Flaw reconstruction in NDE using a limited number of x-ray radiographic projections

One of the major problems in nondestructive evaluation (NDE) is the evaluation of flaw sizes and locations in a limited inspectability environment. In NDE x-ray radiography, this frequently occurs when the geometry of the part under test does not allow x-ray penetration in certain directions. Other times, the inspection setup in the field does not allow for inspection at all angles around the object. This dissertation presents a model based reconstruction technique which requires a small number of x-ray projections from one side of the object under test. The estimation and reconstruction of model parameters rather than the flaw distribution itself requires much less information, thereby reducing the number of required projections. Crack-like flaws are modeled as piecewise linear curves (connected points) and are reconstructed stereographically from at least two projections by matching corresponding endpoints of the linear segments. Volumetric flaws are modeled as ellipsoids and elliptical slices through ellipsoids. The elliptical principal axes lengths, orientation angles and locations are estimated by fitting a forward model to the projection data. The fitting procedure is highly nonlinear and requires stereographic projections to obtain initial estimates of the model parameters. The methods are tested both on simulated and experimental data. Comparisons are made with models from the field of stereology. Finally, analysis of reconstruction errors is presented for both models

Remote sensing of floe size distribution and surface topography

Floe size can be measured by several properties p- for instance, area or mean caliper diameter. Two definitions of floe size distribution seem particularly useful. F(p), the fraction of area covered by floes no smaller than p; and N(p), the number of floes per unit area no smaller than p. Several summertime distributions measured are a graph, their slopes range from -1.7 to -2.5. The variance of an estimate is also calculated

Numerical simulation of coarsening in binary solder alloys

Coarsening in solder alloys is a widely accepted indicator for possible failure of joints in electronic devices. Based on the well-established Cahn–Larché model with logarithmic chemical energy density (Dreyer and Müller, 2001) [20], we present a computational framework for the efficient and reliable simulation of coarsening in binary alloys. Main features are adaptive mesh refinement based on hierarchical error estimates, fast and reliable algebraic solution by multigrid and Schur–Newton multigrid methods, and the quantification of the coarsening speed by the temporal growth of mean phase radii. We provide a detailed description and a numerical assessment of the algorithm and its different components, together with a practical application to a eutectic AgCu brazing alloy

Investigation of Microstructural and Carbon Deposition Effects in SOFC Anodes Through Modelling and Experiments

The investigation of the SOFC anode microstructural properties affected by microstructural parameters and degradation is the focus of this research. Imaging and image processing techniques are developed to achieve quantification of the anode microstructural information. The analytical and Computational Fluid Dynamics based modelling of the microstructure including the degradation effects developed in this work will enable the microstructure optimisation for achieving performance enhancements

Development of Synthetic Coal Char Simulant for Microwave Conversion Studies: A Computationally-Driven Approach

Recent experimental demonstration of new reaction windows for coal char/methane reactions that are less energy-intensive, provides innovation for modular reactors. However, the correlation of the exact mechanism for the enhancement of these reaction windows is not certain. This study investigates the simplification of these experimental studies by developing a well-characterized coal char simulant. The approach involves using a computational approach to screen macroscopic composition to replicate the dielectric and compositional response of actual char. This study is focused on PRB coal char. A discrete element method (DEM) technique was used to simulate the packing of coal chars to give the precise distribution of particle sizes. Micro-CT images of actual coal char were taken and the Feret diameter and particle count were used in DEM simulation. Using the packed DEM geometry, a finite element analysis (FEA) using COMSOL was utilized to solve Maxwell’s equations to match the experimental dielectric properties. Once the required volume fraction and constituents were known, the coal char simulate was synthesized. The comparison of simulation dielectric and actual char dielectric was within 5\% error. The synthetic char was experimentally synthesized and the density of the synthetic dielectric was determined to be 0.5 g/cc and the actual char had a density of 0.4 g/cc. It was determined that the imaginary part of the synthetic char was much larger than the actual char. This was reasoned to be due to the larger electrical conductivity associated with the synthetic char material. Further investigation of the actual char through both optical and scanning tunneling microscopy revealed significant amount of ash content surrounding the char. It is hypothesized that this ash layer coating the char as a result of pyrolysis process is leading to decreased electrical conductivity. A similar FEA approach was used to investigate the particle morphology of a magnetite (Fe$_{3}$O$_{4}$) catalyst embedding on a coal char substrate to understand the localized temperature and electric field enhancements. It was determined that a particle shape significantly influences electric field and localized temperatures. In the absence of the shaped particle the peak electric field strength and subsequently, the volumetric heat flux was two orders of magnitude lower. An optimal geometry and volume fraction to enhance these localized field effects were found during the study

Sparse approximations of protein structure from noisy random projections

Single-particle electron microscopy is a modern technique that biophysicists employ to learn the structure of proteins. It yields data that consist of noisy random projections of the protein structure in random directions, with the added complication that the projection angles cannot be observed. In order to reconstruct a three-dimensional model, the projection directions need to be estimated by use of an ad-hoc starting estimate of the unknown particle. In this paper we propose a methodology that does not rely on knowledge of the projection angles, to construct an objective data-dependent low-resolution approximation of the unknown structure that can serve as such a starting estimate. The approach assumes that the protein admits a suitable sparse representation, and employs discrete $L^1$-regularization (LASSO) as well as notions from shape theory to tackle the peculiar challenges involved in the associated inverse problem. We illustrate the approach by application to the reconstruction of an E. coli protein component called the Klenow fragment.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS479 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org