Chuyu Wei

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.

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.