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.
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.
A Bayesian inference strategy has been used to estimate uncertain inputs to global impurity transport code (GITR) modeling predictions of tungsten erosion and migration in the linear plasma device, PISCES-A. This allows quantification of GITR output uncertainty based on the uncertainties in measured PISCES-A plasma electron density and temperature profiles (n e, T e) used as inputs to GITR. The technique has been applied for comparison to dedicated experiments performed for high (4 × 1022 m-2 s-1) and low (5 × 1021 m-2 s-1) flux 250 eV He-plasma exposed tungsten (W) targets designed to assess the net and gross erosion of tungsten, and corresponding W impurity transport. The W target design and orientation, impurity collector, and diagnostics, have been designed to eliminate complexities associated with tokamak divertor plasma exposures (inclined target, mixed plasma species, re-erosion, etc) to benchmark results against the trace impurity transport model simulated by GITR. The simulated results of the erosion, migration, and re-deposition of W during the experiment from the GITR code coupled to materials response models are presented. Specifically, the modeled and experimental W I emission spectroscopy data for a 429.4 nm line and net erosion through the target and collector mass difference measurements are compared. The methodology provides predictions of observable quantities of interest with quantified uncertainty, allowing estimation of moments, together with the sensitivities to plasma temperature and density.
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.
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.
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.%.
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.
Voronin, Alexey; He, Yunhui; Maclachlan, Scott; Olson, Luke N.; Tuminaro, Raymond S.
A well-known strategy for building effective preconditioners for higher-order discretizations of some PDEs, such as Poisson's equation, is to leverage effective preconditioners for their low-order analogs. In this work, we show that high-quality preconditioners can also be derived for the Taylor–Hood discretization of the Stokes equations in much the same manner. In particular, we investigate the use of geometric multigrid based on the (Formula presented.) discretization of the Stokes operator as a preconditioner for the (Formula presented.) discretization of the Stokes system. We utilize local Fourier analysis to optimize the damping parameters for Vanka and Braess–Sarazin relaxation schemes and to achieve robust convergence. These results are then verified and compared against the measured multigrid performance. While geometric multigrid can be applied directly to the (Formula presented.) system, our ultimate motivation is to apply algebraic multigrid within solvers for (Formula presented.) systems via the (Formula presented.) discretization, which will be considered in a companion paper.
The radiation effects community needs clear, well-documented, neutron energy-dependent responses that can be used in assessing radiation-induced material damage to GaAs semiconductors and for correlating observed radiation-induced changes in the GaAs electronic properties with computed damage metrics. In support of the objective, this document provides: a) a clearly defined set of relevant neutron response functions for use in dosimetry applications; b) clear mathematical expressions for the defined response functions; and c) updated quantitative values for the energy- dependent response functions that reflect the best current nuclear data and modelling. This document recaps the legacy response functions. It then surveys the latest nuclear data and updates the recommended response function to support current GaAs damage studies. A detailed tabulation for six of the energy-dependent response functions is provided in an Appendix.
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.
We report an analysis quantifying the contribution to uncertainty in annual energy projections from uncertainty in ground-measured irradiance. Uncertainty in measured irradiance is quantified for eight instruments by the difference from a well maintained, secondary standard pyranometer which is regarded as truthful. We construct a statistical model of irradiance uncertainty and apply the model to generate a sample of 100 annual time series of irradiance for each instrument. The sample is propagated through a common performance model for a reference photovoltaic system to quantify variation in annual energy. Although the measured irradiance varies from the reference by a few percent (standard deviation of 1-2%) the uncertainty in annual energy is on the order of a fraction of one percent. We propose a model for a factor that represents uncertainty in modeled annual energy that arises from uncertainty in ground-measured irradiance.
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.
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.
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.
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.
Conservation voltage reduction (CVR) is a common technique used by utilities to strategically reduce demand during peak periods. As penetration levels of distributed generation (DG) continue to rise and advanced inverter capabilities become more common, it is unclear how the effectiveness of CVR will be impacted and how CVR interacts with advanced inverter functions. In this work, we investigated the mutual impacts of CVR and DG from photovoltaic (PV) systems (with and without autonomous Volt-VAR enabled). The analysis was conducted on an actual utility dataset, including a feeder model, measurement data from smart meters and intelligent reclosers, and metadata for more than 30 CVR events triggered by the utility over the year. The installed capacity of the modeled PV systems represented 66% of peak load, but reached instantaneous penetrations reached up to 2.5x the load consumption over the year. While the objectives of CVR and autonomous Volt-VAR are opposed to one another, this study found that their interactions were mostly inconsequential since the CVR events occurred when total PV output was low.
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.
Mechanical strength of a 94 wt% debased alumina was measured using ASTM-C1161 specimens fabricated via conventional and lithography-based ceramic manufacturing (LCM) methods. The effects of build orientation and a 1500°C wet hydrogen fire added to the LCM firing sequence on strength were evaluated. A Weibull fit to the conventional flexural specimen data yielded 20 and 356 MPa for the modulus and characteristic strength, respectively. Weibull fits of the data from the LCM specimens yielded moduli between 7.5 and 11.3 and characteristics strengths between 333 and 339 MPa. A Weibull fit to data from LCM specimens subjected to the wet hydrogen fire yielded 14.2 and 376 MPa for the modulus and characteristic strength, respectively. The 95% confidence intervals for all Weibull parameters are reported. Average Archimedes bulk densities of LCM and conventional specimens were 3.732 and 3.730 g/cm3, respectively. Process dependent differences in surface morphology were observed in scanning electron microscope (SEM) images of specimen surfaces. SEM images of LCM specimen cross-sections showed alumina grain texture dependent on build direction, but no evidence of porosity concentrated in planes between printed layers. Fracture surfaces of LCM and conventionally processed specimens revealed hackle lines and mirror regions indicative of fracture initiation at the sample surface rather than the interior.
The integrity of wellbores at the interbed between the caprock and salt is a serious concern in the Big Hill site. For the remediation and life extension of wellbores, more accurate predictions from the global model are needed. The Big Hill global model is improved using the M-D viscoplastic contact surface model and the mesh containing the interbed layer with contact surfaces between the salt and caprock layers, and fault blocks in overburden and caprock layers. The model calibration has been performed based on the cavern volumetric closures obtained from the Caveman calculations. The results agree well from 1991 to the early 2000s. The difference starts to widen after that, it might be because of frequent fluid movement and raw water injection. Therefore, the predictions from this improved model could be used to examine the structural integrity of caverns in Big Hill salt dome.
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.
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.
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.
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.
Xu, Xiaojie; Cho, En J.; Bekker, Logan; Talin, Albert A.; Lee, Elaine; Pascall, Andrew J.; Worsley, Marcus A.; Zhou, Jenny; Cook, Caitlyn C.; Kuntz, Joshua D.; Cho, Seongkoo; Orme, Christine A.
Flexible electronic skin with features that include sensing, processing, and responding to stimuli have transformed human–robot interactions. However, more advanced capabilities, such as human-like self-protection modalities with a sense of pain, sign of injury, and healing, are more challenging. Herein, a novel, flexible, and robust diffusive memristor based on a copolymer of chlorotrifluoroethylene and vinylidene fluoride (FK-800) as an artificial nociceptor (pain sensor) is reported. Devices composed of Ag/FK-800/Pt have outstanding switching endurance >106 cycles, orders of magnitude higher than any other two-terminal polymer/organic memristors in literature (typically 102–103 cycles). In situ conductive atomic force microscopy is employed to dynamically switch individual filaments, which demonstrates that conductive filaments correlate with polymer grain boundaries and FK-800 has superior morphological stability under repeated switching cycles. It is hypothesized that the high thermal stability and high elasticity of FK-800 contribute to the stability under local Joule heating associated with electrical switching. To mimic biological nociceptors, four signature nociceptive characteristics are demonstrated: threshold triggering, no adaptation, relaxation, and sensitization. Lastly, by integrating a triboelectric generator (artificial mechanoreceptor), memristor (artificial nociceptor), and light emitting diode (artificial bruise), the first bioinspired injury response system capable of sensing pain, showing signs of injury, and healing, is demonstrated.
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.
Consider a scalar field f : M → R, where M is a triangulated simplicial mesh in Rd. A level set, or contour, at value v is a connected component of f–1 (v). As v is changed, these contours change topology, merge into each other, or split. Contour trees are concise representations of f that track this contour behavior. The vertices of these trees are the critical points of f, where the gradient is zero. The edges represent changes in the topology of contours. It is a fundamental data structure in data analysis and visualization, and there is significant previous work (both theoretical and practical) on algorithms for constructing contour trees. Suppose M has n vertices, N facets, and t critical points. A classic result of Carr, Snoeyink, and Axen (2000) gives an algorithm that takes O(n log n+Nα(N)) time (where α(·) is the inverse Ackermann function). A further improvement to O(t log t + N) time was given by Chiang et al. All these algorithms involve a global sort of the critical points, a significant computational bottleneck. Unfortunately, lower bounds of Ω(t log t) also exist. We present the first algorithm that can avoid the global sort and has a refined time complexity that depends on the contour tree structure. Intuitively, if the tree is short and fat, we get significant improvements in running time. For a partition of the contour tree into a set of descending paths, P, our algorithm runs in O($\Sigma$pϵP |p| log |p| + tα(t) + N). This is at most O(t log D + N), where D is the diameter of the contour tree. Moreover, it is O(tα(t) + N) for balanced trees, a significant improvement over the previous complexity. Our algorithm requires numerous ideas: partitioning the contour tree into join and split trees, a local growing procedure to iteratively build contour trees, and the use of heavy path decompositions for the time complexity analysis. There is a crucial use of a family of binomial heaps to maintain priorities, ensuring that any comparison made is between comparable nodes of the contour tree. We also prove lower bounds showing that the $\Sigma$pϵP |p| log |p| complexity is inherent to computing contour trees.
An existing shared risk framework designed for assessing and comparing threat-based risks to water utilities is being extended to incorporate electric power. An important differentiating characteristic of this framework is the use of a system-centric rather than an asset-centric approach. This approach allows anonymous sharing of results and enables comparison of assessments across different utilities within an infrastructure sector. By allowing utility owners to compare their assessments with others, they can improve their self-assessments and identification of "unknown unknowns". This document provides an approach for extension of the framework to electric power, including treatment of dependencies and interdependencies. The systems, threats, and mathematical description of associated risks used in a prototype framework are provided. The method is extensible so that additional infrastructure sectors can be incorporated. Preliminary results for a proof of concept calculation are provided.
This report contains a summary of our efforts to correlate head injury simulations predicting intracranial fluid cavitation with clinical assessments of brain injury from blunt impact to the head. Magnetic resonance imaging (MRI) data, collected on traumatic brain injury (TBI) subjects by researchers at the MIND Institute of New Mexico, was acquired for the current work. Specific blunt impact TBI case histories were selected from the TBI data for further study and possible correlation with simulation. Both group and single-subject case histories were examined. We found one single-subject case that was particularly suited for correlation with simulation. Diffusion tensor image (DTI) analysis of the TBI subject identified white matter regions within the brain displaying reductions in fractional anisotropy (FA), an indicator of local damage to the white matter axonal structures. Analysis of functional magnetic resonance image (fMRI) data collected on this individual identified localized regions of the brain displaying hypoactivity, another indicator of brain injury. We conducted high fidelity simulations of head impact experienced by the TBI subject using the Sandia head-neck-torso model and the shock physics computer code CTH. Intracranial fluid cavitation predictions were compared with maps of DTI fractional anisotropy and fMRI hypoactivity to assess whether a possible correlation exists. The ultimate goal of this work is to assess whether one can correlate simulation predictions of intracranial fluid cavitation with the brain injured sites identified by the fMRI and DTI analyses. The outcome of this effort is described in this report.
This report begins with an evaluation of cladding degradation mechanisms deemed important to assessing barrier capability. Unlike similar efforts done in the past, this evaluation accounts for the hypothetical conditions associated with direct dual-purpose canister (DPC) disposal, including conditions resulting from a postulated, in-package, steady-state criticality event. A total of 16 cladding degradation mechanisms are examined assuming direct disposal of DPCs in two different hypothetical repositories: a saturated repository in shale and an unsaturated repository in alluvium.
Biological monitoring logs for Swainson's and Red-tailed Hawk's nesting behaviors during construction at SNL. Logs also include information on other observed wildlife. Three different monitoring logs are present and include images and buffer map.
QOOH radicals are key intermediates in the chain of reactions leading to the autoignition of hydrocarbons and oxygenated organic compounds. They are thought to undergo two main reactions: OH elimination to form a cyclic ether and HO2 elimination to form an alkene. However, theoretical analysis of various substituted hydroperoxyalkyl radicals has found two new pathways: OH transfer and internal H abstraction assisted OH elimination. To determine the importance of these new pathways, their barrier heights for several substituted alkanes were calculated using various quantum chemical theories and compared to those of the well-known pathways. Several cases revealed possible competition with the well-known pathways. Rate coefficients were calculated for propyl systems but further studies will need to complete rate coefficients and branching fractions for all systems analyzed to understand these new reactions’ role in autoignition.
In this paper, we propose a hybrid method that uses stochastic and deterministic search to compute the maximum likelihood estimator of a low-rank count tensor with Poisson loss via state-of-theart local methods. Our approach is inspired by Simulated Annealing for global optimization and allows for fine-grain parameter tuning as well as adaptive updates to algorithm parameters. We present numerical results that indicate our hybrid approach can compute better approximations to the maximum likelihood estimator with less computation than the state-of-the-art methods by themselves.
Aeroseismometery is a novel, cutting edge capability that involves balloon based systems for detecting and geolocating sources of infrasound. The incident infrasound from a range of sources such as volcanos, earthquakes, explosions, supersonic aircraft impinges upon the balloon system causing it to respond dynamically. The dynamic response is post-processed to locate the infrasound source. This report documents the derivation of an analytical model that predicts the balloon dynamics. Governing equations for the system are derived as well as a transfer function relating the infrasound signal to the net force on the balloon components. Experimental measurements of the infrasound signals are convolved with the transfer function and the governing equations numerically time integrated to obtain predictions of the displacement, velocity and acceleration of the balloon system. The predictions are compared to the experimental measurements with good agreement observed. The derivation focuses only on the vertical dynamics of the balloon system. Future work will develop governing equations for the swinging response of the balloon to the incident infrasound.
An in situ counted ion implantation experiment improving the error on the number of ions required to form a single optically active silicon vacancy (SiV) defect in diamond 7-fold compared to timed implantation is presented. Traditional timed implantation relies on a beam current measurement followed by implantation with a preset pulse duration. It is dominated by Poisson statistics, resulting in large errors for low ion numbers. Instead, our in situ detection, measuring the ion number arriving at the substrate, results in a 2-fold improvement of the error on the ion number required to generate a single SiV compared to timed implantation. Through postimplantation analysis, the error is improved 7-fold compared to timed implantation. SiVs are detected by photoluminescence spectroscopy, and the yield of 2.98% is calculated through the photoluminescence count rate. Hanbury-Brown-Twiss interferometry is performed on locations potentially hosting single-photon emitters, confirming that 82% of the locations exhibit single photon emission statistics.
Electrode-scale heterogeneity can combine with complex electrochemical interactions to impede lithium-ion battery performance, particularly during fast charging. This study investigates the influence of electrode heterogeneity at different scales on the lithium-ion battery electrochemical performance under operational extremes. We employ image-based mesoscale simulation in conjunction with a three-dimensional electrochemical model to predict performance variability in 14 graphite electrode X-ray computed tomography data sets. Our analysis reveals that the tortuous anisotropy stemming from the variable particle morphology has a dominating influence on the overall cell performance. Cells with platelet morphology achieve lower capacity, higher heat generation rates, and severe plating under extreme fast charge conditions. On the contrary, the heterogeneity due to the active material clustering alone has minimal impact. Our work suggests that manufacturing electrodes with more homogeneous and isotropic particle morphology will improve electrochemical performance and improve safety, enabling electromobility.
Drinking water has and will continue to be at the foundation of our nation’s well-being and there is a growing interest in United States (US) drinking water quality. Nearly 30% of the United States population obtained their water from community water systems that did not meet federal regulations in 2019. Given the heavy interactions between society and drinking water quality, this study integrates social constructionism, environmental injustice, and sociohydrological systems to evaluate local awareness of drinking water quality issues. By employing text analytics, we explore potential drivers of regional water quality narratives within 25 local news sources across the United States. Specifically, we assess the relationship between printed local newspapers and water quality violations in communities as well as the influence of social, political, and economic factors on the coverage of drinking water quality issues. Results suggest that the volume and/or frequency of local drinking water violations is not directly reflected in local news coverage. Additionally, news coverage varied across sociodemographic features, with a negative relationship between Hispanic populations and news coverage of Lead and Copper Rule, and a positive relationship among non-Hispanic white populations. These findings extend current understanding of variations in local narratives to consider nuances of water quality issues and indicate opportunities for increasing equity in environmental risk communication.
Lithium-sulfur is a "beyond-Li-ion" battery chemistry attractive for its high energy density coupled with low-cost sulfur. Expanding to the MWh required for grid scale energy storage, however, requires a different approach for reasons of safety, scalability, and cost. Here we demonstrate the marriage of the redox-targeting scheme to the engineered Li solid electrolyte interphase (SEI), enabling a scalable, high efficiency, membrane-less Li-S redox flow battery. In this hybrid flow battery architecture, the Li anode is housed in the electrochemical cell, while the solid sulfur is safely kept in a separate catholyte reservoir and electrolyte is pumped over the sulfur and into the electrochemical cell. Electrochemically facile decamethylferrocene and cobaltocene are chosen as redox mediators to kick-start the initial reduction of solid S into soluble polysulfides and final reduction of polysulfides into solid Li2S, precluding the need for conductive carbons. On the anode side, a LiI and LiNO3pretreatment strategy encourages a stable SEI and lessens capacity fade, avoiding use of ion-selective separators. Complementary materials characterization confirms the uniform distribution of LiI in the SEI, while SEM confirms the presence of lower surface area globular Li deposition and UV-vis corroborates evolution of the polysulfide species. Equivalent areal loadings of up to 50 mgScm-2(84 mAh cm-2) are demonstrated, with high capacity and voltage efficiency at 1-2 mgScm-2(973 mAh gS-1and 81.3% VE in static cells and 1142 mAh gS-1and 86.9% VE in flow cells). These results imply that the fundamental Li-S chemistry and SEI engineering strategies can be adapted to the hybrid redox flow battery architecture, obviating the need for ion-selective membranes or flowing carbon additives, and offering a potential pathway for inexpensive, scalable, and safe MWh scale Li-S energy storage.
Hu, Jian Z.; Jaegers, Nicholas R.; Hahn, Nathan T.; Hu, Wenda; Han, Kee S.; Chen, Ying; Sears, Jesse A.; Murugesan, Vijayakumar; Zavadil, Kevin R.; Mueller, Karl T.
Efforts to expand the technological capability of batteries have generated increased interest in divalent cationic systems. Electrolytes used for these electrochemical applications often incorporate cyclic ethers as electrolyte solvents; however, the detailed solvation environments within such systems are not well-understood. To foster insights into the solvation structures of such electrolytes, Ca(TFSI)2and Zn(TFSI)2dissolved in tetrahydrofuran (THF) and 2-methyl-tetrahydrofuran were investigated through multi-nuclear magnetic resonance spectroscopy (17O, 43Ca, and 67Zn NMR) combined with quantum chemistry modeling of NMR chemical shifts. NMR provides spectroscopic fingerprints that readily couple with quantum chemistry to identify a set of most probable solvation structures based on the best agreement between the theoretically predicted and experimentally measured values of chemical shifts. The multi-nuclear approach significantly enhances confidence that the correct solvation structures are identified due to the required simultaneous agreement between theory and experiment for multiple nuclear spins. Furthermore, quantum chemistry modeling provides a comparison of the solvation cluster formation energetics, allowing further refinement of the preferred solvation structures. It is shown that a range of solvation structures coexist in most of these electrolytes, with significant molecular motion and dynamic exchange among the structures. This level of solvation diversity correlates with the solubility of the electrolyte, with Zn(TFSI)2/THF exhibiting the lowest degree of each. Comparisons of analogous Ca2+and Zn2+solvation structures reveal a significant cation size effect that is manifested in significantly reduced cation-solvent bond lengths and thus stronger solvent bonding for Zn2+relative to Ca2+. The strength of this bonding is further reduced by methylation of the cyclic ether ring. Solvation shells containing anions are energetically preferred in all the studied electrolytes, leading to significant quantities of contact ion pairs and consequently neutrally charged clusters. It is likely that the transport and interfacial de-solvation/re-solvation properties of these electrolytes are directed by these anion interactions. These insights into the detailed solvation structures, cation size, and solvent effects, including the molecular dynamics, are fundamentally important for the rational design of electrolytes in multivalent battery electrolyte systems.
Nuclear magnetic resonance (NMR) and nuclear quadrupole resonance (NQR) spectroscopy of bulk quantum materials have provided insight into phenomena, such as quantum phase criticality, magnetism, and superconductivity. With the emergence of nanoscale 2D materials with magnetic phenomena, inductively detected NMR and NQR spectroscopy are not sensitive enough to detect the smaller number of spins in nanomaterials. The nitrogen-vacancy (NV) center in diamond has shown promise in bringing the analytic power of NMR and NQR spectroscopy to the nanoscale. However, due to depth-dependent formation efficiency of the defect centers, noise from surface spins, band bending effects, and the depth dependence of the nuclear magnetic field, there is ambiguity regarding the ideal NV depth for surface NMR of statistically polarized spins. In this work, we prepared a range of shallow NV ensemble layer depths and determined the ideal NV depth by performing NMR spectroscopy on statistically polarized 19F in Fomblin oil on the diamond surface. We found that the measurement time needed to achieve a signal-to-noise ratio of 3 using XY8-N noise spectroscopy has a minimum at an NV ensemble depth of 5.5 ± 1.5 nm for ensembles activated from 100 ppm nitrogen concentration. To demonstrate the sensing capabilities of NV ensembles, we perform NQR spectroscopy on the 11B of hexagonal boron nitride flakes. We compare our best diamond to previous work with a single NV and find that this ensemble provides a shorter measurement time with excitation diameters as small as 4 μm. This analysis provides ideal conditions for further experiments involving NMR/NQR spectroscopy of 2D materials with magnetic properties.
Aqueous aluminum (Al) batteries are posited to be a cheap and energy dense alternative to conventional Li-ion chemistries, but an aqueous electrolyte mediating trivalent aluminum cations (Al3+) warrants greater scrutiny. This study provides a rigorous examination of aqueous Al electrolytes, with the first compelling evidence for a dynamic octahedral solvation structure around the Al3+, without Al-OTf contact ion pairs, even at high concentrations. This solvation behavior and the concomitant, transient electrostatic hydrolysis of Al-OH2 ligands contrasts strongly with previously reported water-in-salt electrolytes, and occurs due to the high charge density of the Lewis acidic Al3+. Nuclear magnetic resonance spectroscopy and other physicochemical measurements quantitatively reveal how species activity evolves with concentration and temperature. This new understanding exposes practical concerns related to the corrosiveness of the acidic aqueous solutions, the degree of hydration of aluminum trifluoromethanesulfonate (Al(OTf)3) salt, and the grossly insufficient reductive stability of the proposed electrolytes (>1 V between HER onset and Al3+/Al). Collectively, these factors constitute multiple fundamental barriers to the feasibility of rechargeable aqueous Al batteries.
We demonstrate that stimulated microwave optical sideband generation using parametric frequency conversion can be utilized as a powerful technique for coherent state detection in atomic physics experiments. The technique has advantages over traditional absorption or polarization rotation-based measurements and enables the isolation of signal photons from probe photons. We outline a theoretical framework that accurately models sideband generation using a density matrix formalism. Using this technique, we demonstrate a novel intrinsic magnetic gradiometer that detects magnetic gradient fields between two spatially separated vapor cells by measuring the frequency of the beat note between sidebands generated within each cell. The sidebands are produced with high efficiency using parametric frequency conversion of a probe beam interacting with Rb87 atoms in a coherent superposition of magnetically sensitive hyperfine ground states. Interference between the sidebands generates a low-frequency beat note whose frequency is determined by the magnetic field gradient between the two vapor cells. In contrast to traditional gradiometers the intermediate step of measuring the magnetic field experienced by the two vapor cells is unnecessary. We show that this technique can be readily implemented in a practical device by demonstrating a compact magnetic gradiometer sensor head with a sensitivity of 25 fT/cm/Hz with a 4.4 cm baseline, while operating in a noisy laboratory environment unshielded from Earth's field.
All-solid-state batteries are often assumed to be safer than conventional Li-ion ones. In this work, we present the first thermodynamic models to quantitatively evaluate solid-state and Li-ion battery heat release under several failure scenarios. The solid-state battery analysis is carried out with an Li7La3Zr2O12 solid electrolyte but can be extended to other configurations using the accompanying spreadsheet. We consider solid-state batteries that include a relatively small amount of liquid electrolyte, which is often added at the cathode to reduce interfacial resistance. While the addition of small amounts of liquid electrolyte increases heat release under specific failure scenarios, it may be small enough that other considerations, such as manufacturability and performance, are more important commercially. We show that short-circuited all-solid-state batteries can reach temperatures significantly higher than conventional Li-ion, which could lead to fire through flammable packaging and/or nearby materials. Our work highlights the need for quantitative safety analyses of solid-state batteries.
Finite-temperature Kohn-Sham density functional theory (KS-DFT) is a widely-used method in warm dense matter (WDM) simulations and diagnostics. Unfortunately, full KS-DFT-molecular dynamics models scale unfavourably with temperature and there remains uncertainty regarding the performance of existing approximate exchange-correlation (XC) functionals under WDM conditions. Of particular concern is the expected explicit dependence of the XC functional on temperature, which is absent from most approximations. Average-atom (AA) models, which significantly reduce the computational cost of KS-DFT calculations, have therefore become an integral part of WDM modeling. In this paper, we present a derivation of a first-principles AA model from the fully-interacting many-body Hamiltonian, carefully analyzing the assumptions made and terms neglected in this reduction. We explore the impact of different choices within this model—such as boundary conditions and XC functionals—on common properties in WDM, for example equation-of-state data, ionization degree and the behavior of the frontier energy levels. Furthermore, drawing upon insights from ground-state KS-DFT, we discuss the likely sources of error in KS-AA models and possible strategies for mitigating such errors.
2,4,dimethyloxetane is an important cyclic ether intermediate that is produced from hydroperoxyalkyl (QOOH) radicals in the low-temperature combustion of n-pentane. However, the reaction mechanisms and rates of consumption pathways remain unclear. In the present work, the pressure- and temperature-dependent kinetics of seven cyclic ether peroxy radicals, which stem from 2,4,dimethyloxetane via H-abstraction and O2 addition, were determined. The automated kinetic workflow code, KinBot, was used to model the complexity of the chemistry in a stereochemically resolved manner and solve the resulting master equations from 300-1000 K and from 0.01-100 atm. The main conclusions from the calculations include (i) diastereomeric cyclic ether peroxy radicals show significantly different reactivities, (ii) the stereochemistry of the peroxy radical determines which QOOH isomerization steps are possible, (iii) conventional QOOH decomposition pathways, such as cyclic ether formation and HO2 elimination, compete with ring-opening reactions, which primarily produce OH radicals, the outcome of which is sensitive to stereochemistry. Ring-opening reactions lead to unique products, such as unsaturated, acyclic peroxy radicals, that form direct connections with species present in other chemical kinetics mechanisms through "cross-over" reactions that may complicate the interpretation of experimental results from combustion of n-pentane and, by extension, other alkanes. For example, one cross-over reaction involving 1-hydroperoxy-4-pentanone-2-yl produces 2-(hydroperoxymethyl)-3-butanone-1-yl, which is an iso-pentane-derived ketohydroperoxide (KHP). At atmospheric pressure, the rate of chemical reactions of all seven peroxy radicals compete with that of collisional stabilization, resulting in well-skipping reactions. However, at 100 atm, only one out of seven peroxy radicals undergoes significant well-skipping reactions. The rates produced from the master equation calculations provide the first foundation for the development of detailed sub-mechanisms for cyclic ether intermediates. In addition, analysis of the complex reaction mechanisms of 2,4-dimethyloxetane-derived peroxy radicals provides insights into the effects of stereoisomers on reaction pathways and product yields.
High-pressure storage and cyclic (de)pressurization of hydrogen gas is known to result in degradation and failure of gas canisters, hoses, linings, and O-rings as the relatively small hydrogen molecule can readily permeate most materials. Hence, identifying material compositions that are less susceptible to hydrogen-induced damage is of significant importance to the hydrogen energy infrastructure. Here, we use classical atomistic molecular dynamics simulations to study hydrogen exposed ethylene-propylene-diene monomer (EPDM) rubber, an elastomer typically used in O-rings. We make chemical modifications to the model by adjusting the crosslink density and report on gas solubility, diffusivity, and molecular restructuring in response to rapid decompression. Our simulations indicate that increases in crosslink density can reduce volumetric expansion during decompression and result in smaller free volume pore sizes. However, these favorable properties for sealing materials come with a tradeoff. At pressure, crosslinks introduce extra free volume, providing potential sites for gas localization, the precursor to cavitation-induced failure.
This guide is meant to assist communities – from residents to energy experts to decision makers – in developing a conceptual microgrid design that meets site-specific energy resilience goals. Using the framework described in this guidebook, stakeholders can come together and start to quantify site-specific vulnerabilities, identify the most significant risks to delivery of electricity, and establish electric outage tolerances across the community. In addition to establishing minimum service needs, this framework encourages communities to consider broader sustainability goals and policy constraints and begin to estimate up-front costs associated with the installation of alternative microgrid solutions. The framework guides a community through data collection and a high-level assessment of its needs, constraints, and priorities, prior to engaging engineers, vendors, and contractors. The first sections of this guidebook provide a high-level primer on electric systems. The latter sections include guidance for step-by-step data gathering and analysis of site conditions. The ultimate product resulting from the stepwise approach is a conceptual microgrid design. A conceptual design is defined as an initial design (10%-20% complete) that considers the specific threats, needs, limitations, and investment options for a given location. Going through this exercise and developing the conceptual microgrid design as a community ensures the same community members who will ultimately live with the solution are the developers of its foundational design. Often, these are also the very same people who understand system tolerances and needs the best and are therefore the ideal candidates for establishing these criteria. Especially when it comes to evaluating critical infrastructure, it is the community that best understands the most critical services. The framework is intended to facilitate a systematic approach to planning for resilience and provide a deeper understanding of how to use a framework to make decisions around microgrid solutions. Like many processes where tradeoffs need to be considered, this is often an iterative process. If this guide serves to help educate and empower communities who are beginning the process of deploying a microgrid, it has met the goal of its authors.
The goal of this project is to reconsider core market and reliability processes that can potentially yield to transformative advances in power grid security, reliability, and efficiency. Current electric power market designs are strongly a function of computing capabilities and limitations that were available in the mid-to-late 1990s, circa deregulation. This includes constructs such as: (1) a 2-tiered day-ahead/real-time market construct; and (2) linearized (“DC”) real power flow approximations in dispatch and pricing. At that time, state-of-the-art computational capabilities could at the limit address deterministic mixed-integer programming formulations of unit commitment (UC) and linear programming formulations of economic dispatch (ED) at limited fidelity and scale. Such constraints forced limited look-ahead time-horizons, crude approximations of AC power flow physics and operations, and artificial partitioning between day-ahead markets, hour(s)-ahead reliability processes, and real-time markets. Consequently, these limitations have resulted in limited security and reliability with increasing out-of-market payments, particularly as uncertainty associated with renewables and distributed energy resources grows.
Social systems are uniquely complex and difficult to study, but understanding them is vital to solving the world’s problems. The Ground Truth program developed a new way of testing the research methods that attempt to understand and leverage the Human Domain and its associated complexities. The program developed simulations of social systems as virtual world test beds. Not only were these simulations able to produce data on future states of the system under various circumstances and scenarios, but their causal ground truth was also explicitly known. Research teams studied these virtual worlds, facilitating deep validation of causal inference, prediction, and prescription methods. The Ground Truth program model provides a way to test and validate research methods to an extent previously impossible, and to study the intricacies and interactions of different components of research.
Recent high profile cyber attacks on critical infrastructures have raised awareness about the severe and widespread impacts that these attacks can have on everyday life. This awareness has spurred research into making industrial control systems and other cyber-physical systems more resilient. A plethora of cyber resilience metrics and frameworks have been proposed for cyber resilience assessments, but these approaches typically assume that data required to populate the metrics is readily available, an assumption that is frequently not valid. This paper describes a new cyber experimentation platform that can be used to generate relevant data and to calculate resilience metrics that quantify how resilient specified industrial control systems are to specified threats. Demonstration of the platform and analysis process are illustrated through a use case involving the control system for a pressurized water reactor.
Systems subjected to dynamic loads often require monitoring of their vibrational response, but limitations on the total number and placement of the measurement sensors can hinder the data-collection process. This paper presents an indirect approach to estimate a system's full-field dynamic response, including all uninstrumented locations, using response measurements from sensors sparsely located on the system. This approach relies on Bayesian inference that utilizes a system model to estimate the full-field response and quantify the uncertainty in these estimates. By casting the estimation problem in the frequency domain, this approach utilizes the modal frequency response functions as a natural, frequency-dependent weighting scheme for the system mode shapes to perform the expansion. This frequency-dependent weighting scheme enables an accurate expansion, even with highly correlated mode shapes that may arise from spatial aliasing due to the limited number of sensors, provided these correlated modes do not have natural frequencies that are closely spaced. Furthermore, the inherent regularization mechanism that arises in this Bayesian-based procedure enables the utilization of the full set of system mode shapes for the expansion, rather than any reduced subset. This approach can produce estimates when considering a single realization of the measured responses, and with some modification, it can also produce estimates for power spectral density matrices measured from many realizations of the responses from statistically stationary random processes. A simply supported beam provides an initial numerical validation, and a cylindrical test article excited by acoustic loads in a reverberation chamber provides experimental validation.
A major challenge in shape optimization is the coupling of finite element method (FEM) codes in a way that facilitates efficient computation of shape derivatives. This is particularly difficult with multiphysics problems involving legacy codes, where the costs of implementing and maintaining shape derivative capabilities are prohibitive. The volume and boundary methods are two approaches to computing shape derivatives. Each has a major drawback: the boundary method is less accurate, while the volume method is more invasive to the FEM code. We introduce the strip method, which computes shape derivatives on a strip adjacent to the boundary. The strip method makes code coupling simple. Like the boundary method, it queries the state and adjoint solutions at quadrature nodes, but requires no knowledge of the FEM code implementations. At the same time, it exhibits the higher accuracy of the volume method. As an added benefit, its computational complexity is comparable to that of the boundary method, that is, it is faster than the volume method. We illustrate the benefits of the strip method with numerical examples.
This report describes the proposed surface sampling techniques and plan for the multi-year Canister Deposition Field Demonstration (CDFD). The CDFD is primarily a dust deposition test that will use three commercial 32PTH2 NUHOMS welded stainless steel storage canisters in Advanced Horizontal Storage Modules, with planned exposure testing for up to 10 years at an operating ISFSI site. One canister will be left at ambient condition, unheated; the other two will have heaters to achieve canister surface temperatures that match, to the degree possible, spent nuclear fuel (SNF) loaded canisters with heat loads of 10 kW and 40 kW. Surface sampling campaigns for dust analysis will take place on a yearly or bi-yearly basis. The goal of the planned dust sampling and analysis is to determine important environmental parameters that impact the potential occurrence of stress corrosion cracking on SNF dry storage canisters. Specifically, measured dust deposition rates and deposited particle sizes will improve parameterization of dust deposition models employed to predict the potential occurrence and timing of stress corrosion cracks on the stainless steel SNF canisters. The size, morphology, and composition of the deposited dust and salt particles will be quantified, as well as the soluble salt load per unit area and the rate of deposition, as a function of canister surface temperature, location, time, and orientation. Previously, a preliminary sampling plan was developed, identifying possible sampling locations on the canister surfaces and sampling intervals; possible sampling methods were also described. Further development of the sampling plan has commenced through three different tasks. First, canister surface roughness, a potentially important parameter for air flow and dust deposition, was characterized at several locations on one of the test canisters. Second, corrosion testing to evaluate the potential lifetime and aging of thermocouple wires, spot welds, and attachments was initiated. Third, hand sampling protocols were developed, and initial testing was carried out. The results of those efforts are presented in this report. The information obtained from the CDFD will be critical for ongoing efforts to develop a detailed understanding of the potential for stress corrosion cracking of SNF dry storage canisters.
A computationally efficient radiative transport model is presented that predicts a camera measurement and accounts for the light reflected and blocked by an object in a scattering medium. The model is in good agreement with experimental data acquired at the Sandia National Laboratory Fog Chamber Facility (SNLFC). The model is applicable in computational imaging to detect, localize, and image objects hidden in scattering media. Here, a statistical approach was implemented to study object detection limits in fog.
Hattar, Khalid M.; Mcgieson, Isak; Bird, Victoriea L.; Barr, Christopher M.; Reed, Bryan W.; Mckeown, Joseph T.; Yi, Feng; Santala, M.K.
The crystallization of an amorphous Ag–In–Sb–Te (AIST) phase change material (PCM) is studied using multiple in situ imaging techniques to directly quantify crystal growth rates over a broad range of temperatures. The measurable growth rates span from ≈ 10–9 to ≈ 20 m/s. Recent results using dynamic transmission electron microscopy (TEM), a photoemission TEM technique, and TEM with sub-framed imaging are reported here and placed into the context of previous growth rate measurements on AIST. Dynamic TEM experiments show a maximum observed crystal growth rate for as-deposited films to be > 20 m/s. It is shown that crystal growth above the glass transition can be imaged in a TEM through use of subframing and a high-frame-rate direct electron detection camera. Challenges associated with the determination of temperature during in situ TEM experiments are described. Preliminary nanocalorimetry results demonstrate the feasibility of collecting thermodynamic data for crystallization of PCMs with simultaneous TEM imaging. Graphical abstract: [Figure not available: see fulltext.]
We employ ultrafast mid-infrared transient absorption spectroscopy to probe the rapid loss of carbonyl ligands from gas-phase nickel tetracarbonyl following ultraviolet photoexcitation at 261 nm. Here, nickel tetracarbonyl undergoes prompt dissociation to produce nickel tricarbonyl in a singlet excited state; this electronically excited tricarbonyl loses another CO group over tens of picoseconds. Our results also suggest the presence of a parallel, concerted dissociation mechanism to produce nickel dicarbonyl in a triplet excited state, which likely dissociates to nickel monocarbonyl. Mechanisms for the formation of these photoproducts in multiple electronic excited states are theoretically predicted with one-dimensional cuts through the potential energy surfaces and computation of spin-orbit coupling constants using equation of motion coupled cluster methods (EOM-CC) and coupled cluster theory with single and double excitations (CCSD). Bond dissociation energies are calculated with CCSD, and anharmonic frequencies of ground and excited state species are computed using density functional theory (DFT) and time-dependent density functional theory (TD-DFT).
Swelling clay hydration/dehydration is important to many environmental and industrial processes. Experimental studies usually probe equilibrium hydration states in an averaged manner and thus cannot capture the fast water transport and structural change in interlayers during hydration/dehydration. Using molecular simulations and thermogravimetric analyses, we observe a two-stage dehydration process. The first stage is controlled by evaporation at the edges: water molecules near hydrophobic sites and the first few water molecules of the hydration shell of cations move fast to particle edges for evaporation. The second stage is controlled by slow desorption of the last 1-2 water molecules from the cations and slow transport through the interlayers. The two-stage dehydration is strongly coupled with interlayer collapse and the coordination number changes of cations, all of which depend on layer charge distribution. This mechanistic interpretation of clay dehydration can be key to the coupled chemomechanical behavior in natural/engineered barriers.