CSTNet: A Dual-Branch Convolutional 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 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.Comment: Submitted to IEEE Transactions on Neural Networks and Learning System

Relative Entropy Regularised TDLAS Tomography for Robust Temperature Imaging

Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography has been widely used for in situ combustion diagnostics, yielding images of both species concentration and temperature. The temperature image is generally obtained from the reconstructed absorbance distributions for two spectral transitions, i.e. two-line thermometry. However, the inherently ill-posed nature of tomographic data inversion leads to noise in each of the reconstructed absorbance distributions. These noise effects propagate into the absorbance ratio and generate artefacts in the retrieved temperature image. To address this problem, we have developed a novel algorithm, which we call Relative Entropy Tomographic RecOnstruction (RETRO), for TDLAS tomography. A relative entropy regularisation is introduced for high-fidelity temperature image retrieval from jointly reconstructed two-line absorbance distributions. We have carried out numerical simulations and proof-of-concept experiments to validate the proposed algorithm. Compared with the well-established Simultaneous Algebraic Reconstruction Technique (SART), the RETRO algorithm significantly improves the quality of the tomographic temperature images, exhibiting excellent robustness against TDLAS tomographic measurement noise. RETRO offers great potential for industrial field applications of TDLAS tomography, where it is common for measurements to be performed in very harsh environments.Comment: Preprint submitted to IEEE Transactions on Instrumentation and Measuremen

Chemical species tomographic imaging of the vapour fuel distribution in a compression-ignition engine

This article reports the first application of chemical species tomography to visualise the in-cylinder fuel vapour concentration distribution during the mixing process in a compression-ignition engine. The engine was operated in motored conditions using nitrogen aspiration and fired conditions using a gasoline-like blend of 50% iso-dodecane and 50% n-dodecane. The tomography system comprises 31 laser beams arranged in a co-planar grid located below the injector. A novel, robust data referencing scheme was employed to condition the acquired data for image reconstruction using the iterative Landweber algorithm. Tomographic images were acquired during the compression stroke at a rate of 13 frames per crank angle degree within the same engine cycle at 1200 r min−1. The temperature-dependent fuel evaporation rate and mixing evolution were observed at different injection timings and intake pressure and temperature conditions. An initial cross-validation of the tomographic images was performed with planar laser-induced fluorescence images, showing good agreement in feature localisation and identification. This is the first time chemical species tomography using near-infrared spectroscopic absorption has been validated under engine conditions, and the first application of chemical species tomography to a compression-ignition engine