Deep neural network inversion for 3D laser absorption imaging of methane in reacting flows

Abstract

Mid-infrared laser absorption imaging of methane in flames is performed with a learning-based approach to the limited view-angle inversion problem. A deep neural network is trained with superimposed Gaussian field distributions of spectral absorption coefficients, and the prediction capability is compared to linear tomography methods at a varying number of view angles for simulated fields representative of a flame pair. Experimental 3D imaging is demonstrated on a methane-oxygen laminar flame doublet (${\lt}\text{cm}$<cm) backlit with tunable radiation from an interband cascade laser near 3.16 µm. Spectrally resolved data at each pixel provide for species-specific projected absorbance. 2D images were collected at six projection angles on a high-speed infrared camera, yielding an aggregate of 27,648 unique lines of sight capturing the scene with a pixel resolution of $\sim 70$∼70 µm. Mole fraction measurements are inferred from the predicted absorption coefficient images using an estimated temperature field, showing consistency with expected values from reactant flow rates. To the authors' knowledge, this work represents the first 3D imaging of methane in a reacting flow.