Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Hydrogen Risk Assessment Models (HyRAM) is a software toolkit that provides a basis for quantitative risk assessment and consequence modeling for hydrogen infrastructure and transportation systems. HyRAM integrates validated, analytical models of hydrogen behavior, statistics, and a standardized QRA approach to generate useful, repeatable data for the safety analysis of various hydrogen systems. HyRAM is a software developed by Sandia National Laboratories for the U.S. Department of Energy. This document demonstrates how to use HyRAM to recreate a hydrogen system and obtain relevant data regarding potential risk. Specific examples are utilized throughout this document, providing detailed tutorials of HyRAM features with respect to hydrogen system safety analysis and risk assessment.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

When making computational simulation predictions of multi-physics engineering systems, sources of uncertainty in the prediction need to be acknowledged and included in the analysis within the current paradigm of striving for simulation credibility. A thermal analysis of an aerospace geometry was performed at Sandia National Laboratories. For this analysis a verification, validation and uncertainty quantification workflow provided structure for the analysis, resulting in the quantification of significant uncertainty sources including spatial numerical error and material property parametric uncertainty. It was hypothesized that the parametric uncertainty and numerical errors were independent and separable for this application. This hypothesis was supported by performing uncertainty quantification simulations at multiple mesh resolutions, while being limited by resources to minimize the number of medium and high resolution simulations. Based on this supported hypothesis, a prediction including parametric uncertainty and a systematic mesh bias are used to make a margin assessment that avoids unnecessary uncertainty obscuring the results and optimizes computing resources.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Polynomial chaos methods have been extensively used to analyze systems in uncertainty quantification. Furthermore, several approaches exist to determine a low-dimensional approximation (or sparse approximation) for some quantity of interest in a model, where just a few orthogonal basis polynomials are required. In this work, we consider linear dynamical systems consisting of ordinary differential equations with random variables. The aim of this paper is to explore methods for producing low-dimensional approximations of the quantity of interest further. We investigate two numerical techniques to compute a low-dimensional representation, which both fit the approximation to a set of samples in the time domain. On the one hand, a frequency domain analysis of a stochastic Galerkin system yields the selection of the basis polynomials. It follows a linear least squares problem. On the other hand, a sparse minimization yields the choice of the basis polynomials by information from the time domain only. An orthogonal matching pursuit produces an approximate solution of the minimization problem. Finally, we compare the two approaches using a test example from a mechanical application.

Abstract not provided.

Several programs at Sandia National Laboratories have adopted energy spectra as a metric to relate the severity of mechanical insults to structural capacity. The purpose being to gain insight into the system's capability, reliability, and to quantify the ultimate margin between the normal operating envelope and the likely system failure point -- a system margin assessment. The fundamental concern with the use of energy metrics was that the applicability domain and implementation details were not completely defined for many problems of interest. The goal of this WSEAT project was to examine that domain of applicability and work out the necessary implementation details. The goal of this project was to provide experimental validation for the energy spectra based methods in the context of margin assessment as they relate to shock environments. The extensive test results concluded that failure predictions using energy methods did not agree with failure predictions using S-N data. As a result, a modification to the energy methods was developed following the form of Basquin's equation to incorporate the power law exponent for fatigue damage. This update to the energy-based framework brings the energy based metrics into agreement with experimental data and historical S-N data.

In thist study, we develop a procedure to utilize error estimates for samples of a surrogate model to compute robust upper and lower bounds on estimates of probabilities of events. We show that these error estimates can also be used in an adaptive algorithm to simultaneously reduce the computational cost and increase the accuracy in estimating probabilities of events using computationally expensive high-fidelity models. Specifically, we introduce the notion of reliability of a sample of a surrogate model, and we prove that utilizing the surrogate model for the reliable samples and the high-fidelity model for the unreliable samples gives precisely the same estimate of the probability of the output event as would be obtained by evaluation of the original model for each sample. The adaptive algorithm uses the additional evaluations of the high-fidelity model for the unreliable samples to locally improve the surrogate model near the limit state, which significantly reduces the number of high-fidelity model evaluations as the limit state is resolved. Numerical results based on a recently developed adjoint-based approach for estimating the error in samples of a surrogate are provided to demonstrate (1) the robustness of the bounds on the probability of an event, and (2) that the adaptive enhancement algorithm provides a more accurate estimate of the probability of the QoI event than standard response surface approximation methods at a lower computational cost.

Using molecular dynamics simulation, we studied the density fluctuations and cavity formation probabilities in aqueous solutions and their effect on the hydration of CO2. With increasing salt concentration, we report an increased probability of observing a larger than the average number of species in the probe volume. Our energetic analyses indicate that the van der Waals and electrostatic interactions between CO2 and aqueous solutions become more favorable with increasing salt concentration, favoring the solubility of CO2 (salting in). However, due to the decreasing number of cavities forming when salt concentration is increased, the solubility of CO2 decreases. The formation of cavities was found to be the primary control on the dissolution of gas, and is responsible for the observed CO2 salting-out effect. Our results provide the fundamental understanding of the density fluctuation in aqueous solutions and the molecular origin of the salting-out effect for real gas.

We present measurements of Josephson junctions containing three magnetic layers with noncollinear magnetizations. The junctions are of the form S/F′/N/F/N/F″/S, where S is superconducting Nb, F′ is either a thin Ni or Permalloy layer with in-plane magnetization, N is the normal metal Cu, F is a synthetic antiferromagnet with magnetization perpendicular to the plane, composed of Pd/Co multilayers on either side of a thin Ru spacer, and F″ is a thin Ni layer with in-plane magnetization. The supercurrent in these junctions decays more slowly as a function of the F-layer thickness than for similar spin-singlet junctions not containing the F′ and F″ layers. The slower decay is the prime signature that the supercurrent in the central part of these junctions is carried by spin-triplet pairs. The junctions containing F′= Permalloy are suitable for future experiments where either the amplitude of the critical current or the ground-state phase difference across the junction is controlled by changing the relative orientations of the magnetizations of the F′ and F″ layers.

High-fidelity detection of iodine species is of utmost importance to the safety of the population in cases of nuclear accidents or advanced nuclear fuel reprocessing. Herein, we describe the success at using impedance spectroscopy to directly detect the real-time adsorption of I2 by a metal-organic framework zeolitic imidazolate framework (ZIF)-8-based sensor. Methanolic suspensions of ZIF-8 were dropcast onto platinum interdigitated electrodes, dried, and exposed to gaseous I2 at 25, 40, or 70 °C. Using an unoptimized sensor geometry, I2 was readily detected at 25 °C in air within 720 s of exposure. The specific response is attributed to the chemical selectivity of the ZIF-8 toward I2. Furthermore, equivalent circuit modeling of the impedance data indicates a >105× decrease in ZIF-8 resistance when 116 wt % I2 is adsorbed by ZIF-8 at 70 °C in air. This irreversible decrease in resistance is accompanied by an irreversible loss in the long-range crystallinity, as evidenced by X-ray diffraction and infrared spectroscopy. Air, argon, methanol, and water were found to produce minimal changes in ZIF-8 impedance. This report demonstrates how selective I2 adsorption by ZIF-8 can be leveraged to create a highly selective sensor using >105× changes in impedance response to enable the direct electrical detection of environmentally relevant gaseous toxins.

Containerization, or OS-level virtualization has taken root within the computing industry. However, container utilization and its impact on performance and functionality within High Performance Computing (HPC) is still relatively undefined. This paper investigates the use of containers with advanced supercomputing and HPC system software. With this, we define a model for parallel MPI application DevOps and deployment using containers to enhance development effort and provide container portability from laptop to clouds or supercomputers. In this endeavor, we extend the use of Sin- gularity containers to a Cray XC-series supercomputer. We use the HPCG and IMB benchmarks to investigate potential points of overhead and scalability with containers on a Cray XC30 testbed system. Furthermore, we also deploy the same containers with Docker on Amazon's Elastic Compute Cloud (EC2), and compare against our Cray supercomputer testbed. Our results indicate that Singularity containers operate at native performance when dynamically linking Cray's MPI libraries on a Cray supercomputer testbed, and that while Amazon EC2 may be useful for initial DevOps and testing, scaling HPC applications better fits supercomputing resources like a Cray.

A monumental shift from conventional lighting technologies (incandescent, fluorescent, high intensity discharge) to LED lighting is currently transpiring. The primary driver for this shift has been energy and associated cost savings. LED lighting is now more efficacious than any of the conventional lighting technologies with room to still improve. Near term, phosphor converted LED packages have the potential for efficacy improvement from 160 lm/W to 255 lm/W. Longer term, color-mixed LED packages have the potential for efficacy levels conceivably as high as 330 lm/W, though reaching these performance levels requires breakthroughs in green and amber LED efficiency. LED package efficacy sets the upper limit to luminaire efficacy, with the luminaire containing its own efficacy loss channels. In this paper, based on analyses performed through the U.S. Department of Energy Solid State Lighting Program, various LED and luminaire loss channels are elucidated, and critical areas for improvement identified. Beyond massive energy savings, LED technology enables a host of new applications and added value not possible or economical with previous lighting technologies. These include connected lighting, lighting tailored for human physiological responses, horticultural lighting, and ecologically conscious lighting. Finally, none of these new applications would be viable if not for the high efficacies that have been achieved, and are themselves just the beginning of what LED lighting can do.

A wave energy converter must be designed to survive and function efficiently, often in highly energetic ocean environments. This represents a challenging engineering problem, comprising systematic failure mode analysis, environmental characterization, modeling, experimental testing, fatigue and extreme response analysis. While, when compared with other ocean systems such as ships and offshore platforms, there is relatively little experience in wave energy converter design, a great deal of recent work has been done within these various areas. Here, this article summarizes the general stages and workflow for wave energy converter design, relying on supporting articles to provide insight. By surveying published work on wave energy converter survival and design response analyses, this paper seeks to provide the reader with an understanding of the different components of this process and the range of methodologies that can be brought to bear. In this way, the reader is provided with a large set of tools to perform design response analyses on wave energy converters.

Inherent advantages of wide bandgap materials make GaN-based devices attractive for power electronics and applications in radiation environments. Recent advances in the availability of wafer-scale, bulk GaN substrates have enabled the production of high quality, low defect density GaN devices, but fundamental studies of carrier transport and radiation hardness in such devices are lacking. Here, we report measurements of the hole diffusion length in low threading dislocation density (TDD), homoepitaxial n-GaN, and high TDD heteroepitaxial n-GaN Schottky diodes before and after irradiation with 2.5 MeV protons at fluences of 4-6 × 1013 protons/cm2. We also characterize the specimens before and after irradiation using electron beam-induced-current (EBIC) imaging, cathodoluminescence, deep level optical spectroscopy (DLOS), steady-state photocapacitance, and lighted capacitance-voltage (LCV) techniques. We observe a substantial reduction in the hole diffusion length following irradiation (50%-55%) and the introduction of electrically active defects which could be attributed to gallium vacancies and associated complexes (VGa-related), carbon impurities (C-related), and gallium interstitials (Gai). EBIC imaging suggests long-range migration and clustering of radiation-induced point defects over distances of ∼500 nm, which suggests mobile Gai. Following irradiation, DLOS and LCV reveal the introduction of a prominent optical energy level at 1.9 eV below the conduction band edge, consistent with the introduction of Gai.

Wind applications require the ability to simulate rotating blades. To support this use-case, a novel design-order sliding mesh algorithm has been developed and deployed. The hybrid method combines the control volume finite element methodology (CVFEM) with concepts found within a discontinuous Galerkin (DG) finite element method (FEM) to manage a sliding mesh. The method has been demonstrated to be design-order for the tested polynomial basis (P=1 and P=2) and has been deployed to provide production simulation capability for a Vestas V27 (225 kW) wind turbine. Other stationary and canonical rotating flow simulations are also presented. As the majority of wind-energy applications are driving extensive usage of hybrid meshes, a foundational study that outlines near-wall numerical behavior for a variety of element topologies is presented. Results indicate that the proposed nonlinear stabilization operator (NSO) is an effective stabilization methodology to control Gibbs phenomena at large cell Peclet numbers. The study also provides practical mesh resolution guidelines for future analysis efforts. Application-driven performance and algorithmic improvements have been carried out to increase robustness of the scheme on hybrid production wind energy meshes. Specifically, the Kokkos-based Nalu Kernel construct outlined in the FY17/Q4 ExaWind milestone has been transitioned to the hybrid mesh regime. This code base is exercised within a full V27 production run. Simulation timings for parallel search and custom ghosting are presented. As the low-Mach application space requires implicit matrix solves, the cost of matrix reinitialization has been evaluated on a variety of production meshes. Results indicate that at low element counts, i.e., fewer than 100 million elements, matrix graph initialization and preconditioner setup times are small. However, as mesh sizes increase, e.g., 500 million elements, simulation time associated with "setup-up" costs can increase to nearly 50% of overall simulation time when using the full Tpetra solver stack and nearly 35% when using a mixed Tpetra- Hypre-based solver stack. The report also highlights the project achievement of surpassing the 1 billion element mesh scale for a production V27 hybrid mesh. A detailed timing breakdown is presented that again suggests work to be done in the setup events associated with the linear system. In order to mitigate these initialization costs, several application paths have been explored, all of which are designed to reduce the frequency of matrix reinitialization. Methods such as removing Jacobian entries on the dynamic matrix columns (in concert with increased inner equation iterations), and lagging of Jacobian entries have reduced setup times at the cost of numerical stability. Artificially increasing, or bloating, the matrix stencil to ensure that full Jacobians are included is developed with results suggesting that this methodology is useful in decreasing reinitialization events without loss of matrix contributions. With the above foundational advances in computational capability, the project is well positioned to begin scientific inquiry on a variety of wind-farm physics such as turbine/turbine wake interactions.

The defense community desires low-power sensors deployed around critical assets for intrusion detection. A piezoelectric microelectromechanical accelerometer is coupled with a complementary metal-oxide-semiconductor comparator to create a near-zero power wakeup system. The accelerometer is designed to operate at resonance and employs aluminum nitride for piezoelectric transduction. At a target frequency of 160 Hz, the accelerometer achieves sensitivities as large as 26 V/g. The system is shown to require only 5.4 nW of power before and after latching. The combined system is shown to wake up to a target frequency signature of a generator while rejecting background noise as well as non-target frequency signatures.

Optimizing thermal generation commitments and dispatch in the presence of high penetrations of renewable resources such as solar energy requires a characterization of their stochastic properties. In this study, we describe novel methods designed to create day-ahead, wide-area probabilistic solar power scenarios based only on historical forecasts and associated observations of solar power production. Each scenario represents a possible trajectory for solar power in next-day operations with an associated probability computed by algorithms that use historical forecast errors. Scenarios are created by segmentation of historic data, fitting non-parametric error distributions using epi-splines, and then computing specific quantiles from these distributions. Additionally, we address the challenge of establishing an upper bound on solar power output. Our specific application driver is for use in stochastic variants of core power systems operations optimization problems, e.g., unit commitment and economic dispatch. These problems require as input a range of possible future realizations of renewables production. However, the utility of such probabilistic scenarios extends to other contexts, e.g., operator and trader situational awareness. Finally, we compare the performance of our approach to a recently proposed method based on quantile regression, and demonstrate that our method performs comparably to this approach in terms of two widely used methods for assessing the quality of probabilistic scenarios: the Energy score and the Variogram score.

We report real-time observations of a phase transition in the ionic solid CaF2, a model AB2 structure in high-pressure physics. Synchrotron x-ray diffraction coupled with dynamic loading to 27.7 GPa, and separately with static compression, follows, in situ, the fluorite to cotunnite structural phase transition, both on nanosecond and on minute time scales. Using Rietveld refinement techniques, we examine the kinetics and hysteresis of the transition. Our results give insight into the kinetic time scale of the fluorite-cotunnite phase transition under shock compression, which is relevant to a number of isomorphic compounds.

A Stillinger-Weber potential is computationally very efficient for molecular dynamics simulations. Despite its simple mathematical form, the Stillinger-Weber potential can be easily parameterized to ensure that crystal structures with tetrahedral bond angles (e.g., diamond-cubic, zinc-blende, and wurtzite) are stable and have the lowest energy. As a result, the Stillinger-Weber potential has been widely used to study a variety of semiconductor elements and alloys. When studying an A-B binary system, however, the Stillinger-Weber potential is associated with two major drawbacks. First, it significantly overestimates the elastic constants of elements A and B, limiting its use for systems involving both compounds and elements (e.g., an A/AB multilayer). Second, it prescribes equal energy for zinc-blende and wurtzite crystals, limiting its use for compounds with large stacking fault energies. Here in this paper, we utilize the polymorphic potential style recently implemented in LAMMPS to develop a modified Stillinger-Weber potential for InGaN that overcomes these two problems.

Attacks authored by state sponsored actors, criminal outfits, ideological enclaves and recreational hackers continue to trouble public and private cyber systems. In order to create and/or maintain an advantage over their adversaries, cyber defenders must pursue novel ways to detect, attribute and respond to offensive operations. Linkography is a topic that has been explored for decades that has found recent application to cyber security. Given the huge amounts of data available for cyber security applications of linkography, we favor semi-automated techniques to exploit this concept. In this paper, we propose a human supervised algorithm that will refine the abstractions used for this bulk approach to linkography. We found this algorithm resulted in automatically generated linkographs with higher accuracies than those derived from static abstractions. These findings suggest that linkography in general and abstraction refinement in particular are viable tools for cyber security practitioners.

We describe pyomo.dae, an open source Python-based modeling framework that enables high-level abstract specification of optimization problems with differential and algebraic equations. The pyomo.dae framework is integrated with the Pyomo open source algebraic modeling language, and is available at http://www.pyomo.org. One key feature of pyomo.dae is that it does not restrict users to standard, predefined forms of differential equations, providing a high degree of modeling flexibility and the ability to express constraints that cannot be easily specified in other modeling frameworks. Other key features of pyomo.dae are the ability to specify optimization problems with high-order differential equations and partial differential equations, defined on restricted domain types, and the ability to automatically transform high-level abstract models into finite-dimensional algebraic problems that can be solved with off-the-shelf solvers. Moreover, pyomo.dae users can leverage existing capabilities of Pyomo to embed differential equation models within stochastic and integer programming models and mathematical programs with equilibrium constraint formulations. Collectively, these features enable the exploration of new modeling concepts, discretization schemes, and the benchmarking of state-of-the-art optimization solvers.

In numerous applications, scientists and engineers acquire varied forms of data that partially characterize the inputs to an underlying physical system. This data is then used to inform decisions such as controls and designs. Consequently, it is critical that the resulting control or design is robust to the inherent uncertainties associated with the unknown probabilistic characterization of the model inputs. Here in this work, we consider optimal control and design problems constrained by partial differential equations with uncertain inputs. We do not assume a known probabilistic model for the inputs, but rather we formulate the problem as a distributionally robust optimization problem where the outer minimization problem determines the control or design, while the inner maximization problem determines the worst-case probability measure that matches desired characteristics of the data. We analyze the inner maximization problem in the space of measures and introduce a novel measure approximation technique, based on the approximation of continuous functions, to discretize the unknown probability measure. Finally, we prove consistency of our approximated min-max problem and conclude with numerical results.

We derive an intrinsically temperature-dependent approximation to the correlation grand potential for many-electron systems in thermodynamical equilibrium in the context of finite-temperature reduced-density-matrix-functional theory (FT-RDMFT). We demonstrate its accuracy by calculating the magnetic phase diagram of the homogeneous electron gas. We compare it to known limits from highly accurate quantum Monte Carlo calculations as well as to phase diagrams obtained within existing exchange-correlation approximations from density functional theory and zero-temperature RDMFT.

Demand for pragmatic alternatives to carbon-intensive fossil fuels is growing more strident. Hydrogen represents an ideal zero-carbon clean energy carrier with high energy density. For hydrogen fuel to compete with alternatives, safe and high capacity storage materials that are readily cycled are imperative. Here, development of such a material, comprised of nickel-doped Mg nanocrystals encapsulated by molecular-sieving reduced graphene oxide (rGO) layers, is reported. While most work on advanced hydrogen storage composites to date endeavor to explore either nanosizing or addition of carbon materials as secondary additives individually, methods to enable both are pioneered: “dual-channel” doping combines the benefits of two different modalities of enhancement. Specifically, both external (rGO strain) and internal (Ni doping) mechanisms are used to efficiently promote both hydriding and dehydriding processes of Mg nanocrystals, simultaneously achieving high hydrogen storage capacity (6.5 wt% in the total composite) and excellent kinetics while maintaining robustness. Furthermore, hydrogen uptake is remarkably accomplished at room temperature and also under 1 bar—as observed during in situ measurements—which is a substantial advance for a reversible metal hydride material. The realization of three complementary functional components in one material breaks new ground in metal hydrides and makes solid-state materials viable candidates for hydrogen-fueled applications.

Ordering nanoparticles into a desired super-structure is often crucial for their technological applications. We use molecular dynamics simulations to study the assembly of nanoparticles in a polymer brush randomly grafted to a planar surface as the solvent evaporates. Initially, the nanoparticles are dispersed in a solvent that wets the polymer brush. After the solvent evaporates, the nanoparticles are either inside the brush or adsorbed at the surface of the brush, depending on the strength of the nanoparticle-polymer interaction. For strong nanoparticle-polymer interactions, a 2-dimensional ordered array is only formed when the brush density is finely tuned to accommodate a single layer of nanoparticles. When the brush density is higher or lower than this optimal value, the distribution of nanoparticles shows large fluctuations in space and the packing order diminishes. For weak nanoparticle-polymer interactions, the nanoparticles order into a hexagonal array on top of the polymer brush as long as the grafting density is high enough to yield a dense brush. An interesting healing effect is observed for a low-grafting-density polymer brush that can become more uniform in the presence of weakly adsorbed nanoparticles.

We outline ideas on desired properties for a new generation of effective core potentials (ECPs) that will allow valence-only calculations to reach the full potential offered by recent advances in many-body wave function methods. The key improvements include consistent use of correlated methods throughout ECP constructions and improved transferability as required for an accurate description of molecular systems over a range of geometries. The guiding principle is the isospectrality of all-electron and ECP Hamiltonians for a subset of valence states. We illustrate these concepts on a few first- and second-row atoms (B, C, N, O, S), and we obtain higher accuracy in transferability than previous constructions while using semi-local ECPs with a small number of parameters. In addition, the constructed ECPs enable many-body calculations of valence properties with higher (or same) accuracy than their all-electron counterparts with uncorrelated cores. This implies that the ECPs include also some of the impacts of core-core and core-valence correlations on valence properties. The results open further prospects for ECP improvements and refinements.

We present DAGSENS, a new approach to parametric transient sensitivity analysis of Differential Algebraic Equation systems (DAEs), such as SPICE-level circuits. The key ideas behind DAGSENS are, (1) to represent the entire sequence of computations from DAE parameters to the objective function (whose sensitivity is needed) as a Directed Acyclic Graph (DAG) called the 'sensitivity DAG', and (2) to compute the required sensitivites efficiently by using dynamic programming techniques to traverse the DAG. DAGSENS is simple, elegant, and easy-to-understand compared to previous approaches; for example, in DAGSENS, one can switch between direct and adjoint sensitivities simply by reversing the direction of DAG traversal. Also, DAGSENS is more powerful than previous approaches because it works for a more general class of objective functions, including those based on 'events' that occur during a transient simulation (e.g., a node voltage crossing a threshold, a phase-locked loop (PLL) achieving lock, a circuit signal reaching its maximum/minimum value, etc.). In this paper, we demonstrate DAGSENS on several electronic and biological applications, including high-speed communication, statistical cell library characterization, and gene expression.

Bilayer van der Waals (vdW) heterostructures such as MoS2/WS2 and MoSe2/WSe2 have attracted much attention recently, particularly because of their type II band alignments and the formation of interlayer exciton as the lowest-energy excitonic state. In this work, we calculate the electronic and optical properties of such heterostructures with the first-principles GW+Bethe-Salpeter Equation (BSE) method and reveal the important role of interlayer coupling in deciding the excited-state properties, including the band alignment and excitonic properties. Our calculation shows that due to the interlayer coupling, the low energy excitons can be widely tuned by a vertical gate field. In particular, the dipole oscillator strength and radiative lifetime of the lowest energy exciton in these bilayer heterostructures is varied by over an order of magnitude within a practical external gate field. We also build a simple model that captures the essential physics behind this tunability and allows the extension of the ab initio results to a large range of electric fields. Our work clarifies the physical picture of interlayer excitons in bilayer vdW heterostructures and predicts a wide range of gate-tunable excited-state properties of 2D optoelectronic devices.

A fully coupled three-dimensional surface and subsurface land model is developed and applied to a site along the Columbia River to simulate three-way interactions among river water, groundwater, and land surface processes. The model features the coupling of the Community Land Model version 4.5 (CLM4.5) and a massively parallel multiphysics reactive transport model (PFLOTRAN). The coupled model, named CP v1.0, is applied to a 400 m × 400 m study domain instrumented with groundwater monitoring wells along the Columbia River shoreline. CP v1.0 simulations are performed at three spatial resolutions (i.e., 2, 10, and 20 m) over a 5-year period to evaluate the impact of hydroclimatic conditions and spatial resolution on simulated variables. Results show that the coupled model is capable of simulating groundwater-river-water interactions driven by river stage variability along managed river reaches, which are of global significance as a result of over 30 000 dams constructed worldwide during the past half-century. Our numerical experiments suggest that the land-surface energy partitioning is strongly modulated by groundwater-river-water interactions through expanding the periodically inundated fraction of the riparian zone, and enhancing moisture availability in the vadose zone via capillary rise in response to the river stage change. Meanwhile, CLM4.5 fails to capture the key hydrologic process (i.e., groundwater-river-water exchange) at the site, and consequently simulates drastically different water and energy budgets. Furthermore, spatial resolution is found to significantly impact the accuracy of estimated the mass exchange rates at the boundaries of the aquifer, and it becomes critical when surface and subsurface become more tightly coupled with groundwater table within 6 to 7 meters below the surface. Inclusion of lateral subsurface flow influenced both the surface energy budget and subsurface transport processes as a result of river-water intrusion into the subsurface in response to an elevated river stage that increased soil moisture for evapotranspiration and suppressed available energy for sensible heat in the warm season. The coupled model developed in this study can be used for improving mechanistic understanding of ecosystem functioning and biogeochemical cycling along river corridors under historical and future hydroclimatic changes. The dataset presented in this study can also serve as a good benchmarking case for testing other integrated models.

Channeled spectropolarimetry measures the spectrally resolved Stokes parameters. A key aspect of this technique is to accurately reconstruct the Stokes parameters from a modulated measurement of the channeled spectropolarimeter. The state-of-the-art reconstruction algorithm uses the Fourier transform to extract the Stokes parameters from channels in the Fourier domain. While this approach is straightforward, it can be sensitive to noise and channel cross-talk, and it imposes bandwidth limitations that cut o high frequency details. To overcome these drawbacks, we present a reconstruction method called compressed channeled spectropolarimetry. In our proposed framework, reconstruction in channeled spectropolarimetry is an underdetermined problem, where we take N measurements and solve for 3N unknown Stokes parameters. We formulate an optimization problem by creating a mathematical model of the channeled spectropolarimeter with inspiration from compressed sensing. We show that our approach o ers greater noise robustness and reconstruction accuracy compared with the Fourier transform technique in simulations and experimental measurements. By demonstrating more accurate reconstructions, we push performance to the native resolution of the sensor, allowing more information to be recovered from a single measurement of a channeled spectropolarimeter.

With the rapid spread in use of Digital Image Correlation (DIC) globally, it is important there be some standard methods of verifying and validating DIC codes. To this end, the DIC Challenge board was formed and is maintained under the auspices of the Society for Experimental Mechanics (SEM) and the international DIC society (iDICs). The goal of the DIC Board and the 2D–DIC Challenge is to supply a set of well-vetted sample images and a set of analysis guidelines for standardized reporting of 2D–DIC results from these sample images, as well as for comparing the inherent accuracy of different approaches and for providing users with a means of assessing their proper implementation. This document will outline the goals of the challenge, describe the image sets that are available, and give a comparison between 12 commercial and academic 2D–DIC codes using two of the challenge image sets.

AlGaN-channel high electron mobility transistors (HEMTs) are among a class of ultra wide-bandgap transistors that are promising candidates for RF and power applications. Long-channel Al_xGa_1-xN HEMTs with x = 0.7 in the channel have been built and evaluated across the -50°C to +200°C temperature range. These devices achieved room temperature drain current as high as 46 mA/mm and were absent of gate leakage until the gate diode forward bias turn-on at ~2.8 V, with a modest -2.2 V threshold voltage. A very large I_on/I_off current ratio, of 8 × 10⁹ was demonstrated. A near ideal subthreshold slope that is just 35% higher than the theoretical limit across the temperature range was characterized. The ohmic contact characteristics were rectifying from -50°C to +50°C and became nearly linear at temperatures above 100°C. An activation energy of 0.55 eV dictates the temperature dependence of off-state leakage.

Atomic motion at grain boundaries is essential to microstructure development, growth and stability of catalysts and other nanostructured materials. However, boundary atomic motion is often too fast to observe in a conventional transmission electron microscope (TEM) and too slow for ultrafast electron microscopy. We report on the entire transformation process of strained Pt icosahedral nanoparticles (ICNPs) into larger FCC crystals, captured at 2.5 ms time resolution using a fast electron camera. Results show slow diffusive dislocation motion at nm/s inside ICNPs and fast surface transformation at μm/s. By characterizing nanoparticle strain, we show that the fast transformation is driven by inhomogeneous surface stress. And interaction with pre-existing defects led to the slowdown of the transformation front inside the nanoparticles. Particle coalescence, assisted by oxygen-induced surface migration at T ≥ 300°C, also played a critical role. Thus by studying transformation in the Pt ICNPs at high time and spatial resolution, we obtain critical insights into the transformation mechanisms in strained Pt nanoparticles.

A power converter capable of converting the 48 V DC output of a photovoltaic panel into 120 V AC at up to 400 W has been demonstrated in a 40 cu. cm. (2.4 cu. in.) module, for a power density of greater than 160 W/cu. in. The module is enabled by the use of GaN and AlGaN field effect transistors (FETs) and diodes operating at higher power densities and higher switching frequencies than conventional silicon power devices. Typical photovoltaic panel converter/inverters have power densities ranging from 3.5-5.0 W/cu. in. and often make use of bulky, low frequency transformers. By using wide- and ultra-wide-bandgap switching devices, the operating frequency has been increased to 150 kHz, eliminating the need to use low frequency charge and current storage elements. The resulting size reduction demonstrates the significant possibilities that the adoption of GaN and AlGaN devices housed in small, 3D printed packages offers in the field of power electronics.

Sophisticated cyber attacks by state-sponsored and criminal actors continue to plague government and industrial infrastructure. Intuitively, partitioning cyber systems into survivable, intrusion tolerant compartments is a good idea. This prevents witting and unwitting insiders from moving laterally and reaching back to their command and control (C2) servers. However, there is a lack of artifacts that can predict the effectiveness of this approach in a realistic setting. We extend earlier work by relaxing simplifying assumptions and providing a new attacker-facing metric. In this article, we propose new closed-form mathematical models and a discrete time simulation to predict three critical statistics: probability of compromise, probability of external host compromise and probability of reachback. The results of our new artifacts agree with one another and with previous work, which suggests they are internally valid and a viable method to evaluate the effectiveness of cyber zone defense.

In order to determine how material characteristics percolate up to system-level improvements in power dissipation for different material systems and device types, we have developed an optimization tool for power diodes. This tool minimizes power dissipation in a diode for a given system operational regime (reverse voltage, forward current density, frequency, duty cycle, and temperature) for a variety of device types and materials. We have carried out diode optimizations for a wide range of system operating points to determine the regimes for which certain power diode materials/devices are favored. In this work, we present results comparing state-of-the-art Si and SiC merged PiN Schottky (MPS) diodes to vertical GaN (v-GaN) PiN diodes and as-yet undeveloped v-GaN Schottky barrier diodes (SBDs). The results of this work show that for all conditions tested, SiC MPS and v-GaN PiN diodes are preferred over Si MPS diodes. v-GaN PiN diodes are preferred over SiC MPS diodes for high-voltage / moderate-frequency operation with the limits of the v-GaN PiN preferred regime, increasing with increasing forward current density. If a v-GaN SBD diode were available, it would be preferred over all other devices at low to moderate voltages, for all frequencies from 100 Hz to 1 MHz.

Extensive all-atom molecular dynamics calculations on the water–squalane interface for nine different loadings with sorbitan monooleate (SPAN80), at T = 300 K, are analyzed for the surface tension equation of state, desorption free-energy profiles as they depend on loading, and to evaluate escape times for adsorbed SPAN80 into the bulk phases. These results suggest that loading only weakly affects accommodation of a SPAN80 molecule by this squalane–water interface. Specifically, the surface tension equation of state is simple through the range of high tension to high loading studied, and the desorption free-energy profiles are weakly dependent on loading here. The perpendicular motion of the centroid of the SPAN80 headgroup ring is well-described by a diffusional model near the minimum of the desorption free-energy profile. Lateral diffusional motion is weakly dependent on loading. Escape times evaluated on the basis of a diffusional model and the desorption free energies are 7 × 10^-2 s (into the squalane) and 3 × 10² h (into the water). Finally, the latter value is consistent with desorption times of related lab-scale experimental work.

This chapter describes the Sandia National Laboratories (SNL) processes for feedback and improvement of radiological operations, including triennial self-assessment (TSA) and radiological process improvement reports (RPIR). This chapter applies to all radiological activities performed by Members of the Workforce (MOW). Certain operations are outside the scope of this manual and do not require self-assessments or use of the RPIR process. See the "Introduction" for exemptions to the requirements of this manual.

This chapter presents requirements and guidance for radiological posting and labeling at Sandia National Laboratories (SNL). This chapter applies to all radiological posting and labeling practices at SNL.

The role of an external field on capillary waves at the liquid-vapor interface of a dipolar fluid is investigated using molecular dynamics simulations. For fields parallel to the interface, the interfacial width squared increases linearly with respect to the logarithm of the size of the interface across all field strengths tested. The value of the slope decreases with increasing field strength, indicating that the field dampens the capillary waves. With the inclusion of the parallel field, the surface stiffness increases with increasing field strength faster than the surface tension. For fields perpendicular to the interface, the interfacial width squared is linear with respect to the logarithm of the size of the interface for small field strengths, and the surface stiffness is less than the surface tension. Above a critical field strength that decreases as the size of the interface increases, the interface becomes unstable due to the increased amplitude of the capillary waves.

Deep learning techniques have demonstrated the ability to perform a variety of object recognition tasks using visible imager data; however, deep learning has not been implemented as a means to autonomously detect and assess targets of interest in a physical security system. We demonstrate the use of transfer learning on a convolutional neural network (CNN) to significantly reduce training time while keeping detection accuracy of physical security relevant targets high. Unlike many detection algorithms employed by video analytics within physical security systems, this method does not rely on temporal data to construct a background scene; targets of interest can halt motion indefinitely and still be detected by the implemented CNN. A key advantage of using deep learning is the ability for a network to improve over time. Periodic retraining can lead to better detection and higher confidence rates. We investigate training data size versus CNN test accuracy using physical security video data. Due to the large number of visible imagers, significant volume of data collected daily, and currently deployed human in the loop ground truth data, physical security systems present a unique environment that is well suited for analysis via CNNs. This could lead to the creation of algorithmic element that reduces human burden and decreases human analyzed nuisance alarms.

There is a desire to detect and assess unmanned aerial systems (UAS) with a high probability of detection and low nuisance alarm rates in numerous fields of security. Currently available solutions rely upon exploiting electronic signals emitted from the UAS. While these methods may enable some degree of security, they fail to address the emerging domain of autonomous UAS that do not transmit or receive information during the course of a mission. We examine frequency analysis of pixel fluctuation over time to exploit the temporal frequency signature present in imagery data of UAS. This signature is present for autonomous or controlled multirotor UAS and allows for lower pixels-on-target detection. The methodology also acts as a method of assessment due to the distinct frequency signatures of UAS when examined against the standard nuisance alarms such as birds or non-UAS electronic signal emitters. The temporal frequency analysis method is paired with machine learning algorithms to demonstrate a UAS detection and assessment method that requires minimal human interaction. The use of the machine learning algorithm allows each necessary human assess to increase the likelihood of autonomous assessment, allowing for increased system performance over time.

Conventional cyber defenses require continual maintenance: virus, firmware, and software updates; costly functional impact tests; and dedicated staff within a security operations center. The conventional defenses require access to external sources for the latest updates. The whitelisted system, however, is ideally a system that can sustain itself freed from external inputs. Cyber-Physical Systems (CPS), have the following unique traits: digital commands are physically observable and verifiable; possible combinations of commands are limited and finite. These CPS traits, combined with a trust anchor to secure an unclonable digital identity (i.e., digitally unclonable function [DUF] - Patent Application #15/183,454; CodeLock), offers an excellent opportunity to explore defenses built on whitelisting approach called 'Trustworthy Design Architecture (TDA).' There exist significant research challenges in defining what are the physically verifiable whitelists as well as the criteria for cyber-physical traits that can be used as the unclonable identity. One goal of the project is to identify a set of physical and/or digital characteristics that can uniquely identify an endpoint. The measurements must have the properties of being reliable, reproducible, and trustworthy. Given that adversaries naturally evolve with any defense, the adversary will have the goal of disrupting or spoofing this process. To protect against such disruptions, we provide a unique system engineering technique, when applied to CPSs (e.g., nuclear processing facilities, critical infrastructures), that will sustain a secure operational state without ever needing external information or active inputs from cybersecurity subject-matter experts (i.e., virus updates, IDS scans, patch management, vulnerability updates). We do this by eliminating system dependencies on external sources for protection. Instead, all internal communication is actively sealed and protected with integrity, authenticity and assurance checks that only cyber identities bound to the physical component can deliver. As CPSs continue to advance (i.e., IoTs, drones, ICSs), resilient-maintenance free solutions are needed to neutralize/reduce cyber risks. TDA is a conceptual system engineering framework specifically designed to address cyber-physical systems that can potentially be maintained and operated without the persistent need or demand for vulnerability or security patch updates.

Physical unclonable functions (PUFs) are devices which are easily probed but difficult to predict. Optical PUFs have been discussed within the literature, with traditional optical PUFs typically using spatial light modulators, coherent illumination, and scattering volumes; however, these systems can be large, expensive, and difficult to maintain alignment in practical conditions. We propose and demonstrate a new kind of optical PUF based on computational imaging and compressive sensing to address these challenges with traditional optical PUFs. This work describes the design, simulation, and prototyping of this computational optical PUF (COPUF) that utilizes incoherent polychromatic illumination passing through an additively manufactured refracting optical polymer element. We demonstrate the ability to pass information through a COPUF using a variety of sampling methods, including the use of compressive sensing. The sensitivity of the COPUF system is also explored. We explore non-traditional PUF configurations enabled by the COPUF architecture. The double COPUF system, which employees two serially connected COPUFs, is proposed and analyzed as a means to authenticate and communicate between two entities that have previously agreed to communicate. This configuration enables estimation of a message inversion key without the calculation of individual COPUF inversion keys at any point in the PUF life cycle. Our results show that it is possible to construct inexpensive optical PUFs using computational imaging. This could lead to new uses of PUFs in places where electrical PUFs cannot be utilized effectively, as low cost tags and seals, and potentially as authenticating and communicating devices.

We consider the task of low-multilinear-rank functional regression, i.e., learning a low-rank parametric representation of functions from scattered real-valued data. Our first contribution is the development and analysis of gradient-based optimization procedures, including stochastic gradient descent and quasi-Newton methods, for learning the parameters of a functional tensor-train (FT) that yields improved accuracy over standard alternating least squares methods. The functional tensor-train uses the tensor-train (TT) representation of low-rank arrays as an ansatz for a class of low-multilinear-rank functions. The FT is represented by a set of matrix-valued functions that contain a set of univariate functions, and the regression task is to learn the parameters of these univariate functions. Our second contribution demonstrates that using nonlinearly parameterized univariate functions, e.g., symmetric kernels with moving centers, within each core can outperform the standard approach of using a linear expansion of basis functions. Our final contributions are new rank adaptation and group-sparsity regularization procedures to minimize overfitting. We use several benchmark problems to demonstrate at least an order of magnitude lower accuracy with gradient-based optimization methods than ALS in the low-sample number regime. We also demonstrate an order of magnitude reduction in accuracy on a test problem resulting from nonlinear parameterizations over linear parameterizations. Finally we compare regression performance with 22 other nonparametric and parametric regression methods on 10 real-world data sets. We achieve top-five accuracy for seven of the data sets and best accuracy for two of the data sets. These rankings are the best amongst parametric models and competetive with the best non-parametric methods.

Counterfeiting or surreptitious modification of electronic systems is of increasing concern, particularly for critical infrastructure and national security systems. Such systems include avionics, medical devices, military systems, and utility infrastructure. We present experimental results from an approach to uniquely identify printed circuit boards (PCBs) on the basis of device unique variations in surface mount passive components and wire trace patterns. We also present an innovative approach for combining measurements of each of these quantities to create unique, random identifiers for each PCB and report the observed entropy, reliability, and uniqueness of the signatures. These unique signatures can be used directly for verifying the integrity and authenticity of the PCBs, or can serve as the basis for generating cryptographic keys for more secure authentication of the devices during system acquisition or field deployment. Our results indicate that the proposed approaches for measuring and combining these quantities are capable of generating high-entropy, unique signatures for PCBs. The techniques explored do not require system designers to utilize specialized manufacturing processes and implementation is low-cost.

This paper describes versions of OPAL, the Occam-Plausibility Algorithm (Farrell et al. in J Comput Phys 295:189–208, 2015) in which the use of Bayesian model plausibilities is replaced with information-theoretic methods, such as the Akaike information criterion and the Bayesian information criterion. Applications to complex systems of coarse-grained molecular models approximating atomistic models of polyethylene materials are described. All of these model selection methods take into account uncertainties in the model, the observational data, the model parameters, and the predicted quantities of interest. A comparison of the models chosen by Bayesian model selection criteria and those chosen by the information-theoretic criteria is given.

Quark nuggets are theoretical objects composed of approximately equal numbers of up, down, and strange quarks and are also called strangelets and nuclearites. They have been proposed as a candidate for dark matter, which constitutes ~85% of the universe's mass and which has been a mystery for decades. Previous efforts to detect quark nuggets assumed that the nuclear-density core interacts directly with the surrounding matter so the stopping power is minimal. Tatsumi found that quark nuggets could well exist as a ferromagnetic liquid with a ∼1012-T magnetic field. We find that the magnetic field produces a magnetopause with surrounding plasma, as the earth's magnetic field produces a magnetopause with the solar wind, and substantially increases their energy deposition rate in matter. We use the magnetopause model to compute the energy deposition as a function of quark-nugget mass and to analyze testing the quark-nugget hypothesis for dark matter by observations in air, water, and land. We conclude the water option is most promising.

Quantum Hall ferromagnetic transitions are typically achieved by increasing the Zeeman energy through in-situ sample rotation, while transitions in systems with pseudo-spin indices can be induced by gate control. We report here a gate-controlled quantum Hall ferromagnetic transition between two real spin states in a conventional two-dimensional system without any in-plane magnetic field. We show that the ratio of the Zeeman splitting to the cyclotron gap in a Ge two-dimensional hole system increases with decreasing density owing to inter-carrier interactions. Below a critical density of ~2.4 × 1010 cm-2, this ratio grows greater than 1, resulting in a ferromagnetic ground state at filling factor ν = 2. At the critical density, a resistance peak due to the formation of microscopic domains of opposite spin orientations is observed. Such gate-controlled spin-polarizations in the quantum Hall regime opens the door to realizing Majorana modes using two-dimensional systems in conventional, low-spin-orbit-coupling semiconductors.

Optical metasurfaces are regular quasi-planar nanopatterns that can apply diverse spatial and spectral transformations to light waves. However, metasurfaces are no longer adjustable after fabrication, and a critical challenge is to realise a technique of tuning their optical properties that is both fast and efficient. We experimentally realise an ultrafast tunable metasurface consisting of subwavelength gallium arsenide nanoparticles supporting Mie-type resonances in the near infrared. Using transient reflectance spectroscopy, we demonstrate a picosecond-scale absolute reflectance modulation of up to 0.35 at the magnetic dipole resonance of the metasurfaces and a spectral shift of the resonance by 30 nm, both achieved at unprecedentedly low pump fluences of less than 400 μJ cm-2. Our findings thereby enable a versatile tool for ultrafast and efficient control of light using light.

Optical nonlinearities are intimately related to the spatial symmetry of the nonlinear media. For example, the second order susceptibility vanishes for centrosymmetric materials under the dipole approximation. The latter concept has been naturally extended to the metamaterials' realm, sometimes leading to the (erroneous) hypothesis that second harmonic (SH) generation is negligible in highly symmetric meta-atoms. In this work we aim to show that such symmetric meta-atoms can radiate SH light efficiently. In particular, we investigate in-plane centrosymmetric meta-atom designs where the approximation for meta-atoms breaks down. In a periodic array this building block allows us to control the directionality of the SH radiation. We conclude by showing that the use of symmetry considerations alone allows for the manipulation of the nonlinear multipolar response of a meta-atom, resulting in e.g. dipolar, quadrupolar, or multipolar emission on demand. This is because the size of the meta-atom is comparable with the free-space wavelength, thus invalidating the dipolar approximation for meta-atoms.

The removal of silica, ubiquitous in produced and industrial waters, by novel mixed oxides is investigated in this present study. We have combined the advantage of high selectivity hydrotalcite (HTC, (Mg6Al2(OH)16(CO3)·4H2O)), with large surface area of active alumina (AA, (Al2O3)) for effective removing of the dissolved silica from cooling tower water. The batch test results indicated the combined HTC/AA is a more effective method for removing silica from CTW than using each of HTC or AA separately. The silica uptake was confirmed by Fourier transform infrared (FTIR), and Energy dispersive spectroscopy (EDS). Results indicate HTC/AA effectively removes silica from cooling tower water (CTW), even in the presence of large concentrations of competing anions, such as Cl−, NO3− HCO3−, CO32− and SO42−. The Single Path Flow Through (SPFT) tests confirmed to rapid uptake of silica by combined HTC/AA during column filtration. The experimental data of silica adsorption fit best to Freundlich isotherm model.

For synthetic aperture radar systems, missing data samples can cause severe image distortion. When multiple, coherent data collections exist and the missing data samples do not overlap between collections, there exists the possibility of replacing data samples between collections. For airborne radar, the known and unknown motions of the aircraft prevent direct data sample replacement to repair image features. This paper presents a method to calculate the necessary phase corrections to enable data sample replacement using only the collected radar data.

All grain boundaries are not equal in their predisposition for fracture due to the complex coupling between lattice geometry, interfacial structure, and mechanical properties. The ability to understand these relationships is crucial to engineer materials resilient to grain boundary fracture. Here, a methodology is presented to isolate the role of grain boundary structure on interfacial fracture properties, such as the tensile strength and work of separation, using atomistic simulations. Instead of constructing sets of grain boundary models within the misorientation/structure space by simply varying the misorientation angle around a fixed misorientation axis, the proposed method creates sets of grain boundary models by means of isocurves associated with important fracture-related properties of the adjoining lattices. Such properties may include anisotropic elastic moduli, the Schmid factor for primary slip, and the propensity for simultaneous slip on multiple slip systems. This approach eliminates the effect of lattice properties from the comparative analysis of interfacial fracture properties and thus enables the identification of structure-property relationships for grain boundaries. As an example, this methodology is implemented to study crack propagation along Ni grain boundaries. Segregated H is used as a means to emphasize differences in the selected grain boundary structures while keeping lattice properties fixed.

Computer simulations at the atomistic scale play an increasing important role in understanding the structure features, and the structure–property relationships of glass and amorphous materials. In this paper, we reviewed atomistic simulation methods ranging from first principles calculations and ab initio molecular dynamics (AIMD) simulations, to classical molecular dynamics (MD), and meso-scale kinetic Monte Carlo (KMC) simulations and their applications to study the reactions and interactions of inorganic glasses with water and the dissolution behaviors of inorganic glasses. Particularly, the use of these simulation methods in understanding the reaction mechanisms of water with oxide glasses, water–glass interfaces, hydrated porous silica gels formation, the structure and properties of multicomponent glasses, and microstructure evolution are reviewed. The advantages and disadvantageous of these simulation methods are discussed and the current challenges and future direction of atomistic simulations in glass dissolution presented.

Atomic motion at grain boundaries is essential to microstructure development, growth and stability of catalysts and other nanostructured materials. However, boundary atomic motion is often too fast to observe in a conventional transmission electron microscope (TEM) and too slow for ultrafast electron microscopy. Here, we report on the entire transformation process of strained Pt icosahedral nanoparticles (ICNPs) into larger FCC crystals, captured at 2.5 ms time resolution using a fast electron camera. Results show slow diffusive dislocation motion at nm/s inside ICNPs and fast surface transformation at μm/s. By characterizing nanoparticle strain, we show that the fast transformation is driven by inhomogeneous surface stress. And interaction with pre-existing defects led to the slowdown of the transformation front inside the nanoparticles. Particle coalescence, assisted by oxygen-induced surface migration at T ≥ 300 °C, also played a critical role. Thus by studying transformation in the Pt ICNPs at high time and spatial resolution, we obtain critical insights into the transformation mechanisms in strained Pt nanoparticles.

An experimental study of migration of tungsten in the DIII-D divertor is described, in which the outer strike point of L-mode plasmas was positioned on a toroidal ring of tungsten-coated metal inserts. Net deposition of tungsten on the divertor just outside the strike point was measured on graphite samples exposed to various plasma durations using the divertor materials evaluation system. Tungsten coverage, measured by Rutherford backscattering spectroscopy (RBS), was found to be low and nearly independent of both radius and exposure time closer to the strike point, whereas farther from the strike point the W coverage was much larger and increased with exposure time. Depth profiles from RBS show this was due to accumulation of thicker mixedmaterial deposits farther from the strike point where the plasma temperature is lower. These results are consistent with a low near-surface steady-state coverage on graphite undergoing net erosion, and continuing accumulation in regions of net deposition. This experiment provides data needed to validate, and further improve computational simulations of erosion and deposition of material on plasma-facing components and transport of impurities in magnetic fusion devices. Such simulations are underway and will be reported later.

With the end of Dennard scaling and the ever-increasing need for more efficient, faster computation, resistive switching devices (ReRAM), often referred to as memristors, are a promising candidate for next generation computer hardware. These devices show particular promise for use in an analog neuromorphic computing accelerator as they can be tuned to multiple states and be updated like the weights in neuromorphic algorithms. Modeling a ReRAM-based neuromorphic computing accelerator requires a compact model capable of correctly simulating the small weight update behavior associated with neuromorphic training. These small updates have a nonlinear dependence on the initial state, which has a significant impact on neural network training. Consequently, we propose the piecewise empirical model (PEM), an empirically derived general purpose compact model that can accurately capture the nonlinearity of an arbitrary two-terminal device to match pulse measurements important for neuromorphic computing applications. By defining the state of the device to be proportional to its current, the model parameters can be extracted from a series of voltages pulses that mimic the behavior of a device in an analog neuromorphic computing accelerator. This allows for a general, accurate, and intuitive compact circuit model that is applicable to different resistance-switching device technologies. In this work, we explain the details of the model, implement the model in the circuit simulator Xyce, and give an example of its usage to model a specific Ta / TaO x device.

We have developed and built a small porous-plug burner based on the original McKenna burner design. The new burner generates a laminar premixed flat flame for use in studies of combustion chemistry and soot formation. The size is particularly relevant for space-constrained, synchrotron-based X-ray diagnostics. In this paper, we present details of the design, construction, operation, and supporting infrastructure for this burner, including engineering attributes that enable its small size. We also present data for charactering the flames produced by this burner. These data include temperature profiles for three premixed sooting ethylene/air flames (equivalence ratios of 1.5, 1.8, and 2.1); temperatures were recorded using direct one-dimensional coherent Raman imaging. We include calculated temperature profiles, and, for one of these ethylene/air flames, we show the carbon and hydrogen content of heavy hydrocarbon species measured using an aerosol mass spectrometer coupled with vacuum ultraviolet photoionization (VUV-AMS) and soot-volume-fraction measurements obtained using laser-induced incandescence. In addition, we provide calculated mole-fraction profiles of selected gas-phase species and characteristic profiles for seven mass peaks from AMS measurements. Using these experimental and calculated results, we discuss the differences between standard McKenna burners and the new miniature porous-plug burner introduced here.

We generate a wide range of models of proppant-packed fractures using discrete element simulations, and measure fracture conductivity using finite element flow simulations. This allows for a controlled computational study of proppant structure and its relationship to fracture conductivity and stress in the proppant pack. For homogeneous multi-layered packings, we observe the expected increase in fracture conductivity with increasing fracture aperture, while the stress on the proppant pack remains nearly constant. This is consistent with the expected behavior in conventional proppant-packed fractures, but the present work offers a novel quantitative analysis with an explicit geometric representation of the proppant particles. In single-layered packings (i.e. proppant monolayers), there is a drastic increase in fracture conductivity as the proppant volume fraction decreases and open flow channels form. However, this also corresponds to a sharp increase in the mechanical stress on the proppant pack, as measured by the maximum normal stress relative to the side crushing strength of typical proppant particles. We also generate a variety of computational geometries that resemble highly heterogeneous proppant packings hypothesized to form during channel fracturing. In some cases, these heterogeneous packings show drastic improvements in conductivity with only moderate increase in the stress on the proppant particles, suggesting that in certain applications these structures are indeed optimal. We also compare our computer-generated structures to micro computed tomography imaging of a manually fractured laboratory-scale shale specimen, and find reasonable agreement in the geometric characteristics.

Analog quantum simulators (AQS) will likely be the first nontrivial application of quantum technology for predictive simulation. However, there remain questions regarding the degree of confidence that can be placed in the results of AQS since they do not naturally incorporate error correction. Specifically, how do we know whether an analog simulation of a quantum model will produce predictions that agree with the ideal model in the presence of inevitable imperfections? At the same time there is a widely held expectation that certain quantum simulation questions will be robust to errors and perturbations in the underlying hardware. Resolving these two points of view is a critical step in making the most of this promising technology. In this work we formalize the notion of AQS reliability by determining sensitivity of AQS outputs to underlying parameters, and formulate conditions for robust simulation. Our approach naturally reveals the importance of model symmetries in dictating the robust properties. To demonstrate the approach, we characterize the robust features of a variety of quantum many-body models.

Algebraic multigrid (AMG) preconditioners are considered for discretized systems of partial differential equations (PDEs) where unknowns associated with different physical quantities are not necessarily colocated at mesh points. Specifically, we investigate a Q2−Q1 mixed finite element discretization of the incompressible Navier–Stokes equations where the number of velocity nodes is much greater than the number of pressure nodes. Consequently, some velocity degrees of freedom (DOFs) are defined at spatial locations where there are no corresponding pressure DOFs. Thus, AMG approaches leveraging this colocated structure are not applicable. This paper instead proposes an automatic AMG coarsening that mimics certain pressure/velocity DOF relationships of the Q2−Q1 discretization. The main idea is to first automatically define coarse pressures in a somewhat standard AMG fashion and then to carefully (but automatically) choose coarse velocity unknowns so that the spatial location relationship between pressure and velocity DOFs resembles that on the finest grid. To define coefficients within the intergrid transfers, an energy minimization AMG (EMIN-AMG) is utilized. EMIN-AMG is not tied to specific coarsening schemes and grid transfer sparsity patterns, and so it is applicable to the proposed coarsening. Numerical results highlighting solver performance are given on Stokes and incompressible Navier–Stokes problems.

A large body of experimental work has established that athermal colloid/polymer mixtures undergo a sequence of transitions from a disordered fluid state to a colloidal crystal to a second disordered phase with increasing polymer concentration. These transitions are driven by polymer-mediated interparticle attraction, which is a function of both the polymer density and size. It has been posited that the disordered state at high polymer density is a consequence of strong interparticle attractions that kinetically inhibit the formation of the colloidal crystal, i.e., the formation of a non-equilibrium gel phase interferes with crystallization. Here we use molecular dynamics simulations and density functional theory on polymers and nanoparticles (NPs) of comparable size and show that the crystal-disordered phase coexistence at high polymer density for sufficiently long chains corresponds to an equilibrium thermodynamic phase transition. While the crystal is, indeed, stabilized at intermediate polymer density by polymer-induced intercolloid attractions, it is destabilized at higher densities because long chains lose significant configurational entropy when they are forced to occupy all of the crystal voids. Our results are in quantitative agreement with existing experimental data and show that, at least in the nanoparticle limit of sufficiently small colloidal particles, the crystal phase only has a modest range of thermodynamic stability.

Molecular structures of kerogen control hydrocarbon production in unconventional reservoirs. Significant progress has been made in developing model representations of various kerogen structures. These models have been widely used for the prediction of gas adsorption and migration in shale matrix. However, using density functional perturbation theory (DFPT) calculations and vibrational spectroscopic measurements, we here show that a large gap may still remain between the existing model representations and actual kerogen structures, therefore calling for new model development. Using DFPT, we calculated Fourier transform infrared (FTIR) spectra for six most widely used kerogen structure models. The computed spectra were then systematically compared to the FTIR absorption spectra collected for kerogen samples isolated from Mancos, Woodford and Marcellus formations representing a wide range of kerogen origin and maturation conditions. Limited agreement between the model predictions and the measurements highlights that the existing kerogen models may still miss some key features in structural representation. A combination of DFPT calculations with spectroscopic measurements may provide a useful diagnostic tool for assessing the adequacy of a proposed structural model as well as for future model development. This approach may eventually help develop comprehensive infrared (IR)-fingerprints for tracing kerogen evolution.

A system is presented that is capable of measuring subnanosecond reverse recovery times of diodes in wide-bandgap materials over a wide range of forward biases (0 - 1 A) and reverse voltages (0 - 10 kV). The system utilizes the step recovery technique and comprises a cable pulser based on a silicon (Si) Photoconductive Semiconductor Switch (PCSS) triggered with an Ultrashort Pulse Laser, a pulse charging circuit, a diode biasing circuit, and resistive and capacitive voltage monitors. The PCSS-based cable pulser transmits a 130 ps rise time pulse down a transmission line to a capacitively coupled diode, which acts as the terminating element of the transmission line. The temporal nature of the pulse reflected by the diode provides the reverse recovery characteristics of the diode, measured with a high bandwidth capacitive probe integrated into the cable pulser. This system was used to measure the reverse recovery times (including the creation and charging of the depletion region) for two Avogy gallium nitride diodes; the initial reverse recovery time was found to be 4 ns and varied minimally over reverse biases of 50-100 V and forward current of 1-100 mA.

In this study we developed an efficient Bayesian inversion framework for interpreting marine seismic Amplitude Versus Angle and Controlled-Source Electromagnetic data for marine reservoir characterization. The framework uses a multi-chain Markov-chain Monte Carlo sampler, which is a hybrid of DiffeRential Evolution Adaptive Metropolis and Adaptive Metropolis samplers. The inversion framework is tested by estimating reservoir-fluid saturations and porosity based on marine seismic and Controlled-Source Electromagnetic data. The multi-chain Markov-chain Monte Carlo is scalable in terms of the number of chains, and is useful for computationally demanding Bayesian model calibration in scientific and engineering problems. As a demonstration, the approach is used to efficiently and accurately estimate the porosity and saturations in a representative layered synthetic reservoir. The results indicate that the seismic Amplitude Versus Angle and Controlled-Source Electromagnetic joint inversion provides better estimation of reservoir saturations than the seismic Amplitude Versus Angle only inversion, especially for the parameters in deep layers. The performance of the inversion approach for various levels of noise in observational data was evaluated — reasonable estimates can be obtained with noise levels up to 25%. Sampling efficiency due to the use of multiple chains was also checked and was found to have almost linear scalability.

We demonstrate a capability of deterministic doping at the single atom level using a combination of direct write focused ion beam and solid-state ion detectors. The focused ion beam system can position a single ion to within 35 nm of a targeted location and the detection system is sensitive to single low energy heavy ions. This platform can be used to deterministically fabricate single atom devices in materials where the nanostructure and ion detectors can be integrated, including donor-based qubits in Si and color centers in diamond.

The porosity of clay aggregates is an important property governing chemical reactions and fluid flow in low-permeability geologic formations and clay-based engineered barrier systems. Pore spaces in clays include interlayer and interparticle pores. Under compaction and dewatering, the size and geometry of such pore spaces may vary significantly (sub-nanometer to microns) depending on ambient physical and chemical conditions. Here we report a molecular dynamics simulation method to construct a complex and realistic clay-like nanoparticle aggregate with interparticle pores and grain boundaries. The model structure is then used to investigate the effect of dewatering and water content on micro-porosity of the aggregates. The results suggest that slow dewatering would create more compact aggregates compared to fast dewatering. Furthermore, the amount of water present in the aggregates strongly affects the particle-particle interactions and hence the aggregate structure. Detailed analyses of particle-particle and water-particle interactions provide a molecular-scale view of porosity and texture development of the aggregates. The simulation method developed here may also aid in modeling the synthesis of nanostructured materials through self-assembly of nanoparticles.

This paper presents a review of the main electrical components that are expected to be present in marine renewable energy arrays. The review is put in context by appraising the current needs of the industry and identifying the key components required in both device and array-scale developments. For each component, electrical, mechanical and cost considerations are discussed; with quantitative data collected during the review made freely available for use by the community via an open access online repository. This data collection updates previous research and addresses gaps specific to emerging offshore technologies, such as marine and floating wind, and provides a comprehensive resource for the techno-economic assessment of offshore energy arrays.

Image processing and stereological techniques were used to characterize the heterogeneity of composite propellant and inform a predictive burn rate model. Composite propellant samples made up of ammonium perchlorate (AP), hydroxyl-terminated polybutadiene (HTPB), and aluminum (Al) were faced with an ion mill and imaged with a scanning electron microscope (SEM) and x-ray tomography (micro-CT). Properties of both the bulk and individual components of the composite propellant were determined from a variety of image processing tools. An algebraic model, based on the improved Beckstead-Derr-Price model developed by Cohen and Strand, was used to predict the steady-state burning of the aluminized composite propellant. In the presented model the presence of aluminum particles within the propellant was introduced. The thermal effects of aluminum particles are accounted for at the solid-gas propellant surface interface and aluminum combustion is considered in the gas phase using a single global reaction. Properties derived from image processing were used directly as model inputs, leading to a sample-specific predictive combustion model.

Anodic stripping voltammetry (ASV) is an analysis technique that permits the selective and quantitative analysis of metal ion species in solution. It is most commonly applied in neutral to acidic electrolyte largely due to inherent metal ion solubility. Bismuth (Bi) is a common film used for ASV due to its good sensitivity, overall stability and insensitivity to O2. ASV, utilizing a Bi film, along with cadmium (Cd) and lead (Pb) as the plating mediators, has recently been adapted to determine zinc (Zn) concentrations in highly alkaline environments (30 % NaOH or 35 % M KOH). Successful analysis of Zn in alkaline relies on the ability of the hydroxide to form soluble metal anion species, such as Bi(OH)4 − and Zn(OH)4 2−. Here, we look to extend this technique to detect and quantify copper (Cu) ions in these highly basic electrolytes. However, in general, the use of ASV to detect and quantify Cu ion concentrations is notoriously difficult as the Cu stripping peak potential overlays with that of Bi from the common Bi film electrode. Here, an ASV method for determining Cu concentration in alkaline solutions is developed utilizing Pb as a deposition mediator. As such, it was found that when analyzing Cu solutions in the presence of Pb, the stripping voltammetry curves present separate and defined Cu stripping peaks. Different analyzes were made to find the best stripping voltammetry performance conditions. As such, an accumulation time of 5 minutes, an accumulation potential of≤−1.45 V vs. Hg/HgO, and a concentration of 35 wt% KOH were determined to be the conditions that presented the best ASV results. Utilizing these conditions, calibration curves in the presence of 5.0 ppm Pb showed the best linear stripping signal correlation with an r-squared value of 0.991 and a limit of detection (LOD) of 0.67 ppm. These results give way to evaluating Cu concentrations using ASV in aqueous alkaline solutions.

Abstract not provided.

In this study, we employ a numerical model to compare the performance of a number of wave energy converter control strategies. The controllers selected for evaluation span a wide range in their requirements for implementation. Each control strategy is evaluated using a single numerical model with a set of sea states to represent a deployment site off the coast of Newport, OR. A number of metrics, ranging from power absorption to kinematics, are employed to provide a comparison of each control strategy's performance that accounts for both relative benefits and costs. The results show a wide range of performances from the different controllers and highlight the need for a holistic design approach which considers control design as a parallel component within the larger process WEC design.

Individual donors in silicon chips are used as quantum bits with extremely low error rates. However, physical realizations have been limited to one donor because their atomic size causes fabrication challenges. Quantum dot qubits, in contrast, are highly adjustable using electrical gate voltages. This adjustability could be leveraged to deterministically couple donors to quantum dots in arrays of qubits. In this work, we demonstrate the coherent interaction of a 31P donor electron with the electron of a metal-oxide-semiconductor quantum dot. We form a logical qubit encoded in the spin singlet and triplet states of the two-electron system. We show that the donor nuclear spin drives coherent rotations between the electronic qubit states through the contact hyperfine interaction. This provides every key element for compact two-electron spin qubits requiring only a single dot and no additional magnetic field gradients, as well as a means to interact with the nuclear spin qubit.

Previous observation of EPDM and FEPM materials aged in thermo-oxidative and thermo-oxidative plus hydrolytic environments revealed an unusual trend: the degradation and disintegration of these polymers in the former case but the ability to maintain mechanical performance and shape in the latter [1]. No abnormalities were observed in the chemical (oxidation rates, FTIR spectra, solvent uptake, gel content, and weight loss vs. temperature) or physical (modulus profile) measurements that could explain these empirically observed aging differences. A second controlled aging test was conducted to verify this trend using only EPDM. Once again it was shown that thermo-oxidative conditions appear to cause more degradative damage (enhanced embrittlement) than observed for the combined thermo-oxidative plus hydrolytic environments. From these data we conclude that water may favorably interfere with normal thermo-oxidative degradation processes. This interference may occur via some combination of chemical and physical property changes in the presence of steam such as: oxidation rate and O2 permeability changes, additional sensitivity to hydrolytic damage, and/or mechanistic changes in relation to pH and hydroperoxide formation.

The high-Affinity receptor for IgE expressed on the surface of mast cells and basophils interacts with antigens, via bound IgE antibody, and triggers secretion of inflammatory mediators that contribute to allergic reactions. To understand how past inputs (memory) influence future inflammatory responses in mast cells, a microfluidic device was used to precisely control exposure of cells to alternating stimulatory and non-stimulatory inputs. We determined that the response to subsequent stimulation depends on the interval of signaling quiescence. For shorter intervals of signaling quiescence, the second response is blunted relative to the first response, whereas longer intervals of quiescence induce an enhanced second response. Through an iterative process of computational modeling and experimental tests, we found that these memory-like phenomena arise from a confluence of rapid, short-lived positive signals driven by the protein tyrosine kinase Syk; slow, long-lived negative signals driven by the lipid phosphatase Ship1; and slower degradation of Ship1 co-factors. This work advances our understanding of mast cell signaling and represents a generalizable approach for investigating the dynamics of signaling systems.

An experiment was conducted in DIII-D to compare gross tungsten (W) erosion on samples exposed to outer strike point (OSP) sweeps in L-mode plasmas for three conditions. These included two phases of resonant magnetic perturbations (RMPs), and a set with no perturbations. Upon RMP application, lobe structures indicative of strike point splitting of the OSP were evident in divertor camera data and on Langmuir probes. Gross W erosion flux, GW, inferred spectroscopically using the S/XB method applied to the 400.9 nm W-I line, was generally in the range ΓW/ΓD +,⊥ = 2 × 10-4 referenced to incident deuterium ion flux ΓD+,⊥, and was increased in the RMP cases by no more than 30% of the level observed in unperturbed discharges. A large reduction in gross erosion (50%) was observed in the private flux region at the W sample for one specific toroidal phase of the RMP field.

Strong stability preserving (SSP) time discretizations preserve the monotonicity properties satisfied by the spatial discretization when coupled with the first order forward Euler, under a certain time-step restriction. The search for high order strong stability preserving time-stepping methods with high order and large allowable time-step has been an active area of research. It is known that implicit SSP Runge–Kutta methods exist only up to sixth order; however, if we restrict ourselves to solving only linear autonomous problems, the order conditions simplify and we can find implicit SSP Runge–Kutta methods of any linear order. In the current work we find implicit SSP Runge–Kutta methods with high linear order pl i n≤ 9 and nonlinear orders p= 2, 3, 4, that are optimal in terms of allowable SSP time-step. Next, we formulate a novel optimization problem for implicit–explicit (IMEX) SSP Runge–Kutta methods and find optimized IMEX SSP Runge–Kutta pairs that have high linear order pl i n≤ 7 and nonlinear orders up to p= 4. We also find implicit methods with large linear stability regions that pair with known explicit SSP Runge–Kutta methods. These methods are then tested on sample problems to demonstrate the sharpness of the SSP coefficient and the typical behavior of these methods on test problems.

To quantify the resolution limits of scanning microwave impedance microscopy (sMIM), we created scanning tunneling microscope (STM)-patterned donor nanostructures in silicon composed of 10 nm lines of highly conductive silicon buried under a protective top cap of silicon, and imaged them with sMIM. This dopant pattern is an ideal test of the resolution and sensitivity of the sMIM technique, as it is made with nm-resolution and offers minimal complications from topography convolution. It has been determined that typical sMIM tips can resolve lines down to ∼80 nm spacing, while resolution is independent of tip geometry as extreme tip wear does not change the resolving power, contrary to traditional scanning capacitance microscopy (SCM). Going forward, sMIM is an ideal technique for qualifying buried patterned devices, potentially allowing for quantitative post-fabrication characterization of donor structures, which may be an important tool for the study of atomic-scale transistors and state of the art quantum computation schemes.

Methods developed to reduce interference in a noisy environment, be it radar target responses or effective communications in the presence of noise for mobile phone users, are vital in delivering a clear usable signal. The methods used to render a cleaner signal can also be used to combine signals of various frequencies. Ground Penetrating Radar (GPR) scans over the same area are no exception. This paper explores using an optimization problem solver, the Expectation Maximization (EM) Algorithm, to define the weights to use to combine multiple GPR scans at different frequencies over the same target area. This approach exploits the Gaussian Mixture Model (GMM) feature of the EM algorithm to produce a cleaner image at depth. Our method demonstrates a measured improvement toward producing a cleaner image.

A sample of natural quartzite rock was submerged in deionized water and illuminated with 1.8-J pulses of 527- nm light with 15-ns duration over an area of 3 cm² [fluence = 0.6 J/cm²]. This relatively low fluence and intensity [40 MW/cm²] were far below the threshold needed for direct ablation via plasma formation or thermal evaporation. With each laser pulse, a small cloud of sub-μm particles was released from the surface and dispersed into the submerging water, forming a long-lived suspension. After one hundred laser pulses, the processing was terminated and the surface of the originally colored quartzite was rendered colorless. The quartzite rock was cut in cross section and the colorless material on the surface was examined with X-ray fluorescence. We report that the transition element Fe was found to be significantly depleted in this colorless layer. This supports the hypothesis that the laser exposure lead to a transient hydrothermal dissolution of the material, followed by a recrystallization process of the SiO₂ that preferentially released iron oxides into the submerging water.

The Accelerator Driven System (ADS) as a reactor to produce fissile material, or for partitioning & transmutation purposes, has potential as an inherently safe reactor since it can be shut down by simply turning off the accelerator. Multiple studies have focused on the intranuclear and internuclear physics involved in the spallation process for high-energy projectiles impacting high-Z targets relevant to ADS applications. To quantify spallation neutronics, and thereby fissile material production, generally requires numerical methods such as MCNP, and such calculations are specific to the geometry under consideration. This study uses published cross sections from the ENDF database and an assumed generic cylindrical geometry for the ADS target and fertile blanket to derive analytic expressions for the production of Pu-239 and U-233, without needing to run MCNP codes, yet matching published data.

This paper explores the viability of neural-networkbased classification of ground surface for vehicles. By classifying road surface in near realtime, improvements in vehicle performance (e.g. braking and cornering) may be possible. Classification performance for many combinations of feature encoding and neural network types are compared. The vehicle used here was an An Audi “S3” with a magnetic suspension system on the sport mode. An NI CompactRIO (or cDAQ) module was used to record from a lowing the cDAQ to communicate with the PCB 352C03 one-axis accelerometer. The accelerometer was firmly attached to the windshield of the car. This work focuses on the classification of four road surfaces (asphalt, dirt, concrete, and sand), though larger target sets were also considered. The most accurate method involved a MATLAB feature extraction package with a back-propagation neural network, yielding an overall accuracy of 97%. Lessons learned from this wide exploration of options may extend to other related classification problems.

This supplementary material complements the article and provides additional information to the chemical mechanism used in this work, boundary conditions for the LES con guration and table generation, comparisons of axial velocities, results from a LES/ nite-rate chemistry (FRC) approach, and results from the LES/DTF/SPF approach with a particular chemistry table that is generated using a single strained premixed amelet solution.

Polyurethane foams are used widely for encapsulation and structural purposes because they are inexpensive, straightforward to process, amenable to a wide range of density variations (1 lb/ft3 - 50 lb/ft3), and able to fill complex molds quickly and effectively. Computational model of the filling and curing process are needed to reduce defects such as voids, out-of-specification density, density gradients, foam decomposition from high temperatures due to exotherms, and incomplete filling. This paper details the development of a computational fluid dynamics model of a moderate density PMDI structural foam, PMDI-10. PMDI is an isocyanate-based polyurethane foam, which is chemically blown with water. The polyol reacts with isocyanate to produces the polymer. PMDI- 10 is catalyzed giving it a short pot life: it foams and polymerizes to a solid within 5 minutes during normal processing. To achieve a higher density, the foam is over-packed to twice or more of its free rise density of 10 lb/ft3. The goal for modeling is to represent the expansion, filling of molds, and the polymerization of the foam. This will be used to reduce defects, optimize the mold design, troubleshoot the processed, and predict the final foam properties. A homogenized continuum model foaming and curing was developed based on reaction kinetics, documented in a recent paper; it uses a simplified mathematical formalism that decouples these two reactions. The chemo-rheology of PMDI is measured experimentally and fit to a generalized- Newtonian viscosity model that is dependent on the extent of cure, gas fraction, and temperature. The conservation equations, including the equations of motion, an energy balance, and three rate equations are solved via a stabilized finite element method. The equations are combined with a level set method to determine the location of the foam-gas interface as it evolves to fill the mold. Understanding the thermal history and loads on the foam due to exothermicity and oven curing is very important to the results, since the kinetics, viscosity, and other material properties are all sensitive to temperature. Results from the model are compared to experimental flow visualization data and post-test X-ray computed tomography (CT) data for the density. Several geometries are investigated including two configurations of a mock structural part and a bar geometry to specifically test the density model. We have found that the model predicts both average density and filling profiles well. However, it under predicts density gradients, especially in the gravity direction. Further model improvements are also discussed for future work.

Process efficiency is a crucial parameter for inertia friction welding (IFW) that is largely unknown at the present time. A new method has been developed to determine the transient profile of the IFW process efficiency by comparing the workpiece torque used to heat and deform the joint region to the total torque. Particularly, the former is measured by a torque load cell attached to the non-rotating workpiece while the latter is calculated from the deceleration rate of flywheel rotation. The experimentally-measured process efficiency for IFW of AISI 1018 steel rods is validated independently by the upset length estimated from an analytical equation of heat balance and the flash profile calculated from a finite element based thermal stress model. The transient behaviors of torque and efficiency during IFW are discussed based on the energy loss to machine bearings and the bond formation at the joint interface.

Design and construct a high-bay facility with penthouse mechanical area that meets the physical and functional requirements as described in this document. Using the Modular High Bay Programming Plan, contractor shall validate programming requirements including equipment to be installed; room sizes and quantities; heating, ventilation, and air- conditioning (HVAC) and electrical requirements, including sizing of equipment based on heat load; and other specific needs. Contractor shall provide the design development, engineering, construction drawings, construction and construction administration services required to completely define, document, and construct the facility to the requirements described in the Building XXX Programming Plan and those referenced herein.

The goal of the DOE OE ESS Safety Roadmap is to foster confidence in the safety and reliability of energy storage systems.

Threaded joints are a common fastening method in applications where disassembly may be required. With a fair amount of investigation of static behavior of threaded joints, less emphasis has been placed on the behavior of threaded joints subjected to transient impact loads. Understanding how energy is transferred across threaded joints under impact loading conditions is critical for improved design and optimization for extreme mechanical environments. Many factors, such as pre - torque, pre - tension load, and impact speed can affect how energy is transferred or dissipated across threaded joins. In addition, high-fidelity numerical simulation of mechanical response of threaded components under blast or impact loading requires reliable experiments and subsequent analyses. In this study, the energy dissipation behavior through a threaded joint under impact loading conditions is investigated using a Kolsky tension bar. The aim is to study possible energy dissipation behavior in both time and frequency domains while the threaded joint remains intact. New analytical methods to understand both time-and frequency-domain behavior of threaded joints are presented. Energy dissipation characteristics through steel-to-steel and steel-to-aluminum threaded joints were then investigated with varying parameters such as pre-torque and impact velocity.

Process induced residual stresses commonly occur in composite structures composed of dissimilar materials. These residual stresses form due to differences in the composite materials coefficients of thermal expansion as well as the shrinkage upon cure exhibited by most thermoset polymer matrix materials. Depending upon the specific geometric details of the composite structure and the materials curing parameters, it is possible that these residual stresses can result in interlaminar delamination and fracture within the composite as well as plastic deformation in the structures metallic materials. It is important to consider potential residual stresses when designing composite parts and their manufacturing processes. However, the experimental determination of residual stresses in prototype parts can be prohibitive, both in terms of financial and temporal costs. As an alternative to physical measurement, it is possible for computational tools to be used to quantify potential residual stresses in composite prototype parts. A simplified method for simulating residual stresses was previously validated with two simple bi-material structures. Continuing on, the objective of this study is to further validate the simplified method for simulating residual stresses for bi-material split rings of different composites and layup variations. The validation process uses uncertainty quantification to develop a distribution of possible simulated residual stress states that are compared to experimentally measured residual stress states of fabricated structures similar to those simulated. The results of the comparisons indicate that the proposed finite element modeling approach is capable of accurately simulating the formation of residual stresses in composite structures and a temperature independent material model is adequate within the composites glassy region.

In the California Industrial General Permit (IGP) 2014-0057-DWQ for storm water monitoring, effective July 1, 2015, there are 21 contaminants that have been assigned NAL (Numeric Action Level) values, both annual and instantaneous.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

An investigation of polyurethane foam filled with known flame retardant fillers including hydroxides, melamine, phosphate-containing compounds, and melamine phosphates was carried out to produce a low-cost material with high flame retardant efficiency. The impact of flame retardant fillers on the physical properties such a s composite foam density, glass transition temperature, storage modulus, and thermal expansion of composite foams was investigated with the goal of synthesizing a robust rigid foam with excellent flame retardant properties.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.