In 2019, Sandia National Laboratories contracted Synapse Energy Economics (Synapse) to research the integration of community and electric utility resilience investment planning as part of the Designing Resilient Communities: A Consequence-Based Approach for Grid Investment (DRC) project. Synapse produced a series of reports to explore the challenges and opportunities in several key areas, including benefit-cost analysis, performance metrics, microgrids, and regulatory mechanisms to promote investments in electric system resilience. This report focuses on regulatory mechanisms to improve resilience. Regulatory mechanisms that improve resilience are approaches that electric utility regulators can use to align utility, customer, and third-party investments with regulatory, ratepayer, community, and other important stakeholder interests and priorities for resilience. Cost-of-service regulation may fail to provide utilities with adequate guidance or incentives regarding community priorities for infrastructure hardening and disaster recovery. The application of other types of regulatory mechanisms to resilience investments can help. This report: characterizes regulatory objective as they apply to resilience; identifies several regulatory mechanisms that are used or can be adapted to improve the resilience of the electric system--including performance-based regulation, integrated planning, tariffs and programs to leverage private investment, alternative lines of business for utilities, enhanced cost recovery, and securitization; provides a case study of each regulatory mechanism; summarizes findings across the case studies; and suggests how these regulatory mechanisms might be improved and applied to resilience moving forward. In this report, we assess the effectiveness of a range of utility regulatory mechanisms at evaluating and prioritizing utility investments in grid resilience. First, we characterize regulatory objectives which underly all regulatory mechanisms. We then describe seven types of regulatory mechanisms that can be used to improve resilience--including performance-based regulation, integrated planning, tariffs and programs to leverage private investment, alternative lines of business for utilities, enhanced cost recovery, and securitization--and provide a case study for each one. We summarize our findings on the extent to which these regulatory mechanisms have supported resilience to date. We conclude with suggestions on how these regulatory mechanisms might be improved and applied to resilience moving forward.
Mcnesby, Kevin; Dean, Steven W.; Benjamin, Richard; Grant, Jesse; Anderson, James; Densmore, John
A simple combination of the Planck blackbody emission law, optical filters, and digital image processing is demonstrated to enable most commercial color cameras (still and video) to be used as an imaging pyrometer for flames and explosions. The hardware and data processing described take advantage of the color filter array (CFA) that is deposited on the surface of the light sensor array present in most digital color cameras. In this work, a triple-pass optical filter incorporated into the camera lens allows light in three 10-nm wide bandpass regions to reach the CFA/light sensor array. These bandpass regions are centered over the maxima in the blue, green, and red transmission regions of the CFA, minimizing the spectral overlap of these regions normally present. A computer algorithm is used to retrieve the blue, green, and red image matrices from camera memory and correct for remaining spectral overlap. A second algorithm calibrates the corrected intensities to a gray body emitter of known temperature, producing a color intensity correction factor for the camera/filter system. The Wien approximation to the Planck blackbody emission law is used to construct temperature images from the three color (blue, green, red) matrices. A short pass filter set eliminates light of wavelengths longer than 750 nm, providing reasonable accuracy (±10%) for temperatures between 1200 and 6000 K. The effectiveness of this system is demonstrated by measuring the temperature of several systems for which the temperature is known.
The most revealing indicator for oxidative processes or state of degraded plastics is usually carbonyl formation, a key step in materials degradation as part of the carbon cycle for man-made materials. Hence, the identification and quantification of carbonyl species with infrared spectroscopy have been the method of choice for generations, thanks to their strong absorbance and being an essential intermediate in carbon oxidation pathways. Despite their importance, precise identification and quantification can be challenging and rigorous fully traceable data are surprisingly rare in the existing literature. An overview of the complexity of carbonyl quantification is presented by the screening of reference compounds in solution with transmission and polymer films with ATR IR spectroscopy, and systematic data analyses. Significant variances in existing data and their past use have been recognized. Guidance is offered how better measurements and data reporting could be accomplished. Experimental variances depend on the combination of uncertainty in exact carbonyl species, extinction coefficient, contributions from neighboring convoluting peaks, matrix interaction phenomena and instrumental variations in primary IR spectral acquisition (refractive index and penetration depth for ATR measurements). In addition, diverging sources for relevant extinction coefficients may exist, based on original spectral acquisition. For common polymer degradation challenges, a relative comparison of carbonyl yields for a material is easily accessible, but quantification for other purposes, such as degradation rates and spatially dependent interpretation, requires thorough experimental validation. All variables highlighted in this overview demonstrate the significant error margins in carbonyl quantification, with exact carbonyl species and extinction coefficients already being major contributors on their own.
Self-assembly of iron oxide nanoparticles (IONPs) into 1D chains is appealing, because of their biocompatibility and higher mobility compared to 2D/3D assemblies while traversing the circulatory passages and blood vessels for in vivo biomedical applications. In this work, parameters such as size, concentration, composition, and magnetic field, responsible for chain formation of IONPs in a dispersion as opposed to spatially confining substrates, are examined. In particular, the monodisperse 27 nm IONPs synthesized by an extended LaMer mechanism are shown to form chains at 4 mT, which are lengthened with applied field reaching 270 nm at 2.2 T. The chain lengths are completely reversible in field. Using a combination of scattering methods and reverse Monte Carlo simulations the formation of chains is directly visualized. The visualization of real-space IONPs assemblies formed in dispersions presents a novel tool for biomedical researchers. This allows for rapid exploration of the behavior of IONPs in solution in a broad parameter space and unambiguous extraction of the parameters of the equilibrium structures. Additionally, it can be extended to study novel assemblies formed by more complex geometries of IONPs.
The critical pitting temperature (CPT) of selective laser melted (SLM) 316 L stainless steel in 1.0 M NaCl was measured and compared with a commercial wrought alloy. Potentiostatic measurements determined a mean CPT value of 16 ± 0.7 °C, 27.5 ± 0.8 °C and 31 ± 1 °C for the wrought alloy, the SLM alloy normal to the build direction and parallel to the build direction, respectively. The lead-in pencil electrode technique was used to study the pit chemistry of the two alloys and to explain the higher CPT values observed for the SLM alloy. A lower critical current density required for passivation in a simulated pit solution was measured for the SLM alloy. Moreover, the ratio of the critical concentration to saturated concentration of dissolving metal cations was found to be higher for the SLM alloy, which was related to its different salt film properties, possibly as a result of the SLM's distinct microstructure.
This paper describes results from an optical-engine investigation of oxygenated fuel effects on ducted fuel injection (DFI) relative to conventional diesel combustion (CDC). Three fuels were tested: a baseline, non-oxygenated No. 2 emissions certification diesel (denoted CFB), and two blends containing potential renewable oxygenates. The first oxygenated blend contained 25 vol% methyl decanoate in CFB (denoted MD25), and the second contained 25 vol% tri-propylene glycol mono-methyl ether in CFB (denoted T25). Whereas DFI and fuel oxygenation primarily curtail soot emissions, intake-oxygen mole fractions of 21% and 16% were employed to explore the potential additional beneficial impact of dilution on engine-out emissions of nitrogen oxides (NOx). It was found that DFI with an oxygenated fuel can attenuate soot incandescence by ~100X (~10X from DFI and an additional ~10X from fuel oxygenation) relative to CDC with conventional diesel fuel, regardless of dilution level and without large effects on other emissions or efficiency. This breaks the soot/NOx trade-off with dilution, enabling simultaneous reductions in both soot and NOx emissions, even with conventional diesel fuel. Significant cyclic variability in soot incandescence for both CDC and DFI suggests that additional improvements in engine-out soot emissions may be possible via improved control of in-cylinder mixture formation and evolution.
Protecting against multi-step attacks of uncertain duration and timing forces defenders into an indefinite, always ongoing, resource-intensive response. To effectively allocate resources, a defender must be able to analyze multi-step attacks under assumption of constantly allocating resources against an uncertain stream of potentially undetected attacks. To achieve this goal, we present a novel methodology that applies a game-theoretic approach to the attack, attacker, and defender data derived from MITRE´s ATT&CK® Framework. Time to complete attack steps is drawn from a probability distribution determined by attacker and defender strategies and capabilities. This constraints attack success parameters and enables comparing different defender resource allocation strategies. By approximating attacker-defender games as Markov processes, we represent the attacker-defender interaction, estimate the attack success parameters, determine the effects of attacker and defender strategies, and maximize opportunities for defender strategy improvements against an uncertain stream of attacks. This novel representation and analysis of multi-step attacks enables defender policy optimization and resource allocation, which we illustrate using the data from MITRE´ s APT3 ATT&CK® Framework.
Carbon dioxide (CO2) is considered the sole culprit for global warming; however, nitrous oxide (N2O), a greenhouse gas (GHG) with approximately 300 times more global warming potential than CO2, accounts for 6% of the GHG emissions in the United States. Seventy five percent of N2O emissions come from synthetic nitrogen (N) fertilizer usage in the agriculture sector primarily due to excess fertilization. Numerous studies have shown that changes in soil management practices, specifically optimizing N fertilizer use and amending soil with organic and humate materials can reverse soil damage and improve a farmer's or land reclamation company's balance sheet. Soil restoration is internationally recognized as one of the lowest cost GHG abatement opportunities available. Profitability improves in two ways: (1) lower operating costs resulting from lower input costs (water and fertilizer); and (2) increased revenue by participation in emerging GHG offsets markets, and water quality trading markets.
We present the results of large scale molecular dynamics simulations aimed at understanding the origins of high friction coefficients in pure metals, and their concomitant reduction in alloys and composites. We utilize a series of targeted simulations to demonstrate that different slip mechanisms are active in the two systems, leading to differing frictional behavior. Specifically, we show that in pure metals, sliding occurs along the crystallographic slip planes, whereas in alloys shear is accommodated by grain boundaries. In pure metals, there is significant grain growth induced by the applied shear stress and the slip planes are commensurate contacts with high friction. However, the presence of dissimilar atoms in alloys suppresses grain growth and stabilizes grain boundaries, leading to low friction via grain boundary sliding. Graphic Abstract: [Figure not available: see fulltext.]
This report is intended to detail the findings of our investigation of the applicability of machine learning to the task of aftershock identification. The ability to automatically identify nuisance aftershock events to reduce analyst workload when searching for events of interest is an important step in improving nuclear monitoring capabilities and while waveform cross - correlation methods have proven successful, they have limitations (e.g., difficulties with spike artifacts, multiple aftershocks in the same window) that machine learning may be able to overcome. Here we apply a Paired Neural Network (PNN) to a dataset consisting of real, high quality signals added to real seismic noises in order to work with controlled, labeled data and establish a baseline of the PNN's capability to identify aftershocks. We compare to waveform cross - correlation and find that the PNN performs well, outperforming waveform cross - correlation when classifying similar waveform pairs, i.e., aftershocks.
Distributed controllers play a prominent role in electric power grid operation. The coordinated failure or malfunction of these controllers is a serious threat, where the resulting mechanisms and consequences are not yet well-known and planned against. If certain controllers are maliciously compromised by an adversary, they can be manipulated to drive the system to an unsafe state. The authors present a strategy for distributed controller defence (SDCD) for improved grid tolerance under conditions of distributed controller compromise. The work of the authors’ first formalises the roles that distributed controllers play and their control support groups using controllability analysis techniques. With these formally defined roles and groups, the authors then present defence strategies for maintaining or regaining system control during such an attack. A general control response framework is presented here for the compromise or failure of distributed controllers using the remaining, operational set. The SDCD approach is successfully demonstrated with a 7-bus system and the IEEE 118-bus system for single and coordinated distributed controller compromise; the results indicate that SDCD is able to significantly reduce system stress and mitigate compromise consequences.
The Geophysical Monitoring System (GMS) State-of-Health User Interface (SOH UI) is a web-based application that allows a user to view and acknowledge the SOH status of stations in the GMS system. The SOH UI will primarily be used by the System Controller, who monitors and controls the system and external data connections. The System Controller uses the station SOH UIs to monitor, detect, and troubleshoot problems with station data availability and quality.
The electric grid is becoming increasingly cyber-physical with the addition of smart technologies, new communication interfaces, and automated grid-support functions. Because of this, it is no longer sufficient to only study the physical system dynamics, but the cyber system must also be monitored as well to examine cyber-physical interactions and effects on the overall system. To address this gap for both operational and security needs, cyber-physical situational awareness is needed to monitor the system to detect any faults or malicious activity. Techniques and models to understand the physical system (the power system operation) exist, but methods to study the cyber system are needed, which can assist in understanding how the network traffic and changes to network conditions affect applications such as data analysis, intrusion detection systems (IDS), and anomaly detection. In this paper, we examine and develop models of data flows in communication networks of cyber-physical systems (CPSs) and explore how network calculus can be utilized to develop those models for CPSs, with a focus on anomaly and intrusion detection. This provides a foundation for methods to examine how changes to behavior in the CPS can be modeled and for investigating cyber effects in CPSs in anomaly detection applications.
Computing k-cores on graphs is an important graph mining target as it provides an efficient means of identifying a graph's dense and cohesive regions. Computing k-cores on hypergraphs has seen recent interest, as many datasets naturally produce hypergraphs. Maintaining k-cores as the underlying data changes is important as graphs are large, growing, and continuously modified. In many practical applications, the graph updates are bursty, both with periods of significant activity and periods of relative calm. Existing maintenance algorithms fail to handle large bursts, and prior parallel approaches on both graphs and hypergraphs fail to scale as available cores increase.We address these problems by presenting two parallel and scalable fully-dynamic batch algorithms for maintaining k-cores on both graphs and hypergraphs. Both algorithms take advantage of the connection between k-cores and h-indices. One algorithm is well suited for large batches and the other for small. We provide the first algorithms that experimentally demonstrate scalability as the number of threads increase while sustaining high change rates in graphs and hypergraphs.
While a great deal of research has been performed to quantify and characterize the wave energy resource, there are still open questions about how a wave energy developer should use this wave resource information to design a wave energy converter device to suit a specific environment or, alternatively, to assess potential deployment locations. It is natural to focus first on the impressive magnitudes of power available from ocean waves, and to be drawn to locations where mean power levels are highest. However, a number of additional factors such as intermittency and capacity factor may be influential in determining economic viability of a wave energy converter, and should therefore be considered at the resource level, so that these factors can influence device design decisions. This study examines a set of wave resource metrics aimed towards this end of bettering accounting for variability in wave energy converter design. The results show distinct regional trends that may factor into project siting and wave energy converter design. Although a definitive solution for the optimal size of a wave energy converter is beyond the reaches of this study, the evidence presented does support the idea that smaller devices with lower power ratings may merit closer consideration.
Optimally-shaped electromagnetic fields have the capacity to coherently control the dynamics of quantum systems and thus offer a promising means for controlling molecular transformations relevant to chemical, biological, and materials applications. Currently, advances in this area are hindered by the prohibitive cost of the quantum dynamics simulations needed to explore the principles and possibilities of molecular control. However, the emergence of nascent quantum-computing devices suggests that efficient simulations of quantum dynamics may be on the horizon. In this article, we study how quantum computers could be employed to design optimally-shaped fields to control molecular systems. We introduce a hybrid algorithm that utilizes a quantum computer for simulating the field-induced quantum dynamics of a molecular system in polynomial time, in combination with a classical optimization approach for updating the field. Qubit encoding methods relevant for molecular control problems are described, and procedures for simulating the quantum dynamics and obtaining the simulation results are discussed. Numerical illustrations are then presented that explicitly treat paradigmatic vibrational and rotational control problems, and also consider how optimally-shaped fields could be used to elucidate the mechanisms of energy transfer in light-harvesting complexes. Resource estimates, as well as a numerical assessment of the impact of hardware noise and the prospects of near-term hardware implementations, are provided for the latter task.
Introduction: Empathy is critical for human interactions to become shared and meaningful, and it is facilitated by the expression and processing of facial emotions. Deficits in empathy and facial emotion recognition are associated with individuals with autism spectrum disorder (ASD), with specific concerns over inaccurate recognition of facial emotion expressions conveying a threat. Yet, the number of evidenced interventions for facial emotion recognition and processing (FERP), emotion, and empathy remains limited, particularly for adults with ASD. Transcranial direct current stimulation (tDCS), a noninvasive brain stimulation, may be a promising treatment modality to safely accelerate or enhance treatment interventions to increase their efficacy. Methods: This study investigates the effectiveness of FERP, emotion, and empathy treatment interventions paired with tDCS for adults with ASD. Verum or sham tDCS was randomly assigned in a within-subjects, double-blinded design with seven adults with ASD without intellectual disability. Outcomes were measured using scores from the Empathy Quotient (EQ) and a FERP test for both verum and sham tDCS. Results: Verum tDCS significantly improved EQ scores and FERP scores for emotions that conveyed threat. Conclusions: These results suggest the potential for increasing the efficacy of treatment interventions by pairing them with tDCS for individuals with ASD.
Most studies of vortex shedding from a circular cylinder in a gas flow have explicitly or implicitly assumed that the no-slip condition applies on the cylinder surface. To investigate the effect of slip, vortex shedding is simulated using molecular gas dynamics (the direct simulation Monte Carlo method) and computational fluid dynamics (the incompressible Navier-Stokes equations with a slip boundary condition). A Reynolds number of 100, a Mach number of 0.3, and a corresponding Knudsen number of 0.0048 are examined. For these conditions, compressibility effects are small, and periodic laminar vortex shedding is obtained. Slip on the cylinder is varied using combinations of diffuse and specular molecular reflections with accommodation coefficients from zero (maximum slip) to unity (minimum slip). Although unrealistic, bounce-back molecular reflections are also examined because they approximate the no-slip boundary condition (zero slip). The results from both methods are in reasonable agreement. The shedding frequency increases slightly as the accommodation coefficient is decreased, and shedding ceases at low accommodation coefficients (large slip). The streamwise and transverse forces decrease as the accommodation coefficient is decreased. Based on the good agreement between the two methods, computational fluid dynamics is used to determine the critical accommodation coefficient below which vortex shedding ceases for Reynolds numbers of 60-100 at a Mach number of 0.3. Conditions to observe the effect of slip on vortex shedding appear to be experimentally realizable, although challenging.
In the present work, premixed combustion in a turbulent boundary layer under auto-ignitive conditions is investigated using direct numerical simulation (DNS). The turbulent inflow of the reactive DNS is obtained by temporal sampling of a corresponding inert DNS of a turbulent boundary layer at a location with Reτ= 360, where Reτ is the friction Reynolds number. The reactants of the DNS are determined by mixing the products of lean natural gas combustion and a H2/N2 fuel jet, resulting in a lean mixture of high temperature with a short ignition delay time. In the free stream the reaction front is stabilized at a streamwise location which can be predicted using the free stream velocity U∞ and the ignition delay time τig. Inside the boundary layer, combustion modifies the near-wall coherent turbulent structures considerably and turbulence results in reaction front wrinkling. The combustion modes in various regions were examined based on the results of displacement velocity, species budget and chemical explosive mode analysis (CEMA). It was indicated that flame propagation prevails in the near-wall region and auto-ignition becomes increasingly important as the wall-normal distance increases. The interactions of turbulence and combustion were studied through statistics of reaction front normal vector and strain rate tensor. It was found that the reaction front normal preferentially aligns with the most compressive strain rate in regions where the effects of heat release on the strain rate are minor and with the most extensive strain rate where its effects are significant. Negative correlations between the wall heat flux and flame quenching distance were observed. A new quenching mode, back-on quenching, was identified. It was found that the heat release rate at the wall is the highest when head-on quenching occurs and lowest when back-on quenching occurs.
Traditional interpolation techniques for particle tracking include binning and convolutional formulas that use pre-determined (i.e., closed-form, parameteric) kernels. In many instances, the particles are introduced as point sources in time and space, so the cloud of particles (either in space or time) is a discrete representation of the Green's function of an underlying PDE. As such, each particle is a sample from the Green's function; therefore, each particle should be distributed according to the Green's function. In short, the kernel of a convolutional interpolation of the particle sample “cloud” should be a replica of the cloud itself. This idea gives rise to an iterative method by which the form of the kernel may be discerned in the process of interpolating the Green's function. When the Green's function is a density, this method is broadly applicable to interpolating a kernel density estimate based on random data drawn from a single distribution. We formulate and construct the algorithm and demonstrate its ability to perform kernel density estimation of skewed and/or heavy-tailed data including breakthrough curves.
Streaks in the buffer layer of wall-bounded turbulence are tracked in time to study their life cycle. Spatially and temporally resolved direct numerical simulation data are used to analyze the strong wall-parallel movements conditioned to low-speed streamwise flow. The analysis of the streaks shows that there is a clear distinction between wall-attached and detached streaks, and that the wall-attached streaks can be further categorized into streaks that are contained in the buffer layer and the ones that reach the outer region. The results reveal that streaks are born in the buffer layer, coalescing with each other to create larger streaks that are still attached to the wall. Once the streak becomes large enough, it starts to meander due to the large streamwise-to-wall-normal aspect ratio, and consequently the elongation in the streamwise direction, which makes it more difficult for the streak to be oriented strictly in the streamwise direction. While the continuous interaction of the streaks allows the superstructure to span extremely long temporal and length scales, individual streak components are relatively small and short-lived. Tall-attached streaks eventually split into wall-attached and wall-detached components. These wall-detached streaks have a strong wall-normal velocity away from the wall, similar to ejections or bursts observed in the literature. Conditionally averaging the flow fields to these split events show that the detached streak has not only a larger wall-normal velocity compared to the wall-attached counterpart, it also has a larger (less negative) streamwise velocity, similar to the velocity field at the tip of a vortex cluster.
This work uses accelerating rate calorimetry to evaluate the impact of cell chemistry, state of charge, cell capacity, and ultimately cell energy density on the total energy release and peak heating rates observed during thermal runaway of Li-ion batteries. While the traditional focus has been using calorimetry to compare different chemistries in cells of similar sizes, this work seeks to better understand how applicable small cell data is to understand the thermal runaway behavior of large cells as well as determine if thermal runaway behaviors can be more generally tied to aspects of lithium-ion cells such as total stored energy and specific energy. We have found a strong linear correlation between the total enthalpy of the thermal runaway process and the stored energy of the cell, apparently independent of cell size and state of charge. We have also shown that peak heating rates and peak temperatures reached during thermal runaway events are more closely tied to specific energy, increasing exponentially in the case of peak heating rates.
This is a progress report on thermal modeling for dual-purpose canister (DPCs) direct disposal that covers several available calculation methods and addresses creep and temperature-dependent properties in a salt repository. Three modeling approaches are demonstrated: A semi-analytical calculation method that uses linear solutions with superposition and imaging, to represent a central waste package in a larger array; A finite difference model of coupled thermal creep, implemented in FLAC2D; and An integrated finite difference thermal-hydrologic modeling approach for repositories in different generic host media, implemented in PFLOTRAN. These approaches are at different levels of maturity, and future work is expected to add refinements and establish the best applications for each.
A discrete direct (DD) model calibration and uncertainty propagation approach is explained and demonstrated on a 4-parameter Johnson-Cook (J-C) strain-rate dependent material strength model for an aluminum alloy. The methodology’s performance is characterized in many trials involving four random realizations of strain-rate dependent material-test data curves per trial, drawn from a large synthetic population. The J-C model is calibrated to particular combinations of the data curves to obtain calibration parameter sets which are then propagated to “Can Crush” structural model predictions to produce samples of predicted response variability. These are processed with appropriate sparse-sample uncertainty quantification (UQ) methods to estimate various statistics of response with an appropriate level of conservatism. This is tested on 16 output quantities (von Mises stresses and equivalent plastic strains) and it is shown that important statistics of the true variabilities of the 16 quantities are bounded with a high success rate that is reasonably predictable and controllable. The DD approach has several advantages over other calibration-UQ approaches like Bayesian inference for capturing and utilizing the information obtained from typically small numbers of replicate experiments in model calibration situations—especially when sparse replicate functional data are involved like force–displacement curves from material tests. The DD methodology is straightforward and efficient for calibration and propagation problems involving aleatory and epistemic uncertainties in calibration experiments, models, and procedures.
The use of evidence theory and associated cumulative plausibility functions (CPFs), cumulative belief functions (CBFs), cumulative distribution functions (CDFs), complementary cumulative plausibility functions (CCPFs), complementary cumulative belief functions (CCBFs), and complementary cumulative distribution functions (CCDFs) in the analysis of time and temperature margins associated with loss of assured safety (LOAS) for one weak link (WL)/two strong link (SL) systems is illustrated. Article content includes cumulative and complementary cumulative belief, plausibility, and probability for (i) SL/ WL failure time margins defined by (time at which SL failure potentially causes LOAS) - (time at which WL failure potentially prevents LOAS), (ii) SL/WL failure temperature margins defined by (the temperature at which SL failure potentially causes LOAS) - (the temperature at which WL failure potentially prevents LOAS), and (iii) SL/SL failure temperature margins defined by (the temperature at which SL failure potentially causes LOAS) - (the temperature of SL whose failure potentially causes LOAS at the time at which WL failure potentially prevents LOAS).
International Journal of Pressure Vessels and Piping
Chatzidakis, Stylianos; Tang, Wei; Payzant, Andrew; Bunn, Jeff; Bryan, Charles R.; Scaglione, John; Wang, Jy A.
Corrosion-resistant welded alloys are frequently used as a leak-tight boundary in critical applications that require confinement of hazardous and/or radioactive substances, including an increasing population of spent nuclear fuel (SNF) canisters. The behavior of residual stresses generated as a result of irregular elastic–plastic deformation during processes such as welding is one of today's key issues to a full understanding of the aging mechanisms that may compromise the confinement boundary. Whether such processes and any subsequent weld repairs, not subjected to post-weld heat treatment, would negatively affect the initial material by introducing through-thickness tensile stresses remains an open question. Here we report the first residual stress measurements using neutron diffraction on the welded joints of a SNF canister. We found significant tensile residual stresses in the as welded sample, indicating that initiation and through-thickness growth of cracks may be possible. Following repair, we observed a stress redistribution and introduction of beneficial compressive stresses. We anticipate our results will improve understanding of confinement susceptibility to aging and guide improvements in repair techniques.
Tritium diffusion in α-Zr containing point defects such as vacancies or self-interstitial atoms (SIAs) is simulated using molecular dynamics. Point defects rapidly aggregate to form extended defects, such as 3D nanoclusters and Frank loops. The geometry of extended defects is affected by the presence of tritium. At low temperature and in the absence of tritium, vacancies aggregate to form stacking fault pyramids. Addition of tritium at these temperatures promotes aggregation of vacancies to form 3D nanoclusters, within which the tritium concentration can be sufficiently high to suggest that these defects may serve as nucleation sites for hydride precipitation. Trapping of tritium in vacancy nanocluster reduces the calculated bulk diffusivity by an amount proportional to the vacancy concentration. At high temperature, vacancy clusters change shape to form planar basal dislocation loops, which bind tritium less strongly, leading to a sharp reduction in the fraction of trapped tritium and a corresponding increase in tritium diffusivity at high temperature. In contrast, SIAs increase tritium diffusion through α-Zr. Analysis of atomic trajectories shows that tritium does not interact directly with SIAs. In conclusion, diffusion enhancement is instead related to expansion of the lattice.
We present a combined experimental and theoretical investigation of the autoignition chemistry of a prototypical cyclic hydrocarbon, cyclopentane. Experiments using a high-pressure photolysis reactor coupled to time-resolved synchrotron VUV photoionization mass spectrometry directly probe the short-lived radical intermediates and products in cyclopentane oxidation reactions. We detect key peroxy radical intermediates ROO and OOQOOH, as well as several hydroperoxides, formed by second O2 addition. Automated quantum chemical calculations map out the R + O2 + O2 reaction channels and demonstrate that the detected intermediates belong to the dominant radical chain-branching pathway: ROO (+ O2) → γ-QOOH + O2 → γ-OOQOOH → products. ROO, OOQOOH, and hydroperoxide products of second-O2 addition undergo extensive dissociative ionization, making their experimental assignment challenging. We use photoionization dynamics calculations to aid in their characterization and report the absolute photoionization spectra of isomerically pure ROO and γ-OOQOOH. A global statistical fit of the observed kinetics enables reliable quantification of the time-resolved concentrations of these elusive, yet critical species, paving the way for detailed comparisons with theoretical predictions from master-equation-based models.
The Human Readiness Level scale complements and supplements the existing technology readiness level scale to support comprehensive and systematic evaluation of human system aspects throughout a system’s life cycle. The objective is to ensure humans can use a fielded technology or system as intended to support mission operations safely and effectively. This article defines the nine human readiness levels in the scale, explains their meaning, and illustrates their application using a helmet-mounted display example.
This report updates the high-level test plan for evaluating surface deposition on three commercial 32PTH2 spent nuclear fuel (SNF) canisters inside NUTECH Horizontal Modular Storage (NUHOMS) Advanced Horizontal Storage Modules (AHSM) from Orano (formerly Transnuclear Inc.) and provides a description of the surface characterization activities that have been conducted to date. The details contained in this report represent the best designs and approaches explored for testing as of this publication. Given the rapidly developing nature of this test program, some of these plans may change to accommodate new objectives or requirements. The goal of the testing is to collect highly defensible and detailed surface deposition measurements from the surface of dry storage canisters in a marine coastal environment to guide chloride-induced stress corrosion crack (CISCC) research. To facilitate surface sampling, the otherwise highly prototypic dry storage systems will not contain SNF but rather will be electrically heated to mimic the thermal-hydraulic-environment. Instrumentation throughout the canister, storage module, and environment will provide an extensive amount of information for the use of model validation. Manual sampling over a comprehensive portion of the canister surface at regular time intervals will offer a high-fidelity quantification of the conditions experienced in a harsh yet realistic environment.
On May 26, 2021, Sandia National Laboratories (SNL) convened a diverse group of experts spanning private industry, academia, the United States military and federal government, and the national laboratories, and hosted a series of panels to gain their insight on critical emergent research and capability development needs to support national cyber strategy objectives. Two panelists of experts presented their prepared remarks, followed by open discussion from over 250 audience members.
Tai, Chia-Tse; Chiu, Po-Yuan; Liu, Chia-You; Kao, Hsiang-Shun; Harris, Charles T.; Lu, Tzu-Ming L.; Hsieh, Chi-Ti; Chang, Shu-Wei; Li, Jiun-Yun
A demonstration of 2D hole gases in GeSn/Ge heterostructures with a mobility as high as 20 000 cm2 V–1 s–1 is given. Both the Shubnikov–de Haas oscillations and integer quantum Hall effect are observed, indicating high sample quality. The Rashba spin-orbit coupling (SOC) is investigated via magneto-transport. Further, a transition from weak localization to weak anti-localization is observed, which shows the tunability of the SOC strength by gating. The magneto-transport data are fitted to the Hikami–Larkin–Nagaoka formula. The phase-coherence and spin-relaxation times, as well as spin-splitting energy and Rashba coefficient of the k-cubic term, are extracted. Furthermore, the analysis reveals that the effects of strain and confinement potential at a high fraction of Sn suppress the Rashba SOC caused by the GeSn/Ge heterostructures.
Ultrafast all-optical switching using Mie resonant metasurfaces requires both on-demand tunability of the wavefront of the light and ultrafast time response. However, devising a switching mechanism that has a high contrast between its "on"and "off"states without compromising speed is challenging. Here, we report the design of a tunable Mie resonant metasurface that achieves this behavior. Our approach utilizes a diffractive array of semiconductor resonators that support both dipolar and quadrupolar Mie resonances. By balancing the strengths of the dipole and quadrupole resonances, we can suppress radiation into the first diffraction order, thus creating a clearly delineated "off"-state at the operating wavelength. Then, we use optical injection of free- carriers to spectrally shift the multipoles and rebalance the multipole strengths, thereby enabling radiation into the diffraction order - all on an ultrafast timescale. We demonstrate ultrafast off-to-on switching with Ion/Ioff ≈ 5 modulation of the diffracted intensity and ultrafast on-to-off switching with Ion/Ioff ≈ 9 modulation. Both switches exhibit a fast τtr ≈ 2.7 ps relaxation time at 215 μJ cm-2 pump fluence. Further, we show that for higher fluences, the temporal response of the metasurface is governed by thermo-optic effects. This combination of multipole engineering with lattice diffraction opens design pathways for tunable metasurface-based integrated devices.
We report a detailed study of the tunnel barriers within a single-hole GaAs/AlGaAs double quantum dot device (DQD). For quantum information applications as well as fundamental studies, careful tuning and reliable measurements of the barriers are important requirements. In order to tune a DQD device adequately into the single-hole electric dipole spin resonance regime, one has to employ a variety of techniques to cover the extended range of tunnel couplings. In this work, we demonstrate four separate techniques, based upon charge sensing, quantum transport, time-resolved pulsing, and electron dipole spin resonance spectroscopy to determine the couplings as a function of relevant gate voltages and magnetic field. Measurements were performed under conditions of both symmetric and asymmetric tunnel couplings to the leads. Good agreement was observed between different techniques when measured under the same conditions. The results indicate that even in this relatively simple circuit, the requirement to tune multiple gates and the consequences of real potential profiles result in non-intuitive dependencies of the couplings as a function of the plunger gate voltage and the magnetic field.
With evolving landscape of DC power transmission and distribution, a reliable and fast protection against faults is critical, especially for medium- and high-voltage applications. Thus, solid-state circuit breakers (SSCB), consisting of cascaded silicon carbide (SiC) junction field-effect transistors (JFET), utilize the intrinsic normally-ON characteristic along with their low ON-resistance. This approach provides an efficient and robust protection solution from detrimental short-circuit events. However, for applications that require high-voltage blocking capability, a proper number of JFETs need be connected in series to achieve the desired blocking voltage rating. Ensuring equal voltage balancing across the JFETs during the switching transitions as well as the blocking stage is critical and hence, this paper presents a novel passive balancing network for series connected JFETs for DC SSCB applications. The dynamic voltage balancing network to synchronize both the turn ON and OFF intervals is described analytically. Moreover, the static voltage balancing network is implemented to establish equal sharing of the total blocking voltage across the series connection of JFETs. The proposed dynamic and steady-state balancing networks are validated by SPICE simulation and experimental results.
We adapt the robust phase estimation algorithm to the evaluation of energy differences between two eigenstates using a quantum computer. This approach does not require controlled unitaries between auxiliary and system registers or even a single auxiliary qubit. As a proof of concept, we calculate the energies of the ground state and low-lying electronic excitations of a hydrogen molecule in a minimal basis on a cloud quantum computer. The denominative robustness of our approach is then quantified in terms of a high tolerance to coherent errors in the state preparation and measurement. Conceptually, we note that all quantum phase estimation algorithms ultimately evaluate eigenvalue differences.
We have carried out quantum Monte Carlo (QMC) calculations of silicon crystal focusing on the accuracy and systematic biases that affect the electronic structure characteristics. The results show that 64 and 216 atom supercells provide an excellent consistency for extrapolated energies per atom in the thermodynamic limit for ground, excited, and ionized states. We have calculated the ground state cohesion energy with both systematic and statistical errors below ≈0.05 eV. The ground state exhibits a fixed-node error of only 1.3(2)% of the correlation energy, suggesting an unusually high accuracy of the corresponding single-reference trial wave function. We obtain a very good agreement between optical and quasiparticle gaps that affirms the marginal impact of excitonic effects. Our most accurate results for band gaps differ from the experiments by about 0.2 eV. This difference is assigned to a combination of residual finite-size and fixed-node errors. We have estimated the crystal Fermi level referenced to vacuum that enabled us to calculate the edges of valence and conduction bands in agreement with experiments.
The first-principles computation of the surfaces of metals is typically accomplished through slab calculations of finite thickness. The extraction of a convergent surface formation energy from slab calculations is dependent upon defining an appropriate bulk reference energy. I describe a method for an independently computed, slab-consistent bulk reference that leads to convergent surface formation energies from slab calculations that also provides realistic uncertainties for the magnitude of unavoidable nonlinear divergence in the surface formation energy with slab thickness. The accuracy is demonstrated on relaxed, unreconstructed low-index aluminum surfaces with slabs with up to 35 layers.
The stability of low-index platinum surfaces and their electronic properties is investigated with density functional theory, toward the goal of understanding the surface structure and electron emission, and identifying precursors to electrical breakdown, on nonideal platinum surfaces. Propensity for electron emission can be related to a local work function, which, in turn, is intimately dependent on the local surface structure. The (1×N) missing row reconstruction of the Pt(110) surface is systematically examined. The (1×3) missing row reconstruction is found to be the lowest in energy, with the (1×2) and (1×4) slightly less stable. In the limit of large (1×N) with wider (111) nanoterraces, the energy accurately approaches the asymptotic limit of the infinite Pt(111) surface. This suggests a local energetic stability of narrow (111) nanoterraces on free Pt surfaces that could be a common structural feature in the complex surface morphologies, leading to work functions consistent with those on thermally grown Pt substrates.
Ashbaugh, Henry S.; Asthagiri, Dilipkumar; Beck, Thomas L.; Rempe, Susan R.
Lawrence Pratt’s career following completion of his Ph.D. at the University of Illinois Urbana Champaign has taken him from Harvard University, to the University of California, Berkeley, and Los Alamos National Laboratory. Most recently, he joined the faculty of the Department of Chemical and Biomolecular Engineering at Tulane University in 2008. Over his career Lawrence has been a leader in theoretical physical chemistry, making influential contributions to a number of areas including the theory of the hydrophobic effect, the development of transition path sampling, contributions to orbital free density functional theory, and the theory of liquids and solutions.
Blast traumatic brain injury is ubiquitous in modern military conflict with significant morbidity and mortality. Yet the mechanism by which blast overpressure waves cause specific intracranial injury in humans remains unclear. Reviewing of both the clinical experience of neurointensivists and neurosurgeons who treated service members exposed to blast have revealed a pattern of injury to cerebral blood vessels, manifested as subarachnoid hemorrhage, pseudoaneurysm, and early diffuse cerebral edema. Additionally, a seminal neuropathologic case series of victims of blast traumatic brain injury (TBI) showed unique astroglial scarring patterns at the following tissue interfaces: subpial glial plate, perivascular, periventricular, and cerebral gray-white interface. The uniting feature of both the clinical and neuropathologic findings in blast TBI is the co-location of injury to material interfaces, be it solid-fluid or solid-solid interface. This motivates the hypothesis that blast TBI is an injury at the intracranial mechanical interfaces. In order to investigate the intracranial interface dynamics, we performed a novel set of computational simulations using a model human head simplified but containing models of gyri, sulci, cerebrospinal fluid (CSF), ventricles, and vasculature with high spatial resolution of the mechanical interfaces. Simulations were performed within a hybrid Eulerian—Lagrangian simulation suite (CTH coupled via Zapotec to Sierra Mechanics). Because of the large computational meshes, simulations required high performance computing resources. Twenty simulations were performed across multiple exposure scenarios—overpressures of 150, 250, and 500 kPa with 1 ms overpressure durations—for multiple blast exposures (front blast, side blast, and wall blast) across large variations in material model parameters (brain shear properties, skull elastic moduli). All simulations predict fluid cavitation within CSF (where intracerebral vasculature reside) with cavitation occurring deep and diffusely into cerebral sulci. These cavitation events are adjacent to high interface strain rates at the subpial glial plate. Larger overpressure simulations (250 and 500kPa) demonstrated intraventricular cavitation—also associated with adjacent high periventricular strain rates. Additionally, models of embedded intraparenchymal vascular structures—with diameters as small as 0.6 mm—predicted intravascular cavitation with adjacent high perivascular strain rates. The co-location of local maxima of strain rates near several of the regions that appear to be preferentially damaged in blast TBI (vascular structures, subpial glial plate, perivascular regions, and periventricular regions) suggest that intracranial interface dynamics may be important in understanding how blast overpressures leads to intracranial injury.
High-energy-density physics is the field of physics concerned with studying matter at extremely high temperatures and densities. Such conditions produce highly nonlinear plasmas, in which several phenomena that can normally be treated independently of one another become strongly coupled. The study of these plasmas is important for our understanding of astrophysics, nuclear fusion and fundamental physics—however, the nonlinearities and strong couplings present in these extreme physical systems makes them very difficult to understand theoretically or to optimize experimentally. Here we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proved far too nonlinear for human researchers. From a fundamental perspective, our understanding can be improved by the way in which machine learning models can rapidly discover complex interactions in large datasets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to approximately daily), thus moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we suggest proposals for the community in terms of research design, training, best practice and support for synthetic diagnostics and data analysis.
We analyze the dislocation content of grain boundary (GB) phase junctions, i.e., line defects separating two different GB phases coexisting on the same GB plane. While regular GB disconnections have been characterized for a variety of interfaces, GB phase junctions formed by GBs with different structures and different numbers of excess atoms have not been previously studied. We apply a general Burgers circuit analysis to calculate the Burgers vectors b of junctions in two ς5 Cu boundaries previously simulated with molecular dynamics. The Burgers vectors of these junctions cannot be described by the displacement shift complete (DSC) lattice alone. We show that, in general, the normal component of b is not equal to the difference in the GB excess volumes but contains another contribution from the numbers of GB atoms per unit area ΔN∗ required to transform one GB phase into another. In the boundaries studied, the latter component dominates and even changes the sign of b. We derive expressions for the normal and tangential components of b in terms of the DSC lattice vectors and the non-DSC part due to ΔN∗ and additional GB excess properties, including excess volume and shears. These expressions provide a connection between GB phase transformations driven by the GB free energy difference and the motion of GB junctions under applied normal and shear stresses. The proposed analysis quantifies b and therefore makes it possible to calculate the elastic part of the energy of these defects, evaluate their contribution to the nucleation barrier during GB phase transformations, and treat elastic interactions with other defects.
Multivariate time series are used in many science and engineering domains, including health-care, astronomy, and high-performance computing. A recent trend is to use machine learning (ML) to process this complex data and these ML-based frameworks are starting to play a critical role for a variety of applications. However, barriers such as user distrust or difficulty of debugging need to be overcome to enable widespread adoption of such frameworks in production systems. To address this challenge, we propose a novel explainability technique, CoMTE, that provides counterfactual explanations for supervised machine learning frameworks on multivariate time series data. Using various machine learning frameworks and data sets, we compare CoMTE with several state-of-the-art explainability methods and show that we outperform existing methods in comprehensibility and robustness. We also show how CoMTE can be used to debug machine learning frameworks and gain a better understanding of the underlying multivariate time series data.
High-performance computing (HPC) researchers have long envisioned scenarios where application workflows could be improved through the use of programmable processing elements embedded in the network fabric. Recently, vendors have introduced programmable Smart Network Interface Cards (SmartNICs) that enable computations to be offloaded to the edge of the network. There is great interest in both the HPC and high-performance data analytics (HPDA) communities in understanding the roles these devices may play in the data paths of upcoming systems. This paper focuses on characterizing both the networking and computing aspects of NVIDIA’s new BlueField-2 SmartNIC when used in a 100Gb/s Ethernet environment. For the networking evaluation we conducted multiple transfer experiments between processors located at the host, the SmartNIC, and a remote host. These tests illuminate how much effort is required to saturate the network and help estimate the processing headroom available on the SmartNIC during transfers. For the computing evaluation we used the stress-ng benchmark to compare the BlueField-2 to other servers and place realistic bounds on the types of offload operations that are appropriate for the hardware. Our findings from this work indicate that while the BlueField-2 provides a flexible means of processing data at the network’s edge, great care must be taken to not overwhelm the hardware. While the host can easily saturate the network link, the SmartNIC’s embedded processors may not have enough computing resources to sustain more than half the expected bandwidth when using kernel-space packet processing. From a computational perspective, encryption operations, memory operations under contention, and on-card IPC operations on the SmartNIC perform significantly better than the general-purpose servers used for comparisons in our experiments. Therefore, applications that mainly focus on these operations may be good candidates for offloading to the SmartNIC.
Plasmas formed in microscale gaps at DC and plasmas formed at radiofrequency (RF) both deviate in behavior compared to the classical Paschen curve, requiring lower voltage to achieve breakdown due to unique processes and dynamics, such as field emission and controlled rates of electron/ion interactions. Both regimes have been investigated independently, using high precision electrode positioning systems for microscale gaps or large, bulky emitters for RF. However, no comprehensive study of the synergistic phenomenon between the two exists. The behavior in such a combined system has the potential to reach sub-10 V breakdown, which combined with the unique electrical properties of microscale plasmas could enable a new class of RF switches, limiters and tuners.This work describes the design and fabrication of novel on-wafer microplasma devices with gaps as small as 100 nm to be operated at GHz frequencies. We used a dual-sacrificial layer process to create devices with microplasma gaps integrated into RF compatible 50 Ω coplanar waveguide transmission lines, which will allow this coupled behaviour to be studied for the first time. These devices are modelled using conventional RF simulations as well as the Sandia code, EMPIRE, which is capable of modelling the breakdown and formation of plasma in microscale gaps driven by high frequencies. Synchronous evaluation of the modelled electrical and breakdown behaviour is used to define device structures, predict behaviour and corroborate results. We further report preliminary independent testing of the microscale gap and RF behaviour. DC testing shows modified-Paschen curve behaviour for plasma gaps at and below four microns, demonstrating decreased breakdown voltage with reduced gap size. Additionally, preliminary S-parameter measurements of as-prepared and connectorized devices have elucidated RF device behaviour. Together, these results provide baseline data that enables future experiments as well as discussion of projected performance and applications for these unique devices.
Studies of size effects on thermal conductivity typically necessitate the fabrication of a comprehensive film thickness series. In this Letter, we demonstrate how material fabricated in a wedged geometry can enable similar, yet higher-throughput measurements to accelerate experimental analysis. Frequency domain thermoreflectance (FDTR) is used to simultaneously determine the thermal conductivity and thickness of a wedged silicon film for thicknesses between 100 nm and 17 μm by considering these features as fitting parameters in a thermal model. FDTR-deduced thicknesses are compared to values obtained from cross-sectional scanning electron microscopy, and corresponding thermal conductivity measurements are compared against several thickness-dependent analytical models based upon solutions to the Boltzmann transport equation. Our results demonstrate how the insight gained from a series of thin films can be obtained via fabrication of a single sample.
The rapidly increasing use of electronics in high-radiation environments and the continued evolution in transistor architectures and materials demand improved methods to characterize the potential damaging effects of radiation on device performance. Here, electron-beam-induced current is used to map hot-carrier transport in model metal-oxide semiconductor field-effect transistors irradiated with a 300 KeV focused He+ beam as a localized line spanning across the gate and bulk Si. By correlating the damage to the electronic properties and combining these results with simulations, the contribution of spatially localized radiation damage on the device characteristics is obtained. This identified damage, caused by the He+ beam, is attributed to localized interfacial Pb centers and delocalized positive fixed-charges, as surmised from simulations. Comprehension of the long-term interaction and mobility of radiation-induced damage are key for future design of rad-hard devices.
Using the local moment counter charge (LMCC) method to accurately represent the asymptotic electrostatic boundary conditions within density functional theory supercell calculations, we present a comprehensive analysis of the atomic structure and energy levels of point defects in cubic silicon carbide (3C-SiC). Finding that the classical long-range dielectric screening outside the supercell induced by a charged defect is a significant contributor to the total energy. we describe and validate a modified Jost screening model to evaluate this polarization energy. This leads to bulk-converged defect levels in finite size supercells. With the LMCC boundary conditions and a standard Perdew-Burke-Ernzerhof (PBE) exchange correlation functional, the computed defect level spectrum exhibits no band gap problem: the range of defect levels spans ∼2.4eV, an effective defect band gap that agrees with the experimental band gap. Comparing with previous literature, our LMCC-PBE defect results are in consistent agreement with the hybrid-exchange functional results of Oda et al. [J. Chem. Phys. 139, 124707 (2013)JCPSA60021-960610.1063/1.4821937] rather than their PBE results. The difference with their PBE results is attributed to their use of a conventional jellium approximation rather than the more rigorous LMCC approach for handling charged supercell boundary conditions. The difference between standard dft and hybrid functional results for defect levels lies not in a band gap problem but rather in solving a boundary condition problem. The LMCC-PBE entirely mitigates the effect of the band gap problem on defect levels. The more computationally economical PBE enables a systematic exploration of 3C-SiC defects, where, most notably, we find that the silicon vacancy undergoes Jahn-Teller-induced distortions from the previously assumed Td symmetry, and that the divacancy, like the silicon vacancy, exhibits a site-shift bistability in p-type conditions.
Inspired by the parallelism and efficiency of the brain, several candidates for artificial synapse devices have been developed for neuromorphic computing, yet a nonlinear and asymmetric synaptic response curve precludes their use for backpropagation, the foundation of modern supervised learning. Spintronic devices - which benefit from high endurance, low power consumption, low latency, and CMOS compatibility - are a promising technology for memory, and domain-wall magnetic tunnel junction (DW-MTJ) devices have been shown to implement synaptic functions such as long-term potentiation and spike-timing dependent plasticity. In this work, we propose a notched DW-MTJ synapse as a candidate for supervised learning. Using micromagnetic simulations at room temperature, we show that notched synapses ensure the non-volatility of the synaptic weight and allow for highly linear, symmetric, and reproducible weight updates using either spin transfer torque (STT) or spin-orbit torque (SOT) mechanisms of DW propagation. We use lookup tables constructed from micromagnetics simulations to model the training of neural networks built with DW-MTJ synapses on both the MNIST and Fashion-MNIST image classification tasks. Accounting for thermal noise and realistic process variations, the DW-MTJ devices achieve classification accuracy close to ideal floating-point updates using both STT and SOT devices at room temperature and at 400 K. Our work establishes the basis for a magnetic artificial synapse that can eventually lead to hardware neural networks with fully spintronic matrix operations implementing machine learning.
TinMan is the first technology to continuously measure thermal neutron intensity during aircraft flight and to define this environment, an important achievement since changes to semiconductors have led electronic parts to become more sensitive to thermal nuetrons that may lead to disturbances in their operation.
Carrasco, Rigo A.; Morath, Christian P.; Grant, Perry C.; Ariyawansa, Gamini; Reyner, C.J.; Stephenson, Chad A.; Kadlec, Clark N.; Hawkins, Samuel D.; Klem, John F.; Steenbergen, Elizabeth H.; Schaefer, Stephen T.; Johnson, Shane R.; Zollner, S.; Webster, Preston T.
Gallium is incorporated into the strain-balanced In(Ga)As/InAsSb superlattice system to achieve the same mid-wave infrared cutoff tunability as conventional Ga-free InAs/InAsSb type-II superlattices, but with an additional degree of design freedom to enable optimization of absorption and transport properties. Time-resolved photoluminescence measurements of InGaAs/InAsSb superlattice characterization- and doped device structures are reported from 77 to 300 K and compared to InAs/InAsSb. The low-injection photoluminescence decay yields the minority carrier lifetime, which is analyzed with a recombination rate model, enabling the determination of the temperature-dependent Shockley-Read-Hall, radiative, and Auger recombination lifetimes and extraction of defect energy levels and capture cross section defect concentration products. The Shockley-Read-Hall-limited lifetime of undoped InGaAs/InAsSb is marginally reduced from 2.3 to 1.4 μs due to the inclusion of Ga; however, given that Ga improves the vertical hole mobility by a factor of >10×, a diffusion-limited InGaAs/InAsSb superlattice nBn could expect a lower bound of 2.5× improvement in diffusion length with significant impact on photodetector quantum efficiency and radiation hardness. At temperatures below 120 K, the doped device structures are Shockley-Read-Hall limited at 0.5 μs, which shows promise for detector applications.
This project is focused on developing advanced combustion strategies for mixing-controlled compression ignition (MCCI, i.e., diesel-cycle) engines that are synergistic with renewable and/or unconventional fuels in a manner that enhances domestic energy security, economic competitiveness, and environmental quality. During this reporting period, the two focus areas were ducted fuel injection (DFI) and surrogate diesel fuels.
Pulsed laser irradiation is used to investigate the local initiation of rapid, self-propagating formation reactions in Al/Pt multilayers. The single pulse direct laser ignition of these 1.6 μm thick freestanding foils was characterized over 10 decades of pulse duration (10 ms to 150 fs). Finite element, reactive heat transport modeling of the near-threshold conditions has identified three distinct ignition pathways. For milli- to microsecond pulses, ignition occurs following sufficient absorption of laser energy to enable diffusion of Al and Pt between layers such that the heat released from the corresponding exothermic reaction overcomes conductive losses outside the laser-irradiated zone. When pulse duration is decreased into the nanosecond regime, heat is concentrated near the surface such that the Al locally melts, and a portion of the top-most bilayers react initially. The favorable kinetics and additional heat enable ignition. Further reducing pulse duration to hundreds of femtoseconds leads to a third ignition pathway. While much of the energy from these pulses is lost to ablation, the remaining heat beneath the crater can be sufficiently concentrated to drive a transverse self-propagating reaction, wherein the heat released from mixing at each interface occurs under kinetic conditions capable of igniting the subsequent layer.
Multisensor networks deployed at nuclear facilities can be leveraged to collect data used as inputs to machine learning models predicting nuclear safeguard relevant information. This work demonstrates an application of this idea by regressing nuclear reactor power levels, a key indicator for nuclear safeguard verification, at the McClellan Nuclear Research Center using data collected by five Merlyn multisensor platforms with LASSO and LSTM models. This work also demonstrates the use of Leave One Node Out to measure the importance of each multisensor for this regression problem providing insight into model explainability and allowing inferential hypotheses about the nuclear facility to be made. This work can be used as a starting point for future development of methods for regression on reactor power levels at nuclear facilities using multisensor network data.
Following the 1986 disastrous accident at Chernobyl, gaining independence following the fall of the former Soviet Union, two revolutions, and on-going Russian intervention, Ukrainea is seeking to expand its use of commercial nuclear energy and to further reduce its dependence on Russia for energy. Nuclear energy in Ukraine has made a significant contribution toward achieving sustainable development and social goals. Ukraine is very vulnerable to the effects of climate change and nuclear is a cornerstone of the country’s effort to combat the effects. With broad support from the U.S. government, Ukraine is cooperating with the U.S. on fresh nuclear fuel supply, civilian nuclear security, outage optimization, spent fuel storage, and future nuclear technology. And is looking to tap U.S. expertise on plant management, and electric grid operations and expansion. NEIc hosted this virtual event to provide an update on nuclear energy projects and plans in Ukraine. Featured speakers included Yaroslav Demchenkov, Deputy Minister of Energy, Taras Kachka, Deputy Minister – Trade Representative, Petro Kotin, CEO, Energoatom,d and Ann K. Ganzer, Senior Bureau Official, Bureau of International Security and Nonproliferation, U.S. Dept. of State. Maria Korsnick, NEI CEO, moderated the discussion. About forty individuals attended representing the Ukraine and U.S. governments, and U.S. suppliers.
This paper serves as the Interface Control Document (ICD) for the Seascape automated test harness developed at Sandia National Laboratories. The primary purposes of the Seascape system are: (1) provide a place for accruing large, curated, labeled data sets useful for developing and evaluating detection and classification algorithms (including, but not limited to, supervised machine learning applications) (2) provide an automated structure for specifying, running and generating reports on algorithm performance. Seascape uses GitLab, Nexus, Solr, and Banana, open source codes, together with code written in the Python language, to automatically provision and configure computational nodes, queue up jobs to accomplish algorithms test runs against the stored data sets, gather the results and generate reports which are then stored in the Nexus artifact server.
Plastics, with their ubiquitous presence in our daily lives and environment, pose an uncomfortable conundrum. Producers and consumers are aware of the value of these organic ingredients in material flow, yet their persistence and disruption to the ecological milieu desperately stipulate a shift in the status quo. Biodegradable plastics-as the name suggests-has its appeal in ensuring the safe return of carbon to ecosystems by complete assimilation of the degraded product as a food source for soil or aquatic microorganisms. However, despite more than a decade of commercial presence, these plastics are still far from replacing the demand for fossil-fuel-based commodity plastics. We discuss this apparent disconnect herein through a material value chain perspective. We review the current state of commercial biodegradable plastics and contrast it against the desired state of the zero-waste-focused circular economy. To close the gap, we suggest critical research needs concerning the structure and properties of biodegradable plastics, testing standards, application development, and waste management. The ultimate success in displacing conventional plastics with biodegradable alternatives will be predicated on collaboration between all stakeholders across the product value chain.
Reactive gas formation in pores of metal–organic frameworks (MOFs) is a known mechanism of framework destruction; understanding those mechanisms for future durability design is key to next generation adsorbents. Herein, an extensive set of ab initio molecular dynamics (AIMD) simulations are used for the first time to predict competitive adsorption of mixed acid gases (NO2 and H2O) and the in-pore reaction mechanisms for a series of rare earth (RE)-DOBDC MOFs. Spontaneous formation of nitrous acid (HONO) is identified as a result of deprotonation of the MOF organic linker, DOBDC. The unique DOBDC coordination to the metal clusters allows for proton transfer from the linker to the NO2 without the presence of H2O and may be a factor in DOBDC MOF durability. This is a previously unreported mechanisms of HONO formation in MOFs. With the presented methodology, prediction of future gas interactions in new nanoporous materials can be achieved.
This paper describes the verification and validation (V&V) framework developed for the stochastic Particle-in-Cell, Direct Simulation Monte Carlo code Aleph. An ideal framework for V&V from the viewpoint of the authors is described where a physics problem is defined, and relevant physics models and parameters to the defined problem are assessed and captured in a Phenomena Identification and Ranking Table (PIRT). Furthermore, numerous V&V examples guided by the PIRT for a simple gas discharge are shown to demonstrate the V&V process applied to a real-world simulation tool with the overall goal to demonstrably increase the confidence in the results for the simulation tool and its predictive capability. Although many examples are provided here to demonstrate elements of the framework, the primary goal of this work is to introduce this framework and not to provide a fully complete implementation, which would be a much longer document. Comparisons and contrasts are made to more usual approaches to V&V, and techniques new to the low-temperature plasma community are introduced. Specific challenges relating to the sufficiency of available data (e.g., cross sections), the limits of ad hoc validation approaches, the additional difficulty of utilizing a stochastic simulation tool, and the extreme cost of formal validation are discussed.
The use of evidence theory and associated cumulative plausibility functions (CPFs), cumulative belief functions (CBFs), cumulative distribution functions (CDFs), complementary cumulative plausibility functions (CCPFs), complementary cumulative belief functions (CCBFs), and complementary cumulative distribution functions (CCDFs) in the analysis of time and temperature margins associated with loss of assured safety (LOAS) for one weak link (WL)/two strong link (SL) systems is illustrated. Article content includes cumulative and complementary cumulative belief, plausibility, and probability for (i) SL/WL failure time margins defined by (time at which SL failure potentially causes LOAS) – (time at which WL failure potentially prevents LOAS), (ii) SL/WL failure temperature margins defined by (the temperature at which SL failure potentially causes LOAS) – (the temperature at which WL failure potentially prevents LOAS), and (iii) SL/SL failure temperature margins defined by (the temperature at which SL failure potentially causes LOAS) – (the temperature of SL whose failure potentially causes LOAS at the time at which WL failure potentially prevents LOAS).
Liquid organic hydrogen carriers such as alcohols and polyols are a high-capacity means of transporting and reversibly storing hydrogen that demands effective catalysts to drive the (de)hydrogenation reactions under mild conditions. We employed a combined theory/experiment approach to develop MOF-74 catalysts for alcohol dehydrogenation and examine the performance of the open metal sites (OMS), which have properties analogous to the active sites in high-performance single-site catalysts and homogeneous catalysts. Methanol dehydrogenation was used as a model reaction system for assessing the performance of five monometallic M-MOF-74 variants (M = Co, Cu, Mg, Mn, Ni). Co-MOF-74 and Ni-MOF-74 give the highest H2 productivity. However, Ni-MOF-74 is unstable under reaction conditions and forms metallic nickel particles. To improve catalyst activity and stability, bimetallic (NixMg1-x)-MOF-74 catalysts were developed that stabilize the Ni OMS and promote the dehydrogenation reaction. An optimal composition exists at (Ni0.32Mg0.68)-MOF-74 that gives the greatest H2 productivity, up to 203 mL gcat-1 min-1 at 300 °C, and maintains 100% selectivity to CO and H2 between 225-275 °C. The optimized catalyst is also active for the dehydrogenation of other alcohols. DFT calculations reveal that synergistic interactions between the open metal site and the organic linker lead to lower reaction barriers in the MOF catalysts compared to the open metal site alone. This work expands the suite of hydrogen-related reactions catalyzed by MOF-74 which includes recent work on hydroformulation and our earlier reports of aryl-ether hydrogenolysis. Moreover, it highlights the use of bimetallic frameworks as an effective strategy for stabilizing a high density of catalytically active open metal sites. This journal is
This interim report is an update of ongoing experimental and modeling work on bentonite material described in Jové Colón et al. (2019, 2020) from past international collaboration activities. As noted in Jové Colón et al. (2020), work on international repository science activities such as FEBEX-DP and DECOVALEX19 is either no longer continuing by the international partners. Nevertheless, research activities on the collected sample materials and field data are still ongoing. Descriptions of these underground research laboratory (URL) R&D activities are described elsewhere (Birkholzer et al. 2019; Jové Colón et al. 2020) but will be explained here when needed. The current reports recent reactive-transport modeling on the leaching of sedimentary rock.
The use of evidence theory and associated cumulative plausibility functions (CPFs), cumulative belief functions (CBFs), cumulative distribution functions (CDFs), complementary cumulative plausibility functions (CCPFs), complementary cumulative belief functions (CCBFs), and complementary cumulative distribution functions (CCDFs) in the analysis of loss of assured safety (LOAS) for weak link (WL)/strong link (SL) systems is introduced and illustrated. Article content includes cumulative and complementary cumulative belief, plausibility, and probability for (i) time at which LOAS occurs for a one WL/two SL system, (ii) time at which a two-link system fails, (iii) temperature at which a two-link system fails, and (iv) temperature at which LOAS occurs for a one WL/two SL system. The presented results can be generalized to systems with more than one WL and two SLs.
In this paper we analyze the noise in macro-particle methods used in plasma physics and fluid dynamics, leading to approaches for minimizing the total error, focusing on electrostatic models in one dimension. We begin by describing kernel density estimation for continuous values of the spatial variable x, expressing the kernel in a form in which its shape and width are represented separately. The covariance matrix of the noise in the density is computed, first for uniform true density. The bandwidth of the covariance matrix C(x,y) is related to the width of the kernel. A feature that stands out is the presence of constant negative terms in the elements of the covariance matrix both on and off-diagonal. These negative correlations are related to the fact that the total number of particles is fixed at each time step; they also lead to the property ∫ C(x,y)dy = 0. We investigate the effect of these negative correlations on the electric field computed by Gauss's law, finding that the noise in the electric field is related to a process called the Ornstein-Uhlenbeck bridge, leading to a covariance matrix of the electric field with variance significantly reduced relative to that of a Brownian process. For non-constant density, p(x), still with continuous x, we analyze the total error in the density estimation and discuss it in terms of bias-variance optimization (BVO). For some characteristic length l, determined by the density and its second derivative, and kernel width h, having too few particles within h leads to too much variance; for h that is large relative to l, there is too much smoothing of the density. The optimum between these two limits is found by BVO. For kernels of the same width, it is shown that this optimum (minimum) is weakly sensitive to the kernel shape. Next, we repeat the analysis for x discretized on a grid. In this case the charge deposition rule is determined by a particle shape. An important property to be respected in the discrete system is the exact preservation of total charge on the grid; this property is necessary to ensure that the electric field is equal at both ends, consistent with periodic boundary conditions. Here, we find that if the particle shapes satisfy a partition of unity property, the particle charge deposited on the grid is conserved exactly. Further, if the particle shape is expressed as the convolution of a kernel with another kernel that satisfies the partition of unity, then the particle shape obeys the partition of unity. This property holds for kernels of arbitrary width, including widths that are not integer multiples of the grid spacing. Furthermore, we show results relaxing the approximations used to do BVO optimization analytically, by doing numerical computations of the total error as a function of the kernel width, on a grid in x. The comparison between numerical and analytical results shows good agreement over a range of particle shapes. We discuss the practical implications of our results, including the criteria for design and implementation of computationally efficient particle shapes that take advantage of the developed theory.
Alternative candidate precursors to [Hf(BH4)4] for low-temperature chemical vapor deposition of hafnium diboride (HfB2) films were identified using density functional theory simulations of molecules with the composition [Hf(BH4)2L2], where L = -OH, -OMe, -O-t-Bu, -NH2, -N═C═O, -N(Me)2, and -N(CH2)5NH2 (1-piperidin-2-amine referred to as Pip2A). Disassociation energies (ED), potential energy surface (PES) scans, ionization potentials, and electron affinities were all calculated to identify the strength of the Hf-L bond and the potential reactivity of the candidate precursor. Ultimately, the low ED (2.07 eV) of the BH4 ligand removal from the Hf atom in [Hf(BH4)4] was partially attributed to an intermediate state where [Hf(BH4)3(H)] and BH3 is formed. Of the candidate precursors investigated, three exhibited a similar mechanism, but only -Pip2A had a PES scan that indicated binding competitive with [Hf(BH4)4], making it a viable candidate for further study.
Rempe, Susan R.; Vangordon, Monika R.; Prignano, Lindsey A.; Dempski, Robert E.; Rick, Steven W.
Channelrhodopsins (ChR) are light-sensitive cation channels used in optogenetics, a technique that applies light to control cells (e.g., neurons) that have been modified genetically to express those channels. Although mutations are known to affect pore kinetics, little is known about how mutations induce changes at the molecular scale. To address this issue, we first measured channel opening and closing rates of a ChR chimera (C1C2) and selected variants (N297D, N297V, and V125L). Then, we used atomistic simulations to correlate those rates with changes in pore structure, hydration, and chemical interactions among key gating residues of C1C2 in both closed and open states. Overall, the experimental results show that C1C2 and its mutants do not behave like ChR2 or its analogous variants, except V125L, making C1C2 a unique channel. Our atomistic simulations confirmed that opening of the channel and initial hydration of the gating regions between helices I, II, III, and VII of the channel occurs with 1) the presence of 13-cis retinal; 2) deprotonation of a glutamic acid gating residue, E129; and 3) subsequent weakening of the central gate hydrogen bond between the same glutamic acid E129 and asparagine N297 in the central region of the pore. Also, an aspartate (D292) is the unambiguous primary proton acceptor for the retinal Schiff base in the hydrated channel.
In this work, experimental results from a study on the evolution of gas jets ejected through the orifices of a pre-chamber in a heavy-duty optical engine are presented. The work examines conditions without fuel inside the main-chamber, which helps to describe the dynamics of the ejected gas jets without the interference of subsequent combustion in the main-chamber. Experimental diagnostics consist of high-speed visible intensified imaging and low-speed infrared imaging. Additionally a one-dimensional gas jet model is used to characterize the spatial distribution of the ejected flow, including parameters such as tip penetration, which are then validated based on experimental results. Different stages in the ejection of pre-chamber jets are identified, with chemical activity restricted to a maximum distance of 5 to 10 orifice diameters downstream of the orifice as indicated by the recorded visible radiation. Sensitivity of cycle-to-cycle variations in pre-chamber jet development to the air-to-fuel ratio in the pre-chamber observed in the experiments is in most part attributed to the variations in the timing of combustion initiation in the pre-chamber. The influence of the ejection flow on the penetration of the gas jet on a cycle-to-cycle basis is presented using the one-dimensional model. The one-dimensional model also indicates that the local flow exhibits highest sensitivity to operating conditions during the start of ejection until the timing when maximum flow is attained. Differences that exist during the decreasing mass-flow ejection time-period tend to smear out in part due to the transient slowdown of the ejection process.
Two-dimensional (2D) metal-boride-derived nanostructures have been a focus of intense research for the past decade, with an emphasis on new synthetic approaches, as well as on the exploration of possible applications in next-generation advanced materials and devices. Their unusual mechanical, electronic, optical, and chemical properties, arising from low dimensionality, present a new paradigm to the science of metal borides that has traditionally focused on their bulk properties. This Perspective discusses the current state of research on metal-boride-derived 2D nanostructures, highlights challenges that must be overcome, and identifies future opportunities to fully utilize their potential.
Interest in wave energy converters to provide autonomous power to various ocean-bound systems, such as autonomous underwater vehicles, sensor systems, and even aquaculture farms, has grown in recent years. The Monterey Bay Aquarium Research Institute has developed and deployed a small two-body point absorber wave energy device suitable to such needs. This paper provides a description of the system to support future open-source access to the device and further the general development of similar wave energy systems. Additionally, to support future control design and system modification efforts, a set of hydrodynamic models are presented and cross-compared. To test the viability of using a linear frequency-domain admittance model for controller tuning, the linear model is compared against four WEC-Sim models of increasing complexity. The linear frequency-domain model is found to be generally adequate for capturing system dynamics, as the model agreement is good and the degree of nonlinearity introduced in the WEC-Sim models is generally less than 2.5%.
We present the development of a pulsed power experimental technique to infer the electrical conductivity of metals from ambient to high energy density conditions. The method is implemented on Thor, a moderate scale (1-2 MA) pulsed power driver. The electrical conductivity of copper at elevated temperature (>4000 K) and pressure (>10 GPa) is determined, and a new tabular material model is developed, guided by density functional theory, which preserves agreement with existing experimental data. Minor modifications (<10%) are found to be necessary to the previous Lee-More-Desjarlais model isotherms in the vicinity of the melt transition in order to account for observed discrepancies with the new experimental data. An analytical model for magnetic direct drive flyer acceleration and Joule heating induced vaporization based on the Tsiolkovsky "rocket equation"is presented to assess sensitivity of the method to minor changes in electrical conductivity.
In this work, we investigate the critical coupling of a single gold disk antenna with a focused beam by evaluating its absorption and scattering using spectral interferometry microcopy.
This work describes the diagnostic implementation and image processing methods to quantitatively measure diesel spray mixing injected into a high-pressure, high-temperature environment. We used a high-repetition-rate pulse-burst laser developed in-house, a high-speed CMOS camera, and optimized the optical configuration to capture Rayleigh scattering images of the vaporized fuel jets inside a constant volume chamber. The experimental installation was modified to reduce reflections and flare levels to maximize the images’ signal-to-noise ratios by anti-reflection coatings on windows and surfaces, as well as series of optical baffles. Because of the specificities of the high-speed system, several image processing techniques had to be developed and implemented to provide quantitative fuel concentration measurements. These methods involve various correction procedures such as camera linearity, laser intensity fluctuation, dynamic background flare, as well as beam-steering effects. Image inpainting was also applied to correct the Rayleigh scattering signal from large scatterers (e.g. particulates). The experiments demonstrate that applying planar laser Rayleigh scattering at high repetition rate to quantitatively resolve the mixing of fuel and ambient gases in diesel jets is challenging, but possible. The thorough analysis of the experimental uncertainty and comparisons to past data prove that such measurements can be accurate, whilst providing valuable information about the mixing processes of high-pressure diesel jets.
This writing examines the DevOps practice from a new perspective, one of understanding its philosophical and scientific nature. DevOps has fundamentally changed the landscape for research and development based on guiding philosophical and scientific principles. Advanced computational technologies and domains have adopted DevOps to enable advanced solution engineering for efficient, quality-assured output. The author provides a concise account of how the philosophy and science of DevOps synergistically defines its essential disposition.
Sparsity, which occurs in both scientific applications and Deep Learning (DL) models, has been a key target of optimization within recent ASIC accelerators due to the potential memory and compute savings. These applications use data stored in a variety of compression formats. We demonstrate that both the compactness of different compression formats and the compute efficiency of the algorithms enabled by them vary across tensor dimensions and amount of sparsity. Since DL and scientific workloads span across all sparsity regions, there can be numerous format combinations for optimizing memory and compute efficiency. Unfortunately, many proposed accelerators operate on one or two fixed format combinations. This work proposes hardware extensions to accelerators for supporting numerous format combinations seamlessly and demonstrates ∼ 4 × speedup over performing format conversions in software.
Among Professor Arthur Gossard's many contributions to crystal growth are those resulting in important improvements in the quality and performance of quantum-well and quantum-dot semiconductor lasers. In celebration of his 85th birthday, we review the development of a semiconductor laser theory that is motivated and guided, in part, by those advances. This theory combines condensed matter theory and laser physics to provide understanding at a microscopic level, i.e., in terms of electrons and holes, and their interaction with the radiation field while influenced by the lattice.
We present a numerical framework for recovering unknown nonautonomous dynamical systems with time-dependent inputs. To circumvent the difficulty presented by the nonautonomous nature of the system, our method transforms the solution state into piecewise integration of the system over a discrete set of time instances. The time-dependent inputs are then locally parameterized by using a proper model, for example, polynomial regression, in the pieces determined by the time instances. This transforms the original system into a piecewise parametric system that is locally time invariant. We then design a deep neural network structure to learn the local models. Once the network model is constructed, it can be iteratively used over time to conduct global system prediction. We provide theoretical analysis of our algorithm and present a number of numerical examples to demonstrate the effectiveness of the method.
Ajantiwalay, Tanvi; Nagel, Lauren; Maloy, Stuart; Hattar, Khalid M.; Mecholsky, John J.; Aitkaliyeva, Assel
Ferritic/martensitic steels, such as HT-9, are known for their complex microstructural features and mechanical properties. In this paper, in-situ micro-tensile tests and traditional fractography methods were utilized to study the fracture behavior of proton-irradiated HT-9 steels. First, to evaluate the viability of micro-tensile tests for nuclear material qualification process, meso‑tensile tests on as-received HT-9 steels were performed. Fracture mechanisms of unirradiated HT-9 steels at both length scales were compared and underlying mechanisms discussed. The direct comparison of micro- and meso‑scale data shows a distinctive size effect demonstrated by the increase in yield stress (YS). Upon completion of initial assessment, specimens were irradiated with 4 MeV+ protons to three fluences, all of which were lower than 0.01 displacements per atom (dpa). As expected, the YS increases with irradiation. However, at 7 × 10−3 dpa, the reversal of the trend was observed, and the YS exhibited sharp decline. We demonstrate that at lower length scales, grain structure has a more profound impact on the mechanical properties of irradiated materials, which provides information needed to fill in the gap in current understanding of the HT-9 fracture at different length scales.
Metamaterials, otherwise known as architected or programmable materials, enable designers to tailor mesoscale topology and shape to achieve unique material properties that are not present in nature. Additionally, with the recent proliferation of additive manufacturing tools across industrial sectors, the ability to readily fabricate geometrically complex metamaterials is now possible. However, in many high-performance applications involving complex multi-physics interactions, design of novel lattice metamaterials is still difficult. Design is primarily guided by human intuition or gradient optimization for simple problems. In this work, we show how machine learning guides discovery of new unit cells that are Pareto optimal for multiple competing objectives; specifically, maximizing elastic stiffness during static loading and minimizing wave speed through the metamaterial during an impact event. Additionally, we show that our artificial intelligence approach works with relatively few (3500) simulation calls.
This report will summarize the group's work to provide recommendations to secure nuclear facilities before, during and after an extreme weather event. Extreme weather events can have drastic impacts to nuclear facilities as seen by the earthquake and subsequent tsunami at the Fukushima Daiichi Nuclear Power Plant in 2011. Recent hurricanes in the United States including Hurricane Harvey demonstrate the devastating effects these storms can have on infrastructure and the surrounding communities. The group is attempting to identify the gaps that potential small modular reactor (SMR) facilities will need to address in order to provide adequate site security before, during and after extreme weather events. This effort proceeded in three parts to provide insights and recommendations to secure Small Modular Reactor facilities for extreme weather events:(1) a literature review of academic articles as well as relevant documents including the existing regulatory framework and recommendations from the IAEA, NRC, and DOE, (2) subject matter expert interviews from a wide variety of security backgrounds, and (3) modeling and simulation on a hypothetical SMR facility. Special attention was paid to the interactions between stakeholders and nuclear facility design considerations, particularly the topics of safety and security. Engineering design issues from safety and security perspectives were discussed and included in simulation. Each step informed the proceeding, with the result including full tabletop scenarios of EWE impacts to security system effectiveness on the hypothetical model. This systems-level analysis provides results to inform recommendations to secure SMR facilities.
Despite hitting major roadblocks in 2-D scaling, NAND flash continues to scale in the vertical direction and dominate the commercial nonvolatile memory market. However, several emerging nonvolatile technologies are under development by major commercial foundries or are already in small volume production, motivated by storage-class memory and embedded application drivers. These include spin-transfer torque magnetic random access memory (STT-MRAM), resistive random access memory (ReRAM), phase change random access memory (PCRAM), and conductive bridge random access memory (CBRAM). Emerging memories have improved resilience to radiation effects compared to flash, which is based on storing charge, and hence may offer an expanded selection from which radiation-tolerant system designers can choose from in the future. This review discusses the material and device physics, fabrication, operational principles, and commercial status of scaled 2-D flash, 3-D flash, and emerging memory technologies. Radiation effects relevant to each of these memories are described, including the physics of and errors caused by total ionizing dose, displacement damage, and single-event effects, with an eye toward the future role of emerging technologies in radiation environments.