Abstract
Measurement of temperature distribution is vital in boilers, heat exchangers, and other industrial applications. Acoustic pyrometry offers the advantage of measuring the temperature in the entire domain in a non-intrusive manner. Acoustic pyrometry involves estimating the temperature of the domain using the time of flight information between the transceivers. Since acoustic pyrometry is an inverse problem, it is sensitive to noise and the number of cells in the domain. Regularization methods help in obtaining feasible solutions. Therefore, the performance of four regularization methods, namely, Tikhonov, modified Tikhonov, Total variation (TV), and iterative reweighted least-squares (IRLS) in reconstructing three different temperature profiles, is studied and compared against the pseudo-inverse method. The effect of noise and the cell ratio (CR) on temperature reconstruction is also studied. Overall, the best results are observed for the coarsest cell ratio and modified Tikhonov with sharp filter regularization.
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