A quality-hierarchical temperature imaging network for TDLAS tomography

Si, Jingjing; Fu, Gengchen; Zhang, Rui; Liu, Chang

doi:10.1109/i2mtc50364.2021.9459814

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…In the realm of tomography methods, optics and acoustics are two typical representatives. The optical method primarily involves tunable diode laser absorption spectroscopy (TD-LAS) [9][10][11]. TDLAS is based on the characteristic spectral absorption of molecules.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Reconstruction of Gas Concentration and Temperature Using Acoustic Tomography

Liu,

Zhu,

Deng

et al. 2024

Sensors

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Acoustic tomography utilizes sensor arrays to collect sound wave signals, enabling non-contact measurement of physical parameters within an area of interest. Compared to optical technologies, acoustic tomography offers the advantages of low cost, low maintenance, and easy installation. Current research in acoustic tomography mainly focuses on reconstruction algorithms for temperature fields, while monitoring the composition and concentration of gases is significant for ensuring safety and improving efficiency, such as in scenarios like boiler furnaces and aviation engine nozzles. In excitable gases, the speed of sound exhibits an S-shaped curve that changes with frequency, a characteristic that could be potentially useful for acoustic tomography. Therefore, this study primarily discusses the quantitative calculation of gas concentration and temperature based on the dispersion of the speed of sound. By employing graphic processing and pattern matching methods, a coupled relationship of the dispersion of the speed of sound with gas concentration and temperature is established. The projection intersection method is used to calculate the concentration and temperature of binary and ternary gas mixtures. Combined with the inversion method, a joint reconstruction method for gas concentration fields and temperature fields based on the dispersion of the speed of sound is developed. The feasibility of the proposed simultaneous reconstruction method for temperature and concentration fields is validated using numerical simulations. Additionally, an acoustic tomography experimental system was set up to conduct reconstruction experiments for binary gas concentration fields and temperature fields, confirming the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Simultaneous Reconstruction of Gas Concentration and Temperature Using Acoustic Tomography

Liu,

Zhu,

Deng

et al. 2024

Sensors

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show abstract

“…Instead of reconstructing static temperature imaging where most studies are currently focusing on [14,16,17], the innovation of this proposed paper is the low-error reconstruction of dynamic flames.…”

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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A Quality-Hierarchical Temperature Imaging Network for TDLAS Tomography

Cheng

et al. 2022

IEEE Trans. Instrum. Meas.

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Tunable diode laser absorption spectroscopy (TDLAS) tomography is a well-established combustion diagnostic technique for imaging two-dimensional cross-sectional distributions of critical flow-field parameters. As two key metrics in TDLAS tomography, reconstruction accuracy and efficiency are generally traded off to satisfy either the requirement of highfidelity image retrieval or rapid tomographic data inversion. In this paper, a novel quality-hierarchical temperature imaging network for TDLAS tomography is developed based on stacked Long Short Term Memory (LSTM). From limited line-of-sight TDLAS measurements, this network outputs two reconstructed temperature images, i.e. a coarse-quality image and a fine-quality image, with different numbers of network layers and consequently different computational costs. The coarse-quality image provides more timely temperature reconstruction, which can satisfy realtime dynamic monitoring of turbulence-chemistry interactions with a temporal resolution of tens of kilo frames per second. On the other hand, the fine-quality image, that can be stored and utilized for offline analysis and diagnosis, further details the temperature reconstruction with more accurate features. Both numerical stimulation and lab-scale experiment validated the accuracy-efficiency trade-off achieved by the proposed qualityhierarchical temperature imaging network.

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A quality-hierarchical temperature imaging network for TDLAS tomography

Cited by 3 publications

References 35 publications

Simultaneous Reconstruction of Gas Concentration and Temperature Using Acoustic Tomography

Simultaneous Reconstruction of Gas Concentration and Temperature Using Acoustic Tomography

Convolutional Neural Network Aided Chemical Species Tomography for Dynamic Temperature Imaging

A Quality-Hierarchical Temperature Imaging Network for TDLAS Tomography

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