Publications

Abstract not provided.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The characterization of the neutron, prompt gamma-ray, and delayed gamma-ray radiation fields in the University of Texas at Austin Nuclear Engineering Teaching Laboratory (NETL) TRIGA reactor for the beam port (BP) 1/5 free-field environment at the 128-inch location adjacent to the core centerline has been accomplished. NETL is being explored as an auxiliary neutron test facility for the Sandia National Laboratories radiation effects sciences research and development campaigns. The NETL reactor is a TRIGA Mark-II pulse and steady-state, above-ground pool-type reactor. NETL is intended as a university research reactor typically used to perform irradiation experiments for students and customers, radioisotope production, as well as a training reactor. Initial criticality of the NETL TRIGA reactor was achieved on March 12, 1992, making it one of the newest test reactor facilities in the US. The neutron energy spectra, uncertainties, and covariance matrices are presented as well as a neutron fluence map of the experiment area of the cavity. For an unmoderated condition, the neutron fluence at the center of BP 1/5, at the adjacent core axial centerline, is about 8.2×1012 n/cm2 per MJ of reactor energy. About 67% of the neutron fluence is below 1 keV and 22% above 100 keV. The 1-MeV Damage-Equivalent Silicon (DES) fluence is roughly 1.6×1012 n/cm2 per MJ of reactor energy.

The Human Readiness Level (HRL) scale is a simple nine-level scale that brings structure and consistency to the real-world application of user-centered design. It enables multidisciplinary consideration of human-focused elements during the system development process. Use of the standardized set of questions comprising the HRL scale results in a single human readiness number that communicates system readiness for human use. The Human Views (HVs) are part of an architecture framework that provides a repository for human-focused system information that can be used during system development to support the evaluation of HRL levels. This paper illustrates how HRLs and HVs can be used in combination to support user-centered design processes. A real-world example for a U.S. Army software modernization program is described to demonstrate application of HRLs and HVs in the context of user-centered design.

Biaxial stress is identified to play an important role in the polar orthorhombic phase stability in hafnium oxide-based ferroelectric thin films. However, the stress state during various stages of wake-up has not yet been quantified. In this work, the stress evolution with field cycling in hafnium zirconium oxide capacitors is evaluated. The remanent polarization of a 20 nm thick hafnium zirconium oxide thin film increases from 9.80 to 15.0 µC cm−2 following 106 field cycles. This increase in remanent polarization is accompanied by a decrease in relative permittivity that indicates that a phase transformation has occurred. The presence of a phase transformation is supported by nano-Fourier transform infrared spectroscopy measurements and scanning transmission electron microscopy that show an increase in ferroelectric phase content following wake-up. The stress of individual devices field cycled between pristine and 106 cycles is quantified using the sin2(ψ) technique, and the biaxial stress is observed to decrease from 4.3 ± 0.2 to 3.2 ± 0.3 GPa. The decrease in stress is attributed, in part, to a phase transformation from the antipolar Pbca phase to the ferroelectric Pca21 phase. This work provides new insight into the mechanisms controlling and/or accompanying polarization wake-up in hafnium oxide-based ferroelectrics.

A novel algorithm for explicit temporal discretization of the variable-density, low-Mach Navier-Stokes equations is presented here. Recognizing there is a redundancy between the mass conservation equation, the equation of state, and the transport equation(s) for the scalar(s) which characterize the thermochemical state, and that it destabilizes explicit methods, we demonstrate how to analytically eliminate the redundancy and propose an iterative scheme to solve the resulting transformed scalar equations. The method obtains second-order accuracy in time regardless of the number of iterations, so one can terminate this subproblem once stability is achieved. Hence, flows with larger density ratios can be simulated while still retaining the efficiency, low cost, and parallelizability of an explicit scheme. The temporal discretization algorithm is used within a pseudospectral direct numerical simulation which extends the method of Kim, Moin, and Moser for incompressible flow [17] to the variable-density, low-Mach setting, where we demonstrate stability for density ratios up to ∼25.7.

Polymer Degradation and Stability

Determining the depths of small crustal earthquakes is challenging in many regions of the world, because most seismic networks are too sparse to resolve trade-offs between depth and origin time with conventional arrival-time methods. Precise and accurate depth estimation is important, because it can help seismologists discriminate between earthquakes and explosions, which is relevant to monitoring nuclear test ban treaties and producing earthquake catalogs that are uncontaminated by mining blasts. Here, we examine the depth sensitivity of several physics-based waveform features for ∼8000 earthquakes in southern California that have well-resolved depths from arrival-time inversion. We focus on small earthquakes (2 < ML < 4) recorded at local distances (< 150 km), for which depth estimation is especially challenging. We find that differential magnitudes (Mw= ML–Mc) are positively correlated with focal depth, implying that coda wave excitation decreases with focal depth. We analyze a simple proxy for relative frequency content, Φ≡ log10 (M0)+3log10 (fc (,and find that source spectra are preferentially enriched in high frequencies, or “blue-shifted,” as focal depth increases. We also find that two spectral amplitude ratios Rg 0.5–2 Hz/Sg 0.5–8 Hz and Pg/Sg at 3–8 Hz decrease as focal depth increases. Using multilinear regression with these features as predictor variables, we develop models that can explain 11%–59% of the variance in depths within 10 subregions and 25% of the depth variance across southern California as a whole. We suggest that incorporating these features into a machine learning workflow could help resolve focal depths in regions that are poorly instrumented and lack large databases of well-located events. Some of the waveform features we evaluate in this study have previously been used as source discriminants, and our results imply that their effectiveness in discrimination is partially because explosions generally occur at shallower depths than earthquakes.

Exploding bridgewire detonators (EBWs) containing pentaerythritol tetranitrate (PETN) exposed to high temperatures may not function following discharge of the design electrical firing signal from a charged capacitor. Knowing functionality of these arbitrarily facing EBWs is crucial when making safety assessments of detonators in accidental fires. Orientation effects are only significant when the PETN is partially melted. The melting temperature can be measured with a differential scanning calorimeter. Nonmelting EBWs will be fully functional provided the detonator never exceeds 406 K (133 °C) for at least 1 h. Conversely, EBWs will not be functional once the average input pellet temperature exceeds 414 K (141 °C) for a least 1 min which is long enough to cause the PETN input pellet to completely melt. Functionality of the EBWs at temperatures between 406 and 414 K will depend on orientation and can be predicted using a stratification model for downward facing detonators but is more complex for arbitrary orientations. A conservative rule of thumb would be to assume that the EBWs are fully functional unless the PETN input pellet has completely melted.

Accident analysis and ensuring power plant safety are pivotal in the nuclear energy sector. Significant strides have been achieved over the past few decades regarding fire protection and safety, primarily centered on design and regulatory compliance. Yet, after the Fukushima accident a decade ago, the imperative to enhance measures against fire, internal flooding, and power loss has intensified. Hence, a comprehensive, multilayered protection strategy against severe accidents is needed. Consequently, gaining a deeper insight into pool fires and their behavior through extensive validated data can greatly aid in improving these measures using advanced validation techniques. A model validation study was performed at Sandia National Laboratories (SNL) in which a 30-cm diameter methanol pool fire was modeled using the SIERRA/Fuego turbulent reacting flow code. This validation study used a standard validation experiment to compare model results against, and conclusions have been published. The fire was modeled with a large eddy simulation (LES) turbulence model with subgrid turbulent kinetic energy closure. Combustion was modeled using a strained laminar flamelet library approach. Radiative heat transfer was accounted for with a model utilizing the gray-gas approximation. In this study, additional validation analysis is performed using the area validation metric (AVM). These activities are done on multiple datasets involving different variables and temporal/spatial ranges and intervals. The results provide insight into the use of the area validation metric on such temporally varying datasets and the importance of physics-aware use of the metric for proper analysis.

We study the problem of multifidelity uncertainty propagation for computationally expensive models. In particular, we consider the general setting where the high-fidelity and low-fidelity models have a dissimilar parameterization both in terms of number of random inputs and their probability distributions, which can be either known in closed form or provided through samples. We derive novel multifidelity Monte Carlo estimators which rely on a shared subspace between the high-fidelity and low-fidelity models where the parameters follow the same probability distribution, i.e., a standard Gaussian. We build the shared space employing normalizing flows to map different probability distributions into a common one, together with linear and nonlinear dimensionality reduction techniques, active subspaces and autoencoders, respectively, which capture the subspaces where the models vary the most. We then compose the existing low-fidelity model with these transformations and construct modified models with an increased correlation with the high-fidelity model, which therefore yield multifidelity estimators with reduced variance. A series of numerical experiments illustrate the properties and advantages of our approaches.

Hydrogen is known to embrittle austenitic stainless steels, which are widely used in high-pressure hydrogen storage and delivery systems, but the mechanisms that lead to such material degradation are still being elucidated. The current work investigates the deformation behavior of single crystal austenitic stainless steel 316L through combined uniaxial tensile testing, characterization and atomistic simulations. Thermally precharged hydrogen is shown to increase the critical resolved shear stress (CRSS) without previously reported deviations from Schmid's law. Molecular dynamics simulations further expose the statistical nature of the hydrogen and vacancy contributions to the CRSS in the presence of alloying. Slip distribution quantification over large in-plane distances (>1 mm), achieved via atomic force microscopy (AFM), highlights the role of hydrogen increasing the degree of slip localization in both single and multiple slip configurations. The most active slip bands accumulate significantly more deformation in hydrogen precharged specimens, with potential implications for damage nucleation. For 〈110〉 tensile loading, slip localization further enhances the activity of secondary slip, increases the density of geometrically necessary dislocations and leads to a distinct lattice rotation behavior compared to hydrogen-free specimens, as evidenced by electron backscatter diffraction (EBSD) maps. The results of this study provide a more comprehensive picture of the deformation aspect of hydrogen embrittlement in austenitic stainless steels.

The valorization of lignin, a currently underutilized component of lignocellulosic biomass, has attracted attention to promote a stable and circular bioeconomy. Successful approaches including thermochemical, biological, and catalytic lignin depolymerization have been demonstrated, enabling opportunities for lignino-refineries and lignocellulosic biorefineries. Although significant progress in lignin valorization has been made, this review describes unexplored opportunities in chemical and biological routes for lignin depolymerization and thereby contributes to economically and environmentally sustainable lignin-utilizing biorefineries. This review also highlights the integration of chemical and biological lignin depolymerization and identifies research gaps while also recommending future directions for scaling processes to establish a lignino-chemical industry.

A technique is proposed for reproducing particle size distributions in three-dimensional simulations of the crushing and comminution of solid materials. The method is designed to produce realistic distributions over a wide range of loading conditions, especially for small fragments. In contrast to most existing methods, the new model does not explicitly treat the small-scale process of fracture. Instead, it uses measured fragment distributions from laboratory tests as the basic material property that is incorporated into the algorithm, providing a data-driven approach. The algorithm is implemented within a nonlocal peridynamic solver, which simulates the underlying continuum mechanics and contact interactions between fragments after they are formed. The technique is illustrated in reproducing fragmentation data from drop weight testing on sandstone samples.

We present large-scale atomistic simulations that reveal triple junction (TJ) segregation in Pt-Au nanocrystalline alloys in agreement with experimental observations. While existing studies suggest grain boundary solute segregation as a route to thermally stabilize nanocrystalline materials with respect to grain coarsening, here we quantitatively show that it is specifically the segregation to TJs that dominates the observed stability of these alloys. Our results reveal that doping the TJs renders them immobile, thereby locking the grain boundary network and hindering its evolution. In dilute alloys, it is shown that grain boundary and TJ segregation are not as effective in mitigating grain coarsening, as the solute content is not sufficient to dope and pin all grain boundaries and TJs. Our work highlights the need to account for TJ segregation effects in order to understand and predict the evolution of nanocrystalline alloys under extreme environments.

Granular metals (GMs), consisting of metal nanoparticles separated by an insulating matrix, frequently serve as a platform for fundamental electron transport studies. However, few technologically mature devices incorporating GMs have been realized, in large part because intrinsic defects (e.g., electron trapping sites and metal/insulator interfacial defects) frequently impede electron transport, particularly in GMs that do not contain noble metals. Here, we demonstrate that such defects can be minimized in molybdenum-silicon nitride (Mo-SiNx) GMs via optimization of the sputter deposition atmosphere. For Mo-SiNx GMs deposited in a mixed Ar/N2 environment, x-ray photoemission spectroscopy shows a 40%-60% reduction of interfacial Mo-silicide defects compared to Mo-SiNx GMs sputtered in a pure Ar environment. Electron transport measurements confirm the reduced defect density; the dc conductivity improved (decreased) by 104-105 and the activation energy for variable-range hopping increased 10×. Since GMs are disordered materials, the GM nanostructure should, theoretically, support a universal power law (UPL) response; in practice, that response is generally overwhelmed by resistive (defective) transport. Here, the defect-minimized Mo-SiNx GMs display a superlinear UPL response, which we quantify as the ratio of the conductivity at 1 MHz to that at dc, Δ σ ω . Remarkably, these GMs display a Δ σ ω up to 107, a three-orders-of-magnitude improved response than previously reported for GMs. By enabling high-performance electric transport with a non-noble metal GM, this work represents an important step toward both new fundamental UPL research and scalable, mature GM device applications.

Frequency-modulated (FM) combs based on active cavities like quantum cascade lasers have recently emerged as promising light sources in many spectral regions. Unlike passive modelocking, which generates amplitude modulation using the field’s amplitude, FM comb formation relies on the generation of phase modulation from the field’s phase. They can therefore be regarded as a phase-domain version of passive modelocking. However, while the ultimate scaling laws of passive modelocking have long been known—Haus showed in 1975 that pulses modelocked by a fast saturable absorber have a bandwidth proportional to effective gain bandwidth—the limits of FM combs have been much less clear. Here, we show that FM combs based on fast gain media are governed by the same fundamental limits, producing combs whose bandwidths are linear in the effective gain bandwidth. Not only do we show theoretically that the diffusive effect of gain curvature limits comb bandwidth, but we also show experimentally how this limit can be increased. By adding carefully designed resonant-loss structures that are evanescently coupled to the cavity of a terahertz laser, we reduce the curvature and increase the effective gain bandwidth of the laser, demonstrating bandwidth enhancement. Our results can better enable the creation of active chip-scale combs and be applied to a wide array of cavity geometries.

This study presents theoretical formulations to evaluate the fundamental parameters and performance characteristics of a bottom-raised oscillating surge wave energy converter (OSWEC) device. Employing a flat plate assumption and potential flow formulation in elliptical coordinates, closed-form equations for the added mass, radiation damping, and excitation forces/torques in the relevant pitch-pitch and surge-pitch directions of motion are developed and used to calculate the system's response amplitude operator and the forces and moments acting on the foundation. The model is benchmarked against numerical simulations using WAMIT and WEC-Sim, showcasing excellent agreement. The sensitivity of plate thickness on the analytical hydrodynamic solutions is investigated over several thickness-to-width ratios ranging from 1:80 to 1:10. The results show that as the thickness of the benchmark OSWEC increases, the deviation of the analytical hydrodynamic coefficients from the numerical solutions grows from 3 % to 25 %. Differences in the excitation forces and torques, however, are contained within 12 %. While the flat plate assumption is a limitation of the proposed analytical model, the error is within a reasonable margin for use in the design space exploration phase before a higher-fidelity (and thus more computationally expensive) model is employed. A parametric study demonstrates the ability of the analytical model to quickly sweep over a domain of OSWEC dimensions, illustrating the analytical model's utility in the early phases of design.

The rise of grid modernization has been prompted by the escalating demand for power, the deteriorating state of infrastructure, and the growing concern regarding the reliability of electric utilities. The smart grid encompasses recent advancements in electronics, technology, telecommunications, and computer capabilities. Smart grid telecommunication frameworks provide bidirectional communication to facilitate grid operations. Software-defined networking (SDN) is a proposed approach for monitoring and regulating telecommunication networks, which allows for enhanced visibility, control, and security in smart grid systems. Nevertheless, the integration of telecommunications infrastructure exposes smart grid networks to potential cyberattacks. Unauthorized individuals may exploit unauthorized access to intercept communications, introduce fabricated data into system measurements, overwhelm communication channels with false data packets, or attack centralized controllers to disable network control. An ongoing, thorough examination of cyber attacks and protection strategies for smart grid networks is essential due to the ever-changing nature of these threats. Previous surveys on smart grid security lack modern methodologies and, to the best of our knowledge, most, if not all, focus on only one sort of attack or protection. This survey examines the most recent security techniques, simultaneous multi-pronged cyber attacks, and defense utilities in order to address the challenges of future SDN smart grid research. The objective is to identify future research requirements, describe the existing security challenges, and highlight emerging threats and their potential impact on the deployment of software-defined smart grid (SD-SG).

The critical stress for cutting of a void and He bubble (generically referred to as a cavity) by edge and screw dislocations has been determined for FCC Fe0.70Cr0.20Ni0.10—close to 300-series stainless steel—over a range of cavity spacings, diameters, pressures, and glide plane positions. The results exhibit anomalous trends with spacing, diameter, and pressure when compared with classical theories for obstacle hardening. These anomalies are attributed to elastic anisotropy and the wide extended dislocation core in low stacking fault energy metals, indicating that caution must be exercised when using perfect dislocations in isotropic solids to study void and bubble hardening. In many simulations with screw dislocations, cross-slip was observed at the void/bubble surface, leading to an additional contribution to strengthening. We refer to this phenomenon as cavity cross-slip locking, and argue that it may be an important contributor to void and bubble hardening.

Seismic waveform data recorded at stations can be thought of as a superposition of the signal from a source of interest and noise from other sources. Frequency-based filtering methods for waveform denoising do not result in desired outcomes when the targeted signal and noise occupy similar frequency bands. Recently, denoising techniques based on deep-learning convolutional neural networks (CNNs), in which a recorded waveform is decomposed into signal and noise components, have led to improved results. These CNN methods, which use short-time Fourier transform representations of the time series, provide signal and noise masks for the input waveform. These masks are used to create denoised signal and designaled noise waveforms, respectively. However, advancements in the field of image denoising have shown the benefits of incorporating discrete wavelet transforms (DWTs) into CNN architectures to create multilevel wavelet CNN (MWCNN) models. The MWCNN model preserves the details of the input due to the good time–frequency localization of the DWT. Here, we use a data set of over 382,000 constructed seismograms recorded by the University of Utah Seismograph Stations network to compare the performance of CNN and MWCNN-based denoising models. Evaluation of both models on constructed test data shows that the MWCNN model outperforms the CNN model in the ability to recover the ground-truth signal component in terms of both waveform similarity and preservation of amplitude information. Model evaluation of real-world data shows that both the CNN and MWCNN models outperform standard band-pass filtering (BPF; average improvement in signal-to-noise ratio of 9.6 and 19.7 dB, respectively, with respect to BPF). Evaluation of continuous data suggests the MWCNN denoiser can improve both signal detection capabilities and phase arrival time estimates.

Radiation and radioactive substances result in the production of radioactive wastes which require safe management and disposal to avoid risks to human health and the environment. To ensure permanent safe disposal, the performance of a deep geological repository for radioactive waste is assessed against internationally agreed risk-based standards. Assessing postclosure safety of the future system's evolution includes screening of features, events, and processes (FEPs) relevant to the situation, their subsequent development into scenarios, and finally the development and execution of safety assessment (SA) models. Global FEP catalogs describe important natural and man-made repository system features and identify events and processes that may affect these features into the future. By combining FEPs, many of which are uncertain, different possible future system evolution scenarios are derived. Repository licensing should consider both the reference or “base” evolution as well as alternative futures that may lead to radiation release, pollution, or exposures. Scenarios are used to derive and consider both base and alternative evolutions, often through production of scenario-specific SA models and the recombination of their results into an assessment of the risk of harm. While the FEP-based scenario development process outlined here has evolved somewhat since its development in the 1980s, the fundamental ideas remain unchanged. A spectrum of common approaches is given here (e.g., bottom–up vs. top–down scenario development, probabilistic vs. bounding handling of uncertainty), related to how individual numerical models for possible futures are converted into a determination as to whether the system is safe (i.e., how aleatoric uncertainty and scenarios are integrated through bounding or Monte Carlo approaches).

The 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the downstream effects of such actions on a complex climate system are not well understood. The development of algorithmic techniques for quantifying relationships between source and impact variables related to a climate event (i.e., a climate pathway) would help inform policy decisions. Data-driven deep learning models have become powerful tools for modeling highly nonlinear relationships and may provide a route to characterize climate variable relationships. In this paper, we explore the use of an echo state network (ESN) for characterizing climate pathways. ESNs are a computationally efficient neural network variation designed for temporal data, and recent work proposes ESNs as a useful tool for forecasting spatiotemporal climate data. However, ESNs are noninterpretable black-box models along with other neural networks. The lack of model transparency poses a hurdle for understanding variable relationships. We address this issue by developing feature importance methods for ESNs in the context of spatiotemporal data to quantify variable relationships captured by the model. We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data. In the climate application, we consider a time period that includes the 1991 volcanic eruption of Mount Pinatubo. This event was a significant stratospheric aerosol injection, which acts as a proxy for an anthropogenic stratospheric aerosol injection. We are able to use the proposed approach to characterize relationships between pathway variables associated with this event that agree with relationships previously identified by climate scientists.

The additive manufacture of compositionally graded Al/Cu parts by laser engineered net shaping (LENS) is demonstrated. The use of a blue light build laser enabled deposition on a Cu substrate. The thermal gradient and rapid solidification inherent to selective laser melting enabled mass transport of Cu up to 4 mm from a Cu substrate through a pure Al deposition, providing a means of producing gradients with finer step sizes than the printed layer thicknesses. Divorcing gradient continuity from layer or particle size makes LENS a potentially enabling technology for the manufacture of graded density impactors for ramp compression experiments. Printing graded structures with pure Al, however, was prevented by the growth of Al2Cu3 dendrites and acicular grains amid a matrix of Al2Cu. A combination of adding TiB2 grain refining powder and actively varying print layer composition suppressed the dendritic growth mode and produced an equiaxed microstructure in a compositionally graded part. Material phase was characterized for crystal structure and nanoindentation hardness to enable a discussion of phase evolution in the rapidly solidifying melt pool of a LENS print.

Atomic cluster expansion (ACE) methods provide a systematic way to describe particle local environments of arbitrary body order. For practical applications it is often required that the basis of cluster functions be symmetrized with respect to rotations and permutations. Existing methodologies yield sets of symmetrized functions that are over-complete. These methodologies thus require an additional numerical procedure, such as singular value decomposition (SVD), to eliminate redundant functions. In this work, it is shown that analytical linear relationships for subsets of cluster functions may be derived using recursion and permutation properties of generalized Wigner symbols. From these relationships, subsets (blocks) of cluster functions can be selected such that, within each block, functions are guaranteed to be linearly independent. It is conjectured that this block-wise independent set of permutation-adapted rotation and permutation invariant (PA-RPI) functions forms a complete, independent basis for ACE. Along with the first analytical proofs of block-wise linear dependence of ACE cluster functions and other theoretical arguments, numerical results are offered to demonstrate this. The utility of the method is demonstrated in the development of an ACE interatomic potential for tantalum. Using the new basis functions in combination with Bayesian compressive sensing sparse regression, some high degree descriptors are observed to persist and help achieve high-accuracy models.

Nuclear power plant (NPP) risk assessment is broadly separated into disciplines of nuclear safety, security, and safeguards. Different analysis methods and computer models have been constructed to analyze each of these as separate disciplines. However, due to the complexity of NPP systems, there are risks that can span all these disciplines and require consideration of safety-security (2S) interactions which allows a more complete understanding of the relationship among these risks. A novel leading simulator/trailing simulator (LS/TS) method is introduced to integrate multiple generic safety and security computer models into a single, holistic 2S analysis. A case study is performed using this novel method to determine its effectiveness. The case study shows that the LS/TS method avoided introducing errors in simulation, compared to the same scenario performed without the LS/TS method. A second case study is then used to illustrate an integrated 2S analysis which shows that different levels of damage to vital equipment from sabotage at a NPP can affect accident evolution by several hours.

The formation of magnesium chloride-hydroxide salts (magnesium hydroxychlorides) has implications for many geochemical processes and technical applications. For this reason, a thermodynamic database for evaluating the Mg(OH)2–MgCl2–H2O ternary system from 0 °C–120 °C has been developed based on extensive experimental solubility data. Internally consistent sets of standard thermodynamic parameters (ΔGf°, ΔHf°, S°, and CP) were derived for several solid phases: 3 Mg(OH)2:MgCl2:8H2O, 9 Mg(OH)2:MgCl2:4H2O, 2 Mg(OH)2:MgCl2:4H2O, 2 Mg(OH)2:MgCl2: 2H2O(s), brucite (Mg(OH)2), bischofite (MgCl2:6H2O), and MgCl2:4H2O. First, estimated values for the thermodynamic parameters were derived using a component addition method. These parameters were combined with standard thermodynamic data for Mg2+(aq) consistent with CODATA (Cox et al., 1989) to generate temperature-dependent Gibbs energies for the dissolution reactions of the solid phases. These data, in combination with values for MgOH+(aq) updated to be consistent with Mg2+-CODATA, were used to compute equilibrium constants and incorporated into a Pitzer thermodynamic database for concentrated electrolyte solutions. Phase solubility diagrams were constructed as a function of temperature and magnesium chloride concentration for comparisons with available experimental data. To improve the fits to the experimental data, reaction equilibrium constants for the Mg-bearing mineral phases, the binary Pitzer parameters for the MgOH+ — Cl− interaction, and the temperature-dependent coefficients for those Pitzer parameters were constrained by experimental phase boundaries and to match phase solubilities. These parameter adjustments resulted in an updated set of standard thermodynamic data and associated temperature-dependent functions. The resulting database has direct applications to investigations of magnesia cement formation and leaching, chemical barrier interactions related to disposition of heat-generating nuclear waste, and evaluation of magnesium-rich salt and brine stabilities at elevated temperatures.

In magnetized liner inertial fusion (MagLIF), a cylindrical liner filled with fusion fuel is imploded with the goal of producing a one-dimensional plasma column at thermonuclear conditions. However, structures attributed to three-dimensional effects are observed in self-emission x-ray images. Despite this, the impact of many experimental inputs on the column morphology has not been characterized. We demonstrate the use of a linear regression analysis to explore correlations between morphology and a wide variety of experimental inputs across 57 MagLIF experiments. Results indicate the possibility of several unexplored effects. For example, we demonstrate that increasing the initial magnetic field correlates with improved stability. Although intuitively expected, this has never been quantitatively assessed in integrated MagLIF experiments. We also demonstrate that azimuthal drive asymmetries resulting from the geometry of the “current return can” appear to measurably impact the morphology. In conjunction with several counterintuitive null results, we expect the observed correlations will encourage further experimental, theoretical, and simulation-based studies. Finally, we note that the method used in this work is general and may be applied to explore not only correlations between input conditions and morphology but also with other experimentally measured quantities.

Finding alloys with specific design properties is challenging due to the large number of possible compositions and the complex interactions between elements. This study introduces a multi-objective Bayesian optimization approach guiding molecular dynamics simulations for discovering high-performance refractory alloys with both targeted intrinsic static thermomechanical properties and also deformation mechanisms occurring during dynamic loading. The objective functions are aiming for excellent thermomechanical stability via a high bulk modulus, a low thermal expansion, a high heat capacity, and for a resilient deformation mechanism maximizing the retention of the BCC phase after shock loading. Contrasting two optimization procedures, we show that the Pareto-optimal solutions are confined to a small performance space when the property objectives display a cooperative relationship. Conversely, the Pareto front is much broader in the performance space when these properties have antagonistic relationships. Density functional theory simulations validate these findings and unveil underlying atomic-bond changes driving property improvements.

Solvent expulsion away from an intervening region between two approaching particles plays important roles in particle aggregation yet remains poorly understood. In this work, we use metadynamics molecular simulations to study the free energy landscape of removing water molecules from gibbsite and pyrophyllite slit pores representing the confined spaces between two approaching particles. For gibbsite, removing water from the intervening region is both entropically and enthalpically unfavorable. The closer the particles approach each other, the harder it is to expel water molecules. For pyrophyllite, water expulsion is spontaneous, which is different from the gibbsite system. A smaller pore makes the water removal more favorable. When water is being drained from the intervening region, single chains of water molecules are observed in gibbsite pore, while in pyrophyllite pore water cluster is usually observed. Water-gibbsite hydrogen bonds help stabilize water chains, while water forms clusters in pyrophyllite pore to maximize the number of hydrogen bonds among themselves. This work provides the first assessment into the energetics and structure of water being drained from the intervening region between two approaching particles during oriented attachment and aggregation.

We present a comprehensive benchmarking framework for evaluating machine-learning approaches applied to phase-field problems. This framework focuses on four key analysis areas crucial for assessing the performance of such approaches in a systematic and structured way. Firstly, interpolation tasks are examined to identify trends in prediction accuracy and accumulation of error over simulation time. Secondly, extrapolation tasks are also evaluated according to the same metrics. Thirdly, the relationship between model performance and data requirements is investigated to understand the impact on predictions and robustness of these approaches. Finally, systematic errors are analyzed to identify specific events or inadvertent rare events triggering high errors. Quantitative metrics evaluating the local and global description of the microstructure evolution, along with other scalar metrics representative of phase-field problems, are used across these four analysis areas. This benchmarking framework provides a path to evaluate the effectiveness and limitations of machine-learning strategies applied to phase-field problems, ultimately facilitating their practical application.

Helium-4-based scintillation detector technology is emerging as a strong alternative to pulse-shape discrimination-capable organic scintillators for fast neutron detection and spectroscopy, particularly in extreme gamma-ray environments. The ⁴He detector is intrinsically insensitive to gamma radiation, as it has a relatively low cross-section for gamma-ray interactions, and the stopping power of electrons in the ⁴He medium is low compared to that of ⁴He recoil nuclei. Consequently, gamma rays can be discriminated by simple energy deposition thresholding instead of the more complex pulse shape analysis. The energy resolution of ⁴He scintillation detectors has not yet been well-characterized over a broad range of energy depositions, which limits the ability to deconvolve the source spectra. In this work, an experiment was performed to characterize the response of an Arktis S670 ⁴He detector to nuclear recoils up to 9 MeV. The ⁴He detector was positioned in the center of a semicircular array of organic scintillation detectors operated in coincidence. Deuterium–deuterium and deuterium–tritium neutron generators provided monoenergetic neutrons, yielding geometrically constrained nuclear recoils ranging from 0.0925 to 8.87 MeV. The detector response provides evidence for scintillation linearity beyond the previously reported energy range. Finally, the measured response was used to develop an energy resolution function applicable to this energy range for use in high-fidelity detector simulations needed by future applications.

Individual lanthanide elements have physical/electronic/magnetic properties that make each useful for specific applications. Several of the lanthanides cations (Ln3+) naturally occur together in the same ores. They are notoriously difficult to separate from each other due to their chemical similarity. Predicting the Ln3+ differential binding energies (ΔΔE) or free energies (ΔΔG) at different binding sites, which are key figures of merit for separation applications, will help design of materials with lanthanide selectivity. We apply ab initio molecular dynamics (AIMD) simulations and density functional theory (DFT) to calculate ΔΔG for Ln3+ coordinated to ligands in water and embedded in metal-organic frameworks (MOFs), and ΔΔE for Ln3+ bonded to functionalized silica surfaces, thus circumventing the need for the computational costly absolute binding (free) energies ΔG and ΔE. Perturbative AIMD simulations of water-inundated simulation cells are applied to examine the selectivity of ligands towards adjacent Ln3+ in the periodic table. Static DFT calculations with a full Ln3+ first coordination shell, while less rigorous, show that all ligands examined with net negative charges are more selective towards the heavier lanthanides than a charge-neutral coordination shell made up of water molecules. Amine groups are predicted to be poor ligands for lanthanide-binding. We also address cooperative ion binding, i.e., using different ligands in concert to enhance lanthanide selectivity.

Analytical and semi–analytical models for stream depletion with transient stream stage drawdown induced by groundwater pumping are developed to address a deficiency in existing models, namely, the use of a fixed stream stage condition at the stream–aquifer interface. Here field data are presented to demonstrate that stream stage drawdown does indeed occur in response to groundwater pumping near aquifer–connected streams. A model that predicts stream depletion with transient stream drawdown is developed based on stream channel mass conservation and finite stream channel storage. The resulting models are shown to reduce to existing fixed–stage models in the limit as stream channel storage becomes infinitely large, and to the confined aquifer flow with a no–flow boundary at the streambed in the limit as stream storage becomes vanishingly small. The model is applied to field measurements of aquifer and stream drawdown, giving estimates of aquifer hydraulic parameters, streambed conductance, and a measure of stream channel storage. The results of the modeling and data analysis presented herein have implications for sustainable groundwater management.

Materials simulations based on direct numerical solvers are accurate but computationally expensive for predicting materials evolution across length- and time-scales, due to the complexity of the underlying evolution equations, the nature of multiscale spatiotemporal interactions, and the need to reach long-time integration. We develop a method that blends direct numerical solvers with neural operators to accelerate such simulations. This methodology is based on the integration of a community numerical solver with a U-Net neural operator, enhanced by a temporal-conditioning mechanism to enable accurate extrapolation and efficient time-to-solution predictions of the dynamics. We demonstrate the effectiveness of this hybrid framework on simulations of microstructure evolution via the phase-field method. Such simulations exhibit high spatial gradients and the co-evolution of different material phases with simultaneous slow and fast materials dynamics. We establish accurate extrapolation of the coupled solver with large speed-up compared to DNS depending on the hybrid strategy utilized. This methodology is generalizable to a broad range of materials simulations, from solid mechanics to fluid dynamics, geophysics, climate, and more.

This dataset is comprised of a library of atomistic structure files and corresponding X-ray diffraction (XRD) profiles and vibrational density of states (VDoS) profiles for bulk single crystal silicon (Si), gold (Au), magnesium (Mg), and iron (Fe) with and without disorder introduced into the atomic structure and with and without mechanical loading. Included with the atomistic structure files are descriptor files that measure the stress state, phase fractions, and dislocation content of the microstructures. All data was generated via molecular dynamics or molecular statics simulations using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code. This dataset can inform the understanding of how local or global changes to a materials microstructure can alter their spectroscopic and diffraction behavior across a variety of initial structure types (cubic diamond, face-centered cubic (FCC), hexagonal close-packed (HCP), and body-centered cubic (BCC) for Si, Au, Mg, and Fe, respectively) and overlapping changes to the microstructure (i.e., both disorder insertion and mechanical loading).

Efficient carbon capture requires engineered porous systems that selectively capture CO2 and have low energy regeneration pathways. Porous liquids (PLs), solvent-based systems containing permanent porosity through the incorporation of a porous host, increase the CO2 adsorption capacity. A proposed mechanism of PL regeneration is the application of isostatic pressure in which the dissolved nanoporous host is compressed to alter the stability of gases in the internal pore. This regeneration mechanism relies on the flexibility of the porous host, which can be evaluated through molecular simulations. Here, the flexibility of porous organic cages (POCs) as representative porous hosts was evaluated, during which pore windows decreased by 10-40% at 6 GPa. POCs with sterically smaller functional groups, such as the 1,2-ethane in the CC1 POC resulted in greater imine cage flexibility relative to those with sterically larger functional groups, such as the cyclohexane in the CC3 POC that protected the imine cage from the application of pressure. Structural changes in the POC also caused CO2 adsorption to be thermodynamically unfavorable beginning at ∼2.2 GPa in the CC1 POC, ∼1.1 GPa in the CC3 POC, and ∼1.0 GPa in the CC13 POC, indicating that the CO2 would be expelled from the POC at or above these pressures. Energy barriers for CO2 desorption from inside the POC varied based on the geometry of the pore window and all the POCs had at least one pore window with a sufficiently low energy barrier to allow for CO2 desorption under ambient temperatures. The results identified that flexibility of the CC1, CC3, or CC13 POCs under compression can result in the expulsion of captured gas molecules.

Abstract not provided.

Abstract not provided.

Stereo high-speed video of photovoltaic modules undergoing laboratory hail tests was processed using digital image correlation to determine module surface deformation during and immediately following impact. The purpose of this work was to demonstrate a methodology for characterizing module impact response differences as a function of construction and incident hail parameters. Video capture and digital image analysis were able to capture out-of-plane module deformation to a resolution of ±0.1 mm at 11 kHz on an in-plane grid of 10 × 10 mm over the area of a 1 × 2 m commercial photovoltaic module. With lighting and optical adjustments, the technique was adaptable to arbitrary module designs, including size, backsheet color, and cell interconnection. Impacts were observed to produce an initially localized dimple in the glass surface, with peak deflection proportional to the square root of incident energy. Subsequent deformation propagation and dissipation were also captured, along with behavior for instances when the module glass fractured. Natural frequencies of the module were identifiable by analyzing module oscillations postimpact. Limitations of the measurement technique were that the impacting ice ball obscured the data field immediately surrounding the point of contact, and both ice and glass fracture events occurred within 100 μs, which was not resolvable at the chosen frame rate. Increasing the frame rate and visualizing the back surface of the impact could be applied to avoid these issues. Applications for these data include validating computational models for hail impacts, identifying the natural frequencies of a module, and identifying damage initiation mechanisms.

Recent experimentally validated alloy design theories have demonstrated nanocrystalline binary alloys that are stable against thermally induced grain growth. An open question is whether such thermal stability also translates to stability under irradiation. In this study, we investigate the response to heavy ion irradiation of a nanocrystalline platinum gold alloy that is known to be thermally stable from previous studies. Heavy ion irradiation was conducted at both room temperature and elevated temperatures on films of nanocrystalline platinum and platinum gold. Using scanning/transmission electron microscopy equipped with energy-dispersive spectroscopy and automated crystallographic orientation mapping, we observe substantial grain growth in the irradiated area compared to the controlled area beyond the range of heavy ions, as well as compositional redistribution under these conditions, and discuss mechanisms underpinning this instability. These findings highlight that grain boundary stability against one external stimulus, such as heat, does not always translate into grain boundary stability under other stimuli, such as displacement damage.

Abstract not provided.

Treatment of lost circulation can represent anywhere from 5 to 25 % of the cost in drilling geothermal wells. The cost of the materials used for lost circulation treatment is less important than their effectiveness at reducing fluid losses. In geothermal systems, the high temperatures (>90 °C) are expected to degrade many commonly used lost circulation materials over time. This degradation could compromise different materials ability to mitigate fluid loss, creating more non-productive time as multiple treatments are needed, but may result in recovering desired permeability zones within the reservoir section over time. This research aimed to study how thermal degradation of eight different lost circulation materials affected their properties relevant to sealing loss zones in geothermal wells. Mass loss experiments were conducted with each material at temperatures of 90–250 °C for 1–42 days to measure the breakdown of the material at geothermal conditions, collecting gases during several experiments to determine the waste produced during degradation. Compaction experiments were conducted with the degraded materials to show how temperatures reduced the rigidity and increased packing of the materials. Viscosity tests were conducted to show the impact of different materials on drilling fluid rheology. Microscope observations were conducted to characterize the alterations to each material due to thermal degradation. Organic materials tend to degrade more than inorganic materials, with organics like microcellulose, cotton seed hulls and sawdust losing 30–50 % of their mass after 1 day of heating at 200 °C, while inorganics like magma fiber only lose ∼5–10 % of its mass after one day of heating at 200 °C. Granular materials are the strongest when compacted despite any mass loss, while fibrous and flaky materials are fairly weak and breakdown easily under stress. The materials do not generally affect fluid rheology unless they have a viscosifying agent as part of the mixture. Microscopic analysis showed that more rigid materials like microcellulose and cedar fiber degrade in brittle manners with splitting and fracturing, while others like cotton seed hulls degrade in more ductile manners forming meshes or clumps of material. The thermal breakdown of lost circulation materials tested suggests that each material should also be classified by its degree of thermal degradability, as at certain temperatures the materials can lose the capability to bridge loss zones around the wellbore.

Radiation source localization is important for nuclear nonproliferation and can be obtained using time-encoded imaging systems with unsegmented detectors. A scintillation crystal can be used with a moving coded-aperture mask to vary the detected count rate produced from radiation sources in the far field. The modulation of observed counts over time can be used to reconstruct an image with the known coded-aperture mask pattern. Current time-encoded imaging systems incorporate cylindrical coded-aperture masks and have limits to their fully coded imaging field-of-view. This work focuses on expanding the field-of-view to 4π by using a novel spherical coded-aperture mask. A regular icosahedron is used to approximate a spherical mask. This icosahedron consists of 20 equilateral triangles; the faces of which are each subdivided into four equilateral triangle-shaped voxels which are then projected onto a spherical surface, creating an 80-voxel coded-aperture mask. These polygonal voxels can be made from high-Z materials for gamma-ray modulation and/or low-Z materials for neutron modulation. In this work, we present Monte Carlo N-Particle (MCNP) simulations and simple models programmed in Mathematica to explore image reconstruction capabilities of this 80-voxel coded-aperture mask.

Abstract not provided.

In order to make design decisions, engineers may seek to identify regions of the design domain that are acceptable in a computationally efficient manner. A design is typically considered acceptable if its reliability with respect to parametric uncertainty exceeds the designer’s desired level of confidence. Despite major advancements in reliability estimation and in design classification via decision boundary estimation, the current literature still lacks a design classification strategy that incorporates parametric uncertainty and desired design confidence. To address this gap, this works offers a novel interpretation of the acceptance region by defining the decision boundary as the hypersurface which isolates the designs that exceed a user-defined level of confidence given parametric uncertainty. This work addresses the construction of this novel decision boundary using computationally efficient algorithms that were developed for reliability analysis and decision boundary estimation. The proposed approach is verified on two physical examples from structural and thermal analysis using Support Vector Machines and Efficient Global Optimization-based contour estimation.

Junctions are fundamental elements that support qubit locomotion in two-dimensional ion trap arrays and enhance connectivity in emerging trapped-ion quantum computers. In surface ion traps they have typically been implemented by shaping radio frequency (RF) electrodes in a single plane to minimize the disturbance to the pseudopotential. However, this method introduces issues related to RF lead routing that can increase power dissipation and the likelihood of voltage breakdown. Here, we propose and simulate a novel two-layer junction design incorporating two perpendicularly rotoreflected (rotated, then reflected) linear ion traps. The traps are vertically separated, and create a trapping potential between their respective planes. The orthogonal orientation of the RF electrodes of each trap relative to the other provides perpendicular axes of confinement that can be used to realize transport in two dimensions. While this design introduces manufacturing and operating challenges, as now two separate structures have to be precisely positioned relative to each other in the vertical direction and optical access from the top is obscured, it obviates the need to route RF leads below the top surface of the trap and eliminates the pseudopotential bumps that occur in typical junctions. In this paper the stability of idealized ion transfer in the new configuration is demonstrated, both by solving the Mathieu equation analytically to identify the stable regions and by numerically modeling ion dynamics. Our novel junction layout has the potential to enhance the flexibility of microfabricated ion trap control to enable large-scale trapped-ion quantum computing.

In a computational fluid model of the atmosphere, the advective transport of trace species, or tracers, can be computationally expensive. For efficiency, models often use semi-Lagrangian advection methods. High-order interpolation semi-Lagrangian (ISL) methods, in particular, can be extremely efficient, if the problem of property preservation specific to them can be addressed. Atmosphere models often use geometrically and logically nonuniform grids for efficiency and, as a result, element-based discretizations. Such grids and discretizations make stability a particular problem for ISL methods. Generally, high-order, element-based ISL methods that use the natural polynomial interpolant associated with a nodal finite-element discretization are unstable. We derive new bases having order of accuracy up to nine, with positive nodal weights, that stabilize the element-based ISL method. We use these bases to construct the linear advection operator in the property-preserving Interpolation Semi-Lagrangian Element-based Transport (Islet) method. Then we discuss key software implementation details. Finally, we show performance results for the Energy Exascale Earth System Model's atmosphere dynamical core, comparing the original and new transport methods. These simulations used up to 27,600 Graphical Processing Units (GPU) on the Oak Ridge Leadership Computing Facility's Summit supercomputer.

High-throughput image segmentation of atomic resolution electron microscopy data poses an ongoing challenge for materials characterization. In this paper, we investigate the application of the polyhedral template matching (PTM) method, a technique widely employed for visualizing three-dimensional (3D) atomistic simulations, to the analysis of two-dimensional (2D) atomic resolution electron microscopy images. This technique is complementary with other atomic resolution data reduction techniques, such as the centrosymmetry parameter, that use the measured atomic peak positions as the starting input. Furthermore, since the template matching process also gives a measure of the local rotation, the method can be used to segment images based on local orientation. We begin by presenting a 2D implementation of the PTM method, suitable for atomic resolution images. We then demonstrate the technique's application to atomic resolution scanning transmission electron microscopy images from close-packed metals, providing examples of the analysis of twins and other grain boundaries in FCC gold and martensite phases in 304 L austenitic stainless steel. Finally, we discuss factors, such as positional errors in the image peak locations, that can affect the accuracy and sensitivity of the structural determinations.

Abstract not provided.

Surface mass loss rates due to sublimation and oxidation at temperatures of 3000–7000 K have been measured in a shock tube for graphite and carbon black (CB) particles. Diagnostics are presented for measuring surface mass loss rates by diffuse backlit illumination extinction imaging and thermal emission. The surface mass loss rate is found by regression fitting extinction and emission signals with an independent spherical primary particle assumption. Measured graphite sublimation and oxidation rates are reported to be an order of magnitude greater than CB sublimation and oxidation rates. It is speculated that the difference between CB and graphite surface mass loss rates is largely due to the primary particle assumption of the presented technique which misrepresents the effective surface area of an aggregate particle where primary particles overlap and shield inner particles. Measured sublimation rates are compared to sublimation models in the literature, and it is seen graphite shows fair agreement with the models while CB underestimates, likely a result of the particle shielding affect not being considered in the sublimation model.

Prior to every ion implantation experiment a simulation of the ion range and other relevant parameters is performed using Monte-Carlo based codes. Although increasing computing power has improved the speed of these calculations, the demands on Monte-Carlo codes are also increasing, requiring evaluation of the optimal number of simulations while ensuring accuracy within threshold bounds. We evaluate the “Stopping and Range of Ions in Matter” (SRIM) code due to its widespread usage. We show how dividing simulations into multiple parallel simulations with different random seeds can lead to calculation speedup and find lower bounds for the required number of ion traces simulated based on an exemplar system of a Ga focused ion beam and a high energy C beam as used in high linear energy transfer testing. Our results indicate simulations can yield results within the underlying data accuracy of SRIM at 10X and 100X shorter simulation time than the SRIM default values.

Abstract not provided.

Charging a Li-ion battery requires Li-ion transport between the cathode and the anode. This Li-ion transport is dependent on (among other factors) the electrostatic environment that the ion encounters within the solid electrolyte interphase (SEI), which separates the anode from the surrounding electrolyte. A previous first-principles work has illuminated the reaction barriers through likely atomistic SEI environments but has had difficulty accurately reflecting the larger electrostatic potential landscape that an ion encounters moving through the SEI. In this work, we apply the recently developed quantum continuum approximation (QCA) technique to provide an equilibrium electronic potentiostat for first-principles interface calculations. Using QCA, we calculate the potential barrier for Li-ion transport through LiF, Li2O, and Li2CO3 SEIs along with LiF-LiF and LiF-Li2O grain boundaries, all paired with Li metal anodes. We demonstrate that the SEI potential barrier is dependent on the electrochemical potentials of the anode in each system. Finally, we use these techniques to estimate the change in the diffusion barrier for a Li ion moving in a LiF SEI as a function of the anode potential. We find that properly accounting for interface and electronic voltage effects significantly lowers reaction barriers compared with previous literature results.

Density-functional theory (DFT) is used to identify phase-equilibria in multi-principal-element and high-entropy alloys (MPEAs/HEAs), including duplex-phase and eutectic microstructures. A combination of composition-dependent formation energy and electronic-structure-based ordering parameters were used to identify a transition from FCC to BCC favoring mixtures, and these predictions experimentally validated in the Al-Co-Cr-Cu-Fe-Ni system. A sharp crossover in lattice structure and dual-phase stability as a function of composition were predicted via DFT and validated experimentally. The impact of solidification kinetics and thermodynamic stability was explored experimentally using a range of techniques, from slow (castings) to rapid (laser remelting), which showed a decoupling of phase fraction from thermal history, i.e., phase fraction was found to be solidification rate-independent, enabling tuning of a multi-modal cell and grain size ranging from nanoscale through macroscale. Strength and ductility tradeoffs for select processing parameters were investigated via uniaxial tension and small-punch testing on specimens manufactured via powder-based additive manufacturing (directed-energy deposition). This work establishes a pathway for design and optimization of next-generation multiphase superalloys via tailoring of structural and chemical ordering in concentrated solid solutions.

The analysis of the work hardening variation with stress reveals insight to operative stress-strain mechanisms in material systems. The onset of plasticity can be assessed and related to ensuing plastic deformation up to the structural instability using one constitutive relationship that incorporates both behaviors of rapid work hardening (Stage 3) and the asymptotic leveling of stress (Stage 4). Results are presented for the mechanical behavior analysis of Ti-6Al-4V wherein the work hardening variation of Stages 3 and 4 are found to: be dependent through a constitutive relationship; be useful in a Hall-Petch formulation of yield strength; and provide the basis for a two point-slope fit method to model the experimental work hardening and stress-strain behavior.

Skyrmions and antiskyrmions are nanoscale swirling textures of magnetic moments formed by chiral interactions between atomic spins in magnetic noncentrosymmetric materials and multilayer films with broken inversion symmetry. These quasiparticles are of interest for use as information carriers in next-generation, low-energy spintronic applications. To develop skyrmion-based memory and logic, we must understand skyrmion-defect interactions with two main goals—determining how skyrmions navigate intrinsic material defects and determining how to engineer disorder for optimal device operation. Here, we introduce a tunable means of creating a skyrmion-antiskyrmion system by engineering the disorder landscape in FeGe using ion irradiation. Specifically, we irradiate epitaxial B20-phase FeGe films with 2.8 MeV Au⁴⁺ ions at varying fluences, inducing amorphous regions within the crystalline matrix. Using low-temperature electrical transport and magnetization measurements, we observe a strong topological Hall effect with a double-peak feature that serves as a signature of skyrmions and antiskyrmions. These results are a step towards the development of information storage devices that use skyrmions and antiskyrmions as storage bits, and our system may serve as a testbed for theoretically predicted phenomena in skyrmion-antiskyrmion crystals.

This manuscript presents a complete framework for the development and verification of physics-informed neural networks with application to the alternating-current power flow (ACPF) equations. Physics-informed neural networks (PINN)s have received considerable interest within power systems communities for their ability to harness underlying physical equations to produce simple neural network architectures that achieve high accuracy using limited training data. The methodology developed in this work builds on existing methods and explores new important aspects around the implementation of PINNs including: (i) obtaining operationally relevant training data, (ii) efficiently training PINNs and using pruning techniques to reduce their complexity, and (iii) globally verifying the worst-case predictions given known physical constraints. The methodology is applied to the IEEE-14 and 118 bus systems where PINNs show substantially improved accuracy in a data-limited setting and attain better guarantees with respect to worst-case predictions.

Abstract not provided.

A thermally driven, micrometer-scale switch technology has been created that utilizes the ErH3/Er2O3 materials system. The technology is comprised of novel thin film switches, interconnects, on-board micro-scale heaters for passive thermal environment sensing, and on-board micro-scale heaters for individualized switch actuation. Switches undergo a thermodynamically stable reduction/oxidation reaction leading to a multi-decade (>11 orders) change in resistance. The resistance contrast remains after cooling to room temperature, making them suitable as thermal fuses. An activation energy of 290 kJ/mol was calculated for the switch reaction, and a thermos-kinetic model was employed to determine switch times of 120 ms at 560 °C with the potential to scale to 1 ms at 680 °C.

Monte Carlo simulations are at the heart of many high-fidelity simulations and analyses for radiation transport systems. As is the case with any complex computational model, it is important to propagate sources of input uncertainty and characterize how they affect model output. Unfortunately, uncertainty quantification (UQ) is made difficult by the stochastic variability that Monte Carlo transport solvers introduce. The standard method to avoid corrupting the UQ statistics with the transport solver noise is to increase the number of particle histories, resulting in very high computational costs. In this contribution, we propose and analyze a sampling estimator based on the law of total variance to compute UQ variance even in the presence of residual noise from Monte Carlo transport calculations. We rigorously derive the statistical properties of the new variance estimator, compare its performance to that of the standard method, and demonstrate its use on neutral particle transport model problems involving both attenuation and scattering physics. We illustrate, both analytically and numerically, the estimator's statistical performance as a function of available computational budget and the distribution of that budget between UQ samples and particle histories. We show analytically and corroborate numerically that the new estimator is unbiased, unlike the standard approach, and is more accurate and precise than the standard estimator for the same computational budget.

Precise control of light-matter interactions at the nanoscale lies at the heart of nanophotonics. However, experimental examination at this length scale is challenging since the corresponding electromagnetic near-field is often confined within volumes below the resolution of conventional optical microscopy. In semiconductor nanophotonics, electromagnetic fields are further restricted within the confines of individual subwavelength resonators, limiting access to critical light-matter interactions in these structures. In this work, we demonstrate that photoelectron emission microscopy (PEEM) can be used for polarization-resolved near-field spectroscopy and imaging of electromagnetic resonances supported by broken-symmetry silicon metasurfaces. We find that the photoemission results, enabled through an in situ potassium surface layer, are consistent with full-wave simulations and far-field reflectance measurements across visible and near-infrared wavelengths. In addition, we uncover a polarization-dependent evolution of collective resonances near the metasurface array edge taking advantage of the far-field excitation and full-field imaging of PEEM. Here, we deduce that coupling between eight resonators or more establishes the collective excitations of this metasurface. All told, we demonstrate that the high-spatial resolution hyperspectral imaging and far-field illumination of PEEM can be leveraged for the metrology of collective, non-local, optical resonances in semiconductor nanophotonic structures.

Abstract not provided.

Bayesian inference with a simple Gaussian error model is used to efficiently compute prediction variances for energies, forces, and stresses in the linear SNAP interatomic potential. The prediction variance is shown to have a strong correlation with the absolute error over approximately 24 orders of magnitude. Using this prediction variance, an active learning algorithm is constructed to iteratively train a potential by selecting the structures with the most uncertain properties from a pool of candidate structures. The relative importance of the energy, force, and stress errors in the objective function is shown to have a strong impact upon the trajectory of their respective net error metrics when running the active learning algorithm. Batched training of different batch sizes is also tested against singular structure updates, and it is found that batches can be used to significantly reduce the number of retraining steps required with only minor impact on the active learning trajectory.

Abstract not provided.

Photonic Doppler Velocimetry (PDV) is a fiber-based measurement amenable to a wide range of experimental conditions. Interference between two optical signals—one Doppler shifted and the other not—is the essential principle in these measurements. A confluence of commercial technologies, largely driven by the telecommunication industry, makes PDV particularly convenient at near-infrared wavelengths. This discussion considers how measurement time scales of interest relate to the design, operation, and analysis of a PDV measurement, starting from the steady state through nanosecond resolution. Benefits and outstanding challenges of PDV are summarized, with comparisons to related diagnostics.

PRX Energy

The Single Volume Scatter Camera (SVSC) Collaboration aims to develop portable neutron imaging systems for a variety of applications in nuclear non-proliferation. Conventional double-scatter neutron imagers are composed of several separate detector volumes organized in at least two planes. A neutron must scatter in two of these detector volumes for its initial trajectory to be reconstructed. As such, these systems typically have a large footprint and poor geometric efficiency. We report on the design and characterization of a prototype monolithic neutron scatter camera that is intended to significantly improve upon the geometrical shortcomings of conventional neutron cameras. The detector consists of a 50 mm×56 mm× 60 mm monolithic block of EJ-204 plastic scintillator instrumented on two faces with arrays of 64 Hamamatsu S13360-6075PE silicon photomultipliers (SiPMs). The electronic crosstalk is limited to < 5% between adjacent channels and < 0.1% between all other channel pairs. SiPMs introduce a significantly elevated dark count rate over PMTs, as well as correlated noise from after-pulsing and optical crosstalk. In this article, we characterize the dark count rate and optical crosstalk and present a modified event reconstruction likelihood function that accounts for them. We find that the average dark count rate per SiPM is 4.3 MHz with a standard deviation of 1.5 MHz among devices. The analysis method we employ to measure internal optical crosstalk also naturally yields the mean and width of the single-electron pulse height. We calculate separate contributions to the width of the single-electron pulse-height from electronic noise and avalanche fluctuations. We demonstrate a timing resolution for a single-photon pulse to be (128 ± 4) ps. Finally, coincidence analysis is employed to measure external (pixel-to-pixel) optical crosstalk. We present a map of the average external crosstalk probability between 2×4 groups of SiPMs, as well as the in-situ timing characteristics extracted from the coincidence analysis. Further work is needed to characterize the performance of the camera at reconstructing single- and double-site interactions, as well as image reconstruction.

In this paper we extend the DGiT multirate framework, developed in Connors and Sockwell (2022) for scalar transmission problems, to a solid–solid interaction (SSI) problem involving two coupled elastic solids and a coupled air–sea model with the rotating, thermal shallow water equations. In so doing we aim to demonstrate the broad applicability of the mathematical theory and governing principles established in Connors and Sockwell (2022) to coupled problems characterized by subproblems evolving at different temporal scales. Multirate time integration algorithms employing different time steps, optimized for the dynamics of each subproblem, can significantly improve simulation efficiency for such coupled problems. However, development of multirate algorithms is a highly non-trivial task due to the coupling, which can impact accuracy, stability or other desired properties such as preservation of system invariants. DGiT provides a general template for multirate time integration that can achieve these properties. To elucidate the manner in which DGiT accomplishes this task, we fully detail each step in the application of the framework to the SSI and air–sea coupled problems. Numerical examples illustrate key properties of the resulting multirate schemes for both problems.

Artificial intelligence (AI) and machine learning (ML) are near-ubiquitous in day-to-day life; from cars with automated driver-assistance, recommender systems, generative content platforms, and large language chatbots. Implementing AI as a tool for international safeguards could significantly decrease the burden on safeguards inspectors and nuclear facility operators. The use of AI would allow inspectors to complete their in-field activities quicker, while identifying patterns and anomalies and freeing inspectors to focus on the uniquely human component of inspections. Sandia National Laboratories has spent the past two and a half years developing on-device machine learning to develop both a digital and robotic assistant. This combined platform, which we term INSPECTA, has numerous on-device machine learning capabilities that have been demonstrated at the laboratory scale. This work describes early successes implementing AI/ML capabilities to reduce the burden of tedious inspector tasks such as seal examination, information recall, note taking, and more.

This data documentation report describes geologic and hydrologic laboratory analysis and data collected in support of site characterization of the Physical Experiment 1 (PE1) testbed, Aqueduct Mesa, Nevada. The documentation includes a summary of laboratory tests performed, discussion of sample selection for assessing heterogeneity of various testbed properties, methods, and results per data type.

The use of structural mechanics models during the design process often leads to the development of models of varying fidelity. Often low-fidelity models are efficient to simulate but lack accuracy, while the high-fidelity counterparts are accurate with less efficiency. This paper presents a multifidelity surrogate modeling approach that combines the accuracy of a high-fidelity finite element model with the efficiency of a low-fidelity model to train an even faster surrogate model that parameterizes the design space of interest. The objective of these models is to predict the nonlinear frequency backbone curves of the Tribomechadynamics research challenge benchmark structure which exhibits simultaneous nonlinearities from frictional contact and geometric nonlinearity. The surrogate model consists of an ensemble of neural networks that learn the mapping between low and high-fidelity data through nonlinear transformations. Bayesian neural networks are used to assess the surrogate model’s uncertainty. Once trained, the multifidelity neural network is used to perform sensitivity analysis to assess the influence of the design parameters on the predicted backbone curves. Additionally, Bayesian calibration is performed to update the input parameter distributions to correlate the model parameters to the collection of experimentally measured backbone curves.

Many imaging techniques for biological systems—like fixation of cells coupled with fluorescence microscopy—provide sharp spatial resolution in reporting locations of individuals at a single moment in time but also destroy the dynamics they intend to capture. These snapshot observations contain no information about individual trajectories, but still encode information about movement and demographic dynamics, especially when combined with a well-motivated biophysical model. The relationship between spatially evolving populations and single-moment representations of their collective locations is well-established with partial differential equations (PDEs) and their inverse problems. However, experimental data is commonly a set of locations whose number is insufficient to approximate a continuous-in-space PDE solution. Here, motivated by popular subcellular imaging data of gene expression, we embrace the stochastic nature of the data and investigate the mathematical foundations of parametrically inferring demographic rates from snapshots of particles undergoing birth, diffusion, and death in a nuclear or cellular domain. Toward inference, we rigorously derive a connection between individual particle paths and their presentation as a Poisson spatial process. Using this framework, we investigate the properties of the resulting inverse problem and study factors that affect quality of inference. One pervasive feature of this experimental regime is the presence of cell-to-cell heterogeneity. Rather than being a hindrance, we show that cell-to-cell geometric heterogeneity can increase the quality of inference on dynamics for certain parameter regimes. Altogether, the results serve as a basis for more detailed investigations of subcellular spatial patterns of RNA molecules and other stochastically evolving populations that can only be observed for single instants in their time evolution.

Abstract As modern neuroscience tools acquire more details about the brain, the need to move towards biological-scale neural simulations continues to grow. However, effective simulations at scale remain a challenge. Beyond just the tooling required to enable parallel execution, there is also the unique structure of the synaptic interconnectivity, which is globally sparse but has relatively high connection density and non-local interactions per neuron. There are also various practicalities to consider in high performance computing applications, such as the need for serializing neural networks to support potentially long-running simulations that require checkpoint-restart. Although acceleration on neuromorphic hardware is also a possibility, development in this space can be difficult as hardware support tends to vary between platforms and software support for larger scale models also tends to be limited. In this paper, we focus our attention on Simulation Tool for Asynchronous Cortical Streams (STACS), a spiking neural network simulator that leverages the Charm++ parallel programming framework, with the goal of supporting biological-scale simulations as well as interoperability between platforms. Central to these goals is the implementation of scalable data structures suitable for efficiently distributing a network across parallel partitions. Here, we discuss a straightforward extension of a parallel data format with a history of use in graph partitioners, which also serves as a portable intermediate representation for different neuromorphic backends. We perform scaling studies on the Summit supercomputer, examining the capabilities of STACS in terms of network build and storage, partitioning, and execution. We highlight how a suitably partitioned, spatially dependent synaptic structure introduces a communication workload well-suited to the multicast communication supported by Charm++. We evaluate the strong and weak scaling behavior for networks on the order of millions of neurons and billions of synapses, and show that STACS achieves competitive levels of parallel efficiency.

Additive manufacturing has established itself to be advantageous beyond small-scale prototyping, now supporting full-scale production of components for a variety of applications. Despite its integration across industries, marine renewable energy technology is one largely untapped application with potential to bolster clean energy production on the global scale. Wave energy converters (WEC) are one specific facet within this realm that could benefit from AM. As such, wire arc additive manufacturing (WAAM) has been identified as a practical method to produce larger scale marine energy components by leveraging cost-effective and readily available A36 steel feedstock material. The flexibility associated with WAAM can benefit production of WEC by producing more complex structural geometries that are challenging to produce traditionally. Additionally, for large components where fine details are less critical, the high deposition rate of WAAM in comparison to traditional wrought techniques could reduce build times by an order of magnitude. In this context of building and supporting WEC, which experience harsh marine environments, an understanding of performance under large loads and corrosive environments must be understood. Hence, WAAM and wrought A36 steel tensile samples were manufactured, and mechanical properties compared under both dry and corroded conditions. The unique microstructure created via the WAAM process was found to directly correlate to the increased ultimate tensile and yield strength compared to the wrought condition. Static corrosion testing in a simulated saltwater environment in parallel with electrochemical testing highlighted an outperformance of corroded WAAM A36 steel than wrought, despite having a slighter higher corrosion rate. Ultimately, this study shows how marine energy systems may benefit from additive manufacturing components and provides a foundation for future applications of WAAM A36 steel.

In this paper, we present a first-order Stress-Hybrid Virtual Element Method (SH-VEM) on six-noded triangular meshes for linear plane elasticity. We adopt the Hellinger–Reissner variational principle to construct a weak equilibrium condition and a stress based projection operator. In each element, the stress projection operator is expressed in terms of the nodal displacements, which leads to a displacement based formulation. This stress-hybrid approach assumes a globally continuous displacement field while the stress field is discontinuous across each element. The stress field is initially represented by divergence-free tensor polynomials based on Airy stress functions, but we also present a formulation that uses a penalty term to enforce the element equilibrium conditions, referred to as the Penalty Stress-Hybrid Virtual Element Method (PSH-VEM). Numerical results are presented for PSH-VEM and SH-VEM, and we compare their convergence to the composite triangle FEM and B-bar VEM on benchmark problems in linear elasticity. The SH-VEM converges optimally in the L2 norm of the displacement, energy seminorm, and the L2 norm of hydrostatic stress. Furthermore, the results reveal that PSH-VEM converges in most cases at a faster rate than the expected optimal rate, but it requires the selection of a suitably chosen penalty parameter.

The goal of this work is to provide a database of quality-checked seismic parameters which can be integrated with the Geologic Framework Model (GFM) for the LYNM-PE1 (Low Yield Nuclear Monitoring – Physical Experiment 1) testbed. We integrated data from geophysical borehole logs, tabletop measurements on collected core, and laboratory measurements.

Recent evaluations of counter-based periodic testing strategies for fault detection in Microprocessor(μP) have shown that only a small set of counters is needed to provide complete coverage of severe faults. Severe faults are defined as faults that leak sensitive information, e.g., an encryption key on the output of a serial port. Alternatively, fault detection can be accomplished by executing instructions that periodically test the control and functional units of the μP. In this paper, we propose a fault detection method that utilizes an ’engineered’ executable program combined with a small set of strategically placed counters in pursuit of a hardware Periodic Built-In-Self-Test (PBIST). We analyze two distinct methods for generating such a binary; the first uses an Automatic Test Generation Pattern (ATPG)-based methodology, and the second uses a process whereby existing counter-based node-monitoring infrastructure is utilized. We show that complete fault coverage of all leakage faults is possible using relatively small binaries with low latency to fault detection and by utilizing only a few strategically placed counters in the μP.

Crystal plasticity finite element method (CPFEM) has been an integrated computational materials engineering (ICME) workhorse to study materials behaviors and structure-property relationships for the last few decades. These relations are mappings from the microstructure space to the materials properties space. Due to the stochastic and random nature of microstructures, there is always some uncertainty associated with materials properties, for example, in homogenized stress-strain curves. For critical applications with strong reliability needs, it is often desirable to quantify the microstructure-induced uncertainty in the context of structure-property relationships. However, this uncertainty quantification (UQ) problem often incurs a large computational cost because many statistically equivalent representative volume elements (SERVEs) are needed. In this article, we apply a multi-level Monte Carlo (MLMC) method to CPFEM to study the uncertainty in stress-strain curves, given an ensemble of SERVEs at multiple mesh resolutions. By using the information at coarse meshes, we show that it is possible to approximate the response at fine meshes with a much reduced computational cost. We focus on problems where the model output is multi-dimensional, which requires us to track multiple quantities of interest (QoIs) at the same time. Our numerical results show that MLMC can accelerate UQ tasks around 2.23×, compared to the classical Monte Carlo (MC) method, which is widely known as ensemble average in the CPFEM literature.

Hydrogen energy storage can be used to achieve goals of national energy security, renewable energy integration, and grid resilience. Adapting underground natural gas storage facility (UNGSF) infrastructure for underground hydrogen storage (UHS) is one method of storing large quantities of hydrogen that has already largely been proven to work for natural gas. There are currently some underground salt caverns in the United States that are being used for hydrogen storage by commercial entities, but it is still a fairly new concept in that it has not been widely deployed nor has it been done with other geologic formations like depleted hydrocarbon reservoirs. Assessments of UHS systems can help identify and evaluate risks to people both working within the facility and residing nearby. This report provides example risk assessment methodologies and analyses for generic wellhead and processing facility configurations, specifically in the context of the risks of unintentional hydrogen releases into the air. Assessment of the hydrogen containment in the subsurface is also critically important for a safety assessment for a UHS facility, but those geomechanical assessments are not included in this report.

Abstract not provided.

We describe a data-driven, multiscale technique to model reactive wetting of a silver–aluminum alloy on a Kovar™ (Fe-Ni-Co alloy) surface. We employ molecular dynamics simulations to elucidate the dependence of surface tension and wetting angle on the drop's composition and temperature. A design of computational experiments is used to efficiently generate training data of surface tension and wetting angle from a limited number of molecular dynamics simulations. The simulation results are used to parameterize models of the material's wetting properties and compute the uncertainty in the models due to limited data. The data-driven models are incorporated into an engineering-scale (continuum) model of a silver–aluminum sessile drop on a Kovar™ substrate. Model predictions of the wetting angle are compared with experiments of pure silver spreading on Kovar™ to quantify the model-form errors introduced by the limited training data versus the simplifications inherent in the molecular dynamics simulations. The paper presents innovations in the determination of “convergence” of noisy MD simulations before they are used to extract the wetting angle and surface tension, and the construction of their models which approximate physio-chemical processes that are left unresolved by the engineering-scale model. Together, these constitute a multiscale approach that integrates molecular-scale information into continuum scale models.

Fluid flow through fractured media is typically governed by the distribution of fracture apertures, which are in turn governed by stress. Consequently, understanding subsurface stress is critical for understanding and predicting subsurface fluid flow. Although laboratory-scale studies have established a sensitive relationship between effective stress and bulk electrical conductivity in crystalline rock, that relationship has not been extensively leveraged to monitor stress evolution at the field scale using electrical or electromagnetic geophysical monitoring approaches. In this paper we demonstrate the use time-lapse 3-dimensional (4D) electrical resistivity tomography to image perturbations in the stress field generated by pressurized borehole packers deployed during shear-stimulation attempts in a 1.25 km deep metamorphic crystalline rock formation.

GaN/InGaN microLEDs are a very promising technology for next-generation displays. Switching control transistors and their integration are key components in achieving high-performance, efficient displays. Monolithic integration of microLEDs with GaN switching devices provides an opportunity to control microLED output power with capacitive (voltage)-controlled rather than current-controlled schemes. This approach can greatly reduce system complexity for the driver circuit arrays while maintaining device opto-electronic performance. In this work, we demonstrate a 3-terminal GaN micro-light emitting transistor that combines a GaN/InGaN blue tunneling-based microLED with a GaN n-channel FET. The integrated device exhibits excellent gate control, drain current control, and optical emission control. This work provides a promising pathway for future monolithic integration of GaN FETs with microLED to enable fast switching, high-efficiency microLED display and communication systems.

Electrical polarization and defect transport are examined in 0.8BaTiO₃–0.2BiZn_0.5Ti_0.5O₃, an attractive capacitor material for high power electronics. Oxygen vacancies are suggested to be the majority charge carrier at or below 250°C with a grain conduction hopping activation energy of 0.97 eV and 0.92 eV for thermally stimulated depolarization current (TSDC) and impedance spectroscopy measurements, respectively. At higher temperature, thermally generated electronic conduction with an activation energy of 1.6 eV is dominant. Significant oxygen vacancy concentration is indicated (up to ~1%) due to cation vacancy formation (i.e., acceptor defects) from observed Bi (and likely Zn) volatility. Oxygen vacancy diffusivity is estimated to be 10^-12.8 cm²/s at 250°C. Low diffusivity and high activation energies are indicative of significant defect interactions. Dipolar oxygen vacancy defects are also indicated, with an activation energy of 0.59 eV from TSDC measurements. In conclusion, the large oxygen vacancy content leads to a short lifetime during high voltage (30 kV/cm), high temperature (250°C) direct current (DC) electrical measurements.

A new strategy is presented for computing anharmonic partition functions for the motion of adsorbates relative to a catalytic surface. Importance sampling is compared with conventional Monte Carlo. The importance sampling is significantly more efficient. This new approach is applied to CH3* on Ni(111) as a test case. The motion of methyl relative to the nickel surface is found to be anharmonic, with significantly higher entropy compared to the standard harmonic oscillator model. The new method is freely available as part of the Minima-Preserving Neural Network within the AdTherm package.

Nanoporous, gas-selective membranes have shown encouraging results for the removal of CO2 from flue gas, yet the optimal design for such membranes is often unknown. Therefore, we used molecular dynamics simulations to elucidate the behavior of CO2 within aqueous and ionic liquid (IL) systems ([EMIM][TFSI] and [OMIM][TFSI]), both confined individually and as an interfacial aqueous/IL system. We found that within aqueous systems the mobility of CO2 is reduced due to interactions between the CO2 oxygens and hydroxyl groups on the pore surface. Within the IL systems, we found that confinement has a greater effect on the [EMIM][TFSI] system as opposed to the [OMIM][TFSI] system. Paradoxically, the larger and more asymmetrical [OMIM]+ molecule undergoes less efficient packing, resulting in fewer confinement effects. Free energy surfaces of the nanoconfined aqueous/IL interface demonstrate that CO2 will transfer spontaneously from the aqueous to the IL phase.

Integrated modeling of plasma-surface interactions provides a comprehensive and self-consistent description of the system, moving the field closer to developing predictive and design capabilities for plasma facing components. One such workflow, including descriptions for the scrape-off-layer plasma, ion-surface interactions and the sub-surface evolution, was previously used to address steady-state scenarios and has recently been extended to incorporate time-dependence and two-way information flow. The new model can address dynamic recycling in transient scenarios, such as the application presented in this paper: the evolution of W samples pre-damaged by helium and exposed to ELMy H-mode plasmas in the DIII-D DiMES. A first set of simulations explored the effect of ELM frequency. This study was discussed in detail in this conference's proceedings and is summarized here. The 2nd set of simulations, which is the focus of this paper, explores the effect of code-coupling frequency. These simulations include initial SOLPS solutions converged to the inter-ELM state, ion impact energy (E_in) and angles (A_in) calculated by hPIC2, and an improved heat transfer description in Xolotl. The model predicts increases in particle fluxes and decreases in heat fluxes by 10%–20% with the coupling time-step. Compared with the first set of simulations, the less shallow impact angle leads to smaller reflection rates and significant D implantation. The higher fraction of implanted flux (and deeper), in particular during ELMs, increases the accumulated D content in the W near-surface region. Future expansion of the workflow includes coupling to hPIC2 and GITR to ensure accurate descriptions of E_in and A_in, and W impurity transport.

Experiments offer incredible value to science, but results must always come with an uncertainty quantification to be meaningful. This requires grappling with sources of uncertainty and how to reduce them. In wind energy, field experiments are sometimes conducted with a control and treatment. In this scenario uncertainty due to bias errors can often be neglected as they impact both control and treatment approximately equally. However, uncertainty due to random errors propagates such that the uncertainty in the difference between the control and treatment is always larger than the random uncertainty in the individual measurements if the sources are uncorrelated. As random uncertainties are usually reduced with additional measurements, there is a need to know the minimum duration of an experiment required to reach acceptable levels of uncertainty. We present a general method to simulate a proposed experiment, calculate uncertainties, and determine both the measurement duration and the experiment duration required to produce statistically significant and converged results. The method is then demonstrated as a case study with a virtual experiment that uses real-world wind resource data and several simulated tip extensions to parameterize results by the expected difference in power. With the method demonstrated herein, experiments can be better planned by accounting for specific details such as controller switching schedules, wind statistics, and postprocess binning procedures such that their impacts on uncertainty can be predicted and the measurement duration needed to achieve statistically significant and converged results can be determined before the experiment.

Stress corrosion cracking behavior of stainless steel 304 L was investigated in full immersion, evaporated artificial sea salt brines (ASW) at 55 °C. It was observed that brines representative of thermodynamically stable brines at lower relative humidity (40% RH, MgCl2-dominant) had a faster crack growth rate than high relative humidity brines (76% RH, NaCl-dominant). Observed crack growth rates (da/dt) under constant stress intensity (K) conditions were determined to be independent of transitioning procedure (rising K or decreasing frequency) regardless of solutions investigated for the orientation presented. Further, positive strain rates had little to no impact on the observed da/dt. The observed behavior suggests an anodic dissolution enhanced hydrogen embrittlement mechanism for SS304L in concentrated ASW environments at 55 °C. Additional explorations further examined environmental influences on da/dt. Nitrate additions to 40% ASW at 55 °C solutions were shown to decrease measured da/dt and further additions stopped measurable crack growth. After sufficient nitrate had been added to fully stifle crack growth, a temperature increase to 75 °C induced cracking again, and a subsequent decrease to 55 °C once again stopped da/dt. These tests demonstrate the importance of ascertaining both brine-specific chemical and dynamic environmental influences on da/dt.

Neural operators, which can act as implicit solution operators of hidden governing equations, have recently become popular tools for learning the responses of complex real-world physical systems. Nevertheless, most neural operator applications have thus far been data-driven and neglect the intrinsic preservation of fundamental physical laws in data. In this work, we introduce a novel integral neural operator architecture called the Peridynamic Neural Operator (PNO) that learns a nonlocal constitutive law from data. This neural operator provides a forward model in the form of state-based peridynamics, with objectivity and momentum balance laws automatically guaranteed. As applications, we demonstrate the expressivity and efficacy of our model in learning complex material behaviors from both synthetic and experimental data sets. We also compare the performances with baseline models that use predefined constitutive laws. We show that, owing to its ability to capture complex responses, our learned neural operator achieves improved accuracy and efficiency. Moreover, by preserving the essential physical laws within the neural network architecture, the PNO is robust in treating noisy data. The method shows generalizability to different domain configurations, external loadings, and discretizations.

We show that the deposition of the solid-state electrolyte LiPON onto films of V2O5 leads to their uniform lithiation of up to 2.2 Li per V2O5, without affecting the Li concentration in the LiPON and its ionic conductivity. Our results indicate that Li incorporation occurs during LiPON deposition, in contrast to earlier mechanisms proposed to explain postdeposition Li transfer between LiPON and LiCoO2. We use our discovery to demonstrate symmetric thin film batteries with a capacity of >270 mAh/g, at a rate of 20C, and 1600 cycles with only 8.4% loss in capacity. We also show how autolithiation can simplify fabrication of Li iontronic transistors attractive for emerging neuromorphic computing applications. Our discovery that LiPON deposition results in autolithiation of the underlying insertion oxide has the potential to substantially simplify and enhance the fabrication process for thin film solid state Li ion batteries and emerging lithium iontronic neuromorphic computing devices.