Applicability of genetic algorithms to reconstruction of projected data from ultrasonic tomography

In this paper simulation studies of the ultrasound computerized tomography (CT) technique employing time of flight data is presented. An enhanced genetic algorithm based reconstruction technique is proposed that is capable of detecting multiple types of inclusions in the test specimen to be reconstructed. It is assumed that the physical properties of the inclusions are known a priori. The preliminary results of our algorithm for a simple configuration are found to be better than those reported with MART1. In addition to being able to identify inclusions of different materials, both the shape and location of the inclusions could be reconstructed using the proposed algorithm. The results are found to be consistent and satisfactory for a wide range of grid sizes and geometries of inclusion(s). Based on the regression analysis an empirical relation between the number of unknowns and the reconstruction time is found which enables one to predict the reconstruction time for higher resolutions

Application of Tomography for Nuclear Safety Applications

Computerized Tomography (CT) was pioneered by astrophysicists and medical diagnostics experts almost 60 years ago. It found its way quickly in the area of noninvasive measurements in multi-phase fluid-flow systems existing in many engineering applications. Nuclear reactor safety involves numerical prediction of thermodynamic parameters of reactors undergoing transients/accidents and one such property is cross-sectional void-faction distribution which plays a crucial role in the heat transfer aspects. The concept of CT provides an experimental non-invasive option to assess these numerical results. This exercise incorporates measurement of void-fraction distribution in flow cross sections that are expected in accident conditions. A simple three-phase (solid-liquid-gas) experiment was carried out at Leibniz University (Hannover) where X-ray CT system was used to image the cross-sectional distribution of void- fractions. This experiment has relevance to the nuclear industry two-phase (steam-water) flow situations. The results also include a novel way of estimating error in experimentally obtained tomographic images of void-fraction distributions

Plasma diagnostics at Aditya Tokamak by two views visible light tomography

•Improved algorithm works equally well for central as well as for peripherical plasma regions.•Entropy optimized smoothening parameters eliminate user dependencies.•Real time fusion grade plasma diagnostics images. This visible light computerized tomography exercise is a part of a project to establish an auxiliary imaging method to assist other imaging facilities at the Institute of Plasma Research (IPR), India. Space constraints around Aditya Tokamak allow only two orthogonal ports. Each port has one detector array (64 sensors) sensitive to the visual spectrum emitted by Hα emission. The objective here is to report the developments on limited view tomography for hot plasma imaging. Spatially filtered entropy maximization algorithm with non-uniform discretization grids is employed. Estimation of unique kernel smoothening parameters (mask size and exponent factor) depends on entropy function and projection data. It removes requirement of any arbitrary/user-based decision for choosing a regularization factor thus minimizes the chance for biasedness or errors. Synthetic projection data is used to analyse the performance of this modification. The error band in the process of recovery remains under acceptable level (less than 15%) irrespective of the origin of the emissions from the core. Reconstructed hot plasma images/profiles from Aditya Tokamak are shown. These profiles may improve the current understanding about (a) plasma–wall interaction or edge plasma turbulence, (b) control and generation of plasma and (c) correlations between theoretical and engineering advancements in Tokamak reactors