Publications

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High speed analog-to-digital converters (ADC), switched-capacitor delay elements, and pulsed radio frequency (RF) systems all require switches in the signal path operating at high switching speeds, providing low resistance when enabled, and providing high signal isolation when disabled. In semiconductor technologies such as CMOS, the enabled state resistance directly scales with the sizing of the switch device, where a larger width switch provides a lower enabled state resistance. As the device width is increased, so is the capacitance formed between the gate, drain, and source of the device.

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The Z Machine at Sandia National Laboratories is a pulsed power facility for high-energy density physics experiments that can shock materials to extreme temperatures and pressures through a focused energy release of up to ∼ 25 MJ in < 100 nanoseconds. It has been in operation for more than two decades and conducts up to ∼ 100 experiments, or “shots,” per year. Based on a set of 74 known shot times from 2018, we determined that Z Machine shots produce detectable ∼ 3–17 Hz ground motion 12 km away at the Albuquerque Seismological Laboratory, New Mexico (ANMO), borehole seismograph, with peak signal at ∼ 7 Hz. The known shot waveforms were used to create a three-component template, leading to the detection of 2339 Z Machine shots since 1998 through single-station cross-correlation. Local seismic magnitude estimates range from local magnitude (ML) -2 to -1.3 and indicate that only a small fraction of the shot energy is transmitted by seismic phases observable at 12 km distance. The most recent major facility renovation, which was intended to decrease mechanical dissipation, is associated with an abrupt decrease in observed seismic amplitudes at ANMO despite stable maximum shot energy. The highly repetitive impulsive sources are well suited to coda-wave interferometry to investigate time-dependent velocity structures. Relative velocity variations (dv/v) show an annual cycle with amplitude of ∼ 0.2%. Local minima are observed in the late spring, and dv/v increases through the summer monsoon rainfall, possibly reflecting patchy saturation as rainfall infiltrates near the eastern edge of the Albuquerque basin. The cumulative results demonstrate that forensic seismology can provide insight into long-term operation of facilities such as pulsed-power laboratories, and that their recurring signals may be valuable for studies of time-dependent structure.

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U.S. nuclear power facilities face increasing challenges in meeting dynamic security requirements caused by evolving and expanding threats while keeping cost reasonable to make nuclear energy competitive. The past approach has often included implementing security features after a facility has been designed and without attention to optimization, which can lead to cost overruns. Incorporating security in the design process can provide robust, cost- effective, and sufficient physical protection systems. The purpose of this work is both to develop a framework for the integration of security into the design phase of High Temperature Gas Reactors (HTGRs) that utilize pebble-based fuels and microreactors. Specifically, this effort focuses on integrating security into the design phase of a model HTGR and microreactor that meets current Nuclear Regulatory Commission (NRC) physical protection requirements and providing advanced solutions to improve physical protection and decrease costs. A suite of tools, including SCRIBE3D©, PATHTRACE© and Blender© were used to model a hypothetical, generic domestic HTGR facility and microreactor facility. Physical protection elements such as sensors, cameras, barriers, and onsite response forces were added to the model based on best practices for physical protection systems. Multiple outsider sabotage scenarios were examined with four-to-eight adversaries to determine security metrics. The results of this work will influence physical protection system designs and facility designs for U.S. domestic HTGRs and microreactors. This work will also demonstrate how a series of experimental and modeling capabilities across the Department of Energy (DOE) Complex can lead to efficient security systems that utilize an onsite response force. The conclusions and recommendations in this document may be applicable to all SMR designs.

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X-ray diffraction (XRD) is a necessary technique for understanding states of materials under static and dynamic loading conditions. The higher-pressure Equation of State (EOS) of many materials can only be explored via shock or ramp compression at temperatures and pressures of interest. While static XRD work has yielded EOS measurements in the 100 - 200 GPa regime, dynamic X-ray diffraction (DXRD) can explore EOS phases in the TPa regime, which closely resembles inner-core planetary conditions. DXRD hinges on the ability to measure the exact phase or phase change of a material while under dynamic loading conditions. Macroscopic diagnostic systems (e.g. velocimetry and pyrometry) can infer a phase change but not identify the specific phase entered by a material. While microscopic (atomic-level) diagnostic systems (e.g. DXRD) have been designed and implemented in Department of Energy’s (DOE) National Laboratories complex, the unique nature of Sandia National Laboratories’ Pulsed Power Facility (Z Machine) prohibits the use of such devices. The destructive nature of Z experiments presents a challenge to data capture and retrieval. Furthermore there are electromagnetic interference, X-ray background, and mechanical constraints to consider. Thus, a multi-part X-ray diagnostic for use on the Z Machine and Z-Beamlet Laser system has been designed and analyzed. Portions of this new DYnamic SCintillator Optic (DYSCO) have been built, tested and fielded. A data analysis software has been written. Finally, the radiance profile of the DYSCO’s scintillator has been characterized through experiments performed at the University of Arizona.

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Induced seismicity is an inherent risk associated with geologic carbon storage (GCS) in deep rock formations that could contain undetected faults prone to failure. Modeling-based risk assessment has been implemented to quantify the potential of injection-induced seismicity, but typically simplified multiscale geologic features or neglected multiphysics coupled mechanisms because of the uncertainty in field data and computational cost of field-scale simulations, which may limit the reliable prediction of seismic hazard caused by industrial-scale CO2 storage. The degree of lateral continuity of the stratigraphic interbedding below the reservoir and depth-dependent fault permeability can enhance or inhibit pore-pressure diffusion and corresponding poroelastic stressing along a basement fault. This study presents a rigorous modeling scheme with optimal geological and operational parameters needed to be considered in seismic monitoring and mitigation strategies for safe GCS.

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Interval Assignment (IA) is the problem of selecting the number of mesh edges (intervals) for each curve for conforming quad and hex meshing. The intervals x is fundamentally integer-valued. Many other approaches perform numerical optimization then convert a floating-point solution into an integer solution, which is slow and error prone. We avoid such steps: we start integer, and stay integer. Incremental Interval Assignment (IIA) uses integer linear algebra (Hermite normal form) to find an initial solution to the meshing constraints, satisfying the integer matrix equation Ax=b. Solving for reduced row echelon form provides integer vectors spanning the nullspace of A. We add vectors from the nullspace to improve the initial solution, maintaining Ax=b. Heuristics find good integer linear combinations of nullspace vectors that provide strict improvement towards variable bounds or goals. IIA always produces an integer solution if one exists. In practice we usually achieve solutions close to the user goals, but there is no guarantee that the solution is optimal, nor even satisfies variable bounds, e.g. has positive intervals. We describe several algorithmic changes since first publication that tend to improve the final solution. The software is freely available.

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Deploying Tidal Energy Converters for electricity generation requires prior-knowledge of the potential Annual Energy Production (AEP) at the site, Ideally using a year-long tidal current record at the proposed site to minimize uncertainty. However, such records are often unavailable. Fortunately, using the periodic nature of tidal variability, the International Electrotechnical Commission Technical Specification for tidal energy resource assessment requires AEP calculation using at least 90 days of tidal current records at each turbine location. The sensitivity of AEP to different record durations has not been fully assessed. This is the goal of our study. The study utilized the U.S. tidal energy geodatabase to simulate tidal currents with various lengths, during 100 years of the 21st century. We then consider two frameworks for evaluating AEP: (a) The long-term (months) fixed instrument (FI) measurement at each proposed tidal turbine location, and (b) one FI measurement and short-term (hours) boat-based moving vessel measurements. Under the two scenarios, we examine the AEP assessed from short tidal current records, including how the AEP uncertainties vary spatially and temporally, and how they are associated with various astronomical factors. This helps provide guidance on choosing the appropriate assessment methodologies to reduce the AEP uncertainties and project cost.

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Characterizing the shallow structure of the Rock Valley region of the Nevada National Security Site is a critical component of the Rock Valley Direct Comparison project. Geophysical data of the region is needed for operational decisions, to constrain geologic models used for simulation, and to facilitate the analysis of future explosive source data. Local measurements of gravity are a key piece of geophysical information that helps to resolve the underlying geologic composition, fault structure, and density characteristics, yet, in the Rock Valley region these measurements are sparse on the scale of the testbed. In this report, we present the details of a recent gravity data acquisition survey designed to collect a dense dataset in the region of interest that complements the existing gravity work but greatly enhances our resolution. This dataset will be integrated with a complementary Los Alamos National Laboratory gravity collection and combined with the existing seismic data in a joint inversion. These measurements were conducted over two weeks with a portable gravimeter and high-resolution GPS and include repeat measurements at a USGS base station as well as reoccupation of gravity sites in the regional dataset. This collection of over 100 new dense gravity measurements will facilitate refinement of the existing Geologic Framework Model and directly complement newly acquired dense seismic data, ultimately improving the project’s ability to investigate the direct comparison of shallow earthquake and explosive sources.

Applying extreme temperature events for future conditions is not straightforward for infrastructure resilience analyses. This work introduces a stochastic model that fills this gap. The model uses at least 50 years of daily extreme temperature records, climate normals with 10%–90% confidence intervals, and shifts/offsets for increased frequency and intensity of heat wave events. Intensity and frequency are shifted based on surface temperature anomaly from 1850–1900 for 32 models from CMIP6. A case study for Worcester, Massachusetts passed 85% of cases using the two-sided Kolmogorov–Smirnov p-value test with 95% confidence for both temperature and duration. Future shifts for several climate scenarios to 2020, 2040, 2060, and 2080 had acceptable errors between the shifted model and 10- and 50-year extreme temperature event thresholds with the largest error being 2.67°C. The model is likely to be flexible enough for other patterns of extreme weather such as extreme precipitation and hurricanes.

Solving large number of small linear systems is increasingly becoming a bottleneck in computational science applications. While dense linear solvers for such systems have been studied before, batched sparse linear solvers are just starting to emerge. In this paper, we discuss algorithms for solving batched sparse linear systems and their implementation in the Kokkos Kernels library. The new algorithms are performance portable and map well to the hierarchical parallelism available in modern accelerator architectures. The sparse matrix vector product (SPMV) kernel is the main performance bottleneck of the Krylov solvers we implement in this work. The implementation of the batched SPMV and its performance are therefore discussed thoroughly in this paper. The implemented kernels are tested on different Central Processing Unit (CPU) and Graphic Processing Unit (GPU) architectures. We also develop batched Conjugate Gradient (CG) and batched Generalized Minimum Residual (GMRES) solvers using the batched SPMV. Our proposed solver was able to solve 20,000 sparse linear systems on V100 GPUs with a mean speedup of 76x and 924x compared to using a parallel sparse solver with a block diagonal system with all the small linear systems, and compared to solving the small systems one at a time, respectively. We see mean speedup of 0.51 compared to dense batched solver of cuSOLVER on V100, while using lot less memory. Thorough performance evaluation on three different architectures and analysis of the performance are presented.

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The purpose of this protocol is to define procedures and practices to be used by the PACT center for field testing of metal halide perovskite (MHP) photovoltaic (PV) modules. The protocol defines the physical, electrical, and analytical configuration of the tests and applies equally to mounting systems at a fixed orientation or sun tracking systems. While standards exist for outdoor testing of conventional PV modules, these do not anticipate the unique electrical behavior of perovskite cells. Further, the existing standards are oriented toward mature, relatively stable products with lifetimes that can be measured on the scale of years to decades. The state of the art for MHP modules is still immature with considerable sample to sample variation among nominally identical modules. Version 0.0 of this protocol does not define a minimum test duration, although the intent is for modules to be fielded for periods ranging for weeks to months. This protocol draws from relevant parts of existing standards, and where necessary includes modifications specific to the behavior of perovskites.

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A new non-neutral generalized Ohm's law (GOL) model for atomic plasmas is presented. This model differs from previous models of this type in that quasi-neutrality is not assumed at any point. Collisional effects due to ionization, recombination, and elastic scattering are included, and an expression for the associated plasma conductivity is derived. An initial set of numerical simulations are considered that compare the GOL model to a two-fluid model in the ideal (collisionless) case. The results demonstrate that solutions obtained from the two models are essentially indistinguishable in most cases when the ion-electron mass ratio is within the range of physical values for atomic plasmas. Additionally, some limitations of the model are discussed.

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Protecting against multi-step attacks of uncertain start times and duration forces the defenders into indefinite, always ongoing, resource-intensive response. To allocate resources effectively, the defender must analyze and respond to an uncertain stream of potentially undetected multiple multi-step attacks and take measures of attack and response intensity over time into account. Such response requires estimation of overall attack success metrics and evaluating effect of defender strategies and actions associated with specific attack steps on overall attack metrics. We present a novel game-theoretic approach GPLADD to attack metrics estimation and demonstrate it on attack data derived from MITRE's ATT&CK Framework and other sources. In GPLADD, the time to complete attack steps is explicit; the attack dynamics emerges from attack graph and attacker-defender capabilities and strategies and therefore reflects 'physics' of attacks. The time the attacker takes to complete an attack step is drawn from a probability distribution determined by attacker and defender strategies and capabilities. This makes time a physical constraint on attack success parameters and enables comparing different defender resource allocation strategies across different attacks. We solve for attack success metrics by approximating attacker-defender games as discrete-time Markov chains and show evaluation of return on detection investments associated with different attack steps. We apply GPLADD to MITRE's APT3 data from ATT&CK Framework and show that there are substantial and un-intuitive differences in estimated real-world vendor performance against a simplified APT3 attack. We focus on metrics that reflect attack difficulty versus attacker ability to remain hidden in the system after gaining control. This enables practical defender optimization and resource allocation against multi-step attacks.

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This work presents measurements of liquid drop deformation and breakup time behind approximately conical shock waves and evaluates the predictive capabilities of low-order models and correlations developed using planar shock experiments. A conical shock was approximated by firing a bullet at Mach 4.5 past a vertical column of water drops with a mean initial diameter of 192 µm. The time-resolved drop position and maximum transverse dimension were characterized using backlit stereo images taken at 500 kHz. The gas density and velocity fields experienced by the drops were estimated using a Reynolds-averaged Navier-Stokes simulation of the bullet. Classical correlations predict drop breakup times and deformation in error by a factor of 3 or more. The Taylor analogy breakup (TAB) model predicts deformed drop diameters that agree within the confidence bounds of the ensemble-averaged experimental values using a dimensionless constant C2 = 2 compared to the accepted value C2 = 2/3. Results demonstrate existing correlations are inadequate for predicting the drop response to the three-dimensional relaxation of the flowfield downstream of a conical-like shock and suggest the TAB model results represent a path toward improved predictions.

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As part of the project “Designing Resilient Communities (DRC): A Consequence-Based Approach for Grid Investment,” funded by the United States (US) Department of Energy’s (DOE) Grid Modernization Laboratory Consortium (GMLC), Sandia National Laboratories (Sandia) partnered with a variety of government, industry, and university participants to develop and test a framework for community resilience planning focused on modernization of the electric grid. This report provides a summary of the development, description, and demonstration of the resulting Resilient Community Design Framework.

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Diesel piston-bowl shape is a key design parameter that affects spray-wall interactions and turbulent flow development, and in turn affects the engine’s thermal efficiency and emissions. It is hypothesized that thermal efficiency can be improved by enhancing squish-region vortices as they are hypothesized to promote fuel-air mixing, leading to faster heat-release rates. However, the strength and longevity of these vortices decrease with advanced injection timings for typical stepped-lip (SL) piston geometries. Dimple stepped-lip (DSL) pistons enhance vortex formation at early injection timings. Previous engine experiments with such a bowl show 1.4% thermal efficiency gains over an SL piston. However, soot was increased dramatically [SAE 2022-01-0400]. In a previous study, a new DSL bowl was designed using non-combusting computational fluid dynamic simulations. This improved DSL bowl is predicted to promote stronger, more rotationally energetic vortices than the baseline DSL piston: it employs shallower, narrower, and steeper-curved dimples that are placed further out into the squish region. In the current experimental study, this improved bowl is tested in a medium-duty diesel engine and compared against the SL piston over an injection timing sweep at low-load and part-load operating conditions. No substantial thermal efficiency gains are achieved at the early injection timing with the improved DSL design, but soot emissions are lowered by 45% relative to the production SL piston, likely due to improved air utilization and soot oxidation. However, these benefits are lost at late injection timings, where the DSL piston renders a lower thermal efficiency than that of the SL piston. Energy balance analyses show higher wall heat transfer with the DSL piston than with the SL piston despite a 1.3% reduction in the piston surface area. Vortex enhancement may not necessarily lead to improved efficiency as more energetic squish-region vortices can lead to higher convective heat transfer losses.

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In this work, we use the Brillouin flow analytic framework to examine the physics of Magnetically Insulated Transmission Lines (MITL). We derive a model applicable to any particle species, including both positive and negative ions, in planar and cylindrical configurations. We then show how to self-consistently solve for two-species simultaneously, using magnetically insulated electrons and positive ions as an example. We require both layers to be spatially separated and magnetically insulated (mutually magnetically insulated); for a 7.5 cm gap with a 2 MV bias voltage, this condition requires magnetic fields in excess of 2.73 T. We see a close match between mutually insulated MITL performance and “superinsulated” (high degree of magnetic insulation) electron-only theory, as may be expected for these high magnetic fields. However, the presence of ions leads to several novel effects: (1) Opposite to electron-only theory, total electron currents increase rather than decrease as the degree of magnetic insulation becomes stronger. The common assumption of neglecting electrons for superinsulated MITL operation must be revisited when ions are present—we calculate up to 20× current enhancement. (2) The electron flow layer thickness increases up to double, due to ion space-charge enhancement. (3) The contributions from both ions and electrons to the MITL flow impedance are calculated. The flow impedance drops by over 50% when ions fill the gap, which can cause significant reflections at the load if not anticipated and degrade performance. Additional effects and results from the inclusion of the ion layer are discussed.

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This document provides the instructions for participating in the 2021 blind photovoltaic (PV) modeling intercomparison organized by the PV Performance Modeling Collaborative (PVPMC). It describes the system configurations, metadata, and other information necessary for the modeling exercise. The practical details of the validation datasets are also described. The datasets were published online in open access in April 2023, after completing the analysis of the results.

We propose primal–dual mesh optimization algorithms that overcome shortcomings of the standard algorithm while retaining some of its desirable features. “Hodge-Optimized Triangulations” defines the “HOT energy” as a bound on the discretization error of the diagonalized Delaunay Hodge star operator. HOT energy is a natural choice for an objective function, but unstable for both mathematical and algorithmic reasons: it has minima for collapsed edges, and its extrapolation to non-regular triangulations is inaccurate and has unbounded minima. We propose a different extrapolation with a stronger theoretical foundation, and avoid extrapolation by recalculating the objective just beyond the flip threshold. We propose new objectives, based on normalizations of the HOT energy, with barriers to edge collapses and other undesirable configurations. We propose mesh improvement algorithms coupling these. When HOT optimization nearly collapses an edge, we actually collapse the edge. Otherwise, we use the barrier objective to update positions and weights and remove vertices. By combining discrete connectivity changes with continuous optimization, we more fully explore the space of possible meshes and obtain higher quality solutions.

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CuCr₂O₄ spinel is a candidate coating material for central receivers in concentrating solar power to protect structural alloys against high temperature oxidation and related degradation. Coating performance and microstructure of dip-coated and sintered coatings is dictated by the initial particle size of the CuCr₂O₄ and sintering temperature, but can be compromised by particle agglomeration. Here in this study, sub-micron particles were synthesised through the Pechini and modified Pechini sol–gel methods. Phase composition was confirmed via X-ray diffraction. Particle growth during calcination of the nanoparticles at different temperatures (650°C, 750°C, 850°C) and times (between 1 and 24 h) was measured via laser diffraction and scanning electron microscopy. The modified Pechini method displayed evidence of smaller particle sizes and greater agglomeration. The kinetics of particle growth observed are consistent with a diffusion limited inhibited grain growth model.

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Annual report for landscaping project WDID 201C399840 inspections.

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Sandia National Laboratories (SNL) has developed a novel reduced order modeling approach. Prioritization of inputs is accomplished using Sobo' indices obtained through a more efficient variance-based global sensitivity analysis. To determine the Sobo' functions, simulated input values are aligned to collocation points to permit the use of Gauss-Lobatto integration, thereby reducing the number of simulation trials needed by more than an order of magnitude compared to standard Monte Carlo approaches. Furthermore, by leveraging the orthogonality of Legendre polynomials in conjunction with those same simulations at the collocation nodes, an efficient fitting method is developed to represent the Sobo' functions from which a reduced order model (ROM) is constructed. The developed method is both more efficient computationally, and the resulting ROM is more accurate. The efficacy of this technique is demonstrated on a nonlinear polynomial test function as well as the nonlinear Ishigami and Sobo' g functions.

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HyRAM+ is a toolkit that includes fast-running models for the unconstrained (i.e., no wall interactions) dispersion and flames for non-premixed fuels. The models were developed for use with hydrogen, but the toolkit was expanded to include propane and methane in a recent release. In this work we validate the dispersion and flame models for these additional fuels, based on reported literature data. The validation efforts spanned a range of release conditions, from subsonic to underexpanded jets and flames for a range of mass flow rates. In general, the dispersion model works well for both propane and methane although the width of the jet/plume is predicted to be wider than observed in some cases. The flame model tends to over-predict the induced buoyancy for low-momentum flames, while the radiative heat flux agrees with the experimental data reasonably well, for both fuels. The models could be improved but give acceptable predictions for propane and methane behavior for the purposes of risk assessment.

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