This report describes a method for evaluating risk from delayed Production Sample Testing (PST), and strategies for reducing test backlogs. Risk contributions to overall risk from delayed PST include a Steady State Risk (SSR) contribution that is inherent in the qualified design and manufacturing processes, and a Non-Homogenous Production Risk (NHPR) contribution from production variation. Both definitions and evaluation techniques for these risk contributions are provided along with correlation to the standard Quality Assurance Process, QA001.2, risk ranking matrix. Methods for tracking test backlogs, and reducing backlogs are also discussed. The delayed PST evaluation method described here includes the merger of both slightly quantitative methods for tracking defect levels and the propensity for production variation with general risk management tracking tools like the likelihood vs. consequence risk ranking matrix. This merger is important because it allows more effective evaluation and communication with regards to delayed PST.
Sandia National Laboratories has long used the Munson-Dawson (M-D) model to predict the geomechanical behavior of salt caverns used to store oil at the Strategic Petroleum Reserve (SPR). Salt creep causes storage caverns to deform inward, thus losing volume. This loss of volume affects the salt above and around the caverns, puts stresses and strains on borehole casings, and creates surface subsidence which affects surface infrastructure. Therefore, accurate evaluation of salt creep behavior drives decisions about cavern operations. Parameters for the M-D model are typically fit against laboratory creep tests, but nearly all historic creep tests have been performed at equivalent stresses of 8 MPa or higher. Creep rates at lower equivalent stresses are very slow, such that tests take months or years to run, and the tests are sensitive to small temperature perturbations (<0.1°C). A recent collaboration between US and German researchers, recently characterized the creep behavior at low equivalent (deviatoric) stresses (<8 MPa) of salt from the Waste Isolation Pilot Plant. In addition, the M-D model was recently extended to include a low stress creep “mechanism”. This paper details new simulations of SPR caverns that use this extended M-D model, called the M-D Viscoplastic model. The results show that the inclusion of low stress creep significantly alters the prediction of steady-state cavern closure behavior and indicate that low stress creep is the dominant displacement mechanism at the dome scale. The implications for evaluating cavern and well integrity are demonstrated by investigating three phenomena: the extent of stress changes around the cavern; the predicted vertical strains applied to wellbore casings; and the evaluation of oscillating stress changes around the cavern due to oil sale cycles and their potential effect on salt fatigue.
Given an input stream S of size N, a φ-heavy hitter is an item that occurs at least φN times in S. The problem of finding heavy-hitters is extensively studied in the database literature.We study a real-time heavy-hitters variant in which an element must be reported shortly after we see its T = φN-th occurrence (and hence it becomes a heavy hitter). We call this the Timely Event Detection (TED) Problem. The TED problem models the needs of many real-world monitoring systems, which demand accurate (i.e., no false negatives) and timely reporting of all events from large, high-speed streams with a low reporting threshold (high sensitivity).Like the classic heavy-hitters problem, solving the TED problem without false-positives requires large space (ω (N) words). Thus in-RAM heavy-hitters algorithms typically sacrifice accuracy (i.e., allow false positives), sensitivity, or timeliness (i.e., use multiple passes).We show how to adapt heavy-hitters algorithms to external memory to solve the TED problem on large high-speed streams while guaranteeing accuracy, sensitivity, and timeliness. Our data structures are limited only by I/O-bandwidth (not latency) and support a tunable tradeoff between reporting delay and I/O overhead. With a small bounded reporting delay, our algorithms incur only a logarithmic I/O overhead.We implement and validate our data structures empirically using the Firehose streaming benchmark. Multi-threaded versions of our structures can scale to process 11M observations per second before becoming CPU bound. In comparison, a naive adaptation of the standard heavy-hitters algorithm to external memory would be limited by the storage device's random I/O throughput, i.e., ≈100K observations per second.
In 2021, functional area drills were held that focused on field sample collection and custody transfer at the sample control hotline for the Radiological Assistance Program (RAP) Consequence Management (CM) program. The overarching goal of these drills were to evaluate the current CM processes using the CBRNResponder mobile and web-based applications. There were several needs identified to improve CM processes and to stream/transfer data across multiple devices with and without internet: (1) A sample check-in process is needed to streamline current processes to reduce errors and create efficiencies, (2) the sample check-in application needs to be deployed as a mobile application and on the browser versions when on-line, and (3) the sample check-in process needs to function in an environment with internet connections and also in a standalone mode when internet is not available.
The essential data for interior and thermal evolution models of the Earth and super-Earths are the density and melting of mantle silicate under extreme conditions. Here, we report an unprecedently high melting temperature of MgSiO3 at 500 GPa by direct shockwave loading of pre-synthesized dense MgSiO3 (bridgmanite) using the Z Pulsed Power Facility. We also present the first high-precision density data of crystalline MgSiO3 to 422 GPa and 7200 K and of silicate melt to 1254 GPa. The experimental density measurements support our density functional theory based molecular dynamics calculations, providing benchmarks for theoretical calculations under extreme conditions. The excellent agreement between experiment and theory provides a reliable reference density profile for super-Earth mantles. Furthermore, the observed upper bound of melting temperature, 9430 K at 500 GPa, provides a critical constraint on the accretion energy required to melt the mantle and the prospect of driving a dynamo in massive rocky planets.
In-silico screening of novel biofuel molecules based on chemical and fuel properties is a critical first step in the biofuel evaluation process due to the significant volumes of samples required for experimental testing, the destructive nature of engine tests, and the costs associated with bench-scale synthesis of novel fuels. Predictive models are limited by training sets of few existing measurements, often containing similar classes of molecules that represent just a subset of the potential molecular fuel space. Software tools can be used to generate every possible molecular descriptor for use as input features, but most of these features are largely irrelevant and training models on datasets with higher dimensionality than size tends to yield poor predictive performance. Feature selection has been shown to improve machine learning models, but correlation-based feature selection fails to provide scientific insight into the underlying mechanisms that determine structure–property relationships. The implementation of causal discovery in feature selection could potentially inform the biofuel design process while also improving model prediction accuracy and robustness to new data. In this study, we investigate the benefits causal-based feature selection might have on both model performance and identification of key molecular substructures. We found that causal-based feature selection performed on par with alternative filtration methods, and that a structural causal model provides valuable scientific insights into the relationships between molecular substructures and fuel properties.
During the various stages of shock loading, many transient modes of deformation can activate and deactivate to affect the final state of a material. In order to fundamentally understand and optimize a shock response, researchers seek the ability to probe these modes in real-time and measure the microstructural evolutions with nanoscale resolution. Neither post-mortem analysis on recovered samples nor continuum-based methods during shock testing meet both requirements. High-speed diffraction offers a solution, but the interpretation of diffractograms suffers numerous debates and uncertainties. By atomistically simulating the shock, X-ray diffraction, and electron diffraction of three representative BCC and FCC metallic systems, we systematically isolated the characteristic fingerprints of salient deformation modes, such as dislocation slip (stacking faults), deformation twinning, and phase transformation as observed in experimental diffractograms. This study demonstrates how to use simulated diffractograms to connect the contributions from concurrent deformation modes to the evolutions of both 1D line profiles and 2D patterns for diffractograms from single crystals. Harnessing these fingerprints alongside information on local pressures and plasticity contributions facilitate the interpretation of shock experiments with cutting-edge resolution in both space and time.
Nutritional stability – a food system’s capacity to provide sufficient nutrients despite disturbance – is an important, yet challenging to measure outcome of diversified agriculture. Using 55 years of data across 184 countries, we assemble 22,000 bipartite crop-nutrient networks to quantify nutritional stability by simulating crop and nutrient loss in a country, and assess its relationship to crop diversity across regions, over time and between imports versus in country production. We find a positive, saturating relationship between crop diversity and nutritional stability across countries, but also show that over time nutritional stability remained stagnant or decreased in all regions except Asia. These results are attributable to diminishing returns on crop diversity, with recent gains in crop diversity among crops with fewer nutrients, or with nutrients already in a country’s food system. Finally, imports are positively associated with crop diversity and nutritional stability, indicating that many countries’ nutritional stability is market exposed.
Sandia provided technical assistance to Energy Cloud to support the assessment of Energy Cloud’s technology for air filtration of the SARS-CoV-2 virus. The Energy Cloud system was developed to filter the SARS-CoV-2 virus from commercial HVAC systems, ducts, and enclosed rooms without generating ozone. The project included a 3-week experimental series with aerosolized SARS-CoV-2 virus at the University of Southern California’s biosafety level 3 (BSL-3) laboratory. USC provided the facility as well as experimentally derived measurements and assessment of the Energy Cloud system efficacy for SARSCoV-2 filtration from air handling systems.
The freely available "Stopping and Range of Ions in Matter"(SRIM) code is used for evaluating ion beam ranges and depth profiles. We present secondary ion mass spectrometry and Rutherford backscattering experimental results of Si samples implanted with low energy Sb ions to evaluate the accuracy of SRIM simulations. We show that the SRIM simulation systematically overestimates the range by 2-6 nm and this overestimation increases for larger ion implantation energy. For the lowest energy implantation investigated, here we find up to a 25% error between the SRIM simulation and the measured range. The ion straggle shows excellent agreement between simulation and experimental results.
Soft-elasticity in monodomain liquid crystal elastomers (LCEs) is promising for impact-absorbing applications where strain energy is ideally absorbed at constant stress. Conventionally, compressive and impact studies on LCEs have not been performed given the notorious difficulty synthesizing sufficiently large monodomain devices. Here, we use direct-ink writing 3D printing to fabricate bulk (>cm3) monodomain LCE devices and study their compressive soft-elasticity over 8 decades of strain rate. At quasi-static rates, the monodomain soft-elastic LCE dissipated 45% of strain energy while comparator materials dissipated less than 20%. At strain rates up to 3000 s−1, our soft-elastic monodomain LCE consistently performed closest to an ideal-impact absorber. Drop testing reveals soft-elasticity as a likely mechanism for effectively reducing the severity of impacts – with soft elastic LCEs offering a Gadd Severity Index 40% lower than a comparable isotropic elastomer. Lastly, we demonstrate tailoring deformation and buckling behavior in monodomain LCEs via the printed director orientation.
We’re happy to report that the full-aperture upgrade project, started in FY18, is now complete and short-pulse target experiments are underway. The table below lists the present performance level of ZPW. Additional laser improvements are in progress to increase the laser energy and pulse contrast along with implementing a correction for achromatic aberrations to reduce the focused spot size and pulse width.
We report on experimental measurements of how an externally imposed magnetic field affects plasma heating by kJ-class, nanosecond laser pulses. The experiments reported here took place in gas cells analogous to magnetized liner inertial fusion targets. We observed significant changes in laser propagation and energy deposition scale lengths when a 12T external magnetic field was imposed in the gas cell. We find evidence that the axial magnetic field reduces radial electron thermal transport, narrows the width of the heated plasma, and increases the axial plasma length. Reduced thermal conductivity increases radial thermal gradients. This enhances radial hydrodynamic expansion and subsequent thermal self-focusing. Our experiments and supporting 3D simulations in helium demonstrate that magnetization leads to higher thermal gradients, higher peak temperatures, more rapid blast wave development, and beam focusing with an applied field of 12T.
LaFleur, Chris B.; Taylor, Gabriel; Putorti Jr., Anthony D.; Salley, Mark H.
This report documents an experimental program designed to investigate High Energy Arcing Fault (HEAF) phenomena for low-voltage metal enclosed switchgear containing aluminum conductors. This report covers full-scale laboratory experiments using representative nuclear power plant (NPP) three-phase electrical equipment. Electrical, thermal, and pressure data were recorded for each experiment and documented in this report. This report covers experiments performed on two low-voltage switchgear units with each unit consisting of two vertical sections. The data collected supports characterization of the low-voltage HEAF hazard and these results will be used to support potential improvements in fire probabilistic risk assessment (PRA) methods. The experiments were performed at KEMA Labs located in Chalfont, Pennsylvania. The experimental design, setup, and execution were completed by staff from the NRC, the National Institute of Standards and Technology (NIST), Sandia National Laboratories (SNL) and KEMA. In addition, representatives from the Electric Power Research Institute (EPRI) observed some of the experimental setup and execution. The HEAF experiments were performed between August 26 and Augsut 29, 2019 on nearidentical Westinghouse Type DS low-voltage metal-enclosed indoor switchgear. The threephase arcing fault was initiated on the aluminum main bus or in select cases on the copper bus stabs near the breaker. These experiments used either nominal 480 volts AC or 600 volts AC. Durations of the experiments ranged from approximately 0.4 s to 8.3 s with fault currents ranging from approximately 9.2 kA to 19.3 kA. Real-time electrical operating conditions, including voltage, current and frequency, were measured during the experiments. Heat fluxes and incident energies were measured with plate thermometers, radiometers, and slug calorimeters at various locations around the electrical enclosures. Environmental measurements of breakdown, conductivity and electromagnetics were also taken. The experiments were documented with normal and high-speed videography, infrared imaging and photography. The results, while limited, indicated the difficulty in maintaining and sustaining low-voltage arcs on aluminum components of sufficient duration and at a single point as observed operating experience.
Combining plasmonic and magnetic properties, namely magneto-plasmonic coupling, inspires great research interest and the search for magneto-plasmonic nanostructure becomes considerably critical. Here we designed a nanopillar-in-matrix structure with core–shell alloyed nanopillars for both BaTiO3 (BTO)-Au0.5Co0.5 (AuCo) and BTO-Au0.25Cu0.25Co0.25Ni0.25 (AuCuCoNi) hybrid systems, i.e., ferromagnetic alloy cores (e.g., Co or CoNi) with plasmonic shells (e.g., Au or Au/Cu). These core–shell alloy nanopillars are uniformly embedded into a dielectric BTO matrix to form a vertically aligned nanocomposite (VAN) structure. Both hybrid systems present excellent epitaxial quality and interesting multi-functionality, e.g., high magnetic anisotropy, magneto-optical coupling response, tailorable plasmonic resonance wavelength, tunable hyperbolic properties and strong optical anisotropy. These alloyed nanopillars-in-matrix designs provide enormous potential for complex hybrid material designs with multi-functionality and demonstrate strong interface enabled magneto-plasmonic coupling along with plasmonic and magnetic performance.
Journal of Water Resources Planning and Management
Haxton, Terranna; Klise, Katherine A.; Laky, Daniel; Murray, Regan; Laird, Carl D.; Burkhardt, Jonathan B.
Drinking water systems commonly use manual or grab sampling to monitor water quality, identify or confirm issues, and verify that corrective or emergency response actions have been effective. In this paper, the effectiveness of regulatory sampling locations for emergency response is explored. An optimization formulation based on the literature was used to identify manual sampling locations to maximize overall nodal coverage of the system. Results showed that sampling locations could be effective in confirming incidents for which they were not designed. When evaluating sampling locations optimized for emergency response against regulatory scenarios, the average performance was reduced by 3%-4%, while using optimized regulatory sampling locations for emergency response reduced performance by 7%-10%. Secondary constraints were also included in the formulation to ensure geographical and water age diversity with minimal impact on the performance. This work highlighted that regulatory sampling locations provide value in responding to an emergency for these networks.
Additively manufactured lattice truss structures, often referred to as architected cellular materials, present significant advantages over conventional structures due to their unique characteristics such as high strength-to-weight ratios and surface area-to-volume ratios. These geometrically complex structures, however, come with concomitant challenges for qualification and inspection. In this study, compression testing interrupted with micro-computed tomography inspection was conducted to monitor the evolution of global and local deformation throughout the loading process of 304 L stainless steel octet truss lattice structures. Both two- and three-dimensional image analysis techniques were leveraged to characterize geometric heterogeneities resulting from the laser powder bed fusion manufacturing process as well as track the structure throughout deformation. Variations from model-predicted behavior resulting from these heterogeneities are considered relative to the predicted and actual responses of the structures during compression to better understand, model, and predict the octet truss lattice structure compression response.
The CSISAR tool is GUI based and very simple to use. The algorithms are robust, and the unique processing flows, that the user is stepped through, virtually eliminate the possibility of error in GEOINT production. An integrated data manager is a key part of the CSISAR system. This data manager keeps track of the data available to a user and informs the user of what data is available and what can be done with that data. This keeps the user from having to be trained in the nuances of the algorithms. CSISAR also has an integrated product manager, which helps the user identify, view and manage previously made products. CSISAR was originally developed in 2010-2011 as a Windows based system. It was updated in 2015 to be a Linux based system. This SAND report is intended to make the Product Description and User’s Guide for CSISAR (originally included within the software) more widely available. New is a brief addition of Linux-specific installation details.
The earth texture with complex morphological geometry and compositions such as shale and carbonate rocks, is typically characterized with sparse field samples because of an expensive and time-consuming characterization process. Accordingly, generating arbitrary large size of the geological texture with similar topological structures at a low computation cost has become one of the key tasks for realistic geomaterial reconstruction and subsequent hydro-mechanical evaluation for science and engineering applications. Recently, generative adversarial neural networks (GANs) have demonstrated a potential of synthesizing input textural images and creating equiprobable geomaterial images for stochastic analysis of hydrogeological properties, for example, the feasibility of CO2 storage sites and exploration of unconventional resources. However, the texture synthesis with the GANs framework is often limited by the computational cost and scalability of the output texture size. In this study, we proposed a spatially assembled GANs (SAGANs) that can generate output images of an arbitrary large size regardless of the size of training images with computational efficiency. The performance of the SAGANs was evaluated with two and three dimensional (2D and 3D) rock image samples widely used in geostatistical reconstruction of the earth texture and Lattice-Boltzmann (LB) simulations were performed to compare pore-scale flow patterns and upscaled permeabilities of training and generated geomaterial images. We demonstrate SAGANs can generate the arbitrary large size of statistical realizations with connectivity and structural properties and flow characteristics similar to training images, and also can generate a variety of realizations even on a single training image. In addition, the computational time was significantly improved compared to standard GANs frameworks.
Understanding the influence of environmental factors on soil organic carbon (SOC) is critical for quantifying and reducing the uncertainty in carbon climate feedback projections under changing environmental conditions. We explored the effect of climatic variables, land cover types, topographic attributes, soil types and bedrock geology on SOC stocks of top 1 m depth across conterminous United States (US) ecoregions. Using 4559 soil profile observations and high-resolution data of environmental factors, we identified dominant environmental controllers of SOC stocks in 21 US ecoregions using geographically weighted regression. We used projected climatic data of SSP126 and SSP585 scenarios from GFDL-ESM 4 Earth System Model of Coupled Model Intercomparison Project phase 6 to predict SOC stock changes across continental US between 2030 and 2100. Both baseline and predicted changes in SOC stocks were compared with SOC stocks represented in GFDL-ESM4 projections. Among 56 environmental predictors, we found 12 as dominant controllers across all ecoregions. The adjusted geospatial model with the 12 environmental controllers showed an R2 of 0.48 in testing dataset. Higher precipitation and lower temperatures were associated with higher levels of SOC stocks in majority of ecoregions. Changes in land cover types (vegetation properties) was important in drier ecosystem as North American deserts, whereas soil types and topography were more important in American prairies. Wetlands of the Everglades was highly sensitive to projected temperature changes. The SOC stocks did not change under SSP126 until 2100, however SOC stocks decreased up to 21% under SSP585. Our results, based on environmental controllers of SOC stocks, help to predict impacts of changing environmental conditions on SOC stocks more reliably and may reduce uncertainties found in both, geospatial and Earth System Models. In addition, the description of different environmental controllers for US ecoregions can help to describe the scope and importance of global and local models.
This report represents an assessment of the gaps in Mechanistic Source Term (MST) and consequence assessment modeling for Molten Salt Reactors (MSRs). The current capabilities for MELCOR and the MELCOR Accident Code System (MACCS) are discussed, along with updates needed in order to address specific needs for MSRs. A test plan developed by Argonne National Laboratories is discussed as addressing some of these gaps, while some will require additional attention. Further recommendations are made on addressing these gaps. This report satisfies the DOE NE Milestone M2RD-21SN0601061 to leverage MELCOR and MACCS to identify parameters of importance for source term assessments for salt spill experiments.
High-quality factor resonant cavities are challenging structures to model in electromagnetics owing to their large sensitivity to minute parameter changes. Therefore, uncertainty quantification (UQ) strategies are pivotal to understanding key parameters affecting the cavity response. We discuss here some of these strategies focusing on shielding effectiveness (SE) properties of a canonical slotted cylindrical cavity that will be used to develop credibility evidence in support of predictions made using computational simulations for this application.
Point defects in SiC are an attractive platform for quantum information and sensing applications because they provide relatively long spin coherence times, optical spin initialization, and spin-dependent fluorescence readout in a fabrication-friendly semiconductor. The ability to precisely place these defects at the optimal location in a host material with nano-scale accuracy is desirable for integration of these quantum systems with traditional electronic and photonic structures. Here, we demonstrate the precise spatial patterning of arrays of silicon vacancy (VSi) emitters in an epitaxial 4H-SiC (0001) layer through mask-less focused ion beam implantation of Li+. We characterize these arrays with high-resolution scanning confocal fluorescence microscopy on the Si-face, observing sharp emission lines primarily coming from the V1 ′ zero-phonon line (ZPL). The implantation dose is varied over 3 orders of magnitude, leading to VSi densities from a few per implantation spot to thousands per spot, with a linear dependence between ZPL emission and implantation dose. Optically-detected magnetic resonance (ODMR) is also performed, confirming the presence of V2 VSi. Our investigation reveals scalable and reproducible defect generation.
Silicon-based layered nanocomposites, comprised of covalent-metal interfaces, have demonstrated elevated resistance to radiation. The amorphization of the crystalline silicon sublayer during irradiation and/or heating can provide an additional mechanism for accommodating irradiation-induced defects. In this study, we investigated the mechanical strength of irradiated Si-based nanocomposites using atomistic modeling. We first examined dose effects on the defect evolution mechanisms near silicon-gold crystalline and amorphous interfaces. Our simulations reveal the growth of an emergent amorphous interfacial layer with increasing dose, a dominant factor mitigating radiation damage. We then examined the effect of radiation on the mechanical strength of silicon-gold multilayers by constructing yield surfaces. These results demonstrate a rapid onset strength loss with dose. Nearly identical behavior is observed in bulk gold, a phenomenon that can be rooted to the formation of radiation-induced stacking fault tetrahedra which dominate the dislocation emission mechanism during mechanical loading. Taken together, these results advance our understanding of the interaction between radiation-induced point defects and metal-covalent interfaces.
A 3 foot x 3 foot x 3 foot aluminum solar collector was manufactured using computer numerical control. The interior of the device included six triangular dimpled fins for enhanced heat transfer. The interior vertical wall on the south side was also dimpled. The solar collector working fluid was based on water, and the collector consisted solely of passive heat transfer mechanisms (no moving parts), making it ideal for off-the-grid and rural applications. Two types of heat transfer experiments were conducted. One experiment had external flat heaters attached on the top and the front side, while the other four sides were insulated. Except for the bottom surface, the second experiment had all its exterior surfaces sprayed with black solar paint to collect as much solar heat as possible. Temperature data as a function of time was collected using 14 thermocouples spread strategically throughout the solar collector. In addition, computational fluid dynamics (CFD) simulations were conducted using the dynamic Smagorinsky large eddy simulation turbulence model. The first simulation considered that both the top and front surfaces were exposed to a fixed temperature of 313.7 K (105 °F), while the remaining four surfaces were insulated. For the second simulation, all conditions were the same, except that the temperature for both heated surfaces was raised to 350 K (170.3 °F). The two temperatures are expected to bound the solar collector operational temperature during the late- Spring, Summer, and early-Fall months. The solar collector design, experimental data, CFD output, and a discussion of five manufacturing approaches and costs are documented in this report.
Organophosphorus hydrolase (OPH) is a metalloenzyme that can hydrolyze organophosphorus agents resulting in products that are generally of reduced toxicity. The best OPH substrate found to date is diethyl p-nitrophenyl phosphate (paraoxon). Most structural and kinetic studies assume that the binding orientation of paraoxon is identical to that of diethyl 4-methylbenzylphosphonate, which is the only substrate analog co-crystallized with OPH. In the current work, we used a combined docking and molecular dynamics (MD) approach to predict the likely binding mode of paraoxon. Then, we used the predicted binding mode to run MD simulations on the wild type (WT) OPH complexed with paraoxon, and OPH mutants complexed with paraoxon. Additionally, we identified three hot-spot residues (D253, H254, and I255) involved in the stability of the OPH active site. We then experimentally assayed single and double mutants involving these residues for paraoxon binding affinity. The binding free energy calculations and the experimental kinetics of the reactions between each OPH mutant and paraoxon show that mutated forms D253E, D253E-H254R, and D253E-I255G exhibit enhanced substrate binding affinity over WT OPH. Interestingly, our experimental results show that the substrate binding affinity of the double mutant D253E-H254R increased by 19-fold compared to WT OPH.
This report discusses the progress on the collaboration between Sandia National Laboratories (Sandia) and Japan Atomic Energy Agency (JAEA) on the sodium fire research in fiscal year (FY) 2021 and is a continuation of the FY2020 progress report. In this report, we only report the changes made to the current sodium pool fire model in MELCOR. We modified and corrected many control functions to enhance the current model from the FY2020 report. This year’s enhancements relate to better agreement of the suspended aerosol measurement from JAEA’s F7 series tests. Staff from Sandia and JAEA conducted the validation studies of the sodium pool fire model in MELCOR. To validate this pool fire model with the latest enhancement, JAEA sodium pool fire experiments (F7-1 and F7-2) were used. The results of the calculation, including the code-to-code comparisons are discussed as well as suggestions for further model improvement. Finally, recommendations are made for new MELCOR simulations for FY2022.
There are urgent calls to action by the NAE, the Nobel Prize Summit, the UN, and global scientists to address and solve, in this decade (2020 – 2030), crucial and widely recognized global challenges to peace and security before they become more complex and more environmentally, financially, and socially costly; before we reach the point of no return.
D'Elia, Marta D.; Elia, Xingjie'; Seleson, Pablo D.; Tian, Xiaochuan; Yu, Yue
Local-to-nonlocal (LtN) coupling refers to a class of methods aimed at combining nonlocal and local modeling descriptions of a given system into a unified coupled representation. This allows to consolidate the accuracy of nonlocal models with the computational expediency of their local counterparts, while often simultaneously removing nonlocal modeling issues such as surface effects. The number and variety of proposed LtN coupling approaches have significantly grown in recent years, yet the field of LtN coupling continues to grow and still has open challenges. This review provides an overview of the state of the art of LtN coupling in the context of nonlocal diffusion and nonlocal mechanics, specifically peridynamics. Furthermore, we present a classification of LtN coupling methods and discuss common features and challenges. The goal of this review is not to provide a preferred way to address LtN coupling but to present a broad perspective of the field, which would serve as guidance for practitioners in the selection of appropriate LtN coupling methods based on the characteristics and needs of the problem under consideration.
This report represents completion of milestone deliverable M2SF-22SN010309082 Annual Status Update for OWL, which is due on November 30, 2021 as part of the fiscal year 2022 (FY2022) work package SF-22SN01030908. This report provides an annual update on status of FY2021 activities for the work package “OWL - Inventory – SNL”. The Online Waste Library (OWL) has been designed to contain information regarding United States (U.S.) Department of Energy (DOE)-managed (as) high-level waste (DHLW), DOE-managed spent nuclear fuel (DSNF), and other wastes that are likely candidates for deep geologic disposal. Links to the current supporting documents for the data are provided when possible; however, no classified or official-use-only (OUO) data are planned to be included in OWL. There may be up to several hundred different DOE-managed wastes that are likely to require deep geologic disposal. This report contains new information on sodium-bonded spent fuel waste types and wastes forms, which are included in the next release of OWL, Version 3.0, on the Sandia National Laboratories (SNL) External Collaboration Network (ECN). The report also provides an update on the effort to include information regarding the types of vessels capable of disposing of DOE-managed waste.
We report low-temperature magneto-transport measurements of an undoped Si/SiGe asymmetric double quantum well heterostructure. The density in both layers is tuned independently utilizing top and bottom gates, allowing the investigation of quantum wells at both imbalanced and matched densities. Integer quantum Hall states at total filling factor ν T = 1 and ν T = 2 are observed in both density regimes, and the evolution of their excitation gaps is reported as a function of the density. The ν T = 1 gap evolution departs from the behavior generally observed for valley splitting in the single layer regime. Furthermore, by comparing the ν T = 2 gap to the single particle tunneling energy, Δ SAS, obtained from Schrödinger-Poisson (SP) simulations, evidence for the onset of spontaneous interlayer coherence is observed for a relative filling fraction imbalance smaller than ∼ 50 %.
In “Interfacial structures and acidity constants (pKa) of goethite from first principles molecular dynamics simulations,” authors Y. Zhang, X. Lui, J. Cheng, and X. Lu apply First Principles molecular dynamics (FPMD, also called Density Functional Theory MD, DFT/MD, or ab initio MD, AIMD), to evaluable the complete set of acidity constants (pKa) of the hydroxyl groups on the most prominent goethite crystal facets. The pKa of these OH and OH$^+_2$ groups are compared with available data from the multisite complexation (MUSIC) model traditionally used to estimate pKa on mineral surfaces. The authors have presented eloquent rationale for the importance and implications of understanding goethite acidity constants in room temperature geochemistry settings. In this paper, I focus on the computational aspects, the strengths of FPMD, and its possibilities.
Predicting the diffusion coefficient of fluids under nanoconfinement is important for many applications including the extraction of shale gas from kerogen and product turnover in porous catalysts. Due to the large number of important variables, including pore shape and size, fluid temperature and density, and the fluid-wall interaction strength, simulating diffusion coefficients using molecular dynamics (MD) in a systematic study could prove to be prohibitively expensive. Here, we use machine learning models trained on a subset of MD data to predict the self-diffusion coefficients of Lennard-Jones fluids in pores. Our MD data set contains 2280 simulations of ideal slit pore, cylindrical pore, and hexagonal pore geometries. We use the forward feature selection method to determine the most useful features (i.e., descriptors) for developing an artificial neutral network (ANN) model with an emphasis on easily acquired features. Our model shows good predictive ability with a coefficient of determination (i.e., R2) of ∼0.99 and a mean squared error of ∼2.9 × 10-5. Finally, we propose an alteration to our feature set that will allow the ANN model to be applied to nonideal pore geometries.
Metal hydrides can store hydrogen isotopes with high volumetric density. In metal tritides, tritium beta decay can result in accumulation of helium within the solid, in some cases exceeding 10 at.% helium after only 4 years of aging. Helium is insoluble in most materials, but often does not readily escape, and instead coalesces to form nanoscale bubbles when helium concentrations are near 1 at.%. Blistering or spallation often occurs at higher concentrations. Radioactive particles shed during this process present a potential safety hazard. This study investigates the effects of high helium concentrations on erbium deuteride (ErD2), a non-radioactive surrogate material for erbium tritide (ErT2). To simulate tritium decay in the surrogate, high doses of 120 keV helium ions were implanted into ErD2 films at room temperature. Scanning and transmission electron microscopy indicated spherical helium bubble formation at a critical concentration of 1.5 at.% and bubble linkage leading to nanoscale crack formation at a concentration of 7.5 at.%. Additionally, crack propagation occurred through the nanocrack region, resulting in spallation extending from the implantation peak to the surface. Electron energy loss spectroscopy was utilized to confirm the presence of high-pressure helium in the nanocracks, suggesting that helium gas plays a predominant role in deformation. This work improves the overall understanding of helium behavior in ErD2 by using modern characterization techniques to determine: the critical helium concentration required for bubble formation, the material failure mechanism at high concentration, and the nanoscale mechanisms responsible for material failure in helium implanted ErD2.
The Dynamic Networks Experiment 2018 (DNE18) was a collaborative effort between Los Alamos National Laboratory (LANL), Sandia National Laboratories (SNL), Lawrence Livermore National Laboratory (LLNL) and Pacific Northwest National Laboratory (PNNL) designed to evaluate methodologies for multi-modal data ingestion and processing. One component of this virtual experiment was a quantitative assessment of current capabilities for infrasound data processing, beginning with the establishment of a baseline for infrasound signal detection. To produce such baselines, SNL and LANL exploited a common dataset of infrasound data recorded across a regional network in Utah from December 2010 through February 2011. We utilize two automated signal detectors, the Adaptive F-Detector (AFD) and the Multivariate Adaptive Learning Detector (MALD) to produce automated signal detection catalogs and an analyst-produced catalog. Comparisons indicate that automatic detectors may be able to identify small amplitude, low SNR events that cannot be identified by analyst review. We document detector performance in terms of precision and recall, demonstrating that the AFD is more precise, but the MALD has higher recall. We use a synthetic dataset of signals embedded in pink noise in order to highlight shortcomings in assessing detection algorithms for low signal to noise ratio signals which are commonly of interest to the nuclear monitoring community. For comparisons utilizing the synthetic dataset, the AFD has higher recall while precision is equal for both detectors. These results indicate that both detectors perform well across a variety of background noise environments; however, both detectors fail to identify repetitive, short duration signals arriving from similar backazimuths. These failures represent specific scenarios that could be targeted for further detector development.
The objective of this report is to accept or reject the hypothesis that the experiments conducted under TP 08-02 Revision 0 (Ismail et al., 2008) were affected by CO2(g) intrusion and sample contamination. The test of the hypothesis is accomplished by comparing the experimental data collected under the protocols of TP 08-02 Revision O and TP 20-01 Revision O (Kirkes and Zhang, 2020). The protocols of TP 20-01 Revision 0 minimize the possibilities of CO2(g) intrusion and sample contamination. The experimental data sets obtained under both TPs will be assessed statistically to see if they are identical or not.
There is an intensive effort to control the nature of attractive interactions between ultrathin semiconductors and metals and to understand its impact on the electronic properties at the junction. Here, we present a photoelectron spectroscopy study on the interface between WS2 films and gold, with a focus on the occupied electronic states near the Brillouin zone center (i.e., the point). To delineate the spectra of WS2 supported on crystalline Au from the suspended WS2, we employ a microscopy approach and a tailored sample structure, in which the WS2/Au junction forms a semi-epitaxial relationship and is adjacent to suspended WS2 regions. The photoelectron spectra, as a function of WS2 thickness, display the expected splitting of the highest occupied states at the point. In multilayer WS2, we discovered variations in the electronic states that spatially align with the crystalline grains of underlying Au. Corroborated by density functional theory calculations, we attribute the electronic structure variations to stacking variations within the WS2 films. We propose that strong interactions exerted by Au grains cause slippage of the interfacing WS2 layer with respect to the rest of the WS2 film. Our findings illustrate that the electronic properties of transition metal dichalcogenides, and more generally 2D layered materials, are physically altered by the interactions with the interfacing materials, in addition to the electron screening and defects that have been widely considered.
Compared to the classical Lanczos algorithm, the s ‐step Lanczos variant has the potential to improve performance by asymptotically decreasing the synchronization cost per iteration. However, this comes at a price; despite being mathematically equivalent, the s ‐step variant may behave quite differently in finite precision, potentially exhibiting greater loss of accuracy and slower convergence relative to the classical algorithm. It has previously been shown that the errors in the s ‐step version follow the same structure as the errors in the classical algorithm, but are amplified by a factor depending on the square of the condition number of the ‐dimensional Krylov bases computed in each outer loop. As the condition number of these s ‐step bases grows (in some cases very quickly) with s , this limits the s values that can be chosen and thus can limit the attainable performance. In this work, we show that if a select few computations in s ‐step Lanczos are performed in double the working precision, the error terms then depend only linearly on the conditioning of the s ‐step bases. This has the potential for drastically improving the numerical behavior of the algorithm with little impact on per‐iteration performance. Our numerical experiments demonstrate the improved numerical behavior possible with the mixed precision approach, and also show that this improved behavior extends to mixed precision s ‐step CG. We present preliminary performance results on NVIDIA V100 GPUs that show that the overhead of extra precision is minimal if one uses precisions implemented in hardware.
This report analyzes experimental data from Test Plans TP 08-02, TP 12-02, and TP 20-01 to add new log K values and Pitzer interaction parameters for Fe, Pb, Mg, Nd and B reactions to the WIPP geochemical thermodynamic database, data0.fm 1.
Characteristic modes on infinite periodic structures are studied using spectral dyadic Green’s functions. This formulation demonstrates that, in contrast to the modal analysis of finite structures, the number of radiating characteristic modes is limited by unit cell size and incident wave vector (i.e., scan angle or phase shift per unit cell). Here, the reflection tensor is decomposed into modal contributions from radiating modes, indicating that characteristic modes are a predictably sparse basis in which to study reflection phenomena.
The interplay of stress, disorder, and Coulomb screening dictating the mobility of doped cadmium oxide (CdO) is examined using Raman spectroscopy to identify the mechanisms driving dopant incorporation and scattering within this emerging infrared optical material. Specifically, multi-wavelength Raman and UV-vis spectroscopies are combined with electrical Hall measurements on a series of yttrium (X = Y) and indium (X = In) doped X:CdO thin-films. Hall measurements confirm n-type doping and establish carrier concentrations and mobilities. Spectral fitting along the low-frequency Raman combination bands, especially the TA+TO(X) mode, reveals that the evolution of strain and disorder within the lattice as a function of dopant concentration is strongly correlated with mobility. Coupling between the electronic and lattice environments was examined through analysis of first- and second-order longitudinal-optical phonon-plasmon coupled modes that monotonically decrease in energy and asymmetrically broaden with increasing dopant concentration. By fitting these trends to an impurity-induced Fröhlich model for the Raman scattering intensity, exciton-phonon and exciton-impurity coupling factors are quantified. These coupling factors indicate a continual decrease in the amount of ionized impurity scattering with increasing dopant concentration and are not as well correlated with mobility. This shows that lattice strain and disorder are the primary determining factors for mobility in donor-doped CdO. In aggregate, the study confirms previously postulated defect equilibrium arguments for dopant incorporation in CdO while at the same time identifying paths for its further refinement.
High‐rate deformation processes of metals entail intense grain refinement and special attention needs to be paid to capture the evolution of microstructure. In this article, a new formulation for coupling Cosserat crystal plasticity and phase field is developed. A common approach is to penalize kinematic incompatibility between lattice orientation and displacement‐based elastic rotation. However, this can lead to significant solution sensitivity to the penalty parameter, resulting in low accuracy and convergence rates. To address these issues, a duality‐based formulation is developed which directly imposes the rotational kinematic compatibility. A weak inf‐sup‐based skew‐symmetric stress projection is introduced to suppress instabilities present in the dual formulation. An additional least squares stabilization is introduced to suppress the spurious lattice rotation with a suitable parameter range derived analytically and validated numerically. The required high‐order continuity is attained by the reproducing kernel approximation. It is observed that equal order displacement‐rotation‐phase field approximations are stable, which allows efficient employment of the same set of shape functions for all independent variables. The proposed formulation is shown to yield superior accuracy and convergence with marginal parameter sensitivity compared to the penalty‐based approach and successfully captures the dominant rotational recrystallization mechanism including block dislocation structures and grain boundary migration.
Dewers, Thomas D.; Reda-Taha, Mahmoud; Stormont, John; Pyrak-Nolte, Laura; Ahmadian, Mohsen; Chapman, David
Borehole cement is used across the range of energy technologies to stabilize casing, to serve as a barrier to behind-casing fluid movement. Cement debonding and other flaws, both at cement interfaces and within the cement itself, can create leakage pathways that can threaten safety to personnel, and wellbore performance, with economic and regulatory consequences. A primary method to assess cement health and wellbore integrity is via acoustic methods. This project was designed with three aims: demonstrate a significant improvement in the interpretation of cement acoustic behavior, both during curing, and in interpreting effects of flaws and evolving interfaces; develop sensor technologies to improve signal-noise ratios and cement acoustic responses; and lastly, provide a borehole demonstration of at least one of these technologies. We have accomplished the first two objectives, and the third, delayed by pandemic health concerns, is proceeding as of this writing via a technology partner with the University of Texas Advanced Energy Consortium.
This project is intended to support the development of new traction drive systems that meet the targets of 100 kW/L for power electronics and 50 kW/L for electric machines with reliable operation to 300,000 miles. To meet these goals, new designs must be identified that make use of state-of-the-art and next-generation electronic materials and design methods. Designs must exploit synergies between components, for example converters designed for high-frequency switching using wide band gap devices and ceramic capacitors. This project includes: (1) a survey of available technologies; (2) the development of design tools that consider the converter volume and performance; (3) exercising the design software to evaluate performance gaps and predict the impact of certain technologies and design approaches, i.e. GaN semiconductors, ceramic capacitors, and select topologies; and (4) building and testing hardware prototypes to validate models and concepts. Early instantiations of the design tools enable co-optimization of the power module and passive elements and provide some design guidance; later instantiations will enable the co-optimization of inverter and machine. Prototype testing begins with evaluation of simpler conversion topologies (i.e. the half-bridge boost converter) and progresses with fabrication of prototype inverter drives.
Elevated temperature and pressure in the earth's subsurface alters the permeability of salt formations, due to changing properties of the salt-brine interface. Molecular dynamics (MD) simulations are used to investigate the mechanisms of temperature and pressure dependence of liquid-solid interfacial tensions of NaCl, KCl, and NaCl-KCl brines in contact with (100) salt surfaces. Salt-brine dihedral angles vary between 55 and 76° across the temperature (300-450 K) and pressure range (0-150 MPa) evaluated. Temperature-dependent brine composition results in elevated dihedral angles of 65-80°, which falls above the reported salt percolation threshold of 60°. Mixed NaCl-KCl brine compositions increased this effect. Elevated temperatures excluded dissolved Na+ ions from the interface, causing the strong temperature dependence of the liquid-solid interfacial tension and the resulting dihedral angle. Therefore, at higher temperature, pressure, and brine concentrations Na-Cl systems may underpredict the dihedral angle. Higher dihedral angles in more realistic mixed brine systems maintain low permeability of salt formations due to changes in the structure and energetics of the salt-brine interface.
Novel materials based on the aluminum oxyhydroxide boehmite phase were prepared using a glycothermal reaction in 1,4-butanediol. Under the synthesis conditions, the atomic structure of the boehmite phase is altered by the glycol solvent in place of the interlayer hydroxyl groups, creating glycoboehmite. The structure of glycoboehmite was examined in detail to determine that glycol molecules are intercalated in a bilayer structure, which would suggest that there is twice the expansion identified previously in the literature. This precursor phase enables synthesis of two new phases that incorporate either polyvinylpyrrolidone or hydroxylpropyl cellulose nonionic polymers. These new materials exhibit changes in morphology, thermal properties, and surface chemistry. All the intercalated phases were investigated using PXRD, HRSTEM, SEM, FT-IR, TGA/DSC, zeta potential titrations, and specific surface area measurement. These intercalation polymers are non-ionic and interact through wetting interactions and hydrogen bonding, rather than by chemisorption or chelation with the aluminum ions in the structure.
The global energy system is undergoing significant changes, including a shift in energy generating technologies to more renewable energy sources. However, the dependence of renewable energy sources on local environmental conditions could also increase disruptions in service through exposures to compound, extreme weather events. By fusing three diverse datasets (operations and maintenance tickets, weather data, and production data), this analysis presents a novel methodology to identify and evaluate performance impacts arising from extreme weather events across diverse geographical regions. Text analysis of maintenance tickets identified snow, hurricanes, and storms as the leading extreme weather events affecting photovoltaic plants in the United States. Statistical techniques and machine learning were then implemented to identify the magnitude and variability of these extreme weather impacts on site performance. Impacts varied between event and non-event days, with snow events causing the greatest reductions in performance (54.5%), followed by hurricanes (12.6%) and storms (1.1%). Machine learning analysis identified key features in determining if a day is categorized as low performing, such as low irradiance, geographic location, weather features, and site size. This analysis improves our understanding of compound, extreme weather event impacts on photovoltaic systems. These insights can inform planning activities, especially as renewable energy continues to expand into new geographic and climatic regions around the world.
This work studies the different types of behavior and inaccuracies that can occur when contact is not adequately accounted for in a dynamical system with freeplay, as the strength of the contact stiffness increases. The MATLAB® ode45 time integration solver, with the built-in Event Location capability, is first validated using past experimental data from a forced Duffing oscillator with freeplay. Next, numerical results utilizing event location are compared to results neglecting event location in order to highlight possible numerical errors and effects on multistable dynamical responses. Inaccuracies tend to occur in two different ways. First, neglecting event location can affect the boundaries between basins of attraction. Second, neglecting event location has little effect on the behaviors of the attractor solutions themselves besides merely resembling poorly converged solutions. Errors are less pronounced at the limits of soft or hard contact stiffness. This study shows the importance of accurately solving piecewise-smooth systems and the existing correlation between the strength of the contact force and possible numerical inaccuracies.
International Conference for High Performance Computing, Networking, Storage and Analysis, SC
Mullowney, Paul; Li, Ruipeng; Thomas, Stephen; Ananthan, Shreyas; Sharma, Ashesh; Rood, Jon S.; Williams, Alan B.; Sprague, Michael A.
The U.S. Department of Energy has identified exascale-class wind farm simulation as critical to wind energy scientific discovery. A primary objective of the ExaWind project is to build high-performance, predictive computational fluid dynamics (CFD) tools that satisfy these modeling needs. GPU accelerators will serve as the computational thoroughbreds of next-generation, exascale-class supercomputers. Here, we report on our efforts in preparing the ExaWind unstructured mesh solver, Nalu-Wind, for exascale-class machines. For computing at this scale, a simple port of the incompressible-flow algorithms to GPUs is insufficient. To achieve high performance, one needs novel algorithms that are application aware, memory efficient, and optimized for the latest-generation GPU devices the result of our efforts are unstructured-mesh simulations of wind turbines that can effectively leverage thousands of GPUs. In particular, we demonstrate a first-of-its-kind, incompressible-flow simulation using Algebraic Multigrid solvers that strong scales to more than 4000 GPUs on the Summit supercomputer.
This work focuses on the space-time reduced-order modeling (ROM) method for solving large-scale uncertainty quantification (UQ) problems with multiple random coefficients. In contrast with the traditional space ROM approach, which performs dimension reduction in the spatial dimension, the space-time ROM approach performs dimension reduction on both the spatial and temporal domains, and thus enables accurate approximate solutions at a low cost. We incorporate the space-time ROM strategy with various classical stochastic UQ propagation methods such as stochastic Galerkin and Monte Carlo. Numerical results demonstrate that our methodology has significant computational advantages compared to state-of-the-art ROM approaches. By testing the approximation errors, we show that there is no obvious loss of simulation accuracy for space-time ROM given its high computational efficiency.
The Galactic/Extra Ultra long Duration Balloon Spectroscopic-Stratospheric Terahertz Observatory (GUSTO), is a NASA balloon-borne project and is scheduled for launch in late 2022. The balloon will carry a spectroscopic telescope that will detect three brightest emission lines from interstellar medium. GUSTO measurements will shed light on the life-cycle of the gases in the Milky Way and Large Magellanic Cloud (LMC). In this study, we will discuss the details of a quantum cascade laser used in the local oscillator for detecting the oxygen line at 4.74 THz.
Heβ spectral line shapes are important for diagnosing temperature and density in many dense plasmas. This work presents Heβ line shapes measured with high spectral resolution from solid-density plasmas with minimized gradients. The line shapes show hallmark features of Stark broadening, including quantifiable redshifts and double-peaked structure with a significant dip between the peaks; these features are compared to models through a Markov chain Monte Carlo framework. Line shape theory using the dipole approximation can fit the width and peak separation of measured line shapes, but it cannot resolve an ambiguity between electron density ne and ion temperature Ti, since both parameters influence the strength of quasistatic ion microfields. Here a line shape model employing a full Coulomb interaction for the electron broadening computes self-consistent line widths and redshifts through the monopole term; redshifts have different dependence on plasma parameters and thus resolve the ne-Ti ambiguity. The measured line shapes indicate densities that are 80-100% of solid, identifying a regime of highly ionized but well-tamped plasma. This analysis also provides the first strong evidence that dense ions and electrons are not in thermal equilibrium, despite equilibration times much shorter than the duration of x-ray emission; cooler ions may arise from nonclassical thermalization rates or anomalous energy transport. The experimental platform and diagnostic technique constitute a promising new approach for studying ion-electron equilibration in dense plasmas.
The performance of solid-state electrochemical systems is intimately tied to the potential and lithium distributions across electrolyte-electrode junctions that give rise to interface impedance. Here, we combine two operando methods, Kelvin probe force microscopy (KPFM) and neutron depth profiling (NDP), to identify the rate-limiting interface in operating Si-LiPON-LiCoO2 solid-state batteries by mapping the contact potential difference (CPD) and the corresponding Li distributions. The contributions from ions, electrons, and interfaces are deconvolved by correlating the CPD profiles with Li-concentration profiles and by comparisons with first-principles-informed modeling. We find that the largest potential drop and variation in the Li concentration occur at the anode-electrolyte interface, with a smaller drop at the cathode-electrolyte interface and a shallow gradient within the bulk electrolyte. Correlating these results with electrochemical impedance spectroscopy following battery cycling at low and high rates confirms a long-standing conjecture linking large potential drops with a rate-limiting interfacial process.
Batteries are an enabling technology for addressing sustainability through the electrification of various forms of transportation (1) and grid storage. (2) Batteries are truly multi-scale, multi-physics devices, and accordingly various theoretical descriptions exist to understand their behavior (3-5) ranging from atomistic details to techno-economic trends. As we explore advanced battery chemistries (6,7) or previously inaccessible aspects of existing ones, (8-10) new theories are required to drive decisions. (11-13) The decisions are influenced by the limitations of the underlying theory. Advanced theories used to understand battery phenomena are complicated and require substantial effort to reproduce. However, such constraints should not limit the insights from these theories. We can strive to make the theoretical research verifiable such that any battery stakeholder can assess the veracity of new theories, sophisticated simulations or elaborate analyses. We distinguish verifiability, which amounts to “Can I trust the results, conclusions and insights and identify the context where they are relevant?”, from reproducibility, which ensures “Would I get the same results if I followed the same steps?” With this motivation, we propose a checklist to guide future reports of theoretical battery research in Table 1. We hereafter discuss our thoughts leading to this and how it helps to consistently document necessary details while allowing complete freedom for creativity of individual researchers. Given the differences between experimental and theoretical studies, the proposed checklist differs from its experimental counterparts. (14,15) This checklist covers all flavors of theoretical battery research, ranging from atomic/molecular calculations (16-19) to mesoscale (20,21) and continuum-scale interactions, (9,22) and techno-economic analysis. (23,24) Finally, as more and more experimental studies analyze raw data, (25) we feel this checklist would be broadly relevant.
The ability to form pristine interfaces after etching and regrowth of GaN is a prerequisite for epitaxial selective area doping, which in turn is needed for the formation of lateral PN junctions and advanced device architectures. In this work, we report the electrical properties of etched-and-regrown GaN PN diodes using an in situ Cl-based precursor, tertiary butylchloride (TBCl). We demonstrated a regrowth diode with I-V characteristics approaching that from a continuously grown reference diode. The sources of unintentional contamination from the silicon (Si) impurity and the mediating effect of Si during the TBCl etching are also investigated in this study. This work points to the potential of in situ TBCl etching toward the realization of GaN lateral PN junctions.
In this study, several uncertainty quantification and sensitivity analysis methods are used to determine the most sensitive geometric and material input parameters of a cantilevered pipeline conveying fluid when uncertainty is introduced to the system at the onset of instability. The full nonlinear equations of motion are modeled using the extended Hamilton’s principle and then discretized using Galerkin’s method. A parametric study is first performed, and the Morris elementary effects are calculated to obtain a preliminary understanding of how the onset speed changes when each parameter is introduced to a ± 5% uncertainty. Then, four different input uncertainty distributions, mainly, uniform and Gaussian distribution, are chosen to investigate how input distributions affect uncertainty in the output. A convergence analysis is used to determine the number of samples needed to maintain simulation accuracy while saving the most computational time. Then, Monte Carlo simulations are run, and the output distributions for each input distribution at ± 1%, ± 3% and ± 5% input uncertainty range are found and discussed. Additionally, the Pearson correlation coefficients are evaluated for different uncertainty ranges. A final Monte Carlo study is performed in which single parameters are held constant while all others still have uncertainty. Overall, the flow speed at the onset of instability is the most sensitive to changes in the outer diameter of the pipe.
An intriguing new class of two-dimensional (2D) materials based on metal-organic frameworks (MOFs) has recently been developed that displays electrical conductivity, a rarity among these nanoporous materials. The emergence of conducting MOFs raises questions about their fundamental electronic properties, but few studies exist in this regard. Here, we present an integrated theory and experimental investigation to probe the effects of metal substitution on the charge transport properties of M-HITP, where M = Ni or Pt and HITP = 2,3,6,7,10,11-hexaiminotriphenylene. The results show that the identity of the M-HITP majority charge carrier can be changed without intentional introduction of electronically active dopants. We observe that the selection of the metal ion substantially affects charge transport. Using the known structure, Ni-HITP, we synthesized a new amorphous material, a-Pt-HITP, which although amorphous is nevertheless found to be porous upon desolvation. Importantly, this new material exhibits p-type charge transport behavior, unlike Ni-HITP, which displays n-type charge transport. These results demonstrate that both p- and n-type materials can be achieved within the same MOF topology through appropriate choice of the metal ion.
A new scaling is derived that yields a Reynolds-number-independent profile for all components of the Reynolds stress in the near-wall region of wall-bounded flows, including channel, pipe and boundary layer flows. The scaling demonstrates the important role played by the wall shear stress fluctuations and how the large eddies determine the Reynolds number dependence of the near-wall turbulence behaviour.
For strongly charged polyelectrolytes in salt-free solutions, we use molecular dynamics simulations of a coarse-grained bead-spring model to calculate overlap concentrations c∗ and chain structure for polymers containing N = 10 to 1600 monomers. Over much of this range, we find that the end-to-end distance R∗ at c∗ increases faster than linearly with increasing N, as chains at the overlap concentration approach strongly extended conformations. This trend results in the overlap concentration c∗ decreasing as a stronger function of N than the classical prediction c∗ ∼N-2. This stronger dependence can be fit either by a logarithmic correction to scaling or by an apparent scaling c∗ ∼N-m, with m > 2.
Dalbey, Keith R.; Eldred, Michael S.; Geraci, Gianluca; Jakeman, John D.; Maupin, Kathryn A.; Monschke, Jason A.; Seidl, Daniel T.; Tran, Anh; Menhorn, Friedrich; Zeng, Xiaoshu
The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.
A new empirical potential for efficient, large scale molecular dynamics simulation of water is presented. The HIPPO (Hydrogen-like Intermolecular Polarizable POtential) force field is based upon the model electron density of a hydrogen-like atom. This framework is used to derive and parametrize individual terms describing charge penetration damped permanent electrostatics, damped polarization, charge transfer, anisotropic Pauli repulsion, and damped dispersion interactions. Initial parameter values were fit to Symmetry Adapted Perturbation Theory (SAPT) energy components for ten water dimer configurations, as well as the radial and angular dependence of the canonical dimer. The SAPT-based parameters were then systematically refined to extend the treatment to water bulk phases. The final HIPPO water model provides a balanced representation of a wide variety of properties of gas phase clusters, liquid water, and ice polymorphs, across a range of temperatures and pressures. This water potential yields a rationalization of water structure, dynamics, and thermodynamics explicitly correlated with an ab initio energy decomposition, while providing a level of accuracy comparable or superior to previous polarizable atomic multipole force fields. The HIPPO water model serves as a cornerstone around which similarly detailed physics-based models can be developed for additional molecular species.
This report describes research and development (R&D) activities conducted during fiscal year 2021 (FY21) specifically related to the Engineered Barrier System (EBS) R&D Work Package in the Spent Fuel and Waste Science and Technology (SFWST) Campaign supported by the United States (U.S.) Department of Energy (DOE). The R&D activities focus on understanding EBS component evolution and interactions within the EBS, as well as interactions between the host media and the EBS. A primary goal is to advance the development of process models that can be implemented directly within the Generic Disposal System Analysis (GDSA) platform or that can contribute to the safety case in some manner such as building confidence, providing further insight into the processes being modeled, establishing better constraints on barrier performance, etc.
This report describes an assessment of flamelet based soot models in a laminar ethylene coflow flame with a good selection of measurements suitable for model validation. Overall flow field and temperature predictions were in good agreement with available measurements. Soot profiles were in good agreement within the flame except for near the centerline where imperfections with the acetylene-based soot-production model are expected to be greatest. The model was challenged to predict the transition between non-sooting and sooting conditions with non-negligible soot emissions predicted even down to small flow rates or flame sizes. This suggests some possible deficiency in the soot oxidation models that might alter the amount of smoke emissions from flames, though this study cannot quantify the magnitude of the effect for large fires.
The Sandia-PRF has built a new capability for the low-temperature plasma community for the simultaneous imaging of molecular rotation/vibration nonequilibrium, electric field, and the distribution of OH radical and formaldehyde in reactive low temperature plasma systems. The system is currently investigating the plasma-assisted deflagration to detonation transition in a micro-combustor channel.
Spray-formed materials have complex microstructures which pose challenges for microscale and mesoscale modeling. To constrain these models, experimental measurements of wave profiles when subjecting the material to dynamic compression are necessary. The use of a gas gun to launch a shock into a material is a traditional method to understand wave propagation and provide information of time-dependent stress variations due to complex microstructures. This data contains information on wave reverberations within a material and provides a boundary condition for simulation. Here we present measurements of the wavespeed and wave profile at the rear surface of tantalum, niobium, and a tantalum/niobium blend subjected to plate impact. Measurements of the Hugoniot elastic limit are compared to previous work and wavespeeds are compared to longitudinal sound velocity measurements to examine wave damping due to the porous microstructure.
The Radiation Protection Center (RPC) of the Iraqi Ministry of Environment continues to evaluate the potential health impacts associated with the Adaya Burial Site, which is located 33 kilometers (20.5 miles) southwest of Mosul. This report documents the radiological analyses of 16 groundwater samples collected from wells located in the vicinity of the Adaya Burial Site and at other sites in northern Iraq. The Adaya Burial Site is a high-risk dump site because a large volume of radioactive material and contaminated soil is located on an unsecure hillside above the village of Tall ar Ragrag. The uranium activities for the 16 water samples in northern Iraq are considered to be naturally occurring and do not indicate artificial (man-made) contamination. With one exception, the alpha spectrometry results for the 16 wells that were sampled in 2019 indicate that the water quality concerning the three uranium isotopes (Uranium-233/234, Uranium-235/236, and Uranium-238) was acceptable for potable purposes (drinking and cooking). However, Well 7 in Mosul had a Uranium-233/234 activity concentration that slightly exceeded the World Health Organization guidance level. Eight of the 16 wells are located in the villages of Tall ar Ragrag and Adaya and had naturally occurring uranium concentrations. Wells in the villages of Tall ar Ragrag and Adaya are located near the Adaya Burial Site and should be sampled on an annual schedule. The list of groundwater analytes should include metals, total uranium, isotopic uranium, gross alpha/beta, gamma spectroscopy, organic compounds, and standard water quality parameters. Our current understanding of the hydrogeologic setting in the vicinity of the Adaya Burial Site is solely based on villager's domestic wells, topographic maps, and satellite imagery. To better understand the hydrogeologic setting, a Groundwater Monitoring Program needs to be developed and should include the installation of twelve groundwater monitoring wells in the vicinity of Tall ar Ragrag and the Adaya Burial Site. Characterization of the limestone aquifer and overlying alluvium is needed. RPC should continue to support health assessments for the villagers in Tall ar Ragrag and Adaya. Collecting samples for surface water (storm water), airborne dust, vegetation, and washway sediment should be conducted on a routine basis. Human access to the Adaya Burial Site needs to be strictly limited. Livestock access on or near the burial site needs to be eliminated. The surface-water exposure pathway is likely a greater threat than the groundwater exposure pathway. Installation of a surface-water diversion or collection system is recommended in order to reduce the potential for humans and livestock to come in contact with contaminated water and sediment. To reduce exposure to villagers, groundwater treatment should be considered if elevated uranium or other contaminants are detected in drinking water. Installing water-treatment systems would likely be quicker to accomplish than remediation and excavation of the Adaya Burial Site. The known potential for human exposure to uranium and metals (such as arsenic, chromium, selenium, and strontium) at the Adaya Burial Site is serious. Additional characterization , mitigation, and remediation efforts should be given a high priority.
The U.S. Department of Energy Solar Energy Technologies Office initiated the Generation 3 Concentrating Solar Power (CSP) program to achieve higher operating temperatures (>700 °C) to enable next-generation CSP high-temperature power cycles such as the supercritical CO2 (sCO2) Brayton Cycle. Three teams were selected to pursue high-temperature gas, liquid, and solid pathways for the heat-transfer media. Phases 1 and 2, which lasted from 2018 – 2020, consisted of design, modeling, and testing activities to further de-risk each of the technologies and develop a design for construction, commissioning, and operation of a pilot-scale facility in Phase 3 (2021 – 2024). This report summarizes the activities in Phases 1 and 2 for the solid-particle pathway led by Sandia National Laboratories. In Phases 1 and 2, Sandia successfully de-risked key elements of the proposed Gen 3 Particle Pilot Plant (G3P3) by improving the design, operation, and performance of key particle component technologies including the receiver, storage bins, particle-to-sCO2 heat exchanger, particle lift, and data acquisition and controls. Modeling and testing of critical components have led to optimized designs that meet desired performance metrics. Detailed drawings, piping and instrumentation diagrams, and process flow diagrams were generated for the integrated system, and structural analyses of the assembled tower structure were performed to demonstrate compliance with relevant codes and standards. Instrumentation and control systems of key subsystems were also demonstrated. Together with Bridgers & Paxton, Bohannan Huston, and Sandia Facilities, we have completed a 100% G3P3 tower design package with stamped engineering drawings suitable for construction bid in Phase 3.
The Energetic Neutrons campaign led by Sandia National Laboratories (SNL) had a successful year testing electronic devices and printed circuit boards (PCBs) under 14 MeV neutron irradiation at OMEGA. During FY21 Sandia’s Neutron Effects Diagnostics (NEDs) and data acquisition systems were upgraded to test novel commercial off-the-shelf and Sandia-fabricated electronic components that support SNL’s National Security mission. The upgrades to the Sandia platform consisted of new cable chains, sample mount fixtures and a new fiber optics platform for testing optoelectronic devices.
Liquefied petroleum gas (LPG) is a viable, cleaner alternative to traditional diesel fuel used in busses and other heavy-duty vehicles and could play a role in helping the US meet its lower emission goals. While the LPG industry has focused efforts on developing vehicles and fueling infrastructure, we must also establish safe parameters for maintenance facilities which are servicing LPG fueled vehicles. Current safety standards aid in the design of maintenance facilities, but additional quantitative analysis is needed to prove safeguards are adequate and suggest improvements where needed. In this report we aim to quantify the amount of flammable mass associated with propane releases from vehicle mounted fuel vessels within enclosed garages. Furthermore, we seek to qualify harm mitigation with variable ventilations and facility layout. To accomplish this we leverage validated computational resources at Sandia National Laboratories to simulate various release scenarios representative of real world vehicles and maintenance facilities. Flow solvers are used to predict the dynamics of fuel systems as well as the evolution of propane during release events. From our simulated results we observe that both inflow and outflow ventilation locations play a critical role in reducing flammable cloud size and potential overpressure values during a possible combustion event.
This project will test the coupling of light emitted from silicon vacancy and nitrogen vacancy defects in diamond into additively manufactured photonic wire bonds toward integration into an "on-chip quantum photonics platform". These defects offer a room-temperature solid state solution for quantum information technologies but suffer from issues such as low activation rate and variable local environments. Photonic wire bonding will allow entanglement of pre-selected solid-state defects alleviating some of these issues and enable simplified integration with other photonic devices. These developments could prove to be key technologies to realize quantum secured networks for national security applications.
Naughton, Brian T.; Jimenez, Tony; Preus, Robert; Summerville, Brent; Whipple, Bradley; Reen, Dylan; Gentle, Jake; Lang, Eric
This document aims to provide guidance on the design and operation of deployable wind systems that provide maximum value to missions in defense and disaster relief. Common characteristics of these missions are shorter planning and execution time horizons and a global scope of potential locations. Compared to conventional wind turbine applications, defense and disaster response applications place a premium on rapid shipping and installation, short-duration operation (days to months), and quick teardown upon mission completion. Furthermore, defense and disaster response applications are less concerned with cost of energy than conventional wind turbine applications. These factors impart design drivers that depart from the features found in conventional distributed wind turbines, thus necessitating unique design guidance. The supporting information for this guidance comes from available relevant references, technical analyses, and input from industry and military stakeholders. This document is not intended to be a comprehensive, prescriptive design specification. This document is intended to serve as a written record of an ongoing discussion of stakeholders about the best currently available design guidance for deployable wind turbines to help facilitate the effective development and acquisition of technology solutions to support mission success. The document is generally organized to provide high-level, focused guidance in the main body, with more extensive supporting details available in the referenced appendices. Section 2 begins with a brief qualitative description of the design guidelines being considered for the deployable wind turbines. Section 3 provides an overview of the characteristics of the mobile power systems commonly used in U.S. military missions. Section 4 covers current military and industry standards and specifications that are relevant to a deployable wind turbine design. Section 5 presents the deployable turbine design guidelines for the application cases.
Reverse engineering (RE) analysts struggle to address critical questions about the safety of binary code accurately and promptly, and their supporting program analysis tools are simply wrong sometimes. The analysis tools have to approximate in order to provide any information at all, but this means that they introduce uncertainty into their results. And those uncertainties chain from analysis to analysis. We hypothesize that exposing sources, impacts, and control of uncertainty to human binary analysts will allow the analysts to approach their hardest problems with high-powered analytic techniques that they know when to trust. Combining expertise in binary analysis algorithms, human cognition, uncertainty quantification, verification and validation, and visualization, we pursue research that should benefit binary software analysis efforts across the board. We find a strong analogy between RE and exploratory data analysis (EDA); we begin to characterize sources and types of uncertainty found in practice in RE (both in the process and in supporting analyses); we explore a domain-specific focus on uncertainty in pointer analysis, showing that more precise models do help analysts answer small information flow questions faster and more accurately; and we test a general population with domain-general sudoku problems, showing that adding "knobs" to an analysis does not significantly slow down performance. This document describes our explorations in uncertainty in binary analysis.
The complexity and associated uncertainties involved with atmospheric-turbine-wake interactions produce challenges for accurate wind farm predictions of generator power and other important quantities of interest (QoIs), even with state-of-the-art high-fidelity atmospheric and turbine models. A comprehensive computational study was undertaken with consideration of simulation methodology, parameter selection, and mesh refinement on atmospheric, turbine, and wake QoIs to identify capability gaps in the validation process. For neutral atmospheric boundary layer conditions, the massively parallel large eddy simulation (LES) code Nalu-Wind was used to produce high-fidelity computations for experimental validation using high-quality meteorological, turbine, and wake measurement data collected at the Department of Energy/Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility located at Texas Tech University's National Wind Institute. The wake analysis showed the simulated lidar model implemented in Nalu-Wind was successful at capturing wake profile trends observed in the experimental lidar data.
Tonopah Test Range (TTR), in support of its testing mission and modernization effort acquired a fleet of new gimballed tracking mounts (GTMs) manufactured by BAE Systems. The new GTMs can be operated remotely during flight tests and provide near real-time target tracking data. Furthermore, test vehicle Time-Space-Position-Information (TSPI) is evaluated using post-test synchronized imagery and pointing angle measurements acquired from each tracking mount. To comply with the Nuclear Enterprise Assurance Program (NEAP), all measurements devices must be certified. In keeping with the NEAP program, qualification of the new GTMs have been assessed to confirm that their pointing angle measurements produce acceptable TSPI results. This study only evaluated the four GTMs as a stand-alone solution and found that the GTMs meet their performance requirement of 0.006 degrees RMS error (or less) for post-processed pointing angles and produced TSPI solution with error volumes on the order of one meter or less. The new GTMs will be utilized in combination with existing optical tracking mounts, which will only improve the accuracy of the resulting TSPI data product. Details regarding the approach, analysis, summary results, and conclusions are presented.