A temperature field reconstruction method based on acoustic thermometry

•A new method of temperature field reconstruction is proposed.•The proposed method solves the problem of missing edge temperature information.•Reconstruction time and accuracy are more advantageous.•The robustness of the reconstruction is good.•The validity of the method in engineering applications...

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Vydáno v:	Measurement : journal of the International Measurement Confederation Ročník 200; s. 111642
Hlavní autoři:	Zhong, Qishui, Chen, Yinsheng, Zhu, Bo, Liao, Shaohui, Shi, Kaibo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 15.08.2022
Témata:	Acoustic thermometry fast iterative shrinkage-thresholding algorithm (FISTA) kernel extreme learning machine (KELM) Temperature field reconstruction Temperature field reconstruction Acoustic thermometry kernel extreme learning machine (KELM) fast iterative shrinkage-thresholding algorithm (FISTA)
ISSN:	0263-2241, 1873-412X
On-line přístup:	Získat plný text
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Shrnutí:	•A new method of temperature field reconstruction is proposed.•The proposed method solves the problem of missing edge temperature information.•Reconstruction time and accuracy are more advantageous.•The robustness of the reconstruction is good.•The validity of the method in engineering applications is experimentally confirmed. In power plant boiler, the temperature field information directly reflects its internal combustion conditions. To ensure the proper running of the whole system and reduce environmental pollution, it is essential to monitor the temperature field inside boiler quickly and accurately. In this paper, a new temperature field reconstruction method based on acoustic thermometry is proposed. First, in order to obtain the temperature of the discrete coarse grid in measurement area, the temperature field reconstruction problem is transformed into an optimization problem, which is solved by the improved monotone fast iterative shrinkage-thresholding algorithm. Then, based on the obtained temperature of coarse grid, the temperature field of the whole measurement area is reconstructed by the kernel extreme learning machine. Compared with existing algorithms, simulation and experimental results show that the proposed method can acquire complete reconstruction results with shorter reconstruction time, better reconstruction accuracy and robustness, and is feasible for engineering applications.
ISSN:	0263-2241 1873-412X
DOI:	10.1016/j.measurement.2022.111642