Publications

Acceptance risk may be estimated using cumulative probability distribution functions applied to populations of measurands and test measurement values. “Simple acceptance” is a decision rule that sets the acceptance range of a test result equal to the tolerance range specification. While acceptance risk is comprised of both consumer risk and producer risk, this paper compares the effects of simple acceptance decision rules and guardbanding decision rules on consumer risk. Consumer risk is also known as the probability of false acceptance. The terms describe the risk of accepting test results as passing when the actual values exceed specification limits. False acceptance is only possible when the true value of a measurand is out of tolerance and the test result indicates that the measurand is within tolerance. Metrologists generally have some information about the test uncertainty regarding a specific acceptance test result. Along with a general lack of knowledge regarding the measurand population parameters, the complicated interplay between risk, dispersion, and central values generally prevents calibration laboratories from fully characterizing acceptance probabilities. Organizations that model the measurand populations can reduce consumer risk by avoiding certification of items whose measurand populations are not well centered. The models in this paper present a recurring trend: guardbanding reduces nonnegligible risks of false acceptance when compared to simple acceptance. However, guardbanding is not the most effective means of acceptance risk mitigation. Systematic characterization of measurand populations can provide the information a calibration laboratory needs to reliably control acceptance risk.

The Radiation Protection Center (RPC) of the Iraqi Ministry of Environment continues to evaluate the potential health impacts associated with the Adaya Burial Site, which is located 33 kilometers (20.5 miles) southwest of Mosul. This report documents the radiological analyses of 16 groundwater samples collected from wells located in the vicinity of the Adaya Burial Site and at other sites in northern Iraq. The Adaya Burial Site is a high-risk dump site because a large volume of radioactive material and contaminated soil is located on an unsecure hillside above the village of Tall ar Ragrag. The uranium activities for the 16 water samples in northern Iraq are considered to be naturally occurring and do not indicate artificial (man-made) contamination. With one exception, the alpha spectrometry results for the 16 wells that were sampled in 2019 indicate that the water quality concerning the three uranium isotopes (Uranium-233/234, Uranium-235/236, and Uranium-238) was acceptable for potable purposes (drinking and cooking). However, Well 7 in Mosul had a Uranium-233/234 activity concentration that slightly exceeded the World Health Organization guidance level. Eight of the 16 wells are located in the villages of Tall ar Ragrag and Adaya and had naturally occurring uranium concentrations. Wells in the villages of Tall ar Ragrag and Adaya are located near the Adaya Burial Site and should be sampled on an annual schedule. The list of groundwater analytes should include metals, total uranium, isotopic uranium, gross alpha/beta, gamma spectroscopy, organic compounds, and standard water quality parameters. Our current understanding of the hydrogeologic setting in the vicinity of the Adaya Burial Site is solely based on villager's domestic wells, topographic maps, and satellite imagery. To better understand the hydrogeologic setting, a Groundwater Monitoring Program needs to be developed and should include the installation of twelve groundwater monitoring wells in the vicinity of Tall ar Ragrag and the Adaya Burial Site. Characterization of the limestone aquifer and overlying alluvium is needed. RPC should continue to support health assessments for the villagers in Tall ar Ragrag and Adaya. Collecting samples for surface water (storm water), airborne dust, vegetation, and washway sediment should be conducted on a routine basis. Human access to the Adaya Burial Site needs to be strictly limited. Livestock access on or near the burial site needs to be eliminated. The surface-water exposure pathway is likely a greater threat than the groundwater exposure pathway. Installation of a surface-water diversion or collection system is recommended in order to reduce the potential for humans and livestock to come in contact with contaminated water and sediment. To reduce exposure to villagers, groundwater treatment should be considered if elevated uranium or other contaminants are detected in drinking water. Installing water-treatment systems would likely be quicker to accomplish than remediation and excavation of the Adaya Burial Site. The known potential for human exposure to uranium and metals (such as arsenic, chromium, selenium, and strontium) at the Adaya Burial Site is serious. Additional characterization , mitigation, and remediation efforts should be given a high priority.

This recommendation document will provide international partners insight on physical protection systems (PPSs) for small modular reactors (SMRs). SMRs create many unique challenges for physical protection that must be addressed in design and implementation. This document will attempt to highlight possible challenges of SMRs and identify potential physical protection recommendations to mitigate these challenges. These recommendations are based on hypothetical SMR facilities and PPSs and their effectiveness against hypothetical adversaries.

The Computer Science Research Institute (CSRI) brings university faculty and students to Sandia National Laboratories for focused collaborative research on Department of Energy (DOE) computer and computational science problems. The institute provides an opportunity for university researches to learn about problems in computer and computational science at DOE laboratories, and help transfer results of their research to programs at the labs. Some specific CSRI research interest areas are: scalable solvers, optimization, algebraic preconditioners, graph-based, discrete, and combinatorial algorithms, uncertainty estimation, validation and verification methods, mesh generation, dynamic load-balancing, virus and other malicious-code defense, visualization, scalable cluster computers, beyond Moore’s Law computing, exascale computing tools and application design, reduced order and multiscale modeling, parallel input/output, and theoretical computer science. The CSRI Summer Program is organized by CSRI and includes a weekly seminar series and the publication of a summer proceedings.

CSPlib is an open source software library for analyzing general ordinary differential equation (ODE) systems and detailed chemical kinetic ODE/DAE systems. It relies on the computational singular perturbation (CSP) method for the analysis of these systems.

To further understand the combustion characteristics and the reaction pathways of acyclic ethers, the oxidation of di-n-propyl ether (DPE) was investigated in a jet-stirred reactor (JSR) combined with a photoionization molecular-beam mass spectrometer. The experiments were carried out at near-atmospheric pressure (700 Torr) and over a temperature range of 425–850 K. Based on the experimental data and previous studies on ether oxidation, a new kinetic model was constructed and used to interpret the oxidation chemistry of DPE. In DPE oxidation, a high reactivity at low temperatures and two negative temperature coefficient (NTC) zones were observed. These behaviors are explained in this work by taking advantage of the obtained species information and the modeling analyses: the two NTC zones are caused by the competition of chain branching and termination reactions of the fuel itself and specific oxidation intermediates, respectively. Furthermore, the general requirements to have double-NTC behavior are discussed. A variety of crucial fuel-specific C6 species, such as ketohydroperoxides and diones, were detected in the species pool of DPE oxidation. Their formation pathways are illuminated based on rate-of-production (ROP) analyses. Propanal was identified as the most abundant small molecule intermediate, and its related reactions have an important impact on the oxidation process of DPE. Both acetic acid and propionic acid were detected in high concentrations. A new formation pathway of propionic acid is proposed and incorporated into the kinetic model to achieve a more accurate prediction for propionic acid mole fractions.

This report provides basic background data on the Manipulate-2020 code. This code is used for processing and "manipulation" of nuclear data in support of radiation metrology applications. The code is made available on the open GitHub repository and is available to the general nuclear data community.

Single case comparisons between severe accident simulations can provide detailed insights into severe accident model behavior, however, they cannot offer insights into model uncertainty, sensitivity to uncertain parameters, or underlying model biases. In this analysis, the single case benchmark comparison of the MELCOR material interaction models for a station blackout (SBO) scenario of a boiling water reactor (BWR) using representative Fukushima Daiichi Unit 1 boundary conditions is expanded to include an uncertainty analysis. As part of this uncertainty analysis, 1200 simulations are performed for each material interaction model (2400 total), with random sampling of 14 uncertain MELCOR input parameters. Input parameters are selected for their impact on models representing core degradation processes. These include candling, fuel rod failure, debris quenching and dryout. The analysis performed here is not a traditional “best-estimate” uncertainty analysis that uses best-estimate parameters or identifies best-estimate figure of merit distributions. Instead, it is an exploratory uncertainty analysis that identifies and interrogates underlying model form biases of the two material interaction models (eutectics and interactive materials models). Uniform distributions are applied to all uncertain parameters to ensure coverage of the model parameter uncertainty space. Key findings from this study include underlying model form biases exhibited by material interaction models, and notable differences in accident progression outcomes between the material interaction models. This uncertainty study extends and confirms the conclusions from the first part of this study, which compared the impact of material interaction modeling on simulation of a short-term station blackout scenario with representative Fukushima Daiichi Unit I boundary conditions. In particular, this study confirms that the eutectics model generally exhibits accelerated degradation and failure of fuel components, the core plate, and the lower head. The eutectics model also has a tendency to exhibit a greater degree of core degradation, greater debris mass formation, and larger debris mass ejection. Finally, the eutectics model exhibits higher maximum temperatures for fuel, cladding, particulate debris, oxidic molten pool, and metallic molten pool components than the interactive materials model; interactive materials model simulations exhibit a soft “limitation” on maximum temperatures that is related to the temperature at which material relocation occurs.

The Computer Science Research Institute (CSRI) brings university faculty and students to Sandia National Laboratories for focused collaborative research on Department of Energy (DOE) computer and computational science problems. The institute provides an opportunity for university researches to learn about problems in computer and computational science at DOE laboratories, and help transfer results of their research to programs at the labs. Some specific CSRI research interest areas are: scalable solvers, optimization, algebraic preconditioners, graph-based, discrete, and combinatorial algorithms, uncertainty estimation, validation and verification methods, mesh generation, dynamic load-balancing, virus and other malicious-code defense, visualization, scalable cluster computers, beyond Moore’s Law computing, exascale computing tools and application design, reduced order and multiscale modeling, parallel input/output, and theoretical computer science. The CSRI Summer Program is organized by CSRI and includes a weekly seminar series and the publication of a summer proceedings.

This work, building on previous efforts, develops a suite of new graph neural network machine learning architectures that generate data-driven prolongators for use in Algebraic Multigrid (AMG). Algebraic Multigrid is a powerful and common technique for solving large, sparse linear systems. Its effectiveness is problem dependent and heavily depends on the choice of the prolongation operator, which interpolates the coarse mesh results onto a finer mesh. Previous work has used recent developments in graph neural networks to learn a prolongation operator from a given coefficient matrix. In this paper, we expand on previous work by exploring architectural enhancements of graph neural networks. A new method for generating a training set is developed which more closely aligns to the test set. Asymptotic error reduction factors are compared on a test suite of 3-dimensional Poisson problems with varying degrees of element stretching. Results show modest improvements in asymptotic error factor over both commonly chosen baselines and learning methods from previous work.

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

The Exascale Computing Project (ECP) is invested in co-design to assure that key applications are ready for exascale computing. Within ECP, the Co-design Center for Particle Applications (CoPA) is addressing challenges faced by particle-based applications across four “sub-motifs”: short-range particle–particle interactions (e.g., those which often dominate molecular dynamics (MD) and smoothed particle hydrodynamics (SPH) methods), long-range particle–particle interactions (e.g., electrostatic MD and gravitational N-body), particle-in-cell (PIC) methods, and linear-scaling electronic structure and quantum molecular dynamics (QMD) algorithms. Our crosscutting co-designed technologies fall into two categories: proxy applications (or “apps”) and libraries. Proxy apps are vehicles used to evaluate the viability of incorporating various types of algorithms, data structures, and architecture-specific optimizations and the associated trade-offs; examples include ExaMiniMD, CabanaMD, CabanaPIC, and ExaSP2. Libraries are modular instantiations that multiple applications can utilize or be built upon; CoPA has developed the Cabana particle library, PROGRESS/BML libraries for QMD, and the SWFFT and fftMPI parallel FFT libraries. Success is measured by identifiable “lessons learned” that are translated either directly into parent production application codes or into libraries, with demonstrated performance and/or productivity improvement. The libraries and their use in CoPA’s ECP application partner codes are also addressed.

Remote communities are increasingly adopting renewable energy, such as wind, as they transition away from diesel energy generation. It is important to understand the benefits and costs of wind energy to isolated systems so that decision‐makers can optimize their choices in these com-munities. There are few examples of valuation of wind energy as a distributed resource and numer-ous differences in valuation approaches, especially in the inclusion of environmental and economic impacts. We apply a distributed wind valuation framework to calculate the benefits and costs of wind in St. Mary’s, Alaska, to the local electric cooperative and to society, finding that the project does not have a favorable benefit‐to‐cost ratio unless societal benefits are included, in which case the benefit‐to‐cost ratio is nearly double. Government funding is important to reducing the initial capital expenditures of this wind project and will likely be the case for projects with similar charac-teristics. Additional fuel savings benefits are potentially possible for this project through technolog-ical additions such as energy storage and advanced controls.

Liquefied petroleum gas (LPG) is a viable, cleaner alternative to traditional diesel fuel used in busses and other heavy-duty vehicles and could play a role in helping the US meet its lower emission goals. While the LPG industry has focused efforts on developing vehicles and fueling infrastructure, we must also establish safe parameters for maintenance facilities which are servicing LPG fueled vehicles. Current safety standards aid in the design of maintenance facilities, but additional quantitative analysis is needed to prove safeguards are adequate and suggest improvements where needed. In this report we aim to quantify the amount of flammable mass associated with propane releases from vehicle mounted fuel vessels within enclosed garages. Furthermore, we seek to qualify harm mitigation with variable ventilations and facility layout. To accomplish this we leverage validated computational resources at Sandia National Laboratories to simulate various release scenarios representative of real world vehicles and maintenance facilities. Flow solvers are used to predict the dynamics of fuel systems as well as the evolution of propane during release events. From our simulated results we observe that both inflow and outflow ventilation locations play a critical role in reducing flammable cloud size and potential overpressure values during a possible combustion event.

In this article, we derive the vacuum electric fields within specific cylindrically symmetric magnetically insulated transmission lines (MITLs) in the limit of an infinite speed of light for an arbitrary time-dependent current. We focus our attention on two types of MITLs: the radial MITL and a spherically curved MITL. We then simulate the motion of charged particles, such as electrons, present in these MITLs due to the vacuum fields. In general, the motion of charged particles due to the vacuum fields is highly nonlinear since the fields are nonlinear functions of spatial coordinates and depend on an arbitrary time-dependent current drive. Using guiding center theory, however, one can describe the gross particle kinetics using a combination of $\textbf {E} \times \textbf {B}$ and $\nabla B$ drifts. In addition, we compare our approximate inner MITL field models and particle kinetics with those from a fully electromagnetic simulation code. We find that the agreement between the approximate model and the electromagnetic simulations is excellent.

This report documents an experimental program designed to investigate High Energy Arcing Fault (HEAF) phenomena for medium voltage electrical switchgear containing aluminum conductors. This report covers full-scale laboratory experiments using representative nuclear power plant (NPP) three-phase electrical equipment. Electrical, thermal, and pressure data were recorded for each experiment and documented in this report. This report covers four of the fourteen planned medium voltage electrical enclosure experiments. Subsequent reports will document the additional experiments performed in the future. The experiments were performed at KEMA Labs located in Chalfont, Pennsylvania. The experimental design, setup, and execution were completed by staff from the United States Nuclear Regulatory Commission (NRC), the National Institute of Standards and Technology (NIST), Sandia National Laboratories (SNL) and KEMA. In addition, representatives from the Electric Power Research Institute (EPRI) observed some of the experimental setups and execution. The HEAF experiments were performed on four near-identical units of General Electric metal-clad medium voltage switchgear. The three-phase arcing fault was initiated on the primary cable connection bus. All four experiments used the same system voltage (6.9 kV) but varied the current and duration. Real-time electrical operating conditions, including voltage, current and frequency, were measured during the experiments. Heat fluxes and incident energies were measured with plate thermometers and slug calorimeters at various locations around the electrical enclosures. Internal enclosure pressures were measured during the experiments. The experiments were documented with normal and high-speed videography, infrared imaging, and photography. Insights from the experimental series included timing information related to enclosure breach, event progression, mass loss measurements for electrodes and steel enclosures, peak pressure rise, particle analysis, along with visual and thermal imaging data to better understand and characterize the hazard. These results will be used in subsequent efforts to advance the state of knowledge related to HEAF.

The peridynamic theory of solid mechanics is applied to modeling the deformation and fracture of micrometer-sized particles made of organic crystalline material. A new peridynamic material model is proposed to reproduce the elastic–plastic response, creep, and fracture that are observed in experiments. The model is implemented in a three-dimensional, meshless Lagrangian simulation code. In the small deformation, elastic regime, the model agrees well with classical Hertzian contact analysis for a sphere compressed between rigid plates. Under higher load, material and geometrical nonlinearity is predicted, leading to fracture. The material parameters for the energetic material CL-20 are evaluated from nanoindentation test data on the cyclic compression and failure of micrometer-sized grains.

Simultaneous data of the quasi-static compaction and electrical conductivity of porous, binary powder mixtures have been collected as a function of bulk density. The powder mixtures consist of a metal conductor, either titanium or iron, an insulator, and pores filled with ambient air. The data show a dependency of the conductivity in terms of relative bulk density and metal volume fraction on conductor type and conductor particle characteristics of size and shape. Finite element models using particle domains generated by discrete element method are used to simulate the bulk conductivity near its threshold while the general effective media equation is used to model the conductivity across the compression regime.

Calcium-silicate-hydrate (C–S–H) represents a key microstructural phase that governs the mechanical properties of concrete at a large scale. Defects in the C–S–H phase are also responsible for the poor ductility and low tensile strength of concrete. Manipulating the microstructure of C–S–H can lead to new cementitious materials with improved structural performance. This paper presents an experimental investigation aiming to characterize a new synthetic polymer-modified synthetic calcium-silicate-hydrate (C–S–H)/styrene-butadiene rubber (SBR) binder. The new C–S–H/SBR binder is produced by calcining calcium carbonate and mixing this with fumed silica (SiO2), deionized water and SBR. Mechanical, physical, chemical and microstructural characterization was conducted to measure the properties of new hardened C–S–H binder. Results from the experimental investigation demonstrate the ability to engineer a new C–S–H binder with low elastic modulus and improved toughness and bond strength by controlling the SBR content and method of C–S–H synthesis. The new binder suggests the possible development of a new family of low-modulus silica-polymer binders that might fit many engineering applications such as cementing oil and gas wells.

Atomically precise ultradoping of silicon is possible with atomic resists, area-selective surface chemistry, and a limited set of hydride and halide precursor molecules, in a process known as atomic precision advanced manufacturing (APAM). It is desirable to expand this set of precursors to include dopants with organic functional groups and here we consider aluminium alkyls, to expand the applicability of APAM. We explore the impurity content and selectivity that results from using trimethyl aluminium and triethyl aluminium precursors on Si(001) to ultradope with aluminium through a hydrogen mask. Comparison of the methylated and ethylated precursors helps us understand the impact of hydrocarbon ligand selection on incorporation surface chemistry. Combining scanning tunneling microscopy and density functional theory calculations, we assess the limitations of both classes of precursor and extract general principles relevant to each.

The myriad detectors and instruments now available for materials characterization provide researchers with an ever-growing suite of tools to probe material behavior. Progress in the development of instrumentation and workflows that enable the collection, and leverage the potential, of various data modalities have provided novel insights into material behavior. Using data across multiple length scales, or performing complementary analyses of in situ and ex situ data, can help reveal a more complete picture of dynamic processes or material structure. However, the accurate combination, or fusion, of these disparate data modalities presents new challenges. Differences in resolution, as well as the varying length scales at which physical phenomena are exploited to generate these data, necessitate novel approaches to accurately interpret and combine these data. Furthermore, the papers within this special topic focus on the collection and fusion of multimodal data to better understand structural materials. From new frameworks and workflows for data segmentation and analysis, process monitoring, enhancing simulations, or interrogating mechanical response, these papers reveal the potential benefits of utilizing multimodal data.

The complexity and associated uncertainties involved with atmospheric-turbine-wake interactions produce challenges for accurate wind farm predictions of generator power and other important quantities of interest (QoIs), even with state-of-the-art high-fidelity atmospheric and turbine models. A comprehensive computational study was undertaken with consideration of simulation methodology, parameter selection, and mesh refinement on atmospheric, turbine, and wake QoIs to identify capability gaps in the validation process. For neutral atmospheric boundary layer conditions, the massively parallel large eddy simulation (LES) code Nalu-Wind was used to produce high-fidelity computations for experimental validation using high-quality meteorological, turbine, and wake measurement data collected at the Department of Energy/Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility located at Texas Tech University's National Wind Institute. The wake analysis showed the simulated lidar model implemented in Nalu-Wind was successful at capturing wake profile trends observed in the experimental lidar data.

A linear performance drop is generally assumed during the photovoltaic (PV) lifetime. However, operational data demonstrate that the PV module degradation rate (Rd) is often nonlinear, which, if neglected, may increase the financial uncertainty. Although nonlinear behavior has been the subject of numerous publications, it was only recently that statistical models able to detect change-points and extract multiple Rd values from PV performance time-series were introduced. A comparative analysis of six open-source libraries, which can detect change-points and calculate nonlinear Rd, is presented in this article. Since the real Rd and change-point locations are unknown in field data, 960 synthetic datasets from six locations and two PV module technologies have been generated using different aggregation and normalization decisions and nonlinear degradation rate patterns. The results demonstrated that coarser temporal aggregation (i.e., monthly vs. weekly), temperature correction, and both PV module technologies and climates with lower seasonality can benefit the change-point detection and Rd extraction. This also raises a concern that statistical models typically deployed for Rd analysis may be highly climatic-and technology-dependent. The comparative analysis of the six approaches demonstrated median mean absolute errors (MAE) ranging from 0.06 to 0.26%/year, given a maximum absolute Rd of 2.9%/year. The median MAE in change-point position detection varied from 3.5 months to 6 years.

Formation of zeolite supported Ag0 clusters depends on a combination of thermodynamically stable atomic configurations, charge balance considerations, and mobility of species on the surface and within pores. Periodic density functional theory (DFT) calculations were performed to evaluate how the location of Al in the mordenite (MOR) framework and humidity control Ag0 nanocluster formation. Four Al framework sites were studied (T1-T4) and the Al positions in the framework were identified by the shifts in the differential Al⋯Al pair distribution function (PDF). Furthermore, structural information about the Ag0 nanoclusters, such as dangling bonds, can be identified by Ag⋯Ag PDF data. For Ag0 formation in vacuum MOR structures with a Si:Al ratio of 5:1 with Al in the T1 position resulted in the most framework flexibility and the lowest Ag0 nanocluster charge, indicating the best result for formation of charge neutral nanoclusters. When water is present, Al in the T3 and T4 positions results in the formation of the smallest average Ag0 nanoclusters plus greater expansion of the O-T-O bond angle than in vacuum, indicating easier diffusion of the Ag0 nanoclusters to the surface. The presence of Al in 4-membered rings and in pairs indicates favorable MOR structures for formation of single Ag atoms, despite the existence of synthesis challenges. Therefore, Al in the T2 position is the least favorable for Ag0 nanocluster formation in both vacuum and in the presence of water. Al in the T1, T3, and T4 positions provides beneficial effects through framework flexibility and changes in nanocluster size or charge that can be leveraged for design of zeolites for formation of metallic nanoclusters.

To support the increasing demands for efficient deep neural network processing, accelerators based on analog in-memory computation of matrix multiplication have recently gained significant attention for reducing the energy of neural network inference. However, analog processing within memory arrays must contend with the issue of parasitic voltage drops across the metal interconnects, which distort the results of the computation and limit the array size. This work analyzes how parasitic resistance affects the end-to-end inference accuracy of state-of-the-art convolutional neural networks, and comprehensively studies how various design decisions at the device, circuit, architecture, and algorithm levels affect the system's sensitivity to parasitic resistance effects. A set of guidelines are provided for how to design analog accelerator hardware that is intrinsically robust to parasitic resistance, without any explicit compensation or re-training of the network parameters.

Rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML), statistical learning, computational learning, and artificial intelligence. In addition to highly visible successes in machine-based natural language translation, playing the game Go, and self-driving cars, these new technologies also have profound implications for computational and experimental science and engineering, as well as for the exascale computing systems that the Department of Energy (DOE) is developing to support those disciplines. Not only do these learning technologies open up exciting opportunities for scientific discovery on exascale systems, they also appear poised to have important implications for the design and use of exascale computers themselves, including high-performance computing (HPC) for ML and ML for HPC. The overarching goal of the ExaLearn co-design project is to provide exascale ML software for use by Exascale Computing Project (ECP) applications, other ECP co-design centers, and DOE experimental facilities and leadership class computing facilities.

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The international safeguards regime desires methods to efficiently verify that facilities are only performing declared activities. Electropotential verification (EPV) is a newly proposed technique that was tested for its feasibility to perform facility design information verification (DIV). EPV works by passing a constant, low voltage current through a conductive system (facility infrastructure of nuclear fuel assembly) and measuring the resulting voltage at various places throughout the infrastructure in order to establish a baseline. Changes made to the system affect these voltage readings, which will deviate from the baseline and indicate that a change to the system was made. For large scale infrastructure such as a nuclear facility DIV, it appears feasible that changes in configuration of the system’s grounding can be detected in real-time, and the location of the change can be inferred from the measured intensity of the change in voltage.

Shear-based deformation processing by hybrid cutting-extrusion and free machining are used to make continuous strip, of thickness up to one millimeter, from low-workability AA6013-T6 in a single deformation step. The intense shear can impose effective strains as large as 2 in the strip without pre-heating of the workpiece. The creation of strip in a single step is facilitated by three factors inherent to the cutting deformation zone: highly confined shear deformation, in situ plastic deformation-induced heating and high hydrostatic pressure. The hybrid cutting-extrusion, which employs a second die located across from the primary cutting tool to constrain the chip geometry, is found to produce strip with smooth surfaces (S_a < 0.4 μm) that is similar to cold-rolled strip. The strips show an elongated grain microstructure that is inclined to the strip surfaces – a shear texture – that is quite different from rolled sheet. Furthermore, this shear texture (inclination) angle is determined by the deformation path. Through control of the deformation parameters such as strain and temperature, a range of microstructures and strengths could be achieved in the strip. When the cutting-based deformation was done at room temperature, without workpiece pre-heating, the starting T6 material was further strengthened by as much as 30% in a single step. In elevated-temperature cutting-extrusion, dynamic recrystallization was observed, resulting in a refined grain size in the strip. Implications for deformation processing of age-hardenable Al alloys into sheet form, and microstructure control therein, are discussed.

We study the structure of the threading edge dislocations, or “elbows,” which are an essential component of the well-known herringbone reconstruction of the (111) surface of Au. Previous work had shown that these dislocations can be stabilized by long-range elastic relaxations into the bulk. However, the validity of the harmonic spring model that had been used to estimate the energies of the dislocations is uncertain. To enable a more refined model of the dislocation energetics, we have imaged the atomic structure of these dislocations using scanning tunneling microscopy. We find that the harmonic spring model does not adequately reproduce the observed structure. We are able to reproduce the structure, however, with a two-dimensional Frenkel-Kontorova (FK) model that uses a pairwise Morse potential to describe the interactions between the top layer Au atoms on a rigid substrate. The parameters of the potential were obtained by fitting the energy of uniaxially compressed phases, or “stripes”, computed with density functional theory, as a function of surface Au density. Within this model, the formation of the threading dislocations remains unfavorable. However, the large forces on the substrate atoms near the threading-dislocation cores, render the assumption of a completely rigid substrate questionable. Indeed, if the FK parameters are modified to account for the relaxation of just one more atomic layer, threading dislocations can, in principle, become favorable, even without bulk elastic relaxations. Additional evidence for a small elbow energy is that our computed change in the Au(111) surface stress tensor caused by the (√ 3 × 22) reconstruction is considerably smaller than previous estimates.

The high-pressure dynamic response of titanium dioxide (TiO 2) is not only of interest because of its numerous industrial applications but also because of its structural similarities to silica (SiO 2). We performed plate impact experiments in a two-stage light gas gun, at peak stresses from 64 to 221 GPa to determine the TiO 2 response along the Hugoniot. The lower stress experiment at 64 GPa shows an elastic behavior followed by an elastic-plastic transition, whereas the high stress experiments above 64 GPa show a single wave structure. Previous shock studies have shown the presence of high-pressure phases (HPP) I (26 GPa) and HPP II (100 GPa); however, our data suggest that the HPP I phase is stable up to 150 GPa. Using a combination of data from our current study and our previous Z-data, we determine that TiO 2 likely melts on the Hugoniot at 157 GPa. Furthermore, our data confirm that TiO 2 is not highly incompressible as shown by a previous study.

The Big Adaptive Rotor (BAR) project was initiated by the U.S. Department of Energy (DOE) in 2018 with the goal of identifying novel technologies that can enable large (>100 meter [m]) blades for low-specific-power wind turbines. Five distinct tasks were completed to achieve this goal: 1. Assessed the trends, impacts, and value of low-specific-power wind turbines; 2. Developed a wind turbine blade cost-reduction road map study; 3. Completed research-and-development opportunity screening; 4. Performed detailed design and analysis; and, 5. Assessed low-cost carbon fiber. These tasks were completed by the national laboratory team consisting of Sandia National Laboratories (Sandia), the National Renewable Energy Laboratory (NREL), and Lawrence Berkeley National Laboratory.

Two-color infrared multiphoton dissociation (2C-IRMPD) spectroscopy is a technique that mitigates spectral distortions due to nonlinear absorption that is inherent to one-color IRMPD. We use a 2C-IRMPD scheme that incorporates two independently tunable IR sources, providing considerable control over the internal energy content and type of spectrum obtained by varying the trap temperature, the time delays and fluences of the two infrared lasers, and whether the first or second laser wavelength is scanned. In this work, we describe the application of this variant of 2C-IRMPD to conformationally complex peptide ions. The 2C-IRMPD technique is used to record near-linear action spectra of both cations and anions with temperatures ranging from 10 to 300 K. We also determine the conditions under which it is possible to record IR spectra of single conformers in a conformational mixture. Furthermore, we demonstrate the capability of the technique to explore conformational unfolding by recording IR spectra with widely varying internal energy in the ion. The protonated peptide ions YGGFL (NH3+-Tyr-Gly-Gly-Phe-Leu, Leu-enkephalin) and YGPAA (NH3+-Tyr-Gly-Pro-Ala-Ala) are used as model systems for exploring the advantages and disadvantages of the method when applied to conformationally complex ions.

Flooding impacts are on the rise globally, and concentrated in urban areas. Currently, there are no operational systems to forecast flooding at spatial resolutions that can facilitate emergency preparedness and response actions mitigating flood impacts. We present a framework for real-time flood modeling and uncertainty quantification that combines the physics of fluid motion with advances in probabilistic methods. The framework overcomes the prohibitive computational demands of high-fidelity modeling in real-time by using a probabilistic learning method relying on surrogate models that are trained prior to a flood event. This shifts the overwhelming burden of computation to the trivial problem of data storage, and enables forecasting of both flood hazard and its uncertainty at scales that are vital for time-critical decision-making before and during extreme events. The framework has the potential to improve flood prediction and analysis and can be extended to other hazard assessments requiring intense high-fidelity computations in real-time.

Structural alloys may experience corrosion when exposed to molten chloride salts due to selective dissolution of active alloying elements. One way to prevent this is to make the molten salt reducing. For the KCl + MgCl₂ eutectic salt mixture, pure Mg can be added to achieve this. However, Mg can form intermetallic compounds with nickel at high temperatures, which may cause alloy embrittlement. This work shows that an optimum level of excess Mg could be added to the molten salt which will prevent corrosion of alloys like 316 H, while not forming any detectable Ni-Mg intermetallic phases on Ni-rich alloy surfaces.

The ability to form pristine interfaces after etching and regrowth of GaN is a prerequisite for epitaxial selective area doping, which in turn is needed for the formation of lateral PN junctions and advanced device architectures. In this work, we report the electrical properties of etched-and-regrown GaN PN diodes using an in situ Cl-based precursor, tertiary butylchloride (TBCl). We demonstrated a regrowth diode with I–V characteristics approaching that from a continuously grown reference diode. The sources of unintentional contamination from the silicon (Si) impurity and the mediating effect of Si during the TBCl etching are also investigated in this study. Furthermore, this work points to the potential of in situ TBCl etching toward the realization of GaN lateral PN junctions.

The following SNL document contains requested radiological survey information, as part of the documentation for the LANL MLU shipment performed by the LANL MLU team the week of October 18th . The surveys were performed in TA-5 October 19th – 21st, 2021. The surveys were for the official shipments of 4 loaded TRUPACTs and 2 empty TRUPACTs. Surveys were completed after the trucks were hitched to their respective trailers.

Waveform modeling is crucial to improving our understanding of observed seismograms. Forward simulation of wavefields provides quantitative methods of testing interactions between complicated source functions and the propagation medium. Here, we discuss three experiments designed to improve under standing of high frequency seismic wave propagation. First, we compare observed and predicted travel times of crustal phases for a set of real observed earthquakes with calculations and synthetic seismograms. Second, we estimate the frequency content of a series of nearly co-located earthquakes of varying magnitude for which we have a relatively well- known 1D velocity model. Third, we apply stochastic perturbations on top of a 3D tomographic model and qualitatively assess how those variations map to differences in the seismograms. While different in scope and aim, these three vignettes illustrate the current state of crustal scale waveform modeling and the potential for future studies to better constrain the structure of the crust.

This work demonstrated both NbN and Nb make good electrodes for stabilizing orthorhombic phase of Hf_0.6Zr_0.4O₂ ferroelectric films. Wake up are < 100 cycles. P_r can be as high as 30 µC/cm² - respectively 14 and 18 µC/cm² here. Further, capacitance suggests an orthorhombic phase can be stabilized. Addition of a linear dielectric under modest thickness can tune the P_r and reduce leakage.

Area-selective atomic layer deposition (AS-ALD) is an appealing bottom-up fabrication technique that can produce atomic-scale device features, overcoming challenges in current industrial techniques such as edge alignment errors. TiCI₄ is a common thermal ALD precursor for Ti0₂ thin films, which are appealing candidates for DRAM capacitors due to their excellent dielectric constants. Hydrogen and chlorine termination passivate the Si surface, allowing for selective deposition of TiCI₄ onto HO-terminated areas. However, selectivity loss occurs after several ALD cycles. Ti oxide nucleates onto surface defects on Cl- and H-Si resists. Previously, the use of H-Si as an ALD resist has been studied extensively, but less work has focused on chemical forces driving nucleation, especially for Cl-Si. Here, formation of defect nuclei was investigated with selectivity loss during Ti0₂ ALD with TiCI₄ and water on the (100) and (111) crystal surfaces of hydrogenated, chlorinated, and oxidized Si.

The galvanostatic intermittent titration technique (GITT) is widely used to evaluate solid-state diffusion coefficients in electrochemical systems. However, the existing analysis methods for GITT data require numerous assumptions, and the derived diffusion coefficients typically are not independently validated. To investigate the validity of the assumptions and derived diffusion coefficients, we employ a direct-pulse fitting method for interpreting the GITT data that involves numerically fitting an electrochemical pulse and subsequent relaxation to a one-dimensional, single-particle, electrochemical model coupled with non-ideal transport to directly evaluate diffusion coefficients. Our non-ideal diffusion coefficients, which are extracted from GITT measurements of the intercalation regime of FeS2 and independently verified through discharge predictions, prove to be 2 orders of magnitude more accurate than ideal diffusion coefficients extracted using conventional methods. We further extend our model to a polydisperse set of particles to show the validity of a single-particle approach when the modeled radius is proportional to the total volume-to-surface-area ratio of the system.

This report documents the activities in a preliminary phase of development for three models: 1) waste package breach model, 2) fuel/basket degradation model, and 3) dual-purpose canister (DPC) crush model. The waste package breach model describes the coupling of mass flow, heat transport, and canister shell deformation in response to a heat-generating (criticality) event. The fuel/basket degradation model describes potential weakening and disaggregation of the DPC structure from corrosion, possibly accelerated by seismic ground motion. Progressively degraded three-dimensional (3D) configurations of the fuel, basket, and shell are generated for future analysis of reactivity (with as-loaded DPC fuel characteristics). Another important application for the fuel/basket degradation model is validation of the two stylized degradation cases currently being used by other investigators for analysis of the as-loaded DPC inventory under disposal conditions. The DPC crush model investigates stability of a typical DPC after breach of the disposal overpack allows fluids from the repository near-field environment to penetrate and externally pressurize the canister shell. Preliminary results show that large deformation of the DPC could occur for external pressure on the order of 10 to 15 MPa, or the shell could be stable (not collapse) with pressure of 20 MPa or greater if the basket plates are fully welded at the connections.

The security of the electric grid and supporting energy systems is crucial to national security. One of the complexities in analyzing the security of energy systems is the safety consequences that may result from accidents. For energy systems, the goal is to ensure that they operate as intended and that any consequences are mitigated or prevented. The integration of safety and security is paramount to protecting these systems from attacks and ensuring that large consequences are prevented. This report describes an integrated safety and security methodology to evaluate cybersecurity events that can lead to large consequences. This novel approach first describes how Systems-Theoretic Process Analysis (STPA) provides a digital causal analysis for Bayesian Networks (BNs). The use of STPA causal analysis provides a systematic approach to constructing BNs that adequately model cyber scenarios that result in consequences. When combined with the technical principles described in Risk-Informed Management of Enterprise Systems (RIMES), a comprehensive risk-informed cybersecurity analysis results that allows decision-makers to prioritize systems that most impact risk.

Solar thermochemical hydrogen (STCH) production is a promising method to generate carbon neutral fuels by splitting water utilizing metal oxide materials and concentrated solar energy. The discovery of materials with enhanced water-splitting performance is critical for STCH to play a major role in the emerging renewable energy portfolio. While perovskite materials have been the focus of many recent efforts, materials screening can be time consuming due to the myriad chemical compositions possible. This can be greatly accelerated through computationally screening materials parameters including oxygen vacancy formation energy, phase stability, and electron effective mass. In this work, the perovskite Gd0.5La0.5Co0.5Fe0.5O3 (GLCF), was computationally determined to be a potential water splitter, and its activity was experimentally demonstrated. During water splitting tests with a thermal reduction temperature of 1,350°C, hydrogen yields of 101 μmol/g and 141 μmol/g were obtained at re-oxidation temperatures of 850 and 1,000°C, respectively, with increasing production observed during subsequent cycles. This is a significant improvement from similar compounds studied before (La0.6Sr0.4Co0.2Fe0.8O3 and LaFe0.75Co0.25O3) that suffer from performance degradation with subsequent cycles. Confirmed with high temperature x-ray diffraction (HT-XRD) patterns under inert and oxidizing atmosphere, the GLCF mainly maintained its phase while some decomposition to Gd2-xLaxO3 was observed.

Bayesian optimization (BO) is an efficient and flexible global optimization framework that is applicable to a very wide range of engineering applications. To leverage the capability of the classical BO, many extensions, including multi-objective, multi-fidelity, parallelization, and latent-variable modeling, have been proposed to address the limitations of the classical BO framework. In this work, we propose a novel multi-objective BO formalism, called srMO-BO-3GP, to solve multi-objective optimization problems in a sequential setting. Three different Gaussian processes (GPs) are stacked together, where each of the GPs is assigned with a different task. The first GP is used to approximate a single-objective computed from the multi-objective definition, the second GP is used to learn the unknown constraints, and the third one is used to learn the uncertain Pareto frontier. At each iteration, a multi-objective augmented Tchebycheff function is adopted to convert multi-objective to single-objective, where the regularization with a regularized ridge term is also introduced to smooth the single-objective function. Finally, we couple the third GP along with the classical BO framework to explore the convergence and diversity of the Pareto frontier by the acquisition function for exploitation and exploration. The proposed framework is demonstrated using several numerical benchmark functions, as well as a thermomechanical finite element model for flip-chip package design optimization.

Two-dimensional (2D) materials with robust ferromagnetic behavior have attracted great interest because of their potential applications in next-generation nanoelectronic devices. Aside from graphene and transition metal dichalcogenides, Bi-based layered oxide materials are a group of prospective candidates due to their superior room-temperature multiferroic response. Here, an ultrathin Bi3Fe2Mn2O10+δ layered supercell (BFMO322 LS) structure was deposited on an LaAlO3 (LAO) (001) substrate using pulsed laser deposition. Microstructural analysis suggests that a layered supercell (LS) structure consisting of two-layer-thick Bi-O slabs and two-layer-thick Mn/Fe-O octahedra slabs was formed on top of the pseudo-perovskite interlayer (IL). A robust saturation magnetization value of 129 and 96 emu cm-3 is achieved in a 12.3 nm thick film in the in-plane (IP) and out-of-plane (OP) directions, respectively. The ferromagnetism, dielectric permittivity, and optical bandgap of the ultrathin BFMO films can be effectively tuned by thickness and morphology variation. In addition, the anisotropy of all ultrathin BFMO films switches from OP dominating to IP dominating as the thickness increases. This study demonstrates the ultrathin BFMO film with tunable multifunctionalities as a promising candidate for novel integrated spintronic devices. This journal is

The following SNL document contains requested forms related to used equipment, as part of the documentation for the MLU shipment that was performed by the LANL MLU team and Crane Services personnel.

Sandia National Labs (SNL)-designed, portable chemical warfare agent (CWA) detection systems consist of three-stages: collection, separation, and detection. We use microfabrication technologies to miniaturize these stages and to reduce the overall size, weight, power, and (potentially) cost of the final system. Our newest system consists of a multi-dimensional separation stage and an miniature ion mobility spectrometer (IMS) detector for unprecedented system sensitivity, selectivity, and depth of target list.

As new and modernized systems are fielded by the U.S. and Russia, and as China expands its nuclear stockpile with 21st century technology, it is important to ask when do new technologies in the nuclear domain actually matter? When do emerging capabilities and replacements of existing systems change the military realities of the world’s nuclear powers and the lived experiences of the people in these countries? Specifically, it is important to consider what are the attributes of emerging weapon systems that may impact nuclear strategic stability, or in even narrower terms, which attributes of newly fielded military systems may make nuclear conflict more likely or less likely? It is through this understanding that policy makers, voters, and the broader nuclear weapons community can evaluate when and how to respond to emerging technology while reducing the likelihood of nuclear escalation.

The following SNL document contains requested radiological survey information, as part of the documentation for the MLU shipment being performed by the LANL MLU team. The survey was performed in TA-5, on October 19th, 2021. This survey was for radiological coverage for the disassembly of two TRUPACTs, the assembly and loading of their payloads, and the reassembly of the TRUPACTs.

Neuromorphic computers are hardware systems that mimic the brain’s computational process phenomenology. This is in contrast to neural network accelerators, such as the Google TPU or the Intel Neural Compute Stick, which seek to accelerate the fundamental computation and data flows of neural network models used in the field of machine learning. Neuromorphic computers emulate the integrate and fire neuron dynamics of the brain to achieve a spiking communication architecture for computation. While neural networks are brain-inspired, they drastically oversimplify the brain’s computation model. Neuromorphic architectures are closer to the true computation model of the brain (albeit, still simplified). Neuromorphic computing models herald a 1000x power improvement over conventional CPU architectures. Sandia National Labs is a major contributor to the research community on neuromorphic systems by performing design analysis, evaluation, and algorithm development for neuromorphic computers. Space-based remote sensing development has been a focused target of funding for exploratory research into neuromorphic systems for their potential advantage in that program area; SNL has led some of these efforts. Recently, neuromorphic application evaluation has reached the NA-22 program area. This same exploratory research and algorithm development should penetrate the unattended ground sensor space for SNL’s mission partners and program areas. Neuromorphic computing paradigms offer a distinct advantage for the SWaP-constrained embedded systems of our diverse sponsor-driven program areas.

The recent introduction of a new generation of "smart NICs" have provided new accelerator platforms that include CPU cores or reconfigurable fabric in addition to traditional networking hardware and packet offloading capabilities. While there are currently several proposals for using these smartNICs for low-latency, in-line packet processing operations, there remains a gap in knowledge as to how they might be used as computational accelerators for traditional high-performance applications. This work aims to look at benchmarks and mini-applications to evaluate possible benefits of using a smartNIC as a compute accelerator for HPC applications. We investigate NVIDIA's current-generation BlueField-2 card, which includes eight Arm CPUs along with a small amount of storage, and we test the networking and data movement performance of these cards compared to a standard Intel server host. We then detail how two different applications, YASK and miniMD can be modified to make more efficient use of the BlueField-2 device with a focus on overlapping computation and communication for operations like neighbor building and halo exchanges. Our results show that while the overall compute performance of these devices is limited, using them with a modified miniMD algorithm allows for potential speedups of 5 to 20% over the host CPU baseline with no loss in simulation accuracy.

Timing spread between the thirty-six Saturn modules affects peak electrical power delivered to the Bremsstrahlung diode and can affect vacuum power flow and impedance behavior of the load. To reduce the module spread, a new megavolt gas-insulated closing switch was developed employing design techniques developed for the Z-machine laser triggered switches while retaining Saturn’s simpler electrical triggering. Two modules were temporarily outfitted with the new switches and used separately into local resistive loads (instead of the usual Saturn electron beam load). A reliable operating point and switch time jitter at that point were the goals of the experiments. The target switch reliability is less than one pre-fire in one thousand switch-shots, and a timing standard deviation of 4 nanoseconds. The switches were able to meet both requirements but the number of tests at the chosen point are limited.

A fade study was performed for the Thermo Electron Model 8825 whole-body dosimeter over a six-month period. The fade equation was evaluated using the method described the Thermo dose calculation algorithm (Thermo, 2007).

The following SNL document contains requested radiological survey information, as part of the documentation for the MLU shipment being performed by the LANL MLU team. The surveys were performed in TA-5, on October 11th - 15th, 2021. These surveys were of the shipping containers, the dunnage container, MLU equipment trailer, and contracted mobile crane.

Vertical GaN p-n diodes combine excellent efficiencies with incredibly fast protection from unwanted electromagnetic pulses.

With this work, we aim to speed up simulation and reduce computational complexity of Converter Dominated Power System (CDPS) within an acceptable accuracy.

In this position paper, we discuss exciting recent advancements in sketching algorithms applied to distributed systems. That is, we look at randomized algorithms that simultaneously reduce the data dimensionality, offer potential privacy benefits, while maintaining verifiably high levels of algorithm accuracy and performance in multi-node computational setups. We look at next steps and discuss the applicability to real systems.

The purpose of this document is to disseminate lessons learned from the Sandia National Laboratories (SNL) Building 1090 modification project and other analytical laboratory related construction projects. The following sections summarize lessons learned at various phases of the project.

The understanding and control of charge carrier interactions with defects at buried insulator/semiconductor interfaces is essential for achieving optimum performance in modern electronics. Here, we report on the use of scanning ultrafast electron microscopy (SUEM) to remotely probe the dynamics of excited carriers at a Si surface buried below a thick thermal oxide. Our measurements illustrate a previously unidentified SUEM contrast mechanism, whereby optical modulation of the space-charge field in the semiconductor modulates the electric field in the thick oxide, thus affecting its secondary electron yield. By analyzing the SUEM contrast as a function of time and laser fluence we demonstrate the diffusion mediated capture of excited carriers by interfacial traps.

Capturing, storing, and sharing biological DNA parts data are integral parts of synthetic biology research. Here, we detail updates to the ICE biological parts registry software platform that enable these processes, describe our implementation of the Web of Registries concept using ICE, and establish Bioparts, a search portal for biological parts available in the public domain. The Web of Registries enables standalone ICE installations to securely connect and form a distributed parts database. This distributed database allows users from one registry to query and access plasmid, strain, (DNA) part, plant seed, and protein entry types in other connected registries. Users can also transfer entries from one ICE registry to another or make them publicly accessible. Bioparts, the new search portal, combines the ease and convenience of modern web search engines with the capabilities of bioinformatics search tools such as BLAST. This portal, available at bioparts.org, allows anyone to search for publicly accessible biological part information (e.g., NCBI, iGEM, SynBioHub, Addgene), including parts publicly accessible through ICE Registries. Additionally, the portal offers a REST API that enables third-party applications and tools to access the portal's functionality programmatically.

The objective of this project was to eliminate and/or render bulk agent unusable by a threat entity via neutralization and/or polymerization of the bulk agent using minimal quantities of additives. We proposed the in situ neutralization and polymerization of bulk chemical agents (CAs) by performing reactions in the existing CA storage container via wet chemical approaches using minimal quantities of chemical based materials. This approach does not require sophisticated equipment, fuel to power generators, electricity to power equipment, or large quantities of decontaminating materials. By utilizing the CA storage container as the batch reactor, the amount of logistical resources can be significantly reduced. Fewer personnel are required since no sophisticated equipment needs to be set up, configured, or operated. Employing the CA storage container as the batch reactor enables the capability to add materials to multiple containers in a short period of time as opposed to processing one container at a time for typical batch reactor approaches. In scenarios where a quick response is required, the material can be added to all the CA containers and left to react on its own without intervention. Any attempt to filter the CA plus material solution will increase the rate of reaction due to increased agitation of the solution.

The dissolution and depletion of chromium (Cr) in salt facing nickel (Ni) alloy surfaces is one of the predominant degradation mechanisms of structural components in molten salt technology. In this work, we use density functional theory to investigate the role of electronic level interactions that may underlie the depletion phenomenon of Cr in a Ni 100 surface exposed to various adsorbed salt species. Our results show that, under vacuum, Ni preferentially segregates to the surface layer. Conversely, in the presence of adsorbed anionic salt species (e.g., chlorine (Cl), fluorine (F) or the impurity oxygen (O)) Cr segregation becomes more favorable. In these cases, Cl has the weakest effect on segregation, while O has the strongest effect. Our analysis reveals the strong correlation between Cr segregation and the amount of valence charge transferred between the Cr atom and surface adsorbate: the greater the charge transfer, the lower the segregation energy. We also show that, when considered, secondary cations screen Cr-anion interactions, which in turn reduce the magnitude of the anions effect on segregation. These results shed light on the role of salt impurities likely play in the overall corrosion phenomena in molten salt environments. This work provides insights into the atomic level interactions fundamental to molten salt corrosion and on the importance of maintaining salt purity.

Using molecular dynamics simulations, we investigate the molecular scale origin of crystal face selectivity when one gibbsite particle attaches to another in water. A comparison of the free energy per unit surface area of particle–particle attachment indicates that particle attachment through edge surfaces, where the edge surfaces are either (1 0 0) or (1 1 0) crystal faces, is more energetically favorable compared to attachment between two basal surfaces (i.e., (0 0 1) crystal faces) or between the basal surface of one particle and the edge surface of another. This result suggests that gibbsite crystals with low basal/edge surface area ratio will preferentially attach through edge surfaces, potentially helping the crystals grow laterally. However, for larger gibbsite particles (high basal/edge surface area ratio) the total free energy, not normalized by surface area, of particle attachment through the basal surfaces is lower (more negative) than attachment through the edge surfaces, indicating that larger gibbsite particles will preferentially aggregate through basal surface attachments. The short-range electrostatic interactions including the interparticle hydrogen bonds from surface –OH groups drive particle attachment, and the dominant contribution to the free energy minimum is enthalpic rather than entropic. However, the enthalpy of basal-edge attachment is significantly offset by the entropy leading to a higher free energy (less negative) compared to that of basal-basal attachment. Study of the free energy for a few imperfect attachments of two particles indicates a higher free energy (i.e., less negative, less stable), compared to a perfect attachment

A synthesis process is presented for experimentally simulating modifications in cosmic dust grains using sequential ion implantations or irradiations followed by thermal annealing. Cosmic silicate dust analogues were prepared via implantation of 20–80 ​keV Fe−, Mg−, and O− ions into commercially available p-type silicon (100) wafers. The as-implanted analogues are amorphous with a Mg/(Fe ​+ ​Mg) ratio of 0.5 tailored to match theoretical abundances in circumstellar dusts. Before the ion implantations were performed, Monte-Carlo-based ion-solid interaction codes were used to model the dynamic redistribution of the implanted atoms in the silicon substrate. 600 ​keV helium ion irradiation was performed on one of the samples before thermal annealing. Two samples were thermally annealed at a temperature appropriate for an M-class stellar wind, 1000 ​K, for 8.3 ​h in a vacuum chamber with a pressure of 1 ​× ​10−7 torr. The elemental depth profiles were extracted utilizing Rutherford Backscattering Spectrometry (RBS) in the samples before and after thermal annealing. X-ray diffraction (XRD) analysis was employed for the identification of various phases in crystalline minerals in the annealed analogues. Transmission electron microscopy (TEM) analysis was utilized to identify specific crystal structures. RBS analysis shows redistribution of the implanted Fe, Mg, and O after thermal annealing due to incorporation into the crystal structures for each sample type. XRD patterns along with TEM analysis showed nanocrystalline Mg and Fe oxides with possible incorporation of additional silicate minerals.

In September of 2020, Arctic sea ice extent was the second-lowest on record. State of the art climate prediction uses Earth system models (ESMs), driven by systems of differential equations representing the laws of physics. Previously, these models have tended to underestimate Arctic sea ice loss. The issue is grave because accurate modeling is critical for economic, ecological, and geopolitical planning. We use machine learning techniques, including random forest regression and Gini importance, to show that the Energy Exascale Earth System Model (E3SM) relies too heavily on just one of the ten chosen climatological quantities to predict September sea ice averages. Furthermore, E3SM gives too much importance to six of those quantities when compared to observed data. Identifying the features that climate models incorrectly rely on should allow climatologists to improve prediction accuracy.

Studying the mechanical behavior of silicon cell fractures is critical for understanding changes in PV module performance. Traditional methods of detecting cell cracks, e.g., electroluminescence (EL) imaging, utilize electrical changes and defects associated with cell fracture. Therefore, these methods reveal crack locations, but do not operate at the time or length scales required to accurately measure other physical properties of cracks, such as separation width and behavior under dynamic loads.

The adoption of digital technology into Instrumentation and Control (I&C) systems in nuclear facilities fundamentally changes the nature of these systems. Greater interconnectivity of reprogrammable, and functionally interdependent control systems has given rise to the need for computer security consideration in digital I&C Systems. The cyber security of I&C systems presents a growing risk to nuclear facilities and requires the development of educational and research tools to ensure the safety of these facilities. Currently there is a major gap in formal educational offerings on cyber security for these Operational Technology (OT) systems. To provide formal cyber security education resources, DOE’s office of International Nuclear Security (INS) partnered with the University of São Paulo (USP) to develop a training course on the cyber security of nuclear facility I&C systems using the hypothetical Nuclear Power Plant, Asherah.

This SNL document contains requested radiological survey information, as part of the documentation for the MLU shipment being performed by the LANL MLU team. The survey was performed in TA-5, on October 6th, 2021. This survey was of two WIPP trailers carrying three empty TRUPACTs each.

Here, integrated silicon microwave pin diodes are exposed to 10-keV X-rays up to a dose of 2 Mrad(SiO₂) and 14-MeV fast neutrons up to a fluence of 2.2×10¹³ cm^-2. Changes in both DC leakage current and small-signal circuit components are examined. Degradation in performance due to total-ionizing dose is shown to be suppressed by non-quasi-static effects during RF operation. Tolerance to displacement damage from fast neutrons is also observed, which is explained using TCAD simulations. Overall, the characterized pin diodes are tolerant to cumulative radiation at levels consistent with space applications such as geosynchronous weather satellites.

The feasibility and component cost of hydrogen rail refueling infrastructure is examined. Example reference stations can inform future studies on components and systems specifically for hydrogen rail refueling facilities. All of the 5 designs considered assumed the bulk storage of liquid hydrogen on-site, from which either gaseous or liquid hydrogen would be dispensed. The first design was estimated to refuel 10 multiple unit trains per day, each train containing 260 kg of gaseous hydrogen at 350 bar on-board. The second base design targeted the refueling of 50 passenger locomotives, each with 400 kg of gaseous hydrogen on-board at 350 bar. Variations from this basic design were made to consider the effect of two different filling times, two different hydrogen compression methods, and two different station design approaches. For each design variation, components were sized, approximate costs were estimated for major components, and physical layouts were created. For both gaseous hydrogen-dispensing base designs, the design of direct-fill using a cryopump design was the lowest cost due to the high cost of the cascade storage system and gas compressor. The last three base designs all assumed that liquid hydrogen was dispensed into tender cars for freight locomotives that required 7,500 kg of liquid hydrogen, and the three different designs assumed that 5, 50, or 200 tender cars were refueled every day. The total component costs are very different for each design, because each design has a very different dispensing capacity. The total component cost for these three designs are driven by the cost of the liquid hydrogen tank; additionally, delivering that much liquid hydrogen to the refueling facility may not be practical. Many of the designs needed the use of multiple evaporators, compressors, and cryopumps operating in parallel to meet required flow rates. In the future, the components identified here can be improved and scaled-up to better fit the needs of heavy-duty refueling facilities. This study provides basic feasibility and first-order design guidance for hydrogen refueling facilities serving emerging rail applications.

We demonstrate the ability to fabricate vertically stacked Si quantum dots (QDs) within SiGe nanowires with QD diameters down to 2 nm. These QDs are formed during high-temperature dry oxidation of Si/SiGe heterostructure pillars, during which Ge diffuses along the pillars' sidewalls and encapsulates the Si layers. Continued oxidation results in QDs with sizes dependent on oxidation time. The formation of a Ge-rich shell that encapsulates the Si QDs is observed, a configuration which is confirmed to be thermodynamically favorable with molecular dynamics and density functional theory. The type-II band alignment of the Si dot/SiGe pillar suggests that charge trapping on the Si QDs is possible, and electron energy loss spectra show that a conduction band offset of at least 200 meV is maintained for even the smallest Si QDs. Our approach is compatible with current Si-based manufacturing processes, offering a new avenue for realizing Si QD devices.

Abstract not provided.

We study wind turbine wakes of rotors operating at high thrust coefficients (CT > 24/25) using large-eddy simulations with a rotating actuator disk model. Wind turbine wakes at high thrust coefficients are different from wakes at low thrust coefficients. Wakes behave differently at high thrust, with increased turbulence and faster recovery. Lower induction in the wake is achieved because wakes in high-thrust conditions recover much faster than in normal operating conditions. This enhanced recovery is possible thanks to the turbulence generated in the near wake. We explore the mechanism behind this behavior and propose a simple model to reproduce it. We also propose a Gaussian fit for the wakes under high-thrust conditions and use it use it to initialize an Ainslie type model within the FAST.Farm framework.

We report a terahertz quantum-cascade vertical-external-cavity surface-emitting laser (QC-VECSEL) emitting around 1.9 THz with up to 10% continuous fractional frequency tuning of a single laser mode. The device shows lasing operation in pulsed mode up to 102K in a high-quality beam, with the maximum output power of 37mW and slope efficiency of 295mW/A at 77 K. Challenges for up-scaling the operating wavelength in QC metasurface VECSELs are identified.

We develop and analyze an optimization-based method for the coupling of a static peridynamic (PD) model and a static classical elasticity model. The approach formulates the coupling as a control problem in which the states are the solutions of the PD and classical equations, the objective is to minimize their mismatch on an overlap of the PD and classical domains, and the controls are virtual volume constraints and boundary conditions applied at the local-nonlocal interface. Our numerical tests performed on three-dimensional geometries illustrate the consistency and accuracy of our method, its numerical convergence, and its applicability to realistic engineering geometries. We demonstrate the coupling strategy as a means to reduce computational expense by confining the nonlocal model to a subdomain of interest, and as a means to transmit local (e.g., traction) boundary conditions applied at a surface to a nonlocal model in the bulk of the domain.

When subjected to certain harmonic oscillations, the gas bubble in a partially liquid-filled, closed, vertical cylinder will break up. Under certain conditions, some of the gas will migrate to the bottom due to Bjerknes forces. At sufficiently large amplitudes, the bubble will break up into gas bubbles at the top and bottom ends of the cylinder. High-speed imaging captured the dynamics of bubble breakup and gas migration. Several parameters were investigated: oscillation frequency, oscillation acceleration, gas volume fraction, and liquid viscosity.

Early Systems Research and Development (ESR&D) is one of the most crucial phases in the product development process. It both blends and blurs the lines between science and engineering, and requires a risk-based, disciplined, and graded approach to effectively manage scope, cost, and complexity of the final product. Many leaders, program managers, and scientists are unwilling to involve systems engineering because of the perception that systems engineering is heavily process oriented, adds unnecessary costs, and should be applied only to mature technologies. The value of systems engineering as applied to ESR&D is unclear to these key individuals. The unfortunate result is that system engineering is not applied to ESR&D. This results in R&D efforts that may have solved the wrong problem, selected the wrong architecture, require technical rework, have difficulty transitioning later maturity levels, and result in higher R&D costs and extended development timelines. This work discusses the difficulty of introducing systems engineering to the research and early development process and their inclination perspectives of researchers, engineers, and managers. The article shall offer potential means to manage the cultural transformation of early adoption of right-sized systems engineering in ESR&D and reverse the attitudinal positions.

Traditional systems engineering demonstrates the importance of customer needs in scoping and defining design requirements; yet, in practice, other human stakeholders are often absent from early lifecycle phases. Human factors are often omitted in practice when evaluating and down-selecting design options due to constraints such as time, money, access to user populations, or difficulty in proving system robustness through the inclusion of human behaviors. Advances in systems engineering increasingly include non-technical influences into the design, deployment, operations, and maintenance of interacting components to achieve common performance objectives. Furthermore, such advances highlight the need to better account for the various roles of human actors to achieve desired performance outcomes in complex systems. Many of these efforts seek to infuse lessons and concepts from human factors (enhanced decision-making through Crew Resource Management), systems safety (Rasmussen's “drift toward danger”) and organization science (Giddens' recurrent human acts leading to emergent behaviors) into systems engineering to better understand how socio-technical interactions impact emergent system performance. Safety and security are examples of complex system performance outcomes that are directly impacted by varying roles of human actors. Using security performance of high consequence facilities as a representative use case, this article will outline the System Context Lenses to understand how to include various roles of human actors into systems engineering design. Several exemplar applications of this organizing lenses will be summarized and used to highlight more generalized insights for the broader systems engineering community.

The U.S. Army Research Office (ARO), in partnership with IARPA, are investigating innovative, efficient, and scalable computer architectures that are capable of executing next-generation large scale data-analytic applications. These applications are increasingly sparse, unstructured, non-local, and heterogeneous. Under the Advanced Graphic Intelligence Logical computing Environment (AGILE) program, Performer teams will be asked to design computer architectures to meet the future needs of the DoD and the Intelligence Community (IC). This design effort will require flexible, scalable, and detailed simulation to assess the performance, efficiency, and validity of their designs. To support AGILE, Sandia National Labs will be providing the AGILE-enhanced Structural Simulation Toolkit (A-SST). This toolkit is a computer architecture simulation framework designed to support fast, parallel, and multi-scale simulation of novel architectures. This document describes the A-SST framework, some of its library of simulation models, and how it may be used by AGILE Performers.

Interest in 3D printing of thermoset resins has increased significantly in recent years. One approach to additive manufacturing of thermoset resins is printing dual-cure resins with direct ink write (DIW). Dual-cure resins are multi-component resins which employ an in situ curable constituent to enable net-shape fabrication while a second constituent and cure mechanism contribute to the final mechanical properties of the printed materials. In this work, the cure kinetics, green strength, printability, and print fidelity of dual-cure epoxy/acrylate thermoset resins are investigated. Resin properties are evaluated as a function of acrylate concentration and in situ UV exposure conditions. The acrylate cure kinetics are probed using photo-differential scanning calorimetry and the impacts of resin composition and UV cure profile on the acrylate extent of conversion are presented. Continuous and pulsed UV cure profiles are shown to affect total conversion due to variances in radical efficiency at different UV intensities and acrylate concentrations. The effects of acrylate concentration on the kinetics of the epoxy thermal cure and the final mechanical properties are also investigated using dynamic mechanical analysis and three-point bend measurements. The glass transition temperature is dependent on formulation, with increasing acrylate content decreasing the Tg. However, the room temperature shear moduli, flexural moduli, strength, strain-to-failure, and toughness values are relatively independent of resin composition. The similarity of the final properties allows for greater flexibility in resin formulation and in situ cure parameters, which can enable the printing of complex parts that require high green strength. We found that the in situ UV print intensities and exposure profiles that are necessary to achieve the best print quality are not, in most cases, the conditions that maximize conversion of the acrylate network. This highlights the importance of developing optimized resin compositions which enable complete cure of the acrylate network by promoting acrylate dark cure or thermal cure.

Respiratory viral infections are considered a major public health threat, and breath metabolomics can provide new ways to detect and understand how specific viruses affect the human pulmonary system. In this pilot study, we characterized the metabolic composition of human breath for an early diagnosis and differentiation of influenza viral infection, as well as other types of upper respiratory viral infections. We first studied the non-specific effects of planned seasonal influenza vaccines on breath metabolites in healthy subjects after receiving the immunization. We then investigated changes in breath content from hospitalized patients with flu-like symptoms and confirmed upper respiratory viral infection. The exhaled breath was sampled using a custom-made breath condenser, and exhaled breath condensate (EBC) samples were analysed using liquid chromatography coupled to quadruplole-time-of-flight mass spectrometer (LC-qTOF). All metabolomic data was analysed using both targeted and untargeted approaches to detect specific known biomarkers from inflammatory and oxidative stress biomarkers, as well as new molecules associated with specific infections. We were able to find clear differences between breath samples collected before and after flu vaccine administration, together with potential biomarkers that are related to inflammatory processes and oxidative stress. Moreover, we were also able to discriminate samples from patients with flu-related symptoms that were diagnosed with confirmatory respiratory viral panels (RVPs). RVP positive and negative differences were identified, as well as differences between specific viruses defined. These results provide very promising information for the further study of the effect of influenza A and other viruses in human systems by using a simple and non-invasive specimen like breath.

Container escapes enable the adversary to execute code on the host from inside an isolated container. Notably, these high severity escape vulnerabilities originate from three sources: (1) container profile misconfigurations, (2) Linux kernel bugs, and (3) container runtime vulnerabilities. While the first two cases have been studied in the literature, no works have investigated the impact of container runtime vulnerabilities. In this paper, to fill this gap, we study 59 CVEs for 11 different container runtimes. As a result of our study, we found that five of the 11 runtimes had nine publicly available PoC container escape exploits covering 13 CVEs. Our further analysis revealed all nine exploits are the result of a host component leaked into the container. Here, we apply a user namespace container defense to prevent the adversary from leveraging leaked host components and demonstrate that the defense stops seven of the nine container escape exploits.

Background: Blockchain distributed ledger technology is just starting to be adopted in genomics and healthcare applications. Despite its increased prevalence in biomedical research applications, skepticism regarding the practicality of blockchain technology for real-world problems is still strong and there are few implementations beyond proof-of-concept. We focus on benchmarking blockchain strategies applied to distributed methods for sharing records of gene-drug interactions. We expect this type of sharing will expedite personalized medicine. Basic Procedures: We generated gene-drug interaction test datasets using the Clinical Pharmacogenetics Implementation Consortium (CPIC) resource. We developed three blockchain-based methods to share patient records on gene-drug interactions: Query Index, Index Everything, and Dual-Scenario Indexing. Main Findings: We achieved a runtime of about 60 s for importing 4,000 gene-drug interaction records from four sites, and about 0.5 s for a data retrieval query. Our results demonstrated that it is feasible to leverage blockchain as a new platform to share data among institutions. Principal Conclusions: We show the benchmarking results of novel blockchain-based methods for institutions to share patient outcomes related to gene-drug interactions. Our findings support blockchain utilization in healthcare, genomic and biomedical applications. The source code is publicly available at https://github.com/tsungtingkuo/genedrug.

Pre-chamber spark-ignition (PCSI), either fueled or non-fueled, is a leading concept with the potential to enable diesel-like efficiency in medium-duty (MD) and heavy-duty (HD) natural gas (NG) engines. However, the inadequate scientific base and simulation tools to describe/predict the underlying processes governing PCSI systems is one of the key barriers to market penetration of PCSI for MD/HD NG engines. To this end, experiments were performed in a heavy-duty, optical, single-cylinder engine fitted with an active fueled PCSI module. The spatial and temporal progress of ignition and subsequent combustion of lean-burn natural gas using PCSI system were studied using optical diagnostic imaging and heat release analysis based on main-chamber and pre-chamber pressure measurements. Optical diagnostics involving simultaneous infrared (IR) and high-speed (30 kfps) broadband and filtered OH* chemiluminescence imaging are used to probe the combustion process. Following the early pressure rise in the pre-chamber, IR imaging reveals initial ejection of unburnt fuel-air mixture from the pre-chamber into the main-chamber. Following this, the pre-chamber gas jets exhibit chemical activity in the vicinity of the pre-chamber region followed by a delayed spread in OH* chemiluminescence, as they continue to penetrate further into the main-chamber. The OH* signal progress radially until the pre-chamber jets merge, which sets up the limit to a first stage, jet-momentum driven, mixing-controlled (temperature field) premixed combustion. This is then followed by the subsequent deceleration of the pre-chamber jets, caused by the decrease in the driving pressure difference (ΔP) as well as charge entrainment, resulting in a flame front evolution, where mixing is not the only driver. Chemical-kinetic calculations probe the possibility of flame propagation or sequential auto-ignition in the second stage of combustion. Finally, key phenomenological features are then summarized so as to provide fundamental insights on the complex underlying fluid-mechanical and chemical-kinetic processes that govern the ignition and subsequent combustion of natural gas near lean-limits in high-efficiency lean-burn natural gas engines employing PCSI system.

Multiphase flow simulation is well-known to be computationally demanding, and modeling large-scale engineered subsurface systems entails significant additional numerical challenges. These challenges arise from: (a) the presence of small-scale discrete features like shafts, tunnels, waste packages, and barriers; (b) the need to accurately represent both the waste form processes at the small spatial scale of the repository and the large-scale transport processes throughout heterogeneous geological formations; (c) the strong contrast in material properties such as porosity and permeability, as well as the nonlinear constitutive relations for multiphase flow. Numerical solution entails discretization of the coupled system of nonlinear governing equations and solving a linear system of equations at each Newton–Raphson iteration. Practical problems require a very large number of unknowns that must be solved efficiently using iterative methods in parallel on high-performance computers. The unique challenges noted above can lead to an ill-conditioned Jacobian matrix and non-convergence with Newton's method due to discontinuous nonlinearity in constitutive models. Moreover, practical applications can require numerous Monte-Carlo simulations to explore uncertainly in material properties, geological heterogeneity, failure scenarios, or other factors; governmental regulatory agencies can mandate these as part of Performance Assessments. Hence there is a need for flexible, robust, and computationally efficient methods for multiphase flow in large-scale engineered subsurface systems. We apply the open-source simulator PFLOTRAN to the practical problem of performance assessment of the US DOE Waste Isolation Pilot Plant (WIPP) site. The simulator employs a finite volume discretization and uses the PETSc parallel framework. We evaluate the performance of several preconditioners for the iterative solution of the linearized Jacobian system; these range from stabilized-biconjugate-gradient with block-Jacobi preconditioning (BCGS) to methods adopted from reservoir modeling, such as the constrained pressure residual (CPR) two-stage preconditioner and flexible generalized residual solver (FGMRES). We also implement within PETSc the general-purpose nonlinear solver, Newton trust-region dogleg Cauchy (NTRDC), which truncates the Newton update or modifies the update with a Cauchy solution that is within the quadratic model trust-region of the objective function. We demonstrate the effectiveness of each method for a series of test problems with increasing difficulty. We find that the NTRDC and FGMRES-CPR-ABF (FCA) preconditioners generally perform best for the test problem having the extreme nonlinear processes, achieving a 50x speed-up compared with BCGS. The most ill-conditioned and extreme nonlinear simulations do not converge with BCGS (as one may expect), but they do complete the simulation with NTRDC and FCA. We also investigate the strong scalability of each method and demonstrate the impact of node-packing upon parallel performance on modern processor architectures.

The broad dissemination of unmanned aerial vehicles (UAV s), specifically quadrotor aircraft, has accelerated their successful use in a wide range of industrial, military, and agricultural applications. Research in the growing field of aerial manipulation (AM) faces many challenges but may enable the next generation of UAV applications. The physical contact required to perform AM tasks results in dynamic coupling with the environment, which may lead to instability with devastating consequences for a UAV in flight. Considering these concerns, this work seeks to determine whether off-the-shelf flight controllers for quadrotor UAV s are suitable for AM applications by investigating the passivity and coupled-stability of quad rotors using generic cascaded position-attitude (CPA) and PX4 flight controllers. Using a planar 3-degree of freedom (DOF) linearized state-space model and two high fidelity 6-DOF models with the CPA and PX4 closed-loop flight controllers, passivity is analyzed during free flight, and stability is analyzed when the UAV is coupled to environments with varying degrees of stiffness. This analysis indicates that quadrotors using the CPA and PX4 flight controllers are non-passive (except for the PX4 controller in the vertical direction with certain vehicle parameters) and may become unstable when the UAV is coupled with environments of certain stiffnesses. Similarities between the results from the linearized 3-DOF model and nonlinear 6-DOF models in the passivity analysis suggest that using an analytical, linear approach is sufficient and potentially useful for vehicle geometry and controller design to improve stability for AM applications.

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LIM1TR (Lithium-Ion Modeling with 1-D Thermal Runaway) is an open-source code that uses the finite volume method to simulate heat transfer and chemical kinetics on a quasi 1-D domain. The target application of this software is to simulate thermal runaway in systems of lithium-ion batteries. The source code for LIM1TR can be found at https://github.com/ajkur/lim1tr. This user guide details the steps required to create and run simulations with LIM1TR starting with setting up the Python environment, generating an input file, and running a simulation. Additional details are provided on the output of LIM1TR as well as extending the code with custom reaction models. This user guide concludes with simple example analyses of common battery thermal runaway scenarios. The corresponding input files and processing scripts can be found in the “Examples” folder in the on-line repository, with select input files included in the appendix of this document.

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This report provides a design study to produce 100% carbon-free electricity for Sandia NM and Kirtland Air Force Base (KAFB) using concentrating solar power (CSP). Annual electricity requirements for both Sandia and KAFB are presented, along with specific load centers that consume a significant and continuous amount of energy. CSP plant designs of 50 MW and 100 MW are then discussed to meet the needs of Sandia NM and the combined electrical needs of both Sandia NM and KAFB. Probabilistic modeling is performed to evaluate inherent uncertainties in performance and cost parameters on total construction costs and the levelized cost of electricity. Total overnight construction costs are expected to range between ~$300M - $400M for the 50 MW CSP plant and between ~$500M - $800M for the 100 MW plant. Annual operations and maintenance (O&M) costs are estimated together with potential offsets in electrical costs and CO₂ emissions. Other considerations such as interconnections, land use and permitting, funding options, and potential agreements and partnerships with Public Service Company of New Mexico (PNM), Western Area Power Administration (WAPA), and other entities are also discussed.

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This report provides detailed documentation of the algorithms that were developed and implemented in the Plato software over the course of the Optimization-based Design for Manufacturing LDRD project.

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This Content Migration Plan provides a framework and methodology for managing and executing the migration of content to the NEFC Program’s on-premises SharePoint 2016 instance, as well as guidelines regarding how to ensure that Knowledge Management Program content, both during and after the migration, is tagged properly. Analysis continues to develop a migration plan for a SharePoint Online instance in a Cloud environment.

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We successfully demonstrated the utility of surface science techniques - namely scanning probe microscopy and thermal desorption spectroscopy - on three different material systems: incipient soot formed during fossil fuel combustion, surface oxides passivating polycrystalline nickel hydrogen uptake, and aluminum hydride cluster formation underpinning solid-state hydrogen fuel storage. For all three material systems, surface science techniques haven proven to probe intricate nanoscale phenomena that are critical to macroscale material behavior. This LDRD has gained insight into early-stage pollution formation, the impacts of common contaminants on tritium flow regulation, and the limitations of solid-state hydrogen fuel storage. Our results support the diversification of national energy technologies.

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Herein, the formulation, parameter sensitivities, and usage methods for the Microstructure-Aware Plasticity (MAP) model are presented. This document is intend to serve as a reference for the underlying theory that constitutes the MAP model and as a practical guide for analysts and future developers on how aspects of this material model influence generalized mechanical behavior.

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Current wind turbine blade materials may not be damage tolerant to the extent necessary to optimize the Levelized Cost of Energy (LCOE) of wind energy plants. Traditionally, wind turbine blades have been designed using a safe-life approach, but advances in inspection techniques and structural health monitoring solutions give rise to the opportunity to design wind turbine blades using a damage tolerant approach. Materials selection is a key element of da mage tolerant design, so the extent of the damage tolerance of alternative materials has been analyzed through a literature review and discussions with industry leaders. Fabrics and resin selection significantly affect the damage tolerance of composites. Changes to fabric architecture may include through-the-thickness (TTT) fibers, stretch-broken carbon fiber (SBCF) composites, and aligned discontinuous fiber reinforced composites (ADFRCs). Previous research has demonstrated that using TTT fibers in creases damage tolerance, but additional research is necessary to demonstrate the effectiveness of SBCFs and ADFRCs in mitigating damage. Several studies have demonstrated increased damage tolerance when toughened resin systems are used. In addition to toughened resin systems, thermoplastics have been shown to be tougher than thermosets. However, thermosets have been traditionally preferred in wind turbine blade manufacturing due to ease of manufacturing. Thermoplastic resin system s have been developed that can be used with conventional manufacturing methods but have yet to be studied for its damage tolerant capabilities. Furthermore, cost and stress analyses on where to effectively implement TTT fibers, SBCF composites, ADFRCs, and toughened resin systems must be executed prior to incorporating new materials into wind turbine blade manufacturing.

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In this work, we have used the well-understood quantum Hall (QH) stripes in high quality two-dimensional electron gases to mimic charge stripes in high transition temperature (Tc) superconductors. The science question we want to address is “Can QH stripes mimic high Tc superconductor stripes and provide a controlled experimental setup to pin-down the role of stripes in high Tc superconductivity?”. We have observed anomalous superconducting transition like behavior in GaAs double quantum well systems (DQWs) when each quantum well (QW) is tuned to the charge stripe states but with different Landau level fillings. Furthermore, we have shown that the transition like behavior is sharper in the DQWs when the two QWs are more strongly coupled. Our results suggest, for the first time, experimental evidence of the paired charge stripes model, which might lead to room-temperature superconductors that have enormously wide applications in computing, energy, and transportation industries. Advancing the science of high transition temperature superconductivity will have a profound impact in advancing energy technologies, ranging from the next generation microchips, new energy transfer grid to public transportation, and thus is important to nation’s energy security and relevant across the landscape of many mission spaces. Sandia has been a leader in materials science research and development. The proposed research takes advantage of Sandia’s state-ofthe-art MBE facilities at the Center for Integrated Nanotechnologies (CINT) and utilizes Sandia’s extensive advanced materials characterization resources. We envision a significant impact on the nation’s energy research and security challenges by investing in this research.

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Energy utilities are evaluating emerging energy technologies to reduce reliance on carbon as an energy carrier. Hydrogen has been identified as a potential substitute for carbon-based fuels that can be blended into other gaseous energy carriers, such as natural gas. However, hydrogen blending into natural gas has important implications on safety which need to be evaluated. Designers and installers of systems that utilize hydrogen gas blending into natural gas distribution systems need to adhere to local building codes and engage with the authority having jurisdiction (AHJ) for safety and permitting approvals. These codes and standards must be considered to understand where safety gaps might be apparent when injecting hydrogen into the natural gas infrastructure. This report generates a list of relevant codes and standards for hydrogen blending on existing, upgraded, or new pipelines. Additionally, a preliminary assessment was made to identify the codes and standards that need to be modified to enable this technology as well as potential gaps due to the unique nature and safety concerns of gaseous hydrogen.

In the planning for FY2020 in the U.S. DOE NE-81 Spent Fuel and Waste Science and Technology (SFWST) Campaign, the DOE requested development of a plan for activities in the Disposal Research (DR) Research and Development (R&D) over a five (5)-year period, and DOE requested periodic updates to this plan. The DR R&D 5-year plan was provided to the DOE based on the FY2020 priorities and program structure (Sassani et al., 2020) and represents a strategic guide to the work within the DR R&D technical areas (i.e., the Control Accounts), focusing on the highest priority technical thrusts. This FY2021 report is the first update to the DR R&D 5-year plan for the SFWST Campaign DR R&D activities. This 5-year plan will be a living document and is planned to be updated periodically to provide review of accomplishments and for prioritization changes based on aspects including mission progress, external technical work, and changes in SFWST Campaign objectives and/or funding levels (i.e., Program Direction). The updates to this 5-year plan will address the DR R&D that has been completed (accomplishments) and the additional knowledge gaps to be investigated, with any updates to the DR R&D priorities for the next stages of activities.

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