Publications

We report a comparison of inferred electron density ( n e ) in a He capacitively-coupled plasma, deduced from laser-collision induced fluorescence measurements, with values computed using a hybrid simulation framework based on particle-in-cell/Monte Carlo collisions simulations and a fluid model for excited He atoms. The studies were carried out for gas pressures between 50 mTorr and 1000 mTorr and peak-to-peak radio-frequency (13.56 MHz) voltages between 150 V and 350 V, in a highly symmetric source equipped with plane-parallel electrodes. A good agreement is found between the experimental and modeling results for n e except at the lowest operating voltages and gas pressures. The (effective) electron temperature ( T e ) values derived by the two methods agree as well reasonably within the plasma bulk. The simulation results are used to compare the density distributions of He+ and various He excited levels and their major populating and de-populating channels at 100 mTorr and 1000 mTorr.

Coastal communities face unique challenges in maintaining continuous service from critical infrastructure. This research advances capabilities for evaluating the impact of using wave energy to desalinate water on the resilience of coastal communities. The study focuses on the feasibility of using wave energy conversion to provide drinking water to communities in need and applying resilience metrics to quantify its impact on the community. To assess the feasibility of wave-powered desalination, this research couples the open-source software Wave Energy Converter SIMulator (WEC-Sim) and Water Network Tool for Resilience (WNTR). This research explores variations in both the wave resource (location, seasonality, and duration) and the ability to maintain drinking water service during a disruption scenario by applying the simulation framework to three case studies, which are based on communities in Puerto Rico. The simulation framework provides a contextualized assessment of the ability of wave-powered desalination to improve the resilience of coastal communities, which can serve as a methodology for future studies seeking the integration of wave-powered desalination with water distribution systems.

Abstract not provided.

Hidden geothermal systems represent a potentially prolific energy resource that could support critical U.S. public and government energy priorities. Basin and Range Investigations for Developing Geothermal Energy (BRIDGE) addressed some the challenges associated with hidden system exploration by prioritizing cost-effective exploration early on through strategic workflow and informed decision-making that mitigates early risk and shifts resources to later exploration stages (e.g., drilling). Sandia National Laboratories partnered with U.S. Navy Geothermal Office, Geologic Geothermal Group, and independent consultants, with additional collaboration with U.S. Geological Survey and private industry. The primary tool of the BRIDGE project was to deploy a regional-scale airborne electromagnetic method to investigate the shallow resistivity structure in areas with high prospectivity. This was followed up at several prospects by a multidisciplinary exploration approach, including additional geologic, geophysical and geochemical studies. A central tenet to the BRIDGE methodology is that zones of low resistivity frequently occur over geothermal systems in the Basin and Range, and when paired with other data constraints, imaging these zones can enable discovery of these systems. In addition to exploring greenfield areas (i.e., Grover Point), the BRIDGE project also flew HTEM resistivity surveys over known geothermal systems including those with established power plants (Don A. Campbell and Salt Wells) and prospects that are known to the literature but remain undeveloped, at least in part, due to a lack of understanding on the location of their producible reservoirs. BRIDGE produced a comprehensive set of data from prospects identified in the Nevada Play Fairway Analysis along with conceptual models for top ranking prospects, wherein all of the observations are used to inform an interpreted model of the system. These models present a range of possible system parameters such as temperature and size, and they are further informed by system analogues in the Basin and Range province and elsewhere. The results of this work leave space for further exploration that may now occur at prospects ‘down the list’ rather than distribution exploration resources evenly across all prospects.

Malicious actors frequently use proxies and VPNs to evade detection and hide their origin. Current challenges to information security include the use of residential proxies to blend in with normal traffic and Man-in-the-Middle phishing proxies that are used to compromise accounts protected with mult-factor authentication. We advance a taxonomy and feature set for the identification of proxied traffic based on the network layer where proxying occurs. We describe how these features apply to common proxy types and how to use these features in the classification of the proxied traffic. Collection of these additional features is feasible using existing network sensors and web servers, while only adding about 30% volume to commonly deployed network sensor logs.

Leading edge erosion (LEE) of wind turbine blades has been identified as a major factor in decreased wind turbine blade lifetimes and energy output over time. Accordingly, the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) has created the Task 46 to undertake cooperative research in the key topic of blade erosion. Participants in the task are given in Table 1.

Scalability is a critical factor in High-Performance Computing (HPC), where optimizing resource usage has a direct impact on cost-effectiveness and time-efficiency. This report presents a strong scaling performance study of the Albany Land Ice (ALI) code across different HPC architectures, towards determining the best configuration to use when running large-scale simulation ensembles.

Abstract not provided.

Abstract not provided.

We report the development of an atomistic-informed, surface-state-dependent predictive model for particle exchange in a carbon-tungsten plasma-surface interface. The predictive model uses machine learning (ML) techniques to learn the energy and angular distributions for particle exchange and rate functions for surface state evolution from molecular dynamics simulations of cumulative bombardment of tungsten by energetic carbon ions. Each predictive component is sensitive to the energy and trajectory of incident plasma species and the surface state. The surface state is represented by a set of surface state descriptors, which were derived from the atomistic surface state for each independent carbon bombardment event. These descriptors are representative of the composition and degree of amorphization of the outermost angstrom of surface material and were chosen to optimize predictive performance for particle exchange at the interface. The distributions for particle exchange (reflection/sputtering) are demonstrated to vary with each surface state descriptor, motivating the development of surface-state-dependent particle exchange models for plasma simulations. The performance of various ML methods was compared, including polynomial quantile regression, artificial neural networks, k-nearest neighbors, and random forest algorithms, with polynomial regression performing the best for interpolation and extrapolation of learned relationships. In addition to the particle exchange model, a neutral network was developed and used to identify data sufficiency throughout surface descriptor space, which will enable real-time feedback during future data production to ensure data is produced where it is most needed, and we provide commentary on improvements to the data production workflow for future endeavors.

There is a need to measure the sensitivity of the image plate scanners used to scan Z shot data. The Carestream HPX-1 image plate scanner was tested, and its performance was characterized to determine the system sensitivity in terms of signal per incident photon as a function of x-ray energy. A Manson source was used to simultaneously expose an Amptek x-ray multi-channel analyzer and image plates, allowing for a comparison of the counts as a function of energy to the signal recorded on the image plates. NIST-certified radioactive sources were used to assign an absolute sensitivity. Results indicate that the HPX-1 scanner response matches the shape of the modeled response, allowing for absolute scaling using NIST-certified radioisotope sources. The HPX-1 scanner shows a stable response with measurements taken over one week. In contrast, the sensitivity of the DITABIS scanner was characterized using the same approach, but it exhibits changes in its response that varied by more than 50% over the span of 4 days.

This document provides an overview of re-start efforts at Sandia National Laboratories (SNL), National Solar Thermal Test Facility (NSTTF), for the DOE SETO SNL Heliostat Refurbishment project. Sandia continues to pursue innovative concentrating solar power (CSP) and thermal (CST) research in order to enhance commercial performance and reduce LCOE and LCOH of concentrating solar energy.

Leading edge erosion (LEE) of wind turbine blades has been identified as a major factor in decreased wind turbine blade lifetimes and energy output over time. Accordingly, the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) has created the Task 46 to undertake cooperative research in the key topic of blade erosion. Participants in the task are given in Table 1.

This report introduces a novel deployable origami baffle designed for telescopes and optical systems, which reduces stray light while maintaining high compactness ratios and low weight. This design leverages the planar nature of the end caps on a cylindrical Kresling origami fold to incorporate mounting points, deployable options, and baffle vanes. The adaptable nature of origami (number of faces, origami geometrical ratios, scaling, etc.) allows the design to easily conform to system requirements, including field of view, deployed length, stowed/deployed stability points, and available volume. Geometric ratios that exhibit bistability in both the stowed and deployed states are discussed in detail, as this results in a rigid structure that maintains its desired configuration. Several designs were conceptualized, and multiple small-scale prototypes were constructed. Potential applications include camera lens hoods, lightweight astronomy telescopes, and deployable baffles for space telescopes and optical systems.

This study supports the Office of Radiological Security’s (ORS) mission of eliminating cesium irradiators by analyzing the supply chain for self-shielded X-ray irradiators (SSXIs), identifying potential risks, and proposing mitigation measures. The research focuses on the primary components of SSXIs, including X-ray tubes, controllers, generators, and coolers or chillers, and evaluates their vulnerabilities using a comprehensive risk matrix framework. The methodology includes subject matter expert (SME) interviews with relevant manufacturers and major stakeholders, a deep literature review, and a meta-analysis of maintenance reports provided by SSXI end users. Results show that while the SSXI market is small, it’s growing, and the highly global nature of the supply chain may create vulnerabilities for critical SSXI components (X-ray tubes are the most vulnerable, followed by generators and controllers). This research communicates necessary information to address concerns of current and future end users, especially those interested in transitioning away from radioactive sources, and informs future policy aimed at supporting the irradiation industry.

Previous experiments utilizing the planar MITL foil platform (T.J. Smith et al. RSI 2021) on the 1-MA, 100-ns Mykonos facility have shown neutral atomic and molecular hydrogen in the gap after rapid heating of the foil surfaces before breakdown. Additionally, previous attempts at using parylene-N as a coating for power flow surfaces on the 1-MA, 100-ns Zebra facility have shown tamping of the electrothermal instability at thicknesses of 50-60 μm (T.M. Hutchinson et al. Phys. Rev. E 2018).

In this portion of the TrojAI evaluation, we focus on the cyber-network-c2-mar2024 dataset. Recall that in this round ResNet18 and ResNet34 neural networks (NN) were trained on the USTC-TFC2016 dataset with the aim of distinguishing between benign versus botnet command and control (c2) packets. A range of bytes from each packet was reformatted into a 28x28 pixel image, and the collection of reformatted packets served as the training (and testing) data for the two ResNet models. For some of the data a trigger watermark was strategically placed to affect various inputs to the NNs. This watermarked, or poisoned, data in turn created a poisoned, or trojaned NN. The data were poisoned in different ways ultimately creating different trojaned NNs. This collection of trojaned NNs was combined with various versions of not trojaned NNs and served as the training and testing data for the performers. The performers’ task was to construct a classifier to distinguish between the trojaned and not trojaned models. It was previously noted that the performers struggled with the cyber-network-c2-mar2024 dataset, motivating this investigation of potential reasons the performers experienced challenges.

Modern Active Electronically Steerable Array (AESA) antennas offer substantial control over transmit and receive antenna beam forming, including via elemental weighting to achieve a desired tapered aperture. However, these tapers also have the adverse side effect of diminishing total transmitted power and/or raising the system noise figure. This is readily calculated, and must be considered for overall radar performance prediction. Independent tapers for transmit and receive arrays might be play.

The Advanced Reactor Cyber Analysis and Development Environment (ARCADE) simplifies the evaluation and assessment of robustness factor and cyber resilience that support secure-by-design for advanced reactor nuclear power plants. In this manner, ARCADE supports risk-informed performance based (RIPB) evaluations of cybersecurity through its integration of plant physics with high-fidelity emulations of control systems. This cross domain approach enables comprehensive analysis of control system sensitivities, cyber-attack scenarios, and their consequences. ARCADE has been custom developed to meet the demands identified in Tier 1 of the Tiered Cyber Analysis (TCA) as outlined in NRC Draft Regulation Guide (RG) 5.96, which provides a RIPB cybersecurity approach for new reactors.

This report calculates resonant Deep Slot transmission and matched uniformly distributed load crosssections at normal incidence, which act as bounds on the power penetration and reception.

This report documents the final design phase of the Critical Experiment Design (CED-2) conducted as part of integral experiment request (IER) 523. The purpose of IER 523 is to determine critical configurations of 35 weight percent (wt%) enriched uranium dioxide beryllium oxide (UO₂-BeO) material driven by an annular ring of Seven Percent Critical Experiment (7uPCX) fuel rods at Sandia National Laboratories (Sandia). The experiments will provide benchmark data on water moderated, intermediately enriched UO2 systems as well as Be nuclear data. The experiment will also provide partial validation for the beryllium oxide (BeO) thermal neutron scattering law (TSL) in the thermal energy range. Experiment design concepts, neutronic analysis results, and proposed paths for continuing the CED process are presented. This report builds on the feasibility and justification of experimental need report (CED-0) and preliminary experiment design report (CED-1) completed in December 2021 and September 2023, respectively [1, 2].

Representing complex systems as graphs enables use of mathematical tools to identify faults or predict failures. Graph nodes correspond to individual modules or subsystems, and edges link coupled system parts. ‘Probes’ measure the node outputs, monitoring the system health for unexpected behavior. Assuming one cannot probe every point, within a system, the fault correlates to a region—not necessarily the specific location. Bayesian networks trained to understand fault patterns can accurately identify the source. The diagnostic tool described aides debugging by pinpointing system failure causes. For predictive maintenance, probe data develop probability distribution functions describing subsystem mean time to failure. Unit lifetime can be estimated through these probability distributions. Two approaches include using Bayesian classifiers to infer the system failure source and developing maintenance schedules by treating systems as collections of random variables. When failure behavior does not follow a closed form function, use of similarity models is proposed.

This report examines the coupling into a circumferential slot array on an infinite cylinder with a finite length interior cavity operating at the fundamental cavity modal frequencies. The directivity gain associated with the interior cavity field is examined, and in this frequency range (as well as lower frequencies), is found to be enhanced by the exterior short circuit current variation with angle driving the slots.

Reference samples with known boron coverage are needed for calibrating measurements of boron deposition in tokamaks where boron is used for wall conditioning to improve fusion plasma performance. This report summarizes recent work at the Sandia Ion Beam Laboratory to fabricate such reference samples. Rutherford backscattering and nuclear reaction analysis were used to determine the boron coverage on reference samples consisting of a thin layer of boron on a silicon substrate.

The recompression closed Brayton cycle (RCBC) test rig at the Sandia Brayton Laboratory provides a development platform to accelerate the commercialization of key technologies for supercritical CO₂ (sCO₂) closed loop Brayton cycles. The test rig enables testing to gain experience and confidence with new technologies, equipment, and processes, and automating monitors and controls will enhance Sandia’s ability to perform the types and amounts of testing needed. This report identifies candidates for automatic monitoring and control to ensure the loop remains within design limits and minimize risk to equipment due to off-normal events or conditions.

This report presents analyses of the AB7 ABCOVE sodium spray fire experiment with the MELCOR code. This code simulates the progression of accident events for analysis and auditing purposes of nuclear facilities during accident conditions. Historically, the ABCOVE experiments have contributed to the validation of aerosol physics and related phenomena. Given advancements in sodium-cooled reactor designs, characterization of the sodium spray combustion may further the review and validation of newly incorporated sodium properties and physics packages, namely, the sodium equations of state (EOS) and the sodium combustion (NAC) package within MELCOR. Previously, the AB5 and AB6 experiments were analyzed with and without the NAC package. This work builds on the previous analyses with a demonstration of the current code capabilities of MELCOR with a more mild Na spray and pool fire scenario.

This document summarizes the activities at the Mykonos Pulsed Power Facility during the calendar year 2024. The first section reports on the yearly shot statistics along with some facility highlights. Section 2 discusses the many improvements we were able to complete this year, thanks to the generous MAAP funding. The last part focuses on each individual campaign and their respective results.

Abstract not provided.

A trilateral agreement has been finalized involving research institutions in Korea, Japan, and the U.S. The project partners are Sandia, LLNL, KIST, KAIST, and AIST. The project title is “Structure-Property Relationships in Metal Alloys for Hydrogen Storage and Processing.” Funding for the U.S. portion of the effort is through NNSA; the PI is Vitalie Stavila. The overall objective of this project is to identify detailed structure-property relationships governing hydrogen separation, purification, storage, and compression in compositionally complex metal alloys.

Spiking neural networks seek to emulate biological computation through interconnected artificial neuron and synapse devices. Spintronic neurons can leverage magnetization physics to mimic biological neuron functions, such as integration tied to magnetic domain wall (DW) propagation in a patterned nanotrack and firing tied to the resistance change of a magnetic tunnel junction (MTJ), captured in the domain wall-magnetic tunnel junction (DW-MTJ) device. Leaking, relaxation of a neuron when it is not under stimulation, is also predicted to be implemented based on DW drift as a DW relaxes to a low energy position, but it has not been well explored or demonstrated in device prototypes. Here, we study DW-MTJ artificial neurons capable of leaky integrate-and-fire (LIF) behavior and demonstrate geometry-dependent leaking dynamics that results in repeatable, tunable LIF operation. Studying the behavior of five different device designs, we show tuning the geometry, stimulating fields and currents, and location of electrical contacts results in a wide range of neuron behavior. Additionally, implementation of an asymmetric notch allows for nonlinear pinning which increased expressivity without sacrificing leaking. The measured behavior is implemented in a simulated spiking neural network that outperforms a 1D model of continuous DW motion and approaches the performance of an ideal LIF activation function. The results show that the analog LIF capability of DW-MTJ neurons combines many desirable neuron functions into a single device, which can result in varied forms of multifunctional neuromorphic computing.

Color centers in the O-band (1260–1360 nm) are crucial for realizing long-coherence quantum network nodes in memory-assisted quantum communications. However, only a limited number of O-band color centers have been thoroughly explored in silicon hosts as spin-photon interfaces. This study explores and compares two promising O-band color centers in silicon for high-fidelity spin-photon interfaces: T and *Cu (transition metal) centers. During T center generation process, we observed the formation and dissolution of other color centers, including the copper-silver related centers with a doublet line around 1312 nm (*Cu$^0_n$), near the optical fiber zero dispersion wavelength (around 1310 nm). We then investigated the photophysics of both T and *Cu centers, focusing on their emission spectra and spin properties. The *Cu$^0_0$ line under a 0.5 T magnetic field demonstrated a 25% broadening, potentially due to spin degeneracy, suggesting that this center can be a promising alternative to T centers.

Fluid-silica interfaces are ubiquitous in chemistry, occurring in both natural geochemical environments and practical applications ranging from separations to catalysis. Simulations of these interfaces have been, and continue to be, a significant avenue for understanding their behavior. A constraining factor, however, is the availability of accurate force fields. Most simulations use traditional “mixing rules” to determine nonbonded dispersion interactions, an approach that has not been critically examined. Here, we present Lennard-Jones parameters for the interaction of carbon dioxide with silica interfaces that are optimized to reproduce density functional theory (DFT)-based binding energies. The modeling is based on the recently developed silica-DDEC force field, whose atomic charges are consistent with DFT calculations. Standard mixing rules are found to predict weaker CO2 binding to silica than that obtained from DFT, an effect corrected by the optimized parameters given here. This behavior extends to other silica force fields (Clayff and Gulmen-Thompson), and the present Lennard-Jones parameters improve their performance as well. The effects of improved Lennard-Jones parameters on the structural and dynamical properties of condensed CO2 in silica slit pores are also examined.

Porous liquids (PLs) are an exciting new class of materials for carbon capture due to their high gas adsorption capacity and ease of industrial implementation. They are composed of sorbent particles suspended in a nonadsorbed solvent, forming a liquid with permanent porosity. While PLs have a vast number of potential compositions based on the number of solvents and sorbent materials available, most of the research has been focused on the selection of the sorbent rather than the solvent. Therefore, PL design criteria on the supramolecular structures of the solvent are explored to create a fundamental understanding of how the solvent enables PL formation for rapid discovery of new PL compositions. Atomistic molecular dynamics simulation of eight solvents with a range of molecular sizes, shapes, and intramolecular bonding was performed, identifying that the shape and size of molecular clusters formed in the solvent are the driving predictor of PL formation rather than the size of the individual solvent molecule. The results demonstrate a significant departure from common approaches to PL formation based on the steric exclusion of solvent molecules from the sorbent via the size of the pore aperture. A modeling and experimental validation study further supports these findings. Through this computational material design study, a previously unexplored mechanism in PL formation, solvent-solvent clustering, is identified as a critical factor for the accelerated discovery of liquid phase carbon capture materials.

We report on low-frequency measurements of few electrons floating on superfluid helium using a bespoke cryogenic cascode amplifier circuit built with off-the-shelf GaAs high-electron-mobility transistors (HEMTs). We integrate this circuit with a charge-coupled device (CCD) to transport the electrons on helium and characterize its performance. Here, we show that this circuit has a signal-to-noise ratio (SNR) of ~ 2 $\frac{e}{√Hz}$ at 102 kHz, an order of magnitude improvement from previous implementations, and provides a compelling alternative to few electron sensing with high-frequency resonators.

Here we look at various forms of spectrum and associated pseudospectrum that can be defined for noncommuting d-tuples of Hermitian elements of a C^$\ast$-algebra. In particular, we focus on the forms of multivariable pseudospectra that are finding applications in physics. The emphasis is on theoretical calculations of examples, in particular for noncommuting pairs and triple of operators on infinite dimensional Hilbert space. In particular, we look at the universal pair of projections in a C^$\ast$ -algebra, the usual position and momentum operators, and triples of tridiagonal operators. We prove a relation between the quadratic pseudospectrum and Clifford pseudospectra, as well as results about how symmetries in a tuple of operators can lead to a symmetry in the various pseudospectra.

Epitaxial regrowth processes are presented for achieving Al-rich aluminum gallium nitride (AlGaN) high electron mobility transistor (HEMTs) with p-type gates with large, positive threshold voltage for enhancement mode operation and low resistance Ohmic contacts. Utilizing a deep gate recess etch into the channel and an epitaxial regrown p-AlGaN gate structure, an Al0.85Ga0.15N barrier/Al0.50Ga0.50N channel HEMT with a large positive threshold voltage (VTH = +3.5 V) and negligible gate leakage is demonstrated. Epitaxial regrowth of AlGaN avoids the use of gate insulators which can suffer from charge trapping effects observed in typical dielectric layers deposited on AlGaN. Low resistance Ohmic contacts (minimum specific contact resistance = 4 × 10−6 Ω cm2, average = 1.8 × 10−4 Ω cm2) are demonstrated in an Al0.85Ga0.15N barrier/Al0.68Ga0.32N channel HEMT by employing epitaxial regrowth of a heavily doped, n-type, reverse compositionally graded epitaxial structure. The combination of low-leakage, large positive threshold p-gates and low resistance Ohmic contacts by the described regrowth processes provide a pathway to realizing high-current, enhancement-mode, Al-rich AlGaN-based ultra-wide bandgap transistors.

The absorption and emission of X-rays in dysprosium-doped yttrium aluminum garnet (YAG:Dy) has produced unexpected thermographic behavior, which is investigated using a combination of finite temperature ab initio molecular dynamic simulations, structural characterization, and electronic structure calculations of X-ray characteristics. Calculated average peak X-ray absorption spectra (XAS) from simulations between 300 and 600 K result in peak intensity loss due to thermalization effects, matching experimentally measured behavior of YAG:Dy. Investigation of atomic snapshots indicates structural factors that correlated with the X-ray behavior, with the first Y-O coordination sphere identified as the primary structural feature unique to high XAS intensity as calculated by radial and pair distribution functions.

A new particle-based reweighting method is developed and demonstrated in the Aleph Particle-in-Cell with Direct Simulation Monte Carlo (PIC-DSMC) program. Novel splitting and merging algorithms ensure that modified particles maintain physically consistent positions and velocities. This method allows a single reweighting simulation to efficiently model plasma evolution over orders of magnitude variation in density, while accurately preserving energy distribution functions (EDFs). Demonstrations on electrostatic sheath and collisional rate dynamics show that reweighting simulations achieve accuracy comparable to fixed weight simulations with substantial computational time savings. This highly performant reweighting method is recommended for modeling plasma applications that require accurate resolution of EDFs or exhibit significant density variations in time or space.

Simulating subsurface contaminant transport at the kilometer-scale often entails modeling reactive flow and transport within and through complex geologic structures. These structures are typically meshed by hand and as a result geologic structure is usually represented by one or a few deterministically generated geological models for uncertainty studies of flow and transport in the subsurface. Uncertainty in geologic structure can have a significant impact on contaminant transport. In this study, the impact of geologic structure on contaminant tracer transport in a shale formation is investigated for a simplified generic deep geologic repository for permanent disposal of spent nuclear fuel. An open-source modeling framework is used to perform a sensitivity analysis study on transport of two tracers from a generic spent nuclear fuel repository with uncertain location of the interfaces between the stratum of the geologic structure. The automated workflow uses sampled realizations of the geological structural model in addition to uncertain flow parameters in a nested sensitivity analysis. Concentration of the tracers at observation points within, in line with, and downstream of the repository are used as the quantities of interest for determining model sensitivity to input parameters and geological realization. Finally, the results of the study indicate that the location of strata interfaces in the geological structure has a first-order impact on tracer transport in the example shale formation, and that this impact may be greater than that of the uncertain flow parameters.

Stabilizing weak clayey soils with lime is an effective method for improving the mechanical properties of soil. However, lime production is an energy-intensive process producing significant CO2 emissions in lime-stabilized soils, which can be counteracted through accelerated carbonation that enhances its engineering performance. The present study evaluates accelerated carbonation of lime-treated soils by adding gaseous (CO2-rich gas), liquid (water-CO2 mixture), and solid (sodium bicarbonate) CO2 sources. Results indicated that samples carbonated with gaseous CO2 exhibited 100% lime carbonation, while samples treated with solid and aqueous sources of CO2 had a mean lime carbonation of 60% and 40%, respectively. All lime-treated-carbonated samples exhibited a mean 50% increase in unconfined compressive strength compared to the untreated samples after a 7-day curing period. Durability evaluation through cyclic wetting and drying indicated that the carbonated samples had higher durability than the untreated samples. X-ray computed tomography showed that adding solid and liquid sources of CO2 facilitated the flocculation of montmorillonite, reducing the porosity. However, a higher dosage of solid CO2 induced clay dispersion, increasing the porosity. X-ray diffraction and thermogravimetric analysis verified CO2 sequestration through the formation of calcite, a thermodynamically stable polymorph of calcium carbonate.

Early-bird communication is a communication/computation overlap technique that leverages fine-grained communication to improve application run-time. Communication is divided such that each individual thread can initiate transmission of its portion of the data upon completion rather than waiting for a dedicated communication phase. The benefit of early-bird communication depends on the completion timing of the individual threads: On the one hand, if all threads are complete at nearly the same time, the overheads of sending multiple messages will accumulate, leading to performance that is worse than if a single message had been sent. On the other hand, if thread completions are spread out in time, those that complete earlier can send data while others continue working, leading to performance that is better than if a single message had been sent. The challenge is that the completion times are currently unknown and can vary based on application, problem size, system software, and underlying hardware. In this paper, we address this lacuna by measuring and evaluating the potential overlap afforded by early-bird communication for a selection of proxy applications. These measurements help us understand whether a given application could benefit from early-bird communication. We present our technique for gathering this data and evaluate data collected from three proxy applications: MiniFE, MiniMD, and MiniQMC. Each application is run on three systems with distinct CPU architectures and strong scales across three run sizes. To characterize the behavior of these workloads, we study the trends of thread timings at both a macro level, across all threads across all runs of an application, and a micro level, that is, within a single process of a single run. We observe that our tested applications exhibit significantly different thread arrival distributions. The machine used had a significant impact, with the window of potential overlap varying by as much as an order of magnitude.

Since the early 2000s, the impending end of Moore’s scaling, as the physical limits to shrinking transistors have been approached, has fueled interest in improving the functionality and efficiency of integrated circuits by employing memristors or two-terminal resistive switches. Formation (or avoidance) of localized conducting channels in many memristors, often called “filaments”, has been established as the basis for their operation. While we understand some qualitative aspects of the physical and thermodynamic origins of conduction localization, there are not yet quantitative models that allow us to predict when they will form or how large they will be. Here we compile observations and explanations of channel formation that have appeared in the literature since the 1930s, show how many of these seemingly unrelated pieces fit together, and outline what is needed to complete the puzzle. This understanding will be a necessary predictive component for the design and fabrication of post-Moore’s-era electronics.

The effect of stress triaxiality on the strain-to-fracture of as-received and photo-oxidized polyamide-6 (PA-6) was investigated using mechanical testing, synchrotron X-ray tomography, and finite element analyses. Mechanical tests were conducted on cylindrical and round notched specimens, where different notch radii were used to vary the stress triaxiality. The specimens were aged by exposure to ultra-violet (UV) radiation at 60∘, causing photo-oxidation. As-received and so-aged specimens were loaded to failure (complete loss of load carrying capacity). For both unaged and aged specimens, a higher triaxiality led to a lower strain-to-fracture. To elucidate the micromechanical damage that mediates fracture in both conditions, specimens with an intermediate notch sharpness were loaded to the peak load, unloaded, and scanned ex situ using synchrotron X-ray tomography. Damage in the unaged bar was found to occur by cavitation and was concentrated at the center of the specimen, where the triaxiality is highest. In the UV-aged bar, a network of inter-connected chemical cracks were found on the notch surface, where the triaxiality is lowest. Finite element analyses were deployed to approximate the local triaxiality at damaged regions in the unaged and UV-aged specimens using a constitutive relation for semicrystalline polymers. From these analyses, the relationship between local triaxiality and strain-to-fracture was quantified for both unaged and photo-oxidized PA-6. Both unaged and photo-oxidized PA-6 showed similar decreases in ductility with triaxiality, hinting at common ductile fracture processes.

Engineering and applied science rely on computational experiments to rigorously study physical systems. The mathematical models used to probe these systems are highly complex, and sampling-intensive studies often require prohibitively many simulations for acceptable accuracy. Surrogate models provide a means of circumventing the high computational expense of sampling such complex models. In particular, polynomial chaos expansions (PCEs) have been successfully used for uncertainty quantification studies of deterministic models where the dominant source of uncertainty is parametric. We discuss an extension to conventional PCE surrogate modeling to enable surrogate construction for stochastic computational models that have intrinsic noise in addition to parametric uncertainty. We develop a PCE surrogate on a joint space of intrinsic and parametric uncertainty, enabled by Rosenblatt transformations, which are evaluated via kernel density estimation of the associated conditional cumulative distributions. Furthermore, we extend the construction to random field data via the Karhunen-Loève expansion. We then take advantage of closed-form solutions for computing PCE Sobol indices to perform a global sensitivity analysis of the model which quantifies the intrinsic noise contribution to the overall model output variance. Additionally, the resulting joint PCE is generative in the sense that it allows generating random realizations at any input parameter setting that are statistically approximately equivalent to realizations from the underlying stochastic model. The method is demonstrated on a chemical catalysis example model and a synthetic example controlled by a parameter that enables a switch from unimodal to bimodal response distributions.

The sensitivity analysis algorithms that have been developed by the radiation transport community in multiple neutron transport codes, such as MCNP and SCALE, are extensively used by fields such as the nuclear criticality community. However, these techniques have seldom been considered for electron transport applications. In the past, the differential-operator method with the single scatter capability has been implemented in Sandia National Laboratories’ Integrated TIGER Series (ITS) coupled electron-photon transport code. This work is meant to extend the available sensitivity estimation techniques in ITS by implementing an adjoint-based sensitivity method, GEAR-MC, to strengthen its sensitivity analysis capabilities. To ensure the accuracy of this method being extended to coupled electron-photon transport, it is compared against the central-difference and differential-operator methodologies to estimate sensitivity coefficients for an experiment performed by McLaughlin and Hussman. Energy deposition sensitivities were calculated using all three methods, and the comparison between them has provided confidence in the accuracy of the newly implemented method. Unlike the current implementation of the differential-operator method in ITS, the GEAR-MC method was implemented with the option to calculate the energy-dependent energy deposition sensitivities, which are the sensitivity coefficients for energy deposition tallies to energy-dependent cross sections. The energy-dependent cross sections could be the cross sections for the material, elements in the material, or reactions of interest for the element. These sensitivities were compared to the energy-integrated sensitivity coefficients and exhibited a maximum percentage difference of 2.15%.

In [R. J. Baraldi and D. P. Kouri, Mathematical Programming, (2022), pp. 1-40], we introduced an inexact trust-region algorithm for minimizing the sum of a smooth nonconvex and nonsmooth convex function. The principle expense of this method is in computing a trial iterate that satisfies the so-called fraction of Cauchy decrease condition—a bound that ensures the trial iterate produces sufficient decrease of the subproblem model. In this paper, we expound on various proximal trust-region subproblem solvers that generalize traditional trust-region methods for smooth unconstrained and convex-constrained problems. We introduce a simplified spectral proximal gradient solver, a truncated nonlinear conjugate gradient solver, and a dogleg method. We compare algorithm performance on examples from data science and PDE-constrained optimization.

The impact of high-altitude electromagnetic pulse events on the electric grid is not fully understood, and validated modeling of mitigations, such as lightning surge arresters (LSAs) is necessary to predict the propagation of very fast transients on the grid. Experimental validation of high frequency models for surge arresters is an active area of research. This article serves to experimentally validate a previously defined ZnO LSA model using four metal-oxide varistor pucks and nanosecond scale pulses to measure voltage and current responses. The SPICE circuit models of the pucks showed good predictability when compared to the measured arrester response when accounting for a testbed inductance of approximately 100 nH. Additionally, the comparatively high capacitance of low-profile arresters show a favorable response to high-speed transients that indicates the potential for effective electromagnetic pulse mitigation with future materials design.

Disordered iron germanium (FeGe) has recently garnered interest as a testbed for a variety of magnetic phenomena as well as for use in magnetic memory and logic applications. This is partially owing to its ability to host skyrmions and antiskyrmions—nanoscale whirlpools of magnetic moments that could serve as information carriers in spintronic devices. In particular, a tunable skyrmion-antiskyrmion system may be created through precise control of the defect landscape in B20-phase FeGe, motivating the development of methods to systematically tune disorder in this material and understand the ensuing structural properties. To this end, we investigate a route for modifying magnetic properties in FeGe. In particular, we irradiate epitaxial B20-phase FeGe films with 2.8 MeV Au4+ ions, which creates a dispersion of amorphized regions that may preferentially host antiskyrmions at densities controlled by the irradiation fluence. To further tune the disorder landscape, we conduct a systematic electron diffraction study with in situ annealing, demonstrating the ability to recrystallize controllable fractions of the material at temperatures ranging from ∼150 to 250 °C. Finally, we describe the crystallization kinetics using the Johnson-Mehl-Avrami-Kolmogorov model, finding that the growth of crystalline grains is consistent with diffusion-controlled one-to-two dimensional growth with a decreasing nucleation rate.

This paper is concerned with inserting three-dimensional computer-aided design (CAD) geometries into meshes composed of hexahedral elements using a volume fraction representation. An adaptive procedure for doing so is presented. The procedure consists of two steps. The first step performs spatial acceleration using a k-d tree. The second step involves subdividing individual hexahedra in an adaptive mesh refinement (AMR)-like fashion and approximating the CAD geometry linearly (as a plane) at the finest subdivision. The procedure requires only two geometric queries from a CAD kernel: determining whether or not a queried spatial coordinate is inside or outside the CAD geometry and determining the closest point on the CAD geometry’s surface from a given spatial coordinate. We prove that the procedure is second-order accurate for sufficiently smooth geometries and sufficiently refined background meshes. We demonstrate the expected order of accuracy is achieved with several verification tests and illustrate the procedure’s effectiveness for several exemplar CAD geometries.

A variational phase field model for dynamic ductile fracture is presented. The model is designed for elasto-viscoplastic materials subjected to rapid deformations in which the effects of heat generation and material softening are dominant. The variational framework allows for the consistent inclusion of plastic dissipation in the heat equation as well as thermal softening. It employs a coalescence function to degrade fracture energy during regimes of high plastic flow. A variationally consistent form of the Johnson–Cook model is developed for use with the framework. Results from various benchmark problems in dynamic ductile fracture are presented to demonstrate capabilities. In particular, the ability of the model to regularize shear band formation and subsequent damage evolution in two- and three-dimensional problems is demonstrated. Importantly, these phenomena are naturally captured through the underlying physics without the need for phenomenological criteria such as stability thresholds for the onset of shear band formation.