  Chemical Species Tomography (CST) has been widely used for in situ imaging of
critical parameters, e.g. species concentration and temperature, in reactive
flows. However, even with state-of-the-art computational algorithms the method
is limited due to the inherently ill-posed and rank-deficient tomographic data
inversion, and by high computational cost. These issues hinder its application
for real-time flow diagnosis. To address them, we present here a novel
CST-based convolutional neural Network (CSTNet) for high-fidelity, rapid, and
simultaneous imaging of species concentration and temperature. CSTNet
introduces a shared feature extractor that incorporates the CST measurement and
sensor layout into the learning network. In addition, a dual-branch
architecture is proposed for image reconstruction with crosstalk decoders that
automatically learn the naturally correlated distributions of species
concentration and temperature. The proposed CSTNet is validated both with
simulated datasets, and with measured data from real flames in experiments
using an industry-oriented sensor. Superior performance is found relative to
previous approaches, in terms of robustness to measurement noise and
millisecond-level computing time. This is the first time, to the best of our
knowledge, that a deep learning-based algorithm for CST has been experimentally
validated for simultaneous imaging of multiple critical parameters in reactive
flows using a low-complexity optical sensor with severely limited number of
laser beams.
