ASCI visualization tool evaluation, Version 2.0

The charter of the ASCI Visualization Common Tools subgroup was to investigate and evaluate 3D scientific visualization tools. As part of that effort, a Tri-Lab evaluation effort was launched in February of 1996. The first step was to agree on a thoroughly documented list of 32 features against which all tool candidates would be evaluated. These evaluation criteria were both gleaned from a user survey and determined from informed extrapolation into the future, particularly as concerns the 3D nature and extremely large size of ASCI data sets. The second step was to winnow a field of 41 candidate tools down to 11. The selection principle was to be as inclusive as practical, retaining every tool that seemed to hold any promise of fulfilling all of ASCI`s visualization needs. These 11 tools were then closely investigated by volunteer evaluators distributed across LANL, LLNL, and SNL. This report contains the results of those evaluations, as well as a discussion of the evaluation philosophy and criteria

Level set modeling and segmentation of diffusion tensor magnetic resonance imaging brain data

Segmentation of anatomical regions of the brain is one of the fundamental problems in medical image analysis. It is traditionally solved by iso-surfacing or through the use of active contours/deformable models on a gray-scale magnetic resonance imaging (MRI) data. We develop a technique that uses anisotropic diffusion properties of brain tissue available from diffusion tensor (DT)-MRI to segment brain structures. We develop a computational pipeline starting from raw diffusion tensor data through computation of invariant anisotropy measures to construction of geometric models of the brain structures. This provides an environment for user-controlled 3-D segmentation of DT-MRI datasets. We use a level set approach to remove noise from the data and to produce smooth, geometric models. We apply our technique to DT-MRI data of a human subject and build models of the isotropic and strongly anisotropic regions of the brain. Once geometric models have been constructed they can be combined to study spatial relationships and quantitatively analyzed to produce the volume and surface area of the segmented regions

Efficient and Open-Source Tool for the Prediction of Thermowell Structural Response

Thermowells are widely used in the aid of the measurement of temperature in high velocity or corrosive flow in large industrial installations. They are susceptible to vortex induced vibration which can be a cause of two types of damage; fatigue failure and resonance failure. Hence it is important to understand the mechanisms that may avoid vortex induced vibration as failure of a thermowell can cause a leak in the pipe or vessel it is installed on. An industry standard for the sizing and installation of a thermowell in order to avoid failure due to vortex induced vibration, hydrostatic pressure or static bending already exists. The standard is thorough and has been amended as recently as 2016 in order to increase safety in working with thermowells. However, it has its shortcomings with some assumptions it makes and when considering unique designs. A unique design of particular interest from industry is that of a cylindrical well with helical strakes attached. This affects the boundary layer of the fluid on the thermowell.In this work, a novel tool is developed for computing the structural response of a thermowell depending on the flow environment in which it is placed in. The tool exploits one-way coupling requiring the physics of fluid flow and solid dynamics. The incompressible Navier-Stokes equations with a RANS turbulence model and a structural modal superposition method are used to solve for the fluid and the solid. An experimental setup was also proposed with the purpose of benchmarking the numerical approach, however, experimental testing was not pursued.The numerical model showed a significant reduction in time dynamic oscillatory force being applied to the thermowell when helical strakes are introduced but an increase in steady state force. Therefore, with the presence of helical strakes, the dynamics stress levels that the thermowell experiences is reduced making the thermowell less susceptible to failure

3D modeling and segmentation of diffusion weighted MRI data

Diffusion weighted magnetic resonance imaging (DW MRI) is a technique that measures the diffusion properties of water molecules to produce a tensor-valued volume dataset. Because water molecules can diffuse more easily along fiber tracts, for example in the brain, rather than across them, diffusion is anisotropic and can be used for segmentation. Segmentation requires the identification of regions with different diffusion properties. In this paper we propose a new set of rotationally invariant diffusion measures which may be used to map the tensor data into a scalar representation. Our invariants may be rapidly computed because they do not require the calculation of eigenvalues. We use these invariants to analyze a 3D DW MRI scan of a human head and build geometric models corresponding to isotropic and anisotropic regions. We then utilize the models to perform quantitative analysis of these regions, for example calculating their surface area and volume

As-Built Modeling of Ojbects for Performance Assessment

The goal of ''as-built'' computational modeling is to incorporate the most representative geometry and material information for an (fabricated or legacy) object into simulations. While most engineering finite element simulations are based on an object's idealized ''as-designed'' configuration with information obtained from technical drawings or computer-aided design models, ''as-built'' modeling uses nondestructive characterization and metrology techniques to provide the feature information. By incorporating more representative geometry and material features as initial conditions, the uncertainty in the simulation results can be reduced, providing a more realistic understanding of the event and object being modeled. In this paper, key steps and technology areas in the as-built modeling framework are: (1) inspection using non-destructive characterization (NDC) and metrology techniques; (2) data reduction (signal and image processing including artifact removal, data sensor fusion, and geometric feature extraction); and (3) engineering and physics analysis using finite element codes. We illustrate the process with a cylindrical phantom and include a discussion of the key concepts and areas that need improvement. Our results show that reasonable as-built initial conditions based on a volume overlap criteria can be achieved and that notable differences between simulations of the as-built and as-designed configurations can be observed for a given load case. Specifically, a volume averaged difference of accumulated plastic strain of 3% and local spatially varying differences up to 10%. The example presented provides motivation and justification to engineering teams for the additional effort required in the as-built modeling of high value parts. Further validation of the approach has been proposed as future work

Great Lakes Research Review 2001

Several years ago, staff from the Great Lakes Program, the Great Lakes Research Consortium, and New York Sea Grant realized an information gap existed between peer reviewed journal articles and newsletter type information related to Great Lakes research. The Great Lakes Research Review was created to fill that gap by providing a substantive overview of research being conducted throughout the basin. It is designed to inform researchers, policy-makers, educators, managers and stakeholders about Great Lakes research efforts, particularly but not exclusively being conducted by scientists affilliated with the Consortium and its member institutions. Each issue has a special theme. Past issues have focused on the fate and transport of toxic substances, the effects of toxics, fisheries issues, and exotic species. The most recent volumes have focused on the Lake Ontario, St. Lawrence River and Lake Erie Ecosystems. The present issue is the second of two describing work related to Lake Erie. We gratefully acknowledge all of the contributing authors who willingly share their research efforts for this publication

Computational methods to engineer process-structure-property relationships in organic electronics: The case of organic photovoltaics

Ever since the Nobel prize winning work by Heeger and his colleagues, organic electronics enjoyed increasing attention from researchers all over the world. While there is a large potential for organic electronics in areas of transistors, solar cells, diodes, flexible displays, RFIDs, smart textiles, smart tattoos, artificial skin, bio-electronics, medical devices and many more, there have been very few applications that reached the market. Organic photovoltaics especially can utilize large market of untapped solar power -- portable and affordable solar conversion devices. While there are several reasons for their unavailability, a major one is the challenge of controlling device morphology at several scales, simultaneously. The morphology is intricately related to the processing of the device and strongly influences performance. Added to this is the unending development of new polymeric materials in search of high power conversion efficiencies. Fully understanding this intricate relationship between materials, processing conditions and power conversion is highly resource and time intensive. The goal of this work is to provide tightly coupled computational routes to these expensive experiments, and demonstrate process control using in-silico experiments. This goal is achieved in multiple stages and is commonly called the process-structure-property loop in material science community. We leverage recent advances in high performance computing (HPC) and high throughput computing (HTC) towards this end. Two open-source software packages were developed: GRATE and PARyOpt. GRATE provides a means to reliably and repeatably quantify TEM images for identifying transport characteristics. It solves the problem of manually quantifying large number of large images with fine details. PARyOpt is a Gaussian process based optimization library that is especially useful for optimizing expensive phenomena. Both these are highly modular and designed to be easily integrated with existing software. It is anticipated that the organic electronics community will use these tools to accelerate discovery and development of new-age devices