A quality-hierarchical temperature imaging network for TDLAS tomography

2021 
Tunable diode laser absorption spectroscopy (TDLAS) tomography is a well-established technique for combustion diagnostics, which is capable of imaging the 2-D distribution of critical flow-field parameters over cross section of the flame. Reconstruction quality and time resolution are two key aspects that need to be compromised in dynamic monitoring. In this paper, we develop a quality-hierarchical TDLAS tomographic algorithm based on Long Short Term Memory (LSTM) network. From limited amount of line-of-sight projections measured on current tomographic field, this algorithm outputs a coarse-quality temperature image and a fine-quality temperature image with different computational costs. Simulation results validated the efficiency-effectiveness trade-off achieved by this quality-hierarchical temperature imaging network.
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