The goal of this work is to develop a fast computed tomography (CT) reconstruction algorithm based on graphics processing units (GPU) that achieves significant improvement over traditional central processing unit (CPU) based implementations. The main challenge in developing a CT algorithm that is capable of handling very large datasets is parallelizing the algorithm in such a way that data transfer does not hinder performance of the reconstruction algorithm. General Purpose Graphics Processing (GPGPU) is a new technology that the Science and Technology (S&T) community is starting to adopt in many fields where CPU-based computing is the norm. GPGPU programming requires a new approach to algorithm development that utilizes massively multi-threaded environments. Multi-threaded algorithms in general are difficult to optimize since performance bottlenecks occur that are non-existent in single-threaded algorithms such as memory latencies. If an efficient GPU-based CT reconstruction algorithm can be developed; computational times could be improved by a factor of 20. Additionally, cost benefits will be realized as commodity graphics hardware could potentially replace expensive supercomputers and high-end workstations. This project will take advantage of the CUDA programming environment and attempt to parallelize the task in such a way that multiple slices of the reconstruction volume are computed simultaneously. This work will also take advantage of the GPU memory by utilizing asynchronous memory transfers, GPU texture memory, and (when possible) pinned host memory so that the memory transfer bottleneck inherent to GPGPU is amortized. Additionally, this work will take advantage of GPU-specific hardware (i.e. fast texture memory, pixel-pipelines, hardware interpolators, and varying memory hierarchy) that will allow for additional performance improvements.
T to compute the radiography properties of various materials, the flux profiles of X-ray sources must be characterized. This report describes the characterization of X-ray beam profiles from a Kimtron industrial 450 kVp radiography system with a Comet MXC-45 HP/11 bipolar oil-cooled X-ray tube. The empirical method described here uses a detector response function to derive photon flux profiles based on data collected with a small cadmium telluride detector. The flux profiles are then reduced to a simple parametric form that enables computation of beam profiles for arbitrary accelerator energies.
Explosive growth in photovoltaic markets has fueled new creative approaches that promise to cut costs and improve reliability of system components. However, market demands require rapid development of these new and innovative technologies in order to compete with more established products and capture market share. Often times diagnostics that assist in R&D do not exist or have not been applied due to the innovative nature of the proposed products. Some diagnostics such as IR imaging, electroluminescence, light IV, dark IV, x-rays, and ultrasound have been employed in the past and continue to serve in development of new products, however, innovative products with new materials, unique geometries, and previously unused manufacturing processes require additional or improved test capabilities. This fast-track product development cycle requires diagnostic capabilities to provide the information that confirms the integrity of manufacturing techniques and provides the feedback that can spawn confidence in process control, reliability and performance. This paper explores the use of digital radiography and computed tomography (CT) with other diagnostics to support photovoltaic R&D and manufacturing applications.
A series of experiments has been performed to allow observation of the foaming process and the collection of temperature, rise rate, and microstructural data. Microfocus video is used in conjunction with particle image velocimetry (PIV) to elucidate the boundary condition at the wall. Rheology, reaction kinetics and density measurements complement the flow visualization. X-ray computed tomography (CT) is used to examine the cured foams to determine density gradients. These data provide input to a continuum level finite element model of the blowing process.
