Publications

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.

Low-velocity impact of 2D woven glass fiber reinforced polymer (GFRP) and carbon fiber reinforced polymer (CFRP) composite laminates was studied experimentally and numerically. Hybrid laminates containing blocked layers of GFRP/CFRP/GFRP with all plies oriented at 0° were investigated. Relatively high impact energies were used to obtain full perforation of the laminate in a low-velocity impact setup. Numerical simulations were carried out using the in-house transient dynamics finite element code, Sierra/SM, developed at Sandia National Laboratories. A three-dimensional continuum damage model was used to describe the response of a woven composite ply. Two methods for handling delamination were considered and compared: (1) cohesive zone modeling and (2) continuum damage mechanics. The reduced model size achieved by omission of the cohesive zone elements produced acceptable results at reduced computational cost. The comparison between different modeling techniques can be used to inform modeling decisions relevant to low velocity impact scenarios. The modeling was validated by comparing with the experimental results and showed good agreement in terms of predicted damage mechanisms and impactor velocity and force histories.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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].

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).

Abstract not provided.

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.

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.

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.

Abstract not provided.

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.

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 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 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.

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.

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.

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.

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.

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.

Influence in the post social media, world-is-flat online social landscape, has gone through an apocalypse level transformation. Trust, the critical component for social cohesion, now develops in a vastly different context from most of human history. We present MADmax, a multi-agent opinion dynamics simulation that utilizes reinforcement learning to evaluate influence strategies in trust-driven social networks. The simulation incorporates a real-world calibrated system dynamics trust model to mediate influence in an agent-based model (ABM) that simulates the evolution of opinions. We employ multi-agent reinforcement learning (MARL) to discover and evaluate influence strategies. Agents collaborate on influence teams, and results offer insight into intra-team competition and inter-team coordination. Additionally, we identify possible indicators of influence campaigns, such as increases in extremism.

Airborne radar for intelligence, surveillance, and reconnaissance has become an indispensable tool, found in both crewed and uncrewed platforms. These radars are usually multimode to engage missions for all manner of phenomenology detection and characterization, and have a long history of success for their intended purposes. Nevertheless, the advance of technology is facilitating enhanced exploitation of generally existing capabilities, as well as enhanced utility with new capabilities. The author identifies several areas of promising research and subsequent development that would provide significant and substantial capability enhancements.

Legacy and modern-day ablation codes typically assume equilibrium pyrolysis gas chemistry. Yet, experimental data suggest that speciation from resin decomposition is far from equilibrium. A thermal and chemical kinetic study was performed on pyrolysis gas advection through a porous char, using the Theoretical Ablative Composite for Open Testing (TACOT) as a demonstrator material. The finite-element tool SIERRA/ Aria simulated the ablation of TACOT under various conditions. Temperature and phenolic decomposition rates generated from Aria were applied as inputs to a simulated network of perfectly stirred reactors (PSRs) in the chemical solver Cantera. A high-fidelity combustion mechanism computed the gas composition and thermal properties of the advecting pyrolyzate. The results indicate that pyrolysis gases do not rapidly achieve chemical equilibrium while traveling through the simulated material. Instead, a highly chemically reactive zone exists in the ablator between 1400 and 2500 K, wherein the modeled pyrolysis gases transition from a chemically frozen state to chemical equilibrium. These finite-rate results demonstrate a significant departure in computed pyrolysis gas properties from those derived from equilibrium solvers. Under the same conditions, finite-rate-derived gas is estimated to provide up to 50% less heat absorption than equilibrium-derived gas. This discrepancy suggests that nonequilibrium pyrolysis gas chemistry could substantially impact ablator material response models.

Power systems have been analyzed and studied as purely physical systems for a long time. Such efforts were critical to the establishment of the power grid as it is today. However, with the increased interest in the integration of renewable energy, the grid is experiencing more vulnerabilities to its operation, stability, and resiliency from the cyber realm. As such, it is crucial to understand the cyber-physical power system interdependencies. In this paper, we advance a Bayesian Network generation algorithm, called CyberDep. CyberDep quantifies cyber-physical interdependencies through conditional probability calculations and aids in analyzing bidirectional data flow dependencies and n-to-1 nodal connections between elements. CyberDep is implemented on a dataset of the cyber-physical emulation of the WSCC 9-bus system, which includes running physical, cyber, and cyber-physical disturbances on the system. The results showcase an improved interdependency quantification and visualization of the n-to-1 probabilistic relationships between the physical and cyber system components.

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%.

Self-assembled quantum dots (QDs) embedded in InGaAs quantum wells (QWs) are used as active regions for photonic-crystal surface-emitting lasers (PCSELs). An epitaxial regrowth method is developed to fabricate the dot-in-well (DWELL) PCSELs. The epitaxial regrowth starts with the growth of a partial laser structure containing bottom cladding, waveguide, active region, and the photonic crystal (PC) layer. The PC layer is patterned to realize the cavity. Subsequently a top cladding layer is regrown to complete the laser structure. During the regrowth of the top cladding layer, the partial laser structure is subjected to high growth temperatures in excess of 600 °C resulting in an unintentional annealing of the active region. This annealing of the active region can alter the QDs by changing their size resulting in a blue shift in photoluminescence (PL) and narrowing PL emission. This effect results in the misaligning of the gain peak and the cavity resonance, resulting in sub-optimal lasing performance. DWELL active regions are known to have better thermal stability compared to both QDs and QWs and could be an ideal candidate for regrown PCSELs. We successfully demonstrate an optically-pumped epitaxially-regrown DWELL PCSEL with an emission wavelength of 1230 nm operating at room temperature. Furthermore, the DWELL active region shows excellent emission wavelength stability and intensity despite the high temperature regrowth process.

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.

We introduce a new training algorithm for deep neural networks that utilize random complex exponential activation functions. Our approach employs a Markov chain Monte Carlo sampling procedure to iteratively train network layers, avoiding global and gradient-based optimization while maintaining error control. It consistently attains the theoretical approximation rate for residual networks with complex exponential activation functions, determined by network complexity. Additionally, it enables efficient learning of multiscale and high-frequency features, producing interpretable parameter distributions. Despite using sinusoidal basis functions, we do not observe Gibbs phenomena in approximating discontinuous target functions.

We demonstrate magnetic anomaly detection (MAD) using an array of 24 commercial induction coil magnetometers with stand-off distances from a pulsed 99.8(3) kA·m2 magnetic dipole source of 260-1200 m. The sparse array is used to estimate the magnetic dipole location, magnitude, and orientation. We demonstrate how independent component analysis (ICA) improves the accuracy and precision of the magnetometer array when estimating the dipole parameters. Using sensor responses recorded from individual source pulses, we estimate the dipole location to within 29 ±; 2 m, the magnitude to within 3 ± kA ·m2, and dipole orientation error to within 19 ± 0.6°.

Traditional Monte Carlo methods for particle transport utilize source iteration to express the solution, the flux density, of the transport equation as a Neumann series. Our contribution is to show that the particle paths simulated within source iteration are associated with the adjoint flux density and the adjoint particle paths are associated with the flux density. We make our assertion rigorous through the use of stochastic calculus by representing the particle path used in source iteration as a solution to a stochastic differential equation (SDE). The solution to the adjoint Boltzmann equation is then expressed in terms of the same SDE, and the solution to the Boltzmann equation is expressed in terms of the SDE associated with the adjoint particle process. An important consequence is that the particle paths used within source iteration simultaneously provide Monte Carlo samples of the flux density and adjoint flux density in the detector and source regions, respectively. The significant practical implication is that particle trajectories can be reused to obtain both forward and adjoint quantities of interest. To the best our knowledge, the reuse of entire particles paths has not appeared in the literature. Monte Carlo simulations are presented to support the reuse of the particle paths.

Legacy and modern-day ablation codes typically assume equilibrium pyrolysis gas chemistry. Yet, experimental data suggest that speciation from resin decomposition is far from equilibrium. A thermal and chemical kinetic study was performed on pyrolysis gas advection through a porous char, using the Theoretical Ablative Composite for Open Testing (TACOT) as a demonstrator material. The finite-element tool SIERRA/ Aria simulated the ablation of TACOT under various conditions. Temperature and phenolic decomposition rates generated from Aria were applied as inputs to a simulated network of perfectly stirred reactors (PSRs) in the chemical solver Cantera. A high-fidelity combustion mechanism computed the gas composition and thermal properties of the advecting pyrolyzate. The results indicate that pyrolysis gases do not rapidly achieve chemical equilibrium while traveling through the simulated material. Instead, a highly chemically reactive zone exists in the ablator between 1400 and 2500 K, wherein the modeled pyrolysis gases transition from a chemically frozen state to chemical equilibrium. These finite-rate results demonstrate a significant departure in computed pyrolysis gas properties from those derived from equilibrium solvers. Under the same conditions, finite-rate-derived gas is estimated to provide up to 50% less heat absorption than equilibrium-derived gas. This discrepancy suggests that nonequilibrium pyrolysis gas chemistry could substantially impact ablator material response models.

Understanding and containing hydrogen isotope diffusion is crucial for many nuclear applications. In situ experiments have consistently shown that radiation significantly enhances isotope diffusion in austenitic stainless steels. Despite extensive research, the mechanism behind this phenomenon remains elusive, as most radiation-induced defects (e.g., vacancies, dislocations, and grain boundaries) typically trap hydrogen, thereby slowing diffusion. While grain boundaries may increase in-plane diffusivity and interstitials may enhance diffusion due to material swelling, these effects are relatively minor. Utilizing an Fe-Ni-Cr-H interatomic potential for stainless steels, we conducted extensive molecular dynamics simulations to investigate the origins of radiation-enhanced diffusion. Our findings reveal that when a system is resolidified, mimicking defects created by radiation displacements, the resulting structure contains a mixture of phases, boundaries, and dislocation networks. This defective structure significantly increases hydrogen diffusivity, enhancing it by approximately 1.7 times at 900 K. These results suggest that the complex defect structures formed during radiation displacements are the primary drivers of the observed diffusion enhancement, providing valuable insights into the mechanisms underlying radiation-enhanced diffusion in nuclear materials.

Carbon capture, utilization, and storage (CCUS) is an important pathway for meeting climate mitigation goals. While the economic viability of CCUS is well understood, previous studies do not evaluate the economic feasibility of carbon capture and storage (CCS) in the Permian Basin specifically regarding the new Section 45Q tax credits. We developed a technoeconomic analysis method, evaluated the economic feasibility of CCS at the acid gas injection (AGI) wells, and assessed the implication of Section 45Q tax credits for CCS at the AGIs. We find that the compressors, well depth, and the permit and monitoring costs drive the facility costs. Compressors are the predominant contributors to capital and operating expenditure driving the levelized cost of CO2 storage. Strategic cost reduction measures identified include 1) sourcing of low-cost electricity and 2) optimizing operational efficiency in well operations. In evaluating the impact of the tax credits on CCS projects, facility scale proved decisive. We found that facilities with an annual injection rate exceeding 10,000 MT storage capacity demonstrate economic viability contingent upon the procurement of inputs at the least cost. The new construction of AGI wells were found to be economically viable at a storage capacity of 100,000 MT. The basin is heavily focused on CCUS (tax credit – $65/MT CO2), which overshadows CCS ($85/MT CO2) opportunities. Balancing the dual objectives of CCS and CCUS requires planning and coordination for optimal resource and pore space utilization to attain the basin's decarbonization potential. We also found that CCS on AGI is a lower cost CCS option as compared to CCS on other industries.

The Direct Simulation Monte Carlo (DSMC) method is utilized to numerically simulate test conditions in the Sandia Hypersonic Shock Tunnel (HST) facility. The setup consists of a hypersonic flow over a cylinder with the freestream at flow speeds of 4-5 km/s in a state of thermal non-equilibrium. We present comparisons of temperatures derived from spectrographic measurements of Nitric Oxide (NO) emission in the ultraviolet (UV) region with predictions from the DSMC solver. Furthermore, we present differences between spectrally banded imaging measurements taken during experiments in the infrared (IR) and UV regions with those obtained from numerical simulations.

There is growing interest to extend low-rank matrix decompositions to multi-way arrays, or tensors. One fundamental low-rank tensor decomposition is the canonical polyadic decomposition (CPD). The challenge of fitting a low-rank, nonnegative CPD model to Poisson-distributed count data is of particular interest. Several popular algorithms use local search methods to approximate the maximum likelihood estimator (MLE) of the Poisson CPD model. This work presents two new algorithms that extend state-of-the-art local methods for Poisson CPD. Hybrid GCP-CPAPR combines Generalized Canonical Decomposition (GCP) with stochastic optimization and CP Alternating Poisson Regression (CPAPR), a deterministic algorithm, to increase the probability of converging to the MLE over either method used alone. Restarted CPAPR with SVDrop uses a heuristic based on the singular values of the CPD model unfoldings to identify convergence toward optimizers that are not the MLE and restarts within the feasible domain of the optimization problem, thus reducing overall computational cost when using a multi-start strategy. We provide empirical evidence that indicates our approaches outperform existing methods with respect to converging to the Poisson CPD MLE.

Appearing as one of the key-components of lithium-ion batteries (LIBs), this work specifically focuses on the additive manufacturing (AM) of custom-shape separators, facilitated by the filament material extrusion process, also called fused deposition modeling (FDM). The development and optimization of composite thermoplastic filament feedstocks combining polypropylene and paraffin wax, followed by the 3D printing of the separator membranes is shown. A post-processing step, based on thermal induced phase separation (TIPS), is introduced to promote porosity formation through removal of the paraffin wax sacrificial phase within the 3D printed items. Separators with different polypropylene/paraffin wax ratios are developed and the impact on printability, mechanical strength, porosity, and electrochemical performances, is thoroughly discussed. X-ray micro-computed tomography is employed to assess the geometric fidelity and to detect printing defects in a complex 3D lattice structure. The performance of the 3D printed porous separators is also compared to a commercial separator. This pioneering research establishes a foundation for the creation of porous separators that can adapt to and conform into 3D printed battery architectures with novel form factors, and also creates opportunities for the use of FDM and TIPS for a wide range of applications that employ porous structures beyond the energy storage field.

In this paper, using 3D Technology Computer-Aided-Design (TCAD) simulations, we show that it is possible to design a static random-access memory (SRAM) using gate-all-around field-effect-transistor (GAA-FET) technology so that it is immune to single alpha particle radiation error. In other words, with the design, there will be no single-event upset (SEU) due to alpha particles. We first use ab initio calculations in PHITS to show that there is a maximum linear energy transfer (LET), LETmax, for the alpha particle in Si and Six Ge1-x. Based on that, by designing a sub-7nm GAA-FET-based SRAM with bottom dielectric isolation (BDI), we show that the SRAM does not flip even if the particle strike is in the worst-case scenario.

The discrimination problem in seismology aims to accurately classify different underground source types based on local, regional, and/or teleseismic observations of ground motion. Typical discriminant approaches are rooted in fundamental, physics-based differences in radiation pattern or wave excitation, which can be frequency-dependent and may not make use of the full waveform. In this article, we explore whether phase and amplitude distances derived from dynamic time warping (DTW) and elastic shape analysis (ESA) can inform event discrimination. We demonstrate the ability to distinguish underground point sources using synthetic waveforms calculated for a 1D Earth model and various source mechanisms. We then apply the method to recorded data from events in the Korean Peninsula, which includes declared nuclear explosions, a collapse event, and naturally occurring earthquakes. Phase and amplitude distances derived from DTW and ESA are then used to classify the event types via dendrogram and k-nearest-neighbor clustering analyses. Using information from the full waveform, we show how different underground sources can be distinguished at regional distances. We highlight the potential of these nonlinear alignment algorithms for discrimination and comment on ways we can extend the framework presented here.

This work introduces a comprehensive simulation tool that provides a robust 1D Schrödinger - Poisson solver for modeling the electrostatics of heterostructures with an arbitrary number of layers, and non-uniform doping profiles along with the treatment of partial ionization of dopants at low temperatures. The effective masses are derived from the first-principles calculations. The solver is used to characterize three Ge1-xSnx/Ge heterostructures with non-uniform doping profiles and determine the subband structure at various temperatures. The simulation results of the sheet carrier densities show excellent agreement with the experimentally extracted data, thus demonstrating the capabilities of the solver.

This article presents a comparative analysis of total ionizing dose (TID) response in GlobalFoundries’ (GF) 12 low-power (LP) and 12LP+12-nm bulk fin field effect transistor (FinFET) technologies using 10-keV X-rays. Our findings show that 12LP+ n-type transistors demonstrate higher sensitivity to TID degradation of the off-state leakage drain current compared to 12LP. Data indicate that for both 12LP and 12LP+, transistors with higher threshold voltages (VTs) exhibit lower off-state drain-source leakage postirradiation compared to transistors with lower VTs. Data consistently show that transistors with fewer fins per transistor show superior TID tolerance, in both 12LP and 12LP+ technologies. Lower VT transistors in both technologies display similar preirradiation leakage currents. On the other hand, higher VT transistors in 12LP+ show lower preirradiation leakage currents than those in 12LP, highlighting that the front-end-of-line of 12LP+ technology has been modified compared to 12LP. p-type devices in 12LP+ presented negligible degradation. Larger TID sensitivity in 12LP+ might be attributed to the implementation of dual-metal gate work functions, reduced halo doping, deeper source/drain (S/D) doping profiles, and/or 12LP+ having narrower fins compared to 12LP.

Surrogate model development is a critical step for uncertainty quantification or other sample-intensive tasks for complex computational models. In this work we develop a multi-output surrogate form using a class of neural networks (NNs) that employ shortcut connections, namely Residual NNs (ResNets). ResNets are known to regularize the surrogate learning problem and improve the efficiency and accuracy of the resulting surrogate. Inspired by the continuous, Neural ODE analogy, we augment ResNets with weight parameterization strategy with respect to ResNet depth. Weight-parameterized ResNets regularize the NN surrogate learning problem and allow better generalization with a drastically reduced number of learnable parameters. We demonstrate that weight-parameterized ResNets are more accurate and efficient than conventional feed-forward multi-layer perceptron networks. We also compare various options for parameterization of the weights as functions of ResNet depth. We demonstrate the results on both synthetic examples and a large scale earth system model of interest.

In this paper, we propose a learning-based method utilizing the Soft Actor-Critic (SAC) algorithm to train a binary Support Vector Machine (SVM) classifier. This classifier is designed to identify valid input spaces in high-dimensional, highly constrained systems while minimizing the total runtime of offline simulations. The simulations adapt their runtime based on the likelihood that a given training input will be informative to the classifier. Furthermore, we introduce a method for using the trained SAC model to predict whether a desired system input is likely to violate constraints, along with a technique to adjust the input as necessary. Additionally, we explore the potential of this model to detect faults or adversarial attacks within the system. The effectiveness of our approach is demonstrated through various simulations of challenging classification problems and a constrained quadrotor model.

Generative modeling has opened new avenues for solving previously intractable materials design problems. However, these new opportunities are accompanied by a drastic increase in the required amount of training data. This is in stark juxtaposition to the high expense and difficulty in curating such large materials datasets. In this work, we propose a novel framework for integrating generative models within an active learning loop. This enables the training of generative models with datasets significantly smaller than what has previously been demonstrated, providing a direct route for their application in data constrained environments. The functionality of this framework is then demonstrated by addressing the challenge of designing polycrystalline textures associated with target anisotropic mechanical properties. The developed protocol exhibited a cost reduction between 14 to 18 times over a randomly sampled experimental design.

Electrochemical random access memory (ECRAM) is an emerging three-terminal nonvolatile memory (NVM) with highly controllable channel conductance which is promising for use as an analog memory (or synapse) in analog in-memory computing (IMC) systems. Energy-efficient analog IMC computing is particularly desirable for power-constrained, high-radiation environments such as satellites. However, little is known about the suitability of ECRAM for use in a total ionizing dose (TID) environment. This work investigates the effect of Co-60 gamma radiation on the channel conductance and noise—two properties critical for analog IMC systems—of a TaOx-based ECRAM up to 17.3 Mrad(SiO2) for both low- and high-channel-conductance state devices. A transient increase in conductance is observed in response to radiation which consists of two elements: an immediate increase in conductivity due to photocurrent and a secondary increase in conductivity, which has a slower rise and saturation and can persist for hours after exposure. This secondary, persistent photoconductivity is attributed to charging caused by hole trapping. These transient effects would not likely occur in a space environment due to the low dose rate compared with this experiment. No permanent change is found in the low conductance state (LCS) following exposure and the minor shift in the high conductance change would be less significant than the regular retention decay in this state. A permanent increase in the random telegraph noise is observed, possibly due to increased traps created in the channel. This work demonstrates that TaOx-based ECRAM is suitable for use in spaceborne analog IMC systems that are subject to significant TID.

In today's communication landscape there are multiple technologies and protocols used for communication between end devices. Within security paradigms for these protocols, integrity management is a common goal of system designers. Communication protocols focused on maintaining message integrity can provide assurance that some received data has not been altered or tampered with. While integrity is often coupled with confidentiality in protocol design, this analysis focuses on an evaluation of only integrity protocols. This work outlines various ways message integrity may be preserved with respect to high performance operational technology (OT) systems. It describes a series of experiments and an evaluation framework used to evaluate the performance of the identified integrity approaches regarding common system design goals. Finally, it addresses the testing environment utilized and closes the work with a summary of experimental results.

The present article summarizes the submissions to the Tribomechadynamics Research Challenge announced in 2021. The task was a blind prediction of the vibration behavior of a system comprising a thin plate clamped on two sides via bolted joints. Both geometric and frictional contact nonlinearities are expected to be relevant. Provided were the CAD models and technical drawings of all parts as well as assembly instructions. The main objective was to predict the frequency and damping ratio of the lowest-frequency mode as function of the amplitude. Many different prediction approaches were pursued, ranging from well-known methods to very recently developed ones. After the submission deadline, the system has been fabricated and tested. The aim of this article is to evaluate the current state of the art in modeling and vibration prediction, and to provide directions for future methodological advancements.

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 increased prevalence of distributed energy resources and microgrids has led to highly variable fault current levels and system configurations in distribution networks. To improve power system resilience during severe weather events, microgrids can be networked outside of their original boundaries to restore service to additional customers. Traditional protective relaying schemes may not be equipped to handle these contingencies. Optimal Adaptive Protection (OAP) algorithms can provide more robust system protection during such events by monitoring the network for system state changes and modifying protective relay settings in near real-time. In this paper an OAP algorithm is applied to systems from three different utilities, modeled in OPAL-RT, and connected to hardware-in-the-loop (HIL) relays. Restoration scenarios are considered starting from islanded microgrids and returning to normal operating conditions. By incorporating OAP after any network switching event, protection security is improved throughout the restoration process.

The complexity of the semiconductor design lifecycle and globalized manufacturing process creates concern over the threat of deliberate malicious alterations, or hardware Trojans, being inserted into microelectronic designs. This has resulted in a significant corpus of hardware Trojan research including Trojan design and benchmarking efforts and development of corresponding metrics and detection and prevention techniques, over the last two decades. In this survey, we first highlight efforts in Trojan design and benchmarking, followed by a cataloging of seminal and recent works in Trojan detection and prevention and their accompanied metrics. Given the volume of literature in this field, this survey considers only pre-silicon techniques. We make this distinction between pre- and post-silicon to properly scope and provide appropriate context into the capabilities of existing hardware Trojan literature. Each major section (design, prevention, and detection) is accompanied by insights, and common pitfalls, which we highlight can be addressed by future research.

We derive a new Galerkin quadrature (GQ) method for S (Formula presented.) calculations that differs from the two methods preceding it in that a matrix inverse for an (Formula presented.) matrix, where (Formula presented.) is the number of directions in the quadrature set, is no longer required. Galerkin quadrature methods are designed for calculations with highly anisotropic scattering. Such methods are not simply special angular quadratures but also are methods for representing the S (Formula presented.) scattering source that offers several advantages relative to the standard scattering source representation when highly truncated Legendre cross-section expansions must be used. Galerkin quadrature methods are also useful when the scattering is moderately anisotropic, but the quadrature being used is not sufficiently accurate for the order of the scattering source expansion that is required. We derive the new method and present computational results showing that its performance for two challenging problems is comparable to those of the two GQ methods that preceded it.

Cross-flow turbines could play a larger role in the diversification of the global energy supply if the impact of more cost-competitive design choices on performance and rotor dynamics was better understood. This study focuses on rotor performance and blade strain measurements while varying the following parameters: blade materials and blade free end length by changing strut support position. Towing tank experiments were performed with a modular 1-meter diameter cross-flow turbine consisting of three NACA 0018 blades with two support struts. One strut was fixed at the lower end of the turbine, while the second strut was adjustable, thereby changing the length of the free end. The blade materials tested were carbon, E-glass, and hollow E-glass fiber composites, in decreasing order of stiffness and cost. High-resolution distributed fiber optic sensors were embedded in two of the three rotor blades for each material and provided hundreds of strain measurements per blade. Turbine performance and blade strain were measured while varying tow speed and tip speed ratio. Performance tests were conducted at towing speeds sufficiently high for the performance to be independent of Reynolds number. E-glass blades and carbon blades performed similarly for the most rigid strut configurations. Higher strain was measured on the E-glass blades, and their performance was reduced for less rigid configurations compared to the carbon fiber blades. The performance of the highly deflective hollow E-glass blades was lower overall and became even more degraded for longer unsupported blade span. The results provide insight into the use of various blade materials in cross-flow turbines and guidance on allowable free end length for each material type.

Sandia will construct a cold flow receiver test platform in order to characterize falling particle curtain receivers at commercially relevant scales. While Sandia has extensive experience in R&D of falling particle curtain receivers, most have been at pilot scale and smaller - on the order of 1 MW_th with characteristic dimensions of nominally 1-2 m to adequately collect solar energy from the heliostat field at the National Solar Thermal Test Facility (NSTTF). However, scaling up receivers to commercially relevant scales (25 MW_th and above) will require a thorough understanding of particle curtain dynamics at larger scales, especially longer drop heights, for design certainty. The goal of this project will be to construct a cold falling particle curtain test rig capable of simulating particle characteristics that are expected in a commercial scale CSP plant, namely the drop height, curtain thickness, and particle mass flow rate (normalized by length of curtain). This will enable data collection on curtain opacity and spread, both of which are correlated to receiver efficiency and reliable construction, for commercially relevant scales. It will also permit validation of numerical models that will enable detailed receiver characterization and design past currently validated scales.

Tank farm workers involved in nuclear cleanup activities perform physically demanding tasks, typically while wearing heavy personal protective equipment (PPE). Exoskeleton devices have the potential to bring considerable benefit to this industry but have not been thoroughly studied in the context of nuclear cleanup. In this paper, we examine the performance of exoskeletons during a series of tasks emulating jobs performed on tank farms while participants wore PPE commonly deployed by tank farm workers. The goal of this study was to evaluate the effects of commercially available lower-body exoskeletons on a user’s gait kinematics and user perceptions. Three participants each tested three lower-body exoskeletons in a 70-min protocol consisting of level treadmill walking, incline treadmill walking, weighted treadmill walking, a weight lifting session, and a hand tool dexterity task. Results were compared to a no exoskeleton baseline condition and evaluated as individual case studies. The three participants showed a wide spectrum of user preferences and adaptations toward the devices. Individual case studies revealed that some users quickly adapted to select devices for certain tasks while others remained hesitant to use the devices. Temporal effects on gait change and perception were also observed for select participants in device usage over the course of the device session. Device benefit varied between tasks, but no conclusive aggregate trends were observed across devices for all tasks. Evidence suggests that device benefits observed for specific tasks may have been overshadowed by the wide array of tasks used in the protocol.

Water security and climate change are important priorities for communities and regions worldwide. The intersections between water and climate change extend across many environmental and human activities. This Primer is intended as an introduction, grounded in examples, for students and others considering the interactions between climate, water, and society. In this Primer, we summarize key intersections between water and climate across four sectors: environment; drinking water, sanitation, and hygiene; food and agriculture; and energy. We begin with an overview of the fundamental water dynamics within each of these four sectors, and then discuss how climate change is impacting water and society within and across these sectors. Emphasizing the relationships and interconnectedness between water and climate change can encourage systems thinking, which can show how activities in one sector may influence activities or outcomes in other sectors. We argue that to achieve a resilient and sustainable water future under climate change, proposed solutions must consider the water–climate nexus to ensure the interconnected roles of water across sectors are not overlooked. Toward that end, we offer an initial set of guiding questions that can be used to inform the development of more holistic climate solutions. This article is categorized under: Science of Water > Water and Environmental Change Engineering Water > Water, Health, and Sanitation Human Water > Value of Water.

Single-event upset (SEU) cross sections are reduced in 176-layer charge trap (CT) 3-D nand devices under proton irradiation when multiple write operations are applied sequentially without the typical erase-before-write. This effect is observed for multiple data patterns and in both single-level cell (SLC) and triple-level cell (TLC) operating modes. SEU cross section calculation methodologies are discussed for highly scaled 3-D devices both with and without the application of rewrites, and potential implications for long-term endurance effects are proposed.

Objective: Real-time determination of human kinematics and kinetics could advance biomechanics research and enable valuable applications of biofeedback and generalizable exoskeleton control. This work aims to investigate a taskindependent, user-independent method for obtaining precise realtime joint state estimation across lower-body joints during a wide variety of tasks. Methods: We developed a generalizable sensing approach using a suit comprised of inertial measurement units (IMUs) and pressure insoles. With the suit, we collected a dataset of 33 tasks commonly performed during construction and hazardous waste cleanup (N = 10). We then trained deep learning user-independent, task-agnostic models to estimate joint lowerbody kinematics and dynamics using only worn sensor data. We likewise computed joint kinematics and dynamics analytically from sensor data to serve as a comparison tool for model results. Results: Our models achieved overall angle estimation root-meansquared-errors (RMSE) of 6.56±.92°, 8.60±1.01°, 7.58±.89°, and 6.00±.73° compared to 13.9±.1.3°, 15.31±1.0°, 10.76±.70°, and 7.56±.48° via analytical methods at the lower back, hip, knee, and ankle, respectively. Likewise, our models achieved overall normalized moment estimation RMSEs of.207±.069 Nm/kg,.242±.044 Nm/kg,.202±.038 Nm/kg, and.193±.034 Nm/kg compared to.306±.036 Nm/kg,.407±.021 Nm/kg, 1.18 ±.022 Nm/kg, and 1.73±.071 Nm/kg via analytical methods at the lower back, hip, knee, and ankle, respectively. Conclusion: These results are comparable to other state-of-the-art wearable sensing systems, establishing deep learning as a viable sensing approach that generalizes to new users and tasks. Significance: This work shows promise for enabling accurate real-world biomechanical data collection and enhancement of biofeedback systems and wearable robot control.

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.

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.

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.

The Direct Simulation Monte Carlo (DSMC) method is utilized to numerically simulate test conditions in the Sandia Hypersonic Shock Tunnel (HST) facility. The setup consists of a hypersonic flow over a cylinder with the freestream at flow speeds of 4-5 km/s in a state of thermal non-equilibrium. We present comparisons of temperatures derived from spectrographic measurements of Nitric Oxide (NO) emission in the ultraviolet (UV) region with predictions from the DSMC solver. Furthermore, we present differences between spectrally banded imaging measurements taken during experiments in the infrared (IR) and UV regions with those obtained from numerical simulations.

Amorphous silicon nitride is a common material in microelectronics devices, which acts as an insulating barrier. Extended annealing times at elevated temperature can initiate crystallization of α-Si3N4, which does not possess the same barrier properties. Molecular dynamics can resolve the fundamental mechanism for α-Si3N4 crystallization and the influence of local environments. We compare two interatomic potentials and conclude that these models predict structural features (e.g., angular distributions and densities) which span the range of experimental measurements. We confirmed these models reproduce experimental estimates of activation energy and leveraged these models to identify crystallization drivers. We conclude that near-Tg, facet-dependent silicon nitride crystal growth rates can be predicted directly by either bulk or interfacial diffusion properties.

Contact nucleation is believed to play a role in liquid-to-solid phase transitions in the atmosphere including ice nucleation and salt efflorescence. Here contact efflorescence of optically levitated ammonium sulfate droplets by collisions with organic particles is probed using a long working-distance optical trap. Two highly viscous water-soluble organic compounds (d-(+)-raffinose and citric acid), and two insoluble highly surface-active organic compounds (stearic acid and cis-pinonic acid) were probed for their ability to induce efflorescence upon contact. While three of the organics showed minimal effectiveness as contact nuclei, cis-pinonic acid showed a remarkable ability to initiate contact efflorescence of ammonium sulfate, occurring near ammonium sulfate’s deliquescence relative humidity. Further analysis of cis-pinonic acid using bright-field microscopy in an electrodynamic balance provided evidence that the cis-pinonic acid particles are crystalline under the laboratory conditions. We suggest that the close lattice match between crystalline ammonium sulfate and crystalline cis-pinonic acid may account for the observed effectiveness in initiating contact efflorescence of ammonium sulfate. In contrast, tests of contact nucleation between cis-pinonic acid and sodium chloride, a pair with a poor lattice match, did not result in efflorescence. These findings suggest that crystalline organic compounds in the atmosphere could act as effective nuclei for contact efflorescence of atmospherically relevant salts, provided they share a compatible lattice structure.

This paper presents a methodology for reducing the complexity of large-scale power network models using spectral clustering, aggregation of electrical components, and cost function approximation. Two approaches are explored using unconstrained and constrained spectral clustering to determine areas for effective system reduction. Once the system areas are determined, both loads and generators by type are aggregated, and their new cost function is approximated through polynomial curve-fitting or statistical methods. The performance of reduced networks is evaluated in terms of their ability to follow the true daily cost of the original system over a 24-hour period considering a set of several days. Two test systems are taken as test beds. Application of the methodology to a modified version of the IEEE 39-bus system reduces it from 17 generators to a 4-bus system and 9 generators with about 93% of accuracy. Similarly, the IEEE 118-bus system is reduced from 19 generators to a 3-bus system with three aggregated units achieving over 99% of accuracy. These findings address scalability challenges and enhance accuracy for high and mid-loading level conditions, and by aggregating thermal units with similar cost functions.

The Na+ super ion conductor (NaSICON, Na1+xZr2SixP3-xO12) is a solid electrolyte well-known for fast, selective Na+ transport at low temperatures, uniquely enabling sodium-based batteries. Producing high-quality NaSICON from solid-state methods, especially when cost-effective, potentially hygroscopic precursors are used, is not trivial. To understand and eliminate the influence of humidity during processing, a scheme was developed to reproducibly yield a high Na+ conductivity (3.75 mS/cm at 25°C, 81.7 mS/cm at 150°C), high density (97%), and machinable NaSICON without the use of binders, sintering aids, or dopants. Controlled humidity studies over 20%–50% RH coupled with thermal, structural, and electrical analysis reveal that calcination temperatures < 1000°C leave NaSICON processing susceptible to water absorption at > 20% RH due to the presence of hygroscopic Na3PO4 and Na2CO3 during shaping, pressing, and sintering. Water absorption results in NaSICON with lower densities, machinability, and Na+ conductivity, due to impaired intergranular Na+ transport. At the other extreme, fully converting precursor to the NaSICON phase at 1230°C before pressing and sintering leads to poor conductivity and density. By calcining at 1000°C, excellent quality NaSICON may be produced under a range of laboratory environments, enabling low-cost production of high-conductivity, machinable NaSICON necessary the ever-growing energy storage market.

The Open-Source Offshore (OSO) airfoils have been developed for research purposes for offshore wind turbines, offering a set of airfoils that align with modern turbine design requirements and industry design practices without proprietary constraints on research use. The eventual airfoil family will target the IEA 22 MW reference wind turbine, which was originally developed with the FFA airfoils. The two airfoils summarized in Table 1 (OSO-21-WT1 and OSO-30-WT1) started development as part of a family of airfoils being designed to target the IEA 22 MW wind turbine. The criteria used to design these airfoils are summarized in Table 1, which aim to encapsulate requirements of modern airfoils for offshore wind turbine applications, and were developed with feedback from industry and research experts. The airfoils were designed using XFOIL and candidate airfoils were then analyzed in RFOIL, which is considered more accurate than XFoil for high lift predictions of thicker airfoils. The design process for a preliminary family of airfoils is available, including a more detailed explanation of the design requirements and metrics similar to those used for these airfoils. Most of the design criteria are met for these two airfoils, with two exceptions. For both airfoils, the L/D Roughness Loss metric is exceeded (42% > 40% goal) and the desired lift coefficient margin over the design value (“CL_Margin”) was moderately exceeded (0.43 > 0.3) while smooth-stall characteristics (computed) were achieved. Note that all of the metrics were computed using RFOIL, and like other new airfoils, these will need to be experimentally validated at a range of Reynolds numbers. The airfoil coordinates will be shared publicly on Sadia National Laboratories’ public Github repository:

Abstract not provided.

While load balancing in distributed-memory computing has been well-studied, we present an innovative approach to this problem: a unified, reduced-order model that combines three key components to describe “work” in a distributed system: computation, communication, and memory. Our model enables an optimizer to explore complex tradeoffs in task placement, such as augmented parallelism, at the expense of data replication increasing memory usage. We propose a fully distributed, heuristic-based load balancing optimization algorithm, and demonstrate that it quickly finds close-to-optimal solutions. We formalize the complex optimization problem as a mixed-integer linear program, and compare it to our strategy. Finally, we show that when applied to an electromagnetics code, our approach obtains up to 2.3x speedups for the imbalanced execution.

Abstract not provided.

Vertical-axis wind turbines (VAWTs) have been the subject of research and development for nearly a century. However, this turbine architecture has fallen in and out of favor on multiple occasions. Beginning in the late 1970s, the U.S. Department of Energy sponsored an extensive experimental program through Sandia National Laboratories which produced a mass of experimental data from several highly instrumented turbines. Turbines designed, built, and tested include the 2 meter, 5 meter, 17 meter, and 34 meter and their respective configurations. This program kicked off a commercial collaboration and resulted in the FloWind turbines. The FloWind turbines had several notable design changes from the experimental turbines that, in conjunction with a general lack of understanding regarding predicting fatigue at the time, led to the majority of the turbines failing prematurely during the late 80s.

Continued development of the additive conductivity material model [1], used to simulate changes in heat transfer that occurs in void generating foam decomposition, has resulted in an improved model and new features. The previous version of the model was calibrated against the Aria Bulk Fluid Element (BFE) solution and proposed a third-order polynomial correction term best captured the increased heat transfer due to voids in the foam. An investigation of the Fuego Conjugate Heat Transfer (CHT) and Aria BFE solutions at several geometries revealed the CHT solution and BFE solution had differing behavior across length scales, especially at smaller scales. Five calibration studies, using the Fuego CHT as the calibration data, were carried out with polynomial functions of 4-th, 3-rd, 2-nd, 1-st and 0-th orders to determine the best correction function that generalized well across length scales. Each polynomial function was calibrated/trained on six different sized geometries and then tested on three uniquely sized geometries. This study revealed that the 1-st order additive conductivity model performed the best. A new feature of void formation scaling was implemented to more realistically capture the heat transfer as voids are created. A scaling term was added to the model to activate the conductivity correction as decomposition progresses.

To understand and develop models for silicon photovoltaic module degradation, accelerated testing is often used, however, outdoor field testing is necessary for validation. Outdoor field testing publications are often limited by the lack of a pristine, control module to compare the fielded module to. In this work, commercially available modules were purchased from seven different manufacturers for outdoor fielding then destructive characterization to investigate packaging material degradation on

In recent years, the procurement of essential materials such as Stainless Steels, Ni-Alloys, and Kovar has become increasingly challenging. Lead times for these materials have extended to over 24 months, placing significant stress on the reliability of attaining these alloys. This situation is exacerbated by the supplier’s minimum purchase requirement of excessively large quantities to meet unique purchase order (PO) requirements which is usually above the maximum requirements of Sandia National Laboratories (SNL). Additionally, the reluctance of foundries to engage in small lot development for SNL specific needs further complicates the procurement process. This white paper explores the reconstitution of SNL’ Melt Laboratory as a strategic solution to mitigate these challenges.

During the development of high-consequence items, test systems should be capable of differenti ating between test failures resulting from narrowly missing requirements versus those indicating potentially catastrophic faults. In many instances, classifying the data corresponds to simply identifying whether measured waveforms have approximately the anticipated shape. Cast in this light, the problem reduces to converting raw data into a form optimal for use with neural network classifiers. This manuscript investigates different means of representing raw data for image classification. Raw data plots and Short Time Fourier Transform (STFT) spectrograms are classified by both custom built, small-scale, Convolution Neural Networks (CNN) and open-source, multi-million parameter, pre-trained deep CNNs. In the case of time varying frequency content, the STFTs provide images with greater detail and can be accurately classified with simpler networks. This requires less mem ory and runs faster than classifying the raw data using the more sophisticated options—making STFTs optimal for applications with memory constraints. STFTs are not a panacea. In some cases the time-domain signal contains useful information that should not be discarded. Rather than using raw data or STFTs, the images can be constructed from both by using red and green channels of an RGB image to visualize the real and imaginary components of the transform, with the raw data occupying the blue channel.

Hardware-in-the-Loop (HIL) methodologies for test system development often require simulations capable of running at MHz speeds on FPGAs. The stringent memory and speed constraints necessitate compromises between model fidelity and execution speed. Numerically solving the underlying governing equations represents the highest accuracy, but slowest responding approach. By storing pre-determined results in look-up-tables (LUT), one can balance speed and accuracy.

The purpose of this paper is to qualitatively explore the question of whether as a power system’s sources and energy storage become more distributed, the power system also tends to become more resilient. The paper is divided into two main parts. After presenting introductory material, Part 1 looks at factors that might limit the ability of ‘fully-distributed’ resources to provide the expected resilience, and Part 2 considers factors that might make a more centralized system more resilient.

Abstract not provided.

The mechanical response of a component is affected by defects, such as porosity, arising from the laser powder bed fusion (LPBF) fabrication process. Thus, it is important to develop accurate and efficient inspection methods for identifying porosity. In this work, porosity identified in an X-ray computed tomography (XCT) volume of a Ti-5553 coupon was compared to pores identified in a serial sectioned volume that represented the ground truth. The porosity of the XCT scan was identified using contrast-based, ISO-based, and machine learning (ML) methods for segmentation. Large inherent porosity was easy to identify, but the ISO thresholding still struggled due to the intensity gradient resulting from both the beam hardening in XCT and the uneven lighting of the serial sectioning panels. Further, the results show that ML-based methods were better suited for identifying small pores and reducing the amount of false positives. Additionally, high strain-rate impact testing was done on some of the XCT samples as well as post-mortem XCT inspection, and the same suite of segmentation and quantification tools were used to identify the large spallation cavities. The comparison of porosity pre- and post-mortem provides insight on the influence of the LPBF porosity on the formation of spall cavities.

Microchannel heat exchanger technology is being pursued for next generation CSP concepts for primary power cycle heat addition and power cycle heat recuperation due to the high heat transfer coefficients and pressure containment advantages of small sCO₂ channels. The economics of future CSP plants as dictated by the SETO 2020 or 2030 targets depend on a heat exchanger with a 30-year lifetime (resisting creep, fatigue, corrosion, erosion) and operational characteristics such as fast ramping and the ability to withstand thermal shock. However, the lifetime and operational limits of microchannel heat exchangers operating at high temperatures, particularly those constructed from high-nickel alloys, are not well known. This uncertainty has resulted in heat exchanger vendors not being able to accurately forecast heat exchanger lifetime as required by customers, specify operational limits as required by process engineers to prevent premature heat exchanger failure, or overdesign heat exchanger which leads to higher cost than necessary.

This report examines the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on operations research, private industry, and government sectors, highlighting their applications in automating processes, enhancing decision-making, and optimizing complex systems. AI/ML technologies have revolutionized industries through predictive maintenance, supply chain optimization, and autonomous systems, while also advancing public safety and defense operations. However, challenges such as data integrity, model transparency, and the need for human oversight persist, particularly in high-consequence environments. The report emphasizes the critical role of explainable AI (XAI) and human-computer interaction models like Human-in-the-Loop (HITL) and Human-on-the-Loop (HOTL) in fostering trust and accountability. Balancing automation with ethical responsibility and transparency is essential for the continued successful integration of AI/ML into operational and strategic decision-making frameworks.

Vertical-axis wind turbines (VAWTs) have been the subject of research and development for nearly a century. However, this turbine architecture has fallen in and out of favor on multiple occasions. Beginning in the late 1970s, the U.S. Department of Energy sponsored an extensive experimental program through Sandia National Laboratories which produced a mass of experimental data from several highly instrumented turbines. Turbines designed, built, and tested include the 2 meter, 5 meter, 17 meter, and 34 meter and their respective configurations. This program kicked off a commercial collaboration and resulted in the FloWind turbines. The FloWind turbines had several notable design changes from the experimental turbines that, in conjunction with a general lack of understanding regarding predicting fatigue at the time, led to the majority of the turbines failing prematurely during the late 80s.

This document summarizes common indices used to characterize climate-related impacts to agricultural production. In particular, we consider indices for three phenomena: 1) drought, 2) growing period, and 3) heat stress (Table 1). It should be noted that these indices are far from exhaustive. Instead, they reflect common concerns related to production of agriculture that are emerging across multiple regions (Gunda et al., 2024). Other indices such as rate of return (Shand et al., 2024) can also be leveraged to support agriculture-related climate analyses as well.

This project is intended to support the development of new traction drive systems that meet the targets of 100 kW/L for power electronics and 50 kW/L for electric machines with reliable operation to 300,000 miles. To meet these goals, new designs must be identified that make use of state-of-the-art and next-generation electronic materials and design methods. Designs must exploit synergies between components, for example converters designed for high-frequency switching using wide band gap (WBG) devices and ceramic capacitors. This project included: (1) a survey of available technologies; (2) investigating new technologies, that for example, reduce volume of thermal management or magnetic components; (3) the development of computer aided design tools that consider the converter volume, reliability, and electrical performance; (4) exercising the design software to evaluate performance gaps and predict the impact of certain technologies and design approaches, i.e. GaN semiconductors, ceramic capacitors, ceramic thermal management components, and select topologies; (5) building and testing hardware prototypes to validate models and concepts. The design tools enable co-optimization of the power module and passive elements and provide some design guidance. At the end of the project, new advanced computing methods, such as machine learning approaches, were considered.

Hydrogen Extremely Low Probability of Rupture (HELPR) is a modular probabilistic fracture mechanics modeling platform developed to assess structural integrity of pipelines for transmission and distribution of hydrogen. HELPR couples fatigue and fracture engineering models with probabilistic methods to generate fast predictions and enables quantification of prediction uncertainty and sensitivity. This user manual serves as a guide through the various analysis features HELPR contains.

Over the past six years, an informal working group has developed to investigate existing sensitivity analysis methods, examine new methods, and identify best practices. The focus is on the use of sensitivity analysis in case studies involving geologic disposal of spent nuclear fuel or nuclear waste. Three additional case studies are presented in this Volume 2 report, including more nonlinear behavior, outputs which exhibit bifurcation, regime changes, and nested sampling.

We have developed methods to expose milligram-scale samples of metals to tritium for studies of aging effects on material properties, primarily intended for metals that reversibly form hydrides. The tritium decays to helium within the solid, leading to profound changes in microstructural features and macroscopic properties. We present our combined experimental and modeling approach, and provide an example of a palladium foil aged 3 months.

Code verification is an essential part of credibility analysis for computational models. It assesses whether the mathematical model is implemented correctly into the code and whether the numerical methods behave consistently, and is done before solution verification and validation. Robust guidance for code verification exists in the literature. However, there is no known, concise guide for selecting the approach based on the model form that also presents an overview of the common elements. This document was written to address this gap as an accessible reference for beginning a code-verification effort.

Abstract not provided.

Code verification is an essential part of credibility analysis for computational models. It assesses whether the mathematical model is implemented correctly into the code and whether the numerical methods behave consistently, and is done before solution verification and validation. Robust guidance for code verification exists in the literature. However, there is no known, concise guide for selecting the approach based on the model form that also presents an overview of the common elements. This document was written to address this gap as an accessible reference for beginning a code-verification effort.

This is a self-contained reference document that derives the equations necessary to build a combined inertial navigation system and error-state Kalman filter. Coordinate transform, linear time invariant system, inertial sensing, and error-state Kalman filtering theory is built up from first principles. This theory is then leveraged to derive the system equations for two combined inertial navigation system and error-state Kalman filters: (1) a 15-state system modeling white-noise-integrating accelerometer and gyroscope biases, and (2) a 39-state system modeling static and first-order Gauss-Markov accelerometer and gyroscope biases, scale factor errors, and cross-axis sensitivity errors.

Salt formations have been explored for the permanent isolation of spent nuclear fuel based on their high thermal conductivity, self-healing nature, and low hydraulic permeability to brine flow. Vacancy defect concentrations in salt complicate fracture mechanics not driven by dislocation dynamics and can influence the resulting surface structure. Classical molecular dynamic simulations were used to simulate tensile testing of salt crystals (halite) with vacancy defect concentrations of up to 0.5 defects/nm³. Increasing defect concentrations resulted in a decrease in ultimate tensile strength and fracture surface energies, driven by increased surface roughness rather than changes in the amount of surface area. Brine–salt surface energies of the fractured surfaces were 0.22 to 0.26 J/m², significantly higher than values reported for atomically flat (100) surfaces at the same brine composition. This change in surface energy increased the brine–salt dihedral angle by ~27°. The dihedral angle threshold for percolation in salt is 60°, and a 27° increase due to rough fracture surfaces identifies a reduction in porosity percolation and a decrease in salt permeability. Therefore, bedded salt and salt domes may be even more stable than those previously predicted from dihedral angle calculations.

Under IER-441, critical experiments were done with and without tantalum test rods within a central test region surrounded by 7uPCX fuel rods. The experiments were done in new critical assembly hardware designed to support the 7uPCX fuel in a 1.02 cm triangular-pitched array. Appendix I is a draft of section 1 of the ICSBEP benchmark evaluation of the experiments.

We demonstrate the capability of a narrow linewidth quantum cascade laser (QCL) to selectively excite a very narrow velocity range of nitric oxide (σ ≤ 7(3) m/s) with a pure ro-vibrational quantum state. By implementing a counter-propagating geometry, the molecules are selectively excited according to the Doppler shift of the ro-vibrational transition frequency such that the velocity width associated with the excited molecules depends only on the QCL linewidth. We demonstrate a velocity distribution limited by the effective linewidth of our free-running QCL (Γ = 3.2 MHz). Our development provides a cost-effective, flexible approach to resolve quantum-state selective chemical dynamics with excellent velocity resolution in a wide variety of molecules with infrared-active transitions. This technique has been formulated to provide ultrahigh collisional energy resolution in molecular beams to delineate final quantum-state product pairs in studies of molecular collisions.

Here, this paper analyzes the impact of the use of Kron reduction on the state variables of a three-phase electrical system, even when it does not meet the necessary conditions for its application. Reduction is applied to a power line model to eliminate the equation corresponding to the neutral conductor of the line. The ATP program is used to model and simulate the behavior of an electrical system considering different degrees of disequilibrium as a reference for the comparison of results. The results show that under certain conditions of disequilibrium the Kron reduction can lead to significant errors in the state variables of the system.

As quantum computing hardware becomes more complex with ongoing design innovations and growing capabilities, the quantum computing community needs increasingly powerful techniques for fabrication failure root-cause analysis. This is especially true for trapped-ion quantum computing. As trapped-ion quantum computing aims to scale to thousands of ions, the electrode numbers are growing to several hundred, with likely integrated photonic components also adding to the electrical and fabrication complexity, making faults even harder to locate. In this work, we used a high-resolution quantum magnetic imaging technique, based on nitrogen-vacancy centers in diamond, to investigate short-circuit faults in an ion trap chip. We imaged currents from these short-circuit faults to ground and compared them to intentionally created faults, finding that the root cause of the faults was failures in the on-chip trench capacitors. This work, where we exploited the performance advantages of a quantum magnetic sensing technique to troubleshoot a piece of quantum computing hardware, is a unique example of the evolving synergy between emerging quantum technologies to achieve capabilities that were previously inaccessible.

Stochastic collocation (SC) is a well-known non-intrusive method of constructing surrogate models for uncertainty quantification. In dynamical systems, SC is especially suited for full-field uncertainty propagation that characterizes the distributions of the high-dimensional solution fields of a model with stochastic input parameters. However, due to the highly nonlinear nature of the parameter-to-solution map in even the simplest dynamical systems, the constructed SC surrogates are often inaccurate. This work presents an alternative approach, where we apply the SC approximation over the dynamics of the model, rather than the solution. By combining the data-driven sparse identification of nonlinear dynamics framework with SC, we construct dynamics surrogates and integrate them through time to construct the surrogate solutions. We demonstrate that the SC-over-dynamics framework leads to smaller errors, both in terms of the approximated system trajectories as well as the model state distributions, when compared against full-field SC applied to the solutions directly. We present numerical evidence of this improvement using three test problems: a chaotic ordinary differential equation, and two partial differential equations from solid mechanics.

Stochastic collocation (SC) is a well-known non-intrusive method of constructing surrogate models for uncertainty quantification. In dynamical systems, SC is especially suited for full-field uncertainty propagation that characterizes the distributions of the high-dimensional solution fields of a model with stochastic input parameters. However, due to the highly nonlinear nature of the parameter-to-solution map in even the simplest dynamical systems, the constructed SC surrogates are often inaccurate. This work presents an alternative approach, where we apply the SC approximation over the dynamics of the model, rather than the solution. By combining the data-driven sparse identification of nonlinear dynamics framework with SC, we construct dynamics surrogates and integrate them through time to construct the surrogate solutions. We demonstrate that the SC-over-dynamics framework leads to smaller errors, both in terms of the approximated system trajectories as well as the model state distributions, when compared against full-field SC applied to the solutions directly. We present numerical evidence of this improvement using three test problems: a chaotic ordinary differential equation, and two partial differential equations from solid mechanics.

The phase transitions and thermodynamics of stoichiometric α-, β-, and γ-UO2(OH)2 polymorphs are investigated using density functional perturbation theory. The pressure-induced β(Pbca) → α(Cmca) phase transition is reproduced by calculations, with a volume reduction of ΔV/V = −14.7% similar to experiment. Consistent with observation, a temperature-driven γ(P21/c) → β(Pbca) phase transition is predicted near 533 K. At 298.15 K, the computed standard molar heat capacity of α-UO2(OH)2 is Cp0 = 112.1 J mol−1 K−1, only 1.6% smaller than the value of Cp0 = 113.96 ± 0.12 J mol−1 K−1 measured by calorimetry. Cp0 = 112.4 and 104.8 J mol−1 K−1 are predicted for the β- and γ-UO2(OH)2 polymorphs, respectively. The calculated molar enthalpy and Gibbs energy functions of the α-, β-, and γ-UO2(OH)2 polymorphs are also reported.

Rare-earth metal-organic frameworks (REMOFs) based on polynuclear metal clusters are an emerging class of materials that have shown promise for CO2 capture and conversion. In this work, copper nanoparticles (CuNPs) were successfully installed on a cluster-based Y(III) MOF to yield a composite material, CuNP-Y-TBAP. The abundance of Cu binding sites on the Y(III) clusters allowed a remarkably high Cu loading to be achieved, and electron microscopy demonstrated that the MOF-supported CuNPs are exceptionally small and monodisperse. CuNP-Y-TBAP was found to be an active heterogeneous catalyst for electrochemical reduction of CO2, yielding CO and CH4 as the primary CO2 reduction products.

Magnetic insulation of electrons prevents losses and can be applied to generating radiation or electron sources for high current and high power applications. Ion emission from the anode may degrade magnetic insulation. We develop equilibrium theory, self-consistently coupling magnetically insulated electron flow with free-flowing injected ions. Generally, ion injection is self-limiting from space charge; however, once insulation strength drops below about 1.2x the magnetic insulation threshold, ion space-charge limits vanish. Further, the gap effectively short-circuits and the electron flow layer asymptotically approaches the anode. In this regime a quasineutral, nonthermal plasma manifests, effectively reducing gap distance and suggesting quasiequilibrium gap closure evolution.

Statistical analysis of tensor-valued data has largely used the tensor-variate normal (TVN) distribution that may be inadequate for data arising from distributions with heavier or lighter tails. We study a general family of elliptically contoured (EC) TV distributions and derive its characterizations, moments, marginal, and conditional distributions. We describe procedures for maximum likelihood estimation from data that are (1) uncorrelated draws from an EC distribution, (2) from a scale mixture of the TVN distribution, and (3) from an underlying but unknown EC distribution, for which we extend Tyler’s robust estimator. A detailed simulation study highlights the benefits of choosing an EC distribution over the TVN for heavier-tailed data. We develop TV classification rules using discriminant analysis and EC errors and show that they better predict cats and dogs from images in the Animal Faces-HQ dataset than the TVN-based rules. A novel tensor-on-tensor regression and TV analysis of variance (TANOVA) framework under EC errors is also demonstrated to better characterize gender, age, and ethnic origin than the usual TVN-based TANOVA in the celebrated labeled faces of the wild dataset.

Scanning electron microscopy (SEM), a century-old technique, is today a ubiquitous method of imaging the surface of nanostructures. However, most SEM detectors simply count the number of secondary electrons from a material of interest, and thereby overlook the rich material information contained within them. Here, by simple modifications to a standard SEM tool, we resolve the momentum and energy information on secondary electrons by directly imaging the electron plume generated by the electron beam of the SEM. Leveraging these spectroscopic imaging capabilities, our technique is able to image lateral electric fields across a prototypical silicon p-n junctions and to distinguish differently doped regions, even when buried beyond depths typically accessible by SEM. Intriguingly, the subsurface sensitivity of this technique reveals unexpectedly strong surface band bending within nominally passivated semiconductor structures, providing useful insights for complex layered component designs, in which interfacial dynamics dictate device operation. These capabilities for noninvasive, multimodal probing of complicated electronic components are crucial in today’s electronic manufacturing but is largely inaccessible even with sophisticated techniques. These results show that seemingly simple SEM can be extended to probe complex and useful material properties.

Entropy is a state variable that may be obtained from any thermodynamically complete equation of state (EOS). However, hydrocode calculations that output the entropy often contain numerical errors; this is not because of the EOS, but rather the solution techniques that are used in hydrocodes (especially Eulerian) such as convection, remapping, and artificial viscosity. In this work, empirical correlations are investigated to reduce the errors in entropy without altering the solution techniques for the conservation of mass, momentum, and energy. Specifically, these correlations are developed for the function of entropy ZS, and they depend upon the net artificial viscous work, as determined via Sandia National Laboratories’ shock physics hydrocode CTH. These results are a continuation of a prior effort to implement the entropy-based CREST reactive burn model in CTH, and they are presented here to stimulate further interest from the shock physics community. Future work is planned to study higher-dimensional shock waves, shock wave interactions, and possible ties between the empirical correlations and a physical law.

Entropy is a state variable that may be obtained from any thermodynamically complete equation of state (EOS). However, hydrocode calculations that output the entropy often contain numerical errors; this is not because of the EOS, but rather the solution techniques that are used in hydrocodes (especially Eulerian) such as convection, remapping, and artificial viscosity. In this work, empirical correlations are investigated to reduce the errors in entropy without altering the solution techniques for the conservation of mass, momentum, and energy. Specifically, these correlations are developed for the function of entropy ZS, and they depend upon the net artificial viscous work, as determined via Sandia National Laboratories’ shock physics hydrocode CTH. These results are a continuation of a prior effort to implement the entropy-based CREST reactive burn model in CTH, and they are presented here to stimulate further interest from the shock physics community. Future work is planned to study higher-dimensional shock waves, shock wave interactions, and possible ties between the empirical correlations and a physical law.

Machine learning (ML) models in the materials sciences that are validated by overly simplistic cross-validation (CV) protocols can yield biased performance estimates for downstream modeling or materials screening tasks. This can be particularly counterproductive for applications where the time and cost of failed validation efforts (experimental synthesis, characterization, and testing) are consequential. We propose a set of standardized and increasingly difficult splitting protocols for chemically and structurally motivated CV that can be followed to validate any ML model for materials discovery. Among several benefits, this enables systematic insights into model generalizability, improvability, and uncertainty, provides benchmarks for fair comparison between competing models with access to differing quantities of data, and systematically reduces possible data leakage through increasingly strict splitting protocols. Performing thorough CV investigations across increasingly strict chemical/structural splitting criteria, local vs. global property prediction tasks, small vs. large datasets, and structure vs. compositional model architectures, some common threads are observed; however, several marked differences exist across these exemplars, indicating the need for comprehensive analysis to fully understand each model's generalization accuracy and potential for materials discovery. For this we provide a general-purpose, featurization-agnostic toolkit, MatFold, to automate reproducible construction of these CV splits and encourage further community use in model benchmarking.

Batteries composed of CFx cathodes have high theoretical specific capacities (>860 mA h g−1). Attempts at realizing such batteries coupled with Li anodes have failed to deliver on this promise, however, due to a discharge voltage plateau below the theoretical maximum lowering the realized energy density and difficulties with recharging the system. In this study, we use first-principles calculations to investigate novel carbon allotropes for these battery systems: graphdiyne and “holey” graphene. We first identify stable flourination structures and calculate their band gaps. We demonstrate that the holes in these carbon allotropes can induce the formation of an amorphous LiF network within the carbon and that this formation may, in fact, be kinetically favored. For structures where amorphous LiF forms within the carbon, we predict it is easier to recharge and higher discharge voltages can be achieved. If the LiF forms outside the carbon product, however, it will be crystalline in form and lead to lower discharge voltages and more difficulty in recharging the systems. Finally, we simulate XPS spectra of representative cases, demonstrating an experimental pathway for determining the reaction pathway of these systems. Our work suggests CFx allotropes with holes in them as potential targets for high capacity, rechargeable cathodes for Li batteries, provided they lead to the formation of amorphous LiF within the C structure.

Ab initio molecular dynamics (AIMD) simulations were carried out to investigate the equation of state of Nb2O5 and its pressure-density relationship under shock conditions. The focus of this study is on the monoclinic B−Nb2O5 (C2/c) polymorph. Enthalpy calculations from AIMD trajectories at 300 K show that the pressure-induced transformation between the thermodynamically most stable crystalline monoclinic parent phase H−Nb2O5 (P2/m) and B−Nb2O5 occurs at ∼1.9 GPa. This H→B transition is energetically more favorable than the H→L(Pmm2) pressure-induced transition recently observed at ∼5.9−9.0 GPa. The predicted shock properties of Nb2O5 polymorphs are also compared to their Nb and NbO2 counterparts to assess the impact of niobium oxidation on shock response.

The inaugural Advancing Chemical Safety and Security Education symposium was held at the 27th IUPAC International Conference on Chemistry Education (ICCE2024). Speakers showcased innovative strategies for seamlessly integrating security concepts into established safety programs, addressing specific needs of diverse academic institutions, and evaluating the effectiveness of different pedagogical approaches. Here, this proceedings publication encapsulates insights from 11 oral presentations, 12 poster presentations, and panel discussions including key recommendations for future advancements in educating chemical safety and security education for academic and industry audiences.

The binding of the sulfate ligand group to monovalent cations in the presence of water is important for many systems. To understand the structure and energetics of sulfate complexes, we use density functional theory to study ethyl sulfate binding to the monovalent cations Li⁺, Na⁺, and K⁺, and to water. The free energies of binding and optimal structures are calculated for a range of the number of ethyl sulfates and waters. Without water, the most optimal structure for all the cations is bidentate binding by two ethyl sulfates, yielding a 4-fold coordination. With water, the lowest free energy structures also have two ethyl sulfates, but the coordination varies with cations. For complexes with water, the four oxygen atoms in the sulfate group enable multiple binding geometries for the cations and for hydrogen bonding with water. Many of these geometries differ in free energy by only a small amount (1–2 kcal/mol), meaning there will be multiple binding configurations in bulk solution. In comparison to the optimal structures for binding to the carboxylate group, there is more variation for binding to the sulfate group as a function of cation type and the number of waters. Further, the polarization of the atoms is significant and varies among the sulfate oxygen atoms. The water oxygen charge is often larger than that of sulfate oxygen, which plays a role in the preference for monodentate ligand binding to cations in the presence of water.

Metasurfaces are highly effective at manipulating classical light in the linear regime; however, effectively controlling the polarization of nonclassical light generated from nonlinear resonant metasurfaces remains a challenge. Here, we present a solution by achieving polarization engineering of frequency-nondegenerate biphotons emitted via spontaneous parametric down-conversion in GaAs metasurfaces, utilizing quasi-bound states in the continuum (qBIC) resonances to enhance biphoton generation. Through comprehensive polarization tomography, we demonstrate that the emitted photons’ polarization directly reflects the qBIC mode’s far-field properties. Furthermore, we show that both the type of qBIC mode and the symmetry of the meta-atoms can be tailored to control each single-photon polarization state, and that the subsequent two-photon polarization states are nearly separable, offering potential applications in the heralded generation of single photons with adjustable polarization. This work provides a significant step toward utilizing metasurfaces to generate quantum light and engineer their polarization, a critical aspect for future quantum technologies.

The following details calibration of a material model for Al7075-T6511. This aluminum alloy is commonly used across a host of engineering applications. Owing to its widespread prevalence, there is great benefit in improving simulation predictions for this alloy. In the present effort, a calibration is performed of its elastic-plastic response accounting for both rate and temperature dependence. The calibration is informed by a series of tests that include specimens of different geometries tested at different rates and temperatures. All specimens are derived from the same barstock, 3.5 inches in diameter. The fitted model itself uses an anisotropic, Hill yield surface coupled with a Johnson-Cook hardening model. Failure predictions are had by means of a modified Wilkins failure criterion. Following calibration of the material model, a validation exercise is performed against platepuncture experiments. These experiments include multiple probe shapes, probe diameters, and plate thicknesses. The puncture experiments are replicated in simulation with mesh studies performed to assess uncertainty. Key quantities of interest, notably the absorbed energy up to failure, are compared between simulation and experiment providing a means to assess the suitability of the calibration in puncture simulations.

It has become increasingly important for microbiology educators to help students learn critical concepts of the discipline. This is particularly true in virology, where current challenges include increasing rates of vaccine hesitancy, misinformation about the COVID-19 pandemic, and controversy surrounding research on pathogens with pandemic potential. Having students learn virology can attract more people to the fieldand increase the number of people who can engage in meaningful discourse about issues relating to the discipline. However, the limited number of virologists who teach undergraduates, combined with the fact that many institutions lack stand-alone virology courses, results in virology often being taught as a limited number of lectures within an undergraduate microbiology course (if it is covered at all), which may or may not be taught by an individual trained as a virologist. To provide a framework to teach virology to undergraduate students, a team of virology educators, with support from the American Society for Virology (ASV), developed curriculum guidelines for use in a stand-alone undergraduate virology course or a virology section within another course (D. B. Kushner et al., J Virol 96:e01305-22, 2022, https://doi.org/10.1128/jvi.01305-22). These guidelines are available at the ASV website (https://asv.org/curriculum-guidelines/). To assist educators in implementing these guidelines, we created examples of measurable learning objectives. This perspective provides details about the virology curriculum guidelines and learning objectives and accompanies the perspective by Boury et al. in this issue of the Journal of Microbiology & Biology Education (25:e00126-24, 2024, https://doi.org/10.1128/jmbe.00126-24) about the recent revision of the microbiology curriculum guidelines overseen by the American Society for Microbiology.

Cyber-physical systems have behaviour that crosses domain boundaries during events such as planned operational changes and malicious disturbances. Traditionally, the cyber and physical systems are monitored separately and use very different toolsets and analysis paradigms. The security and privacy of these cyber-physical systems requires improved understanding of the combined cyber-physical system behaviour and methods for holistic analysis. Therefore, the authors propose leveraging clustering techniques on cyber-physical data from smart grid systems to analyse differences and similarities in behaviour during cyber-, physical-, and cyber-physical disturbances. Since clustering methods are commonly used in data science to examine statistical similarities in order to sort large datasets, these algorithms can assist in identifying useful relationships in cyber-physical systems. Through this analysis, deeper insights can be shared with decision-makers on what cyber and physical components are strongly or weakly linked, what cyber-physical pathways are most traversed, and the criticality of certain cyber-physical nodes or edges. This paper presents several types of clustering methods for cyber-physical graphs of smart grid systems and their application in assessing different types of disturbances for informing cyber-physical situational awareness. The collection of these clustering techniques provide a foundational basis for cyber-physical graph interdependency analysis.

Accurate self-motion estimation is critical for various navigational tasks in mobile robotics. Optic flow provides a means to estimate self-motion using a camera sensor and is particularly valuable in GPS- and radio-denied environments. The present study investigates the influence of different activation functions—ReLU, leaky ReLU, GELU, and Mish—on the accuracy, robustness, and encoding properties of convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs) trained to estimate self-motion from optic flow. Our results demonstrate that networks with ReLU and leaky ReLU activation functions not only achieved superior accuracy in self-motion estimation from novel optic flow patterns but also exhibited greater robustness under challenging conditions. The advantages offered by ReLU and leaky ReLU may stem from their ability to induce sparser representations than GELU and Mish do. Our work characterizes the encoding of optic flow in neural networks and highlights how the sparseness induced by ReLU may enhance robust and accurate self-motion estimation from optic flow.

Dielectric barrier discharges, particularly micro cavity plasma arrays, offer significant potential for plasma-catalytic research due to their ability to ignite plasma in direct contact with a catalytic surface, enabling the observation of plasma-surface interactions. A key factor in their application is the generation of reactive species, such as atomic oxygen, within the cavities. These species can interact with both the surface (e.g. for activation or cleaning) and the gas being treated (e.g. for oxidation). Given the central role of oxygen atoms in plasma catalysis and their use as a model for more complex species, this work investigates the transport of these atoms out of the cavities. Two-photon absorption laser-induced fluorescence spectroscopy with picosecond laser excitation is performed in the volume above the cavities. The results are compared with a basic diffusion model. The reactor operates with a He/O2 mixture at a flow rate of 1 slm and atmospheric pressure. Densities of up to 1016 cm−3 are measured near the surface. Time-dependent measurements show that, at a distance of 350 µm from the surface, a density equilibrium is reached within less than 3 ms of reactor operation. Decay times due to ozone formation after the reactor is turned off are on a similar scale. Spatially resolved measurements show that the oxygen density decreases exponentially from the surface but remains detectable up to approximately 1 mm above the surface, indicating significant application potential. Variations in the O2 admixture show a density maximum at 0.4%, confirming previous helium state enhanced actinometry measurements within the cavities.

In the machine learning problem of multilabel classification, the objective is to determine for each test instance which classes the instance belongs to. In this work, we consider an extension of multilabel classification, called multilabel proportion prediction, in the context of radioisotope identification (RIID) using gamma spectra data. We aim to not only predict radioisotope proportions, but also identify out-of-distribution (OOD) spectra. We achieve this goal by viewing gamma spectra as discrete probability distributions, and based on this perspective, we develop a custom semi-supervised loss function that combines a traditional supervised loss with an unsupervised reconstruction error function. Our approach was motivated by its application to the analysis of short-lived fission products from spent nuclear fuel. In particular, we demonstrate that a neural network model trained with our loss function can successfully predict the relative proportions of 37 radioisotopes simultaneously. The model trained with synthetic data was then applied to measurements taken by Pacific Northwest National Laboratory (PNNL) to conduct analysis typically done by subject-matter experts. We also extend our approach to successfully identify when measurements are OOD, and thus should not be trusted, whether due to the presence of a novel source or novel proportions.

The current present in a galvanic couple can define its resistance or susceptibility to corrosion. However, as the current is dependent upon environmental, material, and geometrical parameters it is experimentally costly to measure. To reduce these costs, Finite Element (FE) simulations can be used to assess the cathodic current but also require experimental inputs to define boundary conditions. Due to these challenges, it is crucial to accelerate predictions and accurately predict the current output for different environments and geometries representative of in-service conditions. Machine learned surrogate models provides a means to accelerate corrosion predictions. However, a one-time cost is incurred in procuring the simulation and experimental dataset necessary to calibrate the surrogate model. Therefore, an active learning protocol is developed through calibration of a low-cost surrogate model for the cathodic current of an exemplar galvanic couple (AA7075-SS304) as a function of environmental and geometric parameters. The surrogate model is calibrated on a dataset of FE simulations, and calculates an acquisition function that identifies specific additional inputs with the maximum potential to improve the current predictions. This is accomplished through a staggered workflow that not only improves and refines prediction, but identifies the points at which the most information is gained, thus enabling expansion to a larger parameter space. The protocols developed and demonstrated in this work provide a powerful tool for screening various forms of corrosion under in-service conditions.

Infrasound generated by earthquakes and explosions is generally detected at receivers at epicentral distances of hundreds to thousands of kilometers. However, proximal (< 50 km) observations are especially important for low-magnitude earthquakes and low-yield explosions that may not generate signals capable of being detected at great ranges. Here, we present on the signals detected on an infrasound array 3 km away from two ML 2.9 earthquakes in the Permian Basin of west Texas. Local infrasound (LIS), generated at receivers during the passage of seismic waves, was detected following each earthquake. Epicentral infrasound (EIS), created at or near the epicenter and propagating away as a sound wave, was also detected. Array processing methods show that the EIS signals arrive from the same direction as the earthquake epicenters and at acoustic speeds. To our knowledge, these are the first observations of laterally propagating EIS at proximal ranges following an earthquake of any magnitude.

Material Testing 2.0 (MT2.0) is a paradigm that advocates for the use of rich, full-field data, such as from digital image correlation and infrared thermography, for material identification. By employing heterogeneous, multi-axial data in conjunction with sophisticated inverse calibration techniques such as finite element model updating and the virtual fields method, MT2.0 aims to reduce the number of specimens needed for material identification and to increase confidence in the calibration results. To support continued development, improvement, and validation of such inverse methods—specifically for rate-dependent, temperature-dependent, and anisotropic metal plasticity models—we provide here a thorough experimental data set for 304L stainless steel sheet metal. The data set includes full-field displacement, strain, and temperature data for seven unique specimen geometries tested at different strain rates and in different material orientations. Commensurate extensometer strain data from tensile dog bones is provided as well for comparison. We believe this complete data set will be a valuable contribution to the experimental and computational mechanics communities, supporting continued advances in material identification methods.

Scalable quantum processors require high-fidelity universal quantum logic operations in a manufacturable physical platform. Donors in silicon provide atomic size, excellent quantum coherence and compatibility with standard semiconductor processing, but no entanglement between donor-bound electron spins has been demonstrated to date. Here we present the experimental demonstration and tomography of universal one- and two-qubit gates in a system of two weakly exchange-coupled electrons, bound to single phosphorus donors introduced in silicon by ion implantation. We observe that the exchange interaction has no effect on the qubit coherence. We quantify the fidelity of the quantum operations using gate set tomography (GST), and we use the universal gate set to create entangled Bell states of the electrons spins, with fidelity 91.3 ± 3.0%, and concurrence 0.87 ± 0.05. These results form the necessary basis for scaling up donor-based quantum computers.

A combined Mode I-II cohesive zone (CZ) elasto-plastic constitutive model, and a two-dimensional (2D) cohesive interface element (CIE) are formulated and implemented at small strain within an ABAQUS User Element (UEL) for simulating 2D crack nucleation and propagation in fluid-saturated porous media. The CZ model mitigates problems of convergence for the global Newton-Raphson solver within ABAQUS, which when combined with a viscous stabilization procedure allows for simulation of post-peak response under load control for coupled poromechanical finite element analysis, such as concrete gravity dam stability analysis. Verification examples are presented, along with a more complex ambient limestone-concrete wedge fracture experiment, water-pressurized concrete wedge experiment, and concrete gravity dam stability analyses. A calibration procedure for estimating the CZ parameters is demonstrated with the limestone-concrete wedge fracture process. For the water-pressurized concrete wedge fracture experiment it is shown that the inherent time-dependence of the poromechanical CIE analysis provides a good match with experimental force versus displacement results at various crack mouth opening rates, yet misses the pore water pressure evolution ahead of the crack tip propagation. This is likely a result of the concrete being partially-saturated in the experiment, whereas the finite element analysis assumes fully water saturated concrete. For the concrete gravity dam analysis, it is shown that base crack opening and associated water uplift pressure leads to a reduced Factor of Safety, which is confirmed by separate analytical calculations.

In response to gradual nanoindentation, the surface of muscovite mica deforms by sudden stochastic nanometer-scale displacement bursts. Here, the statistics of these displacement events are interpreted using a statistical model previously used to model earthquakes to understand how chemically reactive environments alter the surface properties of this material. We show that the statistics of nanoindentation displacement bursts in muscovite mica are tuned by chemomechanical weakening in a manner similar to how the statistics of model events are tuned by a mechanical weakening parameter that describes how easily system-spanning cracks can be nucleated. Because the predictions of this model are independent of any surface defects or structural details, these results suggest this simple model can be universally used to describe chemomechanical weakening in many systems prone to slip avalanches on a wide range of spatio-temporal scales.

Barium titanate (BTO) is a ferroelectric perovskite used in electronics and energy storage systems because of its high dielectric constant. Decreasing the BTO particle size was shown to increase the dielectric constant of the perovskite, which is an intriguing but contested result. We investigated this result by fabricating silicone-matrix nanocomposite specimens containing BTO particles of decreasing diameter. Furthermore, density functional theory modeling was used to understand the interactions at the BTO particle surface. Combining results from experiments and modeling indicated that polymer type, particle surface interactions, and particle surface structure can influence the dielectric properties of polymer-matrix nanocomposites containing BTO.

Solid–water interfaces are crucial for clean water, conventional and renewable energy, and effective nuclear waste management. However, reflecting the complexity of reactive interfaces in continuum-scale models is a challenge, leading to oversimplified representations that often fail to predict real-world behavior. This is because these models use fixed parameters derived by averaging across a wide physicochemical range observed at the molecular scale. Recent studies have revealed the stochastic nature of molecular-level surface sites that define a variety of reaction mechanisms, rates, and products even across a single surface. To bridge the molecular knowledge and predictive continuum-scale models, we propose to represent surface properties with probability distributions rather than with discrete constant values derived by averaging across a heterogeneous surface. This conceptual shift in continuum-scale modeling requires exponentially rising computational power. By incorporating our molecular-scale understanding of solid–water interfaces into continuum-scale models we can pave the way for next generation critical technologies and novel environmental solutions.

Composite materials with different microstructural material symmetries are common in engineering applications where grain structure, alloying and particle/fiber packing are optimized via controlled manufacturing. In fact these microstructural tunings can be done throughout a part to achieve functional gradation and optimization at a structural level. To predict the performance of particular microstructural configuration and thereby overall performance, constitutive models of materials with microstructure are needed. In this work we provide neural network architectures that provide effective homogenization models of materials with anisotropic components. These models satisfy equivariance and material symmetry principles inherently through a combination of equivariant and tensor basis operations. We demonstrate them on datasets of stochastic volume elements with different textures and phases where the material undergoes elastic and plastic deformation, and show that the these network architectures provide significant performance improvements.

Modern lens designs are capable of resolving greater than 10 gigapixels, while advances in camera frame-rate and hyperspectral imaging have made data acquisition rates of Terapixel/second a real possibility. The main bottlenecks preventing such high data-rate systems are power consumption and data storage. In this work, we show that analog photonic encoders could address this challenge, enabling high-speed image compression using orders-of-magnitude lower power than digital electronics. Our approach relies on a silicon-photonics front-end to compress raw image data, foregoing energy-intensive image conditioning and reducing data storage requirements. The compression scheme uses a passive disordered photonic structure to perform kernel-type random projections of the raw image data with minimal power consumption and low latency. A back-end neural network can then reconstruct the original images with structural similarity exceeding 90%. This scheme has the potential to process data streams exceeding Terapixel/second using less than 100 fJ/pixel, providing a path to ultra-high-resolution data and image acquisition systems.

X-rays can provide images when an object is visibly obstructed, allowing for motion measurements via x-ray digital image correlation (DIC). However, x-ray images are path-integrated and contain data for all objects between the source and detector. If multiple objects are present in the x-ray path, conventional DIC algorithms may fail to correlate the x-ray images. A new DIC algorithm called path-integrated (PI)-DIC addresses this issue by reformulating the matching criterion for DIC to account for multiple, independently-moving objects. PI-DIC requires a set of reference x-ray images of each independent object. However, due to experimental constraints, such reference images might not be obtainable from the experiment. This work focuses on the reliability of synthetically-generated reference images, in such cases. A simplified exemplar is used for demonstration purposes, consisting of two aluminum plates with tantalum x-ray DIC patterns undergoing independent rigid translations. Synthetic reference images based on the “as-designed” DIC patterns were generated. However, PI-DIC with the synthetic images suffered some biases due to manufacturing defects of the patterns. A systematic study of seven identified defect types found that an incorrect feature diameter was the most influential defect. Synthetic images were re-generated with the corrected feature diameter, and PI-DIC errors were improved by a factor of 3-4. Final biases ranged from 0.00-0.04 px, and standard uncertainties ranged from 0.06-0.11 px. In conclusion, PI-DIC accurately measured the independent displacement of two plates from a single series of path-integrated x-ray images using synthetically-generated reference images, and the methods and conclusions derived here can be extended to more generalized cases involving stereo PI-DIC for arbitrary specimen geometry and motion. This work thus extends the application space of x-ray imaging for full-field DIC measurements of multiple surfaces or objects in extreme environments where optical DIC is not possible.

Additive manufacturing (AM) technology, specifically 3D printing, holds great promise for in-orbit manufacturing. In-space printing can significantly reduce the mass, cost, and risk of long-term space exploration by enabling replacement parts to be made as needed and reducing dependence on Earth. However, printing in a zero-gravity environment poses challenges due to the absence of a rigid ground for the print platform, which can result in vibrational and rotational forces that may impact printing integrity. To address this issue, this paper proposes a novel linear magnetic position tracking algorithm, named Navigation Integrating Magnets By Linear Estimation (NIMBLE), for dynamic vibration compensation during 3D printing of truss structures in space. Compared to the most commonly used nonlinear optimization method, the NIMBLE algorithm is more than two orders of magnitude faster. With only a single 3-axis magnet sensor and a small NdFeB magnet, the NIMBLE algorithm provides a simple and easily implemented tracking solution for in-orbit 3D printing.

The spatial distribution of electric field due to an imposed electric charge density profile in an infinite slab of dielectric material is derived analytically by integrating Gauss's law. Various charge density distributions are considered, including exponential and power-law forms. The Maxwell stress tensor is used to compute a notional static stress in the material due to the charge density and its electric field. Characteristics of the electric field and stress distributions are computed for example cases in polyethylene, showing that field magnitudes exceeding the dielectric strength would be required in order to achieve a stress exceeding the ultimate tensile strength.

Accurate disease spread modeling is crucial for identifying the severity of outbreaks and planning effective mitigation efforts. To be reliable when applied to new outbreaks, model calibration techniques must be robust. However, current methods frequently forgo calibration verification (a stand-alone process evaluating the calibration procedure) and instead use overall model validation (a process comparing calibrated model results to data) to check calibration processes, which may conceal errors in calibration. In this work, we develop a stochastic agent-based disease spread model to act as a testing environment as we test two calibration methods using simulation-based calibration, which is a synthetic data calibration verification method. The first calibration method is a Bayesian inference approach using an empirically-constructed likelihood and Markov chain Monte Carlo (MCMC) sampling, while the second method is a likelihood-free approach using approximate Bayesian computation (ABC). Simulation-based calibration suggests that there are challenges with the empirical likelihood calculation used in the first calibration method in this context. These issues are alleviated in the ABC approach. Despite these challenges, we note that the first calibration method performs well in a synthetic data model validation test similar to those common in disease spread modeling literature. We conclude that stand-alone calibration verification using synthetic data may benefit epidemiological researchers in identifying model calibration challenges that may be difficult to identify with other commonly used model validation techniques.

The effect of proton implantation as isolation implant and subsequent annealing on the optical absorption and electrical resistivity of low-bandgap p-GaSb is reported. The measured transmittance spectra indicates that implantation creates a distribution of energy levels extending into the bandgap. Electrical measurements show that the average sheet resistance of the implanted layer increases only by an order of magnitude from its pre-implantation value at a proton dose of ∼1013 cm−2 followed by 200 °C annealing. It is also shown that annealing reduces the implantation-induced optical absorption while still retaining a high electrical resistivity.

We present a machine-learning strategy for finite element analysis of solid mechanics wherein we replace complex portions of a computational domain with a data-driven surrogate. In the proposed strategy, we decompose a computational domain into an “outer” coarse-scale domain that we resolve using a finite element method (FEM) and an “inner” fine-scale domain. We then develop a machine-learned (ML) model for the impact of the inner domain on the outer domain. In essence, for solid mechanics, our machine-learned surrogate performs static condensation of the inner domain degrees of freedom. This is achieved by learning the map from displacements on the inner-outer domain interface boundary to forces contributed by the inner domain to the outer domain on the same interface boundary. We consider two such mappings, one that directly maps from displacements to forces without constraints, and one that maps from displacements to forces by virtue of learning a symmetric positive semi-definite (SPSD) stiffness matrix. We demonstrate, in a simplified setting, that learning an SPSD stiffness matrix results in a coarse-scale problem that is well-posed with a unique solution. We present numerical experiments on several exemplars, ranging from finite deformations of a cube to finite deformations with contact of a fastener-bushing geometry. We demonstrate that enforcing an SPSD stiffness matrix drastically improves the robustness and accuracy of FEM–ML coupled simulations, and that the resulting methods can accurately characterize out-of-sample loading configurations with significant speedups over the standard FEM simulations.

In this study we present a replication method to determine surface roughness and to identify surface features when a sample cannot be directly analyzed by conventional techniques. As a demonstration, this method was applied to an unused spent nuclear fuel dry storage canister to determine variation across different surface features. In this study, an initial material down-selection was performed to determine the best molding agent and determined that non-modified Polytek PlatSil23-75 provided the most accurate representation of the surface while providing good usability. Other materials that were considered include Polygel Brush-On 35 polyurethane rubber (with and without Pol-ease 2300 release agent), Polytek PlatSil73-25 silicone rubber (with and without PlatThix thickening agent and Pol-ease 2300 release agent), and Express STD vinylpolysiloxane impression putty. The ability of PlatSil73-25 to create an accurate surface replica was evaluated by creating surface molds of several locations on surface roughness standards representing ISO grade surfaces N3, N5, N7, and N8. Overall, the molds were able to accurately reproduce the expected roughness average (Ra) values, but systematically over-estimated the peak-valley maximum roughness (Rz) values. Using a 3D printed sample cell, several locations across the stainless steel spent nuclear fuel canister were sampled to determine the surface roughness. These measurements provided information regarding variability in normal surface roughness across the canister as well as a detailed evaluation on specific surface features (e.g., welds, grind marks, etc.). The results of these measurements can support development of dry storage canister ageing management programs, as surface roughness is an important factor for surface dust deposition and accumulation. This method can be applied more broadly to different surfaces beyond stainless steel to provide rapid, accurate surface replications for analytical evaluation by profilometry.

The increasing potential of infrastructure disruptions calls for high-quality infrastructure models to be used in resilience analysis and decision making. Unfortunately, many utilities and communities do not have access to accurate and detailed models due to a lack of data and resources. Furthermore, security restrictions on sharing infrastructure models present roadblocks to research, analysis, and decision making. Recent advances in the development of synthetic water distribution models provide a potential solution to this problem. There is an opportunity to improve these methods by leveraging incomplete pipe datasets to aid synthetic network generation. To address this gap, we developed a methodology for synthetic network generation that incorporates partial pipe data using a modification of the minimum cost flow algorithm for network generation and pipe sizing. This methodology demonstrates how partial pipe data can be leveraged to improve site-specific synthetic network generation. For the study area of Mayagüez, Puerto Rico, a synthetic model generated using 50% of real pipe data matches the pressure of the validation system with an average error of 23.5 m of head, which improves upon the average error of 31.6 m of head produced by a synthetic model generated using no data of the real pipes. Additionally, synthetic networks are shown to replicate the pressure response under a disruption scenario of the validation network, suggesting potential use in resilience analysis.

Decarbonizing the glass industry requires alternative melting technology, as current industrial melting practices rely heavily on fossil fuels. Hydrogen has been proposed as an alternative to carbon-based fuels, but the ensuing consequences on the mechanical behavior of the glass remain to be clarified. A critical distinction between hydrogen and carbon-based fuels is the increased generation of water during combustion, which raises the equilibrium solubility of water in the melt and alters the behavior of the resulting glass. A series of five silicate glasses with 80% silica and variable [Na2O]/([H2O] + [Na2O]) ratios were simulated using molecular dynamics to elucidate the effects of water on fracture. Several fracture toughness calculation methods were used in combination with atomistic fracture simulations to examine the effects of hydroxyl content on fracture behavior. This study reveals that the crack propagation pathway is a key metric to understanding fracture toughness. Notably, the fracture propagation path favors hydrogen sites over sodium sites, offering a possible explanation of the experimentally observed effects of water on fracture properties.

The use of photon active interrogation to detect special nuclear material has held significant theoretical promise, as the interrogating source particles, photons, are fundamentally different from one of the main signatures of special nuclear material: neutrons produced in nuclear fission. However, neutrons produced by photonuclear reactions in the accelerator target, collimator, and environment can obscure the fission neutron signal. These (γ,n) neutrons could be discriminated from fission neutrons by their energy spectrum, but common detectors sensitive to the neutron spectrum, like organic scintillators, are typically hampered by the intense photon background characteristic of photon-based active interrogation. In contrast, high-pressure 4He-based scintillation detectors are well -suited to photon active interrogation, as they are similarly sensitive to fast neutrons and can measure their spectrum, but show little response to gamma rays. In this work, a photon active interrogation system utilizing a 4He scintillation detector and a 9 MeV linac-bremsstrahlung x-ray source was experimentally evaluated. The detector was shown to be capable of operating in intense gamma-ray environments and detecting photofission neutrons from 238U when interrogated by this x-ray source. The photofission neutrons show clear spectral separation from (γ,n) neutrons produced in lead, a common shielding material.

Using coarse graining, the upscaled mechanical properties of a solid with small scale heterogeneities are derived. The method maps internal forces at the small scale onto peridynamic bond forces in the coarse grained mesh. These upscaled bond forces are used to calibrate a peridynamic material model with position-dependent parameters. These parameters incorporate mesoscale variations in the statistics of the small scale system. The upscaled peridynamic model can have a much coarser discretization than the original small scale model, allowing larger scale simulations to be performed efficiently. The convergence properties of the method are investigated for representative random microstructures. A bond breakage criterion for the upscaled peridynamic material model is also demonstrated.

Technical key performance indicators (KPIs) are important metrics used to assess and quantitatively summarize various aspects of photovoltaic (PV) systems, including long-term performance, economic viability, and carbon footprint. Herein, a group of experts of the International Energy Agency's Photovoltaic Power Systems Programme Task 13 collect and describ the most important technical KPIs used in the industry. Thereby, a set of best practices for reliably handling PV system data is presented and the impact of data quality and climatic variability on KPI calculation is investigated. The effective use of technical KPIs allows triggering data-driven and informed decisions to optimize PV systems and providing a comprehensive overview of how PV systems operate across different conditions and climates. With the worldwide growth of the PV industry, more companies operate/own PV systems in different regions, where the climatic and seasonal profiles differ. This requires context-aware evaluation of KPIs, or the judicious application of multiple KPIs, to ensure that each asset is evaluated correctly. Beyond that, there is untapped potential in the utilization of KPIs through geospatial mapping and extrapolation of fleet KPIs. This study demonstrates that the uncertainty in KPI estimation is not well understood and depends on data quality, climatic variability, and system configuration.

A deep convolutional neural network (DCNN) was used for microstructure reconstruction using artificial intelligence (MR-AI) by predicting local average fiber orientation distributions (FOD) in a 3D prepreg platelet molded composite (PPMC) pin bracket. To train the MR-AI model, surface strain fields from residual stresses simulated in PPMC plates were used as the input to the DCNN. A training dataset included PPMC plates with various degrees of global fiber alignment, based on the information obtained from high-fidelity flow simulation of a pin bracket. The MR-AI model was then deployed to analyze FOD in the 3D pin bracket by conducting thermo-elastic residual stress analysis. Initially, the MR-AI model was established entirely on the synthetic simulation data. Then, a μCT scan of a physically molded pin bracket was used to create a finite element model that provided data for additional validation of the DCNN model. For the μCT scan finite element pin bracket the MR-AI model predicted the distribution of fiber orientation tensor components with MAE of 0.10 indicating a global prediction error of 10 %. For the flow simulated pin bracket, the MR-AI model predicted the distribution of fiber orientation tensor components with a global prediction error of 11 %. The MR-AI model showed the ability to predict regions of varying alignment in the base and flange of the pin bracket. The proposed MR-AI methodology allows for rapid prediction of FOD in geometrically complex parts and offers a promising path to detecting unique fiber orientation states in molded components.

Controlling microstructural evolution in metallic additive manufacturing (AM) is difficult, especially in producing refined as-built grains instead of coarse, directional grains. Traditional solutions involve adding inoculants to AM feedstocks, but titanium (Ti) alloys cannot employ this approach without producing detrimental secondary phases. Ti-Cu (Ti-copper) alloys offer a solution through constitutional supercooling and/or solid state thermal cycling under AM conditions. This work analyzes a compositionally graded directed energy deposition (DED) Ti-Cu build, single-melt laser tracks, and dilatometric heat treatments to evaluate if, when, and by what mechanism(s) microstructural refinement occurs. Refinement by inoculation of unmelted powder particles was also considered. Constitutional supercooling produced no net microstructural refinement as any equiaxed dendrites which form are remelted with new deposition. This finding agreed with solidification modeling of powder bed fusion-laser beam (PBF-LB) and DED builds. Solid state thermal cycling refined microstructures only during ex-situ dilatometric heat treatments, suggesting build parameter optimization is needed to achieve refinement in-situ. Accidental heterogeneous nucleation on unmelted Ti powder, originating from the different thermophysical properties of Ti and Cu, provided the most significant microstructural refinement. This work systematically assesses the microstructural refinement mechanisms of Ti-Cu in AM builds and offers insights into microstructural control in eutectoid alloys.

Information security and computing, two critical technological challenges for post-digital computation, pose opposing requirements – security (encryption) requires a source of unpredictability, while computing generally requires predictability. Each of these contrasting requirements presently necessitates distinct conventional Si-based hardware units with power-hungry overheads. This work demonstrates Cu0.3Te0.7/HfO2 (‘CuTeHO’) ion-migration-driven memristors that satisfy the contrasting requirements. Under specific operating biases, CuTeHO memristors generate truly random and physically unclonable functions, while under other biases, they perform universal Boolean logic. Using these computing primitives, this work experimentally demonstrates a single system that performs cryptographic key generation, universal Boolean logic operations, and encryption/decryption. Circuit-based calculations reveal the energy and latency advantages of the CuTeHO memristors in these operations. This work illustrates the functional flexibility of memristors in implementing operations with varying component-level requirements.

Laser powder bed fusion (LPBF) additive manufacturing makes near-net-shaped parts with reduced material cost and time, rising as a promising technology to fabricate Ti-6Al-4 V, a widely used titanium alloy in aerospace and medical industries. However, LPBF Ti-6Al-4 V parts produced with 67° rotation between layers, a scan strategy commonly used to reduce microstructure and property inhomogeneity, have varying grain morphologies and weak crystallographic textures that change depending on processing parameters. This study predicts LPBF Ti-6Al-4 V solidification at three energy levels using a finite difference-Monte Carlo method and validates the simulations with large-area electron backscatter diffraction (EBSD) scans. The developed model accurately shows that a 〈001〉 texture forms at low energy and a 〈111〉 texture occurs at higher energies parallel to the build direction but with a lower strength than the textures observed from EBSD. A validated and well-established method of combining spatial correlation and general spherical harmonics representation of texture is developed to calculate a difference score between simulations and experiments. The quantitative comparison enables effective fine-tuning of nucleation density (N0) input, which shows a nonlinear relationship with increasing energy level. Future improvements in texture prediction code and a more comprehensive study of N0 with different energy levels will further advance the optimization of LPBF Ti-6Al-4 V components. These developments contribute a novel understanding of crystallographic texture formation in LPBF Ti-6Al-4 V, the development of robust model validation and calibration pipeline methodologies, and provide a platform for mechanical property prediction and process parameter optimization.

Metal chalcogenides are of interest as electrocatalysts, battery materials, and more. XPS is a valuable tool for studying changes to these materials before and after catalysis, making reference spectra for the pristine materials valuable. Here, we present XPS spectra for a family of nickel sulfoselenide (NiSSe) materials based on the Ni3X2 crystal structure, Ni3S2−xSex. XPS surveys and high-resolution spectra of Ni 2p, S 2p, S 2s, Se 3d, Se 3p, and Se 3s were recorded using Al Kα radiation.

The bulk-boundary correspondence in topological crystalline insulators (TCIs) links the topological properties of the bulk to robust observables on the edges, e.g., the existence of robust edge modes or fractional charge. In one dimension, TCIs protected by reflection symmetry have been realized in a variety of systems in which each unit cell has spatially distributed degrees of freedom (SDOF). However, these realizations exhibit sensitivity of the resulting edge modes to variations in edge termination and to the local breaking of the protective spatial symmetries by inhomogeneity. Here we demonstrate topologically protected edge states in a monoatomic, orbital-based TCI that mitigates both of these issues. By collapsing all SDOF within the unit cell to a singular point in space, we eliminate the ambiguity in unit-cell definition and hence remove a prominent source of boundary termination variability. The topological observables are also more tolerant to disorder in the orbital energies. To validate this concept, we experimentally realize a lattice of mechanical resonators where each resonator acts as an "atom"that harbors two key orbital degrees of freedom having opposite reflection parity. Our measurements of this system provide direct visualization of the sp-hybridization between orbital modes that leads to a nontrivial band inversion in the bulk.

This memo includes the documentation sections for the Library of Advanced Materials for Engineering (LAMÉ) manual for a fiber reinforced polymer composite damage model (Elastic_Orthotropic_FRP_Modular_Damage) in advance of the next Sierra/SM and LAMÉ release consistent with the transition of the model from development to a production capability. This new model capability provides both a set of physically based orthotropic damage criteria as well as orthotropic material softening for woven fiber reinforced polymer composite materials. The documentation sections include short sections on theory, implementation, verification, and user guidance to prescribe the model in a Sierra/SM input file.

The Department of Energy Hydrogen Fuel Cell Technology Office and Wind Energy Technologies Office's Wind-H2-Green Steel/Ammonia project is an initiative to demonstrate the feasibility and efficacy of GW-scale integrated energy systems. The team designed reference facilities that utilize wind- and solar-produced hydrogen for industrial steel and ammonia production. This novel concept warranted review of safety codes and standards as they apply to the designs and the identification of codes and standards gaps. This report reviews hydrogen production and storage codes and standards using reference design specifications from a Minnesota steel plant. Requirements, recommendations, and exclusions for the system were identified. Observed gaps included non-specific salt cavern storage requirements, electrolyzer capacity beyond regulated ranges, and lack of requirements for iron reduction via hydrogen. This report will aide future project design efforts and may provide a basis for safety reviews in new designs for industrial facilities with hydrogen production integration.

Sandia National Laboratories has tested and evaluated three Colt broadband seismometers designed and manufactured by Reftek. The purpose of this seismometer evaluation is to measure performance characteristics in areas such as power consumption, sensitivity, frequency response, full scale, self-noise, dynamic range, calibration system response, and passband. The Colt model of sensors are being evaluated to explore the potential for a future seismometer Type Approval process in the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty (CTBT).

Abstract not provided.

This document provides an overview of re-start efforts at Sandia National Laboratories (SNL), National Solar Thermal Test Facility (NSTTF), for the SNL Molten Salt Test Loop (MSTL). MSTL is one of the world’s only industrial-scale molten salt test systems, capable of testing commercial-scale thermal hydraulic equipment at an industrial scale, required for confident bankability of products. This work was facilitated as part of the Phase 1 re-start effort for facilitating forensics assessments to inform final refurbishments and upgrades costs and schedule information for revitalizing MSTL for use during a separate Phase 2 effort. This work was facilitated to further de-risk a Phase 2 effort pertaining to the tank and the pump, which were considered during Phase 1 to be high-risk items for a successful re-start. Impact from a successful re-start will allow SNL and other industrial molten salt systems companies, such as those in the Market Utilization Report provided in the Phase 1 submission package. An objective of this document is to also ensure longevity of utilization of MSTL after re start.

An Earth-centered, Earth-fixed (ECEF) inertial navigation system must compute the Jacobian of its employed gravitation model with respect to position while time-propagating the error covariance of the system. One commonly used gravitation model is the ‘J2 model’ which is a second-order truncation of the Earth’s spherical harmonic gravitation model. The J2 model is popular because it can quickly and efficiently be evaluated, and the truncation error is small: The ‘J3 term’ --- the third term in the spherical harmonic expansion --- is approximately 1000 times smaller than the J2 term.

Abstract not provided.

This report demonstrates that applying graph theory techniques provides a way to obtain sufficient statistics in finding errors when testing complex state machines. It discusses how to define the tests, then demonstrates how to automatically generate test suites that diversify test cases, subject to constraints. If included within a continuous integration approach, these constructs provide an unbiased means to systematically check for errors within the latest controller software release.

The deployment of heavy-duty (HD) hydrogen fuel cell vehicles that are entering the market now is driving the need for expanded HD hydrogen refueling station infrastructure to meet demand. This expansion must prioritize safety and reliability, necessitating careful consideration of the associated risks. In this study, we use a light-duty (LD) hydrogen refueling station as a comparative tool to quantify the risks for a HD station, which is essentially a scaled-up version of a LD station.

Abstract not provided.

Sandia researchers are addressing the urgent challenge of minimizing dilute and distributed methane emissions. The team is focused on generating stable methane-consuming microbial consortia for deployment in engineered environmental systems. This innovative work aims to produce stable inocula of these consortia and implement viral controls for microbes that generate methane, significantly reducing emissions.

Magnetic reconnection is a fundamental plasma physics process ubiquitous in astrophysics, and important in both magnetic confinement fusion and space weather. The MARZ fundamental science program was recently established on Z to enable the first laboratory astrophysics platform able to access and study the strongly radiatively cooled magnetic reconnection regime. Simulations of this system have successfully used a resistive-MHD approach, but in some regions of parameter space Hall physics has the potential to be important. We describe implementation of a Hall method on a staggered grid resistive-MHD method (compatible with the approach used to model MARZ experiments. We then present a different Hall method based on cell-centered field quantities. Both approaches have been implemented in the Sandia KRAKEN code, to enable us to contrast different numerical Hall-MHD methods within the same HED code.

Abstract not provided.

The Disposal Research & Development (Disposal R&D) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & High-Level Waste Disposition is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). A high priority for Disposal R&D is disposal system modeling (Sassani et al. 2023). The Geologic Disposal Safety Assessment (GDSA) work package is charged with developing a disposal system modeling and analysis capability for evaluating generic disposal system performance for nuclear waste in geologic media.

Abstract not provided.

Downhole logging tools are commonly used to characterize multi-thousand-foot geothermal wells. The elevated temperatures, pressures, and harsh chemical environments present significant challenges for the long-term operation of these tools, especially when real-time data transmission to the surface is required via data cable lines. Teflon-based single or multi-conductor cables with grease-filled cable heads are typically used for downhole tools. However, over extended periods of operation, the grease used to seal the conductors can slowly dissolve into the well fluid, creating electrical shorts and disabling data transmission. Additionally, when temperatures exceed 260 °C, Teflon can soften, potentially allowing parallel conductors to make contact and cause shorts. Between 2009 and 2015, Draka Cableteq USA, now part of the Prysmian Group, developed a multi-conductor/fiber cable and a four-conductor cable capable of operating above 300 °C. While a full study was conducted on the conductor/fiber cable, the evaluation of the four-conductor cable remained incomplete. With the increasing need for long-term high-temperature (HT) operation of logging tools, Sandia National Laboratories is now completing the evaluation of the four-conductor cable. The four-conductor cable has two major novel aspects. Firstly, its glass braid insulation can operate above 300 °C, eliminating the potential for shorts. Secondly, the insulated conductors are encased in metal tubing along the full length of the cable, creating a high-pressure seal between the cable and the tool. This metal tubing eliminates the need for a grease seal, a major limiting factor in the operation time of common cable lines. Sandia National Laboratories will conduct multiple tests to characterize the cable at temperatures above 300 °C and pressures up to 5,000 psi. This cable would enable tools to operate continuously at elevated temperatures, pressures, and in harsh fluids for extended periods, potentially lasting months.

The Burrowing Owl Survey Report for Sandia National Laboratories: 2024 provides data and analysis on occupancy surveys that were conducted during the 2024 survey season. The surveys were conducted in partnership with Kirtland Air Force Base’s Natural Resource Program to maintain and expand on the long-term dataset on burrowing owls on base.

Abstract not provided.

Dynamic mode decomposition (DMD) has become a common technique for constructing surrogate models for dynamical systems from observed system states. The Occupation Kernel DMD (OKDMD) method proposed in (Rosenfeld et al., 2022) and (Rosenfeld et al., 2024) is a Liouville operator based method that builds surrogate models from system state trajectories. Here, this paper proposes an extension of OKDMD to the case when the system states are observed in a streaming fashion, i.e., only a small fraction of the state trajectory is available at a given time. The developed method, Streaming Occupation Kernel DMD (StOKeDMD), accommodates the streaming data input by leveraging properties of specific choices of kernel functions and occupation kernels. We apply the StoKeDMD method as a compression method for streaming data, analyze the memory complexity, and demonstrate the performance of StoKeDMD in the compression of streaming data generated from a Lorenz system and a fluid flow simulation.

Size effects are an unavoidable nuisance in inertial navigation using sensors which are not co-located at the navigational point of interest. When estimating transforms between the navigation point and sensor locations, some trajectories preclude observation of all model parameters. Regularization is proposed to avoid over-fitting size-effects models. The result yields robust size effects compensation in other regions of flight.

The Used Fuel Disposition Campaign (UFDC) was established within the United States (U.S.) Department of Energy (DOE) Office of Nuclear Energy (NE) to conduct research and development (R&D) activities associated with storage, transportation, and disposal of used or spent nuclear fuel (UNF or SNF) and high-level radioactive waste.

This white paper is a primer on physical factors that influence agricultural production and associated touchpoints to climate change. Agricultural production (which includes both crop and livestock production) is critical for food security and supports other economic products, such as textiles and generation of fuels for energy. Various physical factors influence agricultural production, including the crop types being cultivated and livestock being raised; land area and quality; water access and control; fertilizers, pesticides, and antibiotics; labor; and infrastructure associated with processing, storage, and transportation. These factors are impacted by climate change in both chronic and acute ways, from changing temperatures and precipitation patterns to increased prevalence of extreme events and diseases. We draw on examples from around the world to show the complex ways that agricultural production factors and climate interact with local capacities to influence regions around the world.

Sandia National Laboratories (SNL) is advancing technical capabilities used in passive loop seals. The “Puck” seal used a set of International Atomic Energy Agency (IAEA) requirements for new passive loop seals published in 2020 as a design guide. The seal is based on an oxygen-sensitive inner mixture encased in an oxygen-impermeable shell, is monolithic rather than two-part, incorporates self-capturing wire features, contains colored water beads and bubbles formed during processing as unique identifiers (UIDs), and visually indicates tamper (whether from seal body penetration or from seal wire removal) by irreversibly changing the seal body from multi-colored to black. This paper will provide details on the design, development, and testing of Puck seal prototypes.

Key Performance Indicators (KPIs) are an important set of metrics used to assess various aspects of photovoltaic (PV) systems, including their long-term performance, economic viability and carbon footprint. Technical KPIs support data-driven and informed decision-making when optimizing PV systems and provide a comprehensive overview of how PV systems operate across different conditions and climates. Different KPIs are commonly employed throughout the entire value chain of PV projects and can be categorized into technical, economic and sustainability aspects.

This document analyzes the role of Generative Artificial Intelligence (GenAI) tools in cybersecurity, particularly for red teaming. While GenAI accelerates initial security assessments, its effectiveness wanes with complexity, necessitating experienced assessors. The review critiques marketing claims, highlights ethical concerns regarding uncensored models for cybercrime, and advocates for a robust defense strategy supported by skilled professionals.

This report describes the proposed efforts for a three-year (CY23-25) program to develop refractory metal boride/carbide precursors for metal-organic chemical vapor deposition (MOCVD) applications. Reported are the CY24 results on the thermal processing of bis-cyclopentadienyl dialkyl and tetra-alkyl precursors to obtain metal carbide products. Precursors evaluated are commercially available. Materials were processed within in a custom-built MOCVD system at 1000 ⁰C, as well as in a hot isostatic press (HIP) at temperatures of 1000 ⁰C or 1650 ⁰C at pressures of 5000 psi. The products were identified as metal carbide, metal oxide, or a mixture of carbide and oxide phases depending on the starting material and process used. Density functional theory calculations were performed to determine the decomposition mechanism and to inform how ligand choice led to the products.

Reducing lifecycle carbon-dioxide (CO₂) and toxic emissions via electrification or switching to carbon-free fuels is not currently feasible for many off-road, rail, and marine applications. This is due to factors including excessive cost, weight, or size of a battery of sufficient capacity to meet the application requirements, lack of infrastructure, insufficient time for recharging, demanding duty cycles, and severe ambient conditions. The guiding vision for the activities described herein is to enable rapid, cost-effective reductions of the environmental impacts of such applications by using improved, high efficiency engine combustion strategies with currently available and emerging low lifecycle-CO₂ fuels (LLCFs). This report summarizes progress toward achieving this vision in two project areas. The first is a Technology Commercialization Fund (TCF) project focused on facilitating the commercialization of ducted fuel injection (DFI) with LLCFs. The second is a more fundamental, Advanced Combustion Engines (ACE) research project focused on elucidating a new strategy called lean mixing-controlled combustion (LMCC) for use with emerging LLCFs.

This report describes a two-dimensional model of Saturn based on the CASTLE transmission line code. Building on previous modeling efforts, 2D circuit models based on the “chain-link fence” geometry are constructed for pre-ReCap Saturn and post-ReCap Saturn. The 2D model results are in better agreement with data from Shot 4550 measurements of load currents and doses then the previous 1D model. Lower doses (9%) predicted by the new model can be compensated by increasing the load A-K gap.

Abstract not provided.

The Strategic Petroleum Reserve (SPR) is the world’s largest supply of emergency crude oil. The reserve consists of four sites in Louisiana and Texas. Each site stores crude in deep, underground salt caverns. It is the mission of the SPR’s Enhanced Monitoring Program to examine available sensing data to inform our understanding of each site. This report discusses the monitoring data, processes, and results for each of the four sites for fiscal year 2024.

Downhole logging tools are commonly used to characterize multi-thousand-foot geothermal wells. The elevated temperatures, pressures, and harsh chemical environments present significant challenges for the long-term operation of these tools, especially when real-time data transmission to the surface is required via data cable lines. Teflon-based single or multi-conductor cables with grease-filled cable heads are typically used for downhole tools. However, over extended periods of operation, the grease used to seal the conductors can slowly dissolve into the well fluid, creating electrical shorts and disabling data transmission. Additionally, when temperatures exceed 260 °C, Teflon can soften, potentially allowing parallel conductors to make contact and cause shorts. Between 2009 and 2015, Draka Cableteq USA, now part of the Prysmian Group, developed a multi-conductor/fiber cable and a four-conductor cable capable of operating above 300 °C. While a full study was conducted on the conductor/fiber cable, the evaluation of the four-conductor cable remained incomplete. With the increasing need for long-term high-temperature (HT) operation of logging tools, Sandia National Laboratories is now completing the evaluation of the four-conductor cable. The four-conductor cable has two major novel aspects. Firstly, its glass braid insulation can operate above 300 °C, eliminating the potential for shorts. Secondly, the insulated conductors are encased in metal tubing along the full length of the cable, creating a high-pressure seal between the cable and the tool. This metal tubing eliminates the need for a grease seal, a major limiting factor in the operation time of common cable lines. Sandia National Laboratories will conduct multiple tests to characterize the cable at temperatures above 300 °C and pressures up to 5,000 psi. This cable would enable tools to operate continuously at elevated temperatures, pressures, and in harsh fluids for extended periods, potentially lasting months.

Synthetic Aperture Radar (SAR) creates an image of a target scene by coherently processing radar echo returns collected along a flightpath. The quality of the SAR image is inextricably linked to the utility of the image for exploitation supporting the task at hand. Aspects of quality include the fidelity with which it can render the scene being imaged, to include the system’s Impulse Response (IPR) and underlying noise levels/characteristics. Other factors also impact utility.

Abstract not provided.