Publications

Numerical simulation of a repository for heatgenerating nuclear waste in fractured crystalline rock requires a method for simulating coupled heat and fluid flow and reactive radionuclide transport in both porous media (bentonite buffer, surface sediments) and fractured rock (the repository host rock). Discrete fracture networks (DFNs), networks of two-dimensional planes distributed in a three-dimensional domain, are commonly used to simulate isothermal fluid flow and particle transport in fractures, but unless coupled to a continuum, are incapable of simulating heat conduction through the rock matrix, and therefore incapable of capturing the effects of thermally driven fluid fluxes or of coupling chemical processes to thermal processes. We present a method for mapping a stochastically generated DFN to a porous medium domain that allows representation of porous and fractured media in the same domain, captures the behavior of radionuclide transport in fractured rock, and allows simulation of coupled heat and fluid flow including heat conduction through the matrix of the fractured rock. We apply the method within Sandia's Geologic Disposal Safety Assessment (GDSA) framework to conduct a post-closure performance assessment (PA) of a generic repository for commercial spent nuclear fuel in crystalline rock. The three-dimensional, kilometer-scale model domain contains approximately 4.5 million grid cells; grid refinement captures the detail of 3, 360 individual waste packages in 42 disposal drifts. Coupled heat and fluid flow and reactive transport are solved numerically with PFLOTRAN, a massively parallel multiphase flow and reactive transport code. Simulations of multiple fracture realizations were run to 1 million years, and indicate that, because of the channeled nature of fracture flow, thermally-driven fluid fluxes associated with peak repository temperatures may be a primary means of radionuclide transport out of the saturated repository. The channeled nature of fracture flow gives rise to unique challenges in uncertainty and sensitivity quantification, as radionuclide concentrations at any given location outside the repository depend heavily on the distribution of fractures in the domain.

Humic complexation has the potential to increase actinide mobility and hamper waste isolation in geologic nuclear waste repositories. This study shows that humic complexation of tetravalent actinides (Th(IV), U(IV), Np(IV), and Pu(IV)) has been overestimated in past performance assessments of the Waste Isolation Pilot Plant (WIPP). Major reductions are needed for PHUMSIM and PHUMCIM, the equilibrium concentration ratios of humic-bound aqueous actinide to non-colloidal aqueous actinide. These coefficients are currently set at a value of 6.3 based on Th(IV) measurements in particle size fractions of seawater. Actual humic partitioning is expected to be significantly lower in WIPP brines primarily because pH is higher (∼9) and concentrations of competing cations (e.g., Mg2+) are higher. In this work, data from recent studies of Th(IV)-humic, U(IV)-humic, and Ca2+-humic complexation are used to simulate competitive humic complexation under WIPP repository conditions and to estimate new An(IV) PHUMSIM and PHUMCIM values. The new lower coefficients reduce the humic-bound An(IV) concentrations by more than 99%, causing a reduction in total mobile An(IV) concentrations by 85% to 86%, assuming no other type of An(IV) colloid (i.e., intrinsic, microbial, and mineral fragment colloids) is present in significant concentrations.

Technology Readiness Assessment (TRA) is a formal process to aid in defining the remaining R&D needed to bring a new, complex technology system to full technical maturity. A geologic repository for high-level radioactive waste is a prototypical complex system, comprised of novel technologies and complex environmental conditions, but because it is intended to function passively and is comprised of both engineered and geologic barriers, the standard, engineered-system ("hardware") TRA process must be modified. Longstanding precedence employs a Safety Case (or Licensing Case) as the preferred vehicle for assembling all facets of knowledge to make a determination of repository system safety and deployment readiness. However, certain modifications to the established TRA process allow it to be applied advantageously in conjunction with the Safety Case. In particular, an adaptation of the established Features, Events, and Processes (FEPs) methodology can serve as a basis for a "TRA-like" maturity evaluation for various major components and subsystems of a deep geologic repository. The newly proposed Knowledge Readiness Assessment (KRA) process combines the best of both methodologies, i.e., of FEPs analysis and standard TRA evaluation, for establishing confidence in the post-closure performance of major repository components and subsystems.

Following previous experimental evidence of growth and arrest of Richtmyer-Meshkov instabilities in copper, we have used the CTH shock physics code to study and calibrate the effects of material strength at high strain rates. Highly resolved one and two-dimensional simulations were performed using the Johnson-Cook (JC), Mechanical Threshold Stress (MTS), and Preston-Tonks-Wallace (PTW) strength models. The one-dimensional simulations utilized a prescribed homogeneous deformation strain path covering strain rates observed in previous hydrodynamic instability experiments. Spall was modeled using a nominal threshold pressure model (PFRAC) and we use the Mie-Gruneisen equation of state to estimate the volumetric response of the experiments. Our results show good qualitative and quantitative agreement between numerical estimates and prior experiments in the strain rate regimes of interest.

Understanding subsurface fracture network properties at the field scale is important for a number of environmental and economic problems, including siting of spent nuclear fuel repositories, geothermal exploration, and many others. This typically encompasses large volumes of fractured rocks with the properties inferred from the observations at rock outcrops and, if available, from the measurements in exploratory boreholes, quarries, and tunnels. These data are inherently spatially limited and a stochastic model is required to extrapolate the fracture properties over the large volumes of rocks. This study (1) describes three different methods of generating fracture networks developed for use in the fractured continuum model (FCM) and (2) provides a few examples of how these methods impact the predictions of simulated groundwater transport. A detailed analysis of the transport simulations using FCM is provided in the separate paper by the same authors (to be presented at IHLRWM 2017 conference). FCM is based on the effective continuum approaches modified to represent fractures. The permeability of discrete fractures is mapped onto a regular three-dimensional grid. The x-, y-, and z effective permeability values of a grid block are calculated from the tensor. The tensor parameters are fracture aperture, dip, strike, and number of fractures in the grid block (spacing). All three methods use the fracture properties listed above to generate corresponding permeability fields. However, the assumptions and conceptual representation of fracture network from which these properties are derived are very different. The Sequential Gaussian Simulation (SGSim) method does not require an assumption regarding the fracture shape. Fracture aperture, spacing, and orientation are defined based on the field observations. Spatially correlated features (continuation of fracture in the direction of the orientation) are created using spatially correlated random numbers generated with SGSIM code. With this method an exact number of fractures cannot be generated. The Ellipsim method assumes that the fractures are two-dimensional elliptical shapes that can be described with radius and aspect ratio. The knowledge of the fracture (ellipse) radius probability distribution is required. The fracture aperture is calculated from the ellipse radius. For this option an exact number of fractures can be generated. The fracture networks generated with SGSim and Ellipsim are not necessarily connected. The connectivity is achieved indirectly via matrix permeability that can be viewed as the permeability of much smaller fractions. The discrete fracture network (DFN) generator assumes elliptical fracture shapes and requires the same parameters as Ellipsim. The principal difference is in connectivity. The DFN method creates the fracture network connectivity via an iterative process in which not connected clusters of fractures are removed. The permeability fields were generated with FCM using three different methods and the same fracture data set loosely based on the data from an existing site in granite rocks. A few examples of transport simulations are provided to demonstrate the major findings of the comparison.

Three-dimensional large eddy simulations (LES) are used to analyze a springtime Arctic mixed-phase stratocumulus observed on 26 April 2008 during the Indirect and Semi-Direct Aerosol Campaign. Two subgrid-scale turbulence parameterizations are compared. The first scheme is a 1.5-order turbulent kinetic energy (1.5-TKE) parameterization that has been previously applied to boundary layer cloud simulations. The second scheme, Cloud Layers Unified By Binormals (CLUBB), provides higher-order turbulent closure with scale awareness. The simulations, in comparisons with observations, show that both schemes produce the liquid profiles within measurement variability but underpredict ice water mass and overpredict ice number concentration. The simulation using CLUBB underpredicted liquid water path more than the simulation using the 1.5-TKE scheme, so the turbulent length scale and horizontal grid box size were increased to increase liquid water path and reduce dissipative energy. The LES simulations show this stratocumulus cloud to maintain a closed cellular structure, similar to observations. The updraft and downdraft cores self-organize into a larger meso-γ-scale convective pattern with the 1.5-TKE scheme, but the cores remain more isotropic with the CLUBB scheme. Additionally, the cores are often composed of liquid and ice instead of exclusively containing one or the other. These results provide insight into traditionally unresolved and unmeasurable aspects of an Arctic mixed-phase cloud. From analysis, this cloud’s updraft and downdraft cores appear smaller than other closed-cell stratocumulus such as midlatitude stratocumulus and Arctic autumnal mixed-phase stratocumulus due to the weaker downdrafts and lower precipitation rates. Plain Language Summary Low-lying clouds in the Arctic are ubiquitous and important to understand for the near-surface energy balance. These clouds are difficult to measure because of the challenging environment in which they reside. High-resolution models are tools that help fill in knowledge gaps about these clouds. In this work, we compare two different ways to represent fine motion within the cloud and see how the macrophysical properties of the cloud are affected. We found that one representation creates a more energetic cloud, and this type of cloud would exist longer than the other. We also are led to believe in these simulations that these clouds have different internal motions when compared to similar-looking clouds formed at lower latitudes or formed in a different season in the Arctic.

In this paper, we address the needed components to create usable engineering and operational user interfaces (UIs) for airborne Synthetic Aperture Radar (SAR) systems. As airborne SAR technology gains wider acceptance in the remote sensing and Intelligence, Surveillance, and Reconnaissance (ISR) communities, the need for effective and appropriate UIs to command and control these sensors has also increased. However, despite the growing demand for SAR in operational environments, the technology still faces an adoption roadblock, in large part due to the lack of effective UIs. It is common to find operational interfaces that have barely grown beyond the disparate tools engineers and technologists developed to demonstrate an initial concept or system. While sensor usability and utility are common requirements to engineers and operators, their objectives for interacting with the sensor are different. As such, the amount and type of information presented ought to be tailored to the specific application.

This paper details modeling of metal fragmentation of pipe-bomb configurations using the Euler-Lagrange code Zapotec. Zapotec couples the hydrocode CTH with the transient-dynamics finite element code Sierra/SM (also known as Presto) through a step-wise interchange of geometry, state data, and pressure. In this work, three experimental studies of pipe-bomb configurations were simulated using Zapotec, where the metal case was modeled using finite elements and the explosive was modeled with CTH. In the three examples, experimental and simulated debris distributions and early-time debris velocities generally showed excellent agreement. These studies both help build confidence in the use of Zapotec for simulating structural response under shock loadings.

Fast, accurate and robust automatic target recognition (ATR) in optical aerial imagery can provide game-changing advantages to military commanders and personnel. ATR algorithms must reject non-targets with a high degree of confidence in a world with an infinite number of possible input images. Furthermore, they must learn to recognize new targets without requiring massive data collections. Whereas most machine learning algorithms classify data in a closed set manner by mapping inputs to a fixed set of training classes, open set recognizers incorporate constraints that allow for inputs to be labelled as unknown. We have adapted two template-based open set recognizers to use computer generated synthetic images of military aircraft as training data, to provide a baseline for military-grade ATR: (1) a frequentist approach based on probabilistic fusion of extracted image features, and (2) an open set extension to the one-class support vector machine (SVM). These algorithms both use histograms of oriented gradients (HOG) as features as well as artificial augmentation of both real and synthetic image chips to take advantage of minimal training data. Our results show that open set recognizers trained with synthetic data and tested with real data can successfully discriminate real target inputs from non-targets. However, there is still a requirement for some knowledge of the real target in order to calibrate the relationship between synthetic template and target score distributions. We conclude by proposing algorithm modifications that may improve the ability of synthetic data to represent real data.

Fully-polarimetric X-band (9.6 GHz center frequency) VideoSAR with 0.125-meter ground resolution flew collections before, during, and after the fifth Source Physics Experiment (SPE-5) underground chemical explosion. We generate and exploit synthetic aperture RADAR (SAR) and VideoSAR products to characterize surface effects caused by the underground explosion. To our knowledge, this has never been done. Exploited VideoSAR products are "movies" of coherence maps, phase-difference maps, and magnitude imagery. These movies show two-dimensional, time-varying surface movement. However, objects located on the SPE pad created unwanted, vibrating signatures during the event which made registration and coherent processing more difficult. Nevertheless, there is evidence that dynamic changes are captured by VideoSAR during the event. VideoSAR provides a unique, coherent, time-varying measure of surface expression of an underground chemical explosion.

This paper details modeling of metal fragmentation of pipe-bomb configurations using the Euler-Lagrange code Zapotec. Zapotec couples the hydrocode CTH with the transient-dynamics finite element code Sierra/SM (also known as Presto) through a step-wise interchange of geometry, state data, and pressure. In this work, three experimental studies of pipe-bomb configurations were simulated using Zapotec, where the metal case was modeled using finite elements and the explosive was modeled with CTH. In the three examples, experimental and simulated debris distributions and early-time debris velocities generally showed excellent agreement. These studies both help build confidence in the use of Zapotec for simulating structural response under shock loadings.

Classical RANS turbulence models have known deficiencies when applied to jets in crossflow. Identifying the linear Boussinesq stress-strain hypothesis as a major contribution to erroneous prediction, we consider and contrast two machine learning frameworks for turbulence model development. Gene Expression Programming, an evolutionary algorithm that employs a survival of the fittest analogy, and a Deep Neural Network, based on neurological processing, add non-linear terms to the stress-strain relationship. The results are Explicit Algebraic Stress Model-like closures. High fidelity data from an inline jet in crossflow study is used to regress new closures. These models are then tested on a skewed jet to ascertain their predictive efficacy. For both methodologies, a vast improvement over the linear relationship is observed.

The thermal decomposition of H2O2 is an important process in hydrocarbon combustion playing a particularly crucial role in providing a source of radicals at high pressure where it controls the 3rd explosion limit in the H2-O2 system, and also as a branching reaction in intermediatetemperature hydrocarbon oxidation. As such, understanding the uncertainty in the rate expression for this reaction is crucial for predictive combustion computations. Raw experimental measurement data, and its associated noise and uncertainty, is typically unreported in most investigations of elementary reaction rates, making the direct derivation of the joint uncertainty structure of the parameters in rate expressions difficult. To overcome this, we employ a statistical inference procedure, relying on maximum entropy and approximate Bayesian computation methods, and using a two-level nested Markov Chain Monte Carlo algorithm, to arrive at a posterior density on rate parameters for a selected case of laser absorption measurements in a shock tube study, subject to the constraints imposed by the reported experimental statistics. The procedure constructs a set of H2O2 concentration decay profiles consistent with these reported statistics. These consistent data sets are then used to determine the joint posterior density on the rate parameters through straightforward Bayesian inference. Broadly, the method also provides a framework for the replication and comparison of missing data from different experiments, based on reported statistics, for the generation of consensus rate expressions.

With the increasing use of hydrocodes in modelling and system design, benchmarking of software against experiments has become even more vital. While substantial work has been done in this regard, comparisons with temperature data within dynamic experiments are sparse due to experimental limitations. However, novel developments in measurement techniques has enabled the in-flight acquisition of hypervelocity projectile temperature, providing a new source for validation. This is achieved by tracking the decay of an induced magnetic field which is related to conductivity and further correlated to material temperature. As such, an AC-14 bare shaped charge with a copper lining is simulated using CTH, and benchmarked against experimental temperature results observed by Uhlig and Hummer. Particular attention was given to the slug temperature profiles after separation, and the effect of varying equation-of-state and strength models. Simulations are in agreement with experimental results, with a best case of under 2% error between the observed and simulated temperatures for this shaped charge setup. This varied notably (around 20% variance) depending on strength model. Jet structures compare well with radiographic images and are consistent with ALEGRA simulations previously conducted. SAND2017-3686C.

Nuclear power plants are increasingly adding digital components for plant operation, safety, and security. These digital components fill a gap with legacy equipment where replacement components no longer exist. They also benefit operation of the plant by increasing efficiency in power generation, monitoring of equipment and plant parameters, as well as aiding operator control. However, the addition of digital components and systems also adds cyber risks with previously unanalyzed failure modes and attack vectors are introduced with these new systems. These risks are difficult to identify, analyze, and mitigate due to the increasingly complex nature of the digital components and the integration of these components with additional plant processes and communication networks. The research presented in this paper develops a new method that addresses the cyber risk to inform appropriate levels of protection. EPRI and Sandia are working under a Cooperative Research and Development Agreement to develop an effective method of evaluating the cyber risk in production nuclear power facilities. The Cyber Hazards Analysis Risk Methodology (CHARM) focuses on ensuring adequate controls are in place for appropriate cyber protection of the plant from radiological release or generation risk. Existing plant hazards analyses (e.g., PRA, FTA) do not account for software deficiencies or adversarial intent. This method leverages existing plant analyses and MIT’s Systems Theoretic Process Analysis (STPA) to create cyber informed fault trees. These new fault trees will provide the basis for comprehensive cyber risk analysis and help ensure potential gaps in cyber security controls are identified and corrected.

While the low cost and strong safety record of lead-acid batteries make them an appealing option compared to lithium-ion technologies for stationary storage, they can be rapidly degraded by the extended periods of high rate, partial state-of-charge operation required in such applications. Degradation occurs primarily through a process called hard sulfation, where large PbSO4 crystals are formed on the negative battery plates, hindering charge acceptance and reducing battery capacity. Various researchers have found that the addition of some forms of excess carbon to the negative active mass in lead-acid batteries can mitigate hard sulfation, but the mechanism through which this is accomplished is unclear. In this work, the effect of carbon composition and morphology was explored by characterizing four discrete types of carbon additives, then evaluating their effect when added to the negative electrodes within a traditional valve-regulated lead-acid battery design. The cycle life for the carbon modified cells was significantly larger than an unmodified control, with cells containing a mixture of graphitic carbon and carbon black yielding the greatest improvement. The carbons also impacted other electrochemical aspects of the battery (e.g., float current, capacity, etc.) as well as physical characteristics of the negative active mass, such as the specific surface area.

With growing use of carbon fiber-epoxy in transportation systems, it is important to understand fire reaction properties of the composite to ensure passenger safety. Recently, a micro-scale pyrolysis study and macro-scale fire tests were performed using carbon fiber-epoxy at Sandia National Laboratories. Current work focuses on numerical modeling of the material conversion, pyrolysis, and gas-phase combustion that replicate the experiments. Large-eddy simulations (LES) and eddy-dissipation concept (EDC) approach are incorporated in the gas phase along with multiple relevant reaction model methods in the solid phase. The numerical methods that use multi-step pyrolysis rate expressions are validated by thermogravimetric analysis (TGA) results. The pyrolyzed fuel components participate in gas-phase combustion using a turbulent combustion model. The multi-phase combustion capability was further assessed using two cases: a single particle reaction and a solid panel exposed to strong radiant heat. The panel fire test indicates that the model accurately reproduces panel temperature profile while a weaker oxidation is predicted.

When a spherical liquid drop is subjected to a step change in relative gas velocity, aerodynamic forces lead to drop deformation and possible breakup into a number of secondary fragments. To investigate this flow, a digital in-line holography (DIH) diagnostic is proposed which enables rapid quantification of spatial statistics with limited experimental repetition. To overcome the high uncertainty in the depth direction experienced in previous applications of DIH, a crossed-beam, two-view configuration is introduced. With appropriate calibration, this diagnostic is shown to provide accurate quantification of fragment sizes, three-dimensional positions and three-component velocities in a large measurement volume. These capabilities are applied to investigate the aerodynamic breakup of drops at two non-dimensional Weber numbers, We, corresponding to the bag (We = 14) and sheet-thinning (We = 55) regimes. Ensemble average results show the evolution of fragment size and velocity statistics during the course of breakup. Results indicate that mean fragment sizes increase throughout the course of breakup. For the bag breakup case, the evolution of a multi-mode fragment size probability density is observed. This is attributed to separate fragmentation mechanisms for the bag and rim structures. In contrast, for the sheet-thinning case, the fragment size probability density shows only one distinct peak indicating a single fragmentation mechanism. Compared to previous related investigations of this flow, many orders of magnitude more fragments are measured per condition, resulting in a significant improvement in data fidelity. For this reason, this experimental dataset is likely to provide new opportunities for detailed validation of analytic and computational models of this flow.

The Integrated Human Event Analysis System for nuclear power plant internal events at-power application (hereafter "IDHEAS AT-POWER") is a new human reliability analysis (HRA) method developed by the U.S. Nuclear Regulatory Commission (NRC) in collaboration with the Electric Power Research Institute (EPRI). It was developed to provide a structured approach to the qualitative and quantitative analysis of operator actions during internal, at-power nuclear power plant events. The IDHEAS AT-POWER method was tested to evaluate whether its guidance can be practically applied to produce consistent HRA results. This paper presents study findings and final conclusions on the method performance. Lessons learned on study methodology and recommendations for method improvement are also presented.

The directed, head-to-tail self-assembly of microtubule filaments may be generalized in the context of Janus colloidal rods. Specifically, their assembly at the tens of micron-length scale involves a careful balance between long-range electrostatic repulsion and short-range attractive forces. Here we show that the addition of counterion salts increases the rate of directed assembly by screening the electrostatic forces and enhancing the effectiveness of short-range interactions at the microtubule ends.

The Reacting Particle and Boundary Layer (RPBL) model computes the transient-state conditions for a spherical, reacting, porous char particle and its reacting boundary layer. RPBL computes the transport of gaseous species with a Maxwell-Stefan multicomponent approach. Mass transfer diffusion coefficients are corrected to account for a non-stagnant bulk flow condition using a factor based on the Sherwood number. The homogeneous gas phase reactions are modeled with a syngas mechanism, and the heterogeneous reactions are calculated with a six-step reaction mechanism. Both homogeneous and heterogeneous reaction mechanisms are implemented in Cantera. Carbon density (burnout) is computed using the Bhatia and Perlmutter model to estimate the evolution of the specific surface area. Energy equations are solved for the gas temperature and the particle temperature. The physical properties of the particle are computed from the fractions of ash, carbon, and voids in the particle. The void fraction is computed assuming a constant diameter particle during the reaction process. RPBL solves a particle momentum equation in order to estimate the position of the particle in a specific reactor. We performed a validation and uncertainty quantification study with RPBL using experimental char oxidation data obtained in an optically accessible, laminar, entrained flow reactor at Sandia National Laboratories. We used a consistency analysis to compare RPBL and experimental data (with its associated uncertainty) for three coal chars over a range of particle sizes. We found consistency for particle temperature and velocity across all experiments.

The proper consideration of the radiation correction for bare-wire thermocouple measurements requires consideration of the convective and radiative heat transfer of the thermocouple with its surroundings, as well as conductive heat transfer between the thermocouple bead and the connecting thermocouple wires. This has rarely been considered in the past, and to do so has involved complex simulation of the complete thermocouple energy balance. This paper reports on a new, easy-to-implement approach for calculating the proper radiant correction for thermocouples, subject to uncertainties associated with the relevant thermocouple and gas properties and limitations to characterizing convective heat transfer to the thermocouple bead and wires via standard correlations. Examples of the radiation correction computed with this new method as a function of temperature and bead and wire size are given, and are compared with traditional approaches considering heat transfer around either the thermocouple bead or the thermocouple wire.

Fluid-structure interactions were studied on a store with tunable structural natural frequencies in complex cavity flow. Different leading edge geometries, doors, and internal inserts were used to generate cavity pressure fields that were more representative of an actual aircraft bay. The store loading and response was characterized using point pressure and accelerometer measurements. These data were supplemented with high-frequency pressure-sensitive paint applied to both the store and to the cavity floor to capture the three-dimensional nature of the pressure field in the complex configurations. The natural frequencies of the store were then changed to allow a systematic study of mode matching between the structural natural frequencies and the dominant cavity tone frequencies. In the complex cavities, the store responded to the cavity resonant tones not only in the streamwise and wall-normal directions, but also the spanwise direction. That spanwise response to cavity tones was not observed for previous studies in a simple rectangular cavity, because the flow across the store width in the spanwise direction was uniform. This different behavior highlights the importance of using a representative bay geometry for prediction of the structural response of a store in a flight environment.

The combustion of molten metals is an important area of study with applications ranging from solid aluminized rocket propellants to fireworks displays. This work uses digital in-line holography (DIH) to experimentally quantify the three-dimensional position, size, and velocity of aluminum particles during combustion of ammonium perchlorate (AP) based solid-rocket propellants. In addition, spatially resolved particle temperatures are simultaneously measured using two-color imaging pyrometry. To allow for fast characterization of the properties of tens of thousands of particles, automated data processing routines are proposed. Using these methods, statistics from aluminum particles with diameters ranging from 15 to 900 µm are collected at an ambient pressure of 83 kPa. In the first set of DIH experiments, increasing initial propellant temperature is shown to enhance the agglomeration of nascent aluminum at the burning surface, resulting in ejection of large molten aluminum particles into the exhaust plume. The resulting particle number and volume distributions are quantified. In the second set of simultaneous DIH and pyrometry experiments, particle size and velocity relationships as well as temperature statistics are explored. The average measured temperatures are found to be 2640 ± 282 K, which compares well with previous estimates of the range of particle and gas-phase temperatures. The novel methods proposed here represent new capabilities for simultaneous quantification of the joint size, velocity, and temperature statistics during the combustion of molten metal particles. The proposed techniques are expected to be useful for detailed performance assessment of metalized solid-rocket propellants.

This work outlines a case study of charge-induced damage to SOI wafers that caused gate leakage in discrete transistors and static leakage in packaged integrated circuits (ICs). The consequential yield fallout occurred primarily at wafer center. Electrical, optical, and laser-based failure analysis techniques were used to characterize the damage and determine root cause of electrical failure. The failure mechanism was localized to a rinse step during chemical mechanical planarization (CMP). Furthermore, both current-voltage (IV) sweeps and characteristic spatial patterns generated by thermally-induced voltage alteration (TIVA) were used to capture the trends on both packaged ICs and SOI wafers for this type of charge-induced damage; this.led to quick identification of another source of charge-induced damage that affected the post-fab yield.

We present spatial profiles of temperature and soot-volume-fraction statistics from a sooting, 2-m base diameter turbulent pool fire, burning a 10%-toluene/90%-methanol fuel mixture. Dual-pump coherent anti-Stokes Raman scattering and laser-induced incandescence are utilized for simultaneous point measurements of temperature and soot. The research fuel-blend used here results in a lower soot loading than real transportation fuels, but allows us to apply high-fidelity laser diagnostics for spatially resolved measurements in a fully turbulent, buoyant fire of meter-scale base size. Profiles of mean and rms fluctuations are radially resolved across the fire plume, both within the hydrocarbon-rich vapor-dome region near fuel pool, and higher within the actively burning region of the fire. The spatial evolution of the soot and temperature probability density functions is discussed. Soot fluctuations display significant intermittency across the full extent of the fire plume for the research fuel blend used. Simultaneous, spatially overlapped temperature/soot measurements permit us to obtain estimates of joint statistics that are presented as spatially resolved conditional averages across the fire plume, and in terms of a joint pdf obtained by including measurements from multiple spatial locations. Within the actively burning region of the fire, soot is observed to occupy a limited temperature range between ∼1000 and 2000 K, with peak soot concentration occurring at 1600–1700 K across the full radial extent of the fire plume, despite marked changes in the local temperature pdf across the same spatial extent. A wider range of soot temperatures is observed in the fuel vapor-dome region low in the pool fire, with detectable cold soot persisting into conditionally averaged statistics. The results yield insight into soot temperature across a wide spatial extent of a fully turbulent pool fire of meaningful size, which are valuable for development of soot radiative-emission models and for validation of fire fluid-dynamics codes.

A detailed methodology was used to select the sea states tested in the final stage of the Wave Energy Prize (WEPrize), a public prize challenge sponsored by the U.S. Department of Energy [1]. The winner was selected based on two metrics: a threshold value expressing the benefit to effort ratio (ACE metric) and a second metric which included hydrodynamic performance-related quantities (HPQ). HPQ required additional sea states to query aspects of the techno-economic performance not addressed by ACE. Due to the nature of the WEPrize, limited time was allotted to each contestant for testing and thus a limitation on the total sea states was required. However, the applicability of these sea states was required to encompass seven deployment locations representative of the United States West Coast and Hawaii. A cluster analysis was applied to scatter diagrams in order to determine a subset of sea states that could be scaled to find the average annual power flux at each wave climate for the ACE metric. Four additional sea states were selected, including two highly energetic sea states and two bimodal sea states, to evaluate HPQ. These sea states offer a common experimental testing platform for performance in United States deployment climates.

The thermal decomposition of H2O2 is an important process in hydrocarbon combustion playing a particularly crucial role in providing a source of radicals at high pressure where it controls the 3rd explosion limit in the H2-O2 system, and also as a branching reaction in intermediatetemperature hydrocarbon oxidation. As such, understanding the uncertainty in the rate expression for this reaction is crucial for predictive combustion computations. Raw experimental measurement data, and its associated noise and uncertainty, is typically unreported in most investigations of elementary reaction rates, making the direct derivation of the joint uncertainty structure of the parameters in rate expressions difficult. To overcome this, we employ a statistical inference procedure, relying on maximum entropy and approximate Bayesian computation methods, and using a two-level nested Markov Chain Monte Carlo algorithm, to arrive at a posterior density on rate parameters for a selected case of laser absorption measurements in a shock tube study, subject to the constraints imposed by the reported experimental statistics. The procedure constructs a set of H2O2 concentration decay profiles consistent with these reported statistics. These consistent data sets are then used to determine the joint posterior density on the rate parameters through straightforward Bayesian inference. Broadly, the method also provides a framework for the replication and comparison of missing data from different experiments, based on reported statistics, for the generation of consensus rate expressions.

Dynamic materials properties experiments on Sandia National Laboratories Z Machine require increasingly precise electrical current pulse shaping. In the experiment described here, a copper flyer plate is accelerated from rest to 1,600 m/s over a 40 micron flight gap in 50 ns. This flyer then impacts a cerium sample, shock melting the cerium, before subsequent quasi-isentropic ramping to mega-bar pressures. Through predictive simulations, postdicted analysis, and a new computational tool for characterizing inherent Z Machine timing accuracy, qualitative estimates of pulse controllability and experimental design robustness are arrived upon.

Scattering environment conditions, such as fog, pose a challenge for many detection and surveillance active sensing operations in both ground and air platforms. For example, current autonomous vehicles rely on a range of optical sensors that are affected by degraded visual environments. Real-world fog conditions can vary widely depending on the location and environmental conditions during its creation. In our previous work we have shown benefits for increasing signal and range through scattering environments such as fog utilizing polarized light, specifically circular polarization. In this work we investigate the effect of changing fog particle sizes and distributions on polarization persistence for both circularly and linearly polarized light via simulation. We present polarization tracking Monte Carlo results for a range of realistic monodisperse particle sizes as well as varying particle size distributions as a model of scattering environments. We systematically vary the monodisperse particle size, mean particle size of a distribution, particle size distribution width, and number of distribution lobes (bi-modal), as they affect polarized light transmission through a scattering environment. We show that circular polarization signal persists better than linear polarization signal for most variations of the particle distribution parameters.

Scattering environment conditions, such as fog, pose a challenge for many detection and surveillance active sensing operations in both ground and air platforms. For example, current autonomous vehicles rely on a range of optical sensors that are affected by degraded visual environments. Real-world fog conditions can vary widely depending on the location and environmental conditions during its creation. In our previous work we have shown benefits for increasing signal and range through scattering environments such as fog utilizing polarized light, specifically circular polarization. In this work we investigate the effect of changing fog particle sizes and distributions on polarization persistence for both circularly and linearly polarized light via simulation. We present polarization tracking Monte Carlo results for a range of realistic monodisperse particle sizes as well as varying particle size distributions as a model of scattering environments. We systematically vary the monodisperse particle size, mean particle size of a distribution, particle size distribution width, and number of distribution lobes (bi-modal), as they affect polarized light transmission through a scattering environment. We show that circular polarization signal persists better than linear polarization signal for most variations of the particle distribution parameters.

Target heterogeneities, such as cracks, faults, joints, and blocks, are known to influence impact crater morphology on planetary surfaces. We perform a preliminary investigation into how the relationship of target heterogeneity size to projectile size affects the cratering process and final crater morphology for a fixed impact velocity. We use the CTH hydrocode to numerically simulate these impacts into a strong target with idealized heterogeneities where the ratio of the projectile size and heterogeneity size is varied. When the projectile is significantly smaller than the size of the heterogeneities, the pressure field decay is similar to that for a half-space impact into a homogenous target. In contrast, when the projectile size is comparable to or larger than the heterogeneities, we observe more efficient attenuation of the shockwave, resulting in decreased cratering efficiency. The attenuation of the shockwave is caused by rarefaction waves reflecting off of the free surfaces of the heterogeneities and internal energy losses resulting from void space collapse when the target strength is overcome by the impact energy.

Sandia National Laboratories and the National Renewable Energy Laboratory conducted a field campaign at the Scaled Wind Farm Technology (SWiFT) Facility using a customized scanning lidar from the Technical University of Denmark. The results from this field campaign will support the validation of computational models to predict wake dissipation and wake trajectory offset downstream of a stand-alone wind turbine. In particular, regarding the effect of changes in the atmospheric boundary layer inflow state and turbine yaw offset. A key step in this validation process involves quantifying, and reducing, the uncertainty in the wake measurements. The present work summarizes the process that was used to calibrate the alignment of the lidar in order to reduce this source of uncertainty in the experimental data from the SWiFT field test.

Unstructured grid adaptation is a technology that holds the potential to improve the automation and accuracy of computational fluid dynamics and other computational disciplines. Difficulty producing the highly anisotropic elements necessary for simulation on complex curved geometries that satisfies a resolution request has limited this technology's widespread adoption. The Unstructured Grid Adaptation Working Group is an open gathering of researchers working on adapting simplicial meshes to conform to a metric field. Current members span a wide range of institutions including academia, industry, and national laboratories. The purpose of this group is to create a common basis for understanding and improving mesh adaptation. We present our first major contribution: a common set of benchmark cases, including input meshes and analytic metric specifications, that are publicly available to be used for evaluating any mesh adaptation code. We also present the results of several existing codes on these benchmark cases, to illustrate their utility in identifying key challenges common to all codes and important differences between available codes. Future directions are defined to expand this benchmark to mature the technology necessary to impact practical simulation workflows.

Degraded visual environments are a serious concern for modern sensing and surveillance systems. Fog is of interest due to the frequency of its formation along our coastlines disrupting border security and surveillance. Fog presents hurdles in intelligence and reconnaissance by preventing data collection with optical systems for extended periods. We will present recent results from our work in operating optical systems in our controlled fog experimental chamber. This facility is a 180-foot-long, 10-foot-wide, and 10-foot-Tall structure that has over 60 spray nozzles to achieve uniform aerosol coverage with various particle size, distributions, and densities. We will discuss the physical formation of fog in nature and how our generated fog compares. In addition, we will discuss fog distributions and characterization techniques. We will investigate the biases of different methods and discuss the different techniques that are appropriate for realistic environments. Finally, we will compare the data obtained from our characterization studies against accepted models (e.g., MODTRAN) and validate the usage of this unique capability as a controlled experimental realization of natural fog formations. By proving the capability, we will enable the testing and validation of future fog penetrating optical systems and providing a platform for performing optical propagation experimentation in a known, stable, and controlled environment.

Structural dynamics models with localized nonlinearities can be reduced using Hurty/Craig-Bampton component mode synthesis methods. The interior degrees-of-freedom of the linear subcomponents are reduced with a set of dynamic fixedinterface modes while the static constraint modes preserve the physical coordinates at which the nonlinear restoring forces are applied. For finite element models with a highly refined mesh at the boundary, a secondary modal analysis can be performed to reduce the interface down to a truncated set of local-level characteristic constraint modes. In this research, the cost savings and accuracy of the interface reduction technique are evaluated on a simple example problem involving two elastic blocks coming into contact.

The reactions of Criegee intermediates with NO2 have been proposed as a potentially significant source of the important nighttime oxidant NO3, particularly in urban environments where concentrations of ozone, alkenes and NOx are high. However, previous efforts to characterize the yield of NO3 from these reactions have been inconclusive, with many studies failing to detect NO3. In the present work, the reactions of formaldehyde oxide (CH2OO) and acetaldehyde oxide (CH3CHOO) with NO2 are revisited to further explore the product formation over a pressure range of 4-40 Torr. NO3 is not observed; however, temporally resolved and [NO2]-dependent signal is observed at the mass of the Criegee-NO2 adduct for both formaldehyde- and acetaldehyde-oxide systems, and the structure of this adduct is explored through ab initio calculations. The atmospheric implications of the title reaction are investigated through global modelling.

We experimentally demonstrate high-contrast, ultrafast switching of infrared light at 2.1 μm via intraband pumping of a high quality factor perfect absorber made from a highly doped cadmium oxide thin film.

We investigate a novel application of deep neural networks to modeling of errors in prediction of surface pressure fluctuations beneath a compressible, turbulent flow. In this context, the truth solution is given by Direct Numerical Simulation (DNS) data, while the predictive model is a wall-modeled Large Eddy Simulation (LES). The neural network provides a means to map relevant statistical flow-features within the LES solution to errors in prediction of wall pressure spectra. We simulate a number of flat plate turbulent boundary layers using both DNS and wall-modeled LES to build up a database with which to train the neural network. We then apply machine learning techniques to develop an optimized neural network model for the error in terms of relevant flow features.

There has only been one naturally occurring reactor region (Oklo) identified historically. There has to have been other factors that prevented uncontrolled nuclear criticality events. There are higher concentration uranium depositions in the earth's crust than the Oklo region, that did not go critical based on uranium enrichment. There are many papers on the Oklo phenomena which do not address why the uranium did not reach criticality prior to the historical point of 2 billion years ago, nor do they specifically address the lack of radiogenic lead in any of the uranium deposits. Consideration of the lack of lead as a potential indicator of the age of the earth as being a possible factor. Reports which address the leaching effect could consider the reactivity effect of moderation associated with higher enrichment uranium. The lack of radiogenic lead associated with the uranium may or may not be due to leaching. Also, the higher concentration uranium deposits (>15%) were discovered in the 1990s, and reevaluation of the overall effect on a natural reactor criticality were not considered. The high reactivity levels and the low quantity of radiogenic lead identified in uranium tailings, tends to favor a significantly shorter time period or a highly efficient naturally occurring leaching process. A shorter time period would reduce uranium mass and enrichment. Given even a small quantity of moderator would allow an uncontrolled nuclear criticality for high concentration uranium deposits for enrichment between 3 and 8 percent 235 U. The evaluation and analysis of the nuclear criticality safety factors should be evaluated further to document the actual uranium ore grade, and Pb constituents. Identification of the macro-scale quantity (PPM) of radiogenic lead coupled with the NCS factors could be a more useful tool for determining the age of the earth. Further calculations could be considered to determine the impact of different rock formations and materials where uranium is located, and evaluation of the natural leaching of uranium and its decay by-products to associate the effect of radiogenic lead or other materials.

Molecular dynamics simulations of polycrystalline growth of CdTe/CdS heterostructures have been performed. First, CdS was deposited on an amorphous CdS substrate, forming a polycrystalline film. Subsequently, CdTe was deposited on top of the polycrystalline CdS film. Cross-sectional images show grain formation at early stages of the CdS growth. During CdTe deposition, the CdS structure remains almost unchanged. Concurrently, CdTe grain boundary motion was detected after the first 24.4 nanoseconds of CdTe deposition. With the elapse of time, this grain boundary pins along the CdS/CdTe interface, leaving only a small region of epitaxial growth. CdTe grains are larger than CdS grains in agreement with experimental observations in the literature. Crystal phase analysis shows that zinc blende structure dominates over the wurtzite structure inside both CdS and CdTe grains. Composition analysis shows Te and S diffusion to the CdS and CdTe films, respectively. These simulated results may stimulate new ideas for studying and improving CdTe solar cell efficiency.

We demonstrate a silicon photonic transceiver circuit to implement polarization encoding/decoding for DV-QKD. The circuit is capable of encoding BB84 states with >30 dB PER and decoding with >20 dB ER.

The need to avoid SI engine knock often comes with an efficiency penalty, motivating efforts to understand its causes. In this study, the relationship between knock and end-gas autoignition is examined based on experiments in a DISI engine with CR = 12, using three different gasoline fuels. The three gasolines are an alkylate blend (RON=98, MON=97), a blend with high aromatic content (RON=98, MON=88), and a blend of 30% ethanol by volume (RON=98, MON=87). Fuel/air-equivalence ratio (φ) sweeps show that the response of the knock limits to changes in φ varies between the fuels. Furthermore, it is observed that the statistics of knock and heat-release vary in a complex manner between the fuels. Since knock originates from end-gas autoignition, a robust heat-release-based metric of "trace autoignition" is developed to determine knock occurrence. The metric is based on the observation that autoigniting cycles exhibit a faster heat-release rate decay at the end of the combustion event, even if no knock oscillations are detected. In general, the acoustic knock trends match the trends of the "trace autoignition" metric. However, for rich operation, the Alkylate fuel needs to be operated with an average CA50 that is somewhat retarded relative to trace autoignition. Furthermore, the data reveal that the dependence of autoignition on φ varies in manner that relates to the RON-MON octane sensitivity of the fuel. For example, the Alkylate fuel with low octane sensitivity displays no benefit of fuel enrichment, which is in strong contrast to the two high octane-sensitive fuels. Further, the Alkylate fuel shows a strong reduction of its anti-knock quality for lean operation, which correlates with the development of low-temperature heat release.

We experimentally demonstrate amplitude modulators (AMs) with >65 dB extinction across over a 160 nm spectral range. The output optical phase response is also characterized when the amplitude is modulated.

A series of nickel(ii) aryloxide ([Ni(OAr)2(py)x]) precursors was synthesized from an amide-alcohol exchange using [Ni(NR2)2] in the presence of pyridine (py). The H-OAr selected were the mono- and di-ortho-substituted 2-alkyl phenols: alkyl = methyl (H-oMP), iso-propyl (H-oPP), tert-butyl (H-oBP) and 2,6-di-alkyl phenols (alkyl = di-iso-propyl (H-DIP), di-tert-butyl (H-DBP), di-phenyl (H-DPhP)). The crystalline products were solved as solvated monomers and structurally characterized as [Ni(OAr)2(py)x], where x = 4: OAr = oMP (1), oPP (2); x = 3: OAr = oBP (3), DIP (4); x = 2: OAr = DBP (5), DPhP (6). The excited states (singlet or triplet) and various geometries of 1-6 were identified by experimental UV-vis and verified by computational modeling. Magnetic susceptibility of the representative compound 4 was fit to a Curie Weiss model that yielded a magnetic moment of 4.38(3)μB consistent with a Ni2+ center. Compounds 1 and 6 were selected for decomposition studied under solution precipitation routes since they represent the two extremes of coordination. The particle size and crystalline structure were characterized using transmission electron microscopy (TEM) and powder X-ray diffraction (PXRD). The materials isolated from 1 and 6 were found by TEM to form irregular shape nanomaterials (8-15 nm), which by PXRD were found to be Ni0 hcp (PDF: 01-089-7129) and fcc (PDF: 01-070-0989), respectively.

A scaling methodology is described in the present paper for extreme-scale wind turbines (rated at 10 MW or more) that allow their sub-scale turbines to capture their key blade dynamics and aeroelastic deflections. For extreme-scale turbines, such deflections and dynamics can be substantial and are primarily driven by centrifugal, thrust and gravity forces as well as the net torque. Each of these are in turn a function of various wind conditions, including turbulence levels that cause shear, veer, and gust loads. The 13.2 MW rated SNL100-03 rotor design, having a blade length of 100-meters, is herein scaled to the CART3 wind turbine at NREL using 25% geometric scaling and blade mass and wind speed scaled by gravo-aeroelastic constraints. In order to mimic the ultralight structure on the advanced concept extreme-scale design the scaling results indicate that the gravoaeroelastically scaled blades for the CART3 are be three times lighter and 25% longer than the current CART3 blades. A benefit of this scaling approach is that the scaled wind speeds needed for testing are reduced (in this case by a factor of two), allowing testing under extreme gust conditions to be much more easily achieved. Most importantly, this scaling approach can investigate extreme-scale concepts including dynamic behaviors and aeroelastic deflections (including flutter) at an extremely small fraction of the full-scale cost.

Amagat and Dalton mixing-models were analyzed to compare their thermodynamic prediction of shock states. Numerical simulations utilized the Sandra National Laboratories (SNL) shock hydrodynamic code CTH [1]. Simulations modeled the University of New Mexico (UNM) shock tube laboratory experimental series shocking a 1:1 molar mixture of helium (He) and sulfur hexafluoride (SF6). Five input parameters were varied for sensitivity analysis: driver section pressure; driver section density; test section pressure; test section density; and mixture ratio (mole fraction). We show via incremental Latin hypercube sampling (LHS) analysis that significant differences exist between Amagat and Dalton mixing-model predictions. The differences observed in predicted shock speeds, temperatures, and pressures grow more pronounced with higher shock speeds.

A parallel, adaptive overlay grid procedure is proposed for use in generating all-hex meshes for stochastic (SVE) and representative (RVE) volume elements in computational materials modeling. The mesh generation process is outlined including several new advancements such as data filtering to improve mesh quality from voxelated and 3D image sources, improvements to the primal contouring method for constructing material interfaces and pillowing to improve mesh quality at boundaries. We show specific examples in crystal plasticity and syntactic foam modeling that have benefitted from the proposed mesh generation procedure and illustrate results of the procedure with several practical mesh examples.

We study the role of carrier mobility in transparent conducting oxides integrated into epsilon-near-zero modulators. High-mobility materials including CdO enable sub-micron length electroabsorption modulators through >4dB/μm extinction ratios.

Percussive hammers are a promising advance in drilling technology for geothermal since they rely upon rock reduction mechanisms that are well-suited for use in the hard, brittle rock characteristic of geothermal formations. Also known as down-the-hole (DTH) hammers, they are also compatible with low-density fluids that are often used for geothermal drilling. Experience in mining and oil and gas drilling has demonstrated their utility for penetrating hard rock. One limitation to more wide-scale deployment is the ability of the tools to operate at high temperatures (∼300°C) due to elastomers used in the construction and the lubrication required for operation. As part of a United States Department of Energy Funding Opportunity Announcement award, Atlas Copco was tasked with developing a high-temperature DTH capable of being used in geothermal environments. A full-scale development effort including design, build, and testing was pursued for the project. This report summarizes the results of the percussive hammer development efforts between Atlas-Copco Secoroc and Sandia National Labs as part of DE-FOA-EE0005502. Certain design details have been omitted due to the proprietary nature of the information.

Lithium-ion batteries are a central technology to our daily lives with widespread use in mobile devices and electric vehicles. These batteries are also beginning to be widely used in electric grid infrastructure support applications which have stringent safety and reliability requirements. Typically, electrochemical performance data is not available for modelers to validate their simulations, mechanisms, and algorithms for lithium-ion battery performance and lifetime. In this paper, we report on the electrochemical performance of commercial 18650 cells at a variety of temperatures and discharge currents. We found that LiFePO4 is temperature tolerant for discharge currents at or below 10 A whereas LiCoO2, LiNixCoyAl1-x-yO2, and LiNi0.80Mn0.15Co0.05O2 exhibited optimal electrochemical performance when the temperature is maintained at 15◦C. LiNixCoyAl1-x-yO2 showed signs of lithium plating at lower temperatures, evidenced by irreversible capacity loss and emergence of a high-voltage differential capacity peak. Furthermore, all cells need to be monitored for self-heating, as environment temperature and high discharge currents may elicit an unintended abuse condition. Overall, this study shows that lithium-ion batteries are highly application-specific and electrochemical behavior must be well understood for safe and reliable operation. Additionally, data collected in this study is available for anyone to download for further analysis and model validation.

Compressible jet-in-crossflow interactions are poorly simulated using Reynolds-Averaged Navier Stokes (RANS) equations. This is due to model-form errors (physical approximations) in RANS as well as the use of parameter values simply picked from literature (hence- forth, the nominal values of the parameters). Previous work on the Bayesian calibration of RANS models has yielded joint probability densities of C = (Cµ;Cϵ2;Cϵ1), the most influential parameters of the RANS equations. The calibrated values were far more predictive than the nominal parameter values and the advantage held across a range of freestream Mach numbers and jet strengths. In this work we perform Bayesian calibration across a range of Mach numbers and jet strengths and compare the joint densities, with a view of determining whether compressible jet-in-crossflow could be simulated with either a single joint probability density or a point estimate for C. We find that probability densities for ;Cϵ2 agree and also indicate that the range typically used in aerodynamic simulations should be extended. The densities for ;Cϵ1 agree, approximately, with the nominal value. The densities for ;Cµ do not show any clear trend, indicating that they are not strongly constrained by the calibration observables, and in turn, do not affect them much. We also compare the calibrated results to a recently developed analytical model of a jet-in-cross flow interaction. We find that the values of C estimated by the analytical model delivers prediction accuracies comparable to the calibrated joint densities of the parameters across a range of Mach numbers and jet strengths.

High performance computing (HPC) is undergoing a dramatic change in computing architectures. Nextgeneration HPC systems are being based primarily on many-core processing units and general purpose graphics processing units (GPUs). A computing node on a next-generation system can be, and in practice is, heterogeneous in nature, involving multiple memory spaces and multiple execution spaces. This presents a challenge for the development of application codes that wish to compute at the extreme scales afforded by these next-generation HPC technologies and systems - the best parallel programming model for one system is not necessarily the best parallel programming model for another. This inevitably raises the following question: how does an application code achieve high performance on disparate computing architectures without having entirely different, or at least significantly different, code paths, one for each architecture? This question has given rise to the term ‘performance portability’, a notion concerned with porting application code performance from architecture to architecture using a single code base. In this paper, we present the work being done at Sandia National Labs to develop a performance portable compressible CFD code that is targeting the ‘leadership’ class supercomputers the National Nuclear Security Administration (NNSA) is acquiring over the course of the next decade.

Abstract not provided.

High performance computing (HPC) is undergoing a dramatic change in computing architectures. Nextgeneration HPC systems are being based primarily on many-core processing units and general purpose graphics processing units (GPUs). A computing node on a next-generation system can be, and in practice is, heterogeneous in nature, involving multiple memory spaces and multiple execution spaces. This presents a challenge for the development of application codes that wish to compute at the extreme scales afforded by these next-generation HPC technologies and systems - the best parallel programming model for one system is not necessarily the best parallel programming model for another. This inevitably raises the following question: how does an application code achieve high performance on disparate computing architectures without having entirely different, or at least significantly different, code paths, one for each architecture? This question has given rise to the term ‘performance portability’, a notion concerned with porting application code performance from architecture to architecture using a single code base. In this paper, we present the work being done at Sandia National Labs to develop a performance portable compressible CFD code that is targeting the ‘leadership’ class supercomputers the National Nuclear Security Administration (NNSA) is acquiring over the course of the next decade.

Abstract not provided.

Abstract not provided.

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Plasmonic lasers generate coherent long-range or localized surface-plasmon-polaritons (SPPs), where the SPP mode exists at the interface of the metal (or a metallic nanoparticle) and a dielectric. Metallic-cavities sup- porting SPP modes are also utilized for terahertz quantum-cascade lasers (QCLs). Due to subwavelength apertures, plasmonic lasers have highly divergent radiation patterns. Recently, we theoretically and experimentally demonstrated a new technique for implementing distributed-feedback (DFB), which is termed as an antenna- feedback scheme, to establish a hybrid SPP mode in the surrounding medium of a plasmonic laser's cavity with a large wavefront. This technique allows such lasers to radiate in narrow beams without requirement of any specific design considerations for phase-matching. Experimental demonstration is done for terahertz QCLs that show beam-divergence as small as 4-degrees. The antenna-feedback scheme has a characteristic feature in that refractive-index of the laser's surrounding medium affects its radiative frequency in the same vein as refractive- index of the cavity. Hence, any perturbations in the refractive-index of the surrounding medium could lead to large modulation in the laser's emission frequency. Along this line, we report ∼57 GHz reversible, continuous, and mode-hop-free tuning of such QCLs operating at 78 K based on post-process deposition/etching of a dielectric on an already mounted QCL chip. This is the largest tuning range achieved for terahertz QCLs when operating much above the temperature of liquid-Helium. We review the aforementioned experimental results and discuss methods to increase optical power output from terahertz QCLs with antenna-feedback. Peak power output of ∼13 mW is realized for a 3.3 THz QCL operating in a Stirling cooler at 54 K. A new dual-slit photonic structure based on antenna-feedback scheme is proposed to further improve output power as well as provide enhanced tunability.

High-performance computing (HPC) demands high bandwidth and low latency in I/O performance leading to the development of storage systems and I/O software components that strive to provide greater and greater performance. However, capital and energy budgets along with increasing storage capacity requirements have motivated the search for lower cost, large storage systems for HPC. With Burst Buffer technology increasing the bandwidth and reducing the latency for I/O between the compute and storage systems, the back-end storage bandwidth and latency requirements can be reduced, especially underneath an adequately sized modern parallel file system. Cloud computing has led to the development of large, low-cost storage solutions where design has focused on high capacity, availability, and low energy consumption at lowest cost. Cloud computing storage systems leverage duplicates and erasure coding technology to provide high availability at much lower cost than traditional HPC storage systems. Leveraging certain cloud storage infrastructure and concepts in HPC would be valuable economically in terms of cost-effective performance for certain storage tiers. To enable the use of cloud storage technologies for HPC we study the architecture for interfacing cloud storage between the HPC parallel file systems and the archive storage. In this paper, we report our comparison of two erasure coding implementations for the Ceph file system. We compare measurements of various degrees of sharding that are relevant for HPC applications. We show that the Gibraltar GPU Erasure coding library outperforms a CPU implementation of an erasure coding plugin for the Ceph object storage system, opening the potential for new ways to architect such storage systems based on Ceph.

A series of tests have been performed on Sierra White granite subjected to general (true triaxial) states of stress. Tests were performed under constant Lode angle conditions at Lode angles of 23.4, 16.1 and 0°. The constant Lode angle condition was maintained by holding the minimum principal stress constant while increasing the maximum and intermediate principal stress at a predetermined ratio. Tests were performed at minimum principal stresses of 5, 17 and 30 MPa. All of the specimens failed in a brittle manner, with significant dilatant volume strain accumulated, and failure showed a strong dependence on Lode angle. Specimens behaved in a nearly linear elastic manner until approximately 75% of the peak stress was reached. The angle of the failure feature (shear band) was compared to predictions developed by using the Rudnicki and Rice (1975) localization criterion. It was found that there was good agreement (within 7°) between the experimental results and theoretical predictions.

This paper provides a review of current state-of-the-art commercial central receiver systems and emerging technologies intended to increase the outlet temperature to >700 °C. Research on particle-based, gas-based, and liquid-based receiver designs that can achieve these higher temperatures are discussed. Particle-based technologies include directly irradiated designs (free-falling, obstructed, centrifugal) and enclosed designs (gravity fed, fluidized). New gas-based receivers include micro-channel designs and light-trapping configurations that increase the surface area, heat transfer, and solar absorptance to enable higher fluxes and pressures. Liquid-based receivers and materials that are reviewed include high-temperature halide salts (chlorides and fluorides), carbonate salts, and liquid metals (sodium and lead bismuth). Advantages and challenges associated with each of the technologies and receiver designs are presented.

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 and the shrinkage upon cure exhibited by 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 could result in interlaminar delamination or fracture within the composite. Therefore, the consideration of potential residual stresses is important when designing composite parts and their manufacturing processes. However, the experimental determination of residual stresses in prototype parts can be time and cost prohibitive. As an alternative to physical measurement, it is possible for computational tools to be used to quantify potential residual stresses in composite prototype parts. Therefore, the objective of this study is the development of a simplistic method for simulating the residual stresses formed in polymer matrix composite structures. Specifically, a simplified approach accounting for both coefficient of thermal expansion mismatch and polymer shrinkage is implemented within the Sandia National Laboratories' developed SIERRA/SolidMechanics code Adagio. Concurrent with the model development, two simple, bi-material structures composed of a carbon fiber/epoxy composite and aluminum, a flat plate and a cylinder, are fabricated and the residual stresses are quantified through the measurement of deformation. Then, in the process of validating the developed modeling approach with the experimental residual stress data, manufacturing process simulations of the two simple structures are developed and undergo a formal verification and validation process, including a mesh convergence study, sensitivity analysis, and uncertainty quantification. The simulations' final results show adequate agreement with the experimental measurements, indicating the validity of a simple modeling approach, as well as a necessity for the inclusion of material parameter uncertainty in the final residual stress predictions.

Challenge scenarios provide an opportunity to assess the blind, quantitative predictive ability of simulation methods against a previously unseen failure problem. Rather than evaluate the predictions of a single simulation approach, the Sandia Fracture Challenges rely on numerous volunteer teams with expertise in computational mechanics to apply a broad range of computational methods, numerical algorithms, and constitutive models to the challenge.

High-performance computing (HPC) demands high bandwidth and low latency in I/O performance leading to the development of storage systems and I/O software components that strive to provide greater and greater performance. However, capital and energy budgets along with increasing storage capacity requirements have motivated the search for lower cost, large storage systems for HPC. With Burst Buffer technology increasing the bandwidth and reducing the latency for I/O between the compute and storage systems, the back-end storage bandwidth and latency requirements can be reduced, especially underneath an adequately sized modern parallel file system. Cloud computing has led to the development of large, low-cost storage solutions where design has focused on high capacity, availability, and low energy consumption at lowest cost. Cloud computing storage systems leverage duplicates and erasure coding technology to provide high availability at much lower cost than traditional HPC storage systems. Leveraging certain cloud storage infrastructure and concepts in HPC would be valuable economically in terms of cost-effective performance for certain storage tiers. To enable the use of cloud storage technologies for HPC we study the architecture for interfacing cloud storage between the HPC parallel file systems and the archive storage. In this paper, we report our comparison of two erasure coding implementations for the Ceph file system. We compare measurements of various degrees of sharding that are relevant for HPC applications. We show that the Gibraltar GPU Erasure coding library outperforms a CPU implementation of an erasure coding plugin for the Ceph object storage system, opening the potential for new ways to architect such storage systems based on Ceph.

Nuclear power plants and facilities have been implementing digital system upgrades into their previously analog systems for well over twenty years. New nuclear facilities’ control, security, and emergency preparedness systems are almost exclusively built on digital architectures with a high degree of communication between the various systems that are often integrated together into a central control station to aid in operation or security of the facility. As digital systems become more widespread in nuclear facility control system architectures, cyber security related issues have become a significant concern to operators, regulators, governments, and other groups. Among the many concerns related to digital systems and cyber security is the area of common cause and common mode failures. This paper introduces, defines, and discusses some sources of common cause failure from a cyber security perspective: common vector access. This refers to specific access points that an adversary can exploit through a single attack sequence that have the potential to provide relational failures through common cause on multiple components, subsystems, systems, or plants. This paper will further discuss interconnected processes where these access points may exist, the importance of limiting or controlling these pinch points, and some methods of protecting common vector access points.

Pulse-burst particle image velocimetry has been used to acquire time-resolved data at 37.5 kHz of the flow over a finite-width rectangular cavity at Mach 0.8. Power spectra of the particle image velocimetry data reveal four resonance modes that match the frequencies detected simultaneously using high-frequency wall pressure sensors, but whose magnitudes exhibit spatial dependence throughout the cavity. Spatiotemporal cross correlations of velocity to pressure were calculated after bandpass filtering for specific resonance frequencies. Cross-correlation magnitudes express the distribution of resonance energy, revealing local maxima and minima at the edges of the shear layer attributable to wave interference between downstream-and upstream-propagating disturbances. Turbulence intensities were calculated using a triple decomposition and are greatest in the core of the shear layer for higher modes, where resonant energies ordinarily are lower. Most of the energy for the lowest mode lies in the recirculation region and results principally from turbulence rather than resonance. Together, the velocity-pressure cross correlations and the triple-decomposition turbulence intensities explain the sources of energy identified in the spatial distributions of power spectra amplitudes.

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 structure's metallic materials. Therefore, the consideration of potential residual stresses is important 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 composed of aluminum and a carbon fiber/epoxy resin composite. Therefore, the objective of this study is to further validate the simplified method for simulating residual stresses for different composites and more complex structures. The simplified method accounts for both the coefficient of thermal expansion mismatch and polymer shrinkage through the calibration to an experimentally-determined stress-free temperature. This was implemented in Sandia National Laboratories' solid mechanics code, SIERRA, to model split rings with temperature independent and dependent material models. The split rings are comprised of two materials: Aluminum with either a carbon fiber/epoxy resin composite or a glass fiber/epoxy resin composite. Concurrent with the computational efforts, structures similar to those modeled are fabricated and the residual stresses are quantified through the measurement of deformations. The simulations' results are compared to the experimentally observed behaviors for model validation. 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 composite's glassy region. Copyright 2017. Used by CAMX - The Composites and Advanced Materials Expo.

Visual inspection research has a long history spanning the 20th century and continuing to the present day. Current efforts in multiple venues demonstrate that visual inspection continues to have a vital role for many different types of tasks in the 21st century. The nature of this role spans the range from traditional human visual inspection to fully automated detection of defects. Consequently, today's practitioners must not only successfully identify and apply lessons learned from the past, but also explore new areas of research in order to derive solutions for modern day issues such as those presented by introducing automation during inspection. A key lesson from past research indicates that the factors that can degrade performance will persist today, unless care is taken to design the inspection process appropriately.

Ovarian cancer (OvCa) is the fifth leading cause of cancer deaths in women and remains the deadliest gynecological cancer. Our study goal is to examine associations between diagnostic patterns and OvCa stages. We used the data from a web-based survey in which more than 500 women diagnosed with OvCa provided both free text responses and staging information. We employed text mining and natural language processing (NPL) to extract information on clinical diagnostic characteristics, together with 21 dichotomous symptomatic variables, patient-centered advocacy, and polytomous disease severity, with internal validation. We conducted multivariate analyses and developed tree-based classification models with the confirmation of Random Forest to determine important factors in the relationships of the clinical diagnostic characteristics with OvCa stages. Models including the symptoms, patient advocacy tendency, disease severity and doctors' responses as predictors, had a much better predictive power than those limited to doctors' responses alone, indicating that OvCa stage at diagnosis depends on more than just doctors' responses. Although effective early stage diagnosis and treatment remains a challenge, our analysis of patient-centered clinical diagnostic characteristics and symptoms shows that self-advocacy is essential for all women. The frontline physician is critically important in ensuring effective follow-up and timely treatment before diagnosis.

There are gaps in understanding how a cyber-attack would manifest itself within power plants and what these events would look like within the control room from an operator’s perspective. This is especially true for nuclear power plants where safety has much broader consequences than nonnuclear plants. The operating and emergency procedures that operators currently use are likely inadequate for targeted cyber-attacks. This research focuses on understanding how a cyber event would affect the operation of the plant, how an operator would perceive the event, and if the operator’s actions would keep the plant in a safe condition. This research is part of Sandia’s Laboratory Directed Research and Development program where a nuclear power plant cyber model of the control system digital architecture is coupled with a generic pressurized water reactor plant training simulator. Cyber event scenarios will be performed on the coupled system with plant operators. The scenarios simulate plant conditions that may exist during a cyber-attack, component failure, or insider sabotage, and provide an understanding of the displayed information and the actual plant conditions. These scenarios will determine if plant operators can 1) recognize that they are under cyber-attack and 2) take appropriate actions to keep the plant safe. This will also provide the opportunity to assess the operator cognitive workload during such events and identify where improvements might be made. Experiments with nuclear power plant operators will be carried out over FY 2018 and results of the research are expected by the end of FY 2018.

A sodium coolant accident analysis code is necessary to provide regulators with a means of performing confirmatory analyses for future sodium reactor licensing submissions. MELCOR and CONTAIN, which have been employed by the U.S. Nuclear Regulatory Commission for light water reactor licensing, have been traditionally used for Level 2 and Level 3 probabilistic analyses as well as containment design basis accident analysis. To meet future regulatory needs, new models are being added to the MELCOR code for simulation of sodium reactor designs by integrating the existing models developed for separate effects codes into the MELCOR architecture. Sodium properties and equations of state, such as from the SAS4A code, have previously been implemented into MELCOR to replace the water properties and equation of state. Additional specific sodium-related models to address design basis accidents are now being implemented into MELCOR from CONTAIN-LMR. Although the codes are very different in the code architecture, the feasibility fit is being investigated, and the models for the sodium spray fire and the sodium pool fire have been integrated into MELCOR. A new package called Sodium Chemistry (NAC) has been added to MELCOR to handle all sodium related chemistry models for sodium reactor safety applications. Although MELCOR code requires the ambient condition to be above the freezing point of the coolant (e.g., sodium or water), the high relative freezing point of sodium requires MELCOR to handle situations, particularly far from the primary circuit, where the ambient temperatures are usually at room temperature. Because only a single coolant can be modeled in a problem at a time, any presence of water in the problem would be treated as a trace material, an aerosol, in MELCOR. This paper addresses and describe the integration of the sodium models from CONTAIN-LMR, and the testing of the sodium chemistry models in the NAC package of MELCOR that handles sodium type reactor accidents, using available sodium experiments on spray fire and pool fire. In addition, we describe the anticipated sodium models to be completed in this year, such as the atmospheric chemistry model and sodiumconcrete interaction model. Code-to-code comparison between MELCOR and CONTAIN-LMR results, in addition to the experiment code validations, will be demonstrated in this year.

This paper presents a study of alternative heliostat standby aiming strategies and their impact on avian flux hazards and operational performance of a concentrating solar power plant. A mathematical model was developed that predicts the bird-feather temperature as a function of solar irradiance, thermal emittance, convection, and thermal properties of the feather. The irradiance distribution in the airspace above the Ivanpah Unit 2 heliostat field was simulated using a ray-trace model for two different times of the day, four days of the year, and nine different standby aiming strategies. The impact of the alternative aiming strategies on operational performance was assessed by comparing the heliostat slew times from standby position to the receiver for the different aiming strategies. Increased slew times increased a proxy start-up time that reduced the simulated annual energy production. Results showed that spreading the radial aim points around the receiver to a distance of ∼150 m or greater reduced the hazardous exposure times that the feather temperature exceeded the hazard metric of 160 °C. The hazardous exposure times were reduced by ∼23% and 90% at a radial spread of aim points extending to 150 m and 250 m, respectively, but the simulated annual energy production decreased as a result of increased slew times. Single point-focus aiming strategies were also evaluated, but these strategies increased the exposure hazard relative to other aiming strategies.

Previous research at Sandia National Laboratories showed the potential advantages of using light-trapping features which are not currently used in direct tubular receivers. A horizontal bladed receiver arrangement showed the best potential for increasing the effective solar absorptance by increasing the ratio of effective surface area to the aperture footprint. Ray-tracing analyses using SolTrace were performed to understand the light-trapping effects of the bladed receivers, which enable re-reflections between the fins that enhance the effective solar absorptance. A parametric optimization study was performed to determine the best possible configuration with a fixed intrinsic absorptivity of 0.9 and exposed surface area of 1 m2. The resulting design consisted of three vertical panels 0.584 m long and 0.508 m wide and 3 blades 0.508 m long and 0.229 m wide with a downward tilt of 50 degrees from the horizontal. Each blade consisted of two panels which were placed in front of the three vertical panels. The receiver was tested at the National Solar Thermal Test Facility using pressurized air. However, the receiver was designed to operate using supercritical carbon dioxide (sCO2) at 650 °C and 15 MPa for 100,000 hours following the ASME Boiler and Pressure Vessel Code Section VIII Division 1. The air flowed through the leading panel of the blade first, and then recirculated toward the back panel of the blade before flowing through one of the vertical back panels. The test results of the bladed receiver design showed a receiver efficiency increase over a flat receiver panel of ∼5 - 7% (from ∼80% to ∼86%) over a range of average irradiances, while showing that the receiver tubes can withstand temperatures > 800 °C with no issues. Computational fluid dynamics (CFD) modeling using the Discrete Ordinates (DO) radiation model was used to predict the temperature distribution and the resulting receiver efficiencies. The predicted thermal efficiency and surface temperature values correspond to the measured efficiencies and surface temperatures within one standard deviation. In the near future, an sCO2 flow system will be built to expose the receiver to higher pressure and fluid temperatures which could yield higher efficiencies.

Residual stresses can form within composite structures composed of asymmetric laminates during the elevated temperature curing processes common to composite materials. These residual stresses are primarily the result of unbalanced thermal strains that develop throughout the structure due to the composite's orthotropic coefficients of thermal expansion. Furthermore, structures composed of textile, or woven, composite fabrics lend themselves to the formation of these residual stresses, as extreme care must be taken during the lay-up of such parts to ensure that adjacent plies are placed front-to-front or back-to-back, as opposed to front-to-back, to eliminate the potential for any unbalanced thermal strains. Depending upon the specific geometric details of the composite structure of interest, it is possible that these residual stresses could result in fracture within the composite. Therefore, the consideration of potential residual stresses formed throughout the manufacturing process is important. However, the experimental determination of residual stresses in prototype parts can be time and cost prohibitive. As an alternative to physical measurement, it is possible for computational tools to be used to quantify potential residual stresses in composite prototype parts. Therefore, the objectives of this study are two-fold. First, a simplistic method for simulating the residual stresses formed in polymer matrix composite structures is developed within the Sandia National Laboratories' SIERRA/SolidMechanics code Adagio. Subsequently, the required level of model fidelity necessary to provide realistic predictions of a textile composite's residual stress state is determined. Concurrent with the computational activities, asymmetric plates of a woven carbon fiber/epoxy composite are manufactured with varying thicknesses and the residual stresses exhibited by the plates are quantified through the measurement of deformation. The developed computational approach is used to simulate the manufacturing process of these asymmetric plates and final comparisons of the predicted and experimental results show reasonable agreement.

Supercritical carbon dioxide (sCO2) Brayton power cycles have the potential to significantly improve the economic viability of concentrating solar power (CSP) plants by increasing the thermal to electric conversion efficiency from around 35% using high-temperature steam Rankine systems to above 45% depending on the cycle configuration. These systems are the most likely path toward achieving the Department of Energy's (DOE) Office of Energy Efficiency and Renewable Energy (EERE) SunShot targets for CSP tower thermal to electric conversion efficiency above 50% with dry cooling to air at 40 °C and a power block cost of less than 900 $/kWe. Many studies have been conducted to optimize the performance of various sCO2 Brayton cycle configurations in order to achieve high efficiency, and a few have accounted for drivers of cost such as equipment size in the optimization, but complete techno-economic optimization has not been feasible because there are no validated models relating component performance and cost. Reasonably accurate component cost models exist from several sources for conventional equipment including turbines, compressors, and heat exchangers for use in rough order of magnitude cost estimates when assembling a system of conventional equipment. However, cost data from fabricated equipment relevant to sCO2 Brayton cycles is very limited in terms of both supplier variety and performance level with most existing data in the range of 1 MWe power cycles or smaller systems, a single completed system around 7 MWe by Echogen Power Systems, and numerous ROM estimates based on preliminary designs of equipment for 10 MWe systems. This data is highly proprietary as the publication of individual data by any single supplier would damage their market position by potentially allowing other vendors to undercut their stated price rather than competing on reduced manufacturing costs. This paper describes one approach to develop component cost models in order to enable the techno-economic optimization activities needed to guide further research and development while protecting commercially proprietary information from individual vendors. Existing cost models were taken from literature for each major component used in different sCO2 Brayton cycle configurations and adjusted for their magnitude to fit the limited vendor cost data and estimates available. A mean fit curve was developed for each component and used to calculate updated cost comparisons between previously-reviewed sCO2 Brayton cycle configurations for CSP applications including simple recuperated, recompression, cascaded, and mixed-gas combined bifurcation with intercooling cycles. These fitting curves represent an average of the assembled vendor data without revealing any individual vendor cost, and maintain the scaling behavior with performance expected from similar equipment found in literature.

Fuel and oxidizer mixing is a key parameter influencing combustion and emission performance in diesel engines. At the same time, quantitative mixing measurements in automotive sprays are very challenging such that only a few experimental results are available as targets for the development and tuning of numerical models. The caveat is that the experimental data mainly concern the quasi-steady part of the jet, while it can be argued that the injection process in current alternative thermal engines is mostly transient. This work applies planar laser Rayleigh scattering at high-frequency to resolve the development and mixing of vaporized diesel sprays injected in a highly-pressurized environment. The state-of-the-art equipment employed for these experiments include a purposely-built high-power, high-repetition rate pulsed burst laser, optimized optics and a state-of-the-art high-speed CMOS camera. Advanced image processing methods were developed and implemented to mitigate the negative effects of the extreme environments found in diesel engines at the time of injection. The experiments provided two-dimensional mean and variance of the mixture and temperature quantities. The optical system's high spatial and temporal resolution enables tracking of the mixing field with time and space, from which temporally and spatially correlated mixing quantities can be extracted. Further analysis of the detailed mixture and temperature fields offered information about the turbulent mixing process of high-pressure diesel sprays such as scalar dissipation rates or turbulent length scales. Substantial effort was made to assess the uncertainties and limitations of such experimental results due to the optically challenging environment.

The performance of electronic systems for radio-frequency (RF) spectrum analysis is critical for agile radar and communications systems, ISR (intelligence, surveillance, and reconnaissance) operations in challenging electromagnetic (EM) environments, and EM-environment situational awareness. While considerable progress has been made in size, weight, and power (SWaP) and performance metrics in conventional RF technology platforms, fundamental limits make continued improvements increasingly difficult. Alternatively, we propose employing cascaded transduction processes in a chip-scale nano-optomechanical system (NOMS) to achieve a spectral sensor with exceptional signal-linearity, high dynamic range, narrow spectral resolution and ultra-fast sweep times. By leveraging the optimal capabilities of photons and phonons, the system we pursue in this work has performance metrics scalable well beyond the fundamental limitations inherent to all electronic systems. In our device architecture, information processing is performed on wide-bandwidth RF-modulated optical signals by photon-mediated phononic transduction of the modulation to the acoustical-domain for narrow-band filtering, and then back to the optical-domain by phonon-mediated phase modulation (the reverse process). Here, we rely on photonics to efficiently distribute signals for parallel processing, and on phononics for effective and flexible RF-frequency manipulation. This technology is used to create RF-filters that are insensitive to the optical wavelength, with wide center frequency bandwidth selectivity (1-100GHz), ultra-narrow filter bandwidth (1-100MHz), and high dynamic range (70dB), which we will present. Additionally, using this filter as a building block, we will discuss current results and progress toward demonstrating a multichannel-filter with a bandwidth of < 10MHz per channel, while minimizing cumulative optical/acoustic/optical transduced insertion-loss to ideally < 10dB. These proposed metric represent significant improvements over RF-platforms.

In order to bypass the limitation of bulk metallic glasses fabrication, we synthesized thin film metallic glasses to study the corrosion characteristics of a wide atomic% composition range, Mg(35.9-63%)Ca(4.1-21%)Zn(17.9-58.3%), in simulated body fluid. We highlight a clear relationship between Zn content and corrosion current such that Zn-medium metallic glasses exhibit minimum corrosion. In addition, we found higher Zn content leads to a poor in vitro cell viability. These results showcase the benefit of evaluating a larger alloy compositional space to probe the limits of corrosion resistance and prescreen for biocompatible applications.

A study was performed to optimize the geometry of a point absorber style wave energy converter (WEC). An axisymmetric single-body device, moving in heave only, was considered. Design geometries, generated using a parametric definition, were optimized using genetic algorithms. Each geometry was analyzed using a boundary element model (BEM) tool to obtain corresponding frequency domain models. Based on these models, a pseudo-spectral method was applied to develop a control methodology for each geometry. The performance of each design was assessed using a Bretschneider sea state. The objective of optimization is to maximize harvested energy. In this preliminary investigation, a constraint is imposed on the the geometry to guarantee a linear dynamic model would be valid for all geometries generated by the optimization tool. Numerical results are presented for axisymmetric buoy shapes.

Particle-based concentrating solar power (CSP) plants have been proposed to increase operating temperature for integration with higher efficiency power cycles using supercritical carbon dioxide (sCO2). The majority of research to date has focused on the development of high-efficiency and high-temperature particle solar thermal receivers. However, system realization will require the design of a particle/sCO2 heat exchanger as well for delivering thermal energy to the power-cycle working fluid. Recent work has identified moving packed-bed heat exchangers as low-cost alternatives to fluidized-bed heat exchangers, which require additional pumps to fluidize the particles and recuperators to capture the lost heat. However, the reduced heat transfer between the particles and the walls of moving packed-bed heat exchangers, compared to fluidized beds, causes concern with adequately sizing components to meet the thermal duty. Models of moving packed-bed heat exchangers are not currently capable of exploring the design trade-offs in particle size, operating temperature, and residence time. The present work provides a predictive numerical model based on literature correlations capable of designing moving packed-bed heat exchangers as well as investigating the effects of particle size, operating temperature, and particle velocity (residence time). Furthermore, the development of a reliable design tool for moving packed-bed heat exchangers must be validated by predicting experimental results in the operating regime of interest. An experimental system is designed to provide the data necessary for model validation and/or to identify where deficiencies or new constitutive relations are needed.

A linear dynamic model for a wave energy converter (WEC) has been developed based on the results of experimental wave tank testing. Based on this model, a model predictive control (MPC) strategy has been designed and implemented. To assess the performance of this control strategy, a deployment environment off the coast of Newport, OR has been selected and the controller has been used to simulate the WEC response in a set of irregular sea states. To better understand the influence of model accuracy on control performance, an uncertainty analysis has been performed by varying the parameters of the model used for the design of the controller (i.e. the control model), while keeping the WEC dynamic model employed in these simulations (i.e. the plant model) unaltered. The results of this study indicate a relative low sensitivity of the MPC control strategy to uncertainties in the controller model for the specific case studied here.

We consider heuristic and optimal solutions to a discrete geometric bin packing problem that arises in a resource allocation problem. An imaging sensor is assigned to collect data over a large area, but some subregions are more valuable than others. To capture these high-value regions with higher fidelity, we can assign some number of non-overlapping rectangular subsets, called “subfootprints.” The sensor image is partitioned into squares called “chips”, and each chip is further partitioned into pixels. Pixels may have different values. Subfootprints are restricted to rectangular collections of chips, but we are free to choose different rectangle heights, widths, and areas. We seek the optimal arrangement over the family of possible rectangle shapes and sizes. We provide a mixed-integer linear program optimization formulation, as well as a greedy heuristic, to solve this problem. For the meta-problem, we have some freedom to align the chip boundaries to different pixels. However, it is too expensive to solve the optimization formulation for each alignment. However, we show that the greedy heuristic can inform which pixel alignments are worth solving the optimization over. We use a variant of k-means clustering to group greedy solutions by their transport shape-similarity. For each cluster, we run the optimization problem over the greedy layout with the highest value. In practice this efficiently explores the geometric configuration space, and produces solutions close to the global optimum. We show a contrived example using surveillance of the Mississippi River. Our software is available as open-source in the Github repository “GeoPlace .

Information loss from a computation implies energy dissipation due to Landauer’s Principle. Thus, increasing the amount of useful computational work that can be accomplished within a given energy budget will eventually require increasing the degree to which our computing technologies avoid information loss, i.e., are logically reversible. But the traditional definition of logical reversibility is actually more restrictive than is necessary to avoid information loss and energy dissipation due to Landauer’s Principle. As a result, the operations that have traditionally been viewed as the atomic elements of reversible logic, such as Toffoli gates, are not really the simplest primitives that one can use for the design of reversible hardware. Arguably, a complete theoretical framework for reversible computing should provide a more general, parsimonious foundation for practical engineering. To this end, we use a rigorous quantitative formulation of Landauer’s Principle to develop the theory of Generalized Reversible Computing (GRC), which precisely characterizes the minimum requirements for a computation to avoid information loss and the consequent energy dissipation, showing that a much broader range of computations are, in fact, reversible than is acknowledged by traditional reversible computing theory. This paper summarizes the foundations of GRC theory and briefly presents a few of its applications.

Water utilities are vulnerable to a wide variety of human-caused and natural disasters. The Water Network Tool for Resilience (WNTR) is a new open source Python™ package designed to help water utilities investigate resilience of water distribution systems to hazards and evaluate resilience-enhancing actions. In this paper, the WNTR modeling framework is presented and a case study is described that uses WNTR to simulate the effects of an earthquake on a water distribution system. The case study illustrates that the severity of damage is not only a function of system integrity and earthquake magnitude, but also of the available resources and repair strategies used to return the system to normal operating conditions. While earthquakes are particularly concerning since buried water distribution pipelines are highly susceptible to damage, the software framework can be applied to other types of hazards, including power outages and contamination incidents.

Computational imagers fundamentally enable new optical hardware through the use of both physical and algorithmic elements. We report on the creation of a static lensless computational imaging system enabled by this paradigm.

Robust, safe, and reliable grid-scale energy storage continues to be a priority for improved energy surety, expanded integration of renewable energy, and greater system agility required to meet modern dynamic and evolving electrical energy demands. We describe here a new sodium-based battery based on a molten sodium anode, a sodium iodide/aluminum chloride (NaI/AlCl3) cathode, and a high conductivity NaSICON (Na1+xZr2SixP3−xO12) ceramic separator. This NaI battery operates at intermediate temperatures (120–180 °C) and boasts an energy density of >150 Wh kg−1. The energy-dense NaI-AlCl3 ionic liquid catholyte avoids lifetime-limiting plating and intercalation reactions, and the use of earth-abundant elements minimizes materials costs and eliminates economic uncertainties associated with lithium metal. Moreover, the inherent safety of this system under internal mechanical failure is characterized by negligible heat or gas production and benign reaction products (Al, NaCl). Scalability in design is exemplified through evolution from 0.85 to 10 Ah (28 Wh) form factors, displaying lifetime average Coulombic efficiencies of 99.45% and energy efficiencies of 81.96% over dynamic testing lasting >3000 h. This demonstration promises a safe, cost-effective, and long-lifetime technology as an attractive candidate for grid scale storage.

Children with autism spectrum disorders (ASD) and their unaffected siblings (US) are frequent targets of social bullying, which leads to severe physical, emotional, and social consequences. Understanding the risk factors is essential for developing preventative measures. We suggest that one such risk factor may be a difficulty to discriminate different biological body movements (BBM), a task that requires fast and flexible processing and interpretation of complex visual cues, especially during social interactions. Deficits in cognition of BBM have been reported in ASD. Since US display an autism endophenotype we expect that they will also display deficits in social interpretation of BBM. Methods. Participants: 8 US, 8 matched TD children, age 7-14; Tasks/Measurements: Social Blue Man Task: Narrative interpretation with a Latent Dirichlet Allocation [LDA] analysis; Social Experience Questionnaires with children and parents. Results. The US displayed as compared to TD: (i) low self-awareness of social bullying in contrast to high parental reports; (ii) reduced speed in identifying social cues; (iii) lower quality and repetitious wording in social interaction narratives (LDA). Conclusions. US demonstrate social endophenotype of autism reflected in delayed identification, interpretation and verbalization of social cues; these may constitute a high risk factor for becoming a victim of social bullying.

We propose a new post-processing procedure for automatically adjusting node locations of an all-hex mesh to better match the volume of a reference geometry. Hexahedral meshes generated via an overlay grid procedure, where a precise reference geometry representation is unknown or is impractical to use, do not provide for precise volumetric preservation. A discrete volume fraction representation of the reference geometry MI on an overlay grid is compared with a volume fraction representation of a 3D finite element mesh MO. This work proposes a procedure that uses the localized discrepancy between MI and MO to drive node relocation operations to more accurately match a reference geometry. We demonstrate this procedure on a wide range of hexahedral meshes generated with the Sculpt code and show improved volumetric preservation while still maintaining acceptable mesh quality.

We report on the design of a refracting prism array element for use in a computational lensless imaging system. The technique discussed enables creation of a refracting element that maximizes signal on a detector region.

The modeling and simulation of non-traditional imaging systems require holistic consideration of the end-to-end system. We demonstrate this approach through a tolerance analysis of a random scattering lensless imaging system.

The modeling and simulation of non-traditional imaging systems require holistic consideration of the end-to-end system. We demonstrate this approach through a tolerance analysis of a random scattering lensless imaging system.

Data visualizations are used to communicate information to people in a wide variety of contexts, but few tools are available to help visualization designers evaluate the effectiveness of their designs. Visual saliency maps that predict which regions of an image are likely to draw the viewer’s attention could be a useful evaluation tool, but existing models of visual saliency often make poor predictions for abstract data visualizations. These models do not take into account the importance of features like text in visualizations, which may lead to inaccurate saliency maps. In this paper we use data from two eye tracking experiments to investigate attention to text in data visualizations. The data sets were collected under two different task conditions: a memory task and a free viewing task. Across both tasks, the text elements in the visualizations consistently drew attention, especially during early stages of viewing. These findings highlight the need to incorporate additional features into saliency models that will be applied to visualizations.

Laser engineered net shaping (LENS) is an additive manufacturing process that presents a promising method of creating or repairing metal parts not previously feasible with traditional manufacturing methods. The LENS process involves the directed deposition of metal via a laser power source and a spray of metal powder co-located to create and feed a molten pool (also referred to generically as Directed Energy Deposition, DED). DED technologies are being developed for use in prototyping, repair, and manufacturing across a wide variety of materials including stainless steel, titanium, tungsten carbidecobalt, aluminum, and nickel based superalloys. However, barriers to the successful production and qualification of LENS produced or repaired parts remain. This work proposes a finite element (FE) analysis methodology capable of simulating the LENS process at the continuum length scale (i.e. part length scale). This method incorporates an element activation scheme wherein only elements that exceed the material melt temperature during laser heating are activated and carried through to subsequent analysis steps. Following the initial element activation calculation, newly deposited, or activated elements and the associated geometry, are carried through to thermal and mechanical analyses to calculate heat flow due to radiation, convection, and conduction as well as stresses and displacements. The final aim of this work is to develop a validated LENS process simulation capability that can accurately predict temperature history, final part shape, distribution of strength, microstructural properties, and residual stresses based on LENS process parameters.

Lensless imaging systems have the potential to provide new capabilities for lower size and weight configuration than traditional imaging systems. Lensless imagers frequently utilize computational imaging techniques, which moves the complexity of the system away from optical subcomponents and into a calibration process whereby the measurement matrix is estimated. We report on the design, simulation, and prototyping of a lensless imaging system that utilizes a 3D printed optically transparent random scattering element. Development of end-to-end system simulations, which includes simulations of the calibration process, as well as the data processing algorithm used to generate an image from the raw data are presented. These simulations utilize GPU-based raytracing software, and parallelized minimization algorithms to bring complete system simulation times down to the order of seconds. Hardware prototype results are presented, and practical lessons such as the effect of sensor noise on reconstructed image quality are discussed. System performance metrics are proposed and evaluated to discuss image quality in a manner that is relatable to traditional image quality metrics. Various hardware instantiations are discussed.

We show how to place a set of seed points such that a given piecewise linear complex is the union of some faces in the resulting Voronoi diagram. The seeds are placed on sufficiently small spheres centered at input vertices and are arranged into little circles around each half-edge where every seed is mirrored across the associated triangle. The Voronoi faces common to the seeds of such arrangements yield a mesh conforming to the input complex. If the input contains sharp angles, then additional seeds are needed, analogous to nonobtuse refinement. Finally, we propose local optimizations to reduce the number of seeds and output facets.

Microwave spectroscopy probes the rotational transitions of polar molecules in the gas phase and is a proven technique for the detection and identification of short-lived molecules produced from a variety of molecular sources. In this explorative study, we demonstrate that two prerequisites can be met for microwave spectroscopy to become a quantitative tool for the analysis of high-temperature gas mixtures as found in combustion environments. First, we show that the rotational temperature of the targeted species can be sufficiently cooled to allow for a sensitive detection of low-lying rotational states after sampling from hot (∼2200 K) flames. Second, we show that signal intensity profiles can be assembled which, after correcting for the different flame temperatures at various sampling positions, agree well with mole fraction profiles obtained via flame-sampling molecular-beam mass spectrometry. Based on the described results, it is conceivable that rotational spectroscopy can contribute towards the unraveling of complex, high-temperature reaction networks.

In this work we extend compression-based algorithms for deception detection in text. In contrast to approaches that rely on theories for deception to identify feature sets, compression automatically identifies the most significant features. We consider two datasets that allow us to explore deception in opinion (content) and deception in identity (stylometry). Our first approach is to use unsupervised clustering based on a normalized compression distance (NCD) between documents. Our second approach is to use Prediction by Partial Matching (PPM) to train a classifier with conditional probabilities from labeled documents, followed by arithmetic coding (AC) to classify an unknown document based on which label gives the best compression. We find a significant dependence of the classifier on the relative volume of training data used to build the conditional probability distributions of the different labels. Methods are demonstrated to overcome the data size-dependence when analytics, not information transfer, is the goal. Our results indicate that deceptive text contains structure statistically distinct from truthful text, and that this structure can be automatically detected using compression-based algorithms.

We present a new, non-destructive electrical technique, Power Spectrum Analysis (PSA). PSA as described here uses off-normal biasing, an unconventional way of powering microelectronics devices. PSA with off-normal biasing can be used to detect subtle differences between microelectronic devices. These differences, in many cases, cannot be detected by conventional electrical testing. In this paper, we highlight PSA applications related to aging and counterfeit detection.

Lithium-ion battery electrodes are composed of active material particles, binder, and conductive additives that form an electrolyte-filled porous particle composite. The mesoscale (particle-scale) interplay of electrochemistry, mechanical deformation, and transport through this tortuous multi-component network dictates the performance of a battery at the cell-level. Effective electrode properties connect mesoscale phenomena with computationally feasible battery-scale simulations. We utilize published tomography data to reconstruct a large subsection (1000+ particles) of an NMC333 cathode into a computational mesh and extract electrode-scale effective properties from finite element continuum-scale simulations. We present a novel method to preferentially place a composite binder phase throughout the mesostructure, a necessary approach due difficulty distinguishing between non-active phases in tomographic data. We compare stress generation and effective thermal, electrical, and ionic conductivities across several binder placement approaches. Isotropic lithiation-dependent mechanical swelling of the NMC particles and the consideration of strain-dependent composite binder conductivity significantly impact the resulting effective property trends and stresses generated. Our results suggest that composite binder location significantly affects mesoscale behavior, indicating that a binder coating on active particles is not sufficient and that more accurate approaches should be used when calculating effective properties that will inform battery-scale models in this inherently multi-scale battery simulation challenge.

Optimizing communication performance is imperative for largescale computing because communication overheads limit the strong scalability of parallel applications. Today's network cards contain rather powerful processors optimized for data movement. However, these devices are limited to fixed functions, such as remote direct memory access. We develop sPIN, a portable programming model to offload simple packet processing functions to the network card. To demonstrate the potential of the model, we design a cycle-accurate simulation environment by combining the network simulator LogGOPSim and the CPU simulator gem5. We implement offloaded message matching, datatype processing, and collective communications and demonstrate transparent full-application speedups. Furthermore, we show how sPIN can be used to accelerate redundant in-memory filesystems and several other use cases. Our work investigates a portable packet-processing network acceleration model similar to compute acceleration with CUDA or OpenCL. We show how such network acceleration enables an eco-system that can significantly speed up applications and system services.

Structural dynamics models with localized nonlinearities can be reduced using Hurty/Craig-Bampton component mode synthesis methods. The interior degrees-of-freedom of the linear subcomponents are reduced with a set of dynamic fixedinterface modes while the static constraint modes preserve the physical coordinates at which the nonlinear restoring forces are applied. For finite element models with a highly refined mesh at the boundary, a secondary modal analysis can be performed to reduce the interface down to a truncated set of local-level characteristic constraint modes. In this research, the cost savings and accuracy of the interface reduction technique are evaluated on a simple example problem involving two elastic blocks coming into contact.

The rapid reaction of the smallest Criegee intermediate, CH2OO, with water dimers is the dominant removal mechanism for CH2OO in the Earth's atmosphere, but its products are not well understood. This reaction was recently suggested as a significant source of the most abundant tropospheric organic acid, formic acid (HCOOH), which is consistently underpredicted by atmospheric models. However, using time-resolved measurements of reaction kinetics by UV absorption and product analysis by photoionization mass spectrometry, we show that the primary products of this reaction are formaldehyde and hydroxymethyl hydroperoxide (HMHP), with direct HCOOH yields of less than 10%. Incorporating our results into a global chemistry-transport model further reduces HCOOH levels by 10-90%, relative to previous modeling assumptions, which indicates that the reaction CH2OO + water dimer by itself cannot resolve the discrepancy between the measured and predicted HCOOH levels.

An enriched finite element method is described for capillary hydrodynamics including dynamic wetting. The method is enriched via the Conformal Decomposition Finite Element Method (CDFEM). Two formulations are described, one with first-order accuracy and one with second-order accuracy in time. Both formulations utilize a semi-implicit form for the surface tension that is shown to effectively circumvent the explicit capillary time step limit. Sharp interface boundary conditions are developed for capturing the dynamic contact angle as the fluid interface moves along the wall. By virtue of the CDFEM, the contact line is free to move without risk of mesh tangling, but is sharply captured. Multiple problems are used to demonstrate the effectiveness of the methods.

Following previous experimental evidence of growth and arrest of Richtmyer-Meshkov instabilities in copper, we have used the CTH shock physics code to study and calibrate the effects of material strength at high strain rates. Highly resolved one and two-dimensional simulations were performed using the Johnson-Cook (JC), Mechanical Threshold Stress (MTS), and Preston-Tonks-Wallace (PTW) strength models. The one-dimensional simulations utilized a prescribed homogeneous deformation strain path covering strain rates observed in previous hydrodynamic instability experiments. Spall was modeled using a nominal threshold pressure model (PFRAC) and we use the Mie-Gruneisen equation of state to estimate the volumetric response of the experiments. Our results show good qualitative and quantitative agreement between numerical estimates and prior experiments in the strain rate regimes of interest.

Neodymium hydroxide, Nd(OH)3, solubility experiments were carried out in duplicate at room temperature over the pH interval of 10 to 13 for up to 670 days. The ionic radii of Nd(III) is similar to that of Pu(III) and Am(III), so that the former is an appropriate analog for the latter radionuclides. Average concentrations of Nd in solution approach a value of 2.0 × 10-7 mol/L and equilibrium concentrations show no dependence on solution pH. Compared to previous work, the data from this investigation form a continuum across pH-space, indicating agreement amongst data sets.

Industry's inability to reduce logic gates' energy consumption is slowing growth in an important part of the worldwide economy. Some scientists argue that alternative approaches could greatly reduce energy consumption. These approaches entail myriad technical and political issues.

The performance of many of the technologies used in physical protection systems that guard high-value assets are heavily influenced by weather and visibility conditions as well as intruder capabilities. This complicates the already difficult problem of optimizing the design of multi-layered physical protection systems. This paper develops an optimization model for the automatic design of these systems with explicit consideration of the impact of weather and visibility conditions as well as intruder capabilities on system performance. An illustrative case study is provided.

Approximate methods for electronic structure, implemented in sophisticated computer codes and married to ever-more powerful computing platforms, have become invaluable in chemistry and materials science. The maturing and consolidation of quantum chemistry codes since the 1980s, based upon explicitly correlated electronic wave functions, has made them a staple of modern molecular chemistry. Here, the impact of first principles electronic structure in physics and materials science had lagged owing to the extra formal and computational demands of bulk calculations.

Steel grades such as A572 and AISI 4140 are often used for applications where high rate or impact loading may occur. A572 is a hot-rolled carbon steel that is used where a high strength to weight ratio is desired. A grade such as AISI 4140 offers decent corrosion resistance due to higher chromium and molybdenum content and is commonly used in firearm parts, pressurized gas tubes, and structural tubing for roll cages. In these scenarios, the material may undergo high rate loading. Thus, material properties including failure and fracture response at relevant loading rates must be understood so that numerical simulations of impact events accurately capture the deformation and failure/fracture behavior of the involved materials. In this study, the high strain rate tensile response of A572 and 4140 steel are investigated. An increase in yield strength of approximately 28% was observed for 4140 steel when comparing 0.001 s-1 strain rate to 3000 s-1 experiments. A572 showed an increase in yield strength of approximately 52% when the strain rate increased from quasi-static to 2750 s-1. Effects on true stress and strain at failure for the two materials are also discussed.

Low carbon, high strength steel alloys such as Vascomax steels are used in a wide variety of extreme environments due to their high strength, high fracture toughness, and stability over a wide range of temperatures. In this study, Vascomax® C250 steel was dynamically characterized in compression using Kolsky compression bar techniques at two strain rates of 1000 and 3000 s-1. A pair of impedance-matched tungsten carbide platens were implemented to protect damage to the bar ends. The tungsten carbide platens were experimentally calibrated as system compliance which was then properly corrected for actual specimen strain measurements. In addition, elastic indentation of the high-strength compression sample into the platens was also evaluated and showed negligible effect on the specimen strain measurements. The Vascomax® C250 steel exhibited strain-rate effects on the compressive stress-strain curves. The dynamic yield strength was approximately 18% higher than quasi-static yield strength obtained from hardness tests. The dynamic true stress-strain curves of the Vascomax® C250 steel in compression were also computed and then compared with the previously obtained true tensile stress-strain curves at the same strain rates. The Vascomax® C250 steel exhibited a reasonable symmetry in dynamic compression and tensile stress-strain response. However, the fracture strains in dynamic compression were smaller than those in dynamic tension probably due to different fracture mechanisms in the different loading modes.

Researchers are now considering alternatives to the von Neumann computer architecture as a way to improve performance. The current approach of simulating benchmark applications favors continued use of the von Neumann architecture, but architects can help overcome this bias.

Residual stresses can form within composite structures composed of asymmetric laminates during the elevated temperature curing processes common to composite materials. These residual stresses are primarily the result of unbalanced thermal strains that develop throughout the structure due to the composite's orthotropic coefficients of thermal expansion. Furthermore, structures composed of textile, or woven, composite fabrics lend themselves to the formation of these residual stresses, as extreme care must be taken during the lay-up of such parts to ensure that adjacent plies are placed front-to-front or back-to-back, as opposed to front-to-back, to eliminate the potential for any unbalanced thermal strains. Depending upon the specific geometric details of the composite structure of interest, it is possible that these residual stresses could result in fracture within the composite. Therefore, the consideration of potential residual stresses formed throughout the manufacturing process is important. However, the experimental determination of residual stresses in prototype parts can be time and cost prohibitive. As an alternative to physical measurement, it is possible for computational tools to be used to quantify potential residual stresses in composite prototype parts. Therefore, the objectives of this study are two-fold. First, a simplistic method for simulating the residual stresses formed in polymer matrix composite structures is developed within the Sandia National Laboratories' SIERRA/SolidMechanics code Adagio. Subsequently, the required level of model fidelity necessary to provide realistic predictions of a textile composite's residual stress state is determined. Concurrent with the computational activities, asymmetric plates of a woven carbon fiber/epoxy composite are manufactured with varying thicknesses and the residual stresses exhibited by the plates are quantified through the measurement of deformation. The developed computational approach is used to simulate the manufacturing process of these asymmetric plates and final comparisons of the predicted and experimental results show reasonable agreement.

Rather than continue the expensive and time-consuming quest for transistor replacement, the authors argue that 3D chips coupled with new computer architectures can keep Moore's law on its traditional scaling path.

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 structure's metallic materials. Therefore, the consideration of potential residual stresses is important 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 composed of aluminum and a carbon fiber/epoxy resin composite. Therefore, the objective of this study is to further validate the simplified method for simulating residual stresses for different composites and more complex structures. The simplified method accounts for both the coefficient of thermal expansion mismatch and polymer shrinkage through the calibration to an experimentally-determined stress-free temperature. This was implemented in Sandia National Laboratories' solid mechanics code, SIERRA, to model split rings with temperature independent and dependent material models. The split rings are comprised of two materials: Aluminum with either a carbon fiber/epoxy resin composite or a glass fiber/epoxy resin composite. Concurrent with the computational efforts, structures similar to those modeled are fabricated and the residual stresses are quantified through the measurement of deformations. The simulations' results are compared to the experimentally observed behaviors for model validation. 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 composite's glassy region. Copyright 2017. Used by CAMX - The Composites and Advanced Materials Expo.

Many applications, such as PDE based simulations and machine learning, apply BLAS/LAPACK routines to large groups of small matrices. While existing batched BLAS APIs provide meaningful speedup for this problem type, a non-canonical data layout enabling cross-matrix vectorization may provide further significant speedup. In this paper, we propose a new compact data layout that interleaves matrices in blocks according to the SIMD vector length. We combine this compact data layout with a new interface to BLAS/LAPACK routines that can be used within a hierarchical parallel application. Our layout provides up to 14 ×, 45 ×, and 27 × speedup against OpenMP loops around optimized DGEMM, DTRSM and DGETRF kernels, respectively, on the Intel Knights Landing architecture. We discuss the compact batched BLAS/LAPACK implementations in two libraries, KokkosKernels and Intel® Math Kernel Library. We demonstrate the APIs in a line solver for coupled PDEs. Finally, we present detailed performance analysis of our kernels.

Fundamental engine research is primarily conducted under steady-state conditions, in order to better describe boundary conditions which influence the studied phenomena. However, light-duty automobiles are operated, and tested, under heavily transient conditions. This mismatch between studied conditions and in-use conditions is deemed acceptable due to the fundamental knowledge gained from steady-state experiments. Nonetheless, it is useful to characterize the conditions encountered during transient operation and determine if the governing phenomena are unduly influenced by the differences between steady-state and transient operation, and further, whether transient behavior can be reasonably extrapolated from steady-state behavior. The transient operation mode used in this study consists of 20 fired cycles followed by 80 motored cycles, operating on a continuous basis. The intention of the cycle is to provide a significant transient condition, namely the change from motored to knock-limited fired operation, while also maintaining a repeatable cycle that allows for the collection of statistics during quasi- steady-state operation. This study investigates the effect of transient operation on Knock-Limited Combustion Phasing (KL-CA50) compared to steady-state operation. Three compositionally dissimilar matched Research Octane Number (RON) = 98 fuels are used in this study, allowing for the assessment of fuel-specific effects on differences between steady-state and transient operation. This study first characterizes the 20/80 firing cycle described above, before comparing the transient KL-CA50 measurements to the steady-state KL-CA50 measurements. Analysis of both the steady-state and transient results are used to gain insights into the effects of transient operation on end-gas autoignition, relative to steady-state operation and as a function of fuel composition. The results of this study indicate the significant effect that transient operation has on KL-CA50 behavior of a fuel. This is both universal, in that all fuels show responses to the differences in compression temperatures of the charge, as well as fuel specific, in that the fuel response varies based on the fuel's sensitivity to temperature, [O2], and trace species. All fuels showed a significant load extension under transient operation, based on tolerance of higher intake pressures. However, transient operation moved operating conditions to "beyond RON" (Octane Index K < 0) conditions, which favored higher-sensitivity fuels. Based on the analysis of system time constants (e.g. cylinder head temperature dynamic response, exhaust gas temperature dynamic response), it is expected that transient operation, and the benefits for knock-limited operation, are highly influential on drive-cycle performance.

AlGaN-channel high electron mobility transistors (HEMTs) are among a class of ultra wide-bandgap transistors that have a bandgap greater than ∼3.4 eV, beyond that of GaN and SiC, and are promising candidates for RF and power applications. Long-channel AlxGa1-xN HEMTs with x = 0.3 in the channel have been built and evaluated across the −50◦C to +200◦C temperature range. Room temperature drain current of 70 mA/mm, absent of gate leakage, and with a modest −1.3 V threshold voltage was measured. A very large Ion/Ioff current ratio, greater than 108 was demonstrated over the entire temperature range, indicating that off-state leakage is below the measurement limit even at 200◦C. Combined with near ideal subthreshold slope factor that is just 1.3× higher than the theoretical limit across the temperature range, the excellent leakage properties are an attractive characteristic for high temperature operation.

This work identifies classes of cool flame intermediates from nheptane low-temperature oxidation (e.g., < 750 K) in a jet-stirred reactor (JSR) and a cooperative fuel research (CFR) engine. The sampled species from the JSR were analyzed using a synchrotron vacuum ultraviolet radiation photoionization time-of-flight molecular-beam mass spectrometer and an atmospheric pressure chemical ionization orbitrap mass spectrometer; the latter was also used to analyze the sampled species from the CFR engine. The products can be classified by species with molecular formulas of C7H14Ox (x=0-5), C7H12Ox (x=0-4), C7H10Ox (x=0-4), CnH2n (n=2-6), CnH2n-2 (n=4-6), CnH2n+2O (n=1-4, 6), CnH2nO (n=1-6), CnH2n-2O (n=2-6), CnH2n-4O (n=4-6), CnH2n+2O2 (n=0-4, 7), CnH2nO2 (n=1-6), CnH2n-2O2 (n=2-6), CnH2n-4O2 (n=4-7), and CnH2nO3 (n=3-6). The identified intermediate species include mainly alkene, dienes, aldehyde/keto compounds, olefinic aldehyde/keto compounds, diones, cyclic ethers, peroxides, acids, and alcohols/ethers. Reaction pathways forming intermediates with the same carbon number as n-heptane are proposed and discussed. These experimental results should be helpful in the development of kinetic models for n-heptane and longer-chain alkanes.

Rechargeable alkaline Zn/MnO2 batteries are being developed for use as cost-effective grid-scale energy storage devices. Previous studies have shown that limiting the depth of discharge (DOD) of the MnO2 cathode extends cell lifetime while still providing a cost-effective battery system. Herein, a comprehensive study of triethanolamine (TEA) as an additive in Zn/MnO2 limited DOD batteries is provided by examining the effect of TEA in full cells as well as independently on the cathode, anode, separator, and electrolyte. Improvement in cycle-ability of the cathode (on average, 80% of cycled capacity remains after 191 cycles without TEA, 568 cycles with TEA) and a decrease in ionic zinc mobility across Celgard 3501 (7.91 × 10-5 cm2/min without TEA, 3.56 × 10-5 cm2/min with TEA) and Cellophane 350P00 (3.26 × 10-5 cm2/min without TEA, 4.74 × 10-6 cm2/min with TEA) separators upon the addition of TEA are demonstrated. However, TEA increased both the reduction potential of Zn (-0.68 V vs. Hg/HgO without TEA, -0.76 V with TEA) and the solubility of Zn2+ (0.813 M without TEA, 1.023 M with TEA). Overall, the addition of TEA extended the lifetime of limited DOD cells on average by 297%.

Designers of direct-injection compression-ignition engines use a variety of strategies to improve the fuel/charge-gas mixture within the combustion chamber for increased efficiency and reduced pollutant emissions. Strategies include the use of high fuel-injection pressures, multiple injections, small injector orifices, flow swirl, long-ignition-delay conditions, and oxygenated fuels. This is the first journal publication on a new mixing-enhancement strategy for emissions reduction: ducted fuel injection. The concept involves injecting fuel along the axis of a small cylindrical duct within the combustion chamber, to enhance the mixture in the autoignition zone relative to a conventional free-spray configuration (i.e., a fuel spray that is not surrounded by a duct). The results described herein, from initial proof-of-concept experiments conducted in a constant-volume combustion vessel, show dramatically lower soot incandescence from ducted fuel injection than from free sprays over a range of charge-gas conditions that are representative of those in modern direct-injection compression-ignition engines.

The development of the unsteady pressure field on the floor of a rectangular cavity was studied at Mach 0.9 using high-frequency pressure-sensitive paint. Power spectral amplitudes at each cavity resonance exhibit a spatial distribution with an oscillatory pattern; additional maxima and minima appear as the mode number is increased. This spatial distribution also appears in the propagation velocity of modal pressure disturbances. This behavior was tied to the superposition of a downstream-propagating shear-layer disturbance and an upstream-propagating acoustic wave of different amplitudes and convection velocities, consistent with the classical Rossiter model. The summation of these waves generates an interference pattern in the spatial pressure amplitudes and resulting phase velocity of the resonant pressure fluctuations.

Dissolved polysulfides, formed during Li-S battery operation, freely migrate and react with both the Li anode and the sulfur cathode. These soluble polysulfides shuttle between the anode and cathode – the so-called shuttle effect – resulting in an infinite recharge process and poor Columbic efficiency. In this study, water present as an additive in the Li-S battery electrolyte is found to reduce the shuttle effect in Li-S batteries. Batteries where water content was below 50 ppm exhibited a substantial shuttle effect and low charge capacity. Alternatively, addition of 250 ppm water led to stable charge/discharge behavior with high Coulombic efficiency. XPS results show that H2O addition results in the formation of solid electrolyte interphase (SEI) film with more LiOH on Li anode which protects the Li anode from the polysulfides. Batteries cycled without water result in a SEI film with more Li2CO3 likely formed by direct contact between the Li metal and the solvent. Intermediate quantities of H2O in the electrolyte result in high cycle efficiency for the first few cycles which then rapidly decays. This suggests that H2O is consumed during battery cycling, likely by interaction with freshly exposed Li metal formed during Li deposition.

Whilst the physics of both classical evaporation and supercritical fluid mixing are reasonably well characterized and understood in isolation, little is known about the transition from one to the other in the context of liquid fuel systems. The lack of experimental data for microscopic droplets at realistic operating conditions impedes the development of phenomenological and numerical models. To address this issue we performed systematic measurements using high-speed long-distance microscopy, for three single-component fuels (n-heptane, n-dodecane, n-hexadecane), into gas at elevated temperatures (700–1200 K) and pressures (2–11 MPa). We describe these high-speed visualizations and the time evolution of the transition from liquid droplet to fuel vapour at the microscopic level. The measurements show that the classical atomization and vaporisation processes do shift to one where surface tension forces diminish with increasing pressure and temperature, but the transition to diffusive mixing does not occur instantaneously when the fuel enters the chamber. Rather, subcritical liquid structures exhibit surface tension in the near-nozzle region and then, after time surrounded by the hot ambient gas and fuel vapour, undergo a transition to a dense miscible fluid. Although there was clear evidence of surface tension and primary atomization for n-dodecane and n-hexadecane for a period of time at all the above conditions, n-heptane appeared to produce a supercritical fluid from the nozzle outlet when injected at the most elevated conditions (1200 K, 10 MPa). This demonstrates that the time taken by a droplet to transition to diffusive mixing depends on the pressure and temperature of the gas surrounding the droplet as well as the fuel properties. We summarise our observations into a phenomenological model which describes the morphological evolution and transition of microscopic droplets from classical evaporation through a transitional mixing regime and towards diffusive mixing, as a function of operating conditions. We provide criteria for these regime transitions as reduced pressure–temperature correlations, revealing the conditions where transcritical mixing is important to diesel fuel spray mixing.

Sandia National Laboratories and Heat Pipes Laboratory of National Technical University of Ukraine «KPI» have developed several methods of improving robustness of the high-temperature heat pipe wick for their application to concentrating solar power systems with Stirling engine. In this case the wick structures must retain high heat pipe performance with robustness for long-term operation. Recent modeling indicates that wicks based on various fiber combinations could provide the robustness combined with sufficient performance. Results of the development, characterization, modeling, and testing of advanced felt metal wicks that addresses durability issues while maintaining sufficient performance are showed in the paper. The project resulted in an ongoing durability bench-scale heat pipe that simulates wick load conditions required for 80kWth throughput solar receiver, including periodic non-destructive evaluation of the wick durability. Two mock-ups of high-temperature heat pipes were made for long life and performance tests: the first sample with wick based on 12 μm fibers; and the second with hybrid wick based on 30 μm fibers and 6.5 μm fibers. The second heat pipe operation has continued unattended at 775 °C vapor temperature for nearly 13,600 h, with no observable loss of performance or change in startup characteristics.

Ignitability is important to characterize pulverized coal combustion, as it is directly related to flame stability. The current work describes a practical test rig for rapid laboratory analysis of pulverized coal cloud ignition properties. The system has been designed for conventional coal combustion conditions using air as the oxidant. In the current work, the measurement principle of the device is described and its adaption to and applicability for oxy-fuel combustion tests is demonstrated. Four coals with different rank were measured in air and in oxy-fuel atmospheres containing 20–35 vol% O2 in CO2. The major influencing factors for the investigated samples were found to be the coal rank and the gas-phase oxygen concentration, while a minor influence of particle size was observed.

Product formation, in particular ketohydroperoxide formation and decomposition, were investigated in time-resolved, Cl-atom initiated neopentane oxidation experiments in the temperature range 550-675 K using a photoionization time-of-flight mass spectrometer. Ionization light was provided either by Advanced Light Source tunable synchrotron radiation or ∼10.2 eV fixed energy radiation from a H2-discharge lamp. Experiments were performed both at 1-2 atm pressure using a high-pressure reactor and also at ∼9 Torr pressure employing a low-pressure reactor for comparison. Because of the highly symmetric structure of neopentane, ketohydroperoxide signal can be attributed to a 3-hydroperoxy-2,2-dimethylpropanal isomer, i.e. from a γ-ketohydroperoxide (γ-KHP). The photoionization spectra of the γ-KHP measured at low- and high pressures and varying oxygen concentrations agree well with each other, further supporting they originate from the single isomer. Measurements performed in this work also suggest that the "Korcek" mechanism may play an important role in the decomposition of 3-hydroperoxy-2,2-dimethylpropanal, especially at lower temperatures. However, at higher temperatures where γ-KHP decomposition to hydroxyl radical and oxy-radical dominates, oxidation of the oxy-radical yields a new important channel leading to acetone, carbon monoxide, and OH radical. Starting from the initial neopentyl + O2 reaction, this channel releases altogether three OH radicals. A strongly temperature-dependent reaction product is observed at m/z = 100, likely attributable to 2,2-dimethylpropanedial.

Particle accelerators play a key role in a broad set of defense and security applications, including war-fighter and asset protection, cargo inspection, nonproliferation, materials characterization, and stockpile stewardship. Accelerators can replace the high activity radioactive sources that pose a security threat to developing a radiological dispersal device, and, can be used to produce isotopes for medical, industrial, and research purposes. An overview of current and emerging accelerator technologies relevant to addressing the needs of defense and security is presented.

A non-premixed coannular burner in which oxygen concentrations and oxidizer flow rates can be independently varied was developed to investigate the effects of turbulent mixing intensity from oxygen enrichment on soot formation and flame radiation. Local radiation intensities soot concentrations and soot temperatures were measured using a thin-film thermopile planar laser-induced incandescence and two-color imaging pyrometry respectively. The measurements showed that soot formation increased as the oxygen concentration decreases from 100% to 50% helping to moderate a decrease in overall flame radiation. An increase in turbulence intensity had a remarkable effect on flame height soot formation and thermal radiation resulting to decreases in all of these parameters. The soot temperature decreased with a decrease in the oxygen concentration and increased with an increase in turbulent mixing intensity. Thus properly designed oxygen-enriched burners that enhance soot formation for intermediate levels of oxygen purity may be able to achieve thermal radiation intensities as high as 85% of traditional oxy-fuel burners utilizing high-purity oxygen.

Optical coatings with the highest laser damage thresholds rely on clean conditions in the vacuum chamber during the coating deposition process. A low-base pressure in the coating chamber, as well as the ability of the vacuum system to maintain the required pressure during deposition, are important aspects of limiting the amount of defects in an optical coating that could induce laser damage. Our large optics coating chamber at Sandia National Laboratories normally relies on three cryo pumps to maintain low pressures for e-beam coating processes. However, on occasion, one or more of the cryo pumps have been out of commission. In light of this circumstance, we explored how deposition under compromised vacuum conditions resulting from the use of only one or two cryo pumps affects the laser-induced damage thresholds of optical coatings. The coatings of this study consist of HfO2 and SiO2 layer materials and include antireflection coatings for 527 nm at normal incidence and high-reflection coatings for 527 nm at 45-deg angle of incidence in P-polarization.

We present a new meshless method for scalar diffusion equations, which is motivated by their compatible discretizations on primal-dual grids. Unlike the latter though, our approach is truly meshless because it only requires the graph of nearby neighbor connectivity of the discretization points xi. This graph defines a local primal-dual grid complex with a virtual dual grid, in the sense that specification of the dual metric attributes is implicit in the method's construction. Our method combines a topological gradient operator on the local primal grid with a generalized moving least squares approximation of the divergence on the local dual grid. We show that the resulting approximation of the div-grad operator maintains polynomial reproduction to arbitrary orders and yields a meshless method, which attains O(hm) convergence in both L2- and H1-norms, similar to mixed finite element methods. We demonstrate this convergence on curvilinear domains using manufactured solutions in two and three dimensions. Application of the new method to problems with discontinuous coefficients reveals solutions that are qualitatively similar to those of compatible mesh-based discretizations.

Li-air or Li-oxygen batteries promise significantly higher energies than existing commercial battery technologies, yet their development has been hindered by a lack of suitable electrolytes. In this article, we evaluate the physical properties of varied electrolyte compositions to form generalized criteria for electrolyte design. We show that oxygen transport through non-aqueous electrolytes has a critical impact on the discharge rate and capacity of Li-air batteries. Through experiments and molecular dynamics simulations, we highlight that the choice of salt species and concentration have an outsized influence on oxygen solubility, while solvent choice is the major influence on oxygen diffusivity. The stability of superoxide reaction intermediates, key to the oxygen reduction mechanism, is also affected by variations in salt concentration and the choice of solvent. The importance of reactant transport is confirmed through Li-air cell discharge, which demonstrates good agreement between the observed and calculated mass transport-limited currents. These results showcase the impact of electrolyte composition on transport in metal-air batteries and provide guiding principles and simulation-based tools for future electrolyte design.

A “good” speckle pattern enables DIC to make its full-field measurements, but oftentimes this artistic part of the DIC setup takes a considerable amount of time to develop and evaluate for a given optical configuration. A catalog of well-quantified speckle patterns for various fields of view would greatly decrease the time it would take to start making DIC measurements. The purpose of this speckle patterning study is to evaluate various speckling techniques we had readily available in our laboratories for fields of view from around 100 mm down to 5 mm that are common for laboratory-scale experiments. The list of speckling techniques is not exhaustive: spray painting, UV-printing of computer-designed speckle patterns, airbrushing, and particle dispersion. First, we quantified the resolution of our optical configurations for each of the fields of view to determine the smallest speckle we could resolve. Second, we imaged several speckle patterns at each field of view. Third, we quantified the average and standard deviation of the speckle size, speckle contrast, and density to characterize the quality of the speckle pattern. Finally, we performed computer-aided sub-pixel translation of the speckle patterns and ran correlations to examine how well DIC tracked the pattern translations. We discuss our metrics for a “good” speckle pattern and outline how others may perform similar studies for their desired optical configurations.

The breakup of liquid drops is an important phenomenology for many applications. We approach this problem with the objective to improve methods for handling the modeling of the impulse and impact dispersal of liquids in transportation accident scenarios. These scenarios can be distinguished from many other simpler problems, due to the quantity of liquid and the complexity of the intermediate liquid morphology. These differences necessitate alternative approaches to the problem. We have recently implemented a model for inter-particle forces between particles in a Lagrangian/Eulerian CFD code. The inter-particle force model is inspired by molecular dynamics methods, and employs a Lennard-Jones (LJ) attractive force and a spring-based repulsive force. The LJ parameters are related to the bulk fluid properties through a theoretical relationship model. Methods are necessary for modifying the single particle aerodynamic drag term, depending on the new notion of particle connectivity. We want to evaluate these methods for potential utilization in practical simulations. Classical breakup tests for drops in flows suggest a critical Weber number relating to the onset of breakup for a drop. We seek to replicate these data with our model methods as a preliminary step before deploying the method in larger scale practical environments.

Many engineering design problems can be formulated in the framework of partial differential equation (PDE) constrained optimization. The discretization of a PDE leads to multiple levels of resolution with varying degrees of numerical solution accuracy. Coarse discretizations require less computational time at the expense of increased error. Often there are also reduced fidelity models available, with simplifications to the physics models that are computationally easier to solve. This research develops an up to second-order consistent multilevel-multifidelity (MLMF) optimization scheme that exploits the reduced cost resulting from coarse discretization and reduced fidelity to more efficiently converge to the optimum of a fine-grid high-fidelity problem. This scheme distinguishes multilevel approaches applied to discretizations from multifidelity approaches applied to model forms, and navigates both hierarchies to accelerate convergence. Additive, multiplicative, or a combination of both corrections can be applied to the sub-problems to enforce up to second-order consistency with the fine-grid high-fidelity results. The MLMF optimization algorithm is a wrapper around a subproblem optimization solver, and the MLMF scheme is provably convergent if the subproblem optimizer is provably convergent. Heuristics are developed for efficiently tuning optimization tolerances and iterations at each level and fidelity based on relative solution cost. Accelerated convergence is demonstrated for a simple one-dimensional problem and aerodynamic shape optimization of a transonic airfoil.

Vibration and shock qualification testing of components can be an expensive and time-consuming process. If the component is small, often two or more units can be mounted on a fixture and tested simultaneously to reduce test time. There is an inherent danger in simultaneously testing two or more identical components as the fundamental natural frequencies and mode shapes of the individual components will be nearly identical with some slight variation due to manufacturing variability. Testing in this manner can create a situation where closely spaced vibration modes produce unwanted interference between the two units under test. This phenomenon could result in a case where one unit is over-tested while the other is under-tested. This paper presents some experimental results from simultaneously testing pairs of components which show distinct interference between the units. Some analysis will also be presented showing how variations in the components can alter the intended test response, potentially impacting component qualification.

Thin beams and panels subjected to large loadings will behave nonlinearly due to membrane stretch effects as they approach deflections on the order of their thickness; this behavior can be efficiently and accurately modeled using nonlinear reduced order models based on the structure’s linear normal modes. However, the complexity of such reduced order models grows cubically with the number of linear modes in the basis set, making complicated geometries prohibitively expensive to compute. Component mode synthesis techniques may be used to reduce this cost by assembling a set of smaller nonlinear subcomponent models, each of which can be more quickly computed than a nonlinear model of the entire structure. Since geometric nonlinearity is heavily dependent on each structure’s boundary conditions, however, subcomponents of an assembly which are constrained only at their interfaces – such as panels mounted to an underlying frame – prove difficult to treat using existing nonlinear modeling techniques. This work uses Craig-Bampton dynamic substructuring combined with characteristic constraint modes for interface reduction to examine the challenges associated with panel and frame assemblies, with a simple example motivating a discussion of current solutions and future challenges.

Linear structural dynamic models are often used to support system design and qualification. Overall, linear models provide an efficient means for conducting design studies and augmenting test data by recovering un-instrumented or unmeasurable quantities (e.g. stress). Nevertheless, the use of linear models often adds significant conservatism in design and qualification programs by failing to capture critical mechanisms for energy dissipation. Unfortunately, the use of explicit nonlinear models can require unacceptably large efforts in model development and experimental characterization to account for common nonlinearities such as frictional interfaces, macro-slip, and other complex material behavior. The computational requirements are also greater by orders of magnitude. Conversely, modal models are much more computationally efficient and experimentally have shown the ability to capture typical structural nonlinearity. Thus, this work will seek to use modal nonlinear identification techniques to improve the predictive capability of a finite element structural dynamics model. Part I of this paper discusses the experimental aspects of this work. Linear natural frequencies, damping values, and mode shapes are extracted from low excitation level testing. Subsequently, the structure is excited with high level user-defined shaker inputs. The corresponding response data are modally filtered and fit with nonlinear elements to create the nonlinear pseudo-modal model. This is then used to simulate the measured response from a high level excitation experiment which utilized a different type of input. The nonlinear model is then employed in a reduced order, generalized structural dynamics model as discussed in Part II.

Effective mass for a particular mode in a particular direction is classically calculated using a combination of fixed base mode shapes, the mass matrix, and a rigid body mode shape from a finite element model. Recently, an experimental method was developed to calculate effective mass using free experimental mode shapes of a structure on a fixture (the base) along with the measured mass of the fixture and of the test article. The method required three steps. The first step involved constraining all the free modes of the fixture except one rigid body mode in the direction of interest. The second step involved calculating pseudo-modal participation factors for this case. The third step involved constraining the final fixture rigid body degree of freedom and utilizing the constraint matrices with pseudo-modal participation factors to obtain the estimate of the standard modal participation factors which can be converted to effective mass. This work provides a simpler formulation. After the constraint in step one above, the effective masses are calculated directly from the mass normalized mode shapes of the fixture. In most cases this method gives the same answer as the original approach, within experimental error. In some instances, it appears more robust with low signal to noise ratios. It also provides better physical insight as to which modes have significant effective mass in a particular direction. The new approach is illustrated by experimental example.

Linear structural dynamic models are often used to support system design and qualification. Overall, linear models provide an efficient means for conducting design studies and augmenting test data by recovering un-instrumented or unmeasurable quantities (e.g. stress). Nevertheless, the use of linear models often adds significant conservatism in design and qualification programs by failing to capture critical mechanisms for energy dissipation. Unfortunately, the use of explicit nonlinear models can require unacceptably large efforts in model development and experimental characterization to account for common nonlinearities such as frictional interfaces, macro-slip, and other complex material behavior. The computational requirements are also greater by orders of magnitude. Conversely, modal models are much more computationally efficient and experimentally have shown the ability to capture typical structural nonlinearity. Thus, this work will seek to use modal nonlinear identification techniques to improve the predictive capability of a finite element structural dynamics model. Part I of this paper discussed experimental aspects of this work. Part II will consider use of nonlinear modal models in finite element modeling. First, the basic theory and numerical implementation is discussed. Next, the linear structural dynamic model of a configuration of interest is presented and model updating procedures are discussed. Finally, verification exercises are presented for a high level excitation using test data and simulated predictions from a structural dynamics model augmented with models obtained in nonlinear identification efforts.

Metal additive manufacturing (AM) has recently become a desirable process for complex parts across a broad range of applications. However, AM materials often have a varied microstructure due to non-equilibrium solidification conditions. While some adjustments have been made in manufacturing to enhance mechanical traits, very little attention has been directed at understanding the corrosion properties of these materials. The microstructural characteristics of the AM materials may lead to reduction in the corrosion resistance of the AM alloys compared to their conventional counterparts. This presentation explores the corrosion susceptibility of AM stainless steels in aqueous sodium chloride environments as well as industry relevant solutions. Further detailed corrosion studies combined with microstructural characterization provide insight into the microstructural influences on corrosion.

Counting the frequency of small subgraphs is a fundamental technique in network analysis across various domains, most notably in bioinformatics and social networks. The special case of triangle counting has received much attention. Getting results for 4-vertex or 5-vertex patterns is highly challenging, and there are few practical results known that can scale to massive sizes. We introduce an algorithmic framework that can be adopted to count any small pattern in a graph and apply this framework to compute exact counts for all 5-vertex subgraphs. Our framework is built on cutting a pattern into smaller ones, and using counts of smaller patterns to get larger counts. Furthermore, we exploit degree orientations of the graph to reduce runtimes even further. These methods avoid the combinatorial explosion that typical subgraph counting algorithms face. We prove that it suffices to enumerate only four specific subgraphs (three of them have less than 5 vertices) to exactly count all 5-vertex patterns. We perform extensive empirical experiments on a variety of real-world graphs. We are able to compute counts of graphs with tens of millions of edges in minutes on a commodity machine. To the best of our knowledge, this is the first practical algorithm for 5-vertex pattern counting that runs at this scale. A stepping stone to our main algorithm is a fast method for counting all 4-vertex patterns. This algorithm is typically ten times faster than the state of the art 4-vertex counters.

In film cooling flows, it is important to know the temperature distribution resulting from the interaction between a hot main flow and a cooler jet. However, current Reynolds-averaged Navier-Stokes (RANS) models yield poor temperature predictions. A novel approach for RANS modeling of the turbulent heat flux is proposed, in which the simple gradient diffusion hypothesis (GDH) is assumed and a machine learning algorithm is used to infer an improved turbulent diffusivity field. This approach is implemented using three distinct data sets: two are used to train the model and the third is used for validation. The results show that the proposed method produces significant improvement compared to the common RANS closure, especially in the prediction of film cooling effectiveness.

Progressive hedging, though an effective heuristic for solving stochastic mixed integer programs (SMIPs), is not guaranteed to converge in this case. Here, we describe BBPH, a branch and bound algorithm that uses PH at each node in the search tree such that, given sufficient time, it will always converge to a globally optimal solution. In addition to providing a theoretically convergent “wrapper” for PH applied to SMIPs, computational results demonstrate that for some difficult problem instances branch and bound can find improved solutions after exploring only a few nodes.

Novel particle release patterns have been proposed as a means to increase the thermal efficiency of high-temperature falling particle receivers. Innovative release patterns offer the ability to utilize light-trapping and volumetric heating effects as a means to increase particle temperatures over a conventional straight-line particle release pattern. The particle release patterns explored in this work include wave-like patterns and a series of parallel curtains normal to the incident irradiation that have shown favorable results in previous numerical studies at lower particle temperatures. A numerical model has recently been developed of an existing falling particle receiver at the National Solar Thermal Test Facility at Sandia National Laboratories to evaluate these patterns at elevated temperatures necessary to evaluate radiative and convective losses. This model has demonstrated that thermal efficiency gains of 2.5-4.6% could be realized using these patterns compared to the conventional planar release depending on the particle mass flow rate. Increasing the number of parallel curtains, increasing the spacing between curtains, and shifting the particle mass flow rate deeper in the receiver cavity was also found to increase the thermal efficiency. These effects became less significant as the particle mass flow rate increased.

Understanding of subsurface fracture opening and closure is of great importance to oil/gas production, geothermal energy extraction, radioactive waste disposal, and carbon sequestration and storage. Fracture opening and closure involve a complex set of thermal, hydrologic, mechanical and chemical (THMC) processes. In this paper, a fully coupled THMC model for fracture opening and closure is formulated by explicitly accounting for the stress concentration on aperture surface, stress-activated mineral dissolution, pressure solution at contacting asperities, and channel flow dynamics. A model analysis, together with reported laboratory observations, shows that a tangential surface stress created by a far-field compressive normal stress may play an important role in controlling fracture aperture evolution in a stressed geologic medium, a mechanism that has not been considered in any existing models. Based on the model analysis, a necessary condition for aperture opening has been derived. The model provides a reasonable explanation for many salient features of fracture evolution in laboratory experiments, including a spontaneous switch from a permeability reduction to a permeability increase in a static limestone experiment. The work may also help develop a new method for estimating in-situ stress in a reservoir.

Single-laser-shot femtosecond rotational coherent anti-Stokes Raman scattering (fs-RCARS) temperature measurements are performed across a 3- mm line in a turbulent, sooting ethylene jet flame to characterize temperature gradients. A 60-fs pulse is used to excite many rotational Raman transitions, and a 160-ps pulse is used to probe the Raman coherence. The spatial resolution of the measurements is 670 μm in the direction of beam propagation, 200 μm in the direction along the 1D line, and 50 μm in the transverse direction. Measurements have been performed at multiple locations in the jet flame, and the measured temperature are similar to previously recorded point measurements.

Single-laser-shot femtosecond rotational coherent anti-Stokes Raman scattering (fs-RCARS) temperature measurements are performed across a 3- mm line in a turbulent, sooting ethylene jet flame to characterize temperature gradients. A 60-fs pulse is used to excite many rotational Raman transitions, and a 160-ps pulse is used to probe the Raman coherence. The spatial resolution of the measurements is 670 μm in the direction of beam propagation, 200 μm in the direction along the 1D line, and 50 μm in the transverse direction. Measurements have been performed at multiple locations in the jet flame, and the measured temperature are similar to previously recorded point measurements.

Fluid-structure interactions were studied on a 7° half-angle cone in the Purdue Boeing/AFOSR Mach 6 Quiet Tunnel. A thin carbon-composite panel was integrated into the cone and its response to boundary-layer disturbances was measured. Under quiet flow, the cone boundary layer remained laminar. A spark perturber was used to create turbulent spots in the boundary layer at frequencies between 0.1 and 10.5 kHz. Isolated turbulent spots excited a directionally dependent panel response which would last much longer than the spot duration. At higher repetition rates, the panel response did not damp out before the subsequent spot. When the excitation frequency matched a structural natural frequency of the panel, resonance would occur in the directions associated with the mode shape. It was harder to excite spanwise vibration at lower frequencies because of the dominant axial and wall-normal forcing created by the controlled turbulent spots. At higher frequencies, spanwise resonance could be more easily excited, likely because the highly coupled mode shapes associated with those frequencies provided a path for energy transfer.

Biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems. Here, we survey the history of neural-inspired and neuromorphic computing in order to examine the complex and intertwined trajectories of the mathematical theory and hardware developed in this field. Early research focused on adapting existing hardware to emulate the pattern recognition capabilities of living organisms. Contributions from psychologists, mathematicians, engineers, neuroscientists, and other professions were crucial to maturing the field from narrowly-tailored demonstrations to more generalizable systems capable of addressing difficult problem classes such as object detection and speech recognition. Algorithms that leverage fundamental principles found in neuroscience such as hierarchical structure, temporal integration, and robustness to error have been developed, and some of these approaches are achieving world-leading performance on particular data classification tasks. In addition, novel microelectronic hardware is being developed to perform logic and to serve as memory in neuromorphic computing systems with optimized system integration and improved energy efficiency. Key to such advancements was the incorporation of new discoveries in neuroscience research, the transition away from strict structural replication and towards the functional replication of neural systems, and the use of mathematical theory frameworks to guide algorithm and hardware developments.

The purpose of this article is to explore the dependence of calculated postshock thermodynamic properties in shock tube experiments upon the vibrational state of the test gas and upon the uncertainties inherent to calculation inputs. This paper first offers a comparison between state variables calculated according to a Rankine-Hugoniot-equationbased algorithm, known as FROSH, and those derived from shock tube experiments on vibrationally nonequilibrated gases. It is shown that incorrect vibrational relaxation assumptions could lead to errors in temperature as large as8% for 25% oxygen/argon mixtures at 3500 K. Following this demonstration, this article employs the algorithm to show the importance of correct vibrational equilibration assumptions, noting, for instance, that errors in temperature of up to about 2% at 3500 K may be generated for 10% nitrogen/argon mixtures if vibrational relaxation is not treated properly. Finally, this article presents an extensive uncertainty analysis, showing that postshock temperatures can be calculated with root-of-sum-of-square errors of better than ± 1% given sufficiently accurate experimentally measured input parameters.

Due to the increasing prevalence of plenoptic imaging it is necessary to explore the volumetric calibration of this imaging system to correct for inaccuracies due to real world lens distortions and thin lens assumptions in current processing methods. An overview of plenoptic imaging is given and methods of volumetric calibration of a plenoptic camera based on a polynomial mapping function are presented. The accuracy and feasibility of these methods are examined. Preliminary results suggest that use of a 3D polynomial mapping function provides a significant increase in reconstruction accuracy. Depth accuracy of particle location in calibrated volumes was measured to be accurate within 1% of the calculated volume size.

Ferroelastic domain walls provide opportunities for deterministically controlling mechanical, optical, electrical, and thermal energy. Domain wall characterization in micro- and nanoscale systems, where their spacing may be of the order of 100 nm or less is presently limited to only a few techniques, such as piezoresponse force microscopy and transmission electron microscopy. These respective techniques cannot, however, independently characterize domain polarization orientation and domain wall motion in technologically relevant capacitor structures or in a non-destructive manner, thus presenting a limitation of their utility. In this work, we show how backscatter scanning electron microscopy utilizing channeling contrast yield can image the ferroelastic domain structure of ferroelectric films with domain wall spacing as narrow as 10 nm. Combined with electron backscatter diffraction to identify grain orientations, this technique provides information on domain orientation and domain wall type that cannot be readily measured using conventional non-destructive methods. In addition to grain orientation identification, this technique enables dynamic domain structure changes to be observed in functioning capacitors utilizing electrodes that are transparent to the high-energy backscattered electrons. This non-destructive, high-resolution domain imaging technique is applicable to a wide variety of ferroelectric thin films and a multitude of material systems where nanometer-scale crystallographic twin characterization is required.

We propose an algorithm for recovering sparse orthogonal polynomial expansions via collocation. A standard sampling approach for recovering sparse polynomials uses Monte Carlo sampling, from the density of orthogonality, which results in poor function recovery when the polynomial degree is high. Our proposed approach aims to mitigate this limitation by sampling with respect to the weighted equilibrium measure of the parametric domain and subsequently solves a preconditioned'1-minimization problem, where the weights of the diagonal preconditioning matrix are given by evaluations of the Christoffel function. Our algorithm can be applied to a wide class of orthogonal polynomial families on bounded and unbounded domains, including all classical families. We present theoretical analysis to motivate the algorithm and numerical results that show our method is superior to standard Monte Carlo methods in many situations of interest. Numerical examples are also provided to demonstrate that our proposed algorithm leads to comparable or improved accuracy even when compared with Legendre- and Hermite-specific algorithms.

Substructuring methods have been widely used in structural dynamics to divide large, complicated finite element models into smaller substructures. For linear systems, many methods have been developed to reduce the subcomponents down to a low-order set of equations using a special set of component modes, and these are then assembled to approximate the dynamics of a large-scale model. In this paper, a substructuring approach is developed for coupling geometrically nonlinear structures, where each subcomponent is drastically reduced to a low-order set of nonlinear equations using a truncated set of fixed-interface and characteristic constraint modes. The method used to extract the coefficients of the nonlinear reduced-order model is nonintrusive, in that it does not require any modification to the commercial finite element code but computes the reduced-order model from the results of several nonlinear static analyses. The nonlinear reduced-order models are then assembled to approximate the nonlinear differential equations of the global assembly. The method is demonstrated on the coupling of two geometrically nonlinear plates with simple supports at all edges. The plates are joined at a continuous interface through the rotational degrees of freedom, and the nonlinear normal modes of the assembled equations are computed to validate the models. The proposed substructuring approach reduces a 12,861-degree-of-freedom model down to only 23 degrees of freedom while still accurately reproducing the nonlinear normal modes.

Despite the known degrading impact of high nuisance and false alarm rates (NAR/FAR) on operator performance, analyses of security systems often ignores operator performance. We developed a model to analyze the impact of nuisance alarm rates on operator performance and on overall system performance. The model demonstrates that current methods that do not account for operator performance produce optimistic estimates of system performance. As shown in our model, even low NAR/FAR levels and the associated alarm queueing effect can increase operator detect and response time, which in turn reduces the amount of time the response force has to interrupt the intruder. An illustrative analysis shows that alarm processing times can be higher than the assessment time due to queue wait times and that systems with only one or two operators can become overwhelmed as NAR increases, decreasing system performance.

We created a simulation model to investigate potential links between the actions of violent extremist organizations (VEOs), people in the VEO’s home country, and diaspora communities from that country living in the West. We created this model using the DYMATICA framework, which uses a hybrid cognitive system dynamics modeling strategy to simulate behaviors based on psycho-social theory. Initial results of the model are given, focusing on increases to VEO funding and recruiting resulting from an invasion of the VEO’s home country. Western intervention, prejudice, and economic drivers are also considered.

Next generation learning ecosystems will be comprised of intelligent, adaptive environments that utilize one’s cultural footprints to co-create shared narratives and facilitate intercultural understanding. The present paper discusses why digital footprints, cultural signposts, intercultural agents, and transmedia learning are needed to realize relevant learning in virtual environments. The paper introduces notions that may impact the design of culturally-aware information technology for distributed learning are presented.

Economic feasibility of geologic carbon storage demands sustaining large storage rates without damaging caprock seals. Reactivation of pre-existing or newly formed fractures may provide a leakage pathway across caprock layers. In this study, we apply an equivalent continuum approach within a finite element framework to model the fluid-pressure-induced reactivation of pre-existing fractures within the caprock, during high-rate injection of super-critical CO2 into a brine-saturated reservoir in a hypothetical system, using realistic geomechanical and fluid properties. We investigate the impact of reservoir to caprock layer thickness, wellbore orientation, and injection rate on overall performance of the system with respect to caprock failure and leakage. We find that vertical wells result in locally higher reservoir pressures relative to horizontal injection wells for the same injection rate, with high pressure inducing caprock leakage along reactivated opening-mode fractures in the caprock. After prolonged injection, leakage along reactivated fractures in the caprock is always higher for vertical than horizontal injection wells. Furthermore, we find that low ratios of reservoir to caprock thickness favor high excess pressure and thus fracture reactivation in the caprock. Injection into thick reservoir units thus lowers the risk associated with CO2 leakage.

Short circuit current (Isc) depends on the effective irradiance incident upon a PV module. Effective irradiance is highly correlated with broadband irradiance, but can vary slightly as the spectral content of the incident light changes. We explore using a few spectral wavelengths with broadband irradiance to predict Isc for ten modules of varying technologies (silicon, CIGS, CdTe). The goal is to identify a few spectral wavelengths that could be easily (and economically) measured to improve PV performance modeling.

Fungi in the Candida genus are the most common fungal pathogens. They not only cause high morbidity and mortality but can also cost billions of dollars in healthcare. To alleviate this burden, early and accurate identification of Candida species is necessary. However, standard identification procedures can take days and have a large false negative error. The method described in this study takes advantage of hyperspectral confocal fluorescence microscopy, which enables the capability to quickly and accurately identify and characterize the unique autofluorescence spectra from different Candida species with up to 84% accuracy when grown in conditions that closely mimic physiological conditions.

This paper describes methods that a distribution engineer could use to determine advanced inverter settings to improve distribution system performance. These settings are for fixed power factor, volt-var, and volt-watt functionality. Depending on the level of detail that is desired, different methods are proposed to determine single settings applicable for all advanced inverters on a feeder or unique settings for each individual inverter. Seven distinctly different utility distribution feeders are analyzed to simulate the potential benefit in terms of hosting capacity, system losses, and reactive power attained with each method to determine the advanced inverter settings.

PVLIB is a set of open source modeling functions that allow users to simulate most aspects of PV system performance. The functions, in Matlab and Python, are freely available under a BSD 3 clause open source license. The Matlab version is maintained by Sandia and is available on the PV Performance Modeling Collaborative (PVPMC) website (pvpmc.sandia.gov). The Python version is available on GitHub with packages easily installable through conda and pip. New functions were released on the Matlab version 1.3 in January 2016 and are actively being ported to Python.

Fault-tolerance poses a major challenge for future large-scale systems. Active research into coordinated, uncoordinated, and hybrid checkpointing systems has explored how the introduction of asynchrony can address anticipated scalability issues. While fully uncoordinated approaches have been shown to have significant delays, the degree of sychronization required to keep overheads low has not yet been significantly addressed. In this paper, we use a simulation-based approach to show the impact of synchronization on local checkpoint activity. Specifically, we show the degree of synchronization needed to keep the impacts of local checkpointing low is attainable with current technology for a number of key production HPC workloads. Our work provides a critical analysis and comparison of synchronization and local checkpointing. This enables users and system administrators to fine-tune the checkpointing scheme to the application and system characteristics available.

As PV and wind power penetrations in utility balancing areas increase, it is important to understand how they will impact net load. We investigate daily and seasonal trends in solar power generation, wind power generation, and net load. Quantitative metrics are used to compare scenarios with no PV or wind, PV plus wind, only PV, or only wind. PV plus wind scenarios are found to have a larger reduction in maximum net load and smaller ranges between maximum and minimum load than PV only or wind only scenarios, showing that PV plus wind can be a beneficial combination.

The use of the term 'availability' to describe a photovoltaic (PV) system and power plant has been fraught with confusion for many years. A term that is meant to describe equipment operational status is often omitted, misapplied or inaccurately combined with PV performance metrics due to attempts to measure performance and reliability through the lens of traditional power plant language. This paper discusses three areas where current research in standards, contract language and performance modeling is improving the way availability is used with regards to photovoltaic systems and power plants.

When an optical coating is damaged, deposited incorrectly, or is otherwise unsuitable, the conventional method to restore the optic often entails repolishing the optic surface, which can incur a large cost and long lead time. We propose three alternative options to repolishing, including (i) burying the unsuitable coating under another optical coating, (ii) using ion milling to etch the unsuitable coating completely from the optic surface and then recoating the optic, and (iii) using ion milling to etch through a number of unsuitable layers, leaving the rest of the coating intact, and then recoating the layers that were etched. Repairs were made on test optics with dielectric mirror coatings according to the above three options. The mirror coatings to be repaired were quarter wave stacks of HfO2 and SiO2 layers for high reflection at 1054 nm at 45 deg incidence in P-polarization. One of the coating layers was purposely deposited incorrectly as Hf metal instead of HfO2 to evaluate the ability of each repair method to restore the coating's high laser-induced damage threshold (LIDT) of 64.0 J/cm2. The repaired coating with the highest resistance to laser-induced damage was achieved using repair method (ii) with an LIDT of 49.0 to 61.0 J/cm2.

Photovoltaic (PV) distributed energy resources (DER) have reached approximately 27 GW in the U.S., and the solar penetration rate continues to increase. This growth is expected to continue, causing challenges for grid operators who must maintain grid stability, reliability, and resiliency. To minimize adverse effects on the performance of electrical power system (EPS) with increasing levels of variable renewable generation, photovoltaic inverters must implement grid-support capabilities, allowing the DER to actively participate in grid support operations and remain connected during short-term voltage and frequency anomalies. These functions include voltage and frequency regulation features that adjust DER active and reactive power at the point of common coupling. To evaluate the risk of these functions conflicting with traditional distribution system voltage regulation equipment, researchers used several methods to quantify EPS-support function response times for autonomous voltage regulation functions (volt-var function). Based on this study, no adverse interactions between PV inverters with volt-var functions and load tap changing transformers or capacitor banks were discovered.

The Sandia Array Performance Model (SAPM), a semi-empirical model for predicting PV system power, has been in use for more than a decade. While several studies have presented laboratory intercomparisons of measurements and analysis, detailed procedures for determining model coefficients have never been published. Independent test laboratories must develop in-house procedures to determine SAPM coefficients, which contributes to uncertainty in the resulting models. In response to requests from commercial laboratories and module manufacturers, Sandia has formally documented the measurement and analysis methods as a supplement to the original model description. In this paper we present a description of the measurement procedures and an example analysis for calibrating the SAPM.

The Sandia Array Performance Model (SAPM), a semi-empirical model for predicting PV system power, has been in use for more than a decade. While several studies have presented laboratory intercomparisons of measurements and analysis, detailed procedures for determining model coefficients have never been published. Independent test laboratories must develop in-house procedures to determine SAPM coefficients, which contributes to uncertainty in the resulting models. In response to requests from commercial laboratories and module manufacturers, Sandia has formally documented the measurement and analysis methods as a supplement to the original model description. In this paper we present a description of the measurement procedures and an example analysis for calibrating the SAPM.

The geological disposal of nuclear waste is based on the multi-barrier concept, comprising various engineered and natural barriers, to confine the radioactive waste and isolate it from the biosphere. Some of the planned repositories for high-level nuclear waste will be hosted in fractured crystalline rock formations. The potential of these formations to act as natural transport barriers is related to two coupled processes: diffusion into the rock matrix and sorption onto the mineral surfaces available in the rock matrix. Different in situ and laboratory experiments have pointed out the ubiquitous heterogeneous nature of the rock matrix: mineral surfaces and pore space are distributed in complex microstructures and their distribution is far from being homogeneous (as typically assumed by Darcy-scale coarse reactive transport models). In this work, we use a synthetically generated fracture–matrix system to assess the implications of grain-scale physical and mineralogical heterogeneity on cesium transport and retention. The resulting grain-scale reactive transport model is solved using high-performance computing technologies, and the results are compared with those derived from two alternative models, denoted as upscaled models, where mineral abundance is averaged over the matrix volume. In the grain-scale model, the penetration of cesium into the matrix is faster and the penetration front is uneven and finger-shaped. The analysis of the cesium breakthrough curves computed at two different points in the fracture shows that the upscaled models provide later first-arrival time estimates compared to the grain-scale model. The breakthrough curves computed with the three models converge at late times. These results suggest that spatially averaged upscaled parameters of sorption site distribution can be used to predict the late-time behavior of breakthrough curves but could be inadequate to simulate the early behavior.

Photovoltaic (PV) distributed energy resources (DER) have reached approximately 27 GW in the U.S., and the solar penetration rate continues to increase. This growth is expected to continue, causing challenges for grid operators who must maintain grid stability, reliability, and resiliency. To minimize adverse effects on the performance of electrical power system (EPS) with increasing levels of variable renewable generation, photovoltaic inverters must implement grid-support capabilities, allowing the DER to actively participate in grid support operations and remain connected during short-term voltage and frequency anomalies. These functions include voltage and frequency regulation features that adjust DER active and reactive power at the point of common coupling. To evaluate the risk of these functions conflicting with traditional distribution system voltage regulation equipment, researchers used several methods to quantify EPS-support function response times for autonomous voltage regulation functions (volt-var function). Based on this study, no adverse interactions between PV inverters with volt-var functions and load tap changing transformers or capacitor banks were discovered.

Fluctuating boundary layer wall shear stress can be an important loading component for structures subjected to turbulent boundary layer flows. While normal force loading via wall pressure fluctuation is relatively well described analytically, there is a dearth of information for wall shear behavior. Starting with an approximate acoustic analogy we derive simple approximate expressions for both wall pressure and wall shear fluctuations behavior utilizing a Taylor hypothesis based analogy between streamwise and temporal fluctuations. Analytical results include longitudinal spatial correlation, autocorrelation, frequency spectrum, RMS intensity and longitudinal and lateral coherence expressions. While coefficients in these expressions usually require some empirical input they nonetheless provide useful predictions for functional behavior. Comparison of the models with available literature data sets suggests reasonable agreement. Dedicated high fidelity numerical computations (direct numerical simulations) for a supersonic boundary layer are used to further explore the efficacy of these models. The analytical models for wall pressure fluctuation and wall shear fluctuation spectral density compare well for low frequency with the simulations when Reynolds number effects are included in the pressure fluctuation intensity. The approximate analytical models developed here provide a physics-based connection between classical empirical expressions and more complete experimental and computational descriptions.

Photovoltaic system monitoring can generate vast amounts of data. Analytical methods are required to post-process this data into useful information. Pecos is open source software designed to address this need. The software was developed at Sandia National Laboratories and is compatible with performance models in PVLIB-Python. The software can be used to automatically run a series of quality control tests and generate reports which include performance metrics, test results, and graphics.

High-frequency solar variability is an important to grid integration studies, but ground measurements are scarce. The high resolution irradiance algorithm (HRIA) has the ability to produce 4-sceond resolution global horizontal irradiance (GHI) samples, at locations across North America. However, the HRIA has not been extensively validated. In this work, we evaluate the HRIA against a database of 10 high-frequency ground-based measurements of irradiance. The evaluation focuses on variability-based metrics. This results in a greater understanding of the errors in the HRIA as well as suggestions for improvement to the HRIA.

High-frequency solar variability is an important to grid integration studies, but ground measurements are scarce. The high resolution irradiance algorithm (HRIA) has the ability to produce 4-sceond resolution global horizontal irradiance (GHI) samples, at locations across North America. However, the HRIA has not been extensively validated. In this work, we evaluate the HRIA against a database of 10 high-frequency ground-based measurements of irradiance. The evaluation focuses on variability-based metrics. This results in a greater understanding of the errors in the HRIA as well as suggestions for improvement to the HRIA.

The goal of this effort was to assess the effect of high penetration solar deployment on the small signal stability of the western North American power system (wNAPS). Small signal stability is concerned with the system response to small disturbances, where the system is operating in a linear region. The study area consisted of the region governed by the Western Electricity Coordinating Council (WECC). General Electric's Positive Sequence Load Flow software (PSLF®) was employed to simulate the power system. A resistive brake insertion was employed to stimulate the system. The data was then analyzed in MATLAB1® using subspace methods (Eigensystem Realization Algorithm). Two different WECC base cases were analyzed: 2022 light spring and 2016 heavy summer. Each base case was also modified to increase the percentage of wind and solar. In order to keep power flows the same, the modified cases replaced conventional generation with renewable generation. The replacements were performed on a regional basis so that solar and wind were placed in suitable locations. The main finding was that increased renewable penetration increases the frequency of inter-area modes, with minimal impact on damping. The slight increase in mode frequency was consistent with the loss of inertia as conventional generation is replaced with wind and solar. Then, distributed control of renewable generation was assessed as a potential mitigation, along with an analysis of the impact of communications latency on the distributed control algorithms.

This paper describes methods that a distribution engineer could use to determine advanced inverter settings to improve distribution system performance. These settings are for fixed power factor, volt-var, and volt-watt functionality. Depending on the level of detail that is desired, different methods are proposed to determine single settings applicable for all advanced inverters on a feeder or unique settings for each individual inverter. Seven distinctly different utility distribution feeders are analyzed to simulate the potential benefit in terms of hosting capacity, system losses, and reactive power attained with each method to determine the advanced inverter settings.

A control algorithm is designed to smooth the variability of PV power output using distributed batteries. The tradeoff between smoothing and battery size is shown. It is also demonstrated that large numbers of highly distributed current, voltage, and irradiance sensors can be utilized to control the distributed storage in a more optimal manner. It is also demonstrated that centralized energy storage control for PV ramp rate smoothing requires very fast communication, typically less than a 15-second update rate. Finally, advanced inverter dynamic reactive current is shown to provide voltage variability smoothing, hence reducing the number of voltage regulator tap changes without energy storage.

A control algorithm is designed to smooth the variability of PV power output using distributed batteries. The tradeoff between smoothing and battery size is shown. It is also demonstrated that large numbers of highly distributed current, voltage, and irradiance sensors can be utilized to control the distributed storage in a more optimal manner. It is also demonstrated that centralized energy storage control for PV ramp rate smoothing requires very fast communication, typically less than a 15-second update rate. Finally, advanced inverter dynamic reactive current is shown to provide voltage variability smoothing, hence reducing the number of voltage regulator tap changes without energy storage.

The research presented in this paper compares five real-time control strategies for the power output of a large number of distributed PV systems in a large distribution feeder circuit. Both real and reactive power controls are considered with the goal of minimizing network over-voltage violations caused by high penetrations of PV generation. The control parameters are adjusted to maximize the effectiveness of each control. The controls are then compared based on their ability to achieve multiple objectives. These objectives include minimizing the total number of voltage violations, minimizing the total amount of PV energy curtailed or reactive power generated, and maximizing the fairness of any control action among all PV systems. The controls are simulated on the OpenDSS platform using time series load and spatially-distributed irradiance data.

PVLIB is a set of open source modeling functions that allow users to simulate most aspects of PV system performance. The functions, in Matlab and Python, are freely available under a BSD 3 clause open source license. The Matlab version is maintained by Sandia and is available on the PV Performance Modeling Collaborative (PVPMC) website (pvpmc.sandia.gov). The Python version is available on GitHub with packages easily installable through conda and pip. New functions were released on the Matlab version 1.3 in January 2016 and are actively being ported to Python.

Determination of module temperature coefficients for voltage, current and power requires measuring the average of cell temperatures. Conventional practice is to place thermocouples or resistive temperature devices (RTDs) at a few locations on a module's back surface and to average the readings, which may not accurately represent the average temperature over all cells. We investigate the suitability of averaging RTDs, which measure average temperature along a 1m length, to accurately measure the average cell temperature when determining temperature coefficients outdoors.

Current-voltage (I-V) curve traces of photovoltaic (PV) systems can provide detailed information for diagnosing fault conditions. The present work implemented an in situ, automatic I-V curve tracer system coupled with Support Vector Machine and a Gaussian Process algorithms to classify and estimate abnormal and normal PV performance. The approach successfully identified normal and fault conditions. In addition, the Gaussian Process regression algorithm was used to estimate ideal I-V curves based on a given irradiance and temperature condition. The estimation results were then used to calculate the lost power due to the fault condition.

A two-dimensional direct numerical simulation of the ignition at diesel engine-relevant conditions was performed at 40 atm and at 900 K ambient temperature using dimethyl ether (DME) as the fuel with a 30 species reduced chemical mechanism. At these conditions similar to diesel fuel DME exhibited two-stage ignition. The low-temperature chemistry analysis revealed a "spotty" first-stage autoignition in lean regions which transitions to a diffusively supported cool-flame and then propagates up the local mixture fraction gradient towards richer regions. The cool-flame speed was much faster than can be attributed to spatial gradients in first-stage ignition delay time in homogeneous reactors and it caused a shortening of the second-stage ignition delay times compared to a homogeneous reactor wherein the shortening became more pronounced at richer mixtures. Multiple high-temperature ignition kernels were noted over a range of rich mixtures that are much richer than the homogeneous most reactive mixture and most kernels formed much earlier than suggested by the homogeneous ignition delay time of the corresponding local mixture.

Monitoring of photovoltaic (PV) systems can maintain efficient operations. However, extensive monitoring of large quantities of data can be a cumbersome process. The present work introduces a simple, inexpensive, yet effective data monitoring strategy for detecting faults and determining lost revenues automatically. This was achieved through the deployment of Raspberry Pi (RPI) device at a PV system's combiner box. The RPI was programmed to collect PV data through Modbus communications, and store the data locally in a MySQL database. Then, using a Gaussian Process Regression algorithm the RPI device was able to accurately estimate string level current, voltage, and power values. The device could also detect system faults using a Support Vector Novelty Detection algorithm. Finally, the RPI was programmed to output the potential lost revenue caused by the abnormal condition. The system analytics information was then displayed on a user interface. The interface could be accessed by operations personal to direct maintenance activity so that critical issues can be solved quickly.

The effect of uncertainty in the energy partition function on the silicon displacement damage metric is presented. Through the use of a Total Monte Carlo approach, the effect of uncertainty in the underlying electronic and nuclear ion interaction potentials, which are used to define the damage partition function, is propagated into an uncertainty in the silicon damage metric. This uncertainty is expressed as an energy-dependent covariance matrix which permits this uncertainty component to be combined with other uncertainty components, e.g. uncertainty due to the knowledge of the nuclear interaction data or to the treatment of the damage in the threshold displacement region. This approach provides a rigorous treatment of uncertainty due to the damage metric which can then be propagated in uncertainty estimates for various applications, e.g. when examining damage equivalence between different neutron sources. A strong energy-dependent correlation is found in this uncertainty component.

The capability to characterize the neutron energy spectrum and fluence received by a test object is crucial to understanding the damage effects observed in electronic components. For nuclear research reactors and high energy density physics facilities this can pose exceptional challenges, especially with low level neutron fluences. An ASTM test method for characterizing neutron environments utilizes the 2N2222A transistor as a 1-MeV equivalent neutron fluence sensor and is applicable for environments with 1 × 1012 - 1 × 1014 1 -MeV(Si)-Eqv.-n/cm2. In this work we seek to extend the range of this test method to lower fluence environments utilizing the 2N1486 transistor. The 2N1486 is shown to be an effective neutron displacement damage sensor as low as 1 × 1010 1-MeV(Si)-Eqv.-n/cm2.

We studied the effect of light ion and heavy ion irradiations on pnp Si BJTs. A mismatch in DLTS deep peak amplitude for devices with same final gain but irradiated with different ion species was observed. Also, different ions cause different gain degradation when the DLTS spectra are matched. Pre-dosed ion-irradiated samples show that ion induced ionization does not account for the differences in DLTS peak height but isochronal annealing studies suggest that light ions produce more VP defects than heavy ions to compensate for the lack of clusters that heavy ions produce. The creation of defect clusters by heavy ions is evident by the higher content of E4 and V2∗ defects compared to light ions.

Total ionizing dose results are provided, showing the effects of different threshold adjust implant processes and irradiation bias conditions of 14-nm FinFETs. Minimal radiation-induced threshold voltage shift across a variety of transistor types is observed. Off-state leakage current of nMOSFET transistors exhibits a strong gate bias dependence, indicating electrostatic gate control of the sub-fin region and the corresponding parasitic conduction path are the largest concern for radiation hardness in FinFET technology. The high-Vth transistors exhibit the best irradiation performance across all bias conditions, showing a reasonably small change in off-state leakage current and Vth, while the low-Vth transistors exhibit a larger change in off-state leakage current. The "worst-case" bias condition during irradiation for both pull-down and pass-gate nMOSFETs in static random access memory is determined to be the on-state (Vgs = Vdd). We find the nMOSFET pull-down and pass-gate transistors of the SRAM bit-cell show less radiation-induced degradation due to transistor geometry and channel doping differences than the low-Vth transistor. Near-threshold operation is presented as a methodology for reducing radiation-induced increases in off-state device leakage current. In a 14-nm FinFET technology, the modeling indicates devices with high channel stop doping show the most robust response to TID allowing stable operation of ring oscillators and the SRAM bit-cell with minimal shift in critical operating characteristics.

Despite the known degrading impact of high nuisance and false alarm rates (NAR/FAR) on operator performance, analyses of security systems often ignores operator performance. We developed a model to analyze the impact of nuisance alarm rates on operator performance and on overall system performance. The model demonstrates that current methods that do not account for operator performance produce optimistic estimates of system performance. As shown in our model, even low NAR/FAR levels and the associated alarm queueing effect can increase operator detect and response time, which in turn reduces the amount of time the response force has to interrupt the intruder. An illustrative analysis shows that alarm processing times can be higher than the assessment time due to queue wait times and that systems with only one or two operators can become overwhelmed as NAR increases, decreasing system performance.

Turbulent fluctuations of the scalar dissipation rate have a major impact on extinction in non-premixed combustion. Recently, an unsteady extinction criterion has been developed (Hewson, 2013) that predicts extinction dependent on the duration and the magnitude of dissipation rate fluctuations exceeding a critical quenching value; this quantity is referred to as the dissipation impulse. The magnitude of the dissipation impulse corresponding to unsteady extinction is related to the difficulty with which a flamelet is exintguished, based on the steady-state S-curve. In this paper we evaluate this new extinction criterion for more realistic dissipation rates by evolving a stochastic Ornstein-Uhlenbeck process for the dissipation rate. A comparison between unsteady flamelet evolution using this dissipation rate and the extinction criterion exhibit good agreement. The rate of predicted extinction is examined over a range of Damköhler and Reynolds numbers and over a range of the extinction difficulty. The results suggest that the rate of extinction is proportional to the average dissipation rate and the area under the dissipation rate probability density function exceeding the steady-state quenching value. It is also inversely related to the actual probability that this steady-state quenching dissipation rate is observed and the difficulty of extinction associated with the distance between the upper and middle branches of the S-curve.

The formation of one- and two-ring aromatic species in near atmospheric- pressure opposed-flow diffusion flames of 1,3-butadiene (1,3-C4 H6 ) was studied. The chemical structures of two different 1,3-C4 H6 /Ar-O2/Ar flames were studied using flame-sampling molecular-beam mass spectrometry with both electron and single-photon ionization. Generally a very good agreement was observed between the experimental and modelling data, allowing for a meaningful reaction path analysis. In the formation of aromatic species up to naphthalene, it was essential to improve the fulvene and the C5 chemistry description in the mechanism. Benzene was formed mainly via fulvene through the reactions of the C4H5 isomers with C2H2. The n-C4H5 radical reacted with CH3 forming 1,3-pentadiene (C5H8), which was subsequently oxidized to form the naphthalene precursor cyclopentadienyl (C5H5). Oxidation of naphthalene is predicted to be a contributor to the formation of phenylacetylene (C8H6), implying that consumption reactions can be of the same importance as molecular growth reactions.

The Waste Isolation Pilot Plant (WIPP), located in southeastern New Mexico of the United States (U.S.), has been developed by the U.S. Department of Energy (DOE) for the geologic disposal of transuranic (TRU) waste. The DOE demonstrates compliance with the WIPP containment requirements by means of performance assessment (PA) calculations. WIPP PA calculations estimate the probability and consequence of potential radionuclide releases from the repository to the accessible environment for a regulatory period of 10,000 years after facility closure. WIPP PA models are used (in part) to support the repository recertification process that occurs at five-year intervals following the receipt of the first waste shipment at the site in 1999. The PA executed in support of the 2014 Compliance Recertification Application (CRA-2014) for WIPP includes a number of parameter, implementation, and repository feature changes. Among these changes are the incorporation of a new panel closure system design, additional mined volume in the north end of the repository, a refinement to the PA representation of WIPP waste shear strength, and a gas generation rate refinement. These changes are briefly discussed, as is their cumulative impact on regulatory compliance for the facility. The federal recertification status of the WIPP is also discussed.

Except in extreme weather conditions, Synthetic aperture radar (SAR) is a remote sensing technology that can operate day or night. SAR can provide surveillance by making multiple passes over a wide area. For object-based intelligence, it is convenient to use these multiple passes to segment and classify the SAR images into objects that identify various terrains and man-made structures that we call "static-features." Our approach is unique in that we have multiple SAR passes of an area over a long period of time (on the order of weeks). From these many SAR images of the same area, we can combine SAR images from different times to create a variety of SAR products. For example, we introduce a novel SAR image product that captures how different regions decorrelate at different rates. From these many SAR products, we exact superpixels or groups of connected pixels that describe a homogenous region. Using pixels contained within a superpixel we develop a series of one-class classification algorithms using a goodness-of-fit metric that classifies terrains of interest in each SAR product for each superpixel. To combine the results from many SAR products we use P-value fusion. The result is a classification and a confidence about the different classes. To enforce spatial consistency, we represent the confidence labeling of the superpixels as a conditional random field and infer the most likely labeling by maximize the posterior probability of the random field. The result is a colorized SAR image where each color represents a different terrain class.

This work has applied a suite of long-term-reliability accelerated tests to a variety of module-level power electronics (MLPE) devices (such as microinverters and optimizers) from five different manufacturers. This dataset is one of the first (only the paper by Parker et al. entitled 'Dominant factors affecting reliability of alternating current photovoltaic modules,' in Proc. 42nd IEEE Photovoltaic Spec. Conf. , 2015, is reported for reliability testing in the literature), as well as the largest, experimental sets in public literature, both in the sample size (five manufacturers including both dc/dc and dc/ac units and 20 units for each test) and the number of experiments (six different experimental test conditions) for MLPE devices. The accelerated stress tests (thermal cycling test per IEC 61215 profile, damp heat test per IEC 61215 profile, and static temperature tests at 100 and 125 °C) were performed under powered and unpowered conditions. The first independent long-term experimental data regarding damp heat and grid transient testing, as well as the longest term (>9 month) testing of MLPE units reported in the literature for thermal cycling and high-temperature operating life, are included in these experiments. Additionally, this work is the first to show in situ power measurements, as well as periodic efficiency measurements over a series of experimental tests, demonstrating whether certain tests result in long-term degradation or immediate catastrophic failures. The result of this testing highlights the performance of MLPE units under the application of several accelerated environmental stressors.

The dentate gyrus forms a critical link between the entorhinal cortex and CA3 by providing a sparse version of the signal. Concurrent with this increase in sparsity, a widely accepted theory suggests the dentate gyrus performs pattern separation-similar inputs yield decorrelated outputs. Although an active region of study and theory, few logically rigorous arguments detail the dentate gyrus's (DG) coding.We suggest a theoretically tractable, combinatorial model for this action. The model provides formal methods for a highly redundant, arbitrarily sparse, and decorrelated output signal. To explore the value of this model framework, we assess how suitable it is for two notable aspects of DG coding: how it can handle the highly structured grid cell representation in the input entorhinal cortex region and the presence of adult neurogenesis, which has been proposed to produce a heterogeneous code in the DG.We find tailoring themodel to grid cell input yields expansion parameters consistent with the literature. In addition, the heterogeneous coding reflects activity gradation observed experimentally. Finally,we connect this approach with more conventional binary threshold neural circuit models via a formal embedding.

An underserved niche exists for data mining tools in complex analytical environments. We propose three attributes of analytical tool development that facilitates rapid operationalization of new tools into complex, dynamic environments: accessibility, adaptability, and extendibility. Accessibility we define as the ability to load data into an analytical system quickly and seamlessly. Adaptability we define as the ability to apply a tool rapidly to new, unanticipated use cases. Extendibility we define as the ability to create new functionality “in the field” where it is being used and, if needed, harden that new functionality into a new, more permanent user interface. Distributed “big data” systems generally do not optimize for these attributes, creating an underserved niche for new analytical tools. In this paper we will define the problem, examine the three attributes, and describe the architecture of an example system called Citrus that we have built and use that is especially focused on these three attributes.

Additively manufactured (AM) austenitic stainless steels are intriguing candidates for the storage of gaseous hydrogen isotopes because complex vessel geometries can be built more easily than by using conventional machining options. Parts built with AM stainless steel tend to have excellent mechanical properties (with tensile strength, ductility, fatigue crack growth, and fracture toughness comparable to or exceeding that of wrought austenitic stainless steel). However, the solidification microstructures produced by AM processing differ substantially from the microstructures of wrought material. Some features may affect permeability, including some amount of porosity and a greater amount of ferrite. Because the diffusivity of hydrogen in ferrite is greater than in austenite (six orders of magnitude at ambient temperature), care must be taken to retain the performance that is taken for granted due to the base alloy chemistry. Furthermore, AM parts tend to have greater dislocation densities and greater amounts of carbon, nitrogen, and oxygen. These features, along with the austenite/ferrite interfaces, may contribute to greater hydrogen trapping. We report the results of our studies of deuterium transport in various austenitic (304L, 316, and 316L) steels produced by AM. Manufacturing by Powder Bed Fusion (PBF) and two different blown powder methods are considered here (Laser Engineered Net Shaping® (LENS®) and a Direct Laser Powder Deposition (DLPD) method with a higher laser power)). The hydrogen permeability (an equilibrium property) changes negligibly (less than a factor of 2), regardless of chemistry and processing method, when tested between 150 and 500°C. This is despite increases in ferrite content up to FN=2.7. However, AM materials exhibit greater hydrogen isotope trapping, as measured by permeation transients, thermal desorption spectra, and inert gas fusion measurement. The trapping energies are likely modest (<10 kJ/mol), but may indicate a larger population of trap sites than in conventional 300-series stainless steels.

Flexible open celled foams are commonly used for energy absorption in packaging. Over time polymers can suffer from aging by becoming stiffer and more brittle. This change in stiffness can affect the foam’s performance in a low velocity impact event. In this study, the compressive properties of new open-cell flexible polyurethane foam were compared to those obtained from aged open-cell polyurethane foam that had been in service for approximately 25 years. The foams tested had densities of 10 and 15 pcf. These low density foams provided a significant challenge to machine cylindrical compression specimens that were 1 “in height and 1” in diameter. Details of the machining process are discussed. The compressive properties obtained for both aged and new foams included testing at various strain rates (0.05. 0.10, 5 s-1) and temperatures (-54, RT, 74 °C). Results show that aging of flexible polyurethane foam does not have much of an effect on its compressive properties.