PERFORMANCE EVALUATION OF ITERATIVE TOMOGRAPHIC ALGORITHMS APPLIED TORECONSTRUCTION OF A 3-DIMENSIONAL TEMPERATURE-FIELD

Iterative tomographic algorithms have been applied to the reconstructi on of a three-dimensional temperature field (from its projections) for Rayleigh-Benard-type natural-convection problems. Nine distinct algor ithms with varying numbers of projections and projection angles have b een considered. The three-dimensional temperature field is sliced into a set of two-dimensional planes and reconstruction algorithms are app lied to each individual plane. Projection of the temperature field is interpreted as a path integral along a line in the appropriate directi on. The integrals are evaluated numerically and are assumed to represe nt exact data. Errors in reconstruction are defined with field data as reference and are used to compare one algorithm with respect to anoth er. The algorithms used in this work can be broadly classified into th ree groups: additive algebraic reconstruction technique (ART) multipli cative algebraic reconstruction technique (MART), and maximization rec onstruction technique (MRT). Additive ART shows a systematic convergen ce with respect to number of the projections and the value of the rela xation parameter. MART algorithms produce less error at convergence co mpared to additive ARTs but converge only at low values of relaxation parameter. In the present work the MRT algorithm shows intermediate pe rformance when compared to ART asd MART Increasing noise level in proj ection data increases the error in the reconstructed field. The maximu m and root-mean-square errors are highest in ART and lowest in MART fo r a given projection data. Increasing noise levels in projection data decrease the convergence rates. For all algorithms, a 20% noise level is seen as an upper limit beyond which the reconstructed field is bare ly recognizable.