A Two-Stage Acoustic Tomography Method for Temperature Distribution Reconstruction

doi:10.12096/j.2096-4528.pgt.24056

Abstract

Abstract:

Objectives To improve the reconstruction accuracy of temperature distribution in acoustic tomography, a two-stage reconstruction algorithm based on compressed sensing and multi-scale dilated convolution is proposed. Methods First, a compressed sensing model for reconstructing temperature distribution in acoustic tomography is developed, and the orthogonal matching pursuit (OMP) algorithm is used to obtain a coarse-grid temperature distribution. Then, a multi-scale convolutional neural network is established to predict the fine-grid temperature distribution reconstruction results, where a compensation channel for original measurement is incorporated to enhance the utilization of prior information. Three numerical models of typical temperature distributions are established and compared with the OMP, algebraic reconstruction technique (ART), simultaneous algebraic reconstruction technique (SART), and Landweber algorithms. Results The proposed algorithm achieves an average relative error and a root mean square error of 0.45% and 0.64%, respectively, with reconstruction errors lower than those of other algorithms. Conclusions The two-stage temperature distribution reconstruction algorithm can effectively alleviate the ill-posedness of the temperature distribution reconstruction problem, thereby improving the reconstruction accuracy.

Key words: acoustic tomography, high-resolution reconstruction, compressed sensing, multi-scale dilated convolution, orthogonal matching pursuit

CLC Number:

TK 31

Lifeng ZHANG, Xianghu DONG. A Two-Stage Acoustic Tomography Method for Temperature Distribution Reconstruction[J]. Power Generation Technology, 2025, 46(5): 1050-1058.

Figures/Tables 17

Fig. 1 Measurement area and acoustic wave paths

Fig. 2 Flowchart of temperature distribution reconstruction

Fig. 3 Schematic diagram of compressed sensing

Fig. 4 Flowchart of OMP algorithm

Fig. 5 Schematic diagram of SE channel attention mechanism

Fig. 6 Principle of dilated convolution

Fig. 7 Multi-scale dilated convolution module

Fig. 8 Image decomposition and recombination

Fig. 9 Diagram of network architecture

Fig. 10 MSDC network training process

Fig. 11 Single-peak reconstruction

Tab. 1 Reconstruction errors of single-peak temperature distribution

重建算法	E_ARE	E_RMSE
Landweber	4.58	6.97
ART	4.27	6.61
SART	3.77	6.46
OMP	0.57	0.88
OMP-MSDC	0.31	0.45

Fig. 12 Double-peak reconstruction

Tab. 2 Reconstruction errors of double-peak temperature distribution

重建算法	E_ARE	E_RMSE
Landweber	6.43	8.79
ART	6.39	9.30
SART	5.78	8.03
OMP	0.81	1.19
OMP-MSDC	0.44	0.64

Fig. 13 Four-peak reconstruction

Tab. 3 Reconstruction errors of four-peak temperature distribution

重建算法	E_ARE	E_RMSE
Landweber	7.21	9.13
ART	8.0	10.48
SART	6.83	8.14
OMP	1.48	1.96
OMP-MSDC	0.58	0.79

Fig. 14 Reconstruction errors of temperature field at different noise levels

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