Publications

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The U.S. Strategic Petroleum Reserve (SPR) is a crude oil storage system run by the U.S. Department of Energy (DOE). The reserve consists of 60 active storage caverns spread across four sites in Louisiana and Texas, near the Gulf of Mexico. Beginning in 2016, the SPR began executing U.S. congressionally mandated oil sales. The configuration of the reserve, with a total capacity of greater than 700 MMB, requires raw water to be used instead of saturated brine for oil withdrawals such as for sales. All sales will produce leaching within the caverns used for oil delivery. Thirty-six caverns had a combined total of over 29 MMB of water injected from CY18-CY19 for mandatory sales. Leaching effects were monitored in these caverns to understand how the sales operations may impact the long-term integrity of the caverns. While frequent sonars are the best way to monitor changes in cavern shape, they can be resource intensive for the number of caverns involved in sales and exchanges. An intermediate option is to model the leaching effects and see if any concerning features develop. The leaching effects were modeled here using the Sandia Solution Mining Code (SANSMIC). The results indicate that leaching induced features are not of concern in the majority of the caverns, 32 of 36. Four caverns, BH-107, BH-108, BH-114 and WH-114 have features that may grow with additional leaching and should be monitored as leaching continues in those caverns. Six caverns had post sale sonars which were compared with SANSMIC results. SANSMIC was able to capture the leaching well. A deviation in the SANSMIC and sonar cavern shapes was observed near the cavern floor in caverns with significant floor rise, a process not captured by SANSMIC. These results suggest SANSMIC is a useful tool for monitoring changes in cavern shape due to leaching effects related to sales and exchanges.

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In response to the COVID-19 pandemic, the Exascale Computing Project’s (ECP) Interoperable Design of Extreme-scale Application Software (IDEAS) productivity team launched the panel series Strategies for Working Remotely to facilitate informal, cross-organizational dialog in the absence of face-to-face meetings. In a time of pandemic, organizations increasingly need to reach across perceived boundaries to learn from each other, so that we can move beyond stand-alone silos to more connected multidisciplinary and multiorganizational configurations. The present paper argues that the unplanned transition to remote work, overuse of electronic communication, and need to unlearn habits associated with an overreliance on face-to-face, created unique opportunities to learn from the situation and accelerate cross-institutional cooperation and collaboration through online community dialog facilitated by informal panel discussions. Recommendations for facilitating online panel discussions to foster cross-organizational dialog are provided by applying the Simulation Experience Design Method.

China is endeavoring to build nuclear power plants (NPPs) in numerous countries around the globe - an initiative that has the potential to strengthen Chinas political and economic influences on those countries. This study provides an overview of the situation and considers the issues involved in such partnerships with China. In order to assess Chinas ability to follow through with its agreements, this study also presents a technical review of its NPP production capability.

This is the second in a sequence of three Hardware Evaluation milestones that provide insight into the following questions: What are the sources of excess data movement across all levels of the memory hierarchy, going out to the network fabric? What can be done at various levels of the hardware/software hierarchy to reduce excess data movement? How does reduced data movement track application performance? The results of this study can be used to suggest where the DOE supercomputing facilities, working with their hardware vendors, can optimize aspects of the system to reduce excess data movement. Quantitative analysis will also benefit systems software and applications to optimize caching and data layout strategies. Another potential avenue is to answer cost-benefit questions, such as those involving memory capacity versus latency and bandwidth. This milestone focuses on techniques to reduce data movement, quantitatively evaluates the efficacy of the techniques in accomplishing that goal, and measures how performance tracks data movement reduction. We study a small collection of benchmarks and proxy mini-apps that run on pre-exascale GPUs and on the Accelsim GPU simulator. Our approach has two thrusts: to measure advanced data movement reduction directives and techniques on the newest available GPUs, and to evaluate our benchmark set on simulated GPUs configured with architectural refinements to reduce data movement.

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he Corrective Action Management Unit (CAMU) at Sandia National Laboratories, New Mexico (SNL/NM) consists of a containment cell and ancillary systems that underwent closure in 2003 in accordance with the Closure Plan in Appendix D of the Class 3 Permit Modification (SNL/NM September 1997). The containment cell was closed with wastes in place. On January 27, 2015, the New Mexico Environment Department issued the Hazardous Waste Facility Operating Permit (Permit) for Sandia National Laboratories (NMED January 2015) to the U.S. Department of Energy/National Nuclear Security Administration (DOE/NNSA) and its Management and Operating (M&O) contractor. The current M&O contractor is National Technology & Engineering Solutions of Sandia, LLC (NTESS). The Permit became effective February 26, 2015. The CAMU is undergoing post-closure care in accordance with the Permit, as revised and updated. This CAMU Report of Post-Closure Care Activities documents all activities and results for calendar year (CY) 2020, as required by the Permit.

The purpose of this CWL Annual Post-Closure Care Report is to document monitoring, inspection, maintenance, and repair activities conducted during CY 2020 as required by PCCP Attachment 1, Section 1.12 (NMED October 2009 and subsequent revisions). This annual report documents post-closure care activities conducted from January through December 2020 and fulfills the PCCP requirement for annual reporting to the NMED.

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This report focuses on the two primary goals set forth in Sandia’s TAFI effort, referred to here under the name Kebab. The first goal is to overlay a trajectory onto a large database of historical trajectories, all with very different sampling rates than the original track. We demonstrate a fast method to accomplish this, even for databases that hold over a million tracks. The second goal is to then demonstrate that these matched historical trajectories can be used to make predictions about unknown qualities associated with the original trajectory. As part of this work, we also examine the problem of defining the qualities of a trajectory in a reproducible way.

Superstorm Sandy caused a major disruption to passenger-rail and other commuter systems throughout New York and New Jersey. To address this issue, New Jersey Transit (NJT) established the NJ TRANSITGRID project, an effort designed to power bus, ferry, and limited passenger-rail service during natural or man-made disasters. Given the importance of these transportation systems, NJT partnered with Sandia National Laboratories (Sandia) to assess the cyber-resilience of the information systems that monitor and control the electrical systems within the microgrid. The Sandia “tabletop” assessment is based on the most recent 20% design packages. From this assessment, the Sandia team identified several security areas that were undefined or did not implement industry best practices. Finally, the Sandia team presented possible follow-on assessment activities and recommended investigating multiple hardening technologies. Addressing these findings and adding state-of-the-art detection and mitigation technologies will help ensure the NJ TRANSITGRID is built with more comprehensive cyber-resilience features.

The GMS Station State-of-Health (SOH) monitoring capability provides the system controller the ability to view current SOH values and calculated statistics for stations and channels, view trend plots of SOH values, be notified when station SOH status changes, and acknowledge or quiet notifications while the station issues are being investigated. The SOH monitoring capability includes components to acquire CD 1.1 protocol station data, extract SOH information from the raw data packets, process the raw SOH information for display, store the SOH information, and display the SOH information in an interactive display. All these components use system and processing configuration to provide the system controller mission-relevant information about station health. This document is a guide to setting the processing configuration for GMS SOH monitoring.

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.1.1 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

The long-term x-ray diffraction (XRD) detector scheme compatible with Z-containment experiments will involve conversion of the diffracted x-rays to optical light, which will be transported away from the Z-Dynamic Materials Properties (DMP) load and detected on a fast-gated camera. In this so-called DIffraction SCintillator Optic (DISCO) scheme , the scintillator is coupled to a long, coherent imaging fiber bundle using a custom lens system with high numerical aperture. In addition, the DISCO diagnostic incorporates time-gating to allow measurement only during the short time window of the x-ray pulse in which XRD occurs, thereby significantly reducing unwanted background generated by the Z-DMP load. Dynamic compression experiments were performed at the Chama target chamber to evaluate the DISCO diagnostic . Specifically, a Zr sample was laser-shocked with the Chaco laser while the Z-Beamlet (ZBL) laser was used to generate x-rays, which enabled time-gated 6.7-keV XRD patterns from the compressed Zr sample to be obtained.

Modeling and simulation of the legacy HERMES III Magnetically Insulated Transmission Line (MITL) has been performed using EMPHASIS, an unstructured time-domain electromagnetic (UTDEM) particle-in-cell (PIC) simulation software. This design when used lost roughly half of its current before the pulse reached the load. The cause of the current loss in the MITL was found to be the vacuum impedance changes along the MITL. The MITL was then redesigned to maintain constant impedance and simulated in EMPHASIS once again. Following predicting simulation results, the new MITL was then built, installed, and tested, showing minimal current loss and good agreement with simulation and theoretical results, all of which are reported here. Additionally, an analysis of experimental voltage calculation techniques using cathode and anode currents is performed and compared to simulation results.

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

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The effects of internal hydrogen on the deformation microstructures of 304L austenitic stainless steel have been characterized using electron backscattered diffraction (EBSD), transmission Kikuchi diffraction (TKD), high-resolution scanning transmission electron microscopy (HRSTEM), and nanoprobe diffraction. Samples, both thermally precharged with hydrogen and without thermal precharging, were subjected to tensile deformation of 5 and 20 pct true strain followed by multiple microscopic interrogations. Internal hydrogen produced widespread stacking faults within the as-forged initially unstrained material. While planar deformation bands developed with tensile strain in both the hydrogen-precharged and non-precharged material, the character of these bands changed with the presence of internal hydrogen. As shown by nanobeam diffraction and HRSTEM observations, in the absence of internal hydrogen, the bands were predominantly composed of twins, whereas for samples deformed in the presence of internal hydrogen, ε-martensite became more pronounced and the density of deformation bands increased. For the 20 pct strain condition, α'-martensite was observed at the intersection of ε-martensite bands in hydrogen-precharged samples, whereas in non-precharged samples α'-martensite was only observed along grain boundaries. We hypothesize that the increased prevalence of α'-martensite is a secondary effect of increased ε-martensite and deformation band density due to internal hydrogen and is not a signature of internal hydrogen itself.

Sandia National Laboratories (SNL) Employee Health Services (EHS) program believes that good health is essential to getting the most out of life, which is why we offer a variety of worksite wellness programs that put employees in control of their own health. These programs are based on current research and strategies that are proven to minimize risk and decrease the impact of illness through early detection and treatment. Services includes Health Assessments, the Virgin Pulse (VP) online wellness platform, and Health Action Plans (HAP) which include one-on-one education, organizational health initiatives, a video library, an events calendar, onsite fitness facilities, and group fitness classes. Participation in these programs help employees and their spouses earn funding for their Health Reimbursement Accounts (HRAs). The EHS Health Action Plan (HAP) initiative targets the key health risks identified through the 8-15-80 model by engaging employees to change their own healthcare story. EHS identified the most prevalent of the health risks amongst our population through Health Risk Assessment (HRA) and used that data to build Health Action Plans (HAPs). The 2020 Health Action Plans addressed improving upon inadequate sleep, lack of energy, stress, weight, physical inactivity, cardiometabolic issues (hypertension, high cholesterol, diabetes), low back pain, allergies, asthma, digestive health, tobacco use, and living well to maintaining low risk for those individuals who do not have a chronic condition to manage. These plans connect employees with our onsite registered dietitians, fitness professionals, health coaches, physical therapists, and physicians as appropriate. In addition to addressing the physical aspects of health, the plans also emphasized pre/post-assessments to encourage building behavioral and emotional skills that promote health and facilitate lifestyle changes over a minimum of three months. Just one risk reduction or behavior change can make an impact on the health of the participant’s Division as well as Sandia’s overall risk levels and ultimately its healthcare costs. Sandia employees achieved an Overall Wellness Score of 69 based on the WellSource Health Assessment (the same as last year). A score of 70-100 is considered “Doing Well”, and a score of 40-69 is in the “Caution” category. Overall in CY 2019, Sandia saw a 34% participation rate in Health Action Plan (HAP) programs (up from 33% in CY 2019) with an 89% completion rate (4% lower than CY 2019). Overall, 5,039 (347 more than CY 2019) individuals participated in 8,329 HAPs, which is 1,005 more than the last calendar year (7,324).

Here, we performed ab initio molecular dynamics (AIMD) simulations to benchmark bulk liquid structures and to evaluate results from all-atom force field molecular dynamics (FFMD) simulations with the generalized Amber force field (GAFF) for organophosphorus (OP) and organochlorine (OC) compounds. Our work also addresses the current and important topic of force field validation, applied here to a set of nonaqueous organic liquids. Our approach differs from standard treatments, which validate force fields based on thermodynamic data. Utilizing radial distribution functions (RDFs), our results show that GAFF reproduces the AIMD-predicted asymmetric liquid structures moderately well for OP compounds that contain bulky alkyl groups. Among the OCs, RDFs obtained from FFMD overlap well with AIMD results, with some offsets in position and peak structuring. However, re-parameterization of GAFF for some OCs is needed to reproduce fully the liquid structures predicted by AIMD. The offsets between AIMD and FFMD peak positions suggest inconsistencies in the developed force fields, but, in general, GAFF is able to capture short-ranged and long-ranged interactions of OPs and OCs observed in AIMD. Along with the local coordination structure, we also compared enthalpies of vaporization. Overall, calculated bulk properties from FFMD compared reasonably well with experimental values, suggesting that small improvements within the FF should focus on parameters that adjust the bulk liquid structures of these compounds.

Helium is frequently used as a working medium for the generation of plasmas and is capable of energetic photon emissions. These energetic photon emissions are often attributed to the formation of helium excimer and subsequent photon emission. When the plasma device is exposed to another gas, such as nitrogen, this energetic photon emission can cause photoionization and further ionization wave penetration into the additional gas. Often ignored are the helium resonance emissions that are assumed to be radiation trapped and therefore not pertinent to photoionization. Here, experimental evidence for the presence of helium atomic emission in a pulsed discharge at ten's of Torr is shown. Simulations of a discharge in similar conditions agree with the experimental measurements. In this context, the role of atomic and molecular helium light emission on photoionization of molecular nitrogen in an ionization wave is studied using a kinetic modeling approach that accounts for radiation dynamics in a developing low-temperature plasma. Three different mixtures of helium at a total pressure of 250 Torr are studied in simulation. Photoionization of the nitrogen molecule by vacuum ultraviolet helium emission is used as the only seed source ahead of the ionization front. It is found that even though radiation trapped, the atomic helium emission lines are the significant source of photoionization of nitrogen. The significant effect of radiation trapped photon emission on ionization wave dynamics demonstrates the need to consider these radiation dynamics in plasma reactors where self-absorbed radiation is ignored.

A small number of associating groups incorporated onto a polymer backbone have dramatic effects on the mobility and viscoelastic response of the macromolecules in melts. These associating groups assemble, driving the formation of clusters, whose lifetime affects the properties of the polymers. Here, we probe the effects of the interaction strength on the structure and dynamics of two topologies, linear and star polymer melts, and further investigate blends of associative and non-associating polymers using molecular dynamics simulations. Polymer chains of approximately one entanglement length are described by a bead-spring model, and the associating groups are incorporated in the form of interacting beads with an interaction strength between them that is varied from 1 to 20 kBT. We find that, for all melts and blends, interaction of a few kBT between the associating groups drives cluster formation, where the size of the clusters increases with increasing interaction strength. These clusters act as physical crosslinkers, which slow the chain mobility. Blends of chains with and without associating groups macroscopically phase separate for interaction strength between the associating groups of a few kBT and above. For weakly interacting associating groups, the static structure function S(q) is well fit by functional form predicted by the random phase approximation where a clear deviation occurs as phase segregation takes place, providing a quantitative assessment of phase segregation.

Wire X-pinches (WXPs) have been studied comprehensively as fast (∼ 1 ns pulse width), small (∼ 1 μm) x-ray sources, created by twisting two or more fine wires into an "X"to produce a localized region of extreme magnetic pressure at the cross-point. Recently, two alternatives to the traditional WXP have arisen: The hybrid X-pinch (HXP), composed of two conical electrodes bridged by a thin wire or capillary, and the laser-cut foil X-pinch (LCXP), cut from a thin foil using a laser. We present a comparison of copper wire, hybrid, and laser-cut foil X-pinches on a single experimental platform: UC San Diego's ∼ 200 kA, 150 ns rise time GenASIS driver. All configurations produced 1-2 ns pulse width, ≤ 5 μm soft x-ray (Cu L-shell, ∼ 1 keV) sources (resolutions diagnostically limited) with comparable fluxes. WXP results varied with linear mass and wire count, but consistently showed separate pinch and electron-beam-driven sources. LCXPs produced the brightest (∼ 1 MW), smallest (≤ 5 μm) Cu K-shell sources, and spectroscopic data showed both H-like Cu K α lines indicative of source temperatures ≥ 2 keV, and cold K α (∼ 8050 eV) characteristic of electron beam generated sources, which were not separately resolved on other diagnostics (within 1-2 ns and ≤ 200 μm). HXPs produced minimal K-shell emission and reliably single, bright, and small L-shell sources after modifications to shape the early current pulse through them. Benefits and drawbacks for each configuration are discussed to provide potential X-pinch users with the information required to choose the configuration best suited to their needs.

Maximum pit sizes were predicted for dilute and concentrated NaCl and MgCl2 solutions as well as sea-salt brine solutions corresponding to 40% relative humidity (RH) (MgCl2-rich) and 76% RH (NaCl-rich) at 25 °C. A quantitative method was developed to capture the effects of various cathode evolution phenomena including precipitation and dehydration reactions. Additionally, the sensitivity of the model to input parameters was explored. Despite one's intuition, the highest chloride concentration (roughly 10.3 M Cl−) did not produce the largest predicted pit size as the ohmic drop was more severe in concentrated MgCl2 solutions. Therefore, the largest predicted pits were calculated for saturated NaCl (roughly 5 M Cl−). Next, it was determined that pit size predictions are most sensitive to model input parameters for concentrated brines. However, when the effects of cathodic reactions on brine chemistry are considered, the sensitivity to input parameters is decreased. Although there was not one main input parameter that influenced pit size predictions, two main categories were identified. Under similar chloride concentrations (similar RH), the water layer thickness (WL), and pit stability product, (i·x)sf, are the most influential factors. When varying chloride concentrations (RH), changes in WL, the brine specific cathodic kinetics on the external surface (captured in the equivalent current density (ieq)), and conductivity (κo) are the most influential parameters. Finally, it was noted that dehydration reactions coupled with precipitation in the cathode will have the largest effect on predicted pit size, and cause the most significant inhibition of corrosion damage.

Semiconductor saturable absorber mirrors (SESAMs) enable passive modelocking of several ultrafast solid-state lasers. Conventionally, SESAMs in the 1-µm wavelength range have employed InGaAs quantum wells (QWs) as absorbers. Here we demonstrate a SESAM based on InAs/GaAs submonolayer quantum dots (SML QDs) capable of generating femtosecond pulses by passively modelocking a vertical-external-cavity surface-emitting laser (VECSEL). Structural measurements are carried out to verify the quality and composition of the QDs. Modelocking experiments with a VECSEL and the QD SESAM in a ring cavity configuration yield pulses as short as 185 fs at 1025 nm. Compared to a traditional QW absorber, SML QD SESAMs exhibit ~ 25% faster recovery times. This also translates to slower power degradation rates or higher damage thresholds in SML QD SESAMs.

Pulsed-laser induced single event current measurements on two geometries of waveguide-integrated germanium photodiodes were conducted over a range of operating voltages to examine the impact of photodiode geometry on the transient response. Vertical PIN photodiodes exhibit transients with a duration that is relatively independent of the operating voltage while the transient duration in lateral PIN photodiodes depends on operating voltage. Furthermore, the experimental measurements facilitate identification of device dimensions that impact the transient response. In this work, these results can be used to identify potential radiation mitigation strategies for photodiodes operating in a radiation environment. Understanding the implications of design choices is critical for designing integrated photonic systems that balance system performance with tolerance for radiation degradation.

Heavy ion, focused x-ray, and pulsed laser single event transient experiments are performed on a silicon epitaxial diode. Collected charge, transient rise times, and transient fall times are calculated and compared between the different sources. It is observed that these transient shape characteristics depend on the source (ion, x-ray, or laser), even when similar amounts of charge are generated. Finally, the observed differences are examined and explained in terms of basic charge collection mechanisms.

Epitaxial lithium niobate (LNO) thin films are an attractive material for devices across a broad range of fields, including optics, acoustics, and electronics. These applications demand high-quality thin films without in-plane growth domains to reduce the optical/acoustical losses and optimize efficiency. Twin-free single-domain-like growth has been achieved previously, but it requires specific growth conditions that might be hard to replicate. In this work, a versatile nanocomposite-seeded approach is demonstrated as an effective approach to grow single-domain epitaxial lithium niobate thin films. Films are grown through a pulsed laser deposition method and growth conditions are optimized to achieve high-quality epitaxial film. A comprehensive microstructure characterization is performed and optical properties are measured. A piezoelectric acoustic resonator device is developed to demonstrate the future potential of the nanocomposite-seeded approach for high-quality LNO growth for radio frequency (RF) applications.

Industrial accidents, such as the Fukushima and Chernobyl disasters, release harmful chemicals into the environment, covering large geographical areas. Natural flora may serve as biological sensors for detecting metal contamination, such as cesium. Spectral detection of plant stresses typically employs a few select wavelengths and often cannot distinguish between different stress phenotypes. In this study, we apply hyperspectral reflectance imaging in the visible and near-infrared along with multivariate curve resolution (MCR) analysis to identify unique spectral signatures of three stresses in Arabidopsis thaliana: salt, copper, and cesium. While all stress conditions result in common stress physiology, hyperspectral reflectance imaging and MCR analysis produced unique spectral signatures that enabled classification of each stress. As the level of potassium was previously shown to affect cesium stress in plants, the response of A. thaliana to cesium stress under variable levels of potassium was also investigated. Increased levels of potassium reduced the spectral response of A. thaliana to cesium and prevented changes to chloroplast cellular organization. While metal stress mechanisms may vary under different environmental conditions, this study demonstrates that hyperspectral reflectance imaging with MCR analysis can distinguish metal stress phenotypes, providing the potential to detect metal contamination across large geographical areas.

In this position paper we will address challenges and opportunities relating to the design and codesign of application specific circuits. Given our background as computational scientists, our perspective is from the viewpoint of a highly motivated application developer as opposed to career computer architects

In this work, we present modal-based methods for model calibration in structural dynamics, and address several key challenges in the solution of gradient-based optimization problems with eigenvalues and eigenvectors, including the solution of singular Helmholtz problems encountered in sensitivity calculations, non-differentiable objective functions caused by mode swapping during optimization, and cases with repeated eigenvalues. Unlike previous literature that relied on direct solution of the eigenvector adjoint equations, we present a parallel iterative domain decomposition strategy (Adjoint Computation via Modal Superposition with Truncation Augmentation) for the solution of the singular Helmholtz problems. For problems with repeated eigenvalues we present a novel Mode Separation via Projection algorithm, and in order to address mode swapping between inverse iterations we present a novel Injective mode ordering metric. We present the implementation of these methods in a massively parallel finite element framework with the ability to use measured modal data to extract unknown structural model parameters from large complex problems. A series of increasingly complex numerical examples are presented that demonstrate the implementation and performance of the methods in a massively parallel finite element framework [7,5], using gradient-based optimization techniques in the Rapid Optimization Library (ROL) [21].

Grain boundaries in metallic materials can exist in a wide range of stable and metastable structures. In addition, the properties of a grain boundary may be altered through solute segregation. In this work, we present a formulation that combines the spectrum of embrittling potencies associated with solute segregation with site-occupancy statistics. As a prototype problem, we illustrate the relation between segregation and embrittlement in the case of S segregation to grain boundaries in Ni. To obtain a population of site segregation energies, we perform molecular statics calculations on 378 different symmetric-tilt grain boundaries and their free surface equivalents, using an embedded-atom method interatomic potential developed specifically for studying embrittlement. Our results show that it is important to consider both the energies associated with embrittlement and the probability of occupancy to describe the general embrittling nature of a grain boundary. When analyzed in isolation, certain grain boundaries show large embrittling potencies; however, that effect is diminished when the probability of S segregation to that grain boundary is considered within a polycrystal. We propose a new quantity, the embrittling estimator, which not only categorizes grain boundaries as embrittling or strengthening, but also considers site occupancy probabilities, so that the embrittlement behavior of grain boundaries within a network of grain boundaries can be compared. Finally, we examine the relationship between embrittlement behavior and innate grain boundary properties, such as the free volume, and find statistical evidence that the complex nature of embrittlement cannot be explained by linear correlations with excess volumes or energies. Ultimately, this combined approach provides a theoretical tool to assist grain boundary engineering of metastable alloys.

Here we examine approximate geometrically self-similar solutions to a parabolic free boundary value problem applied to alluvial fan surface morphology and growth. Alluvial fans are fan- or cone-shaped sedimentary deposits caused by the rapid deposition of sediment from a canyon discharging onto a flatter plain. Longitudinal, topographic profiles of fans can be readily described by a seemingly time independent dimensionless profile (DeChant et al., 1999). However, because an alluvial fan can be expected to grow over time, it is clear that this “steady” profile is certainly time dependent and can be described using a space-time self-similar solution. In an experimental and theory-based study, Guerit et al. (2014) developed a self-similar (or as they describe it a self-affine) linear solution based upon an approximate first order small parameter expansion solution for a 1-d homogeneous nonlinear diffusion equation. Direct substitution of this result into a linear diffusion equation suggests that this first order expression may not fully satisfy the associated governing equation. In contrast, we develop a more complete solution based upon a modeled approximation for the axi-symmetric formulation such that the associated temporal behavior is consistent with a 1/3 time power-law as described by Reitz and Jerolmack (2014). The resulting expression is an exact solution to a linear heat equation. We emphasize that a small parameter is not inherent to the resulting profile result and is not included in our model development. Though developed using rather different approaches, the formal solution developed here is in good agreement with the simple polynomial described by DeChant et al. (1999) suggesting that this self-similar solution is a suitable time dependent representation of alluvial fan longitudinal profile form and improves on earlier work.

Nanocrystalline (NC) metals suffer from an intrinsic thermal instability; their crystalline grains undergo rapid coarsening during processing treatments or under service conditions. Grain boundary (GB) solute segregation has been proposed to mitigate grain growth and thermally stabilize the grain structures of NC metals. However, the role of GB character in solute segregation and thermal stability of NC metals remains poorly understood. Herein, we employ high resolution microscopy techniques, atomistic simulations, and theoretical analysis to investigate and characterize the impact of GB character on segregation behavior and thermal stability in a model NC Pt-Au alloy. High resolution electron microscopy along with X-ray energy dispersive spectroscopy and automated crystallographic orientation mapping is used to obtain spatially correlated Pt crystal orientation, GB misorientation, and Au solute concentration data. Atomistic simulations of polycrystalline Pt-Au systems are used to reveal the plethora of GB segregation profiles as a function of GB misorientation and the corresponding impact on grain growth processes. With the aid of theoretical models of interface segregation, the experimental data for GB concentration profiles are used to extract GB segregation energies, which are then used to elucidate the impact of GB character on solute drag effects. Our results highlight the paramount role of GB character in solute segregation behavior. In broad terms, our approach provides future avenues to employ GB segregation as a microstructure design strategy to develop NC metallic alloys with tailored microstructures. This journal is

We propose a novel synthesis of observational and simulated data (climatological and biological) to enhance understanding of the real-world interplay between climate (here, the water cycle) and the epidemiology of water cycle-driven infectious disease. Aligning with Focal Area 2, predictive modeling using AI techniques, we will develop systems of hierarchical models to discover latent features of the water cycle. Our approach will leverage state-of-the-art in Artificial Intelligence (AI) to measure the degree to which climate change-driven shifts in water cycle can be predicted by supplementing sparse and irregular climate data with water cycle-driven infectious disease resources.

Coronal mass ejections (CMEs) occur when long-lived magnetic flux ropes (MFRs) anchored to the solar surface destabilize and erupt away from the Sun. This destabilization is often described in terms of an ideal magnetohydrodynamic instability called the torus instability. It occurs when the external magnetic field decreases sufficiently fast such that its decay index, n = -z θ(ln B) θz, is larger than a critical value, n > ncr, where ncr = 1.5 for a full, large aspect ratio torus. However, when this is applied to solar MFRs, a range of conflicting values for ncr is found in the literature. To investigate this discrepancy, we have conducted laboratory experiments on arched, line-tied flux ropes and applied a theoretical model of the torus instability. Our model describes an MFR as a partial torus with foot points anchored in a conducting surface and numerically calculates various magnetic forces on it. This calculation yields better predictions of ncr that take into account the specific parameters of the MFR. We describe a systematic methodology to properly translate laboratory results to their solar counterparts, provided that the MFRs have a sufficiently small edge safety factor or, equivalently, a large enough twist. After this translation, our model predicts that ncr in solar conditions falls near ncr ~ 0.9 solar and within a larger range of ncr ~ (0.7, 1.2) solar, depending on the parameters. The methodology of translating laboratory MFRs to their solar counterparts enables quantitative investigations of CME initiation through laboratory experiments. These experiments allow for new physics insights that are required for better predictions of space weather events but are difficult to obtain otherwise.

Dynamic interface instabilities such as Rayleigh–Taylor, Kelvin–Helmholtz, and Richtmyer–Meshkov are important in a number of physical phenomena. Besides meriting study because of their role in natural events and man-made applications, they can also be used to study constitutive properties of materials in extreme conditions. Both RTI and RMI configurations have been used to study the strength of solids at high strain rates, though RMI has largely been limited to zero or ambient pressure. Recently, advances in imaging have allowed tamped RMI experiments to be performed in which the pressure is maintained above ambient. In this study, we examine the tamped RMI for determining material strength. Through simulation, we explore the behavior of the jetting material and examine the sensitivity of jetting to material properties. We identify simple scaling laws that relate the key physical parameters controlling jetting, which are compared to previous results from the literature. We use these scaling law and other considerations to examine issues associated with tamped RMI experiments.

Fracture resistance of pipeline welds from a range of strength grades and welding techniques was measured in air and 21 MPa hydrogen gas, including electric resistance weld of X52, friction stir weld of X100 and gas metal arc welds (GMAW) of X52, X65 and X100. Welds exhibited a decrease in fracture resistance in hydrogen compared to complementary tests in air. A general trend was observed that fracture resistance in 21 MPa hydrogen gas decreased with increasing yield strength. To accommodate material constraints, two different fracture coupon geometries were used in this study, which were shown to yield similar fracture resistance values in air and 21 MPa hydrogen gas; values using different coupons resulted in less than 15% difference. In addition, fracture coupons were removed from controlled locations in select welds to examine the potential influence of orientation and residual stress. The two orientations examined in the X100 GMAW exhibited negligible differences in fracture resistance in air and, similarly, negligible differences in hydrogen. Residual stress exhibited a modest influence on fracture resistance; however, a consistent trend was not observed between tests in air and hydrogen, suggesting further studies are necessary to better understand the influence of residual stress. A comparison of welds and base metals tested in hydrogen gas showed similar susceptibility to hydrogen-assisted fracture. The overall dominant factor in determining the susceptibility to fracture resistance in hydrogen is the yield strength.

Abstract not provided.

In this study, we report on the thermal conductivity of amorphous carbon generated in diamond via nitrogen ion implantation (N 3 + at 16.5 MeV). Transmission electron microscopy techniques demonstrate amorphous band formation about the longitudinal projected range, localized approximately 7 μm beneath the sample surface. While high-frequency time-domain thermoreflectance measurements provide insight into the thermal properties of the near-surface preceding the longitudinal projected range depth, a complimentary technique, steady-state thermoreflectance, is used to probe the thermal conductivity at depths which could not otherwise be resolved. Through measurements with a series of focusing objective lenses for the laser spot size, we find the thermal conductivity of the amorphous region to be approximately 1.4 W m-1 K-1, which is comparable to that measured for amorphous carbon films fabricated through other techniques.

The solid-electrolyte interphase (SEI) that forms on lithium ion battery (LIB) anodes prevents degradation-causing transfer of electrons to the electrolyte. Grain boundaries (GBs) between different SEI components, like LiF, have been suggested to accelerate Li+transport. However, using the non-equilibrium Green's function technique with density functional theory (NEGF-DFT), we find that GBs enhance electron tunneling in thin LiF films by 1-2 orders of magnitude, depending on the bias. Extrapolating to thicker films using the Wentzel-Kramers-Brillouin (WKB) method emphasizes that safer batteries require passivation of GBs in the SEI.

Objectives: Introduce a sensor selection approach to assist test engineers with MIMO test design. Demonstrate the capability of the approach. Approach: Define a desired response from a field model. Supply the sensor selection technique to two lab models, with different boundary conditions than the “field” model. Compare the laboratory response to the field response using sensors selected from the approach.

Summary: Sensor selection approach for MIMO testing was defined and demonstrated on a model of a practical system. Laboratory response obtained using the approach matched the field response well. Information about how many control sensors, and where to locate sensors can be gleaned from the approach.

The outer core of the Earth is composed primarily of liquid iron, and the inner core boundary is governed by the intersection of the melt line and the geotherm. While there are many studies on the thermodynamic equation of state for solid iron, the equation of state of liquid iron is relatively unexplored. In this work, we use dynamic compression to diagnose the high-pressure liquid equation of state of iron by utilizing the shock-ramp capability at Sandia National Laboratories’ Z-Machine. This technique enables measurements of material states off the Hugoniot by initially shocking samples and subsequently driving a further, shockless compression. Planetary studies benefit greatly from isentropic, off-Hugoniot experiments since they can cover pressure-temperature (P-T) conditions that are close to adiabatic profiles found in planetary interiors. We used this method to drive iron to P-T conditions similar to those of the Earth’s outer-inner core boundary, along an elevated-temperature isentrope in the liquid from 275 GPa to 400 GPa. We derive the equation of state using a hybrid backward integration – forward Lagrangian technique on particle velocity traces to determine the pressure-density history of the sample. Our results are in excellent agreement with SESAME 92141, a previously published equation of state table. With our data and previous experimental data on liquid iron we provide new information on the iron melting line and derive new parameters for a Vinet-based equation of state. The table and our parameterized equation of state are applied to provide an updated means of modeling the pressure, mass, and density of liquid iron cores in exoplanetary interiors.

In this work, the frequency-dependent ferroelectric properties of 45 nm (Al,Sc)N films sputter deposited on complementary metal–oxide–semiconductor (CMOS)-compatible Al metal electrodes are measured and compared. Low in-plane compressive stress (-10 ± 20 MPa) is observed in (Al,Sc)N thin films deposited on Al electrodes. The (Al,Sc)N films exhibit an imprint in the measured coercive fields (E_c) of -4.3/+5.3 MV cm^-1 at 10 kHz. Using positive-up negative-down (PUND) measurements, ferroelectric switching is observed within ≈200 ns of an applied voltage pulse, which demonstrates the ability of ferroelectric (Al,Sc)N to achieve the fast read/write speeds desired in memory devices.

Each year there are millions of dollars spent on the research and production of high-resolution detectors. This research indicates that the pursuit of higher resolution detectors is not always necessary. The terminal resolution of a NaI detector, or highest detector resolution, at which identification algorithms fail to identify highly enriched uranium (HEU) was evaluated using GADRAS, Genie, and GammaVision. GADRAS employs a template matching algorithm, while Genie and GammaVision utilize a mathematical approach for peak search and identification. The NaI spectra utilized for evaluation were generated using the GADRAS Inject tab and source modeling functions. Each spectrum included terrestrial and cosmic background from Dallas, TX. The resolutions for each spectrum were increased from a default 8.92% to a point where each algorithm would fail to identify ²³⁵U from a HEU source. Six different source configurations were used in this research: bare HEU, 50% shielded HEU, 90% shielded HEU, bare HEU with an interference source of ⁹⁹mTc, bare HEU with ⁹⁹mTc both shielded 50%, and bare HEU with ⁹⁹mTc both shielded 90%. T

The role of local molecular structure on calculated 13C and 19F NMR chemical shifts for graphite fluoride materials was explored by using gauge-including projector augmented wave (GIPAW) computational methods for different periodic crystal polymorphs and density functional theory (DFT) gauge-including atomic orbital (GIAO) computational methods for individual graphite fluoride platelets, i.e., fluorinated graphene (FG). The impact of stacking sequences, d-spacing, and ring conformations on fully fluorinated graphite fluoride structures was investigated. A range of different defects including Stone-Wales, F and C vacancies, void formation, and F inversion were also evaluated using FG structures. These calculations show that distinct chemical shift signatures exist for many of these polymorphs and defects, therefore providing a basis for spectral assignment and development of models describing the mean local CF structure in disordered graphite fluoride materials.

MLIR (Multi-Level Intermediate Representation), is an extensible compiler framework that supports high-level data structures and operation constructs. These higher-level code representations are particularly applicable to the artificial intelligence and machine learning (AI/ML) domain, allowing developers to more easily support upcoming heterogeneous AI/ML accelerators and develop flexible domain specific compilers/frameworks with higher-level intermediate representations (IRs) and advanced compiler optimizations. The result of using MLIR within the LLVM compiler framework is expected to yield significant improvement in the quality of generated machine code, which in turn will result in improved performance and hardware efficiency

This article proposes several advances to sparse nonnegative matrix factorization (SNMF) as a way to identify large-scale patterns in urban traffic data. The input to our model is traffic counts organized by time and location. Nonnegative matrix factorization additively decomposes this information, organized as a matrix, into a linear sum of temporal signatures. Penalty terms encourage this factorization to concentrate on only a few temporal signatures, with weights which are not too large. Our interest here is to quantify and compare the regularity of traffic behavior, particularly across different broad temporal windows. In addition to the rank and error, we adapt a measure introduced by Hoyer to quantify sparsity in the representation. Combining these, we construct several curves which quantify error as a function of rank (the number of possible signatures) and sparsity; as rank goes up and sparsity goes down, the approximation can be better and the error should decreases. Plots of several such curves corresponding to different time windows leads to a way to compare disorder/order at different time scalewindows. In this paper, we apply our algorithms and procedures to study a taxi traffic dataset from New York City. In this dataset, we find weekly periodicity in the signatures, which allows us an extra framework for identifying outliers as significant deviations from weekly medians. We then apply our seasonal disorder analysis to the New York City traffic data and seasonal (spring, summer, winter, fall) time windows. We do find seasonal differences in traffic order.

Cement is widely used in wellbores to stabilize the steel casing used in wellbore operations for oil and gas production, enhanced geothermal systems and carbon sequestration, and to limit fluid movement between sub-surface strata. Flaws such as microcracks in wellbore cement can lead to leakage along the wellbore compromising wellbore integrity. There is an increasing need for methods to monitor cement crack propagation in wellbore environments. In this study, we develop and report the first cementitious sensors capable of exhibiting high frequency acoustic bandgaps (ABGs) using carbon nanotubes (CNTs). Computational simulations of a sensor unit cell are used to design cement-multi walled carbon nanotubes (MWCNTs) sensors that show a wide bandgap. When the cement-MWCNTs sensors is embedded in cement specimens, bandgaps were measured experimentally under 300 kHz and under 600 kHz, consistent with the computationally predicted bandgaps in the range of 290–360 kHz, 410–460 kHz and 515–585 kHz. These bandgap features were absent in homogeneous cement specimens. X-ray tomographic reconstructions showed microscopic debonding at cement-MWCNTs sensor interface. Frequency response analysis of a three-dimensional computational model indicated a shift of frequency of minimum transmission due to the interface debonding, but no perturbation of bandgap response was observed. Here, the cement-MWCNTs sensors developed in this study show the potential of a packed CNT inclusion material in cementitious matrix to create ABGs in a cement matrix.

Abstract not provided.

Geothermal energy can provide answers to many of America’s essential energy questions. The United States has tremendous geothermal resources, as illustrated by the results of the DOE GeoVision analysis, but technical and non-technical barriers have historically stood in the way of widespread deployment of geothermal energy. The U.S. Department of Energy’s Geothermal Technologies Office within the Office of Energy Efficiency and Renewable Energy has invested more than $470 million in research and development (R&D) since 2015 to meet its three strategic goals: (1) unlock the potential of enhanced geothermal systems, (2) advance technologies to increase geothermal energy on the U.S. electricity grid, and (3) support R&D to expand geothermal energy opportunities throughout the United States. Here, we describe many of those R&D initiatives and outlines future directions in geothermal research.

Pyruvic acid, a representative alpha-keto carboxylic acid, is one of the few organic molecules destroyed in the troposphere by solar radiation rather than by reactions with free radicals. To date, only its stable final products were identified, often with contribution from secondary chemistry, making it difficult to elucidate photodissociation mechanisms following excitation to the lowest singlet excited-state (S₁) and the role of the internal hydrogen bond in the most-stable Tc conformer. Using multiplexed photoionization mass spectrometry we report the first direct experimental evidence, via the observation of singlet methylhydroxycarbene (MHC) following 351 nm excitation, supporting the decarboxylation mechanism previously proposed. Decarboxylation to MHC + CO₂ represents 97–100% of product branching at 351 nm. We observe vinyl alcohol and acetaldehyde, which we attribute to isomerization of MHC. We also observe a 3 ± 2% yield of the Norrish Type I photoproducts CH₃CO + DOCO, but only from d₁-pyruvic acid. At 4 Torr pressure, we measure a photodissociation quantum yield of $1.0^{+0}_{–0.4}$, consistent with IUPAC recommendations. However, our measured product branching fractions disagree with IUPAC. In light of previous calculations, these results support a mechanism in which hydrogen transfer on the S₁ excited state occurs at least partially by tunneling, in competition with intersystem crossing to the T₁ state. Here, we present the first evidence of a bimolecular reaction of MHC in the gas phase, where MHC reacts with pyruvic acid to produce a C₄H₈O₂ product. This observation implies that some MHC produced from pyruvic acid in Earth's troposphere will be stabilized and participate in chemical reactions with O₂ and H₂O, and should be considered in atmospheric modeling.

A main challenge for integrating the intermittent photovoltaic (PV) power generation remains the accuracy of day-ahead forecasts and the establishment of robust performing methods. The purpose of this work is to address these technological challenges by evaluating the day-ahead PV production forecasting performance of different machine learning models under different supervised learning regimes and minimal input features. Specifically, the day-ahead forecasting capability of Bayesian neural network (BNN), support vector regression (SVR), and regression tree (RT) models was investigated by employing the same dataset for training and performance verification, thus enabling a valid comparison. The training regime analysis demonstrated that the performance of the investigated models was strongly dependent on the timeframe of the train set, training data sequence, and application of irradiance condition filters. Furthermore, accurate results were obtained utilizing only the measured power output and other calculated parameters for training. Consequently, useful information is provided for establishing a robust day-ahead forecasting methodology that utilizes calculated input parameters and an optimal supervised learning approach. Finally, the obtained results demonstrated that the optimally constructed BNN outperformed all other machine learning models achieving forecasting accuracies lower than 5%.

The outer membrane (OM) of Gram-negative (G-) bacteria presents a barrier for many classes of antibacterial agents. Lipopolysaccharide (LPS), present in the outer leaflet of the OM, is stabilized by divalent cations and is considered to be the major impediment for antibacterial agent permeation. However, the actual affinities of major antibiotic classes toward LPS have not yet been determined. In the present work, we use Langmuir monolayers formed from E. coli Re and Rd types of LPS to record pressure-area isotherms in the presence of antimicrobial agents. Our observations suggest three general types of interactions. First, some antimicrobials demonstrated no measurable interactions with LPS. This lack of interaction in the case of cefsulodin, a third-generation cephalosporin antibiotic, correlates with its low efficacy against G-bacteria. Ampicillin and ciprofloxacin also show no interactions with LPS, but in contrast to cefsulodin, both exhibit good efficacy against G-bacteria, indicating permeation through common porins. Second, we observe substantial intercalation of the more hydrophobic antibiotics, novobiocin, rifampicin, azithromycin, and telithromycin, into relaxed LPS monolayers. These largely repartition back to the subphase with monolayer compression. We find that the hydrophobic area, charge, and dipole all show correlations with both the mole fraction of antibiotic retained in the monolayer at the monolayer-bilayer equivalence pressure and the efficacies of these antibiotics against G-bacteria. Third, amine-rich gentamicin and the cationic antimicrobial peptides polymyxin B and colistin show no hydrophobic insertion but are instead strongly driven into the polar LPS layer by electrostatic interactions in a pressure-independent manner. Their intercalation stably increases the area per molecule (by up to 20%), which indicates massive formation of defects in the LPS layer. These defects support a self-promoted permeation mechanism of these antibiotics through the OM, which explains the high efficacy and specificity of these antimicrobials against G-bacteria.

This paper presents the first systematic comparison between south-facing monofacial and bifacial photovoltaic (PV) modules, as well as between south-facing bifacial and vertical east-west facing bifacial PV modules in Alaska. The state’s solar industry, driven by the high price of energy and dropping equipment costs, is quickly growing. The challenges posed by extreme sun angles in Alaska’s northern regions also present opportunities for unique system designs. Annual bifacial gains of 21% were observed between side by side south-facing monofacial and bifacial modules. Vertical east-west bifacial modules had virtually the same annual production as south-facing latitude tilt bifacial modules, but with different energy production profiles.

Applications of fiber optic sensors to battery monitoring have been increasing due to the growing need of enhanced battery management systems with accurate state estimations. The goal of this review is to discuss the advancements enabling the practical implementation of battery internal parameter measurements including local temperature, strain, pressure, and refractive index for general operation, as well as the external measurements such as temperature gradients and vent gas sensing for thermal runaway imminent detection. A reasonable matching is discussed between fiber optic sensors of different range capabilities with battery systems of three levels of scales, namely electric vehicle and heavy-duty electric truck battery packs, and grid-scale battery systems. The advantages of fiber optic sensors over electrical sensors are discussed, while electrochemical stability issues of fiber-implanted batteries are critically assessed. This review also includes the estimated sensing system costs for typical fiber optic sensors and identifies the high interrogation cost as one of the limitations in their practical deployment into batteries. Finally, future perspectives are considered in the implementation of fiber optics into high-value battery applications such as grid-scale energy storage fault detection and prediction systems.

Abstract not provided.

Abstract not provided.

Herein we report the progress towards an organic glass scintillator with fast and thermal neutron sensitivity providing “triple” pulse shape discrimination (PSD) through the inclusion of a boron-incorporated aromatic molecule. The commercially available molecule 2-(p-tolyl)-1,3,2-dioxaborinane (TDB) can be readily synthesized in one step using inexpensive materials and incorporated into the organic glass scintillator at 20% by weight or 0.25% 10B by mass. In addition, we demonstrate that TDB can be easily scaled up and formulated into organic glass scintillator samples to produce a thermal neutron capture signal with a light yield equivalent to 120.4 ± 3.7 keVee, which is the highest value reported in the literature to date.

Over the past decade as Moore's Law has slowed, the need for new forms of computation that can provide sustainable performance improvements has risen. A new method, called in situ computing, has shown great potential to accelerate matrix vector multiplication (MVM), an important kernel for a diverse range of applications from neural networks to scientific computing. Existing in situ accelerators for scientific computing, however, have a significant limitation: These accelerators provide no acceleration for preconditioning-A key bottleneck in linear solvers and in scientific computing workflows. This paper enables in situ acceleration for state-of-The-Art linear solvers by demonstrating how to use a new in situ matrix inversion accelerator for analog preconditioning. As existing techniques that enable high precision and scalability for in situ MVM are inapplicable to in situ matrix inversion, new techniques to compensate for circuit non-idealities are proposed. Additionally, a new approach to bit slicing that enables splitting operands across multiple devices without external digital logic is proposed. For scalability, this paper demonstrates how in situ matrix inversion kernels can work in tandem with existing domain decomposition techniques to accelerate the solutions of arbitrarily large linear systems. The analog kernel can be directly integrated into existing preconditioning workflows, leveraging several well-optimized numerical linear algebra tools to improve the behavior of the circuit. The result is an analog preconditioner that is more effective (up to 50% fewer iterations) than the widely used incomplete LU factorization preconditioner, ILU(0), while also reducing the energy and execution time of each approximate solve operation by 1025x and 105x respectively.

Over the past decade as Moore's Law has slowed, the need for new forms of computation that can provide sustainable performance improvements has risen. A new method, called in situ computing, has shown great potential to accelerate matrix vector multiplication (MVM), an important kernel for a diverse range of applications from neural networks to scientific computing. Existing in situ accelerators for scientific computing, however, have a significant limitation: These accelerators provide no acceleration for preconditioning-A key bottleneck in linear solvers and in scientific computing workflows. This paper enables in situ acceleration for state-of-The-Art linear solvers by demonstrating how to use a new in situ matrix inversion accelerator for analog preconditioning. As existing techniques that enable high precision and scalability for in situ MVM are inapplicable to in situ matrix inversion, new techniques to compensate for circuit non-idealities are proposed. Additionally, a new approach to bit slicing that enables splitting operands across multiple devices without external digital logic is proposed. For scalability, this paper demonstrates how in situ matrix inversion kernels can work in tandem with existing domain decomposition techniques to accelerate the solutions of arbitrarily large linear systems. The analog kernel can be directly integrated into existing preconditioning workflows, leveraging several well-optimized numerical linear algebra tools to improve the behavior of the circuit. The result is an analog preconditioner that is more effective (up to 50% fewer iterations) than the widely used incomplete LU factorization preconditioner, ILU(0), while also reducing the energy and execution time of each approximate solve operation by 1025x and 105x respectively.

Millimeter-sized CD foils fielded close (order mm) to inertial confinement fusion (ICF) implosions have been proposed as a game-changer for improving energy resolution and allowing time-resolution in neutron spectrum measurements using the magnetic recoil technique. This paper presents results from initial experiments testing this concept for direct drive ICF at the OMEGA Laser Facility. While the foils are shown to produce reasonable signals, inferred spectral broadening is seen to be high (∼5 keV) and signal levels are low (by ∼20%) compared to expectation. Before this type of foil is used for precision experiments, the foil mount must be improved, oxygen uptake in the foils must be better characterized, and impact of uncontrolled foil motion prior to detection must be investigated.

Metamaterials derive their unusual properties from their architected structure, which generally consists of a repeating unit cell designed to perform a particular function. However, existing metamaterials are, with few exceptions, physically continuous throughout their volume, and thus cannot take advantage of multi-body behavior or contact interactions. Here we introduce the concept of multi-body interpenetrating lattices, where two or more lattices interlace through the same volume without any direct connection to each other. This new design freedom allows us to create architected interpenetrating structures where energy transfer is controlled by surface interactions. As a result, multifunctional or composite-like responses can be achieved even with only a single print material. While the geometry defining interpenetrating lattices has been studied since the days of Euclid, additive manufacturing allows us to turn these mathematical concepts into physical objects with programmable interface-dominated properties. In this first study on interpenetrating lattices, we reveal remarkable mechanical properties including improved toughness, multi-stable/negative stiffness behavior, and electromechanical coupling.

This work proposes a windowed least-squares (WLS) approach for model reduction of dynamical systems. The proposed approach sequentially minimizes the time-continuous full-order-model residual within a low-dimensional space–time trial subspace over time windows. The approach comprises a generalization of existing model reduction approaches, as particular instances of the methodology recover Galerkin, least-squares Petrov–Galerkin (LSPG), and space–time LSPG projection. In addition, the approach addresses key deficiencies in existing model reduction techniques, e.g., the dependence of LSPG and space–time LSPG projection on the time discretization and the exponential growth in time exhibited by a posteriori error bounds for both Galerkin and LSPG projection. We consider two types of space–time trial subspaces within the proposed approach: one that reduces only the spatial dimension of the full-order model, and one that reduces both the spatial and temporal dimensions of the full-order model. For each type of trial subspace, we consider two different solution techniques: direct (i.e., discretize then optimize) and indirect (i.e., optimize then discretize). Numerical experiments conducted using trial subspaces characterized by spatial dimension reduction demonstrate that the WLS approach can yield more accurate solutions with lower space–time residuals than Galerkin and LSPG projection.

Electron-temperature (Te) measurements in implosions provide valuable diagnostic information, as Te is unaffected by residual flows and other non-thermal effects unlike ion temperature inferred from a fusion product spectrum. In OMEGA cryogenic implosions, measurement of Te(t) can be used to investigate effects related to time-resolved hot-spot energy balance. The proposed diagnostic utilizes five fast-rise (15 ps) scintillator channels with distinct X-ray filtering. Titanium and stepped aluminum filtering were chosen to maximize detector sensitivity in the 10 keV-20 keV range, as it has been shown that these x rays have similar density and temperature weighting to the emitted deuterium-tritium fusion neutrons. Initial data collected using a prototype nosecone on the existing neutron temporal diagnostic demonstrate the validity of this diagnostic technique. The proposed system will be capable of measuring spatially integrated Te(t) with 20 ps time resolution and <10% uncertainty at peak emission in cryogenic DT implosions.

This paper addresses the issue of overfitting while calibrating unknown parameters of over-parameterized physics-based models with noisy and incomplete observations. A semi-analytical Bayesian framework of nonlinear sparse Bayesian learning (NSBL) is proposed to identify sparsity among model parameters during Bayesian inversion. NSBL offers significant advantages over machine learning algorithm of sparse Bayesian learning (SBL) for physics-based models, such as 1) the likelihood function or the posterior parameter distribution is not required to be Gaussian, and 2) prior parameter knowledge is incorporated into sparse learning (i.e. not all parameters are treated as questionable). NSBL employs the concept of automatic relevance determination (ARD) to facilitate sparsity among questionable parameters through parameterized prior distributions. The analytical tractability of NSBL is enabled by employing Gaussian ARD priors and by building a Gaussian mixture-model approximation of the posterior parameter distribution that excludes the contribution of ARD priors. Subsequently, type-II maximum likelihood is executed using Newton's method whereby the evidence and its gradient and Hessian information are computed in a semi-analytical fashion. We show numerically and analytically that SBL is a special case of NSBL for linear regression models. Subsequently, a linear regression example involving multimodality in both parameter posterior pdf and model evidence is considered to demonstrate the performance of NSBL in cases where SBL is inapplicable. Next, NSBL is applied to identify sparsity among the damping coefficients of a mass-spring-damper model of a shear building frame. These numerical studies demonstrate the robustness and efficiency of NSBL in alleviating overfitting during Bayesian inversion of nonlinear physics-based models.

Background: Using a thin-walled tube torsion test to characterize a material’s shear response is a well-known technique; however, the thin walled specimen tends to buckle before reaching large shear deformation and failure. An alternative technique is the surface stress method (Nadai 1950; Wu et al. J Test Eval 20:396–402, 1992), which derives a shear stress-strain curve from the torque-angular displacement relationship of a solid cylindrical bar. The solid bar torsion test uniquely stabilizes the deformation which allows us to control and explore very large shear deformation up to failure. However, this method has rarely been considered in the literature, possibly due to the complexity of the analysis and experimental issues such as twist measurement and specimen uniformity. Objective: In this investigation, we develop a method to measure the large angular displacement in the solid bar torsion experiments to study the large shear deformation of two common engineering materials, Al6061-T6 and SS304L, which have distinctive hardening behaviors. Methods: Modern stereo-DIC methods were applied to make deformation measurements. The large angular displacement of the specimen posed challenges for the DIC analysis. An analysis method using multiple reference configurations and transformation of deformation gradient is developed to make the large shear deformation measurement successful. Results: We successfully applied the solid bar torsion experiment and the new analysis method to measure the large shear deformation of Al6061-T6 and SS304L till specimen failure. The engineering shear strains at failure are on the order of 2–3 for Al6061-T6 and 3–4 for SS304L. Shear stress-strain curves of Al6061-T6 and SS304L are also obtained. Conclusions: Solid bar torsion experiments coupled with 3D-DIC technique and the new analysis method of deformation gradient transformation enable measurement of very large shear deformation up to specimen failure.

Data integrity is crucial for the performance and reliability analysis of photovoltaic (PV) systems, since actual in-field measurements commonly exhibit invalid data caused by outages and component failures. The scope of this paper is to present a complete methodology for PV data processing and quality verification in order to ensure improved PV performance and reliability analyses. Data quality routines (DQRs) were developed to ensure data fidelity by detecting and reconstructing invalid data through a sequence of filtering stages and inference techniques. The obtained results verified that PV performance and reliability analyses are sensitive to the fidelity of data and, therefore, time series reconstruction should be handled appropriately. To mitigate the bias effects of 10% or less invalid data, the listwise deletion technique provided accurate results for performance analytics (exhibited a maximum absolute percentage error of 0.92%). When missing data rates exceed 10%, data inference techniques yield more accurate results. The evaluation of missing power measurements demonstrated that time series reconstruction by applying the Sandia PV Array Performance Model yielded the lowest error among the investigated data inference techniques for PV performance analysis, with an absolute percentage error less than 0.71%, even at 40% missing data rate levels. The verification of the routines was performed on historical datasets from two different locations (desert and steppe climates). The proposed methodology provides a set of standardized analytical procedures to ensure the validity of performance and reliability evaluations that are performed over the lifetime of PV systems.

The complex postdetonation geologic structures that form after an underground nuclear explosion are hard to constrain because increased heterogeneity around the damage zone affects seismic waves that propagate through the explosion site. Generally, a vertical rub-ble-filled structure known as a chimney is formed after an underground nuclear explosion that is composed of debris that falls into the subsurface cavity generated by the explosion. Compared with chimneys that collapse fully, leaving a surface crater, partially collapsed chimneys can have remnant subsurface cavities left in place above collapsed rubble. The 1964 nuclear test HADDOCK, conducted at the Nevada test site (now the Nevada National Security Site), formed a partially collapsed chimney with no surface crater. Understanding the subsurface structure of these features has significant national security applications, such as aiding the study of suspected underground nuclear explosions under a treaty verification. In this study, we investigated the subsurface architecture of the HADDOCK legacy nuclear test using hybrid 2D–3D active source seismic reflection and refraction data. The seismic data were acquired using 275 survey shots from the Seismic Hammer (a 13,000 kg weight drop) and 65 survey shots from a smaller accelerated weight drop, both recorded by ∼ 1000 three-component 5 Hz geophones. First-arrival, P-wave tomographic modeling shows a low-velocity anomaly at ∼ 200 m depth, likely an air-filled cavity caused by partial collapse of the rock column into the temporary post-detonation cavity. A high-velocity anomaly between 20 and 60 m depth represents spall-related compaction of the shallow alluvium. Hints of low velocities are also present near the burial depth ( ∼ 364 m). The reflection seismic data show a prominent subhorizontal reflector at ∼ 300 m depth, a short-curved reflector at ∼ 200 m, and a high-amplitude reflector at ∼ 50 m depth. Comparisons of the reflection sections to synthetic data and borehole stratigraphy suggest that these features correspond to the alluvium–tuff contact, the partial collapse cavity, and the spalled layer, respectively.

This communication describes a novel series of linear and crosslinked polyurethanes (PUs) and their selective depolymerization under mild conditions. Two unique polyols are synthesized bearing unsaturated units in a configuration designed to favor ring-closing metathesis (RCM) to five- and six-membered cycloalkenes. These polyols are co-polymerized with toluene diisocyanate to generate linear PUs and trifunctional hexamethylene- and diphenylmethane-based isocyanates to generate crosslinked PUs. The polyol design is such that the RCM reaction cleaves the backbone of the polymer chain. Upon exposure to dilute solutions of Grubbs’ catalyst under ambient conditions, the PUs are rapidly depolymerized to low molecular weight, soluble products bearing vinyl and cycloalkene functionalities. These functionalities enable further re-polymerization by traditional strategies for polymerization of double bonds. It is anticipated that this general approach can be expanded to develop a range of chemically recyclable condensation polymers that are readily depolymerized by orthogonal metathesis chemistry.

Land-use conflicts created by the growth of solar photovoltaics (PV) can be mitigated by applying the concept of agrivoltaics, that is, the co-development of land for both PV and agricultural purposes, to commercial-scale solar installations. In this study, we present a conceptual design for a novel agrivoltaic system based on pasture-fed rabbit farming and provide the technical, environmental and economic analyses to demonstrate the viability of the concept. Included in our analysis are the economic advantages to the PV operator of grazing rabbits at a density sufficient to control vegetative growth, thus reducing the economic and environmental costs of mowing; the dual-revenue stream from the sale of both rabbits and electricity, contrasted with estimates of the capital-investment costs for rabbits co-located with, and also independent of, PV; and the economic value to the rabbit farmer of higher colony-growth rates (made possible by the shading and predator protection provided by the PV arrays and of reduced fencing costs, which are the largest capital cost, by being able to leverage the PV systems for rabbit fencing. We also provide an environmental analysis that suggests that rabbit-PV farming is a pathway to a measurable reduction in agriculturally-generated greenhouse-gas emissions. Our calculations indicate that the co-location of solar and rabbit farms is a viable form of agrivoltaics, increasing overall site revenue by 2.5%–24.0% above projected electricity revenue depending on location and rental/ownership of rabbits, while providing a high-value agricultural product that, on a per weight basis, has significantly less environmental impact than cattle.

1 The exponential growth of data has driven technology providers to develop new protocols, such as cache coherent interconnects and memory semantic fabrics, to help users and facilities leverage advances in memory technologies to satisfy these growing memory and storage demands. Using these new protocols, fabric-Attached memories (FAM) can be directly attached to a system interconnect and be easily integrated with a variety of processing elements (PEs). Moreover, systems that support FAM can be smoothly upgraded and allow multiple PEs to share the FAM memory pools using well-defined protocols. The sharing of FAM between PEs allows efficient data sharing, improves memory utilization, reduces cost by allowing flexible integration of different PEs and memory modules from several vendors, and makes it easier to upgrade the system. One promising use-case for FAMs is in High-Performance Compute (HPC) systems, where the underutilization of memory is a major challenge. However, adopting FAMs in HPC systems brings new challenges. In addition to cost, flexibility, and efficiency, one particular problem that requires rethinking is virtual memory support for security and performance. To address these challenges, this paper presents decoupled access control and address translation (DeACT), a novel virtual memory implementation that supports HPC systems equipped with FAM. Compared to the state-of-The-Art two-level translation approach, DeACT achieves speedup of up to 4.59x (1.8x on average) without compromising security.1Part of this work was done when Vamsee was working under the supervision of Amro Awad at UCF. Amro Awad is now with the ECE Department at NC State.

A key challenge to nonlocal models is the analytical complexity of deriving them from first principles, and frequently their use is justified a posteriori. In this work we extract nonlocal models from data, circumventing these challenges and providing data-driven justification for the resulting model form. Extracting data-driven surrogates is a major challenge for machine learning (ML) approaches, due to nonlinearities and lack of convexity — it is particularly challenging to extract surrogates which are provably well-posed and numerically stable. Our scheme not only yields a convex optimization problem, but also allows extraction of nonlocal models whose kernels may be partially negative while maintaining well-posedness even in small-data regimes. To achieve this, based on established nonlocal theory, we embed in our algorithm sufficient conditions on the non-positive part of the kernel that guarantee well-posedness of the learnt operator. These conditions are imposed as inequality constraints to meet the requisite conditions of the nonlocal theory. We demonstrate this workflow for a range of applications, including reproduction of manufactured nonlocal kernels; numerical homogenization of Darcy flow associated with a heterogeneous periodic microstructure; nonlocal approximation to high-order local transport phenomena; and approximation of globally supported fractional diffusion operators by truncated kernels.

The swelling of clay at high temperature and pressure is important for applications including nuclear waste storage but is not well understood. A molecular dynamics study of the swelling of Na montmorillonite in water at several temperatures (T = 298, 400, and 500 K) and water environment pressures (Pe = 5 and 100 MPa) is reported here. Adopting a rarely used setup that enables swelling pressure to be resolved with an accuracy of ~1 MPa, the swelling pressure was computed at basal spacings 1.6–2.6 nm (or 2–5 water layers between neighboring clay sheets), which has not been widely studied before. At T = 298 K and Pe = 5 MPa, swelling pressure Ps oscillates at d-spacing d smaller than 2.2 nm and decays monotonically as d increases. Increasing T to 500 K but keeping Pe at 5 MPa, Ps remains oscillatory at small d, but its repulsive peak at d = 2.2 nm shifts to ~2.0 nm and Ps at different d-spacings can grow more attractive or repulsive. At d > 2.0 nm, Ps is weakened greatly. Keeping T at 500 K and increasing Pe to 100 MPa, Ps recovers toward that at T = 298 K and Pe = 5 MPa, however, the repulsive peak at d = 2.0 nm remains the same. The opposite effects of increasing temperature and pressure on the density and dielectric screening of water, which control ion correlations and thus double layer repulsion, are essential for understanding the observed swelling pressure at elevated temperatures and its response to environment pressures.

Background:: Subcritical crack growth can occur in a brittle material when the stress intensity factor is smaller than the fracture toughness if an oxidizing agent (such as water) is present at the crack tip. Objective:: We present a novel bi-material beam specimen which can measure environmentally assisted crack growth rates. The specimen is “self-loaded” by residual stress and requires no external loading. Methods:: Two materials with different coefficient of thermal expansion are diffusion bonded at high temperature. After cooling to room temperature a subcritical crack is driven by thermal residual stresses. A finite element model is used to design the specimen geometry in terms of material properties in order to achieve the desired crack tip driving force. Results:: The specimen is designed so that the crack driving force decreases as the crack extends, thus enabling the measurement of the crack velocity versus driving force relationship with a single test. The method is demonstrated by measuring slow crack growth data in soda lime silicate glass and validated by comparison to previously published data. Conclusions:: The self-loaded nature of the specimen makes it ideal for measuring the very low crack velocities needed to predict brittle failure at long lifetimes.

The CHAMP experience is summarized.

Magneto-optical (MO) coupling incorporates photon-induced change of magnetic polarization that can be adopted in ultrafast switching, optical isolators, mode convertors, and optical data storage components for advanced optical integrated circuits. However, integrating plasmonic, magnetic, and dielectric properties in one single material system poses challenges since one natural material can hardly possess all these functionalities. Here, co-deposition of a three-phase heterostructure composed of a durable conductive nitride matrix with embedded core–shell vertically aligned nanopillars, is demonstrated. The unique coupling between ferromagnetic NiO core and atomically sharp plasmonic Au shell enables strong MO activity out-of-plane at room temperature. Further, a template growth process is applied, which significantly enhances the ordering of the nanopillar array. The ordered nanostructure offers two schemes of spin polarization which result in stronger antisymmetry of Kerr rotation. The presented complex hybrid metamaterial platform with strong magnetic and optical anisotropies is promising for tunable and modulated all-optical-based nanodevices.

Ground 304 stainless steel (SS) samples were exposed to sea salt particles at 35 °C and two relative humidity (RH) levels for durations ranging from 1 week to 2 years. For all exposure times, pit number density and total pit volume at 40% RH were observed to be considerably greater than those at 76% RH. Statistical analysis of distributions of pit populations for both RH conditions showed that pit number density and total pit volume increased rapidly at first but slowed as exposure time increased. Cross-hatched features were observed in the 40% RH pits while ellipsoidal, faceted pits were observed at 76% RH. Optical profilometry indicated that most pits were not hemispherical. X-ray tomography provided evidence of undercutting and fissures. Piecewise curve fitting modeled the 40% RH data closely, predicting that corrosion damage would eventually plateau. However, a similar treatment of the 76% RH data suggested that corrosion damage would continuously increase, which implied that the piecewise power-law fit was limited in its ability to model atmospheric corrosion generally. Based on these observations, the operative mechanisms determining long-term corrosion behavior were hypothesized to be different depending on the RH of exposure.

The present work addresses the need for an efficient, versatile, accurate and open-source numerical tool to be used during the design stage of wave energy converters (WECs). The device considered here is the heaving point-absorber developed and tested by Sandia National Laboratories. The smoothed particle hydrodynamics (SPH) method, as implemented in DualSPHysics, is proposed since its meshless approach presents some important advantages when simulating floating devices. The dynamics of the power take-off system are also modelled by coupling DualSPHysics with the multi-physics library Project Chrono. A satisfactory matching between experimental and numerical results is obtained for: (i) the heave response of the device when forced via its actuator; (ii) the vertical forces acting on the fixed device under regular waves and; (iii) the heave response of the WEC under the action of both regular waves and the actuator force. This proves the ability of the numerical approach proposed to simulate accurately the fluid–structure interaction along with the WEC’s closed-loop control system. In addition, radiation models built from the experimental and WAMIT results are compared with DualSPHysics by plotting the intrinsic impedance in the frequency domain, showing that the SPH method can be also employed for system identification.

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Tunnel junction devices grown monolithically by metal organic chemical vapor deposition were optimized for minimization of the tunnel junction voltage drop. Two device structures were studied: an all-GaN homojunction tunnel junction and a graded InGaN heterojunction-based tunnel junction. This work reports a record-low voltage drop in the graded-InGaN heterojunction based tunnel junction device structure achieving a de-embedded tunnel junction voltage drop of 0.17 V at 100 A/cm2. The experimental data were compared with a theoretical model developed through technology computer-aided design (TCAD) simulations that offer a physics-based approach to understanding the key components of the design space, which lead to a more efficient tunnel junction.

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Gaussian processes and other kernel-based methods are used extensively to construct approximations of multivariate data sets. The accuracy of these approximations is dependent on the data used. This paper presents a computationally efficient algorithm to greedily select training samples that minimize the weighted L^p error of kernel-based approximations for a given number of data. The method successively generates nested samples, with the goal of minimizing the error in high probability regions of densities specified by users. The algorithm presented is extremely simple and can be implemented using existing pivoted Cholesky factorization methods. Training samples are generated in batches which allows training data to be evaluated (labeled) in parallel. For smooth kernels, the algorithm performs comparably with the greedy integrated variance design but has significantly lower complexity. Numerical experiments demonstrate the efficacy of the approach for bounded, unbounded, multi-modal and non-tensor product densities. We also show how to use the proposed algorithm to efficiently generate surrogates for inferring unknown model parameters from data using Bayesian inference.

This project has four main technical objectives: 1) Develop and improve bifacial performance models by adding the capability to evaluate electrical behavior and performance of bifacial modules and arrays under realistic field conditions including irradiance variability caused by racking, module frame, and position in the array. 2) Instrument and monitor performance of fielded bifacial systems to validate performance models and to measure, analyze and publish on bifacial energy gain. These should include both research and commercial bifacial systems and cover a variety of deployment applications. 3) Evaluate optimal bifacial system designs using simulations leveraging high-performance computing, and also using full sized and miniaturized experimental field deployments. 4) Establish and contribute to international test standards for bifacial system performance, testing, and safety, and work with the community to establish installation and siting best practices.

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This document describes the draft ruleset for Assent, an experimental wargame to practice consensus making in a crisis scenario.

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This analysis shows that when lower density crude oil is injected into the top of an underground salt storage cavern containing more dense crude, separate oil phases can form and coexist indefinitely. This has been observed at the U.S. Strategic Petroleum Reserve in spite of geothermal heating and natural convection, which tend to mix the contents of containers with significant vertical extent subjected to wall and bottom heating. Such persistent layering can create operational challenges for meeting delivery specifications if high-value, low-vapor pressure oil becomes trapped below incoming low-density, high-vapor pressure oil, effectively blocking access to the lower layers until the top layer is removed. Previous conceptual models assumed that the oil injection process mixed incoming oil with resident oil in a storage cavern, forming a single oil phase with relatively homogeneous properties. Here, a review of historical data from the Strategic Petroleum Reserve revealed that several caverns contain multiple oil layers. As a result, oil layering needs to be another variable considered when planning oil movements at SPR in order to optimize low-vapor pressure oil availability to assist in oil delivery blending.

The objectives of this project are as follows: 1) Reduce uncertainty in PV performance models by developing and validating new and improved models and submodes. 2) Create and manage an open source repository of modeling functions and data. 3) Build and grow the PV Performance Modeling Collaborative 4) Represent the US in the IEA PVPS Task 13 Working group.

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Elucidating the mechanisms responsible for sub-microsecond desorption of water and other impurities from electrode surfaces at high heating rates is crucial for understanding pulsed power behavior. Ionization of desorbed impurities in the vacuum regions causes power or current loss; devising methods to limit such desorption during the short time scale of pulsed power is needed to improve corresponding applications. Previous molecular modeling studies have strongly suggested that, under high vacuum conditions, the amount of water impurity desorbing from oxide surfaces on metal electrodes is at a sub-monolayer level at room temperature, which appears insufficient to explain observed pulsed power energy losses at high current densities. In this work, we apply Density Functional Theory (DFT) techniques to show that hydrogen trapped inside iron metal can diffuse into hematite (α-Fe₂O₃) on the metal surface, ultimately reacting with the oxide to form Fe(II) and H₂O. The latter desorbs at elevated temperature and may explain the anomalous amount of desorbed impurity inferred from pulsed-power experiments. We also apply a suite of characterization techniques to demonstrate that when iron metal is heated to 650 °C, the dominant surface oxide component becomes α-Fe₂O₃. The oxide facets exposed are found to be a mixture of (0001), (10-10), and others, in agreement with the DFT models used.

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The effect of nitrogen on the surfaces of polycrystalline ITER-grade tungsten and a tungsten single crystal were studied with low energy ion scattering (LEIS) and direct recoil spectroscopy (DRS). LEIS and DRS measurements on both tungsten surfaces were performed in an ultra-high vacuum system as various quantities of N₂ were introduced into the chamber through a variable leak valve. The obtained ion energy spectra reveal that nitrogen was readily adsorbed onto the surface, in turn limiting the amount of hydrogen that could be adsorbed onto the surface. These results not only provide insight into how the presence of nitrogen on tungsten surfaces may play a role in hydrogen adsorption and retention, but also serve to benchmark models being developed to describe the H-N-W system.

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