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

Laser absorption spectroscopy tomography is an effective combustion diagnostic method for obtaining simultaneous two-dimensional distribution measurements of temperature and gas molar concentrations. For the reconstruction process of complex combustion flames, a new algorithm named 'multi-iterative adaptive optimization regularization' (MIARO) is proposed. This algorithm is a further development of another algorithm known as the 'modified adaptive algebraic reconstruction technique' (MAART) with the improvement of the initial value and adaptive regularization parameter selections. In MIARO, the problem of the MAART's initial value sensitivity is compensated for, and in addition, reconstruction parameters are also introduced into the regularization so that both the quality of reconstruction and the convergence of regularization are guaranteed. In butane burner experiments, an average relative error of 1.82% was achieved with MIARO, compared to 2.44% with MAART, which is a significant reduction of 25.1%. The simulation and experimental results clearly demonstrate that the MIARO algorithm can be used to reconstruct dynamic combustion fields and eliminate boundary artifacts with improved measurement accuracy and robustness.

Automated summary

Laser absorption spectroscopy tomography is an effective combustion diagnostic method for obtaining simultaneous measurements of temperature and gas molar concentrations. The proposed MIARO algorithm is a further development of the MAART algorithm with improvements in the initial value and adaptive regularization parameter selections. In experiments, MIARO achieved an average relative error reduction of 25.1% compared to MAART, demonstrating improved measurement accuracy and robustness in reconstructing dynamic combustion fields and eliminating boundary artifacts.