Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography has been widely used for in situ combustion diagnostics, yielding images of both species concentration and temperature. The temperature image is generally obtained from the reconstructed absorbance distributions for two spectral transitions, i.e. two-line thermometry. However, the inherently ill-posed nature of tomographic data inversion leads to noise in each of the reconstructed absorbance distributions. These noise effects propagate into the absorbance ratio and generate artefacts in the retrieved temperature image. To address this problem, we have developed a novel algorithm, which we call Relative Entropy Tomographic RecOnstruction (RETRO), for TDLAS tomography. A relative entropy regularisation is introduced for high-fidelity temperature image retrieval from jointly reconstructed two-line absorbance distributions. We have carried out numerical simulations and proof-of-concept experiments to validate the proposed algorithm. Compared with the wellestablished Simultaneous Algebraic Reconstruction Technique (SART), the RETRO algorithm significantly improves the quality of the tomographic temperature images, exhibiting excellent robustness against TDLAS tomographic measurement noise. RETRO offers great potential for industrial field applications of TDLAS tomography, where it is common for measurements to be performed in very harsh environments.
Acoustic tomography can be used to measure temperature field from the time-of-flight (TOF). In order to capture real-time temperature field change and accurately yield quantitative temperature image, two improvements on the conventional acoustic tomography system are studied: simultaneous acoustic transmission and TOF collection along multiple ray paths, and offline iteration reconstruction algorithm. In system operation, all the acoustic transceivers send the modulated and filtered wideband Kasami sequence simultaneously to facilitate fast and accurate TOF measurements using cross-correlation detection. For image reconstruction, iteration process is separated and executed offline beforehand to shorten computational time for online temperature field reconstruction. The feasibility and effectiveness of the developed methods are validated in simulation study. Simulation results show that the proposed method can reduce the processing time per frame from 160 ms to 20 ms, and the reconstruction error remains less than 5%. Hence the proposed method has the potential of monitoring rapid temperature change with good temporal and spatial resolution.
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