In this work, laser absorption spectroscopy techniques are expanded in spatial resolution capability by utilizing a high-speed infrared camera to image flow fields backlit with tunable mid-wave infrared laser radiation. The laser absorption imaging (LAI) method yields spectrally-resolved and spatially-rich datasets from which quantitative species and temperature profiles can be generated using tomographic reconstruction. Access to the mid-wave infrared (3–5 µm) enables imaging of fuels, intermediates, and products of combustion in canonical small-diameter flames (< 1 cm). Example 1D measurements and 2D reconstructions of ethane (3.34 µm), carbon monoxide (4.97 µm), and carbon dioxide (4.19 µm) in an axisymmetric laminar flame are presented and discussed. LAI is shown to significantly enhance spatio-temporal data bandwidth (∼400 simultaneously sampled lines-of-sight) and resolution (∼50 µm) compared to other tomographic absorption spectroscopy techniques, and with a simplified optical arrangement.

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 ( < 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 ∼ 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.

A deep learning method for laser absorption tomography was developed to effectively integrate physical priors related to flow-field thermochemistry and transport. Mid-fidelity reacting flow simulations were coupled with a forward molecular absorption model to train a deep neural network that performs the tomographic inversion of laser absorption images to predict temperature and species fields in flames. The method was evaluated through numerical simulation and experimental testing in benchtop laminar flames. The target flow-fields involved a spatially-convolved laminar ethylene-flame doublet, backlit with tunable radiation from a quantum cascade laser near 4.85 µm probing rovibrational absorption transitions of carbon monoxide. 2D images were collected at 11 different projection angles, yielding an aggregate of 50,688 unique lines of sight capturing the scene with a pixel resolution of approximately 70 µm. A convolutional neural network was introduced to efficiently generate temperature and species profiles and trained with a large dataset of large-eddy simulations of laminar flames at variable conditions. The learning-based approach to the inversion problem was found to more accurately predict species and temperature fields of the flame with fewer projection angles, reduce convergence time, and expand the field domain relative to classical linear tomography.