In order to accurately describe defect accumulation in heterogeneous microstructures and under complex irradiation conditions, simulation methods are needed that can explicitly account for the effect of nonhomogeneous microstructures on damage accumulation. In this project, an advanced simulation tool called spatially resolved stochastic cluster dynamics (SRSCD) is developed for this purpose. The proposed approach relies on solving spatially resolved coupled rate equations of standard cluster dynamics methods in a kinetic Monte Carlo scheme. Large-scale simulations of radiation damage in polycrystalline materials are enabled through several improvements made to this method, including a pseudo-adaptive meshing scheme for cascade implantation and implementation of this method in a synchronous parallel kinetic Monte Carlo framework.
This Environmental Restoration Operations (ER) Consolidated Quarterly Report (ER Quarterly Report) provides the status of ongoing corrective action activities being implemented by Sandia National Laboratories, New Mexico (SNL/NM) for the April, May, and June 2016 quarterly reporting period.
A model-scale wave tank test was conducted in the interest of improving control systems design of wave energy converters (WECs). The success of most control strategies is based directly upon the availability of a reduced-order model with the ability to capture the dynamics of the system with sufficient accuracy. For this reason, the test described in this report, which is the first in a series of planned tests on WEC controls, focused on system identification (system ID) and model validation.
This document presents the facility-recommended characterization of the neutron, prompt gamma-ray, and delayed gamma-ray radiation fields in the Annular Core Research Reactor (ACRR) for the cadmium-polyethylene (CdPoly) bucket in the central cavity on the 32-inch pedestal at the core centerline. The designation for this environment is ACRR-CdPoly-CC-32-cl. The neutron, prompt gamma-ray, and delayed gamma-ray energy spectra, uncertainties, and covariance matrices are presented as well as radial and axial neutron and gamma-ray fluence profiles within the experiment area of the bucket. Recommended constants are given to facilitate the conversion of various dosimetry readings into radiation metrics desired by experimenters. Representative pulse operations are presented with conversion examples. Acknowledgements The authors wish to thank the Annular Core Research Reactor staff and the Radiation Metrology Laboratory staff for their support of this work. Also thanks to Drew Tonigan for helping field the activation experiments in ACRR, David Samuel for helping to finalize the drawings and get the parts fabricated, and Elliot Pelfrey for preparing the active dosimetry plots.
Aria is a Galerkin finite element based program for solving coupled-physics problems described by systems of PDEs and is capable of solving nonlinear, implicit, transient and direct-to-steady state problems in two and three dimensions on parallel architectures. The suite of physics currently supported by Aria includes thermal energy transport, species transport, and electrostatics as well as generalized scalar, vector and tensor transport equations. Additionally, Aria includes support for manufacturing process flows via the incompressible Navier-Stokes equations specialized to a low Reynolds number (Re < 1) regime. Enhanced modeling support of manufacturing processing is made possible through use of either arbitrary Lagrangian- Eulerian (ALE) and level set based free and moving boundary tracking in conjunction with quasi-static nonlinear elastic solid mechanics for mesh control. Coupled physics problems are solved in several ways including fully-coupled Newton's method with analytic or numerical sensitivities, fully-coupled Newton- Krylov methods and a loosely-coupled nonlinear iteration about subsets of the system that are solved using combinations of the aforementioned methods. Error estimation, uniform and dynamic h-adaptivity and dynamic load balancing are some of Aria's more advanced capabilities. Aria is based upon the Sierra Framework.
This report documents conceptual design development for the Deep Borehole Field Test (DBFT), including test packages (simulated waste packages, not containing waste) and a system for demonstrating emplacement and retrieval of those packages in the planned Field Test Borehole (FTB). For the DBFT to have demonstration value, it must be based on conceptualization of a deep borehole disposal (DBD) system. This document therefore identifies key options for a DBD system, describes an updated reference DBD concept, and derives a recommended concept for the DBFT demonstration.
In July, 2016, the Electric Power Research Institute and industry partners performed a field test at the Maine Yankee Nuclear Site, located near Wiscasset, Maine. The primary goal of the field test was to evaluate the use of robots in surveying the surface of an in-service interim storage canister within an overpack; however, as part of the demonstration, dust and soluble salt samples were collected from horizontal surfaces within the interim storage system. The storage system is a vertical system made by NAC International, consisting of a steel-lined concrete overpack containing a 304 stainless steel (SS) welded storage canister. The canister did not contain spent fuel but rather greater-than-class-C waste, which did not generate significant heat, limiting airflow through the storage system. The surfaces that were sampled for deposits included the top of the shield plug, the side of the canister, and a shelf at the bottom of the overpack, just below the level of the pillar on which the canister sits. The samples were sent to Sandia National Laboratories for analysis. This report summarizes the results of those analyses. Because the primary goal of the field test was to evaluate the use of robots in surveying the surface of the canister within the overpack, collection of dust samples was carried out in a qualitative fashion, using paper filters and sponges as the sampling media. The sampling focused mostly on determining the composition of soluble salts present in the dust. It was anticipated that a wet substrate would more effectively extract soluble salts from the surface that was sampled, so both the sponges and the filter paper were wetted prior to being applied to the surface of the metal. Sampling was accomplished by simply pressing the damp substrate against the metal surface for two minutes, and then removing it. It is unlikely that the sampling method quantitatively collected dust or salts from the metal surface; however, both substrates did extract a significant amount of material. The paper filters collected both particles, trapped within the cellulose fibers of the filter, and salts, while the sponges collected only the soluble salts, with very few particles. Upon delivery to Sandia, both collection media were analyzed using the same methods. The soluble salts were leached from the substrates and analyzed via ion chromatography, and insoluble minerals were analyzed by scanning electron microscopy and energy dispersive X-ray spectroscopy. The insoluble minerals were found to consist largely of terrestrially-derived mineral fragments, dominantly quartz and biotite. Large (mm-sized) aggregates of calcium carbonate, calcium silicate, and calcium aluminum silicate were also present. The aggregates had one flat, smooth surface and one well crystallized surface, and were interpreted to be efflorescence on the inside of the overpack and in the vent, formed by seepage of cement pore fluids through joints in the steel liner of the overpack. Such efflorescence was commonly observed during the boroscope inspection of the storage system at the site. The material may have flaked off and fallen to the point where the dust was collected, or may have brushed off onto the sponges when the robot was retrieved through the inlet vent. Chemical analysis showed that the soluble salts on the shield plug were Ca- and Na-rich, with lesser K and minor Mg; the anionic component was dominated by SO 4 and Cl, with minor amounts of NO 3 . The cation composition of the soluble salts from the overpack shelf and the canister surface was similar to the filter samples, but the anions differed significantly, being dominantly NO 3 with lesser Cl and only trace SO 4 . The salts appear to represent a mixture of sea-salts (probably partially converted to nitrates and sulfates by particle-gas conversion reactions) and continental salt aerosols. Ammonium, a common component in continental aerosols, was not observed and may have been lost by degassing from the canister surface or after collection during sample storage and transportation. The demonstration at Maine Yankee has shown that the robot and sampling method used for the test can successfully be used to collect soluble salts from metal surfaces within an interim storage system overpack. The results were consistent from sample to sample, suggesting that a representative sample of the soluble salts was being collected. However, it is unlikely that the salt samples collected here represent quantitative sampling of the salts on the surfaces evaluated; for that reason, chloride densities per unit area are not presented here. It should also be noted that the relevance to storage systems at the site that contain SNF may be limited, because a heat- generating canister will result in greater airflow through the overpack, affecting dust deposition rates and possibly salt compositions.
Global nuclear energy has reached a critical juncture. The footprint of nuclear energy is growing and will continue to grow in coming decades to meet increasing global energy demands, desires for energy security, and mounting concerns about climate change. This growth includes construction of reactors in countries new to the nuclear energy enterprise, in addition to expansion of existing programs. The lack of operational experience coupled with weak regulatory systems in some countries raises the potential of a nuclear accident. The expansion of nuclear energy is also met with an increasingly complex threat environment, with threats to nuclear security from non-state actors as well as the continued risks of state proliferation. The trend towards increasingly digitized and networked nuclear facilities significantly expands operational uncertainty and adds complexity to implementing safeguards and security. These factors merit fresh consideration of potential safety, safeguards, security, and cyber (3SC) risks, as well as approaches for managing those risks in an integrated, sustainable, and internationally cooperative manner. In an effort to explore the emerging challenges and cooperative solutions to the global expansion of nuclear energy The George Washington University Elliott School of International Affairs and Sandia National Laboratories convened a group of more than thirty experts from government, national laboratories, non-government organizations, and academia on May 5th, 2016 at George Washington University to discuss these issues in a not-for-attribution environment.
The subject of this report is the performance portability of the Aeras global atmosphere dynamical core (implemented within the Albany multi-physics code) to new and emerging architecture machines using the Kokkos library and programming model. We describe the process of refactoring the finite element assembly process for the 3D hydrostatic model in Aeras and highlight common issues associated with development on GPU architectures. After giving detailed build and execute instructions for Aeras with MPI, OpenMP and CUDA on the Shannon cluster at Sandia National Laboratories and the Titan supercomputer at Oak Ridge National Laboratory, we evaluate the per- formance of the code on a canonical test case known as the baroclinic instability problem. We show a speedup of up to 4 times on 8 OpenMP threads, but we were unable to achieve a speedup on the GPU due to memory constraints. We conclude by providing methods for improving the performance of the code for future optimization.
Although using standard Taylor series coefficients for finite-difference operators is optimal in the sense that in the limit of infinitesimal space and time discretization, the solution approaches the correct analytic solution to the acousto-dynamic system of differential equations, other finite-difference operators may provide optimal computational run time given certain error bounds or source bandwidth constraints. This report describes the results of investigation of alternative optimal finite-difference coefficients based on several optimization/accuracy scenarios and provides recommendations for minimizing run time while retaining error within given error bounds.
In this report we discuss a new maritime surveillance and detection concept based on Raman scattering of water molecules. Using a scanning lidar that detects Raman scattered photons from water, the absence or change of signal indicates the presence of a non-water object. With sufficient spatial resolution a negative two dimensional image of the object can be generated by the scanning lidar. Because Raman scattering is an inelastic process with a relatively large wavelength shift for water, this concept completely avoids the problematic elastic scattering for objects at or very close to the water surface. Elastic scattering makes it difficult to discriminate between water and dark objects at or near the water surface especially when automated detection is required. It is also difficult to deal with elastic scattering from the bottom surface for shallow waters. The maximum detection depth for this concept is limited by the attenuation of the excitation and return Raman light in water. If excitation in the UV is used, fluorescence can be used for discrimination between organic and non-organic objects. Range gating can be used for this concept for detection of objects below a specified depth. In this report we develop a lidar model for this concept to estimate the number of detected Raman photons for variable lidar parameters and depths in the presence of the solar background. We also report on the results of proof-of-concept measurements using the Sandia Ares lidar with excitation at 355 nm. The measurements show good agreement with the lidar model predictions. The detected number of photons for typical lidar parameter shows the concept is viable and applicable to a variety of day and nighttime detection scenarios. This concept has many potential applications including near-surface mine detection, swimmer detection for security purposes, wide area search, as well as other civilian applications.
Standard Unified Modeling Mapping and Integration Toolkit (SUMMIT) is a software capability developed by Sandia National Laboratories (SNL) under the direction and funding of the Department of Homeland Security (DHS), Science and Technology Directorate (S&T). SUMMIT is a scalable platform technology for linking together “best-in-class” models, data, and simulation tools to enable analysts, emergency planners and incident managers to more effectively, economically, and rapidly prepare, analyze, train, and respond to real or potential incidents. The SUMMIT software architecture was created under the Integrated Modeling, Mapping, and Simulation (IMMS) Project and was motivated by the issuance of Homeland Security Presidential Directive 8 (HSPD-8) which called for continuous improvement of our Nation’s preparedness to respond to catastrophic events. Furthermore, a state-of-the-art training and exercise facility called the National Exercise Simulation Center (NESC) was established within the Federal Emergency Management Agency (FEMA) headquarters.
Cyber defense is an asymmetric battle today. We need to understand better what options are available for providing defenders with possible advantages. Our project combines machine learning, optimization, and game theory to obscure our defensive posture from the information the adversaries are able to observe. The main conceptual contribution of this research is to separate the problem of prediction, for which machine learning is used, and the problem of computing optimal operational decisions based on such predictions, coupled with a model of adversarial response. This research includes modeling of the attacker and defender, formulation of useful optimization models for studying adversarial interactions, and user studies to measure the impact of the modeling approaches in realistic settings.
The American Indian Research & Education Initiative (AIREI) is a pilot program that started in 2011 and is funded by the US Department of Energy (DOE) Economic Impact & Diversity and National Nuclear Security Administration in partnership with the American Indian Higher Education Consortium (AIHEC) and the American Indian Science and Engineering Society. AIREI brings science, technology, engineering, and mathematics (STEM) research and education funding to Tribal Colleges and Universities (TCU) and other US universities. AIREI has funded eight schools, including four pairs of tribal colleges and mainstream universities, in order for student and faculty research teams to bring energy projects to tribal lands. The research team from Southwest Indian Polytechnic Institute (SIPI) and Northern Arizona University (NAU) has performed a student-centric research and analysis feasibility study of a potential utility-scale solar power plant on the Jemez Pueblo reservation trust land. The research team from Navajo Technical University (NTU) and Arizona State University (ASU) has assessed the effectiveness of solar photovoltaic (PV) system designs in meeting the electricity demands of Navajo Tribal homes and public buildings in addition to the development of a solar technology curriculum that incorporates the outcomes of this study, helping to advance PV system design and installations on local Tribal lands. The Little Big Horn College (LBHC) and Montana State University-Bozeman (MSUB) team has developed fast growing strains of nitrogen-fixing cyanobacteria to help advance carbon capture and sequestration (CCS) technologies. The research supported the Crow Nation reservation as it evaluates opportunities for coal-to-liquid fuel and CCS projects. The Sinte Gleska University (SGU) and South Dakota School of Mines (SDSM) team developed computer modeling and simulation technologies to evaluate the feasibility of oil and gas development from the Niobrara Formation on the Rosebud Sioux reservation. Through this project, the students developed skills in applied energy-related research involving computer simulation, chemistry, geology, and petroleum engineering. AIREI supports collaboration between these universities and connects the teams with the technological expertise and mentorship opportunities provided through Sandia National Laboratories (Sandia). AIHEC consists of 37 American Indian tribally controlled colleges around the nation and provides technical assistance through professional development workshops, strategic planning meetings, and information sharing strategies.
Gamble, Marc; Van Der Wijngaart, Rob; Teranishi, Keita; Parashar, Manish
This document provides a specification of Fenix, a software library compatible with the Message Passing Interface (MPI) to support fault recovery without application shutdown. The library consists of two modules. The first, termed process recovery , restores an application to a consistent state after it has suffered a loss of one or more MPI processes (ranks). The second specifies functions the user can invoke to store application data in Fenix managed redundant storage, and to retrieve it from that storage after process recovery.
Polyurethane is a complex multiphase material that evolves from a viscous liquid to a system of percolating bubbles, which are created via a CO2 generating reaction. The continuous phase polymerizes to a solid during the foaming process generating heat. Foams introduced into a mold increase their volume up to tenfold, and the dynamics of the expansion process may lead to voids and will produce gradients in density and degree of polymerization. These inhomogeneities can lead to structural stability issues upon aging. For instance, structural components in weapon systems have been shown to change shape as they age depending on their molding history, which can threaten critical tolerances. The purpose of this project is to develop a Cradle-to-Grave multiphysics model, which allows us to predict the material properties of foam from its birth through aging in the stockpile, where its dimensional stability is important.