Over the last three years the Neurons to Algorithms (N2A) LDRD project teams has built infrastructure to discover computational structures in the brain. This consists of a modeling language, a tool that enables model development and simulation in that language, and initial connections with the Neuroinformatics community, a group working toward similar goals. The approach of N2A is to express large complex systems like the brain as populations of a discrete part types that have specific structural relationships with each other, along with internal and structural dynamics. Such an evolving mathematical system may be able to capture the essence of neural processing, and ultimately of thought itself. This final report is a cover for the actual products of the project: the N2A Language Specification, the N2A Application, and a journal paper summarizing our methods.
This is the final report on the LDRD, though the interested reader is referred to the ANS Transactions paper which more thoroughly documents the technical work of this project.
Density-functional theory calculations, ab-initio molecular dynamics, and the Kubo-Greenwood formula are applied to predict electrical conductivity in Ta2Ox (0 x 5) as a function of composition, phase, and temperature, where additional focus is given to various oxidation states of the O monovacancy (VOn; n=0,1+,2+). Our calculations of DC conductivity at 300K agree well with experimental measurements taken on Ta2Ox thin films and bulk Ta2O5 powder-sintered pellets, although simulation accuracy can be improved for the most insulating, stoichiometric compositions. Our conductivity calculations and further interrogation of the O-deficient Ta2O5 electronic structure provide further theoretical basis to substantiate VO0 as a donor dopant in Ta2O5 and other metal oxides. Furthermore, this dopant-like behavior appears specific to neutral VO cases in both Ta2O5 and TiO2 and was not observed in other oxidation states. This suggests that reduction and oxidation reactions may effectively act as donor activation and deactivation mechanisms, respectively, for VO0 in transition metal oxides.
Molybdenum electrical interconnects for thermoelectric modules were produced by air plasma spraying a 30%CE%BCm size molybdenum powder through a laser-cut Kapton tape mask. Initial feasibility demonstrations showed that the molybdenum coating exhibited excellent feature and spacing retention (~170%CE%BCm), adhered to bismuth-telluride, and exhibited electrical conductivity appropriate for use as a thermoelectric module interconnect. A design of experiments approach was used to optimize air plasma spray process conditions to produce a molybdenum coating with low electrical resistivity. Finally, a molybdenum coating was successfully produced on a fullscale thermoelectric module. After the addition of a final titanium/gold layer deposited on top of the molybdenum coating, the full scale module exhibited an electrical resistivity of 128%CE%A9, approaching the theoretical resistivity value for the 6mm module leg of 112%CE%A9. Importantly, air plasma sprayed molybdenum did not show significant chemical reaction with bismuth-telluride substrate at the coating/substrate interface. The molybdenum coating microstructure consisted of lamellar splats containing columnar grains. Air plasma sprayed molybdenum embedded deeply (several microns) into the bismuth-telluride substrate, leading to good adhesion between the coating and the substrate. Clusters of round pores (and cracks radiating from the pores) were found immediately beneath the molybdenum coating. These pores are believed to result from tellurium vaporization during the spray process where the molten molybdenum droplets (2623%C2%B0C) transferred their heat of solidification to the substrate at the moment of impact. Substrate cooling during the molybdenum deposition process was recommended to mitigate tellurium vaporization in future studies.
This report describes X-cut lithium niobates (LiNbO3) utilization for voltage sensing by monitoring the acoustic wave propagation changes through LiNbO3 resulting from applied voltage. Direct current (DC), alternating current (AC) and pulsed voltage signals were applied to the crystal. Voltage induced shift in acoustic wave propagation time scaled quadratically for DC and AC voltages and linearly for pulsed voltages. The measured values ranged from 10 - 273 ps and 189 ps 2 ns for DC and non-DC voltages, respectively. Data suggests LiNbO3 has a frequency sensitive response to voltage. If voltage source error is eliminated through physical modeling from the uncertainty budget, the sensors U95 estimated combined uncertainty could decrease to ~0.025% for DC, AC, and pulsed voltage measurements.
The objective of this work was to describe several of the ductile failure criteria com- monly used to solve practical problems. The following failure models were considered: equivalent plastic strain, equivalent plastic strain in tension, maximum shear, Mohr- Coulomb, Wellman's tearing parameter, Johnson-Cook and BCJ MEM. The document presents the main characteristics of each failure model as well as sample failure predic- tions for simple proportional loading stress histories in three dimensions and in plane stress. Plasticity calculations prior to failure were conducted with a simple, linear hardening, J2 plasticity model. The resulting failure envelopes were plotted in prin- cipal stress space and plastic strain space, where the dependence on stress triaxiality and Lode angle are clearly visible. This information may help analysts select a ductile fracture model for a practical problem and help interpret analysis results.
The recently developed Magnetically Applied Pressure-Shear (MAPS) experimental technique to measure material shear strength at high pressures on magneto-hydrodynamic (MHD) drive pulsed power platforms was fielded on August 16, 2013 on shot Z2544 utilizing hardware set A0283A. Several technical and engineering challenges were overcome in the process leading to the attempt to measure the dynamic strength of NNSA Ta at 50 GPa. The MAPS technique relies on the ability to apply an external magnetic field properly aligned and time correlated with the MHD pulse. The load design had to be modified to accommodate the external field coils and additional support was required to manage stresses from the pulsed magnets. Further, this represents the first time transverse velocity interferometry has been applied to diagnose a shot at Z. All subsystems performed well with only minor issues related to the new feed design which can be easily addressed by modifying the current pulse shape. Despite the success of each new component, the experiment failed to measure strength in the samples due to spallation failure, most likely in the diamond anvils. To address this issue, hydrocode simulations are being used to evaluate a modified design using LiF windows to minimize tension in the diamond and prevent spall. Another option to eliminate the diamond material from the experiment is also being investigated.
The Predictive Capability Maturity Model (PCMM) is an expert elicitation tool designed to characterize and communicate completeness of the approaches used for computational model definition, verification, validation, and uncertainty quantification associated for an intended application. The primary application of this tool at Sandia National Laboratories (SNL) has been for physics-based computational simulations in support of nuclear weapons applications. The two main goals of a PCMM evaluation are 1) the communication of computational simulation capability, accurately and transparently, and 2) the development of input for effective planning. As a result of the increasing importance of computational simulation to SNLs mission, the PCMM has evolved through multiple generations with the goal to provide more clarity, rigor, and completeness in its application. This report describes the approach used to develop the fourth generation of the PCMM.
This report describes the theoretical background on modeling electron transport in the presence of electric and magnetic fields by incorporating the effects of the Lorentz force on electron motion into the Boltzmann transport equation. Electromagnetic fields alter the electron energy and trajectory continuously, and these effects can be characterized mathematically by differential operators in terms of electron energy and direction. Numerical solution techniques, based on the discrete-ordinates and finite-element methods, are developed and implemented in an existing radiation transport code, SCEPTRE.
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.
Atomistic-scale behavior drives performance in many micro- and nano-fluidic systems, such as mircrofludic mixers and electrical energy storage devices. Bringing this information into the traditionally continuum models used for engineering analysis has proved challenging. This work describes one such approach to address this issue by developing atomistic-to-continuum multi scale and multi physics methods to enable molecular dynamics (MD) representations of atoms to incorporated into continuum simulations. Coupling is achieved by imposing constraints based on fluxes of conserved quantities between the two regions described by one of these models. The impact of electric fields and surface charges are also critical, hence, methodologies to extend finite-element (FE) MD electric field solvers have been derived to account for these effects. Finally, the continuum description can have inconsistencies with the coarse-grained MD dynamics, so FE equations based on MD statistics were derived to facilitate the multi scale coupling. Examples are shown relevant to nanofluidic systems, such as pore flow, Couette flow, and electric double layer.
This report documents work conducted in FY13 to conduct a feasibility study on thermal spray coated cathodes to be used in the RITS-6 accelerator in an attempt to improve surface uniformity and repeatability. Currently, the cathodes are coated with colloidal silver by means of painting by hand. It is believed that improving the cathode coating process could simplify experimental setup and improve flash x-ray radiographic performance. This report documents the experimental setup and summarizes the results of our feasibility study. Lastly, it describes the path forward and potential challenges that must be overcome in order to improve the process for creating uniform and repeatable silver coatings for cathodes.
We present the results of a three-year LDRD project focused on understanding microstructural evolution and related property changes in Zr-based nuclear cladding materials towards the development of high fidelity predictive simulations for long term dry storage. Experiments and modeling efforts have focused on the effects of hydride formation and accumulation of irradiation defects. Key results include: determination of the influence of composition and defect structures on hydride formation; measurement of the electrochemical property differences between hydride and parent material for understanding and predicting corrosion resistance; in situ environmental transmission electron microscope observation of hydride formation; development of a predictive simulation for mechanical property changes as a function of irradiation dose; novel test method development for microtensile testing of ionirradiated material to simulate the effect of neutron irradiation on mechanical properties; and successful demonstration of an Idaho National Labs-based sample preparation and shipping method for subsequent Sandia-based analysis of post-reactor cladding.
This document provides a detailed discussion and a guide for the use of the RadCat 6.0 Graphical User Interface input file generator for the RADTRAN code, Version 6. RadCat 6.0 integrates the newest analysis capabilities of RADTRAN 6.0, including an economic model, updated loss-of-lead shielding model, a new ingestion dose model, and unit conversion. As of this writing, the RADTRAN version in use is RADTRAN 6.02.
At sufficiently high energies, the wavelengths of electrons and photons are short enough to only interact with one atom at time, leading to the popular %E2%80%9Cindependent-atom approximation%E2%80%9D. We attempted to incorporate atomic structure in the generation of cross sections (which embody the modeled physics) to improve transport at lower energies. We document our successes and failures. This was a three-year LDRD project. The core team consisted of a radiation-transport expert, a solid-state physicist, and two DFT experts.
This report describes an Early Career Laboratory Directed Research and Development (LDRD) project to develop an interface tracking model for droplet electrocoalescence. Many fluid-based technologies rely on electrical fields to control the motion of droplets, e.g. microfluidic devices for high-speed droplet sorting, solution separation for chemical detectors, and purification of biodiesel fuel. Precise control over droplets is crucial to these applications. However, electric fields can induce complex and unpredictable fluid dynamics. Recent experiments (Ristenpart et al. 2009) have demonstrated that oppositely charged droplets bounce rather than coalesce in the presence of strong electric fields. A transient aqueous bridge forms between approaching drops prior to pinch-off. This observation applies to many types of fluids, but neither theory nor experiments have been able to offer a satisfactory explanation. Analytic hydrodynamic approximations for interfaces become invalid near coalescence, and therefore detailed numerical simulations are necessary. This is a computationally challenging problem that involves tracking a moving interface and solving complex multi-physics and multi-scale dynamics, which are beyond the capabilities of most state-of-the-art simulations. An interface-tracking model for electro-coalescence can provide a new perspective to a variety of applications in which interfacial physics are coupled with electrodynamics, including electro-osmosis, fabrication of microelectronics, fuel atomization, oil dehydration, nuclear waste reprocessing and solution separation for chemical detectors. We present a conformal decomposition finite element (CDFEM) interface-tracking method for the electrohydrodynamics of two-phase flow to demonstrate electro-coalescence. CDFEM is a sharp interface method that decomposes elements along fluid-fluid boundaries and uses a level set function to represent the interface.
Chronic infection with Hepatitis C virus (HCV) results in cirrhosis, liver cancer and death. As the nations largest provider of care for HCV, US Veterans Health Administration (VHA) invests extensive resources in the diagnosis and treatment of the disease. This report documents modeling and analysis of HCV treatment dynamics performed for the VHA aimed at improving service delivery efficiency. System dynamics modeling of disease treatment demonstrated the benefits of early detection and the role of comorbidities in disease progress and patient mortality. Preliminary modeling showed that adherence to rigorous treatment protocols is a primary determinant of treatment success. In depth meta-analysis revealed correlations of adherence and various psycho-social factors. This initial meta-analysis indicates areas where substantial improvement in patient outcomes can potentially result from VA programs which incorporate these factors into their design.
Active brazes have been used for many years to produce bonds between metal and ceramic objects. By including a relatively small of a reactive additive to the braze one seeks to improve the wetting and spreading behavior of the braze. The additive modifies the substrate, either by a chemical surface reaction or possibly by alloying. By its nature, the joining process with active brazes is a complex nonequilibrium non-steady state process that couples chemical reaction, reactant and product diffusion to the rheology and wetting behavior of the braze. Most of the these subprocesses are taking place in the interfacial region, most are difficult to access by experiment. To improve the control over the brazing process, one requires a better understanding of the melting of the active braze, rate of the chemical reaction, reactant and product diffusion rates, nonequilibrium composition-dependent surface tension as well as the viscosity. This report identifies ways in which modeling and theory can assist in improving our understanding.
Visible spectroscopy is a powerful diagnostic, allowing plasma parameters ranging from temperature and density to electric and magnetic fields to be measured. Spectroscopic dopants are commonly introduced to make these measurements. On Z, dopants are introduced passively (i.e. a salt deposited on a current-carrying surface); however, in some cases, passive doping can limit the times and locations at which measurements can be made. Active doping utilizes an auxiliary energy source to disperse the dopant independently from the rest of the experiment. The objective of this LDRD project was to explore laser ablation as a method of actively introducing spectroscopic dopants. Ideally, the laser energy would be delivered to the dopant via fiber optic, which would eliminate the need for time-intensive laser alignments in the Z chamber. Experiments conducted in a light lab to assess the feasibility of fibercoupled and open-beam laser-ablated doping are discussed.
We develop a novel calibration approach to address the problem of predictive ke RANS simulations of jet-incrossflow. Our approach is based on the hypothesis that predictive ke parameters can be obtained by estimating them from a strongly vortical flow, specifically, flow over a square cylinder. In this study, we estimate three ke parameters, C%CE%BC, Ce2 and Ce1 by fitting 2D RANS simulations to experimental data. We use polynomial surrogates of 2D RANS for this purpose. We conduct an ensemble of 2D RANS runs using samples of (C%CE%BC;Ce2;Ce1) and regress Reynolds stresses to the samples using a simple polynomial. We then use this surrogate of the 2D RANS model to infer a joint distribution for the ke parameters by solving a Bayesian inverse problem, conditioned on the experimental data. The calibrated (C%CE%BC;Ce2;Ce1) distribution is used to seed an ensemble of 3D jet-in-crossflow simulations. We compare the ensemble's predictions of the flowfield, at two planes, to PIV measurements and estimate the predictive skill of the calibrated 3D RANS model. We also compare it against 3D RANS predictions using the nominal (uncalibrated) values of (C%CE%BC;Ce2;Ce1), and find that calibration delivers a significant improvement to the predictive skill of the 3D RANS model. We repeat the calibration using surrogate models based on kriging and find that the calibration, based on these more accurate models, is not much better that those obtained with simple polynomial surrogates. We discuss the reasons for this rather surprising outcome.
As alternative energy generating devices (i.e., solar, wind, etc) are added onto the electrical energy grid (AC grid), irregularities in the available electricity due to natural occurrences (i.e., clouds reducing solar input or wind burst increasing wind powered turbines) will be dramatically increased. Due to their almost instantaneous response, modern flywheel-based energy storage devices can act a mechanical mechanism to regulate the AC grid; however, improved spin speeds will be required to meet the necessary energy levels to balance these green energy variances. Focusing on composite flywheels, we have investigated methods for improving the spin speeds based on materials needs. The so-called composite flywheels are composed of carbon fiber (C-fiber), glass fiber, and a glue (resin) to hold them together. For this effort, we have focused on the addition of fillers to the resin in order to improve its properties. Based on the high loads required for standard meso-sized fillers, this project investigated the utility of ceramic nanofillers since they can be added at very low load levels due to their high surface area. The impact that TiO2 nanowires had on the final strength of the flywheel material was determined by a three-point-bend test. The results of the introduction of nanomaterials demonstrated an increase in strength of the flywheels C-fiber-resin moiety, with an upper limit of a 30% increase being reported. An analysis of the economic impact concerning the utilization of the nanowires was undertaken and after accounting for new-technology and additional production costs, return on improved-nanocomposite investment was approximated at 4-6% per year over the 20-year expected service life. Further, it was determined based on the 30% improvement in strength, this change may enable a 20-30% reduction in flywheel energy storage cost ($/kW-h).
This paper proposes a tolerance bound approach for determining sample sizes. With this new methodology we begin to think of sample size in the context of uncertainty exceeding margin. As the sample size decreases the uncertainty in the estimate of margin increases. This can be problematic when the margin is small and only a few units are available for testing. In this case there may be a true underlying positive margin to requirements but the uncertainty may be too large to conclude we have sufficient margin to those requirements with a high level of statistical confidence. Therefore, we provide a methodology for choosing a sample size large enough such that an estimated QMU uncertainty based on the tolerance bound approach will be smaller than the estimated margin (assuming there is positive margin). This ensures that the estimated tolerance bound will be within performance requirements and the tolerance ratio will be greater than one, supporting a conclusion that we have sufficient margin to the performance requirements. In addition, this paper explores the relationship between margin, uncertainty, and sample size and provides an approach and recommendations for quantifying risk when sample sizes are limited.
Bioweapons and emerging infectious diseases pose growing threats to our national security. Both natural disease outbreak and outbreaks due to a bioterrorist attack are a challenge to detect, taking days after the outbreak to identify since most outbreaks are only recognized through reportable diseases by health departments and reports of unusual diseases by clinicians. In recent decades, arthropod-borne viruses (arboviruses) have emerged as some of the most significant threats to human health. They emerge, often unexpectedly, from cryptic transmission foci causing localized outbreaks that can rapidly spread to multiple continents due to increased human travel and trade. Currently, diagnosis of acute infections requires amplification of viral nucleic acids, which can be costly, highly specific, technically challenging and time consuming. No diagnostic devices suitable for use at the bedside or in an outbreak setting currently exist. The original goals of this project were to 1) develop two highly sensitive and specific diagnostic assays for detecting RNA from a wide range of arboviruses; one based on an electrochemical approach and the other a fluorescent based assay and 2) develop prototype microfluidic diagnostic platforms for preclinical and field testing that utilize the assays developed in goal 1. We generated and characterized suitable primers for West Nile Virus RNA detection. Both optical and electrochemical transduction technologies were developed for DNA-RNA hybridization detection and were implemented in microfluidic diagnostic sensing platforms that were developed in this project.
We performed optical electric field measurements ion nanosecond time scales using the electrooptic crystal beta barium borate (BBO). Tests were based on a preliminary bench top design intended to be a proofofprinciple stepping stone towards a modulardesign optical Efield diagnostic that has no metal in the interrogated environment. The long term goal is to field a modular version of the diagnostic in experiments on large scale xray source facilities, or similarly harsh environments.
In the supercritical CO2-water-mineral systems relevant to subsurface CO2 sequestration, interfacial processes at the supercritical fluid-mineral interface will strongly affect core- and reservoir-scale hydrologic properties. Experimental and theoretical studies have shown that water films will form on mineral surfaces in supercritical CO2, but will be thinner than those that form in vadose zone environments at any given matric potential. The theoretical model presented here allows assessment of water saturation as a function of matric potential, a critical step for evaluating relative permeabilities the CO2 sequestration environment. The experimental water adsorption studies, using Quartz Crystal Microbalance and Fourier Transform Infrared Spectroscopy methods, confirm the major conclusions of the adsorption/condensation model. Additional data provided by the FTIR study is that CO2 intercalation into clays, if it occurs, does not involve carbonate or bicarbonate formation, or significant restriction of CO2 mobility. We have shown that the water film that forms in supercritical CO2 is reactive with common rock-forming minerals, including albite, orthoclase, labradorite, and muscovite. The experimental data indicate that reactivity is a function of water film thickness; at an activity of water of 0.9, the greatest extent of reaction in scCO2 occurred in areas (step edges, surface pits) where capillary condensation thickened the water films. This suggests that dissolution/precipitation reactions may occur preferentially in small pores and pore throats, where it may have a disproportionately large effect on rock hydrologic properties. Finally, a theoretical model is presented here that describes the formation and movement of CO2 ganglia in porous media, allowing assessment of the effect of pore size and structural heterogeneity on capillary trapping efficiency. The model results also suggest possible engineering approaches for optimizing trapping capacity and for monitoring ganglion formation in the subsurface.
As with other large healthcare organizations, medical adverse events at the Department of Veterans Affairs (VA) facilities can expose patients to unforeseen negative risks. VHA leadership recognizes that properly handled disclosure of adverse events can minimize potential harm to patients and negative consequences for the effective functioning of the organization. The work documented here seeks to help improve the disclosure process by situating it within the broader theoretical framework of issues management, and to identify opportunities for process improvement through modeling disclosure and reactions to disclosure. The computational model will allow a variety of disclosure actions to be tested across a range of incident scenarios. Our conceptual model will be refined in collaboration with domain experts, especially by continuing to draw on insights from VA Study of the Communication of Adverse Large-Scale Events (SCALE) project researchers.
Corrosion tests at 400, 500, and 680ÀC were performed using four high temperature alloys; 347SS, 321SS In625, and HA230. Molten salt chemistry was monitored over time through analysis of nitrite, carbonate, and dissolved metals. Metallography was performed on alloys at 500 and 680ÀC, due to the relatively thin oxide scale observed at 400ÀC. At 500ÀC, corrosion of iron based alloys took the form of chromium depletion and iron oxides, while nickel based alloys also had chromium depletion and formation of NiO. Chromium was detected in relatively low concentrations at this temperature. At 680ÀC, significant surface corrosion occurred with metal losses greater than 450microns/year after 1025hours of exposure. Iron based alloys formed complex iron, sodium, and chromium oxides. Some data suggests grain boundary chromium depletion of 321SS. Nickel alloys formed NiO and metallic nickel corrosion morphologies, with HA230 displaying significant internal oxidation in the form of chromia. Nickel alloys both exhibited worse corrosion than iron based alloys likely due to preferential dissolution of chromium, molybdenum, and tungsten.
The performance, reproducibility and reliability of metal joints are complex functions of the detailed history of physical processes involved in their creation. Prediction and control of these processes constitutes an intrinsically challenging multi-physics problem involving heating and melting a metal alloy and reactive wetting. Understanding this process requires coupling strong molecularscale chemistry at the interface with microscopic (diffusion) and macroscopic mass transport (flow) inside the liquid followed by subsequent cooling and solidification of the new metal mixture. The final joint displays compositional heterogeneity and its resulting microstructure largely determines the success or failure of the entire component. At present there exists no computational tool at Sandia that can predict the formation and success of a braze joint, as current capabilities lack the ability to capture surface/interface reactions and their effect on interface properties. This situation precludes us from implementing a proactive strategy to deal with joining problems. Here, we describe what is needed to arrive at a predictive modeling and simulation capability for multicomponent metals with complicated phase diagrams for melting and solidification, incorporating dissolutive and composition-dependent wetting.
This report documents work conducted in FY13 on electrical discharge experiments performed to develop predictive computational models of the fundamental processes of surface breakdown in the vicinity of high-permittivity material interfaces. Further, experiments were conducted to determine if free carrier electrons could be excited into the conduction band thus lowering the effective breakdown voltage when UV photons (4.66 eV) from a high energy pulsed laser were incident on the rutile sample. This report documents the numerical approach, the experimental setup, and summarizes the data and simulations. Lastly, it describes the path forward and challenges that must be overcome in order to improve future experiments for characterizing the breakdown behavior for rutile.
Infectious diseases can spread rapidly through healthcare facilities, resulting in widespread illness among vulnerable patients. Computational models of disease spread are useful for evaluating mitigation strategies under different scenarios. This report describes two infectious disease models built for the US Department of Veteran Affairs (VA) motivated by a Varicella outbreak in a VA facility. The first model simulates disease spread within a notional contact network representing staff and patients. Several interventions, along with initial infection counts and intervention delay, were evaluated for effectiveness at preventing disease spread. The second model adds staff categories, location, scheduling, and variable contact rates to improve resolution. This model achieved more accurate infection counts and enabled a more rigorous evaluation of comparative effectiveness of interventions.
An ideal 3He detector replacement for the near- to medium-term future will use materials that are easy to produce and well understood, while maintaining thermal neutron detection efficiency and gamma rejection close to the 3He standard. Toward this end, we investigated the use of standard alkali halide scintillators interfaced with 6Li and read out with photomultiplier tubes (PMTs). Thermal neutrons are captured on 6Li with high efficiency, emitting high-energy and triton (3H) reaction products. These particles deposit energy in the scintillator, providing a thermal neutron signal; discrimination against gamma interactions is possible via pulse shape discrimination (PSD), since heavy particles produce faster pulses in alkali halide crystals. We constructed and tested two classes of detectors based on this concept. In one case 6Li is used as a dopant in polycrystalline NaI; in the other case a thin Li foil is used as a conversion layer. In the configurations studied here, these systems are sensitive to both gamma and neutron radiation, with discrimination between the two and good energy resolution for gamma spectroscopy. We present results from our investigations, including measurements of the neutron efficiency and gamma rejection for the two detector types. We also show a comparison with Cs2LiYCl6:Ce (CLYC), which is emerging as the standard scintillator for simultaneous gamma and thermal neutron detection, and also allows PSD. We conclude that 6Li foil with CsI scintillating crystals has near-term promise as a thermal neutron detector in applications previously dominated by 3He detectors. The other approach, 6Li-doped alkali halides, has some potential, but require more work to understand material properties and improve fabrication processes.
Two categories of challenges confront the developer of computational spray models: those related to the computation and those related to the physics. Regarding the computation, the trend towards heterogeneous, multi- and many-core platforms will require considerable re-engineering of codes written for the current supercomputing platforms. Regarding the physics, accurate methods for transferring mass, momentum and energy from the dispersed phase onto the carrier fluid grid have so far eluded modelers. Significant challenges also lie at the intersection between these two categories. To be competitive, any physics model must be expressible in a parallel algorithm that performs well on evolving computer platforms. This work created an application based on a software architecture where the physics and software concerns are separated in a way that adds flexibility to both. The develop spray-tracking package includes an application programming interface (API) that abstracts away the platform-dependent parallelization concerns, enabling the scientific programmer to write serial code that the API resolves into parallel processes and threads of execution. The project also developed the infrastructure required to provide similar APIs to other application. The API allow object-oriented Fortran applications direct interaction with Trilinos to support memory management of distributed objects in central processing units (CPU) and graphic processing units (GPU) nodes for applications using C++.
This report summarizes the result of a NEAMS project focused on the use of reliability methods within the RAVEN and RELAP-7 software framework for assessing failure probabilities as part of probabilistic risk assessment for nuclear power plants. RAVEN is a software tool under development at the Idaho National Laboratory that acts as the control logic driver and post-processing tool for the newly developed Thermal-Hydraulic code RELAP-7. Dakota is a software tool developed at Sandia National Laboratories containing optimization, sensitivity analysis, and uncertainty quantification algorithms. Reliability methods are algorithms which transform the uncertainty problem to an optimization problem to solve for the failure probability, given uncertainty on problem inputs and a failure threshold on an output response. The goal of this work is to demonstrate the use of reliability methods in Dakota with RAVEN/RELAP-7. These capabilities are demonstrated on a demonstration of a Station Blackout analysis of a simplified Pressurized Water Reactor (PWR).
Most materials microstructural evolution processes progress with multiple processes occurring simultaneously. In this work, we have concentrated on the processes that are active in nuclear materials, in particular, nuclear fuels. These processes are coarsening, nucleation, differential diffusion, phase transformation, radiation-induced defect formation and swelling, often with temperature gradients present. All these couple and contribute to evolution that is unique to nuclear fuels and materials. Hybrid model that combines elements from the Potts Monte Carlo, phase-field models and others have been developed to address these multiple physical processes. These models are described and applied to several processes in this report. An important feature of the models developed are that they are coded as applications within SPPARKS, a Sandiadeveloped framework for simulation at the mesoscale of microstructural evolution processes by kinetic Monte Carlo methods. This makes these codes readily accessible and adaptable for future applications.
We created interactive demonstration activities for Take Our Daughters&Sons to Work Day (TODSTWD) 2013 in order to promote general interest in chemistry and also generate awareness of the type of work our laboratories can perform. %E2%80%9CCurious about Mars Rover Curiosity?%E2%80%9D performed an elemental analysis on rocks brought to our lab using the same technique utilized on the planet Mars by the NASA robotic explorer Curiosity. %E2%80%9CFood is Chemistry?%E2%80%9D utilized a mass spectrometer to measure, in seconds, each participant's breath in order to identify the food item consumed for the activity. A total of over 130 children participated in these activities over a 3 hour block, and feedback was positive. This document reports the materials (including handouts), experimental procedures, and lessons learned so that future demonstrations can benefit from the baseline work performed. We also present example results used to prepare the Food activity and example results collected during the Curiosity demo.
Radiation transport calculations were performed to compute the angular tallies for scattered gamma-rays as a function of distance, height, and environment. Greens Functions were then used to encapsulate the results a reusable transformation function. The calculations represent the transport of photons throughout scattering surfaces that surround sources and detectors, such as the ground and walls. Utilization of these calculations in GADRAS (Gamma Detector Response and Analysis Software) enables accurate computation of environmental scattering for a variety of environments and source configurations. This capability, which agrees well with numerous experimental benchmark measurements, is now deployed with GADRAS Version 18.2 as the basis for the computation of scattered radiation.
This SAND report summarizes the activities and outcomes of the Network and Ensemble Enabled Entity Extraction in Information Text (NEEEEIT) LDRD project, which addressed improving the accuracy of conditional random fields for named entity recognition through the use of ensemble methods.
Resistive random access memory (ReRAM) has become a promising candidate for next-generation high-performance non-volatile memory that operates by electrically tuning resistance states via modulating vacancy concentrations. Here, we demonstrate a wafer-scale process for resistive switching in tantalum oxide that is completely CMOS compatible. The resulting devices are forming-free and with greater than 1x105 cycle endurance.
We design a space efficient algorithm that approximates the transitivity (global clustering coefficient) and total triangle count with only a single pass through a graph given as a stream of edges. Our procedure is based on the classic probabilistic result, the birthday paradox. When the transitivity is constant and there are more edges than wedges (common properties for social networks), we can prove that our algorithm requires O( p n) space (n is the number of vertices) to provide accurate estimates. We run a detailed set of experiments on a variety of real graphs and demonstrate that the memory requirement of the algorithm is a tiny fraction of the graph. For example, even for a graph with 200 million edges, our algorithm stores just 60,000 edges to give accurate results. Being a single pass streaming algorithm, our procedure also maintains a real-Time estimate of the transitivity/number of triangles of a graph, by storing a miniscule fraction of edges.
Nations using borosilicate glass as an immobilization material for radioactive waste have reinforced the importance of scientific collaboration to obtain a consensus on the mechanisms controlling the long-term dissolution rate of glass. This goal is deemed to be crucial for the development of reliable performance assessment models for geological disposal. The collaborating laboratories all conduct fundamental and/or applied research using modern materials science techniques. This paper briefly reviews the radioactive waste vitrification programs of the six participant nations and summarizes the current state of glass corrosion science, emphasizing the common scientific needs and justifications for on-going initiatives.
Everyday problem solving requires the ability to go beyond experience by efficiently encoding and manipulating new information, i.e., fluid intelligence (Gf) [1]. Performance in tasks involving Gf, such as logical and abstract reasoning, has been shown to rely on distributed neural networks, with a crucial role played by prefrontal regions [2]. Synchronization of neuronal activity in the gamma band is a ubiquitous phenomenon within the brain; however, no evidence of its causal involvement in cognition exists to date [3]. Here, we show an enhancement of Gf ability in a cognitive task induced by exogenous rhythmic stimulation within the gamma band. Imperceptible alternating current [4] delivered through the scalp over the left middle frontal gyrus resulted in a frequency-specific shortening of the time required to find the correct solution in a visuospatial abstract reasoning task classically employed to measure Gf abilities (i.e., Raven’s matrices) [5]. Crucially, gamma-band stimulation (γ-tACS) selectively enhanced performance only on more complex trials involving conditional/logical reasoning. The finding presented here supports a direct involvement of gamma oscillatory activity in the mechanisms underlying higher-order human cognition.
