On the regularization for nonlinear tomographic absorption spectroscopy

Dai, Jinghang; Yu, Tao; Xu, Lijun

doi:10.1016/j.jqsrt.2017.11.016

Cited by 27 publications

(5 citation statements)

References 32 publications

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“…Häber et al retrieved the tomographic multispecies visualization of laminar and turbulent methane/air diffusion flames based on a kind of variation Tikhonov's regularization (Häber et al, 2020a;Häber et al, 2020b). As another type of priori information of temperature field, Total Variance (TV) indicates the sparseness of field (Cai et al, 2013b;Dai et al, 2018). Compared with Tikhonov's regularization, TV regularization shows better performance in preserving sharp discontinuities between distinct regions of reconstruction domain, which is capable for providing representative features of combustion such as the flame front (Rudin et al, 1992;Strong and Chan, 2003).…”

Section: Wfmentioning

confidence: 99%

A Survey for 3D Flame Chemiluminescence Tomography: Theory, Algorithms, and Applications

Jin

Situ

2022

Front. Photon.

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Combustion diagnostics play an essential role in energy engineering, transportation, and aerospace industries, which has great potential in combustion efficiency improvement and polluting emission control. The three-dimensional (3D) visualization of the combustion field and the measurement of key physical parameters such as temperature, species concentration, and velocity during the combustion process are important topics in the field of combustion diagnostics. Benefiting from the non-contact and non-intrusive advantages of the optical detection method as well as the advantages of the 3D full-field measurement of the measured field by computational tomography, flame chemiluminescence tomography (FCT) has the ability to realize non-intrusive and instantaneous 3D quantitative measurement and 3D full-field visualization of key physical parameters in the combustion process, which has crucial research significance in combustion diagnostics. In this study, we review the progress of FCT technique. First, we provide an extensive review of practical applications of FCT in state-of-the-art combustion diagnostics and research. Then, the basic concepts and mathematical theory of FCT are elaborated. Finally, we introduce the conventional reconstruction algorithm and proceed to more popular artificial intelligence-based algorithms.

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Section: Wfmentioning

confidence: 99%

A Survey for 3D Flame Chemiluminescence Tomography: Theory, Algorithms, and Applications

Jin

Situ

2022

Front. Photon.

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“…However, as in the case of linear (monochromatic) AT, A is either underdetermined or ill-conditioned so prior information must be added to regularize the estimates. Previous work has employed a gradient-based penalty [30] or heuristic filter [31], to promote spatial smoothness, or a total variation penalty [9], to obtain smooth regions separated by a sharp discontinuity (characteristic of a flame front, for instance).…”

Section: Nonlinear Hatmentioning

confidence: 99%

Multiparameter gas sensing with linear hyperspectral absorption tomography

Grauer¹,

Emmert²,

Sanders³

et al. 2019

Meas. Sci. Technol.

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Hyperspectral absorption tomography (HAT) reconstructs the distribution of key gas parameters, including composition, pressure, and temperature, from multi-beam absorbance data with numerous spectral resolution elements. There is a nonlinear relationship between the parameters of interest and the spectral absorption coefficient, which must be incorporated into the tomography algorithm. Nonlinear HAT simultaneously reconstructs the composition and temperature of a gas by minimizing a single nonconvex objective function, which combines the light attenuation and spectroscopy models, using a metaheuristic technique. The time required for this computation depends, strongly, on the assumed heuristics, but the high computational cost limits the problem size and, hence, the obtainable spatial resolution. Conversely, linear HAT reconstructs the absorption coefficient for each measurement wavenumber, individually, exploiting the linear structure of the underlying tomography problem. Local spectra are then post-processed with a spectroscopic model to recover multiple parameters. The linear technique enables accurate reconstructions on a high-resolution grid by way of an established statistical imaging algorithm. Moreover, local spectra can be employed to gauge phenomena such as multi-species broadening and line mixing with a calibrated regression model. We simulate linear and nonlinear HAT and reconstruct experimental absorbance data using the former approach to demonstrate its superior performance. Nonlinear reconstructions required a 100-fold computational effort compared to linear HAT. In our experimental test, we reconstructed the mole fraction, pressure, and temperature of water vapor in a stagnation flow, which represents the first three-parameter laser absorption tomography experiment. Simulated and experimental results in our paper make a comprehensive case for linear HAT compared to the nonlinear method.

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“…However, limited by the insufficient number of line-ofsight measurements, tomographic data inversion is inherently ill-posed and rank-deficient [6], which causes less accuracy and increased instability in the reconstructed images. Although various computational tomographic algorithms impose determined a priori for regularization [7][8][9], it is still challenging to eliminate the effects of artefacts. Instead of highly depending on the mathematical formulation, the deep learning technique provides an alternative approach to solve these ill-posed inverse problems [10].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Aided Chemical Species Tomography for Dynamic Temperature Imaging

Zhang

Enemali

et al. 2022

2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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Chemical Species Tomography (CST) using Tunable Diode Laser Absorption Spectroscopy (TDLAS) is an in-situ technique to reconstruct the two-dimensional temperature distributions in combustion diagnosis. However, limited by the lack of projection data, traditionally computational tomographic algorithms are inherently rankdeficient, causing artefacts and severe uncertainty in the retrieved images. Recently, data-driven approaches, such as deep learning algorithms, have been validated to be more accurate and stable for CST. However, most attempts modelled the phantoms using two-dimensional Gaussian profiles to construct the training set, enabling reconstruction of only simple and static temperature fields and can seldom retrieve the dynamic and instantaneous temperature imaging. To address this problem, we use Fire Dynamics Simulator (FDS) to simulate the dynamic and fire-driven reacting flows for training set construction. Based on this training set, a Convolutional Neural Network (CNN) is designed. This newly introduced method is validated by numerical simulation, indicating good accuracy and sensitivity in monitoring dynamic flames.

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On the regularization for nonlinear tomographic absorption spectroscopy

Cited by 27 publications

References 32 publications

A Survey for 3D Flame Chemiluminescence Tomography: Theory, Algorithms, and Applications

A Survey for 3D Flame Chemiluminescence Tomography: Theory, Algorithms, and Applications

Multiparameter gas sensing with linear hyperspectral absorption tomography

Convolutional Neural Network Aided Chemical Species Tomography for Dynamic Temperature Imaging

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