3D Temperature Distribution Reconstruction in Furnace Based on Acoustic Tomography

Kong, Qian; Jiang, Genshan; Liu, Yuechao; Sun, Jianhao

doi:10.1155/2019/1830965

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…Yan et al 1,13 used Markov RBF, and solved the inverse problem by singular value decomposition (SVD) with Tikhonov regularization. Kong et al 7,20 used inverse multiquadrics RBF with a linear polynomial, and solved the inverse problem by a truncated generalized SVD method. Yu et al 21 used multiquadric RBF and solved the inverse problem by SVD.…”

Section: Introductionmentioning

confidence: 99%

Temperature Distribution Reconstruction Method for Acoustic Tomography Based on Compressed Sensing

et al. 2022

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Acoustic tomography (AT) is one of a few non-contact measurement techniques that can present information about the temperature distribution. Its successful application greatly depends on the performance of the reconstruction algorithm. In this paper, a temperature distribution reconstruction method based on compressed sensing (CS) is proposed. Firstly, a measurement matrix of an AT system in a CS framework is established. Secondly, a sparse basis is selected based on the mutual coherence between the measurement matrix and sparse basis. Thirdly, an improvement of the orthogonal matching pursuit (OMP) algorithm, called the IMOMP algorithm, is proposed for pursuing efficiency in recovering sparse signals. Reconstruction experiments of Gaussian sparse signals showed that IMOMP was better than OMP in both success ratio and running time, and the selection method of sparse basis was effective. Finally, a temperature distribution reconstruction algorithm based on compressed sensing, that is, the CS-IMOMP algorithm, is proposed. Simulation and experiment results show that, compared with the least square algorithm and the Simultaneous Iterative Reconstruction Technique algorithm, the CS-IMOMP algorithm has smaller reconstruction errors and provides more accurate information about the temperature distribution.

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Section: Introductionmentioning

confidence: 99%

Temperature Distribution Reconstruction Method for Acoustic Tomography Based on Compressed Sensing

et al. 2022

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“…Unlike the above-mentioned algorithm, some scholars did not assume that the sound slowness (temperature) distribution in each grid was uniform when developing the mathematical model. Instead, they used a linear combination of RBFs to estimate the sound slowness distribution; i.e., Markov RBF [ 10 , 20 ], inverse multiquadrics RBF [ 21 , 22 ], multiquadric RBF [ 23 ], reflected sigmoidal RBF [ 24 ], hybrid RBF of exponential kernel and cubic kernel [ 25 ] and Gaussian RBF [ 5 ]. This kind of algorithm allows N > M ; in order to address seriously ill-conditioned inverse problems, these algorithms employed methods such as singular value decomposition (SVD), Tikhonov regularization and so on.…”

Section: Introductionmentioning

confidence: 99%

Temperature Field Reconstruction Method for Acoustic Tomography Based on Multi-Dictionary Learning

Wei

Yan

Zhou

2022

Sensors

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A reconstruction algorithm is proposed, based on multi-dictionary learning (MDL), to improve the reconstruction quality of acoustic tomography for complex temperature fields. Its aim is to improve the under-determination of the inverse problem by the sparse representation of the sound slowness signal (i.e., reciprocal of sound velocity). In the MDL algorithm, the K-SVD dictionary learning algorithm is used to construct corresponding sparse dictionaries for sound slowness signals of different types of temperature fields; the KNN peak-type classifier is employed for the joint use of multiple dictionaries; the orthogonal matching pursuit (OMP) algorithm is used to obtain the sparse representation of sound slowness signal in the sparse domain; then, the temperature distribution is obtained by using the relationship between sound slowness and temperature. Simulation and actual temperature distribution reconstruction experiments show that the MDL algorithm has smaller reconstruction errors and provides more accurate information about the temperature field, compared with the compressed sensing and improved orthogonal matching pursuit (CS-IMOMP) algorithm, which is an algorithm based on compressed sensing and improved orthogonal matching pursuit (in the CS-IMOMP, DFT dictionary is used), the least square algorithm (LSA) and the simultaneous iterative reconstruction technique (SIRT).

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“…Acoustic tomography (AT), as a nonintrusive temperature measurement technique, has the advantages of wide measuring range, short measuring time delay, and good environment adaptability. erefore, it is widely applied in temperature measurement for boiler hearth [1][2][3][4][5], gas [6][7][8], granary [9], hydrothermal solution [10][11][12], and noninvasive thermometry for materials [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…e analysis of the furnace temperature field can guide the adjustment of the fuel mixing ratio, which is conducive to improving the combustion efficiency, reducing the emission of pollutants, and optimizing the combustion state of the boiler. Since the combustion process of boilers is characterized by harsh environment, complex and variable state, and local high temperature [1,3], it is difficult to obtain an accurate temperature field in the furnace with traditional contact temperature measurement. With the reconstruction algorithm for temperature distribution based on AT, which is a nonintrusive temperature measurement technique, the temperature distribution of the measure area can be obtained by measuring the time-of-flight (TOF) of multiple ultrasonic propagation paths in the measure area.…”

Section: Introductionmentioning

confidence: 99%

A Method for Reconstruction of Boiler Combustion Temperature Field Based on Acoustic Tomography

Zhou

et al. 2021

Mathematical Problems in Engineering

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Acoustic tomography (AT), as a noninvasive temperature measurement method, can achieve temperature field measurement in harsh environments. In order to achieve the measurement of the temperature distribution in the furnace and improve the accuracy of AT reconstruction, a temperature field reconstruction algorithm based on the radial basis function (RBF) interpolation method optimized by the evaluation function (EF-RBFI for short) is proposed. Based on a small amount of temperature data obtained by the least square method (LSM), the RBF is used for interpolation. And, the functional relationship between the parameter of RBF and the root-mean-square (RMS) error of the reconstruction results is established in this paper, which serves as the objective function for the effect evaluation, so as to determine the optimal parameter of RBF. The detailed temperature description of the entire measured temperature field is finally established. Through the reconstruction of three different types of temperature fields provided by Dongfang Boiler Works, the results and error analysis show that the EF-RBFI algorithm can describe the temperature distribution information of the measured combustion area globally and is able to reconstruct the temperature field with high precision.

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3D Temperature Distribution Reconstruction in Furnace Based on Acoustic Tomography

Cited by 7 publications

References 27 publications

Temperature Distribution Reconstruction Method for Acoustic Tomography Based on Compressed Sensing

Temperature Distribution Reconstruction Method for Acoustic Tomography Based on Compressed Sensing

Temperature Field Reconstruction Method for Acoustic Tomography Based on Multi-Dictionary Learning

A Method for Reconstruction of Boiler Combustion Temperature Field Based on Acoustic Tomography

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