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

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 co...

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Bibliographic Details
Published in:Optics letters 2020-04, Vol.45 (8), p.2447-2450
Main Authors: Wei, Chuyu, Schwarm, Kevin K, Pineda, Daniel I, Spearrin, R Mitchell
Format: Article
Language:English
Subjects:
Absorption spectra
Absorptivity
Artificial neural networks
Cascade lasers
Computer simulation
Flames
Flow velocity
Forecasting
Infrared cameras
Infrared imaging
Infrared lasers
Lasers
Machine learning
Methane
Neural networks
Pixels
Reacting flow
Temperature distribution
Three dimensional imaging
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Summary: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 (${\lt}\text{cm}$
ISSN:0146-9592
1539-4794
DOI:10.1364/OL.391834