Reconstruction method for inversion problems in an acoustic tomography based temperature distribution measurement

In industrial areas, temperature distribution information provides a powerful data support for improving system efficiency, reducing pollutant emission, ensuring safety operation, etc. As a noninvasive measurement technology, acoustic tomography (AT) has been widely used to measure temperature distribution where the efficiency of the reconstruction algorithm is crucial for the reliability of the measurement results. Different from traditional reconstruction techniques, in this paper a two-phase reconstruction method is proposed to ameliorate the reconstruction accuracy (RA). In the first phase, the measurement domain is discretized by a coarse square grid to reduce the number of unknown variables to mitigate the ill-posed nature of the AT inverse problem. By taking into consideration the inaccuracy of the measured time-of-flight data, a new cost function is constructed to improve the robustness of the estimation, and a grey wolf optimizer is used to solve the proposed cost function to obtain the temperature distribution on the coarse grid. In the second phase, the Adaboost.RT based BP neural network algorithm is developed for predicting the temperature distribution on the refined grid in accordance with the temperature distribution data estimated in the first phase. Numerical simulations and experiment measurement results validate the superiority of the proposed reconstruction algorithm in improving the robustness and RA.