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.