Publications

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Entangling gates in trapped-ion quantum computers are most often applied to stationary ions with initial motional distributions that are thermal and close to the ground state, while those demonstrations that involve transport generally use sympathetic cooling to reinitialize the motional state prior to applying a gate. Future systems with more ions, however, will face greater nonthermal excitation due to increased amounts of ion transport and exacerbated by longer operational times and variations over the trap array. In addition, pregate sympathetic cooling may be limited due to time costs and laser access constraints. In this paper, we analyze the impact of such coherent motional excitation on entangling-gate error by performing simulations of Mølmer-Sørenson (MS) gates on a pair of trapped-ion qubits with both thermal and coherent excitation present in a shared motional mode at the start of the gate. We quantify how a small amount of coherent displacement erodes gate performance in the presence of experimental noise, and we demonstrate that adjusting the relative phase between the initial coherent displacement and the displacement induced by the gate or using Walsh modulation can suppress this error. We then use experimental data from transported ions to analyze the impact of coherent displacement on MS-gate error under realistic conditions.

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The development of chemistry is reported to implement selective dual-wavelength olefin metathesis polymerization for continuous additive manufacturing (AM). A resin formulation based on dicyclopentadiene is produced using a latent olefin metathesis catalyst, various photosensitizers (PSs) and photobase generators (PBGs) to achieve efficient initiation at one wavelength (e.g., blue light) and fast catalyst decomposition and polymerization deactivation at a second (e.g., UV-light). This process enables 2D stereolithographic (SLA) printing, either using photomasks or patterned, collimated light. Importantly, the same process is readily adapted for 3D continuous AM, with printing rates of 36 mm h–1 for patterned light and up to 180 mm h–1 using un-patterned, high intensity light.

Soil carbon can be divided into two categories: organic and inorganic. Soil inorganic carbon (SIC) is present in carbonate minerals in the soil and is often found in dry, arid regions. Examples of SIC include calcium carbonate (CaCO₃) and magnesium carbonate (MgCO₃), both of which play important roles in soil health. Soil organic carbon (SOC) is found in fresh plant matter (available SOC) and as humus or charcoal (inert SOC). Both types of carbon act as storage in the global carbon cycle. As a carbon sink, soil carbon has the potential to store carbon that would otherwise remain in the atmosphere as CO₂, one of the primary greenhouse emissions. As such, soil is under increasing attention and research to be used as a sequestration (i.e., isolation) method to reduce the amount of carbon in the atmosphere. This type of carbon sequestration is called biological sequestration. SOC typically stores carbon for several decades (depending on decomposition rates) while SIC can store carbon for more than 70,000 years. Common sequestration techniques for SOC usually fall under the category of land management: planting perennials, keeping plant residue and composting, reducing tilling, and other agricultural practices that vary by region. SIC sequestration through carbonates naturally takes thousands of years but there have been studies to increase SIC sequestration through the addition of silicates.

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We provide corrections to the slot capacitance and inverse inductance per unit length for slot gasket groove geometries using an approximate conformal mapping approach. We also provide corrections for abrupt step changes in slot width along with boundary discontinuity conditions for implementation in the various slot models.

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Neutron double scatter imaging exploits the kinematics of neutron elastic scattering to enable emission imaging of neutron sources. Due to the relatively low coincidence detection efficiency of fast neutrons in organic scintillator arrays, imaging efficiency for double scatter cameras can also be low. One method to realize significant gains in neutron coincidence detection efficiency is to develop neutron double scatter detectors which employ monolithic blocks of organic scintillator, instrumented with photosensor arrays on multiple faces to enable 3D position and multi-interaction time pickoff. Silicon photomultipliers (SiPMs) have several advantageous characteristics for this approach, including high photon detection efficiency (PDE), good single photon time resolution (SPTR), high gain that translates to single photon counting capabilities, and ability to be tiled into large arrays with high packing fraction and photosensitive area fill factor. However, they also have a tradeoff in high uncorrelated and correlated noise rates (dark counts from thermionic emissions and optical photon crosstalk generated during avalanche) which may complicate event positioning algorithms. We have evaluated the noise characteristics and SPTR of Hamamatsu S13360-6075 SiPMs with low noise, fast electronic readout for integration into a monolithic neutron scatter camera prototype. The sensors and electronic readout were implemented in a small-scale prototype detector in order to estimate expected noise performance for a monolithic neutron scatter camera and perform proof-of-concept measurements for scintillation photon counting and three-dimensional event positioning.

This document provides a description of the model evaluation protocol (MEP) database for fires involving liquefied natural gas (LNG) and processing fuels at LNG facilities. The purpose of the MEP is to provide procedures regarding the assessment of a model's suitability to predict thermal exclusion zones resulting from a fire. The database includes measurements from pool fire, jet fire, and fireball experiments which are provided in a spreadsheet. Users are to enter model results into the spreadsheet which automatically generates statistical performance measures and graphical comparisons with the experimental data. The intent of this document is to provide a description of the experiments and of the procedure required to carry out the validation portion of the MEP. In addition, the statistical performance measures, measurements for comparisons, and parameter variation are provided.

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Distributed generation (DG) sources like photovoltaic (PV) systems with advanced inverters are able to perform grid-support functions, like autonomous Volt-VAR that attempts to mitigate voltage issues by injecting or consuming reactive power. However, the Volt-VAR function operates with VAR priority, meaning real power may be curtailed to provide additional reactive power support. Since some locations on the grid may be more prone to higher voltages than others, PV systems installed at those locations may be forced to curtail more power, adversely impacting the value of that PV system. Adaptive Volt-VAR (AVV) could be implemented as an alternative, whereby the Volt-VAR reference voltage changes over time, but this functionality has not been well-explored in the literature. In this work, the potential benefits and grid impacts of AVV were investigated using yearlong quasi-static time-series (QSTS) simulations. After testing a variety of allowable AVV settings, we found that even with aggressive settings AVV resulted in <0.01% real power curtailment and significantly reduced the reactive power support required from the PV inverter compared to conventional Volt-VAR but did not provide much mitigation for extreme voltage conditions. The reactive power support provided by AVV was injected to oppose large deviations in voltage (in either direction), indicating that it could be useful for other applications like reducing voltage flicker or minimizing interactions with other voltage regulating devices.

A new adaptive rational interpolation method is proposed to obtain the wideband frequency response of a resonant cavity simulated with the method of moments (MoM). This interpolation method uses both the Loewner matrix to construct a rational expression for the solution vector of MoM's matrix system and an error estimator generated by the solution vectors and their derivatives. This error estimator is implemented in the adaptive procedure to gain a minimum set of frequencies and solution vectors required in the interpolation. The resulting set of frequencies and solution vectors is applied to interpolate other system variables, such as shielding effectiveness and input impedance. Numerical results of a slotted cylindrical cavity supporting high-quality factor resonances are presented, showing that the new rational interpolation method is accurate and efficient in interpolating the complicated resonant response of the solution vector functions.

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Thermal spray processing of metals and respective blends is becoming increasingly attractive due to the unique properties such as increased yield strength, low ductility, and differences in tensile and compressive strengths that result from microstructural features due to the spray process compared to other additive manufacturing methods. Here we report the results of plate impact experiments applied to Controlled Atmosphere Plasma Spray deposits of tantalum (Ta), niobium (Nb), and a tantalum-niobium blend (TaNb). These methods allowed for definition of the Hugoniot for each material type and the assessment of the Hugoniot Elastic Limit (HEL). Spallation experiments were conducted, and soft recovery of each material type allowed for scanning electron microscopy to characterize the fracture mechanism during tensile loading.

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Abstract: Here, we report the high pressure phase and morphology behavior of ordered anatase titanium dioxide (TiO2) nanocrystal arrays. One-dimensional TiO2 nanorods and nanorices were synthesized and self-assembled into ordered mesostructures. Their phase and morphological transitions at both atomic scale and mesoscale under pressure were studied using in situ synchrotron wide- and small-angle x-ray scattering (WAXS and SAXS) techniques. At the atomic scale, synchrotron WAXS reveals a pressure-induced irreversible amorphization up to 35 GPa in both samples but with different onset pressures. On the mesoscale, no clear phase transformations were observed up to 20 GPa by synchrotron SAXS. Intriguingly, sintering of TiO2 nanorods at mesoscale into nano-squares or nano-rectangles, as well as nanorices into nanowires, were observed for the first time by transmission electron microscopy. Such pressure-induced nanoparticle phase-amorphization and morphological changes provide valuable insights for design and engineering structurally stable nanomaterials. Impact statement: The high pressure behavior of nanocrystals (NCs) continues to be of interest, as previous studies have demonstrated that an externally applied pressure can serve as an efficient tool to induce structural phase transitions of NC assemblies at both the atomic scale and mesoscale without altering any chemistry by manipulating NC interatomic and interparticle distances. In addition, the high pressure generated deviatoric stress has been proven to be able to force adjacent NCs to connect and fuse into new crystalline nanostructures. Although the atomic structural evolution of TiO2 NCs under pressure has been widely investigated in the past decades, open questions remain regarding the mesoscale phase transition and morphology of TiO2 NC assemblies as a function of pressure. Therefore, in this work, systemic high pressure experiments on ordered arrays of TiO2 nanorods and nanorices were conducted by employing wide/small angle x-ray scattering techniques. The sintering of TiO2 assemblies at mesoscale into various nanostructures under pressure were revealed by transmission electron microscopy. Overall, this high pressure work fills the current gap in research on the mesoscale phase behavior of TiO2 assemblies. The observed morphology tunability attained by applying pressure opens new pathways for engineering nanomaterials and optimizing their collective properties through mechanical compression stresses. Graphical abstract: [Figure not available: see fulltext.].

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Hydrogen is an important resource for many different industries throughout the world, including refining, manufacturing, and as a direct energy source. Hydrogen production, through methods such as steam methane reforming, has been developed over several decades. There is a large global demand for hydrogen from these industries and safe production and distribution are paramount for hydrogen systems. Codes and standards have been developed to reduce the risk associated with hydrogen accidents to the public. These codes and standards are similar to those in other industries in which there is inherent risk to the public, such as gasoline and natural gas production and distribution. Although there will always be a risk to the public in these types of fuels, the codes and standards are developed to reduce the likelihood of an accident occurring and reduce the severity of impact, should one occur. This report reviews the current state of hydrogen in the United States and outlines the codes and standards that ensure safe operation of hydrogen systems. The total hydrogen demand and use in different industries is identified. Additionally, the current landscape of hydrogen production and fueling stations in the United States is outlined. The safety of hydrogen systems is discussed through an overview of the purpose, methods, and content included in codes and standards. As outlined in this safety overview, the risk to the public in operation of hydrogen generation facilities and fueling stations is reduced through implementation of appropriate measures. Codes, such as NFPA 2, ensure that the risk associated with a hydrogen system is no greater than the risk presented by gasoline refueling stations.

This document provides a description of the model evaluation protocol (MEP) for pool fires, jet fires, and fireballs involving liquefied natural gas (LNG), refrigerant fluids, and byproducts at LNG facilities. The purpose of the MEP is to provide procedures regarding the assessment of a model's suitability to predict heat flux from fires. Three components, namely, a scientific assessment, model verification, and model validation comprise the MEP. The evaluation of a model satisfying these three components is to be documented in the form of a model evaluation report (MER). Discussion of models for the prediction of fire, detailed information on each of the three MEP components, the MEP procedure regarding new versions of previously approved models, and the format of the model evaluation report (MER) are provided.

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We present a surrogate modeling framework for conservatively estimating measures of risk from limited realizations of an expensive physical experiment or computational simulation. Risk measures combine objective probabilities with the subjective values of a decision maker to quantify anticipated outcomes. Given a set of samples, we construct a surrogate model that produces estimates of risk measures that are always greater than their empirical approximations obtained from the training data. These surrogate models limit over-confidence in reliability and safety assessments and produce estimates of risk measures that converge much faster to the true value than purely sample-based estimates. We first detail the construction of conservative surrogate models that can be tailored to a stakeholder's risk preferences and then present an approach, based on stochastic orders, for constructing surrogate models that are conservative with respect to families of risk measures. Our surrogate models include biases that permit them to conservatively estimate the target risk measures. We provide theoretical results that show that these biases decay at the same rate as the L2 error in the surrogate model. Numerical demonstrations confirm that risk-adapted surrogate models do indeed overestimate the target risk measures while converging at the expected rate.

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This manual describes the installation and use of the Xyce™ XDM Netlist Translator. XDM simplifies the translation of netlists generated by commercial circuit simulator tools into Xyce-compatible netlists. XDM currently supports translation from PSpice, HSPICE, and Spectre netlists into Xyce™ netlists.

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Optical fiber diagnostics are extensively used in pulsed power experiments, such as the Sandia Z machine. However, radiation produced in a pulsed power environment can significantly affect these measurements. Catastrophic fiber darkening may be mitigated with shielding, but no flexible material can stop all radiation produced by the machine and/or target. Radiation-induced refractive index modulations are particularly challenging for optical interferometry. Several approaches for radiation-tolerant photonic Doppler velocimetry are discussed here.

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Digital light processing (DLP) 3D printing is an additive manufacturing process that utilizes light patterns to photopolymerize a liquid resin into a solid. Due to the accuracy of modern digital micromirror devices (DMD) and recent advances in resin chemistry, it is now possible to create functionally graded structures using different light intensity values, also known as grayscale DLP (g-DLP). Different intensities of light lead to differences in the polymer crosslinking density after curing, which ultimately produces a part with gradients of material properties. However, g-DLP is a complicated process. First, the DLP printing is a highly coupled chemical and physical process that involves light propagation, chemical reactions, species diffusion, heat transfer, volume shrinkage, and changes in mechanical behaviors of the curing resin. Second, in g-DLP, light gradients create strong in plane gradients of chemical species concentrations in the curing liquid resin due to the strong dependence of light intensity on the rate of monomer crosslinking. Furthermore, light gradients through the depth create concentration gradients due to the degree of cure dependent light absorption and the use of photoabsorbers. These complex physical features of the printing process must be understood in order to properly control printing parameters such as light exposure time, printing speed, and grayscale variations to achieve accurate mechanical properties. In this paper, a photopolymerization reaction–diffusion model is developed and used in conjunction with experiments to investigate the coupled effects of light propagation, chemical reaction rates, and species diffusion during g-DLP 3D printing. The model is implemented numerically utilizing the finite difference method and simulation results are compared to experimental findings of simple printed structures. The agreement between experimental and model predictions of simple quantities of interest, such as geometric feature sizes, shows that the model can capture the overcure due to free-radical and other species diffusion during printing when grayscale patterns are employed. This model lays the groundwork for future extensions that can incorporate more complex coupled physics such as heat transfer, volume shrinkage, and material property evolution, which are critically important in utilizing g-DLP 3D printing for the fabrication of high-performance parts which excellent geometric and material property tolerances.

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A proof-of-concept tool, the Produced Water-Economic, Socio, Environmental Simulation model (PW-ESESim), was developed to support ease of analysis. The tool was designed to facilitate head-to-head comparison of alternative produced water source, treatment, and reuse water management strategies. A graphical user interface (GUI) guides the user through the selection and design of alternative produced water treatment and reuse strategies and the associated health and safety risk and economic benefits. At the highest conceptual level, alternative water strategies include the selection of a source water (locally or regionally available produced water), treatment strategy (pre-treatment, physical, chemical, biological, desalination, and post-treatment processes) and product water purpose (e.g., irrigation, industrial processing, environmental). After selection of these details, the PW-ESESim output a number of key economic, societal, environmental, public/ecological health and safety metrics to support user decision-making; specific examples include, cost of treatment, improvements in freshwater availability, human and ecologic health impacts and growth in local jobs and the economy. Through the simulation of different produced water treatment and management strategies, tradeoffs are identified and used to inform fit-for-purpose produced water treatment and reuse management decisions. While the tool was initially designed using Southeastern New Mexico (Permian Basin) as a case study, the general design of the PW-ESESim model can be extended to support other oil and gas regions of the U.S.

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Accelerated aging studies of β CL-20 thin films deposited on glass surfaces in different environments (N2, air, air/water) were conducted. Samples were analyzed with attenuated total reflectance infrared (ATR-IR) spectroscopy. Spectral features of molecular lattice inclusions in CL-20 films aged in an air/water environment were observed. The features occurred after β CL-20 polymorph transformation to α CL-20 and were accompanied by the appearance of lattice water peaks. To assist ATR-IR peak assignment, density functional theory studies were performed, and IR spectra of α CL-20 lattice inclusions of small molecules that were identified as degradation products of CL-20 were computed. Simulated spectra of NO2, HNCO, N2O, and CO2 incorporated into partially hydrated α CL-20 matched the experimental spectrum of β CL-20 thin films aged in air/water.

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Data is a valuable commodity, and it is often dispersed over multiple entities. Sharing data or models created from the data is not simple due to concerns regarding security, privacy, ownership, and model inversion. This limitation in sharing can hinder model training and development. Federated learning can enable data or model sharing across multiple entities that control local data without having to share or exchange the data themselves. Differential privacy is a conceptual framework that brings strong mathematical guarantee for privacy protection and helps provide a quantifiable privacy guarantee to any data or models shared. The concepts of federated learning and differential privacy are introduced along with possible connections. Lastly, some open discussion topics on how federated learning and differential privacy can tied to AI-Enhanced co-design of microelectronics are highlighted.

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Neural networks are largely based on matrix computations. During forward inference, the most heavily used compute kernel is the matrix-vector multiplication (MVM): $W \vec{x} $. Inference is a first frontier for the deployment of next-generation hardware for neural network applications, as it is more readily deployed in edge devices, such as mobile devices or embedded processors with size, weight, and power constraints. Inference is also easier to implement in analog systems than training, which has more stringent device requirements. The main processing kernel used during inference is the MVM.

We present results from ramp compression experiments on high-purity Zr that show the α→ω, ω→β, as well as reverse β→ω phase transitions. Simulations with a multiphase equation of state and phenomenological kinetic model match the experimental wave profiles well. While the dynamic α→ω transition occurs ∼9GPa above the equilibrium phase boundary, the ω→β transition occurs within 0.9 GPa of equilibrium. We estimate that the dynamic compression path intersects the equilibrium ω-β line at P=29.2GPa, and T=490K. The thermodynamic path in the interior of the sample lies ∼100K above the isentrope at the point of the ω→β transition. Approximately half of this dissipative temperature rise is due to plastic work, and half is due to the nonequilibrium α→ω transition. The inferred rate of the α→ω transition is several orders of magnitude higher than that measured in dynamic diamond anvil cell (DDAC) experiments in an overlapping pressure range. We discuss a model for the influence of shear stress on the nucleation rate. We find that the shear stress sji has the same effect on the nucleation rate as a pressure increase δP=cϵijsji/(ΔV/V), where c is a geometric constant ∼1 and ϵij are the transformation shear strains. The small fractional volume change ΔV/V≈0.1 at the α→ω transition amplifies the effect of shear stress, and we estimate that for this case δP is in the range of several GPa. Correcting our transition rate to a hydrostatic rate brings it approximately into line with the DDAC results, suggesting that shear stress plays a significant role in the transformation rate.

The apparent ion temperature and mean velocity of the dense deuterium tritium fuel layer of an inertial confinement fusion target near peak compression have been measured using backscatter neutron spectroscopy. The average isotropic residual kinetic energy of the dense deuterium tritium fuel is estimated using the mean velocity measurement to be ∼103 J across an ensemble of experiments. The apparent ion-temperature measurements from high-implosion velocity experiments are larger than expected from radiation-hydrodynamic simulations and are consistent with enhanced levels of shell decompression. These results suggest that high-mode instabilities may saturate the scaling of implosion performance with the implosion velocity for laser-direct-drive implosions.

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This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users' Guide. The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce. This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users' Guide.

Sandia National Laboratories (SNL) is a multimission laboratory located in Albuquerque, New Mexico, and is one of three National Nuclear Security Administration research and development laboratories located in the United States. Recently, SNL’s Emergency Response Team (ERT) responded to an incident involving a sulfur dioxide (SO2)-fixed monitor, setting off the alarm inside a laboratory and in the adjacent hallway. The potential sources for the alarm were various experiments involving batteries and an uninterrupted power supply (UPS) in the immediate area.

Free-floating balloons are an emerging platform for infrasound recording, but they cannot host arrays sufficiently wide for multi-sensor acoustic direction finding techniques. Because infrasound waves are longitudinal, the balloon motion in response to acoustic loading can be used to determine the signal azimuth. This technique, called “aeroseismometry,” permits sparse balloon-borne networks to geolocate acoustic sources. This is demonstrated by using an aeroseismometer on a stratospheric balloon to measure the direction of arrival of acoustic waves from successive ground chemical explosions. A geolocation algorithm adapted from hydroacoustics is then used to calculate the location of the explosions.

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The MELCOR Accident Consequence Code System (MACCS) is used by Nuclear Regulatory Commission (NRC) and various national and international organizations for probabilistic consequence analysis of nuclear power accidents. This User Guide is intended to assist analysts in understanding the MACCS/WinMACCS model and to provide information regarding the code. This user guide version describes MACCS Version 4.0. Features that have been added to MACCS in subsequent versions are described in separate documentation. This User Guide provides a brief description of the model history, explains how to set up and execute a problem, and informs the user of the definition of various input parameters and any constraints placed on those parameters. This report is part of a series of reports documenting MACCS. Other reports include the MACCS Theory Manual, MACCS Verification Report, Technical Bases for Consequence Analyses Using MACCS, as well as documentation for preprocessor codes including SecPop, MelMACCS, and COMIDA2.

Niobium (Nb)-doped lead-tin-zirconate-titanate (PSZT) ceramics near the lead-zirconate-titanate 95/5 orthorhombic AFE-rhombohedral FE morphotropic phase boundary (PSZT 13.5/81/5.5 -1.6Nb) were prepared with up to 10 mol.% of hafnium (Hf) substituted for zirconium. The ceramics were prepared by a traditional solid-state synthesis route and sintered to near full density at 1150°C for 6 h in sealed alumina crucibles with self-same material as the lead vapor source. All compositions were ∼98% dense with no detectable secondary phases by X-ray diffraction. The grain size was ∼3 μm for all compositions, consisting of equiaxed grains with intergranular porosity. The compositions exhibited remnant polarization values of ∼32 μC/cm2. Depolarization by the hydrostatic pressure-induced FE-AFE phase transition occurred at 310 MPa for all compositions, resulting in a total depolarization output of 32.4 μC/cm2 for the PSZT ceramics. Evaluation of the R3c-R3m and R3m-Pm (Formula presented.) m phase transition temperatures by impedance spectroscopy showed temperatures on heating ranging from 86 to 92°C and 186 to 182°C, respectively, for increasing nominal Hf content. Thermal hysteresis of the phase transitions was also observed in the ceramics, with the transition temperature on cooling being 1–4°C lower. The study demonstrated that the PSZT ceramics are relatively insensitive to variations in Hf content in the range of 0 to 10 mol.%.

Sandia National Laboratories in collaboration with the National Renewable Energy Laboratory outline a framework for developing a solar fuels roadmap based on novel concepts for hybridizing gas-splitting thermochemical cycle s with high-temperature electro chemical steps. We call this concept SoHyTEC, a Solar Hybrid Thermochemical-Electrochemical Cycle. The strategy focuses on transforming purely thermochemical cycles that split water (H₂O) and carbon dioxide (CO₂) to produce hydrogen (H 2 ) and carbon monoxide (CO) , respectively, the fundamental chemical building blocks for diverse fuels and chemicals , by substituting thermochemical reactions with high-temperature electrochemical steps. By invoking high-temperature electrochemistry, the energy required to complete the gas-splitting cycle is divided into a thermal component (process temperature) and an electrical component (applied voltage). These components, sourced from solar energy, are independently variable knobs to maximize overall process efficiency. Furthermore, a small applied voltage can reduce cycle process temperature by hundreds of degrees , opening the door to cost-effective solar concentrators and practical receiver/reactor de signs. Using the SoHyTEC concept as a backdrop, we outline a framework that advocates developing methods for automating information gathering, critically evaluating thermochemical cycles for adapting into SoHyTEC, establishing requirements based on thermodynamic analysis, and developing a model-based approach to benchmarking a SoHyTEC system against a baseline concentrating solar thermal integrated electrolysis plant. We feel these framework elements are a necessary precursor to creating a robust and adaptive technology development roadmap for producing solar fuels using SoHyTEC. In one example, we introduce high-temperature electrochemistry as a method to manipulate a fully stoichiometric two-step metal oxide cycle that circumvents costly separation processes and ultra-high cycle temperatures. We also identify and group water-splitting chemistries that are conceptually amenable to hybridization.

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The increasing availability of advanced metering infrastructure (AMI) data has led to significant improvements in load modeling accuracy. However, since many AMI devices were installed to facilitate billing practices, few utilities record or store reactive power demand measurements from their AMI. When reactive power measurements are unavailable, simplifying assumptions are often applied for load modeling purposes, such as applying constant power factors to the loads. The objective of this work is to quantify the impact that reactive power load modeling practices can have on distribution system analysis, with a particular focus on evaluating the behaviors of distributed photovoltaic (PV) systems with advanced inverter capabilities. Quasi-static time-series simulations were conducted after applying a variety of reactive power load modeling approaches, and the results were compared to a baseline scenario in which real and reactive power measurements were available at all customer locations on the circuit. Overall, it was observed that applying constant power factors to loads can lead to significant errors when evaluating customer voltage profiles, but that performing per-phase time-series reactive power allocation can be utilized to reduce these errors by about 6x, on average, resulting in more accurate evaluations of advanced inverter functions.

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The Department of Energy maintains an up-to-date documentation of the number of available full drawdowns of each of the caverns owned by the Strategic Petroleum Reserve (SPR). This information is important for assessing the SPR's ability to deliver oil to domestic oil companies expeditiously if national or world events dictate a rapid sale and deployment of the oil reserves. Sandia was directed to develop and implement a process to continuously assess and report the evolution of drawdown capacity, the subject of this report. A cavern has an available drawdown if after that drawdown, the long-term stability of the cavern, the cavern field, or the oil quality are not compromised. Thus, determining the number of a vailable drawdowns requires the consideration of several factors regarding cavern and wellbore integrity and stability, including stress states caused by cavern geometry and operations, salt damage caused by dilatant and tensile stresses, the effect of enhanced creep on wellbore integrity, and the sympathetic stress effect of operations on neighboring caverns. A consensus has now been built regarding the assessment of drawdown capabilities and risks for the SPR caverns (Sobolik et al., 2014; Sobolik 2016). The process involves an initial assessment of the pillar-to-diameter (P/D) ratio for each cavern with respect to neighboring caverns. A large pillar thickness between adjacent caverns should be strong enough to withstand the stresses induced by closure of the caverns due to salt creep. The first evaluation of P/D includes a calculation of the evolution of P/D after a number of full cavern drawdowns. The most common storage industry standard is to keep this value greater than 1.0, which should ensure a pillar thick enough to prevent loss of fluids to the surrounding rock mass. However, many of the SPR caverns currently have a P/D less than 1.0 or will likely have a low P/D after one or two full drawdowns. For these caverns, it is important to examine the s tructural integrity with more detail using geomechanical models. Finite - element geomechanical models have been used to determine the stress states in the pillars following successive drawdowns. By computing the tensile and dilatant stresses in the salt, areas of potential structural instability can be identified that may represent "red flags" for additional drawdowns. These analyses have found that many caverns will maintain structural integrity even when grown via drawdowns to dimensions resulting in a P/D of less than 1.0. The analyses have also confirmed that certain caverns should only be completely drawn down one time. As the SPR caverns are utilized and partial drawdowns are performed to remove oil from the caverns (e.g., for occasional oil sales , purchases, or exchanges authorized by the Congress or the President), the changes to the cavern caused by these procedures must be tracked and accounted for so that an ongoing assessment of the cavern's drawdown capacity may be continued. A proposed methodology for assessing and tracking the available drawdowns for each cavern was presented in Sobolik et al. (2018). This report is the latest in a series of annual reports, and it includes the baseline available drawdowns for each cavern, and the most recent assessment of the evolution of drawdown expenditure for several caverns.

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Photovoltaic modules are subjected to various mechanical stressors in their deployment environments, ranging from installation handling to wind and snow loads. Damage incurred during these mechanical events has the potential to initiate subsequent degradation mechanisms, reducing useful module lifespan. Thus, characterizing the mechanical state of photovoltaic modules is pertinent to the development of reliable packaging designs. In this work, photovoltaic modules with strain gauges directly incorporated into the module laminate were fabricated and subjected to mechanical loading to characterize internal strains within the module when under load. These experimental measurements were then compared against results obtained by high-fidelity finite-element simulations. The simulation results showed reasonable agreement in the strain values over time; however, there were large discrepancies in the magnitudes of these strains. Both the instrumentation technique and the finite-element simulations have areas where they can improve. These areas of improvement have been documented. Despite the observed discrepancies between the experimental and simulated results, the module instrumentation proved to be a useful gauge in monitoring and characterizing the mechanical state. With some process improvements, this method could potentially be applied to other environments that a photovoltaic module will encounter in its lifetime that are known to cause damage and degrade performance.

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The use of an electrochemical dissolution process is shown to remove the recast layer contamination from the surfaces of electrical-discharge-machining cut components, as well as the interior exposed surfaces of the structure. The solution chemistry, cell potential, and exposure time are all relevant interdependent variables. Optimization of the electrode geometry should be made for each type of component. For the case of Cu-Zn recast contamination of 300-series alloy components, surface composition analysis indicates that complete electrochemical dissolution is achieved using a dilute solution of nitric acid (HNO₃). For example, electrochemical dissolution of the Cu-Zn recast is accomplished at 1.2 V cell potential using a 20% nitric solution and an exposure time of 4 h. The use of a nitric acid bath was specifically chosen since it’s chemically compatible and will not degrade the host alloy or the component. In sum, an electrochemically driven dissolution process can be tailored to remove of the recast contamination without affecting the integrity of the host component structure and its dimensional tolerances.

In this article, we present a general methodology to combine the Discontinuous PetrovGalerkin (DPG) method in space and time in the context of methods of lines for transient advection-reaction problems. We first introduce a semidiscretization in space with a DPG method redefining the ideas of optimal testing and practicality of the method in this context. Then, we apply the recently developed DPG-based time-marching scheme, which is of exponential-type, to the resulting system of Ordinary Differential Equations (ODEs). We also discuss how to efficiently compute the action of the exponential of the matrix coming from the space semidiscretization without assembling the full matrix. Finally, we verify the proposed method for 1D+time advection-reaction problems showing optimal convergence rates for smooth solutions and more stable results for linear conservation laws comparing to the classical exponential integrators.

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This report documents the fatigue code SIESTA that has been used for recently here at Sandia National Laboratories. It is written in two parts: the first as a user manual and the second as a theory manual. Currently employed in SIESTA are stress-life cycle approaches. Clients have requested the use of standards in particular analyses; therefore, the American Society of Mechanical Engineers, Boiler Pressure Vessel Code fatigue standards have been implemented. These include an elastic, an elastic-plastic, and a weld fatigue method. All three methods use a Max-Min cycle counting method that is appropriate for non-proportional loading. A Signed von Mises method that used a Rainflow Cycle Counting Method is also implemented. The Signed von Mises with the Rainflow Cycle Counting Method is appropriate for proportional loading. Several verification examples are noted and include comparisons to experimental data.

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Sandia National Laboratories (SNL) is performing a test campaign for the Department of Energy (DOE) Office of Cybersecurity, Energy Security, and Emergency Response (CESER) to address high-altitude electromagnetic pulse (HEMP) vulnerability of critical components of generation stations, with focus on early-time (E1) HEMP. The campaign seeks to establish response and damage thresholds for these critical elements in response to reasonable HEMP threat levels as a means for determining where vulnerabilities may exist or where mitigations may be needed. This report provides component vulnerability test results that will help to inform site vulnerability assessments and HEMP mitigation planning.

Abstract: An innovative biomimetic method has been developed to synthesize layered nanocomposite coatings using silica and sugar-derived carbon to mimic the formation of a natural seashell structure. The layered nanocomposites are fabricated through alternate coatings of condensed silica and sugar. Sugar-derived carbon is a cost-effective material as well as environmentally friendly. Pyrolysis of sugar will form polycyclic aromatic carbon sheets, i.e., carbon black. The resulting final nanocomposite coatings can survive temperatures of more than 1150 °C and potentially up to 1650 °C. These coatings have strong mechanical properties, with hardness of more than 11 GPa and elastic modulus of 120 GPa, which are 80% greater than those of pure silica. The layered coatings have many applications, such as shielding in the form of mechanical barriers, body armor, and space debris shields. Graphical abstract: [Figure not available: see fulltext.]

Advanced Battery Management Systems (BMS) play a vital role in monitoring, predicting, and controlling the performance of lithium-ion batteries. BMS employing sophisticated electrochemical models can help increase battery cycle life and minimize charging time. However, in order to realize the full potential of electrochemical model-based BMS, it is critical to ensure accurate predictions and proper model parameterization. The accuracy of the predictions of an electrochemical model is dependent on the accuracy of its parameters, the values of which might change with battery cycling and aging. Parameter estimation for an electrochemical model is generally challenging due to the nonlinear nature and computational complexity of the model equations. To this end, this work utilizes the recently proposed Tanks-in-Series model for Li-ion batteries (J.Electrochem. Soc., 167, 013534 (2020)) to perform parameter estimation. The Tanks-in-Series approach allows for substantially faster parameter estimation compared to the original pseudo two-dimensional (p2D) model. The objective of this work is thus to demonstrate the gain in computational efficiency from the Tanks-in-Series approach. A sensitivity analysis of model parameters is also performed to benchmark the fidelity of the Tanks-in-Series model.

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