Simultaneous measurement of flame temperature and species concentration distribution from nonlinear tomographic absorption spectroscopy

This research introduces the evolution strategy based on covariance matrix adaption algorithm, which has been proven applicable for solving highly inseparable and nonlinear optimization problems, to resolve the nonlinear tomography absorption spectroscopy inverse optimization problem and simultaneously reconstruct a two-dimensional temperature and species concentration distribution fields in an absorption flame. Severe ill-posedness and crosstalk issues exist during multi-parameter field simultaneous reconstruction. To alleviate the ill-posedness in nonlinear tomography absorption spectroscopy equations, two regularization methods, Tikhonov regularization and the regularization based on the generalized Gaussian Markov random field, are applied to reconstruct the temperature distribution field. The impacts of different regularization factors (the temperature and the concentration regularization factors) are investigated as well, and optimal intervals are suggested for reference. Simulation results show that the evolution strategy based on covariance matrix adaption algorithm worked well in retrieving multi-parameter distribution fields on both symmetric and asymmetric flame distribution models. Moreover, the evolution strategy based on covariance matrix adaption algorithm alleviated the severe crosstalk issue between temperature and species concentration to improve the accuracy of the reconstructed species concentration distribution field.