Toward Customized Spatial Resolution in TDLAS Tomography

Liu, Chang; Tsekenis, Stylianos-Alexios; Polydorides, Nick; McCann, H.

doi:10.1109/jsen.2018.2884085

Cited by 30 publications

(22 citation statements)

References 28 publications

(38 reference statements)

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“…Hence, the prior gives a lower bound for the spatial resolution, and when this lower bound is reached additional measurement beams do not significantly improve resolution. This agrees with studies of the influence of the beam arrangement on resolution using Tsekenis approach [31]: the resolution approaches a limiting value, although this could not directly be explained using Tsekenis empirical approach.…”

Section: Resultssupporting

confidence: 86%

Quantifying the spatial resolution of the maximum a posteriori estimate in linear, rank-deficient, Bayesian hard field tomography

Emmert,

Wagner,

Daun

2020

Preprint

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Image based diagnostics are interpreted in the context of spatial resolution. The same is true for tomographic image reconstruction. Current empirically driven approaches to quantify spatial resolution [1] rely on a deterministic formulation based on point-spread functions which neglect the statistical prior information, that is integral to rank-deficient tomography. We propose a statistical spatial resolution measure based on the covariance of the reconstruction (point estimate) and show that the prior information acts as a lower limit for the spatial resolution. Furthermore, the spatial resolution measure can be employed for designing tomographic systems under consideration of spatial inhomogeneity of spatial resolution.

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Section: Resultssupporting

confidence: 86%

Quantifying the spatial resolution of the maximum a posteriori estimate in linear, rank-deficient, Bayesian hard field tomography

Emmert,

Wagner,

Daun

2020

Preprint

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“…where 𝑅 , = 𝑋 , + 𝑌 , (17) Each sample of mux S2f/1f shown in Fig. 2 is demodulated from the accumulated c modulation periods.…”

Section: B Wms-based Qp Sensing Scheme For Cstmentioning

confidence: 99%

“…Industrial CST is commonly implemented in harsh environments, suffering from significant noise in the line-ofsight (LoS) TDLAS measurements [14][15][16], dozens of which are required in order to achieve adequate spatial resolution [17]. The major sources of noise include electronic noise, optical noise, and environmental noise [18,19].…”

Section: Introductionmentioning

confidence: 99%

Cost-Effective Quasi-Parallel Sensing Instrumentation for Industrial Chemical Species Tomography

Enemali

Zhang

McCann

et al. 2022

IEEE Trans. Ind. Electron.

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“…Given no prior information of the target field, it has been shown that a regular beam array, i.e. equiangular projections and equispaced laser beams within each projection, leads to uniform distribution of the sampling deficiency across the RoI [32]. Therefore, a parallel beam arrangement is adopted in this work, as shown in Fig.…”

Section: Numerical Simulationmentioning

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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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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Toward Customized Spatial Resolution in TDLAS Tomography

Cited by 30 publications

References 28 publications

Quantifying the spatial resolution of the maximum a posteriori estimate in linear, rank-deficient, Bayesian hard field tomography

Quantifying the spatial resolution of the maximum a posteriori estimate in linear, rank-deficient, Bayesian hard field tomography

Cost-Effective Quasi-Parallel Sensing Instrumentation for Industrial Chemical Species Tomography

Relative Entropy Regularized TDLAS Tomography for Robust Temperature Imaging

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