Publications

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The Terry Turbine Expanded Operating Band Project is currently conducting testing at Texas A&M University, and the resulting data has been incorporated into MELCOR models of the Terry turbines used in nuclear power plants. These improved models have produced improvements in the Fukushima Daiichi Unit 2 simulations while providing new insights into the behavior of the plant. The development of future experimental test efforts is ongoing. Development of and refinements to the plans for full-scale steam and steam-water turbine ingestion testing has been performed. These full-scale steam-based tests will complement the testing occurring at Texas A&M University, and will resolve the remaining questions regarding scale or working fluid. Planning work has also begun for future testing intended to explore the uncontrolled RCIC self-regulation theorized to have occurred in Fukushima Daiichi Unit 2.

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This document summarily provides brief descriptions of the MELCOR code enhancement made between code revision number 11932 and 14959. Revision 11932 represents the last official code release; therefore, the modeling features described within this document are provided to assist users that update to the newest official MELCOR code release, 14959. Along with the newly updated MELCOR Users' Guide and Reference Manual, users will be aware and able to assess the new capabilities for their modeling and analysis applications. Following the official release an addendum section has been added to this report detailing modifications made to the official release which support the accompanying patch release. The addendums address user reported issues and previously known issues within the official code release which extends the original Quick look document to also support the patch release. Furthermore, the addendums section documents the recent changes to input records in the Users' Guide applicable to the patch release and corrects a few issues in the revision 14959 release as well.

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Crystal plasticity-finite element method (CP-FEM) is now widely used to understand the mechanical response of polycrystalline materials. However, quantitative mesh convergence tests and verification of the necessary size of polycrystalline representative volume elements (RVE) are often overlooked in CP-FEM simulations. Mesh convergence studies in CP-FEM models are more challenging compared to conventional finite element analysis (FEA) as they are not only computationally expensive but also require explicit discretization of individual grains using many finite elements. Resolving each grains within a polycrystalline domain complicates mesh convergence study since mesh convergence is strongly affected by the initial crystal orientations of grains and local loading conditions. In this work, large-scale CP-FEM simulations of single crystals and polycrystals are conducted to study mesh sensitivity in CP-FEM models. Various factors that may affect the mesh convergence in CP-FEM simulations, such as initial textures, hardening models and boundary conditions are investigated. In addition, the total number of grains required to obtain adequate RVE is investigated. This work provides a list of guidelines for mesh convergence and RVE generation in CP-FEM modeling.

Sandia National Laboratories (SNL) National Solar Thermal Test Facility (NSTTF) and Tech Library have been collaborating over the course of the FY19 period to establish and maintain the first and only digital collection in the world of Concentrating Solar Power (CSP) related historical documents, dating back to the CSP program inception here at Sandia in the 1970's thru to the present. The unclassified, unrestricted (UUR) collection, comprised of internally generated Sandia documents as well as a significant number of external reports will be searchable via both the Sandia website and OSTI, DOE's document repository. DOE is currently championing efforts to get the collection launched, where international partners, which include Australia and Germany, plan to forward related documents to be included in the CSP archive. Advancing this transformative project will make the CSP collection accessible to the Sandia and global communities.

This monthly report is intended to communicate the status of North Slope ARM facilities managed by Sandia National Labs.

We present a study of the transport properties of thermally generated spin currents in an insulating ferrimagnetic-antiferromagnetic-ferrimagnetic trilayer over a wide range of temperature. Spin currents generated by the spin Seebeck effect (SSE) in a yttrium iron garnet (YIG) YIG/NiO/YIG trilayer on a gadolinium gallium garnet (GGG) substrate were detected using the inverse spin Hall effect (ISHE) in Pt. By studying samples with different NiO thicknesses, the spin diffusion length of NiO was determined to be ∼3.8 nm at room temperature. Surprisingly, a large increase of the SSE signal was observed below 30 K, and the field dependence of the signal closely follows a Brillouin function for an S=7/2 spin. The increase of the SSE signal at low temperatures could thus be associated with the paramagnetic SSE from the GGG substrate. Besides, a broad peak in the SSE response was observed around 100 K. These observations are important in understanding the generation and transport properties of spin currents through magnetic insulators and the role of a paramagnetic substrate in spin current generation.

This research objective of this EELDRD study was to learn to electrodeposit Pt Au alloys with independently controlled composition and grain size. What was accomplished was the capability to electrodeposit PtAu alloys with controlled composition and a nanocrysolline grain size. Nanocrystalline metals as a class and, specifically, the Pt_0.9Au_0.1 alloy developed in 2015-17 via sputtering at Sandia National Labs have clear advantages in strength, wear resistance, and fatigue tolerance over commercially-available structural alloys. With this capability befitting coating of complex components and implementable at existing vendors, we can upgrade the electrical contact component reliability of selected Labs systems.

Cylindrical dog-bone (or dumbbell) shaped samples have become a common design for dynamic tensile tests of ductile materials with a Kolsky tension bar. When a direct measurement of displacement between the bar ends is used to calculate the specimen strain, the actual strain in the specimen gage section is overestimated due to strain in the specimen shoulder and needs to be corrected. The currently available correction method works well for elastic-perfectly plastic materials but may not be applicable to materials that exhibit significant work-hardening behavior. In this study, we developed a new specimen strain correction method for materials possessing an elastic-plastic with linear work-hardening stress–strain response. A Kolsky tension bar test of a Fe-49Co-2V alloy (known by trade names Hiperco and Permendur) was used to demonstrate the new specimen strain correction method. This new correction method was also used to correct specimen strains in Kolsky tension bar experiments on two other materials: 4140 alloy, and 304L-VAR stainless steel, which had different work-hardening behavior.

Slabs of cast calcium sulfate dihydrate have been used for decades as a heat transfer barrier in commercial and residential construction and are assessed in accordance with the parameters defined by ASTM C1396. The study described herein hypothesizes that minor impurities have significant effects on the high-temperature performance of these casts. Five thermocouples are cast into a 20-mm-thick slab at approximately 4-mm intervals are measured between ambient and approximately 1000°C in a furnace. Thermal diffusivity and inertia are estimated from this temperature profile. This study compares natural, flue gas desulfurized (FGD) and reagent grade hemihydrates that have been converted into the dihydrate form with distilled water; the common additives kaolin and borax are used to modify the cast. The thermocouple data allow an effective thermal diffusivity (α′) and an effective thermal inertia (I′) to be calculated. The hemihydrate source and kaolin content are found to affect the high-temperature performance; the FGD source increases the thermal inertia, and kaolin inhibits the formation of other borate compounds through intercalation.

A comprehensive, chemically detailed mechanism for the combustion of 2-methyl-2-butene and n-pentane is presented to provide insights into the different sooting tendencies of these two structurally different C5 hydrocarbons. A hierarchically assembled mechanism has been developed to specifically target speciation data from low-pressure premixed flames of 2-methyl-2-butene [Ruwe et al., Combust. Flame, 175, 34-46, 2017] and newly measured mole fraction data for a fuel-rich (ɸ=1.8) n-pentane flame, in which species profiles up to phenol were quantified. The partially isomer-resolved chemical composition of this flame was determined using flame-sampling molecular-beam mass spectrometry with single-photon ionization by tunable, synchrotron-generated vacuum-ultraviolet radiation. The presented model, which includes a newly determined, consistent set of the thermochemistry data for the C5 species, presents overall satisfactory capabilities to predict the mole fraction profiles of common combustion intermediates. The analysis of the model predictions revealed the fuel-structure dependencies (i.e. saturated vs. unsaturated and linear vs. branched) of the formation of small aromatic species that are considered as soot precursors. The propensity of the 2-methyl-2-butene flame to form larger concentrations of aromatic species was traced back to the readily available formation routes of several small precursor molecules and the efficient formation of “first aromatic rings” beyond benzene.

Recent event-related brain potential (ERP) experiments have demonstrated parafoveal N400 expectancy and congruity effects, showing that semantic information can be accessed from words in parafoveal vision (a conclusion also supported by some eye-tracking work). At the same time, it is unclear how higher-order integrative aspects of language comprehension unfold across the visual field during reading. In the current study, we recorded ERPs in a parafoveal flanker paradigm, while readers were instructed to read passively for comprehension or to judge the plausibility of sentences in which target words varied in their semantic expectancy and congruity. We directly replicated prior work showing graded N400 effects for parafoveal viewing, which are then not duplicated when the target words are processed foveally. Critically, although N400 effects were not modulated by task goals, a posteriorly distributed late positive component thought to reflect semantic integration processes was observed to semantic incongruities only in the plausibility judgment task. However, this effect was observed at a considerable delay, appearing only after words had moved into foveal vision. Our findings thus suggest that semantic access can be initiated in parafoveal vision, whereas central foveal vision may be necessary to enact higher-order (and task-dependent) integrative processing.

The Hydrogen Materials—Advanced Research Consortium (HyMARC) is the core storage material research team of the DOE/EERE Fuel Cell Technologies Office (FCTO) and is comprised of Sandia National Laboratories (Livermore, CA; SNL), Lawrence Livermore National Laboratory (LLNL), and Lawrence Berkeley Laboratory (LBNL). Its objective is to overcome critical scientific barriers limiting the use of solid-state materials for vehicular hydrogen storage, thereby enabling design and discovery of breakthrough storage materials. Over the three-year lifetime of the project, HyMARC "moved the bar" relative to compressed gas storage by identifying the most promising material improvement strategies, obtaining thermodynamic data that was either missing or inaccurate in the literature, and filling major gaps in the toolkit of computational models. The HyMARC team also developed many new capabilities in the areas of material synthesis and characterization that address specific roadblocks to discovery of successful storage materials.

High-harmonic generation (HHG) is a signature optical phenomenon of strongly driven, nonlinear optical systems. Specifically, the understanding of the HHG process in rare gases has played a key role in the development of attosecond science1. Recently, HHG has also been reported in solids, providing novel opportunities such as controlling strong-field and attosecond processes in dense optical media down to the nanoscale2. Here, we report HHG from a low-loss, indium-doped cadmium oxide thin film by leveraging the epsilon-near-zero (ENZ) effect3–8, whereby the real part of the material’s permittivity in certain spectral ranges vanishes, as well as the associated large resonant enhancement of the driving laser field. We find that ENZ-assisted harmonics exhibit a pronounced spectral redshift as well as linewidth broadening, resulting from the photo induced electron heating and the consequent time-dependent ENZ wavelength of the material. Our results provide a new platform to study strong-field and ultrafast electron dynamics in ENZ materials, reveal new degrees of freedom for spectral and temporal control of HHG, and open up the possibilities of compact solid-state attosecond light sources.

The creation of microelectromechanical systems (MEMS) that can operate through elevated temperatures would enable systems diagnostics and controls that are not possible with conventional-off-the-shelf components. The integration of silicon carbide (SiC) with aluminum nitride (AlN) has led to the fabrication of devices that can withstand elevated temperature anneals >935 °C. The results from a piezoelectric micromachined ultrasonic transducer (PMUT) and a microresonator are reported as demonstrations of the fabrication process. Testing the PMUT response before and after annealing at 935 °C led to a change in resonant frequency of less than 1%, which is attributable to a shift in film stress. The response of the microresonator was RF tested in situ up to 500 °C and showed no degradation in its electromechanical coupling coefficient. The resonant frequency decreased with temperature due to the temperature coefficient of Young's modulus, and the quality factor decreased with temperature and remained unrecoverable upon cooling. The degradation in the quality factor is suspected to be a result of oxidation of the titatium nitride (TiN) top electrode, which increases the resistivity and leads to an unrecoverable reduction in the quality factor. The robust piezoelectric response of AlN at these temperatures show that AlN is a very promising candidate for elevated temperature applications.

Fluid inclusions are found within mineral crystals or along grain boundaries in many sedimentary rocks, notably in evaporite formations, and can migrate along a thermal or hydro-mechanical gradient. Shale and salt rocks have been considered potential host rocks for radioactive waste disposal, due to their low permeability. Previously stagnant inclusions may become mobilised by a perturbation of the in situ state by a geotechnical installation or the emplacement of heat-generating waste. The migration of fluid inclusions can thus have important impacts on the long-term performance of a geologic repository for high-level radioactive waste disposal. As a part of the international research project DECOVALEX-2019, two aspects of fluid inclusion migration in rock salt are currently investigated under different boundary conditions: a) altered hydro-mechanical conditions as a consequence of tunnel excavation or borehole drilling and b) coupled thermo-hydro-mechanical-chemical conditions during the heating period of the post-closure phase of a repository. To obtain a mechanistic understanding of underlying physical processes for fluid inclusion migration, a multi-scale modelling strategy has been developed. Microscale hydraulic and time-dependent mechanical conditions related to the creep behaviour of rock salt are constrained by considering the macroscale stress evolution of an underground excavation. An analysis using a coupled two-phase flow and elasto-plastic model with a consideration of permeability variation indicates that a pathway dilation along the halite grain boundary may increase the permeability by two orders of magnitude. The calculated high flow velocity may explain the fast pressure build-up observed in the field. In addition, a mathematical model for the migration and morphological evolution of a single fluid inclusion under a thermal gradient has been formulated. A first-order analysis of the model leads to a simple mathematical expression that is able to explain the key observations of thermally driven inclusion migration in salt. Finally, numerical methods such as a phase field method for solving a moving boundary problem of fluid inclusion migration have also been explored.

Gold-plated copper alloys are used extensively in electrical contacts where diffusional processes are known to cause contact degradation. An in situ transmission electron microscopy (TEM) heating study was carried out to provide fundamental understanding of the aging phenomena in reasonable timescales. Samples to visualize the interface in TEM were prepared by focused ion beam (FIB) microscopy and heated in situ up to 350°C while holding at intermediate temperatures to enable imaging. The grain boundaries in Au coatings, specifically the columnar boundaries, provided rapid pathways for diffusion of Cu all the way to the Au surface. This unequal diffusion created vacancies in Cu which coalesced into Kirkendall voids. This in situ technique has been applied to visualize the diffusion pathways in electroplated and sputtered Au films deposited directly on Cu, as well the role of Ni and NiP as barrier layers for mitigating Cu diffusion.

Microstructure reconstruction problems are usually limited to the representation with finitely many number of phases, e.g. binary and ternary. However, images of microstructure obtained through experimental, for example, using microscope, are often represented as a RGB or grayscale image. Because the phase-based representation is discrete, more rigid, and provides less flexibility in modeling the microstructure, as compared to RGB or grayscale image, there is a loss of information in the conversion. In this paper, a microstructure reconstruction method, which produces images at the fidelity of experimental microscopy, i.e. RGB or grayscale image, is proposed without introducing any physics-based microstructure descriptor. Furthermore, the image texture is preserved and the microstructure image is represented with continuous variables (as in RGB or grayscale images), instead of binary or categorical variables, which results in a high-fidelity image of microstructure reconstruction. The advantage of the proposed method is its quality of reconstruction, which can be applied to any other binary or multiphase 2D microstructure. The proposed method can be thought of as a subsampling approach to expand the microstructure dataset, while preserving its image texture. Moreover, the size of the reconstructed image is more flexible, compared to other machine learning microstructure reconstruction method, where the size must be fixed beforehand. In addition, the proposed method is capable of joining the microstructure images taken at different locations to reconstruct a larger microstructure image. A significant advantage of the proposed method is to remedy the data scarcity problem in materials science, where experimental data is scare and hard to obtain. The proposed method can also be applied to generate statistically equivalent microstructures, which has a strong implication in microstructure-related uncertainty quantification applications. The proposed microstructure reconstruction method is demonstrated with the UltraHigh Carbon Steel micrograph DataBase (UHCSDB).

We have created a demonstration permissioned Distributed Ledger Technology (DLT) datastore for the UF₆ cylinder tracking safeguards use-case utilizing the Ethereum DLT framework and using Solidity for smart contract code. Our demonstration creates a simulated dataset representing tracking of 75,000 UF₆ cylinders across 11 example nuclear facilities worldwide. Our DLT system allows for easy input and reading of shipping and receiving data, including a Graphical User Interface (GUI). Sandia’s Emulytics capability was leveraged to help create the DLT node network and assess performance. We find that our DLT prototype can easily handle to ~150,000 UF₆ cylinder shipments per year worldwide, without any excessive computational or storage burden on the IAEA or Member States. Next steps could include a demonstration to the IAEA and potentially demonstrating integration with TradeLens, a DLT in use by a consortium of international shipping companies representing over half of world shipping trade.

The purpose of this document is to discuss the construction of two MACCS dose conversion factor (DCF) files in some detail, an older file created in 2007 named FGR13DCF.inp and a newer file created in 2018 called FGR13GyEquiv_RevA.inp. Very briefly, the difference between the two files is that the older file follows the standard conventions of assigning a radiation weighting factor of 20 for alpha radiation for all tissues and organs; whereas, the newer file complies with the FGR 13 health effects modeling and uses modified radiation weighting factors (referred to as relative biological effectiveness (RBE) factors) for alpha radiation of 10 for breast and of 1 for red bone marrow. During an intermediate period the creation of these two DCF files, a file called FGR13GyEquiv was created and used for the SOARCA calculations. This file was not released to the MACCS user community, but it is also discussed briefly in this document. DCF files are used by MACCS to convert air and ground radionuclide concentrations to doses to an organ or to the whole body. The MACCS calculation considers duration and shielding factor for each exposure pathway including cloudshine, groundshine, inhalation, and ingestion. Dose coefficients for cloudshine and groundshine are expressed as dose rates; dose coefficients for inhalation and ingestion are expressed as committed doses. In this document, dose coefficients (newer ICRP terminology) is used to describe the values contained in dose conversion factor (older ICRP terminology) files.

Reduction is an operation performed on the values of two or more key-value pairs that share the same key. Reduction of sparse data streams finds application in a wide variety of domains such as data and graph analytics, cybersecurity, machine learning, and HPC applications. However, these applications exhibit low locality of reference, rendering traditional architectures and data representations inefficient. This article presents MetaStrider, a significant algorithmic and architectural enhancement to the state-of-the-art, SuperStrider. Furthermore, these enhancements enable a variety of parallel, memory-centric architectures that we propose, resulting in demonstrated performance that scales near-linearly with available memory-level parallelism.

Selective laser melting (SLM) is a powder-based additive manufacturing technique which creates parts by fusing together successive layers of powder with a laser. The quality of produced parts is highly dependent on the proper selection of processing parameters, requiring significant testing and experimentation to determine parameters for a given machine and material. Computational modeling could potentially be used to shorten this process by identifying parameters through simulation. However, simulating complete SLM builds is challenging due to the difference in scale between the size of the particles and laser used in the build and the size of the part produced. Often, continuum models are employed which approximate the powder as a continuous medium to avoid the need to model powder particles individually. While computationally expedient, continuum models require as inputs effective material properties for the powder which are often difficult to obtain experimentally. Building on previous works which have developed methods for estimating these effective properties along with their uncertainties through the use of detailed models, this work presents a part scale continuum model capable of predicting residual thermal stresses in an SLM build with uncertainty estimates. Model predictions are compared to experimental measurements from the literature.

The New York State Public Service Commission recently made significant changes to the compensation mechanisms for distributed energy resources, such as solar generation. The new mechanisms, called the Value of Distributed Energy Resources (VDER), alter the value proposition of potential installations. In particular, multiple time-of-generation based pricing alternatives were established, which could lead to potential benefits from pairing energy storage systems with solar installations. This paper presents the calculations to maximize revenue from a solar photovoltaic and energy storage system installation operating under the VDER pricing structures. Two systems in two different zones within the New York Independent System Operator area were modeled. The impact of AC versus DC energy storage system interconnections with solar generation resources was also explored. The results show that energy storage systems could generate significant revenue depending on the pricing alternative being targeted and the zone selected for the project.

The presentation includes shot details, WDPE hardware, and images.

Motivation. Critical infrastructures are large, complex engineered systems that must be operated robustly under abnormal conditions resulting from natural hazards or intentional acts. For example, electric power systems must be robust to line faults, water utilities must rapidly mitigate contamination incidents, and computing networks must adapt to adversarial intrusions to protect critical information. Problem. It is difficult for decision-makers within resiliency analysis in critical infrastructure to optimize designs and develop effective response strategies that can account for uncertainties. Facing incomplete information and the sheer scope that a natural hazard or attack vector may incorporate, response can be ineffective without reliable, scalable decision support tools. These problems are intrinsically nonlinear and involve discrete decisions, and unfortunately, existing off-the-shelf mathematical programming methods cannot support optimization-based decision-making of these nonlinear at scale. Method/Approach and Results. This project emphasized development of fundamental optimization strategies that supported real- time mitigation and response for critical infrastructures. In particular, the project developed multi- tree approaches based on piecewise outer-approximations for solution of mixed-integer nonlinear programming (MINLP) problems. These techniques alternate between an MILP or MISOCP relaxation to obtain a lower bound and candidate discrete solutions and an NLP subproblem to obtain upper bounds. Using tailored relaxations based on problem structure, these methods were used to solve several key applications in resilience and response of critical infrastructure. This work resulted in two open-source, copyrighted software packages: CORAMIN (https://github.com/Coramin/Coramin) -- an object-oriented mathematical programming framework that supports tailored multi-tree algorithms for solution of large- scale mixed-integer nonlinear programming; and EGRET (Electrical Grid Research and Engineering Toolkit) (https://github.com/grid- parity-exchange/Egret) -- a declarative mathematical programming framework built upon CORAMIN and Pyomo for formulation and solution of resilience and operations problems in power grid systems. Furthermore, these tools resulted in several important published results, including the following: The first known global optimization approach that could solve the unit-commitment problem with nonlinear power flow constraints on medium-sized test problems; Improved parallel optimization-based bounds tightening and strengthening of relaxations of AC power flow constraints; and, Optimization-based approaches for improved grid resilience and use of demand response to improve grid resilience with reduction in capital requirements. Result Implications. This project developed first-of-a-kind algorithms for decision-making in critical infrastructure resilience operations and planning, as well as a next-generation toolkit for MINLP researchers. These approaches leveraged high-performance computing architectures to solve some of the largest, most challenging nonlinear discrete optimization problems to global optimality, and these successes were captured in open-source software to enable optimization-based decision-making, and efficient solution of MINLP formulations for electric power transmission grids.

The Alternative Fuels Risk Assessment Models (A1tRAM) toolkit combines Quantitative Risk Assessment (QRA) with simulations of unignited dispersion, ignited turbulent diffusion flames, and indoor accumulation with delayed ignition of fuels. The models of the physical phenomena need to be validated for each of the fuels in the toolkit. This report shows the validation for methane which is being used as a surrogate for natural gas. For the unignited dispersion model, seven previously published experiments from credible sources were used to validate. The validation looked at gas concentrations with respect to the distance from the release point. Four of these were underexpanded jets (i.e. release velocity equal to or greater than local speed of sound) and the other three subsonic releases. The methane plume model in AltRAM matched both varieties well, with higher accuracy for the underexpanded releases. For the jet flame model, we compared the heat flux and thermal radiation data reported from five separate turbulent jet flame experiments to the quantities calculated by A1tRAM. Four of the five datasets were for underexpanded diffusion jets flames. While the results still match well enough to give a good estimate of what is occurring, the error is higher than what was seen with the plume model. For the underexpanded flames A1tRAM provided reasonable approximations, which would lead to conservative risk assessments. Some modeling errors can be attributed to environmental effects (i.e. wind) since most large scale flame experiments are conducted outdoors. A1tRAM has been shown to be a reasonably accurate tool for calculating the concentration or flame properties of natural gas releases. Improvements could still be made for the plume of subsonic releases and radiative heat fluxes to reduce the conservative nature of these predictions. These models can provide valuable information for the risk assessment of natural gas infrastructure.

The hydrokinetic industry has advanced beyond its initial testing phase with full-scale projects being introduced, constructed and tested globally. However primary hurdles such as reducing the cost of these systems, optimizing individual systems and arrays and balancing energy extraction with environmental impact still requires attention prior to achieving commercial success. The present study addresses the advances and limitations of near-zero head hydrokinetic technologies and the possibility of increased potential and applicability when enhancement techniques within the design, implementation and operational phases are considered. Its goal is threefold: to review small-scale state-of-the-art near-zero hydrokinetic-current-energy-conversion-technologies, to assess barriers including gaps in knowledge, information and data as well as assess time and resource limitations of water-infrastructure owners and operators. A case study summarizes the design and implementation of the first permanent modern hydrokinetic installation in South Africa where improved outputs were achieved through optimization during each design and operation phase. An economic analysis validates a competitive levelized cost of energy and further emphasizes the broad potential that is relatively unexplored within existing water-infrastructure.

Anion hydration is complicated by H-bond between neighboring water molecules in addition to H-bond donation to the anion. This situation leads to competing structures and anharmonic vibrations for simple clusters like (H2O)nCl-. This study applies quasi-chemical theory to study anion hydration and exploits dynamics calculations on isolated clusters to account for anharmonicity. Comparing singly hydrated halide clusters, classic H-bond donation to the anion occurs for F-, while Cl- clusters exhibit flexible dipole-dominated interactions. The predicted Cl- – F- hydration free energy difference agrees with experiment, a significant theoretical step for addressing issues like Hofmeister ranking and selectivity in ion channels.

This study investigates the mechanical and corrosion properties of as-built and annealed equiatomic CoCrFeMnNi alloy produced by laser-based directed energy deposition (DED) Additive Manufacturing (AM). The high cooling rates of DED produced a single-phase, cellular microstructure with cells on the order of 4 μm in diameter and inter-cellular regions that were enriched in Mn and Ni. Annealing created a chemically homogeneous recrystallized microstructure with a high density of annealing twins. The average yield strength of the as-built condition was 424 MPa and exceeded the annealed condition (232 MPa), however; the strain hardening rate was lower for the as-built material stemming from higher dislocation density associated with DED parts and the fine cell size. In general, the yield strength, ultimate tensile strength, and elongation-to-failure for the as-built material exceeded values from previous studies that explored other AM techniques to produce the CoCrFeMnNi alloy. Ductile fracture occurred for all specimens with dimple initiation associated with nanoscale oxide inclusions. The breakdown potential (onset of pitting corrosion) was similar for the as-built and annealed conditions at 0.40 VAg/AgCl when immersed in 0.6 M NaCl. Pit morphology/propagation for the as-built condition exhibited preferential corrosion of inter-cellular Ni/Mn regions leading to a tortuous pit bottom and cover, while the annealed conditions pits resembled lacy pits similar to 304 L steel. A passive oxide film depleted in Cr cations with substantial incorporation of Mn cations is proposed as the primary mechanism for local corrosion susceptibility of the CoCrFeMnNi alloy.

In preparation for the next phase of the Source Physics Experiments, we acquired an active-source seismic dataset along two transects totaling more than 30 km in length at Yucca Flat, Nevada, on the Nevada National Security Site. Yucca Flat is a sedimentary basin which has hosted more than 650 underground nuclear tests (UGTs). The survey source was a novel 13,000 kg modified industrial pile driver. This weight drop source proved to be broadband and repeatable, richer in low frequencies (1-3 Hz) than traditional vibrator sources and capable of producing peak particle velocities similar to those produced by a 50 kg explosive charge. In this study, we performed a joint inversion of P-wave refraction travel times and Rayleigh-wave phase-velocity dispersion curves for the P- and S-wave velocity structure of Yucca Flat. Phase-velocity surface-wave dispersion measurements were obtained via the refraction microtremor method on 1 km arrays, with 80% overlap. Our P-wave velocity models verify and expand the current understanding of Yucca Flat’s subsurface geometry and bulk properties such as depth to Paleozoic basement and shallow alluvium velocity. Areas of disagreement between this study and the current geologic model of Yucca Flat (derived from borehole studies) generally correlate with areas of widely spaced borehole control points. This provides an opportunity to update the existing model, which is used for modeling groundwater flow and radionuclide transport. Scattering caused by UGT-related high-contrast velocity anomalies substantially reduced the number and frequency bandwidth of usable dispersion picks. The S-wave velocity models presented in this study agree with existing basin-wide studies of Yucca Flat, but are compromised by diminished surface-wave coherence as a product of this scattering. As nuclear nonproliferation monitoring moves from teleseismic to regional or even local distances, such high-frequency (>5 Hz) scattering could prove challenging when attempting to discriminate events in areas of previous testing.

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 %3C 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.

The classic models for ductile fracture of metals were based on experimental observations dating back to the 1950’s. Using advanced microscopy techniques and modeling algorithms that have been developed over the past several decades, it is possible now to examine the micro- and nano-scale mechanisms of ductile rupture in more detail. This new information enables a revised understanding of the ductile rupture process under quasi-static room temperature conditions in ductile pure metals and alloys containing hard particles. While ductile rupture has traditionally been viewed through the lens of nucleation-growth-and-coalescence, a new taxonomy is proposed involving the competition or cooperation of up to seven distinct rupture mechanisms. Generally, void nucleation via vacancy condensation is not rate limiting, but is extensive within localized shear bands of intense deformation. Instead, the controlling process appears to be the development of intense local dislocation activity which enables void growth via dislocation absorption.

This paper takes a critical look at the materials aspects of thermal runaway of lithium-ion batteries and correlates contributions from individual cell components to thermal runaway trends. An accelerating rate calorimeter (ARC) was used to evaluate commercial lithium-ion cells based on LiCoO2 (LCO), LiFePO4 (LFP), and LiNixCoyAl1-x-yO2 (NCA) at various states of charge (SOC). Cells were disassembled and the component properties were evaluated by thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and temperature-resolved X-ray diffraction (TR-XRD). The whole cell thermal runaway onset temperature decreases and peak heating rate increases with SOC due to cathode destabilization. LCO and NCA cathodes are metastable, with NCA cells exhibiting the highest thermal runaway rates. By contrast, the LFP cathode is stable to >500 °C, even when charged. For anodes, the decomposition and whole cell self-heating onset temperature is generally independent of SOC. DSC exotherm onset temperatures of the anodes were generally within 10 °C of the onset of self-heating in whole cell ARC. However, onset temperatures of the cathodes were typically observed above the ARC onset of whole cell runaway. This systematic evaluation of component to whole cell degradation provides a scientific basis for future thermal modeling and design of safer cells.

Industry seems to have the isolated-single-turbine-in-a-flat-field problem "solved". Wide-scale deployment and cost competitiveness require large wind plants — tens to hundreds of turbines in complex terrain. Predictive wind farm simulation is an exascale problem.

We demonstrate all-optical switching of high quality factor quasibound states in the continuum resonances in broken symmetry GaAs metasurfaces. By slightly breaking the symmetry of the GaAs nanoresonators, we enable leakage of symmetry protected bound states in the continuum (BICs) to free space that results in sharp spectral resonances with high quality factors of ∼500. We tune the resulting quasi-BIC resonances with ultrafast optical pumping at 800 nm and observe a 10 nm spectral blue shift of the resonance with pump fluences of less than 100 μJ cm-2. The spectral shift is achieved in an ultrafast time scale (<2.5 ps) and is caused by a shift in the refractive index mediated by the injection of free carriers into the GaAs resonators. An absolute reflectance change of 0.31 is measured with 150 μJ cm-2. Our results demonstrate a proof-of-concept that these broken symmetry metasurfaces can be modulated or switched at ultrafast switching speeds with higher contrast at low optical fluences (<100 μJ cm-2) than conventional Mie-metasurfaces.

This milestone shows a demonstration of the surface map model to map the surface temperature for single-phase computational fluid dynamics simulation data from STAR-CCM+ to the subchannel code CTF using a two-step process that captures global and local rod surface temperature behavior based on simulation boundary conditions and position. This model can be used to improve results from CTF by transforming surface temperatures obtained from CTF to model data that resembles data from STAR-CCM+. A summary of the current two-step model process and results of the initial investigations with a single subchannel and a 5 x 5 set of fuel rods are shown and discussed within, with suggestions for further improvements.

Harden and optimize the ROCm based AMD GPU backend, develop a prototype backend for the Intel ECP Path Forward architecture, and improve the existing prototype Remote Memory Space capabilities.

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Fuel Cycle Technology (FCT) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). Two high priorities for SFWST disposal R&D are design concept development and disposal system modeling. These priorities are directly addressed in the SFWST Geologic Disposal Safety Assessment (GDSA) control account, which is charged with developing a geologic repository system modeling and analysis capability, and the associated software, GDSA Framework, for evaluating disposal system performance for nuclear waste in geologic media. GDSA Framework is supported by SFWST Campaign and its predecessor the Used Fuel Disposition (UFD) campaign.

We demonstrate a platform for phase and amplitude modulation in silicon nitride photonic integrated circuits via piezo-optomechanical coupling using tightly mechanically coupled aluminum nitride actuators. The platform, fabricated in a CMOS foundry, enables scalable active photonic integrated circuits for visible wavelengths, and the piezoelectric actuation functions without performance degradation down to cryogenic temperatures. As an example of the potential of the platform, we demonstrate a compact (∼40 µm diameter) silicon nitride ring resonator modulator operating at 780 nm with intrinsic quality factors in excess of 1.5 million, >10 dB change in extinction ratio with 2 V applied, a switching time less than 4 ns, and a switching energy of 0.5 pJ/bit. We characterize the exemplary device at room temperature and 7 K. At 7 K, the device obtains a resistance of approximately 20 teraohms, allowing it to operate with sub-picowatt electrical power dissipation. We further demonstrate a Mach-Zehnder modulator constructed in the same platform with piezoelectrically tunable phase shifting arms, with 750 ns switching time constant and 20 nW steady-state power dissipation at room temperature.

A mechanism to electrically tune the frequency of terahertz quantum cascade lasers (QCLs) is developed that allows for tuning, while the QCL is operated close to its peak bias and temperature. Two optically coupled but electrically isolated cavities are used in which the bias of a control cavity tunes the resonant-mode of the coupled QCL cavity independent of the QCL's operating bias. Approximately 4 GHz electrical tuning is realized for a 3.6 THz distributed-feedback QCL operating in pulsed mode at 58 K in a Stirling cooler. The single-mode QCL emits near-constant peak-power in the range of 5 - 5.3 mW through the tuning range and radiates in a narrow single-lobed beam with a far-field divergence of ∼ 4 ° × 11 °. The superlattice structure of the QCL is designed to implement a low-voltage intersubband absorption transition that is detuned from that of its gain transition, the strength of which could be controlled sensitively with applied voltage utilizing resonant-tunneling injection of electrons in the absorption subband. The tuning is realized by the application of small bias voltages (∼ 6 - 7 V) and requires a narrow bias range (∼ 1 V, ∼ 40 A / cm 2) to traverse across the entire tuning range, and the method should be generally applicable to all intersubband lasers including mid-infrared QCLs.

As demands for memory-intensive applications continue to grow, the memory capacity of each computing node is expected to grow at a similar pace. In high-performance computing (HPC) systems, the memory capacity per compute node is decided upon the most demanding application that would likely run on such system, and hence the average capacity per node in future HPC systems is expected to grow significantly. However, since HPC systems run many applications with different capacity demands, a large percentage of the overall memory capacity will likely be underutilized; memory modules can be thought of as private memory for its corresponding computing node. Thus, as HPC systems are moving towards the exascale era, a better utilization of memory is strongly desired. Moreover, upgrading memory system requires significant efforts. Fortunately, disaggregated memory systems promise better utilization by defining regions of global memory, typically referred to as memory blades, which can be accessed by all computing nodes in the system, thus achieving much better utilization. Disaggregated memory systems are expected to be built using dense, power-efficient memory technologies. Thus, emerging nonvolatile memories (NVMs) are placing themselves as the main building blocks for such systems. However, NVMs are slower than DRAM. Therefore, it is expected that each computing node would have a small local memory that is based on either HBM or DRAM, whereas a large shared NVM memory would be accessible by all nodes. Managing such system with global and local memory requires a novel hardware/software co-design to initiate page migration between global and local memory to maximize performance while enabling access to huge shared memory. In this paper we provide support to migrate pages, investigate such memory management aspects and the major system-level aspects that can affect design decisions in disaggregated NVM systems

This report documents the completion of milestone STPM12-19 Documented Kokkos application usecases. The goal of this milestone was to develop use case examples for common patterns users implement with Kokkos. This work was performed in the fourth quarter of FY19 and resulted in use case descriptions available in the Kokkos Wiki, with code examples.

This report documents the completion of milestone STPRO4-25 Harden and optimize the ROCm based AMD GPU backend, develop a prototype backend for the Intel ECP Path Forward architecture, and improve the existing prototype Remote Memory Space capabilities. The ROCM code was hardened up to the point of passing all Kokkos unit tests - then AMD deprecated the programming model, forcing us to start over in FY20 with HIP. The Intel ECP Path Forward architecture prototype was developed with some initial capabilities on simulators - but plans changed, so that work will not continue. Instead SYCL will be developed as a backend for Aurora. Remote Spaces was improved. Development is ongoing part of a collaboration with NVIDIA.

This report documents the completion of milestone STPRO4-24 "Provide high quality (production) Kokkos support and consultation for ASC applications and libraries.". The Kokkos team resolved 344 issues reported to github (no explicit tracking of ASC vs non-ASC was performed). We engaged actively on the Kokkos slack channel, which now averages about 60 unique users per week. A survey of ASC customers was conducted with regards to the experienced support. The feedback indicates that the ASC customers are satisfied by Kokkos' support efforts.

This report documents the completion of milestone STPRO4-26 Engaging the C++ Committee. The Kokkos team attended the three C++ Committee meetings in San Diego, Hawaii, and Cologne with multiple members, updated multiple in-flight proposals (e.g. MDSpan, atomic ref), contributed to numerous proposals central for future capabilities in C++ (e.g. executors, affinity) and organized a new effort to introduce a Basic Linear Algebra library into the C++ standard. We also implemented a production quality version of mdspan as the basis for replacing the vast majority of the implementation of Kokkos::View, and thus start the transitioning of one of the core features in Kokkos to its future replacement.

In this paper, we study, analyze, and validate some important zero-dimensional physics-based models for vanadium redox batch cell (VRBC) systems and formulate an adequate physics-based model that can predict the battery performance accurately. In the model formulation process, a systems approach to multiple parameters estimation has been conducted using VRBC systems at low C-rates (~C/30). In this batch cell system, the effect of ions' crossover through the membrane is dominant, and therefore, the capacity loss phenomena can be explicitly observed. Paradoxically, this means that using the batch system might be a better approach for identifying a more suitable model describing the effect of ions transport. Next, we propose an efficient systems approach, which enables to help understand the battery performance quickly by estimating all parameters of the battery system. Finally, open source codes, executable files, and experimental data are provided to enable people's access to robust and accurate models and optimizers. In battery simulations, different models and optimizers describing the same systems produce different values of the estimated parameters. Providing an open access platform can accelerate the process to arrive at robust models and optimizers by continuous modification from the users' side.

The fillers R&D program, mostly experimental, is part of a broader R&D program that includes new process modeling and performance assessment of criticality effects and the overall importance of criticality to repository performance (consequence screening). A literature research and consultation effort with experts by Hardin and Brady (2018) identified several potentially effective and workable filler materials including cements (primarily phosphate based), molten-metal alloys, and low-temperature glasses. Filler attributes were defined and the preliminary lists were compared qualitatively. Further comparative analysis will be done (e.g., cost estimates) after experimental screening has narrowed the list of alternatives. The following cement filler compositions were selected for experimental development work and accelerated testing in FY19: Aluminum phosphate cements (APCs); more specifically aluminum oxide / aluminum phosphate (Al₂O₃/ AlPO₄) cements in which Al₂O₃ serves as the filler material bound by an AlPO₄ binder formed by the reaction of Al₂O₃ with H₃PO₄; Calcium phosphate cements (CPCs); more specifically composed of pure or nearly pure hydroxyapatite (Ca₅(PO₄)₃(OH)); Magnesium potassium phosphate cements (MKPs) composed of magnesium oxide / magnesium potassium phosphate (MgO / MgKPO₄) cements in which MgO serves as the filler and MgKPO₄ serves as the binder formed by the reaction of MgO with monopotassium phosphate (KH₂PO₄) and tricalcium phosphate ((Ca₃(PO₄)₂); Two additional potential cement materials were explored preliminarily as the result of: (1) continued literature investigations into other filler candidates (wollastonite-based phosphate ceramic) and (2) the experimental discovery of a well-consolidated fly ash phosphate cement during the evaluation of fly ash as a potential filler material with Al₂O₃in APCs. Fly ash phosphate cements, more specifically in which a fly ash material composed primarily of mullite and quartz serves as the filler and is reacted with H₃PO₄ to form amorphous phosphate phase(s) as the binder; Wollastonite aluminum phosphate cements (WAPC), specifically wollastonite / aluminum phosphate (CaSiO₃/ AlPO₄) in which CaSiO₃ serves as the filler material and AlPO₄ serves as the binder formed by Al(OH)₃ or metakaolin as Al sources and H₃PO₄ or ammonium dihydrogen phosphate (ADP) (NH₄H₂PO₄) as phosphate sources. The FY19 effort focused on the optimization of compositions and subsequent processing of these five materials to achieve dense and well-consolidated monolithic samples with relatively low porosity. Once these goals were met basic material properties screening evaluations were performed including an assessment of dissolution resistance in water at elevated temperature (200 °C) and mechanical testing including unconfined compressive strength (UCS) testing. To date, the aluminum phosphate cements (APCs) appear to show the most promise for continued development. They are easily prepared and form smooth pourable slurries that remain stable for days with relatively low viscosities of several thousand centipoise (cP). They are then set at elevated temperatures (e.g., 170 °C) under ambient (0.1 MPa) or elevated pressure (~1MPa). Overall, they demonstrate the best dissolution resistance in water at elevated temperature (200 °C) and good compressive strengths. However, additional effort is required to optimize the APC slurry formulations and the process used for thermal curing these materials. The calcium phosphate cements (CPCs) can be formed at room temperature to produce a well-consolidated body. However, their slurry viscosities are very high (and difficult to measure) and they exhibit relatively short cure times of 2 to 3 hours. Also, dissolution resistance is very poor, the poorest of all the cements examined The same is the case for the small number of MKP cements fabricated; they cure very quickly (10 minutes or less) and disintegrate within a few hours upon immersion in distilled water. Surprisingly, fly ash reacts with phosphoric acid to form dense and well-consolidated cements but the mixture rapidly sets at room temperature (less than 30 minutes) and the subsequent conversion of the binder to an amorphous phosphate phase(s) as a function of temperature is complicated. Finally, the wollastonite aluminum phosphate cements (WAPC) are easily prepared and form smooth pourable slurries that remain stable for several hours. They are then set at 130 °C. A WAPC sample exhibited the highest compressive strengths of all the materials we evaluated but in general their dissolution resistance to water is poor.

Neuromorphic computers based on analogue neural networks aim to substantially lower computing power by reducing the need to shuttle data between memory and logic units. Artificial synapses containing nonvolatile analogue conductance states enable direct computation using memory elements; however, most nonvolatile analogue memories require high write voltages and large current densities and are accompanied by nonlinear and unpredictable weight updates. Here, we develop an inorganic redox transistor based on electrochemical lithium-ion insertion into Li_XTiO₂ that displays linear weight updates at both low current densities and low write voltages. The write voltage, as low as 200 mV at room temperature, is achieved by minimizing the open-circuit voltage and using a low-voltage diffusive memristor selector. We further show that the Li_XTiO₂ redox transistor can achieve an extremely sharp transistor subthreshold slope of just 40 mV/decade when operating in an electrochemically driven phase transformation regime.

How does a structure fail in an explosion? Is there an engineering design opportunity to mitigate the effects of a blast? These are questions that may be addressed with simulation; however, a computational simulation capability to answer these questions must include developments in material physics modeling and numerical algorithms that we believe are possible, but do not yet exist. This LDRD project proposes to develop the physics models and computational approaches to create a computational simulation approach to answer these physics and engineering questions.

An overview of the Uintah code and benchmark case is presented. Unitah provides a parallel, adaptive, multi-physics framework and solves time-dependent PDEs in parallel.

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy Office of Nuclear Energy, Office of Spent Fuel and Waste Disposition (SFWD), has been conducting research and development on generic deep geologic disposal systems (i.e., geologic repositories). This report describes specific activities in the second half of Fiscal Year (FY) 2019 associated with the Geologic Disposal Safety Assessment (GDSA) Repository Systems Analysis (RSA) work package within the SFWST Campaign. The overall objective of the GDSA RSA work package is to develop generic deep geologic repository concepts and system performance assessment (PA) models in several host-rock environments, and to simulate and analyze these generic repository concepts and models using the GDSA Framework toolkit, and other tools as needed.

Keto-hydroperoxides (KHPs) are reactive, partially oxidized intermediates that play a central role in chain-branching reactions during the gas-phase low-temperature oxidation of hydrocarbons and oxygenated species. Although multiple isomeric forms of the KHP intermediate are possible in complex oxidation environments when multiple reactant radicals exist that contain nonequivalent O2 addition sites, isomer-resolved data of KHPs have not been reported. In this work, we provide partially isomer-resolved detection and quantification of the KHPs that form during the low-temperature oxidation of tetrahydrofuran (THF, cycl.-O-CH2CH2CH2CH2-). We describe how these short-lived KHPs were detected, identified, and quantified using integrated experimental and theoretical approaches. The experimental approaches were based on direct molecular-beam sampling from a jet-stirred reactor operated at near-atmospheric pressure and at temperatures between 500 and 700 K, followed by mass spectrometry with single-photon ionization via tunable synchrotron-generated vacuum-ultraviolet radiation, and the identification of fragmentation patterns. The interpretation of the experiments was guided by theoretical calculations of ionization thresholds, fragment appearance energies, and photoionization cross sections. On the basis of the experimentally observed and theoretically calculated ionization and fragment appearance energies, KHP isomers could be distinguished as originating from H-abstraction reactions from either the α-C adjacent to the O atom or the β-C atoms. Temperature-dependent concentration profiles of the partially resolved isomeric KHP intermediates were determined in the range of 500-700 K, and the results indicate that the observed KHP isomers are formed overwhelmingly (∼99%) from the α-C THF radical. Comparisons of the partially isomer-resolved quantification of the KHPs to up-to-date kinetic modeling results reveal new opportunities for the development of a next-generation THF oxidation mechanism.

A sensitive volume is developed using pulsed laser-induced collected charge for two bias conditions in an epitaxial silicon diode. These sensitive volumes show good agreement with experimental two photon absorption laser-induced collected charge at a variety of focal positions and pulse energies. When compared to ion-induced collected charge, the laser-based sensitive volume over predicts the experimental collected charge at low bias and agrees at high bias. Here, a sensitive volume based on ion-induced collected charge adequately describes the ion experimental results at both biases. Differences in the amount of potential modulation explain the differences between the ion-and laser-based sensitive volumes at the lower bias. Truncation of potential modulation by the highly doped substrate at the higher bias results in similar sensitive volumes.

The relationship between the structure and thermodynamic properties of schoepite, an important uranyl phase with formula [(UO₂)₈O₂(OH)₁₂]·12H₂O formed upon corrosion of UO₂, has been investigated within the framework of density functional perturbation theory (DFPT). Experimental crystallographic lattice parameters are well reproduced in this study using standard DFT. Phonon calculations within the quasi-harmonic approximation predict standard molar entropy and isobaric heat capacity of S⁰ = 179.60 J mol^-1 K^-1 and C⁰_P = 157.4 J mol^-1 K^-1 at 298.15 K, i.e., ~6% and ~4% larger than existing DFPT-D2 calculations. The computed variation of the standard molar isobaric heat capacity with water content from schoepite (UO₃·xH₂O, x = 2.25) to dehydrated schoepite (x = 1) is predicted to be essentially linear along isotherms ranging from 100 to 500 K. Finally, these findings have important implications for the dehydration of layered uranyl corrosion phases and hygroscopic materials.

To ensure reliable and predictable service in the electrical grid between distributed renewable distributed energy resources (DERs) it is important to gauge the level of trust present within critical components and DER aggregators (DERAs). Although trust throughout a smart grid is temporal and dynamically varies according to measured states, it is possible to accurately formulate communications and service level strategies based on such trust measurements. Utilizing an effective set of machine learning and statistical methods, it is shown that establishment of trust levels between DERAs using behavioral pattern analysis is possible. Further, it is also shown that the establishment of such trust can facilitate simple secure communications routing between DERAs. Providing secure routing between DERAs enables a grid operator to maintain service level agreements to its customers, reduce the attack surface and increase operational resiliency.

Tensile properties, fatigue crack initiation, fatigue crack growth rate, and fatigue life are evaluated in 304L austenitic stainless steel fabricated by directed energy deposition (DED). Large lack of fusion (LoF) defects (often >1 mm in length) significantly reduce ultimate tensile strength and ductility, as well as accelerate fatigue crack initiation and reduce fatigue life. In comparison, small spherical defects (<100 μm in diameter) have less effect on tensile and fatigue properties. Fatigue crack growth rate is less severely affected by defects than other properties, showing only local acceleration in the proximity of LoF defects. Therefore, shorter fatigue life is attributed to the role of LoF defects on facilitating fatigue crack initiation and to a lesser extent fatigue crack propagation. Additionally, the fatigue life can be normalized for defects by considering their effect on ultimate tensile strength, suggesting that in the limit of low defect population, the fatigue strength of additively manufactured stainless steel is similar to conventional wrought materials.

Abstract not provided.

SNAP potentials are inter-atomic potentials for molecular dynamics that enable simulations at accuracy levels comparable to density functional theory(DFT) at a fraction of the cost. As such, SNAP scales to on the order of 10⁴ — 10⁶ atoms. In this work, we explore CPU optimization of potentials computation using SIMD. We note that efficient use of SIMD is non-obvious as the application features an irregular iteration space for various potential terms, necessitating use of SIMD across atoms in a cross matrix, batched fashion. We present a preliminary analytic model to determine the correct batch size for several CPU architectures across several vendors, and show end-to-end speedups between 1.66x and 3.22x compared to the original.

There have been recent efforts towards the development of biologically-inspired neuromorphic devices and architecture. Here, we show a synapse circuit that is designed to perform spike-timing-dependent plasticity which works with the leaky, integrate, and fire neuron in a neuromorphic computing architecture. The circuit consists of a three-terminal magnetic tunnel junction with a mobile domain wall between two low-pass filters and has been modeled in SPICE. The results show that the current flowing through the synapse is highly correlated to the timing delay between the pre-synaptic and post-synaptic neurons. Using micromagnetic simulations, we show that introducing notches along the length of the domain wall track pins the domain wall at each successive notch to properly respond to the timing between the input and output current pulses of the circuit, producing a multi-state resistance representing synaptic weights. We show in SPICE that a notch-free ideal magnetic device also shows spike-timing dependent plasticity in response to the circuit current. This work is key progress towards making more bio-realistic artificial synapses with multiple weights, which can be trained online with a promise of CMOS compatibility and energy efficiency.

The aim and scope of this project was the development of a capability to prepare high-quality, epitaxial beta gallium oxide films by oxide reactive molecular-beam epitaxy. The purpose was to demonstrate that beta gallium oxide could be grown by such a method using Sandia’s existing oxide molecular-beam epitaxy instrument. The key activity in this project was the installation of a gallium oxide capability on the Sandia instrument. This required the acquisition of several custom items for the instrument, including: a gallium effusion cell, appropriate cell power supplies and temperature controllers, a shutter to block beam flux, installation of an existing ozone generator with a directed gas nozzle and controlled leak valve, and re-routing the chilled water system to accommodate the cell. In addition, beta gallium oxide single crystals were acquired and their surfaces characterized by reflection high energy electron diffraction.

Diffraction series data have been acquired and analyzed via multivariate statistical analysis. For two different data series analyzed, the data analysis was able to reduce the raw diffraction data series into a much smaller easier-to-interpret solution consisting mainly of crystallographic phase and orientation information.

EmrE is a small, homodimeric membrane transporter that exploits the established pH gradient across the E. coli inner membrane to export polyaromatic cations that might otherwise inhibit cellular growth. While herculean efforts through experimental studies have established many fundamental facts about the specificity and rate of substrate transport in EmrE, the low resolution of the available structures have hampered efforts to tie those findings to the EmrE coupling mechanism between proton and small molecule substrates. Here we present a full three-dimensional structure of EmrE optimized against available cyro-EM data to delineate the critical interactions by which EmrE regulates its conformation. We use the generated structural model to conduct equilibrium and nonequilibrium molecular dynamics simulations to probe EmrE dynamics under different substrate loading states, representing different states in the transport cycle. The model is stable under extended simulation, and reveals that water dynamics within the EmrE lumen change substantially with the loading state. The water dynamics cause hydrogen bonding networks to shift radically when the protonation states change for a pair of solvent-exposed glutamate residues (E14) within the lumen of the transporter, which are proposed to act as proton binding sites during the transport cycle. One specific hydrogen bond from a tyrosine (Y60) of one monomer to a glutamate (E14) on the opposite monomer is especially critical, as it locks the protein conformation when the glutamate is deprotonated. Furthermore, the hydrogen bond provided by Y60 lowers the pK_a of the interacting glutamate relative to its partner on the opposite monomer such that it will protonate second, establishing the need for both glutamates to be protonated for the hydrogen bond to break and a substrate-free transition to take place.

Thin films are materials systems that are widely used in applications ranging from electronics and optical devices to industrial and biomedical ones. However, these systems are unstable against various homogenization processes and aging mechanisms (grain growth, coarsening, surface evolution, and diffusion of species) even at low service temperatures. In this work, we examine the role of various aspects of microstructure (grain boundary types and characters, free surfaces, surface diffusion, and thermal grooves) on the thermal aging of such systems. Existing experimental tools will be leveraged to characterize thin films, i.e., in-situ quantitative thermal annealing, and provide direct comparisons to predications emerging from a recently developed meso-scale model via Precession Electron Diffraction (PED). Parametric studies will be conducted to gain insights on the role of each of the aforementioned aspects of microstructure on the dynamics. Herein, we will focus on “hard” gold thin films (with alloying elements like Co, Ni, or Fe) as they are materials of choice in a wide range of applications at Sandia. Initially, pure gold systems are examined and future studies will focus on microstructure evolution in the presence of alloying elements and second-phase particles.

Density functional theory (DFT) is undergoing a shift from a descriptive to a predictive tool in the field of solid state physics, heralded by a spike in “high-throughput” studies. However, methods to rigorously evaluate the validity and accuracy of these studies is lacking, raising serious questions when simulation and experiment disagree. In response, we have developed the V-DM/16 test set, designed to evaluate the experimental accuracy of DFT’s various implementations for pe riodic transition metal solids. Our test set evaluates 26 transition metal elements and 80 transition metal alloys across three physical observables: lattice constants, elastic coefficients, and formation energy of alloys. Whether or not a functional can accurately evaluate the formation energy offers key insights into whether the relevant physics are being captured in a simulation, an especially impor tant question in transition metals where active d-electrons can thwart the accuracy of an otherwise well-performing functional. Our test set captures a wide variety of cases where the unique physics present in transition metal binaries can undermine the effectiveness of “traditional” functionals. By application of the V/DM-16 test set, we aim to better characterize the performance of existing functionals on transition metals, and to offer a new tool to rigorously evaluate the performance of new functionals in the future.

Symmetric four-point bending (S4PB) and anti-symmetric four-point bending (AS4PB) were applied to assess the effect of hydrogen on crack initiation and propagation in a stable, nitrogen-strengthened austenitic stainless steel (21Cr-6Ni-9Mn). Specimens of a high strength aluminum alloy (AA2219-T851), which has also been identified as hydrogen-compatible, were used for test method development prior to completing the stainless steel test matrix. Single edge notched bend (SEN(B)) specimens were extracted from forged 21Cr-6Ni-9Mn bar and AA2219 plate. The 21Cr-6Ni-9Mn specimens were then hydrogen charged with ~200 wt. ppm hydrogen; AA2219 specimens were not charged. Aluminum specimens were tested in S4PB to induced mode I (pure bending), and AS4PB to attain varying levels of mode I/II mixity and mode II (pure shear), where the ratio of mode I to mode II varies with the position of the crack plane relative to the load line. After test method troubleshooting and validation, hydrogen charged stainless steel specimens were then subjected to mode I and two ratios of mixed mode I/II. Mode II loading was not achieved due to high load limitations. Analyses of fracture profiles for both materials reveal a marked effect of loading mode mixity on initial crack propagation orientation, however a specific contribution of hydrogen was not readily identifiable. Fracture initiation toughness in the presence of hydrogen, J_IH, was calculated following the J-integral approach; the mode I J_IH calculated for stainless steel samples fractured in S4PB were consistent with published values determined from compact tension specimens. The peak loads required to initiate fracture during AS4PB far exceeded those required during S4PB. This is attributed to the increasing shear force applied to the specimens as the degree of mode mixity increases. Ultimately, understanding the fracture response of hydrogen exposed stainless steels subjected to mixed mode I/II loading is critical for designing hydrogen containment vessels or gas transfer systems (GTS).

Numerical simulations of the cardiovascular system are affected by uncertainties arising from a substantial lack of data related to the boundary conditions and the physical parameters of the mathematical models. Quantifying the impact of this uncertainty on the numerical results along the circulatory network is challenged by the complexity of both the morphology of the domain and the local dynamics. Here, we propose to integrate (i) the Transverse Enriched Pipe Element Methods (TEPEM) as a reduced-order model for effectively computing the 3D local hemodynamics; and (ii) a combination of uncertainty quantification via Polynomial Chaos Expansion and classical relaxation methods – called network uncertainty quantification (NetUQ) – for effectively propagating random variables that encode uncertainties throughout the networks. The findings demonstrate the computational effectiveness of computing the propagation of uncertainties in networks with nontrivial topology, including portions of the cerebral and the coronary systems.

The purpose of this report is to review technical issues relevant to the performance evaluation of dry storage systems during vacuum drying and long-term storage operations. It also provides updates on experimental components under development that are vital for pursuing advanced studies. Validation of the extent of water removal in a multi-assembly dry storage system using an industrial vacuum drying procedure is needed, as operational conditions leading to incomplete drying may have potential impacts on the fuel, cladding, and other components in the system. Water remaining in canisters/casks upon completion of vacuum drying can lead to cladding corrosion, embrittlement, and breaching, as well as fuel degradation. Therefore, additional information is needed to evaluate the potential impacts of water retention on extended long-term dry storage. A general lack of data and experience modeling the drying process necessitates the testing of advanced concepts focused on the simulation of industrial vacuum drying. Smaller-scale tests that incorporate relevant physics and well-controlled boundary conditions are necessary to provide insight and guidance to the modeling of prototypic systems undergoing drying processes. This report describes the development and testing of waterproof, electrically-heated spent fuel rod simulators as a proof of concept to enable experimental simulation of the entire dewatering and drying process. This report also describes the preliminary development of specially-designed, unheated mock fuel rods for monitoring internal rod pressures and studying water removal from simulated failed fuel rods. A variety of moisture monitoring instrumentation is also being considered and will be downselected for the tracking of dewpoints of gas samples. The effects of cladding oxidation and crud on water retention in dry storage systems can be explored via separate effects tests (SETs) that would measure chemisorbed and physisorbed water content on cladding samples. The concepts listed above will be incorporated into an advanced dry cask simulator with multiple fuel assemblies in order to account for important inter-assembly heat-transfer physics. Plans are described for harvesting up to five full-length 5x5 laterally truncated assemblies from commercial 17x17 PWR skeleton components with the goal of constructing this simulator.

This report has been accepted for publication in the journal Fusion Science and Technology, in the special issue associated with the Tritium 2016 conference, where the work was presented. Scanning calorimetry of a confined, reversible hydrogen sorbent material has been previously proposed as a method to determine compositions of unknown mixtures of diatomic hydrogen isotopologues and helium. Application of this concept could result in greater process knowledge during the handling of these gases. Previously published studies have focused on mixtures that do not include tritium. This paper focuses on modeling to predict the effect of tritium in mixtures of the isotopologues on a calorimetry scan. The model predicts that tritium can be measured with a sensitivity comparable to that observed for hydrogen-deuterium mixtures, and that under some conditions, it may be possible to determine the atomic fractions of all three isotopes in a gas mixture.

The linear energy-momentum dispersion which arises from graphene’s underlying honeycomb lattice gives graphene its unique electronic properties unfound in conventional semiconductors. Theoretically speaking, when an electrostatic potential with hexagonal or honeycomb symmetry is imposed onto a two-dimensional electron/hole system, the band structure is modified in a way that the same linear energy-momentum dispersion could exist. Experimentally, there has not been any evidence from transport demonstrating the so-called “artificial graphene”. In this project, we attempt to create an artificial superlattice potential with hexagonal symmetry for two dimensional carriers in an undoped SiGe heterostructure by patterning a nanoscale hole array in a metallic gate. Using undoped heterostructures allows us to access a very wide density range, which covers the magic densities at which the Dirac points are expected. A process flow for fabricating such field-effect-transistor devices with a lattice constant as small as 90 nm is reported. Magneto-transport measurements performed at 0.3 K show that the superlattice potential in the quantum well in which the two-dimensional system resides is indeed modulated by the gates. However, no signature of the sought-after linear dispersion is observed in the transport data.

Magnetically driven experiments supporting pulsed-power utilize a wide range of configurations, including wire-arrays, gas-puffs, flyer plates, and cylindrical liners. This experimental flexibility is critical to supporting radiation effects, dynamic materials, magneto-inertial-fusion (MIF), and basic high energy density laboratory physics (HEDP) efforts. Ultimately, the rate at which these efforts progress is limited by our understanding of the complex plasma physics of these systems. Our effort has been to begin to develop an advanced algorithmic structure and a R&D code implementation for a plasma physics simulation capability based on the five-moment multi-fluid / full-Maxwell plasma model. This model can be used for inclusion of multiple fluid species (e.g., electrons, multiple charge state ions, and neutrals) and allows for generalized collisional interactions between species, models for ionization/recombination, magnetized Braginskii collisional transport, dissipative effects, and can be readily extended to incorporate radiation transport physics. In the context of pulsed-power simulations this advanced model will help to allow SNL to computationally simulate the dense continuum regions of the physical load (e.g. liner implosions, flyer plates) as well as partial power-flow losses in the final gap region of the inner MITL. In this report we briefly summarize results of applying a preliminary version of this model in the context of verification type problems, and some initial magnetic implosion relevant prototype problems. The MIF relevant prototype problems include results from fully-implicit / implicit-explicit (IMEX) resistive MHD as well as full multifluid EM plasma formulations.

The inextensible cylindrical shell theory and lubrication theory combine into a model for the elastohydrodynamics of a rolling-imprint modality of nanoimprint lithography (NIL). Foil-bearing theory describes the formation of the lubrication gap due to relative motion between a tensioned substrate and a rigid, cylindrical surface. Reproduction of the results of foil-bearing theory for both stiff and perfectly flexible substrates validates this coupled model and reveals a highly predictable region of uniformity that provides low shear stress conditions ideal for UV-cure. These results show theoretical limitations that are used to construct an operating window for predicting rolling-mode NIL process feasibility.

Efficiency bottlenecks inherent to conventional computing in executing neural algorithms have spurred the development of novel devices capable of “in-memory” computing. Commonly known as “memristors,” a variety of device concepts including conducting bridge, vacancy filament, phase change, and other types have been proposed as promising elements in artificial neural networks for executing inference and learning algorithms. In this article, we review the recent advances in memristor technology for neuromorphic computing and discuss strategies for addressing the most significant performance challenges, including nonlinearity, high read/write currents, and endurance. As an alternative to two-terminal memristors, we introduce the three-terminal electrochemical memory based on the redox transistor (RT), which uses a gate to tune the redox state of the channel. Decoupling the “read” and “write” operations using a third terminal and storage of information as a charge-compensated redox reaction in the bulk of the transistor enables high-density information storage. These properties enable low-energy operation without compromising analog performance and nonvolatility. Finally, we discuss the RT operating mechanisms using organic and inorganic materials, approaches for array integration, and prospects for achieving the device density and switching speeds necessary to make electrochemical memory competitive with established digital technology.

Engineered solid-liquid interfaces will play an important role in the development of future energy storage and conversion (ESC) devices. In the present study, defective graphene oxide (GO) and reduced graphene oxide (rGO) structures were used as engineered interfaces to tune the selectivity and activity of Pt disk electrodes. GO was deposited on Pt electrodes via the Langmuir-Blodgett technique, which provided compact and uniform GO films, and these films were subsequently converted to rGO by thermal reduction. Electrochemical measurements revealed that both GO and rGO interfaces on Pt electrodes exhibit selectivity toward the oxygen reduction reaction (ORR), but they do not have an impact on the activity of the hydrogen oxidation reaction in acidic environments. Scanning transmission electron microscopy at atomic resolution, along with Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM), revealed possible diffusion sites for H2 and O2 gas molecules and functional groups relevant to the selectivity and activity of these surfaces. Based on these insights, rGO interfaces are further demonstrated to exhibit enhanced activity for the ORR in nonaqueous environments and demonstrate the power of our ex situ engineering approach for the development of next-generation ESC devices.

Accurate and efficient constitutive modeling remains a cornerstone issue for solid mechanics analysis. Over the years, the LAMÉ advanced material model library has grown to address this challenge by implementing models capable of describing material systems spanning soft polymers to stiff ceramics including both isotropic and anisotropic responses. Inelastic behaviors including (visco)plasticity, damage, and fracture have all incorporated for use in various analyses. This multitude of options and flexibility, however, comes at the cost of many capabilities, features, and responses and the ensuing complexity in the resulting implementation. Therefore, to enhance confidence and enable the utilization of the LAMÉ library in application, this effort seeks to document and verify the various models in the LAMÉ library. Specifically, the broader strategy, organization, and interface of the library itself is first presented. The physical theory, numerical implementation, and user guide for a large set of models is then discussed. Importantly, a number of verification tests are performed with each model to not only have confidence in the model itself but also highlight some important response characteristics and features that may be of interest to end-users. Finally, in looking ahead to the future, approaches to add material models to this library and further expand the capabilities are presented.

Presented in this document is a small portion of the tests that exist in the Sierra/SolidMechanics (Sierra/SM) verification test suite. Most of these tests are run nightly with the Sierra/SM code suite, and the results of the test are checked versus the correct analytical result. For each of the tests presented in this document, the test setup, a description of the analytic solution, and comparison of the Sierra/SM code results to the analytic solution is provided. Mesh convergence is also checked on a nightly basis for several of these tests. This document can be used to confirm that a given code capability is verified or referenced as a compilation of example problems. Additional example problems are provided in the Sierra/SM Example Problems Manual. Note, many other verification tests exist in the Sierra/SM test suite, but have not yet been included in this manual.

In this work, a novel computational procedure, based on the principles of flat-histogram Monte Carlo, is developed for facile prediction of the adsorption thermodynamics of intrinsically flexible adsorbents. We then demonstrate how an accurate prediction of methane deliverable capacity in a metal–organic framework (MOF) with significant intrinsic flexibility requires use of such a method. Dynamic side chains in the framework respond to methane adsorbates and reorganize to exhibit a more conducive pore space at high adsorbate densities while simultaneously providing a less conducive pore space at low adsorbate densities. This “responsive pore” MOF achieves ~20% higher deliverable capacity than if the framework were rigid and elucidates a strategy for designing high deliverable capacity MOFs in the future.

At Sandia National Laboratories, I spent the summer of 2016 interning at the Combustion Research Facility. As the title suggests, the main goal of the facility is to investigate combustion with the goal of optimizing engine efficiency and reducing pollutants and emissions. At the lab, I worked in the field of spectroscopy, lasers and optics. Specifically, I was involved in a research group that investigated innovative methods of probing matter and chemical reactions with laser light.

Combined picosecond (10^-12 s) and femtosecond (10^-15 s) laser pulses can give sensitive, low-noise measurements of important quantities in reacting flows, such as species concentrations and temperature. Emmanuel’s work focused on the development of an instrument for tailoring the time profile of picosecond laser pulses for use in nonlinear optical spectroscopic methods created from broad bandwidth femtosecond pulses. In addition to constructing the device, Emmanuel produced a LabView-based automation code, building off skills he developed in a previous CCI internship at Sandia.

Radiation-imaging devices are important to the nuclear security and safeguards communities due to their ability to detect and localize radioactive sources. Because of their low natural background, difficulty to shield, and unique association with SNM, fast-neutron imaging provides a promising means for the detection of SNM. To make these R&D systems useful for end-user applications, robust reconstruction and analysis algorithms must be developed that provide detailed information on the location, energy spectrum, and intervening material. To date, deconvolution, match filtering, and MLEM have been demonstrated. However, the information provided is limited to an approximate location of the source.

In this report Chenhall presents a new Monte Carlo (MC) transport method developed as part of his PhD thesis work funded via fellowship by Sandia National Laboratories. The model is used to simulate the electron thermal transport within inertial confinement fusion (ICF) type problems. The new model aims to improve upon the currently used implicit Schurtz, Nicolai, and Busquet (iSNB) method, in particular by using finite particle ranges in comparison to the exponential solution of a diffusion method and by improved higher order angular modeling. The new method is being developed using the 1D LILAC and 2D DRACO codes developed by the Rochester Laboratory for Laser Energetics. The model is compared to iSNB for several ICF type problems: Omega shot 60303 a shock timing experiment, Omega shot 68951 a cryo target implosion and a NIF phase plate polar direct drive design. Overall, the MC method performs at least as well as the iSNB method and appears to indicate a lower predicted preheat ahead of the shock fronts. However, the difference in overall results between iSNB and MC transport is small for the choice of test problems and further simulations will need to be run to fully understand the difference between the models.

Abstract not provided.

Abstract not provided.

An initial foray into the design of specimens that can be used to provide data about the quasistatic ductile failure of metals when subjected to shear-dominated (low triaxiality) states of stress was undertaken. Four specimen geometries made from two materials with different ductility (Al 7075, lower ductility and steel A286, higher ductility) were considered as candidates. Based on results from analysis and experimentation, it seems that two show promise for further consideration. Whereas preliminary results indicate that the Johnson-Cook model fit the failure data for Al 7075 well, it did not fit the data for steel A286. Further work is needed to consolidate the results and evaluate other failure models that may fit the steel data better, as well as to extend the results of this work to the dynamic loading regime.

Concern over Arctic methane (CH₄) emissions has increased following recent discoveries of poorly understood sources and predictions that methane emissions from known sources will grow as Arctic temperatures increase. New efforts are required to detect increases and explain sources without being confounded by the multiple sources. Methods for distinguishing different sources are critical. We conducted measurements of atmospheric methane and source tracers and performed baseline global atmospheric modeling to begin assessing the climate impact of changes in atmospheric methane. The goal of this project was to address uncertainties in Arctic methane sources and their potential impact on climate by (1) deploying newly developed trace-gas analyzers for measurements of methane, methane isotopologues, ethane, and other tracers of methane sources in the Barrow, AK, (2) characterizing methane sources using high-resolution atmospheric chemical transport models and tracer measurements, and (3) modeling Arctic climate using the state-of-the-art high- resolution Spectral Element Community Atmosphere Model (CAM-SE).

In experiments conducted on the Z-machine at Sandia National Laboratories, dynamic material properties cannot be analyzed using traditional analytic methods, necessitating solving an inverse problem. Bayesian model calibration is a statistical framework for solving an inverse problem to estimate parameters input into a computational model in the presence of multiple uncertainties. Disentangling input parameter uncertainty and model misspecification is often poorly identified problem. When using computational models for physical parameter estimation, the issue of parameter identifiability must be carefully considered to obtain accurate and precise estimates of physical parameters. Additionally, in dynamic material properties applications, the experimental output is a function, velocity over time. While we can sample an arbitrarily large number of points from the measured velocity, these curves only contain a finite amount of information about the calibration parameters. In this report, we propose modifications to the Bayesian model calibration framework to simplify and improve the estimation of physical parameters with functional outputs. Specifically, we propose scaling the likelihood function by an effective sample size rather than modeling the discrepancy function; and modularizing input nuisance parameters with weakly identified parameters. We evaluate the performance of these proposed methods using a statistical simulation study and then apply these methods to estimate parameters of the tantalum equation of state. We conclude that these proposed methods can provide simple, fast, and statistically valid alternatives to the full Bayesian model calibration procedure; and that these methods can be used to estimate parameters of the equation of state for tantalum.

A 150 lb_f thrust class, modular, bi-propellant, rocket engine/gas-generator and supporting test infrastructure has been developed in a cooperative effort between Sandia National Laboratories and the New Mexico Institute of Mining and Technology’s (NMIMT’s) Energetic Materials Research and Testing Center (EMRTC). This modular test engine design consists of a head end fuel-oxidizer injector, a spark ignition gaseous H₂/O₂ torch igniter, combustion chamber and nozzle module. This robust design allows for rapid configuration changes as well as economical repair should hardware become damaged in testing. The engine interfaces with a permanently installed pressurizing system capable of delivering liquid nitrous oxide and a variety of liquid fuels for both rocket engine development and propellant performance evaluation. The regulated high pressure systems allow for delivery of liquefied gases above their saturation pressure as well as allowing for high pressure rocket engine/gas-generator operation. The facility test cell houses a 1 ton thrust capacity test stand leaving room for larger scale engine development.

Color centers (defect complexes such as SiV) in diamond have shown potential in fields ranging from metrology, cybersecurity to quantum computation. Demonstrations in these fields have pushed the envelope of state-of-the-art operations – for example, single photon sources (SPS) making use of SiV centers in diamond for quantum key distribution have demonstrated all the requirements for SPS operation including: (1) stable operation with second correlation function <<1, (2) electrically driven single photon emission and (3) compatibility with frequenc y conversion to telecommunication frequencies. To-date, however, all these demonstrations have been on lab-scale one-off devices. The key question behind how to deterministically fabricate these devices, namely activation yield has been overlooked. For context, Si based semiconductor devices are hugely successful because we have a high activation yield for implanted dopants. This is not yet true for diamond color centers. As currently understood, the color center yield is dominated by a lack of vacancies in the immediate area of the implantation. We propose to optimize the activation yield of color center using a combination of (1) focused single ion implantation with in-situ detection to count the number of implanted Si ions and (2) localized point defect (vacancy) creation using a focused Li ion beam to improve the yield. These experiments build on the unique capabilities of the SNL nanoImplanter (nI) to produce focused ion beam with spatial resolution of < 10 nm of both Si and Li ions. This work will also leverage our world-leading single ion implantation and detection capabilities.

The effort to develop larger-scale computing systems introduces a set of related challenges: Large machines are more difficult to synchronize. The sheer quantity of hardware introduces more opportunities for errors. New approaches to hardware, such as low-energy or neuromorphic devices are not directly programmable by traditional methods.

This project seeks to leverage various hyperspectral tensor products for the purposes of target classification/detection/prediction. In addition to hyperspectral, these products may be images, time series, geometries, or other modalities. The scenarios in which the targets of interest must be identified are typically from remote sensing platforms such as satellites. As such, there are numerous real-world constraints that drive algorithmic formulation. Cost, complexity, and feasibility of the algorithm should all be considered. Targets of interest are exceedingly rare, and collecting many data samples is prohibitively expensive. Furthermore, model interpretability is paramount due to the application space. The goal of this project is to develop a constrained supervised tensor factorization framework for use on hyperspectral data products. Supervised tensor factorizations already exist in the literature, although they have not seen widespread adoption in the remote sensing domain. The novelty of this project will be the formulation and inclusion of constraints that take into account mission considerations and physics based limits to learn a factorization that is both physically interpretable and mission deployable. This will represent a new contribution to the field of remote sensing for performing supervised learning tasks with hyperspectral data.

A transition to sustainable energy is among society’s greatest challenges. With this change, the development of more efficient, safe, and cost-effective batteries is also necessary. Batteries are becoming ever more integral to today’s technology rich world. 3 Intermittent power sources, such as wind and solar, require storage devices to deliver a consistent energy supply. More ecofriendly, electric vehicles, have limited travel distances due to low energy density batteries. Phones and other personal electronics are also reliant on battery performance. An encouraging improvement in specific energy from current, lithium (Li) ion batteries, which are already nearing the bounds of their performance potential, are lithium sulfur (Li-S) batteries.

This project investigates the behavior of a venting feature on a hermetically sealed volume under abnormal thermal environments. A pressure profile is used to simulate internal pressure build up due to thermal decomposition of foam caused by temperatures approaching 800K. For the purposes of his scoping study a small can composed of 304L stainless steel is used to emulate the problem at hand and simplify the model. The venting feature can be described as an elliptical thinned area with X-like scoring marks on the circumferential surface of the can where the highest stresses are located without the feature. The feature variables include elliptical size, depth, and quantity to optimize the venting feature geometry to vent at a desired pressure range. Cubit, SIERRA: Solid Mechanics, and Ensight modeling programs are used to simulate and analyze several iterations of this feature to determine the most optimal geometry. The model indicates through the simulations that the feature does in fact vent in a predictable manner and can be used in a variety of pressure vessel applications where uncontrolled over pressurizations are undesirable. Future work includes model validation tests, looking at different geometries, and using a more accurate failure criterion.

Abstract not provided.

Abstract not provided.

Researchers at Sandia National Laboratories have developed a high-fidelity virtual model of the human head, neck, and torso to investigate the details of life-threatening injury to the central nervous, respiratory, and cardiovascular systems as a result of blast exposure and behind-armor blunt trauma. This model set is comprised of separate head-neck and torso models that can be used independently or combined to investigate comprehensive injury to life-critical organs as a result of blast, blunt impact, and/or projectile penetration. The Sandia head-neck-torso model represents a 60th percentile human male from the waist up possessing anatomically correct distributions of bone, white and gray brain matter, falx & tentorium membranes, spinal cord, intervertebral disks, cartilage, vasculature, blood, airways, lungs, heart, liver, stomach, kidneys, spleen, muscle, and fat/skin.

As new memory technologies appear on the market, there is a growing push to incorporate them into future architectures. Compared to traditional DDR DRAM, these technologies provide appealing advantages such as increased bandwidth or non-volatility. However, the technologies have significant downsides as well including higher cost, manufacturing complexity, and for non-volatile memories, higher latency and wear-out limitations. As such, no technology has emerged as a clear technological and economic winner. As a result, systems are turning to the concept of multi-level memory, or mixing multiple memory technologies in a single system to balance cost, performance, and reliability.

Distributed Phase Plates (DPP) are used in laser experiments to create homogenous intensity distributions of a distinct shape at the location of the laser focus. Such focal shaping helps with controlling the intensity that is impeding on the target. To efficiently use a DPP, the exact size and shape of the focal distribution is of critical importance. We recorded direct images of the focal distribution with ideal continuous-wave (CW) alignment lasers and with laser pulses delivered by the Z-Beamlet facility. As necessary to protect the imaging sensors, laser pulses will not be performed by full system shots, but rather with limited energy on so-called 'rod-shots', in which Z-Beamlet's main amplifiers do not engage. The images are subsequently analyzed for characteristic radii and shape. All characterizations were performed at the Pecos target area of Sandia with a lens of 3.2 m focal length.

This is an addendum to the Sierra/SolidMechanics 4.54 User's Guide that documents additional capabilities available only in alternate versions of the Sierra/SolidMechanics (Sierra/SM) code. These alternate versions are enhanced to provide capabilities that are regulated under the U.S. Department of State's International Traffic in Arms Regulations (ITAR) export control rules. The ITAR regulated codes are only distributed to entities that comply with the ITAR export control requirements. The ITAR enhancements to Sierra/SM include material models with an energy-dependent pressure response (appropriate for very large deformations and strain rates) and capabilities for blast modeling. This document is an addendum only; the standard Sierra/SolidMechanics 4.54 User's Guide should be referenced for most general descriptions of code capability and use.

Presented in this document are tests that exist in the Sierra/SolidMechanics example problem suite, which is a subset of the Sierra/SM regression and performance test suite. These examples showcase common and advanced code capabilities. A wide variety of other regression and verification tests exist in the Sierra/SM test suite that are not included in this manual.

Presented in this document are the theoretical aspects of capabilities contained in the Sierra/SM code. This manuscript serves as an ideal starting point for understanding the theoretical foundations of the code. For a comprehensive study of these capabilities, the reader is encouraged to explore the many references to scientific articles and textbooks contained in this manual. It is important to point out that some capabilities are still in development and may not be presented in this document. Further updates to this manuscript will be made as these capabilities come closer to production level.

Sierra/SolidMechanics (Sierra/SM) is a Lagrangian, three-dimensional finite element analysis code for solids and structures subjected to extensive contact and large deformations, encompassing explicit and implicit dynamic as well as quasistatic loading regimes. This document supplements the primary Sierra/SM 4.54 User's Guide, describing capabilities specific to Goodyear analysis use cases, including additional implicit solver options, material models, finite element formulations, and contact settings.

Accurate modeling of viscoelasticity remains an important consideration for a variety of materials (e.g. polymers and inorganic glasses). As such, over the previous decades a substantial body of work has been dedicated to developing appropriate constitutive models for viscoelasticity ranging from initial considerations of linear thermoviscoelasticity to more complex non-linear formulations incorporating fictive temperatures or potential energy clocks including the use of both internal state variable(ISV) and hereditary integral representations. Nonetheless, relatively limited (in comparison to plasticity) attention has been paid to the numerical integration of such schemes. In terms of integral based formulations, Taylor et al. first considered the problem of the integration of a linear viscoelasticity model. That work focused on the integration of the hereditary integrals and demonstrated improved performance of the new scheme with a custom finite element code over an existing finite difference reference. Chambers and Becker, using a free volume based shift factor, also considered the integration of the hereditary integrals and the impact on the problem of a pressurized thick-walled cylinder and developed an adaptive scheme to bound the error. Chambers later developed three-point Gauss and composite integration schemes for the hereditary integrals and noted improved accuracy. With respect to ISV-based schemes, formulations for the non-linear Schapery model have been proposed. However, in those efforts greater attention was paid to convergence of the non-linear solution scheme than impact of numerical integration. Various authors (e.g. Holzapfel and Simo and Hughes) have also studied the use of convolution integrals with differential forms of ISVs for temperature-independent formulations. Regardless, while the "potential energy clock" (PEC) and "simplified potential energy clock"(SPEC) models have been used to study a variety of non-linear responses (e.g.), limited attention has been paid to the numerical performance. As will be discussed later, the "clock" at the center of the formulations includes temperature and complex history dependence making the numerical integration of such a model even more challenging. Thus, in the current work an initial effort towards characterizing the numerical integration of the constitutive model through simplified problems is performed. To that end, in Section 2 the theory of the model is briefly presented while the numerical integration is discussed in Section 3. Results of various studies characterizing the numerical behavior and performance are then given in Section 4. Finally, some concluding remarks and thoughts for follow on works are provided in Section 5.

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & Waste Disposition (SFWD) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). Two high priorities for SFWST disposal R&D are design concept development and disposal system modeling (DOE 2011, Table 6). These priorities are directly addressed in the SFWST Geologic Disposal Safety Assessment (GDSA) work package, which is charged with developing a disposal system modeling and analysis capability for evaluating disposal system performance for nuclear waste in geologic media.

Lithium batteries provide high energy density storage with applications ranging from consumer electronics to electric vehicles. However, they have a limited lifespan and experience capacity loss with aging. Multiple mechanisms contribute to battery aging. The battery binder plays two important roles in the electrodes, and the damage it sustains during cycling may play a role in the degradation of the overall battery performance. Mechanical stress during battery operations occurs as a result of the swelling and shrinking of the electrodes because of the movement of lithium with cycling. The yield stress of the swollen polyvinylidene fluoride carbon black (PVDFCB) binder was measured at approximately 4MPa for PVDF with carbon black CB weight fractions between 10-30% swollen in propylene carbonate. This is far less stress than is typically experienced in an electrode during cycling. The effects of this permanent damage to the binder were explored by measuring the conductivity loss with strains in excess of the binder yield.

This document outlines the gradient-based digital image correlation (DIC) formulation used in DICe, the Digital Image Correlation Engine (Sandia’s open source DIC code). The gradient-based algorithm implemented in DICe directly reflects the formulation presented here. Every effort is made to point out any simplifications or assumptions involved in the implementation. The focus of this document is on determination of the motion parameters. Computing strain is not discussed herein.

The ethical, legal, and social issues (ELSI) surrounding Artificial Intelligence (AI) can have as great of an impact on the technologies’ success as technical issues such as safety, reliability, and security. Addressing these risks can counter potential program failures, legal and ethical battles, constraints to scientific research, and product vulnerabilities. This paper presents a surety engineering framework and process that can be applied to AI to identify and address technical, ethical, legal and societal risks. Extending sound engineering practices to incorporate a method to “engineer” ELSI can offer the scientific rigor required to significantly reduce the risk of AI vulnerabilities. Modeling the specification, design, evaluation and quality/risk indicators for AI provides a foundation for a risk-informed decision process that can benefit researchers and stakeholders alike as they use it to critically examine both substantial and intangible risks.

The review was conducted on May 9-10, 2016 at the University of Utah. Overall the review team was impressed with the work presented and found that the CCMSC had met or exceeded the Year 2 milestones. Specific details, comments and recommendations are included in this document.

The concept of total-internal-reflection elastic metasurface (TIR-MS)was recently proposed [1]and employed within flexible planar waveguides in order to create highly subwavelength sound-hard barriers impenetrable to low frequency elastic waves. The underlying physical mechanism relies on the design of engineered interfaces exhibiting extreme phase gradients such that any incoming wave at, approximately, any incidence will experience total-internal-reflection conditions. At the design frequency, the metasurface exhibits a large phase gradient such that, in accordance with the generalized Snell's law, the first critical angle is virtually always exceeded. It is worth noting that in practical realizations, the actual total reflection performance might vary depending on the angle of incidence. This dependence is due to the discrete implementation of the metasurface which results in diffraction effects. This paper presents the results of an experimental study that explores the vibration isolation performance of TIR-MS when applied to structures made of complex combinations of different elastic waveguides (e.g. bolted assemblies of beams, plates, and shells). Such system can be seen as a prototypical structure emulating mechanical assemblies of practical interest for many engineering applications. Experimental results confirm that, when the TIR-MS is embedded in the host waveguide, significant vibration isolation capabilities are achieved under quasi-omnidirectional incidence and highly subwavelength excitation conditions (i.e. the ratio of the operating wavelength to the width of the TIR-MS is approximately 5.25). These experimental results suggest new interesting directions to achieve vibration isolation and mechanical energy filtering for practical engineering systems.

Silicone elastomer filled with glass micro balloons (GMB) is an elastomeric syntactic foam used in electronics and component packaging for encapsulation, potting, stress-relief layer, and electrical insulation purposes. Under mechanical loading, the reinforcing phase, namely the GMBs embedded in the elastomer matrix, may break or delaminate, leading to internal damage and macroscale stiffness degradation, which can alter the material's protective capacity against mechanical shock and vibration. The degree of damage is controlled by the loading history, delamination, and failure behavior of the GMBs. We investigate the GMB failure behavior in this work wherein we present an indentation experiment to measure the force required to fail individual GMBs that are either embedded in the elastomer matrix or adhered to the surface of an elastomer layer. The indentation apparatus is augmented with an inverted optical microscope to enable in situ imaging of the GMB. Failure modes for the embedded or non-embedded GMBs are discussed based on the morphology of the broken GMBs and the measured failure forces. We also measure the adhesion energy between the glass balloon and the elastomer, based on which the possibility of delamination between the GMB and the surrounding elastomer matrix during the failure process is evaluated. Our results can facilitate the development of a failure criterion of GMBs which is necessary for establishing a physics-based constitutive model to describe the macroscopic damage mechanics of elastomeric syntactic foams.

Arctic sea ice plays an important role in the global climate. Sea ice models governed by physical equations have been used to simulate the state of the ice including characteristics such as ice thickness, concentration, and motion. More recent models also attempt to capture features such as fractures or leads in the ice. These simulated features can be partially misaligned or misshapen when compared to observational data, whether due to numerical approximation or incomplete physics. In order to make realistic forecasts and improve understanding of the underlying processes, it is necessary to calibrate the numerical model to field data. Traditional calibration methods based on generalized least-square metrics are flawed for linear features such as sea ice cracks. We develop a statistical emulation and calibration framework that accounts for feature misalignment and misshapenness, which involves optimally aligning model output with observed features using cutting-edge image registration techniques. This work can also have application to other physical models which produce coherent structures. Supplementary materials accompanying this paper appear online.

Metallic alloys are extensively utilized in applications where extreme loading and environmental conditions occur and engineering reliability of components or structures made of such materials is a significant concern in applications. Adiabatic heating in these materials during high-rate deformation is of great interest to analysts, experimentalists, and modelers due to a reduction in strength that is produced. Capturing the thermosoftening caused by adiabatic heating is critical in material model development to precisely predict the dynamic response of materials and structures at high rates of loading. In addition to strain rate effect, the Johnson–Cook (JC) model includes a term to describe the effect of either environmental or adiabatic temperature rise. The standard expression of the JC model requires quantitative knowledge of temperature rise, but it can be challenging to obtain in situ temperature measurements, especially in dynamic experiments. The temperature rise can be calculated from plastic work with a predetermined Taylor-Quinney (TQ) coefficient. However, the TQ coefficient is difficult to determine since it may be strain and strain-rate dependent. In this study, we modified the JC model with a power-law strain rate effect and an explicit form of strain- and strain-rate-dependent thermosoftening due to adiabatic temperature rise to describe the strain-rate-dependent tensile stress–strain response, prior to the onset of necking, for 304L stainless steel, A572, and 4140 steels. The modified JC model was also used to describe the true stress–strain response during necking for A572 and 4140 steels at various strain rates. The results predicted with the modified JC model agreed with the tensile experimental data reasonably well.

Gate length dependent (80 nm–5000 mm) radio frequency measurements to extract saturation velocity are reported for Al0.85Ga0.15N/Al0.7Ga0.3N high electron mobility transistors fabricated into radio frequency devices using electron beam lithography. Direct current characterization revealed the threshold voltage shifting positively with increasing gate length, with devices changing from depletion mode to enhancement mode when the gate length was greater than or equal to 450 nm. Transconductance varied from 10 mS/mm to 25 mS/mm, with the 450 nm device having the highest values. Maximum drain current density was 268 mA/mm at 10 V gate bias. Scattering-parameter characterization revealed a maximum unity gain bandwidth (fT) of 28 GHz, achieved by the 80 nm gate length device. A saturation velocity value of 3.8 × 106 cm/s, or 35% of the maximum saturation velocity reported for GaN, was extracted from the fT measurements.

The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) ribonucleoprotein (RNP) complex is an RNA-guided DNA-nuclease that is part of the bacterial adaptive immune system. CRISPR/Cas9 RNP has been adapted for targeted genome editing within cells and whole organisms with new applications vastly outpacing detection and quantification of gene-editing reagents. Detection of the CRISPR/Cas9 RNP within biological samples is critical for assessing gene-editing reagent delivery efficiency, retention, persistence, and distribution within living organisms. Conventional detection methods are effective, yet the expense and lack of scalability for antibody-based affinity reagents limit these techniques for clinical and/or field settings. This necessitates the development of low cost, scalable CRISPR/Cas9 RNP affinity reagents as alternatives or augments to antibodies. Herein, we report the development of the Streptococcus pyogenes anti-CRISPR/Cas9 protein, AcrIIA4, as a novel affinity reagent. An engineered cysteine linker enables covalent immobilization of AcrIIA4 onto glassy carbon electrodes functionalized via aryl diazonium chemistry for detection of CRISPR/Cas9 RNP by electrochemical, fluorescent, and colorimetric methods. Electrochemical measurements achieve a detection of 280 pM RNP in reaction buffer and 8 nM RNP in biologically representative conditions. Our results demonstrate the ability of anti-CRISPR proteins to serve as robust, specific, flexible, and economical recognition elements in biosensing/quantification devices for CRISPR/Cas9 RNP.

A coarse-grained model previously used to simulate Nafion using dissipative particle dynamics (DPD) is modified to describe sulfonated Diels-Alder poly(phenylene) (SDAPP) polymers. The model includes a proton-hopping mechanism similar to the Grotthuss mechanism. The intramolecular parameters for SDAPP are derived from atomistic molecular dynamics (MD) simulation using the iterative Boltzmann inversion. The polymer radii of gyration, domain morphologies, and cluster distributions obtained from our DPD model are in good agreement with previous atomistic MD simulations. As found in the atomistic simulations, the DPD simulations predict that the SDAPP nanophase separates into hydrophobic polymer domains and hydrophilic domains that percolate through the system at sufficiently high sulfonation and hydration levels. Increasing sulfonation and/or hydration leads to larger proton and water diffusion constants, in agreement with experimental measurements in SDAPP. In the DPD simulations, the proton hopping (Grotthuss) mechanism becomes important as sulfonation and hydration increase, in qualitative agreement with experiment. The turning on of the hopping mechanism also roughly correlates with the point at which the DPD simulations exhibit clear percolated, hydrophilic domains, demonstrating the important effects of morphology on proton transport.

Abstract not provided.

Sandia has a legacy of leadership in the advancement of high performance computing (HPC) at extreme scales. First-of-a-kind scalable distributed-memory parallel platforms such as the Intel Paragon, ASCI Red (the world’s first teraflops computer), and Red Storm (co-developed with Cray) helped form the basis for one of the most successful supercomputer product lines ever: the Cray XT series. Sandia also has pioneered system software elements—including lightweight operating systems, the Portals network programming interface, advanced interconnection network designs, and scalable I/O— that are critical to achieving scalability on large computing systems.

To achieve exascale computing, fundamental hardware architectures must change. The most significant consequence of this assertion is the impact on the scientific and engineering applications that run on current high performance computing (HPC) systems, many of which codify years of scientific domain knowledge and refinements for contemporary computer systems. In order to adapt to exascale architectures, developers must be able to reason about new hardware and determine what programming models and algorithms will provide the best blend of performance and energy efficiency into the future. While many details of the exascale architectures are undefined, an abstract machine model is designed to allow application developers to focus on the aspects of the machine that are important or relevant to performance and code structure. These models are intended as communication aids between application developers and hardware architects during the co-design process. We use the term proxy architecture to describe a parameterized version of an abstract machine model, with the parameters added to elucidate potential speeds and capacities of key hardware components. These more detailed architectural models are formulated to enable discussion between the developers of analytic models and simulators and computer hardware architects. They allow for application performance analysis and hardware optimization opportunities. In this report our goal is to provide the application development community with a set of models that can help software developers prepare for exascale. In addition, through the use of proxy architectures, we can enable a more concrete exploration of how well new and evolving application codes map onto future architectures. This second version of the document addresses system scale considerations and provides a system-level abstract machine model with proxy architecture information.

A number of construction and demolition projects are planned for the 10-year period evaluated in this Sandia National Laboratories, New Mexico (SNL/NM) site-wide environmental impact statement (SWEIS). Construction and decommissioning and demolition (D&D) activities are continually being accomplished at SNL/NM as new facilities are brought on line to replace older, less efficient facilities. The construction and D&D projects presented in this appendix are projected to take place, assuming funding and administrative/regulatory approvals are obtained, under both the No Action Alternative and the Expanded Operations Alternative. Under the Reduced Operations Alternative, no new construction projects were assumed to take place, and D&D activities were assumed to be limited to those needed to maintain a safe operating environment on the site.

The Center for Integrated Nanotechnologies (CINT) is a Department of Energy/Office of Science Nanoscale Science Research Center (NSRC), operating as a national user facility devoted to establishing the scientific principles that govern nanoscale integration. Nanoscale integration is defined as assembling diverse nanoscale materials across length scales to design and achieve new properties and functionality. The CINT Theory and Simulation of Nanoscale Phenomena thrust is the component of CINT dedicated to developing and applying theory to enable nanoscale integration. Our focus is on understanding and simulating the unique behavior of integrated materials and systems with nanoscale structure. This mission is achieved through collaborations with CINT Users, between thrust scientists, and with CINT scientists from other thrusts. Our research is focused on three science directions that together form the basis for integration at the nanoscale, namely (i) Hierarchical structure and dynamics in soft matter, (ii) Excitation and Transport in Nanostructured Systems, and (iii) Emergent phenomena at surfaces and interfaces. A broad spectrum of techniques is developed and applied including continuum fluid theory, atomistic and coarse-grained molecular dynamics simulations, static and dynamic electronic structure calculations, multiscale modeling, low-energy effective Hamiltonian methods, and perturbative and exact quantum many-body approaches. These tools are applied to physical systems of interest to CINT Users, the other CINT thrusts, and the general scientific community with the goals of understanding and controlling the interactions between nanoscale building blocks to assemble specific integrated structures, controlling energy transfer and other interactions over multiple length scales, and designing and exploiting the interactions within assembled structures to achieve new materials functionality.

The opportunities presented by nanomaterials are exciting and broad, with revolutionary implications spanning energy technologies, electronics, computing, sensing capabilities and biomedical diagnostics. Deriving the ultimate benefit from these materials will require the controlled assembly of diverse nanoscale materials across multiple length scales to design and achieve new properties and functionality, in other words, nanomaterials integration.

Positive thermal expansion can cause significant stress or even catastrophic device failure in applications where materials are placed in confined environments. At material interfaces such as coatings, thermal expansion effects can also lead to cracking and peeling behavior. The ability to impart controlled thermal expansion properties in an array of designs via additive manufacturing technologies would mitigate such problems and bring significant value to various materials science and engineering challenges. Negative thermal expansion materials are of interest for composite material applications whereby they can compensate for the behavior of a positive thermal expansion matrix. This Truman Fellowship LDRD research project presents complimentary experimental and molecular modeling results towards the fundamental understanding and development of metal-organic framework (MOF) materials with controlled thermal expansion properties. Design strategies for imparting precisely tailored negative, zero, and positive thermal expansion regimes in MOFs are studied and the implications of these design strategies for the use of MOFs as an emergent negative thermal expansion material class are examined. Challenges towards exploiting this nanoscale behavior at length scales relevant to composite material systems are introduced. ACKNOWLEDGEMENTS I will be forever grateful to the Truman Fellowship Selection Committee for providing me with the opportunity to pursue this research project. The unique opportunity to carry out this exciting scientific research with all of the resources and support that Sandia has to offer has been a truly rewarding experience. I would like to especially thank Yolanda Moreno for her endless assistance throughout my time as a Truman Fellow. It is hard to imagine a more supportive and stimulating scientific environment to carry out this research. I would also like to thank all of my colleagues and the individuals that have collaborated with me throughout this Fellowship, both internal and external to Sandia. Many of you appear as co-authors on the publications resulting from this LDRD, including especially fruitful collaborations with researchers at the Georgia Institute of Technology and the University of Amsterdam. I cannot express enough my gratitude for the significant role that you all played in shaping my educational experience and the success during my time at Sandia as a Truman Fellow.

We will develop Malliavin estimators for Monte Carlo radiation transport by formulating the governing jump stochastic differential equation and deriving the applicable estimators that produce sensitivities for our equations. Efficient and effective sensitivity can be used for design optimization and uncertainty quantification with broad utilization for radiation environments. The technology demonstration will lower development risk for other particle-based simulation methods.

To modify existing Bruker connectors for the BCU-II cooler system to allow use with older style MAS probes where the cooling gas is the bearing gas. The use of the BCU will allow lower temperature MAS experiments without the need to use N₂ liquid exchangers.

This document presents a Tribal Telecom/Internet Service Provider (ISP) internet protocol, version 6 (IPv6) business planning advisory. It includes examples of business areas, services, challenges, benefits, and a vision for the future.

Abstract not provided.

Although its demise has been frequently predicted, the Message Passing Interface (MPI) remains the dominant programming model for scientific applications running on high-performance computing (HPC) systems. MPI specifies powerful semantics for interprocess communication that have enabled scientists to write applications for simulating important physical phenomena. However, these semantics have also presented several significant challenges. For example, the existence of wildcard values has made the efficient enforcement of MPI message matching semantics challenging. Significant research has been dedicated to accelerating MPI message matching. One common approach has been to offload matching to dedicated hardware. One of the challenges that hardware designers have faced is knowing how to size hardware structures to accommodate outstanding match requests. Applications that exceed the capacity of specialized hardware typically must fall back to storing match requests in bulk memory, e.g. DRAM on the host processor. In this paper, we examine the implications of hardware matching and develop guidance on sizing hardware matching structure to strike a balance between minimizing expensive dedicated hardware resources and overall matching performance. By examining the message matching behavior of several important HPC workloads, we show that when specialized hardware matching is not dramatically faster than matching in memory the offload hardware's match queue capacity can be reduced without significantly increasing match time. On the other hand, effectively exploiting the benefits of very fast specialized matching hardware requires sufficient storage resources to ensure that every search completes in the specialized hardware. The data and analysis in this paper provide important guidance for designers of MPI message matching hardware.

Most experimental setups and environment specifications define acceleration loads on the component. However, Sierra Structural Dynamics cannot apply acceleration boundary conditions in modal transient analysis. Modal analysis of these systems and environments must be done through the application of a huge artificial force to a large fictitious point mass. Introducing a large mass into the analysis is a common source of numerical error. In this report we detail a mathematical procedure to directly apply acceleration boundary conditions in modal analyses without the requirement of adding a non-physical mass to the system. We prototype and demonstrate this procedure in Matlab and scope the work required to integrate this procedure into Sierra Structural Dynamics.

This manuscript comprises the final report for the 1-year, FY19 LDRD project "Rigorous Data Fusion for Computationally Expensive Simulations," wherein an alternative approach to Bayesian calibration was developed based a new sampling technique called VoroSpokes. Vorospokes is a novel quadrature and sampling framework defined with respect to Voronoi tessellations of bounded domains in $R^d$ developed within this project. In this work, we first establish local quadrature and sampling results on convex polytopes using randomly directed rays, or spokes, to approximate the quantities of interest for a specified target function. A theoretical justification for both procedures is provided along with empirical results demonstrating the unbiased convergence in the resulting estimates/samples. The local quadrature and sampling procedures are then extended to global procedures defined on more general domains by applying the local results to the cells of a Voronoi tessellation covering the domain in consideration. We then demonstrate how the proposed global sampling procedure can be used to define a natural framework for adaptively constructing Voronoi Piecewise Surrogate (VPS) approximations based on local error estimates. Finally, we show that the adaptive VPS procedure can be used to form a surrogate model approximation to a specified, potentially unnormalized, density function, and that the global sampling procedure can be used to efficiently draw independent samples from the surrogate density in parallel. The performance of the resulting VoroSpokes sampling framework is assessed on a collection of Bayesian inference problems and is shown to provide highly accurate posterior predictions which align with the results obtained using traditional methods such as Gibbs sampling and random-walk Markov Chain Monte Carlo (MCMC). Importantly, the proposed framework provides a foundation for performing Bayesian inference tasks which is entirely independent from the theory of Markov chains.

As with any quantum computing platform, semiconductor quantum dot devices require sophisticated hardware and controls for operation. The increasing complexity of quantum dot devices necessitates the advancement of automated control software and image recognition techniques for rapidly evaluating charge stability diagrams. We use an image analysis toolbox developed in Python to automate the calibration of virtual gates, a process that previously involved a large amount of user intervention. Moreover, we show that straightforward feedback protocols can be used to simultaneously tune multiple tunnel couplings in a triple quantum dot in a computer automated fashion. Finally, we adopt the use of a "tunnel coupling lever arm" to model the interdot barrier gate response and discuss how it can be used to more rapidly tune interdot tunnel couplings to the gigahertz values that are compatible with exchange gates.

This milestone was created to ensure the Sandia FOUS program has the needed levels of project direction, programmatic information and an escalation path (if needed) for their role within the deployment and operations of the Astra cluster.

The current standard Bayesian approach to model calibration, which assigns a Gaussian process prior to the discrepancy term, often suffers from issues of unidentifiability and computational complexity and instability. When the goal is to quantify uncertainty in physical parameters for extrapolative prediction, then there is no need to perform inference on the discrepancy term. With this in mind, we introduce Gibbs posteriors as an alternative Bayesian method for model calibration, which updates the prior with a loss function connecting the data to the parameter. The target of inference is the physical parameter value which minimizes the expected loss. We propose to tune the loss scale of the Gibbs posterior to maintain nominal frequentist coverage under assumptions of the form of model discrepancy, and present a bootstrap implementation for approximating coverage rates. Our approach is highly modular, allowing an analyst to easily encode a wide variety of such assumptions. Furthermore, we provide a principled method of combining posteriors calculated from data subsets. We apply our methods to data from an experiment measuring the material properties of tantalum.

To identify the critical issues that affect the evolution of microstructure during additive manufacturing, we investigated the influence of process parameters on the evolution of the dimensional and surface quality, microstructure, internal defects, and mechanical properties in 316L stainless steel (SS) components fabricated using laser engineered net shaping (LENS®), a directed energy deposition (DED) additive manufacturing (AM) technique. The results show that the accumulation of un-melted powder particles on the side walls of deposited sections can be avoided by selecting a laser under-focused condition. Moreover, we report that the variation of melt pool width is more sensitive to laser power than to the depth of the melt pool. The formation of a so-called “hierarchical” microstructure with cellular morphology is attributable to a combination of layer deposition and rapid solidification, which are characteristics of AM. Finally, we discuss microstructure evolution and defect formation, particularly the formation of multiple interfaces and the presence of un-melted powder particles and pores, in light of the dynamic convective fluid flow and rapid solidification that occur in the melt pool. X-ray computed tomography (X-CT) was used to precisely map the spatial distribution of pores in the DED components. The evolution of microstructure during DED is discussed in the context of related thermal phenomena in an effort to provide fundamental insight into the mechanisms that govern defect formation.

Background: The efficient biological production of industrially and economically important compounds is a challenging problem. Brute-force determination of the optimal pathways to efficient production of a target chemical in a chassis organism is computationally intractable. Many current methods provide a single solution to this problem, but fail to provide all optimal pathways, optional sub-optimal solutions or hybrid biological/non-biological solutions. Results: Here we present RetSynth, software with a novel algorithm for determining all optimal biological pathways given a starting biological chassis and target chemical. By dynamically selecting constraints, the number of potential pathways scales by the number of fully independent pathways and not by the number of overall reactions or size of the metabolic network. This feature allows all optimal pathways to be determined for a large number of chemicals and for a large corpus of potential chassis organisms. Additionally, this software contains other features including the ability to collect data from metabolic repositories, perform flux balance analysis, and to view optimal pathways identified by our algorithm using a built-in visualization module. This software also identifies sub-optimal pathways and allows incorporation of non-biological chemical reactions, which may be performed after metabolic production of precursor molecules. Conclusions: The novel algorithm designed for RetSynth streamlines an arduous and complex process in metabolic engineering. Our stand-alone software allows the identification of candidate optimal and additional sub-optimal pathways, and provides the user with necessary ranking criteria such as target yield to decide which route to select for target production. Furthermore, the ability to incorporate non-biological reactions into the final steps allows determination of pathways to production for targets that cannot be solely produced biologically. With this comprehensive suite of features RetSynth exceeds any open-source software or webservice currently available for identifying optimal pathways for target production.

This report documents the completion of milestone STPM12-17 Kokkos Training Bootcamp. The goal of this milestone was to hold a combined tutorial and hackathon bootcamp event for the Kokkos community and prospective users. The Kokkos Bootcamp event was held at Argonne National Laboratories from August 27 — August 29, 2019. Attendance being lower than expected (we believe largely due to bad timing), the team focused with a select set of ECP partners on early work in preparation for Aurora. In particular we evaluated issues posed by exposing SYCL and OpenMP target offload to applications via the Kokkos Pro Model.

Here we report molecular level details regarding the adsorption of sarin (GB) gas in a prototypical zirconium-based metal-organic framework (MOF, UiO-66). By combining predictive modeling and experimental spectroscopic techniques, we unambiguously identify several unique bindings sites within the MOF, using the P=O stretch frequency of GB as a probe. Remarkable agreement between predicted and experimental IR spectrum is demonstrated. As previously hypothesized, the undercoordinated Lewis acid metal site is the most favorable binding site. Yet multiple sites participate in the adsorption process; specifically, the Zr-chelated hydroxyl groups form hydrogen bonds with the GB molecule, and GB weakly interacts with fully coordinated metals. Importantly, this work highlights that subtle orientational effects of bound GB are observable via shifts in characteristic vibrational modes; this finding has large implications for degradation rates and opens a new route for future materials design.

We describe algorithms for creating probabilistic scenarios for the situation when the underlying forecast methodology is modeled as being more (or less) accurate than it has been historically. Such scenarios can be used in studies that extend into the future and may need to consider the possibility that forecast technology will improve. Our approach can also be used to generate alternative realizations of renewable energy production that are consistent with historical forecast accuracy, in effect serving as a method for creating families of realistic alternatives — which are often critical in simulation-based analysis methodologies.

Data fields sampled on irregularly spaced points arise in many applications in the sciences and engineering. For regular grids, Convolutional Neural Networks (CNNs) have been successfully used to gaining benefits from weight sharing and invariances. We generalize CNNs by introducing methods for data on unstructured point clouds based on Generalized Moving Least Squares (GMLS). GMLS is a nonparametric technique for estimating linear bounded functionals from scattered data, and has recently been used in the literature for solving partial differential equations. By parameterizing the GMLS estimator, we obtain learning methods for operators with unstructured stencils. In GMLS-Nets the necessary calculations are local, readily parallelizable, and the estimator is supported by a rigorous approximation theory. We show how the framework may be used for unstructured physical data sets to perform functional regression to identify associated differential operators and to regress quantities of interest. The results suggest the architectures to be an attractive foundation for data-driven model development in scientific machine learning applications.

Interest in studying power flow dynamics has grown in recent years, with new power flow diagnostics being developed at Sandia National Laboratories for the Z Pulsed Power Facility. Presently, the only power flow loads that have been studied are cylindrical static or imploding loads that are driven by synchronous short pulse (100 ns rise time). Presented is a design that utilizes the dynamic materials properties program's stripline geometry in a high voltage pulsed shaped (asymmetric asynchronous) driving mode. This design has exhibited repeatable current loss with a large time-varying inductance that is well matched to the machine at pulse initialization but which triples to high inductance in 800 ns. Evidence is presented that plasma not captured in the magnetohydrodynamic approximation and ill represented by any of our existing predictive pulsed power codes is adversely affecting load current delivery. The authors believe this design could be of great interest to the experimental and modeling communities for studying power flow dynamics.

Distiller's grains are a byproduct of corn ethanol production and provide an opportunity for increasing the economic viability and sustainability of the overall grain-to-fuels process. Typically, these grains are dried and sold as a ruminant feed adjunct. This study considers utilization of the residuals in a novel supplementary fermentation process to produce two products, enriched protein and fusel alcohols. The value-added proposition and environmental impact of this second fermentation step for distiller's grains are evaluated by considering three different processing scenarios. Techno-economic results show the minimum protein selling price, assuming fusel alcohol products are valued at $0.79 per liter gasoline equivalent, ranges between $1.65-$2.48 kg protein-1 for the different cases. Environmental impacts of the systems were evaluated through life cycle assessment. Results show a baseline emission results of 17 g CO2-eq (MJ fuel)-1 for the fuel product and 10.3 kg CO2-eq kg protein-1 for the protein product. Sensitivity to allocation methods show a dramatic impact with results ranging between -8 to 140 g CO2-eq (MJ fuel)-1 for the fuel product and -0.3 to 6.4 kg CO2-eq kg protein-1 for the protein product. The discussion is focused on the potential impact of the technology on corn ethanol production economics and sustainability.

The Department of Energy(DOE), Office of Nuclear Energy (NE), Spent Fuel and Waste Science and Technology (SFWST) program is performing research and development in the area of commercial spent nuclear fuel (SNF) long term storage and transportation. This program is being conducted under the provisions of the Nuclear Waste Policy Act (NWPA) of 1982 and its amendments that require the DOE to take title to and manage SNF after storage at the utility reactor site. This report is a condensed version of previous gap reports (Hanson 2012 and Hanson 2019) with up-dated gap priority assessments. The gap priorities have been updated from Hanson 2019 because 2019 is based on R&D performed through 2017. Much important work has been done since 2017 that requires a change in a few of the priority rankings to better focus the near-term R&D program. Background material, regulatory positions, operational and inventory status, and prioritization schemes are discussed in detail in Hanson 2012/2019, and are not repeated in this report. One exception is an overview of the prioritization criteria for reference. This is meant to give the reader an appreciation of the framework for prioritization of the identified gaps. A complete discussion of the prioritization scheme is provided in Hanson 2019.

Programmable accelerators have become commonplace in modern computing systems. Advances in programming models and the availability of massive amounts of data have created a space for massively parallel accelerators capable of maintaining context for thousands of concurrent threads resident on-chip. These threads are grouped and interleaved on a cycle-by-cycle basis among several massively parallel computing cores. One path for the design of future supercomputers relies on an ability to model the performance of these massively parallel cores at scale. The SST framework has been proven to scale up to run simulations containing tens of thousands of nodes. A previous report described the initial integration of the open-source, execution-driven GPU simulator, GPGPU-Sim, into the SST framework. This report discusses the results of the integration and how to use the new GPU component in SST. It also provides examples of what it can be used to analyze and a correlation study showing how closely the execution matches that of a Nvidia V100 GPU when running kernels and mini-apps.

Inertial confinement fusion facilities generate implosions at speeds greater than 100 km/s, and measuring the material velocities is important and challenging. We have developed a new velocimetry technique that uses time-stretched spectral interferometry to increase the measurable velocity range normally limited by the detector bandwidth. In this approach, the signal is encoded on a chirped laser pulse that is stretched in time to reduce the beat frequency before detection. We demonstrate the technique on an imploding liner experiment at the Sandia National Laboratories’ Z machine, where beat frequencies in excess of 50 GHz were measured with 20 GHz bandwidth detection.

The authors exposed a radiatively cooled, Li-filled tantalum (Ta) heat pipe (HP) to a H plasma in Magnum PSI continuously for ˜2 h. We kept the overall heat load on the inclined HP constant and varied the tilt to give peak heat fluxes of ˜7.5–13 MW/m2. The peak temperature reached ˜1250 °C. This paper describes the post-test analysis and discusses Li HPs with materials other than Ta for fusion. A companion paper describes the experiment.

Extensive deployment of interoperable distributed energy resources (DER) is increasing the power system cyber security attack surface. National and jurisdictional interconnection standards require DER to include a range of autonomous and commanded grid-support functions, which can drastically influence power quality, voltage, and bulk system frequency. Here, the authors investigate the impact to the cyber-physical power system in scenarios where communications and operations of DER are controlled by an adversary. The findings show that each grid-support function exposes the power system to distinct types and magnitudes of risk. The physical impact from cyber actions was analysed in cases of DER providing distribution system voltage regulation and transmission system support. Finally, recommendations are presented for minimising the risk using engineered parameter limits and segmenting the control network to minimise common-mode vulnerabilities.

We consider a joint-chance constraint (JCC) as a union of sets, and approximate this union using bounds from classical probability theory. When these bounds are used in an optimization model constrained by the JCC, we obtain corresponding upper and lower bounds on the optimal objective function value. We compare the strength of these bounds against each other under two different sampling schemes, and observe that a larger correlation between the uncertainties tends to result in more computationally challenging optimization models. We also observe the same set of inequalities to provide the tightest upper and lower bounds in our computational experiments.

We consider a joint-chance constraint (JCC) as a union of sets, and approximate this union using bounds from classical probability theory. When these bounds are used in an optimization model constrained by the JCC, we obtain corresponding upper and lower bounds on the optimal objective function value. We compare the strength of these bounds against each other under two different sampling schemes, and observe that a larger correlation between the uncertainties tends to result in more computationally challenging optimization models. We also observe the same set of inequalities to provide the tightest upper and lower bounds in our computational experiments.

The non-classical linear Boltzmann equation (NCLBE) is a recently developed framework based on non-classical transport theory for modeling the expected value of particle flux in an arbitrary stochastic medium. Provided with a non-classical cross-section for a given statistical description of a medium, any transport problem in that medium may be solved. Previous work has been limited in the types of material variability considered and has not explicitly introduced finite boundaries and sources. In this work the solution approach for the NCLBE in multidimensional media with finite boundaries is outlined. The discrete ordinates method with an implicit discretization of the pathlength variable is used to leverage sweeping methods for the transport operator. In addition, several convenient approximations for non-classical cross-sections are introduced based on existing theories of stochastic media. The solution approach is verified against random realizations of a Gaussian process medium in a square enclosure.

Variability in the predicted cost of energy of an ocean energy converter array is more substantial than for other forms of energy generation, due to the combined stochastic action of weather conditions and failures. If the variability is great enough, then this may influence future financial decisions. This paper provides the unique contribution of quantifying variability in the predicted cost of energy and introduces a framework for investigating reduction of variability through investment in components. Following review of existing methodologies for parametric analysis of ocean energy array design, the development of the DTOcean software tool is presented. DTOcean can quantify variability by simulating the design, deployment and operation of arrays with higher complexity than previous models, designing sub-systems at component level. A case study of a theoretical floating wave energy converter array is used to demonstrate that the variability in levelised cost of energy (LCOE) can be greatest for the smallest arrays and that investment in improved component reliability can reduce both the variability and most likely value of LCOE. A hypothetical study of improved electrical cables and connectors shows reductions in LCOE up to 2.51% and reductions in the variability of LCOE of over 50%; these minima occur for different combinations of components.

Applications at the intersection of quantum and EM physics are becoming more prevalent in the engineering community. Interestingly, many of these applications require solving purely classical EM problems to characterize the most important dynamics of the system. As a result, computational electromagnetics (CEM) can play a vital role in this new area. However, the classical problems that typically need to be solved are the broadband analysis of near-field scattering problems in complicated regions with multiscale and/or subwavelength features. Recently, potential-based time domain integral equations (TDIEs) have been investigated to solve these traditionally challenging CEM problems [1], [2]. However, for these methods to be applicable, they must be robustly stable when analyzing complicated geometries over broad bandwidths.

In this work we present a performance exploration on Eager K-truss, a linear-algebraic formulation of the K-truss graph algorithm. We address performance issues related to load imbalance of parallel tasks in symmetric, triangular graphs by presenting a fine-grained parallel approach to executing the support computation. This approach also increases available parallelism, making it amenable to GPU execution. We demonstrate our fine-grained parallel approach using implementations in Kokkos and evaluate them on an Intel Skylake CPU and an Nvidia Tesla V100 GPU. Overall, we observe between a 1.261. 48x improvement on the CPU and a 9.97-16.92x improvement on the GPU due to our fine-grained parallel formulation.

Over the last decade, hardware advances have led to the feasibility of training and inference for very large deep neural networks. Sparsified deep neural networks (DNNs) can greatly reduce memory costs and increase throughput of standard DNNs, if loss of accuracy can be controlled. The IEEE HPEC Sparse Deep Neural Network Graph Challenge serves as a testbed for algorithmic and implementation advances to maximize computational performance of sparse deep neural networks. We base our sparse network for DNNs, KK-SpDNN, on the sparse linear algebra kernels within the Kokkos Kernels library. Using the sparse matrix-matrix multiplication in Kokkos Kernels allows us to reuse a highly optimized kernel. We focus on reducing the single node and multi-node runtimes for 12 sparse networks. We test KK-SpDNN on Intel Skylake and Knights Landing architectures and see 120-500x improvement on single node performance over the serial reference implementation. We run in data-parallel mode with MPI to further speed up network inference, ultimately obtaining an edge processing rate of 1.16e+12 on 20 Skylake nodes. This translates to a 13x speed up on 20 nodes compared to our highly optimized multithreaded implementation on a single Skylake node.

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We present the detector response comparison between a 10x10 pixellated array of scintillator read out with Anger logic using four 2" Hamamatsu R7724-100 super bialkali photomultiplier tubes (PMT) and a custom Silicon photomultiplier (SiPM) board consisting of 100 C-series 6x6 mm SiPMs from SensL. An array of these pixellated detectors are currently used in the Neutron Coded Aperture (NCA) imaging system. The energy, timing and pulse shape discrimination response using both readout schemes are presented, along with an analysis of multiple scatter events occurring within the block. An evaluation of the impact of photodetector readout on the overall detection efficiency and imaging accuracy is presented.

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Network security researchers often rely on Emulytics^TM to provide a way to evaluate the safety and security of real world systems. This work involves running a large number of virtual machines on a distributed platform to observe how software and hardware will respond to different types of attacks. While Emulytics^TM software such as minimega provide a scalable system for conducting experiments, the sheer volume of network traffic produced in an experiment can easily exceed the rate at which data can be recorded for offline analysis. As such, researchers must perform live analytics, narrow their monitoring scope or accept that they must run an experiment multiple times to capture all the information they require. In support of Sandia's commitment to Emulytics^TM, we are developing new storage components for the Carlin cluster that will enable researchers to capture significantly more network traffic from their experiments. This report provides a summary of Haoda Wang's initial investigation of how new AMD Epyc storage nodes can be adapted to perform packet capture at 100Gbps speeds with minimal loss. This work found that the NVMe storage capabilities of the Epyc architecture are suitable for capturing 100Gbps Ethernet traffic. While capturing traffic with existing libraries was surprisingly challenging, we were able to develop a DPDK-based software tool that recorded network traffic to disk with minimal packet loss.

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Under high-rate loading in tension, metals can sustain much larger tensile stresses for sub-microsecond time periods than would be possible under quasi-static conditions. This type of failure, known as spall, is not adequately reproduced by hydrocodes with commonly used failure models. The Spall Kinetics Model treats spall by incorporating a time scale into the process of failure. Under sufficiently strong tensile states of stress, damage accumulates over this time scale, which can be thought of as an incubation time. The time scale depends on the previous loading history of the material, reflecting possible damage by a shock wave. The model acts by modifying the hydrostatic pressure that is predicted by any equation of state and is therefore simple to implement. Examples illustrate the ability of the model to reproduce the spall stress and resulting release waves in plate impact experiments on stainless steel.

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