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10 August 2023 Neural network-based tomographic reconstruction of high-speed interferometric and Schlieren image data for density and velocity detection
Johannes Gürtler, Sami Tasmany, Robert Kuschmierz, Jakob Woisetschläger, Jürgen Czarske
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Proceedings Volume 12621, Multimodal Sensing and Artificial Intelligence: Technologies and Applications III; 126210L (2023) https://doi.org/10.1117/12.2673762
Event: SPIE Optical Metrology, 2023, Munich, Germany
Abstract
Multimodal sensing is presented based on the measurement of density oscillations in swirl-stabilized flames, where density is coupled to physical quantities like heat release rate or sound pressure and advection velocity. The measurement approach is a high-speed camera based interferometer combined with a multi-camera background oriented Schlieren system, both enabling line of sight detection and therefore require solution of the inverse problem by tomographic reconstruction. Those reconstructions are realized using a neural network, resulting in the three-dimensional distribution of local thermoacoustic oscillations. Finally, the advection velocity of those oscillating vortex structures is calculated by image correlation.
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Johannes Gürtler, Sami Tasmany, Robert Kuschmierz, Jakob Woisetschläger, and Jürgen Czarske "Neural network-based tomographic reconstruction of high-speed interferometric and Schlieren image data for density and velocity detection", Proc. SPIE 12621, Multimodal Sensing and Artificial Intelligence: Technologies and Applications III, 126210L (10 August 2023); https://doi.org/10.1117/12.2673762
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KEYWORDS
Neural networks
Tomography
Combustion
Image restoration
Interferometry
Flame
High speed cameras
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