A Spatially Progressive Neural Network for Locally/Globally Prioritized TDLAS Tomography

Abstract

Tunable diode laser absorption spectroscopy tomography (TDLAST) has been widely applied for imaging two-dimensional distributions of industrial flow-field parameters, e.g., temperature and species concentration. Two main interested imaging objectives in TDLAST are the local combustion and its radiation in the entire sensing region. State-of-the-art algorithms were developed to retrieve either of the two objectives. In this paper, we address the both by developing a novel multi-output imaging neural network, named as Spatially Progressive Neural Network (SpaProNet). This network consists of locally and globally prioritized reconstruction stages. The former enables hierarchical imaging of the finely resolved and highly accurate local combustion, but coarsely resolved background. The later retrieves a fine-resolved image for the entire sensing region, at the cost of slightly trading off the reconstruction accuracy in the combustion zone. Furthermore, the proposed network is driven by the hydrodynamics of the real reactive flows, in which the training dataset is obtained from large eddy simulation. The proposed SpaProNet is validated by both simulation and lab-scale experiment. In all test cases, the visual and quantitative metric comparisons show that the proposed SpaProNet outperforms the existing methods from the following two perspectives: a) the locally prioritized stage provides ever-better accuracy in the combustion zone; b) the globally prioritized stage shows turbulence-indicative accuracy in the entire sensing region for diagnosis of heat radiation from the flame and flame-air interactions.

Perspectives on instrumentation development for chemical species tomography in reactive-flow diagnosis

“…Examples of this approach have been attempted by deriving the physical information from large eddy simulation (LES). The LES results were used to train end-to-end neural networks, which directly reconstruct the absorption fields from the CST measurements [100,101]. These attempts indeed allow for incorporation of thermochemistry and flow-field transport models into image reconstruction.…”

Reconstruction Algorithm Optimization Based on Multi-Iteration Adaptive Regularity for Laser Absorption Spectroscopy Tomography

“…TDLAT has been gradually developed and has been widely applied [13]. By detecting the spectral data for multiple angles and laser paths through the measured field, combined with image reconstruction algorithms, the visualization of temperature and component concentration distributions in complex combustion fields is achieved using TDLAT, which was used in the experiments in this research [14,15]. Two-dimensional distribution reconstruction is essentially the process of solving the physical properties of each reconstruction parameter of a target region based on the measured projection values and sensitivity matrix of a measurement system.…”