A New Algorithm for Reconstructing Two-Dimensional Temperature Distribution by Ultrasonic Thermometry

Abstract

Temperature, especially temperature distribution, is one of the most fundamental and vital parameters for theoretical study and control of various industrial applications. In this paper, ultrasonic thermometry to reconstruct temperature distribution is investigated, referring to the dependence of ultrasound velocity on temperature. In practical applications of this ultrasonic technique, reconstruction algorithm based on least square method is commonly used. However, it has a limitation that the amount of divided blocks of measure area cannot exceed the amount of effective travel paths, which eventually leads to its inability to offer sufficient temperature information. To make up for this defect, an improved reconstruction algorithm based on least square method and multiquadric interpolation is presented. And then, its reconstruction performance is validated via numerical studies using four temperature distribution models with different complexity and is compared with that of algorithm based on least square method. Comparison and analysis indicate that the algorithm presented in this paper has more excellent reconstruction performance, as the reconstructed temperature distributions will not lose information near the edge of area while with small errors, and its mean reconstruction time is short enough that can meet the real-time demand.

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

“…In the temperature distribution reconstruction, the measure area is divided into N unit blocks. And, due to the limitation of LSM, N should be less than the number of propagation paths M [25]. Assuming that the ultrasonic velocity is evenly distributed in each unit block, then from equation ( 4), the corresponding TOF of ultrasound in kth selected propagation path can be rewritten as…”

“…Using the Kriging model with both local and global statistical characteristics, Yan et al [24] used the Kriging method to interpolate and extrapolate a small number of pixels after reconstructing them with LSM to get the temperature description of the whole area. Using the good ability of multiquadric interpolation to process sparse data, Shen et al [25] proposed a reconstruction algorithm based on LSM and multiquadric interpolation so that the reconstruction results will not lose information at the edge of the area. However, how to improve the accuracy of temperature field reconstruction and the quality of reconstruction remains an open problem.…”

3D Temperature Distribution Reconstruction in Furnace Based on Acoustic Tomography

“…Acoustic tomography, as an important and efficient method, has been applied to measure the temperature fields in furnaces to improve the combustion efficiency of the boiler and ensure the safe operation of the boiler. At present, the research on temperature field reconstruction by AT is mainly conducted in two dimensions [9][10][11]. 3D temperature field reconstruction is discussed by only few researchers [12,13].…”

Two‐Dimensional Temperature Field Distribution Reconstruction Based on Least Square Method and Radial Basis Function Approximation

“…Reconstructing temperature field distribution using acoustic imaging is an inverse process which depends on reconstruction algorithm. One of the most widely used reconstruction algorithms is least square algorithm (LSA) [12][13][14]. But, this algorithm could not complete an entire temperature field distribution reconstruction due to its flaw of interpolation range.…”