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Two-Step TDLAS Tomographic Reconstruction for Temperature Imaging

Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography is a well-accepted technique for noninvasive combustion diagnosis, which can generate images for 2-dimensional distribution of critical flow parameters in the combustion field, such as temperature and species concentration. Among them, temperature imaging is usually realized from ratios of absorbance distributions reconstructed for two preselected spectral transitions, i.e. two-line thermometry. Many computational tomographic algorithms have been proposed to reconstruct the absorbance distributions from limited line-of-sight TDLAS measurements. However, due to the ill-posedness of this inverse problem, reconstruction accuracy of traditional computational tomographic algorithms is usually not high, which leads to severe artefacts in the reconstructed temperature image. In this work, a two-step TDLAS tomographic reconstruction method is proposed for temperature imaging. The first step reconstructs the absorbance distributions with a priori information of the smoothness of distributions of flow parameters, while the second step supplements detailed information from residual TDLAS measurements of the first step. Simulation results show that this two-step method improves the quality of reconstructed temperature images compared to Landweber algorithm and Algebraic Reconstruction Technique (ART) with Total Variation (TV) regularization.