A Quality-Hierarchical Temperature Imaging Network for TDLAS Tomography

Abstract

Tunable diode laser absorption spectroscopy (TDLAS) tomography is a well-established combustion diagnostic technique for imaging 2-D cross-sectional distributions of critical flow-field parameters. As two key metrics in TDLAS tomography, reconstruction accuracy and efficiency are generally traded off to satisfy either the requirement of high-fidelity image retrieval or rapid tomographic data inversion. In this article, a novel quality-hierarchical temperature imaging network for TDLAS tomography is developed based on stacked long short-term memory (LSTM). From limited line-of-sight TDLAS measurements, this network outputs two reconstructed temperature images, i.e., a coarse-quality image and a fine-quality image, with different numbers of network layers and consequently different computational costs. The coarse-quality image provides more timely temperature reconstruction, which can satisfy real-time dynamic monitoring of turbulence-chemistry interactions with a temporal resolution of tens of kilo frames per second. In contrast, the fine-quality image, which can be stored and utilized for offline analysis and diagnosis, further details the temperature reconstruction with more accurate features. Both numerical stimulation and lab-scale experiment validated the accuracy-efficiency tradeoff achieved by the proposed quality-hierarchical temperature imaging network.