Iterative computed tomography reconstruction of void fraction in a fuel bundle on unstructured mesh

Abstract

The experimental investigation of internal two-phase flow structures in high pressure/high temperature systems is technologically challenging and limited to a few instrumentation techniques. For complex fuel bundle geometries, the use of Computed Tomography (CT) allows for relatively high spatial resolution (0.2 to 1 mm) and non-intrusive measurement of time-averaged void fraction. One of the main issues using standard structured reconstruction mesh is due to the loss of accuracy near the test section boundaries, for instance within the pixels overlapping both the two-phase coolant and the heated rods. In this work, a void fraction reconstruction methodology based on unstructured mesh is developed to accurately bound relevant attenuation regions within the considered test section. This method eliminates any overlapping reconstruction pixels and allows straightforward applications of knowledge-based, low noise, algebraic iterative reconstruction techniques (ART, SIRT, etc.). Within the same consistent framework, CT scan forward projections can be simulated for various selected cases (single-phase, arbitrary-two-phase void fraction distributions, etc.) and reconstructed to assess the performance of both the CT scan setup (e.g., projection data sampling and number of projections) and the reconstruction technique. The method is applied to the void fraction reconstruction of gamma CT data in a 5 × 5 fuel bundle geometry measured at the Westinghouse FRIGG thermal–hydraulic test facility operating under prototypical conditions of Boiling Water Reactor. The unstructured mesh is designed to match the features of the test section (isolation, pressure vessel, water gap, fuel channel, heated rods and coolant) with high-resolution in the domain of interest (coolant) and very low-resolution in all other domains with known attenuation coefficients. As compared to previous void fraction reconstruction attempts for this data set, the new reconstruction methodology allows significant improvements in void fraction resolution and noise reduction.