Hierarchical Temperature Imaging Using Pseudo-Inversed Convolutional Neural Network Aided TDLAS Tomography

As an in situ combustion diagnostic tool, Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography has been widely used for imaging of two-dimensional temperature distributions in reactive flows. Compared with the computational tomographic algorithms, Convolutional Neural Networks (CNNs) have been proofed to be more robust and accurate for image reconstruction, particularly in case of limited access of laser beams in the Region of Interest (RoI). In practice, flame in the RoI that requires to be reconstructed with good spatial resolution is commonly surrounded by low-temperature background. Although the background is not of high interest, spectroscopic absorption still exists due to heat dissipation and gas convection. Therefore, we propose a Pseudo-Inversed CNN (PI-CNN) for hierarchical temperature imaging that (a) uses efficiently the training and learning resources for temperature imaging in the RoI with good spatial resolution, and (b) reconstructs the less spatially resolved background temperature by adequately addressing the integrity of the spectroscopic absorption model. In comparison with the traditional CNN, the newly introduced pseudo inversion of the RoI sensitivity matrix is more penetrating for revealing the inherent correlation between the projection data and the RoI to be reconstructed, thus prioritising the temperature imaging in the RoI with high accuracy and high computational efficiency. In this paper, the proposed algorithm was validated by both numerical simulation and lab-scale experiment, indicating good agreement between the phantoms and the high-fidelity reconstructions.

Target-Dependent Chemical Species Tomography with Hybrid Meshing of Sensing Regions

This paper develops a hybrid-size meshing scheme for target-dependent imaging in Chemical Species Tomography (CST). The traditional implementation of CST generally places the target field in the central region of laser sensing, the so-called Region of Interest (RoI), with uniform-size meshes. The centre of the RoI locates at the midpoint between the laser emitters and receivers, while the size of the RoI is empirically determined by the optical layout. A too small RoI cannot make the most use of laser beams, while a too large one leads to much severer rank deficiency in CST. To solve the above-mentioned issues, we introduce hybrid-size meshes in the entire region of laser sensing, with dense ones in the RoI to detail the target flow field and sparse ones out of the RoI to fully consider the physically existing laser absorption. The proposed scheme was both numerically and experimentally validated using a CST sensor with 32 laser beams. The images reconstructed using the hybrid-size meshing scheme show better accuracy and finer profile of the target flow, compared with those reconstructed using the traditionally uniform-size meshing. The proposed hybrid-size meshing scheme significantly facilitates the industrial application of CST towards practical combustors, in which the combustion zone is bypassed by cooling air. In these scenarios, the proposed scheme can better characterise the combustion zone with dense meshes, while maintaining the integrity of the physical model by considering the absorption in the bypass air with sparse meshes.