Two-step tomographic reconstructions of temperature and species concentration in a flame based on laser absorption measurements with a rotation platform

Huang, Xia; Kan, Ruifeng; Xu, Zhenyu; He, Yabai; Liu, Jianguo; Chen, Bing; Yang, Chenguang; Lu, Yuexiang; Wei, Min; Zhang, Guangle

doi:10.1016/j.optlaseng.2016.09.005

Cited by 49 publications

(16 citation statements)

References 28 publications

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“…This work focuses on substantial improvement on temperature imaging in TDLAS tomography. The improved quality of temperature images will also contribute to a better accuracy of the gas concentration distributions that can be subsequently solved by linear tomographic algorithms [15]. Fig.…”

Section: Resultsmentioning

confidence: 99%

“…To mitigate the instability of the reconstructions, a variety of computational tomographic algorithms have been applied in TDLAS tomography, e.g. Algebraic Reconstruction Technique (ART) [15], Simultaneous Algebraic Reconstruction Technique (SART) [3,16], Landweber algorithm [4,17], and Tikhonov regularisation [18], by formulating the inverse problem with a heuristically determined prior, e.g. smoothness of the true temperature distributions.…”

Section: Introductionmentioning

confidence: 99%

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Relative Entropy Regularized TDLAS Tomography for Robust Temperature Imaging

Bao

Zhang

Enemali

et al. 2021

IEEE Trans. Instrum. Meas.

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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 wellestablished 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.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Relative Entropy Regularized TDLAS Tomography for Robust Temperature Imaging

Bao

Zhang

Enemali

et al. 2021

IEEE Trans. Instrum. Meas.

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“…In the state-of-the-art simulation of laminar flames [5], [6], [8], [15], [17], [18], the cross section temperature distribution has been assumed to be a Gaussian profile or the combination of multiple Gaussian profiles. We follow this assumption and use Gaussian profiles to simulate the laminar flames in our study.…”

Section: A Datasetmentioning

confidence: 99%

“…1) algebraic techniques based on linear back projection, e.g., algebraic reconstruction technique (ART) [5] and Landweber algorithm [1], [6]; 2) regularization methods, e.g., Tikhonov regularization [7]; 3) global optimization, e.g., simulated annealing [8]; 4) statistical inversion, e.g., covariance estimation [9]; 5) dimensional reduction techniques, e.g., surrogate functions method [10]. As discussed in [11], these algorithms can, to some extent, mitigate the difficulty of robust image reconstruction in CST.…”

Section: Introductionmentioning

confidence: 99%

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

Jiang

Zhang

et al. 2023

IEEE Trans. Neural Netw. Learning Syst.

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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 rankdeficient 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.

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“…A relaxation factor is necessary in SART to keep the noise low and to reduce problems with streak artifacts. More applications of ART in the linear LAS tomographic measurements can be found in (144,(147)(148)(149)(150)(151)(152)(153).…”

Section: Tomographic Algorithmsmentioning

confidence: 99%

Laser absorption spectroscopy for combustion diagnosis in reactive flows: A review

Liu

2018

Applied Spectroscopy Reviews

167

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Laser absorption spectroscopy (LAS) has been rapidly developed and widely applied to combustion diagnosis in recent decades. As a costeffective tool for measuring multiple combustion parameters, LAS provides unique properties in terms of accuracy and sensitivity for understanding the reactions and kinetics in reactive flows. Line-ofsight and tomographic LAS techniques have stimulated numerous applications and been proved to be robust for in situ combustion diagnosis in uniform and non-uniform combustion fields, respectively. This review highlights the breakthroughs in the evolution of LAS techniques from the viewpoints of key principles, sensors and instrumentations developed for combustion diagnosis, with particular emphasis on a series of spatially-resolved LAS techniques with their recent applications on obtaining high-fidelity measurement results with minimal intrusion to the practical combustors. Along the way, we note some challenges and requirements for further development of the LAS-based combustion diagnosis.

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Two-step tomographic reconstructions of temperature and species concentration in a flame based on laser absorption measurements with a rotation platform

Cited by 49 publications

References 28 publications

Relative Entropy Regularized TDLAS Tomography for Robust Temperature Imaging

Relative Entropy Regularized TDLAS Tomography for Robust Temperature Imaging

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

Laser absorption spectroscopy for combustion diagnosis in reactive flows: A review

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