Publications

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We are developing computational models to elucidate the expansion and dynamic filling process of a polyurethane foam, PMDI. The polyurethane of interest is chemically blown, where carbon dioxide is produced via the reaction of water, the blowing agent, and isocyanate. The isocyanate also reacts with polyol in a competing reaction, which produces the polymer. Here we detail the experiments needed to populate a processing model and provide parameters for the model based on these experiments. The model entails solving the conservation equations, including the equations of motion, an energy balance, and two rate equations for the polymerization and foaming reactions, following a simplified mathematical formalism that decouples these two reactions. Parameters for the polymerization kinetics model are reported based on infrared spectrophotometry. Parameters describing the gas generating reaction are reported based on measurements of volume, temperature and pressure evolution with time. A foam rheology model is proposed and parameters determined through steady-shear and oscillatory tests. Heat of reaction and heat capacity are determined through differential scanning calorimetry. Thermal conductivity of the foam as a function of density is measured using a transient method based on the theory of the transient plane source technique. Finally, density variations of the resulting solid foam in several simple geometries are directly measured by sectioning and sampling mass, as well as through x-ray computed tomography. These density measurements will be useful for model validation once the complete model is implemented in an engineering code.

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Estimation of the x-ray attenuation properties of an object with respect to the energy emitted from the source is a challenging task for traditional Bremsstrahlung sources. This exploratory work attempts to estimate the x-ray attenuation profile for the energy range of a given Bremsstrahlung profile. Previous work has shown that calculating a single effective attenuation value for a polychromatic source is not accurate due to the non-linearities associated with the image formation process. Instead, we completely characterize the imaging system virtually and utilize an iterative search method/constrained optimization technique to approximate the attenuation profile of the object of interest. This work presents preliminary results from various approaches that were investigated. The early results illustrate the challenges associated with these techniques and the potential for obtaining an accurate estimate of the attenuation profile for objects composed of homogeneous materials.

This paper will investigate energy-efficiency for various real-world industrial computed-tomography reconstruction algorithms, both CPU- and GPU-based implementations. This work shows that the energy required for a given reconstruction is based on performance and problem size. There are many ways to describe performance and energy efficiency, thus this work will investigate multiple metrics including performance-per-watt, energy-delay product, and energy consumption. This work found that irregular GPU-based approaches1 realized tremendous savings in energy consumption when compared to CPU implementations while also significantly improving the performanceper- watt and energy-delay product metrics. Additional energy savings and other metric improvement was realized on the GPU-based reconstructions by improving storage I/O by implementing a parallel MIMD-like modularization of the compute and I/O tasks.

Estimation of the x-ray attenuation properties of an object with respect to the energy emitted from the source is a challenging task for traditional Bremsstrahlung sources. This exploratory work attempts to estimate the x-ray attenuation profile for the energy range of a given Bremsstrahlung profile. Previous work has shown that calculating a single effective attenuation value for a polychromatic source is not accurate due to the non-linearities associated with the image formation process. Instead, we completely characterize the imaging system virtually and utilize an iterative search method/constrained optimization technique to approximate the attenuation profile of the object of interest. This work presents preliminary results from various approaches that were investigated. The early results illustrate the challenges associated with these techniques and the potential for obtaining an accurate estimate of the attenuation profile for objects composed of homogeneous materials.

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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.

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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.

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There has been much work done in implementing various GPU-based Computed Tomography reconstruction algorithms for medical applications showing tremendous improvement in computational performance. While many of these reconstruction algorithms could also be applied to industrial-scale datasets, the performance gains may be modest to non-existent due to a combination of algorithmic, hardware, or scalability limitations. Previous work presented showed an irregular dynamic approach to GPU-Reconstruction kernel execution for industrial-scale reconstructions that dramatically improved voxel processing throughput. However, the improved kernel execution magnified other system bottlenecks such as host memory bandwidth and storage read/write bandwidth, thus hindering performance gains. This paper presents a multi-GPU-based reconstruction algorithm capable of efficiently reconstructing large volumes (between 64 gigavoxels and 1 teravoxel volumes) not only faster than traditional CPU- and GPU-based reconstruction algorithms but also while consuming significantly less energy. The reconstruction algorithm exploits the irregular kernel approach from previous work as well as a modularized MIMD-like environment, heterogeneous parallelism, as well as macro- and micro-scale dynamic task allocation. The result is a portable and flexible reconstruction algorithm capable of executing on a wide range of architectures including mobile computers, workstations, supercomputers, and modestly-sized hetero or homogeneous clusters with any number of graphics processors. © 2013 IEEE.

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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.

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