Tomographic imaging of carbon dioxide in the exhaust plume of large commercial aero-engines.

We report here the first implementation of chemically specific imaging in the exhaust plume of a gas turbine typical of those used for propulsion in commercial aircraft. The method used is chemical species tomography (CST) and the target species is CO2, absorbing in the near-infrared at 1999.4 nm. A total of 126 beams propagate transverse to the plume axis, along 7 m paths in a coplanar geometry, to probe a central region of diameter ≈1.5m. The CO2 absorption spectrum is measured using tunable diode laser spectroscopy with wavelength modulation, using the second harmonic to first harmonic (2f/1f) ratio method. The engine is operated over the full range of thrust, while data are recorded in a quasi-simultaneous mode at frame rates of 1.25 and 0.3125 Hz. Various data inversion methodologies are considered and presented for image reconstruction. At all thrust levels a persistent ring structure of high CO2 concentration is observed in the central region of the measurement plane, with a raised region in the middle of the plume assumed to be due to the engine's boat tail. With its potential to target various exhaust species, the CST method outlined here offers a new approach to turbine combustion research, turbine engine development, and aviation fuel research and development.

CSTNet: A Dual-Branch Convolutional Neural Network for Imaging of Reactive Flows Using Chemical Species Tomography.

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 convolutional neural network, namely CSTNet, for high-fidelity, rapid, and simultaneous imaging of species concentration and temperature using CST. CSTNet introduces a shared feature extractor that incorporates the CST measurements and sensor layout into the learning network. In addition, a dual-branch decoder with internal crosstalk, which automatically learns the naturally correlated distributions of species concentration and temperature, is proposed for image reconstructions. 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 reconstruction accuracy and robustness to measurement noise. This is the first time, to the best of our knowledge, that a deep learning-based method for CST has been experimentally validated for simultaneous imaging of multiple critical parameters in reactive flows using a low-complexity optical sensor with a severely limited number of laser beams.