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

Two-dimensional temperature measurement in a high temperature and high pressure combustor using CT-TDLAS with a wide scanning laser at 1335-1375nm

Tunable diode laser absorption spectroscopy (TDLAS) technology is a developing method for temperature and species concentration measurements with the features of non-contact, high precision, high sensitivity, etc. The difficulty of two-dimensional (2D) temperature measurement in actual combustors has not yet been solved because of pressure broadening of absorption spectra, optical accessibility, etc. In this study, the combination of computed tomography (CT) and TDLAS with a wide scanning laser at 1335-1375nm has been applied to a combustor for 2D temperature measurement in high temperature of 300-2000K and high pressure of 0.1-2.5MPa condition. An external cavity type laser diode with wide wavelength range scanning at 1335-1375nm was used to evaluate the broadened H2O absorption spectra due to the high temperature and high pressure effect. The spectroscopic database in high temperature of 300-2000K and high pressure of 0.1-5.0MPa condition has been revised to improve the accuracy for temperature quantitative analysis. CT reconstruction accuracy was also evaluated in different cases, which presented the consistent temperature distribution between CT reconstruction and assumed distributions. The spatial and temporal distributions of temperature in the high temperature and high pressure combustor were measured successfully by CT-TDLAS using the revised spectroscopic database

Hierarchical temperature imaging using pseudoinversed convolutional neural network aided TDLAS tomography

As an in situ combustion diagnostic tool, Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography has been widely used for imaging of two-dimensional temperature distributions in reactive flows. Compared with the computational tomographic algorithms, Convolutional Neural Networks (CNNs) have been proofed to be more robust and accurate for image reconstruction, particularly in case of limited access of laser beams in the Region of Interest (RoI). In practice, flame in the RoI that requires to be reconstructed with good spatial resolution is commonly surrounded by low-temperature background. Although the background is not of high interest, spectroscopic absorption still exists due to heat dissipation and gas convection. Therefore, we propose a Pseudo-Inversed CNN (PI-CNN) for hierarchical temperature imaging that (a) uses efficiently the training and learning resources for temperature imaging in the RoI with good spatial resolution, and (b) reconstructs the less spatially resolved background temperature by adequately addressing the integrity of the spectroscopic absorption model. In comparison with the traditional CNN, the newly introduced pseudo inversion of the RoI sensitivity matrix is more penetrating for revealing the inherent correlation between the projection data and the RoI to be reconstructed, thus prioritising the temperature imaging in the RoI with high accuracy and high computational efficiency. In this paper, the proposed algorithm was validated by both numerical simulation and lab-scale experiment, indicating good agreement between the phantoms and the high-fidelity reconstructions.Comment: Submitted to IEEE Transactions on Instrumentation and Measuremen

A stability and spatial-resolution enhanced laser absorption spectroscopy tomographic sensor for complex combustion flame diagnosis

A novel stable laser absorption spectroscopy (LAS) tomographic sensor with enhanced stability and spatial resolution is developed and applied to complex combustion flame diagnosis. The sensor reduces the need for laser collimation and alignment even in extremely harsh environments and improves the stability of the received laser signal. Furthermore, a new miniaturized laser emission module was designed to achieve multi-degree of freedom adjustment. The full optical paths can be sampled by 8 receivers, with such arrangement, the equipment cost can be greatly reduced, at the same time, the spatial resolution is improved. In fact, 100 emitted laser paths are realized in a limited space of 200mm×200 mm with the highest spatial resolution of 1.67mm×1.67 mm. The stability and penetrating spatial resolution of the LAS tomographic sensor were validated by both simulation and field experiments on the afterburner flames. Tests under two representative experiment states, i.e., the main combustion and the afterburner operation states, were conducted. Results show that the error under the main combustion state was about 4.32% and, 5.38% at the afterburner operation state. It has been proven that this proposed sensor can provide better tomographic measurements for combustion diagnosis, as an effective tool for improving performances of afterburners

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