Benchmark evaluation of tomographic algorithms for simultaneous reconstruction of temperature and volume fraction fields of soot and metal-oxide nanoparticles in non-uniform flames

With the growing applications of nanofluid flame, the monitoring and controlling of its combustion process is of paramount importance. Thus, it is necessary to develop diagnosing methods which can simultaneously image important parameters such as temperature and volume fractions of soot, metal-oxide nanoparticles. Tomographic emission spectroscopy is an effective method which has been proposed for this purpose. However, the inversion process was only reported with least-squares QR decomposition (LSQR) so far and there are numerous well-established reconstruction algorithms which have not been utilized yet. Thus, this work aims to perform systematic comparative studies on several representative algorithms for the inversion process. In the simulative studies, algorithms including Tikhonov regularization, algebraic reconstruction technique (ART), LSQR, Landweber algorithm, maximum likelihood expectation maximization (MLEM), and ordered subset expectation maximization (OSEM) were discussed. The effects of the number of iterations, the signal-to-noise ratio, and the number of projections and the calibration error in projection angles on the performance of the algorithms were investigated. Advice on selecting the suitable algorithms under different application conditions is then provided according to the extensive numerical studies.