this project claims that four-dimensional detection of local thermoacoustic oscillations, based on the combination of CLIV with 3D-BOS, DTV and DNN as binding element, will reveal local and coupled information on combustion, acoustics and fluid dynamics. METHODS: The project is organized in a conventional way from the point of view of time schedule - design or redesign, construction and procurement, testing and measurement, and final experimental qualification.LEVEL OF ORGINALITY: This new approach is supported by the recent paradigm shift in digital camera technology and the rapid development of artificial intelligence (deep learning), both due to the increased computational power available. The DNN approach also adds an innovative element to the combination of the other, more established techniques, and will booster experimental combustion research, as well as, aeroacoustics and fluid dynamics.