Time-resolved laser absorption imaging of ethane at 2 kHz in unsteady partially premixed flames

Four-dimensional laser absorption cinematography of species and temperature dynamics at 2 kHz in reacting flows

A four-dimensional (4D) mid-infrared laser absorption imaging technique has been developed and demonstrated for quantitative, time-resolved, volumetric measurements of temperature and species concentration in dynamic combustion flows. This technique employs a dual high-speed infrared camera setup to capture turnable radiation from a quantum cascade laser near 4.85 µm to resolve rovibrational absorption transitions of carbon monoxide at two orthogonal projection angles. The laser is modulated with a customized waveform to adaptively resolve two target transitions with an increased density of data samples in proximity to the transition peaks, therefore ensuring accurate and quantitative spectral interpretation while minimizing the required frame rate. A 3D masked Tikhonov regularized inversion was performed to reconstruct spectrally resolved absorbance at every grid point of each frame, which enables subsequent interpretation of local gas properties in time. These methods are applied to achieve quantitative 4D cinematography of temperature and carbon monoxide in a propagating C2H4/O2 flame with a spatial pixel resolution of ∼70 µm and a temporal resolution of 2 kHz.

Quantitative Tomographic Laser Absorption Imaging of Atomic Potassium during Combustion of Potassium Chloride Salt and Biomass

Gaseous potassium (K) species play an important role in biomass combustion processes, and imaging techniques are powerful tools to investigate the related gas-phase chemistry. Here, laser absorption imaging of gaseous atomic K in flames is implemented using tunable diode laser absorption spectroscopy at 769.9 nm and a high-speed complementary metal oxide semiconductor (CMOS) camera recording at 30 kfps. Atomic K absorption spectra are acquired for each camera pixel in a field of view of 28 × 28 mm at a rate of 100 Hz. The technique is used to determine the spatial distribution of atomic K concentration during the conversion of potassium chloride (KCl) salt and wheat straw particles in a laminar premixed CH₄/air flame with an image pixel resolution of up to 120 μm. Due to axisymmetry in setup geometry and, consequently, atomic K distributions, the radial atomic K concentration fields could be reconstructed by one-dimensional tomography. For the KCl sample, the K concentration field was in excellent agreement with previous point measurements. In the case of wheat straw, atomic K concentrations of around 3 ppm were observed in a cylindrical flame during devolatilization. In the char conversion phase, a spherical layer of atomic K, with concentrations reaching 25 ppm, was found within 5 mm of the particle surface, while the concentration rapidly decreased to sub-ppm levels along the vertical axis. In both cases, a thin (∼1 mm) layer without any atomic K was observed in close vicinity to the particle, suggesting that the potassium was initially not released in its atomic form.

Physics-trained neural network for sparse-view volumetric laser absorption imaging of species and temperature in reacting flows

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.

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 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.