We consider the numerical solution of parameterized linear systems where the system matrix, the solution, and the right-hand side are parameterized by a set of uncertain input parameters. We explore spectral methods in which the solutions are approximated in a chosen finite-dimensional subspace. It has been shown that the stochastic Galerkin projection technique fails to minimize any measure of the solution error [A. Mugler and H.-J. Starkloff, ESAIM Math. Model. Numer. Anal., 47 (2013), pp. 1237-1263]. As a remedy for this, we propose a novel stochatic least-squares Petrov-Galerkin (LSPG) method. The proposed method is optimal in the sense that it produces the solution that minimizes a weighted2-norm of the residual over all solutions in a given finite-dimensional subspace. Moreover, the method can be adapted to minimize the solution error in different weighted2-norms by simply applying a weighting function within the least-squares formulation. In addition, a goal-oriented seminorm induced by an output quantity of interest can be minimized by defining a weighting function as a linear functional of the solution. We establish optimality and error bounds for the proposed method, and extensive numerical experiments show that the weighted LSPG method outperforms other spectral methods in minimizing corresponding target weighted norms.
In this work, a Pitzer model is developed for the K+(Na+)-Am(OH)4−-Cl−-OH− system based on Am(OH)3(s) solubility data in highly alkaline KOH solutions. Under highly alkaline conditions, the solubility reaction of Am(OH)3(s) is expressed as: Solubilities of Am(OH)3(s) based on the above reaction are modeled as a function of KOH concentrations. The stability constant for Am(OH)4− is evaluated using Am(OH)3(s) solubility data in KOH solutions up to 12 mol•kg-1 taken from the literature. The Pitzer interaction parameters related to Al(OH)4- are used as analogs for the interaction parameters involving Am(OH)4- to obtain the stability constant for Am(OH)4-. The for the reaction is -11.34 ± 0.15 (2σ).
In many applications the resolution of small-scale heterogeneities remains a significant hurdle to robust and reliable predictive simulations. In particular, while material variability at the mesoscale plays a fundamental role in processes such as material failure, the resolution required to capture mechanisms at this scale is often computationally intractable. Multiscale methods aim to overcome this difficulty through judicious choice of a subscale problem and a robust manner of passing information between scales. One promising approach is the multiscale finite element method, which increases the fidelity of macroscale simulations by solving lower-scale problems that produce enriched multiscale basis functions. In this study, we present the first work toward application of the multiscale finite element method to the nonlocal peridynamic theory of solid mechanics. This is achieved within the context of a discontinuous Galerkin framework that facilitates the description of material discontinuities and does not assume the existence of spatial derivatives. Analysis of the resulting nonlocal multiscale finite element method is achieved using the ambulant Galerkin method, developed here with sufficient generality to allow for application to multiscale finite element methods for both local and nonlocal models that satisfy minimal assumptions. We conclude with preliminary results on a mixed-locality multiscale finite element method in which a nonlocal model is applied at the fine scale and a local model at the coarse scale.
Evaluating the effectiveness of data visualizations is a challenging undertaking and often relies on one-off studies that test a visualization in the context of one specific task. Researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles that could be applied to assessing the effectiveness of data visualizations in a more rapid and generalizable manner. One possibility for such a tool is a model of visual saliency for data visualizations. Visual saliency models are typically based on the properties of the human visual cortex and predict which areas of a scene have visual features (e.g. color, luminance, edges) that are likely to draw a viewer's attention. While these models can accurately predict where viewers will look in a natural scene, they typically do not perform well for abstract data visualizations. In this paper, we discuss the reasons for the poor performance of existing saliency models when applied to data visualizations. We introduce the Data Visualization Saliency (DVS) model, a saliency model tailored to address some of these weaknesses, and we test the performance of the DVS model and existing saliency models by comparing the saliency maps produced by the models to eye tracking data obtained from human viewers. Finally, we describe how modified saliency models could be used as general tools for assessing the effectiveness of visualizations, including the strengths and weaknesses of this approach.
We present the design, characterization, and testing of a laboratory prototype radiological search and localization system. The system, based on time-encoded imaging, uses the attenuation signature of neutrons in time, induced by the geometrical layout and motion of the system. We have demonstrated the ability to detect a ∼1mCi252Cf radiological source at 100m standoff with 90% detection efficiency and 10% false positives against background in 12min. This same detection efficiency is met at 15s for a 40m standoff, and 1.2s for a 20m standoff.
Efficient design of wave energy converters requires an accurate understanding of expected loads and responses during the deployment lifetime of a device. A study has been conducted to better understand best-practices for prediction of design responses in a wave energy converter. A case-study was performed in which a simplified wave energy converter was analyzed to predict several important device design responses. The application and performance of a full long-term analysis, in which numerical simulations were used to predict the device response for a large number of distinct sea states, was studied. Environmental characterization and selection of sea states for this analysis at the intended deployment site were performed using principle-components analysis. The full long-term analysis applied here was shown to be stable when implemented with a relatively low number of sea states and convergent with an increasing number of sea states. As the number of sea states utilized in the analysis was increased, predicted response levels did not change appreciably. However, uncertainty in the response levels was reduced as more sea states were utilized.
This work extends recent methods to calculate dynamic substructuring predictions of a weakly nonlinear structure using nonlinear pseudo-modal models. In previous works, constitutive joint models (such as the modal Iwan element) were used to capture the nonlinearity of each subcomponent on a mode-by-mode basis. This work uses simpler polynomial stiffness and damping elements to capture nonlinear dynamics from more diverse jointed connections including large continuous interfaces. The proposed method requires that the modes of the system remain distinct and uncoupled in the amplitude range of interest. A windowed sinusoidal loading is used to excite each experimental subcomponent mode in order to identify the nonlinear pseudo-modal models. This allows for a higher modal amplitude to be achieved when fitting these models and extends the applicable amplitude range of this method. Once subcomponent modal models have been experimentally extracted for each mode, the Transmission Simulator method is implemented to assemble the subcomponent models into a nonlinear assembled prediction. Numerical integration methods are used to evaluate this prediction compared to a truth test of the nonlinear assembly.
The first metal-organic framework exhibiting thermally activated delayed fluorescence (TADF) was developed. The zirconium-based framework (UiO-68-dpa) uses a newly designed linker composed of a terphenyl backbone, an electron-accepting carboxyl group, and an electron-donating diphenylamine and exhibits green TADF emission with a photoluminescence quantum yield of 30% and high thermal stability.
When very few samples of a random quantity are available from a source distribution or probability density function (PDF) of unknown shape, it is usually not possible to accurately infer the PDF from which the data samples come. Then a significant component of epistemic uncertainty exists concerning the source distribution of random or aleatory variability. For many engineering purposes, including design and risk analysis, one would normally want to avoid inference related under-estimation of important quantities such as response variance, and failure probabilities. Recent research has established the practicality and effectiveness of a class of simple and inexpensive UQ Methods for reasonable conservative estimation of such quantities when only sparse samples of a random quantity are available. This class of UQ methods is explained, demonstrated, and analyzed in this paper within the context of the Sandia Cantilever Beam End-to-End UQ Problem, Part A.1. Several sets of sparse replicate data are involved and several representative uncertainty quantities are to be estimated: A) beam deflection variability, in particular the 2.5 to 97.5 percentile “central 95%” range of the sparsely sampled PDF of deflection; and B) a small exceedance probability associated with a tail of the PDF integrated beyond a specified deflection tolerance.
Duan, Lian; Choudhari, Meelan M.; Chou, Amanda; Munoz, Federico; Ali, Syed R.C.; Radespiel, Rolf; Schilden, Thomas; Schroder, Wolfgang; Marineau, Eric C.; Casper, Katya M.; Chaudhry, Ross S.; Candler, Graham V.; Gray, Kathryn; Sweeney, Cameron J.; Schneider, Steven P.
While low disturbance (“quiet”) hypersonic wind tunnels are believed to provide more reliable extrapolation of boundary layer transition behavior from ground to flight, the presently available quiet facilities are limited to Mach 6, moderate Reynolds numbers, low freestream enthalpy, and subscale models. As a result, only conventional (“noisy”) wind tunnels can reproduce both Reynolds numbers and enthalpies of hypersonic flight configurations, and must therefore be used for flight vehicle test and evaluation involving high Mach number, high enthalpy, and larger models. This article outlines the recent progress and achievements in the characterization of tunnel noise that have resulted from the coordinated effort within the AVT-240 specialists group on hypersonic boundary layer transition prediction. New Direct Numerical Simulation (DNS) datasets elucidate the physics of noise generation inside the turbulent nozzle wall boundary layer, characterize the spatiotemporal structure of the freestream noise, and account for the propagation and transfer of the freestream disturbances to a pitot-mounted sensor. The new experimental measurements cover a range of conventional wind tunnels with different sizes and Mach numbers from 6 to 14 and extend the database of freestream fluctuations within the spectral range of boundary layer instability waves over commonly tested models. Prospects for applying the computational and measurement datasets for developing mechanism-based transition prediction models are discussed.
Al-Based Al-Cu alloys have a very high strength to density ratio, and are therefore important materials for transportation systems including vehicles and aircrafts. These alloys also appear to have a high resistance to hydrogen embrittlement, and as a result, are being explored for hydrogen related applications. To enable fundamental studies of mechanical behavior of Al-Cu alloys under hydrogen environments, we have developed an Al-Cu-H bond-order potential according to the formalism implemented in the molecular dynamics code LAMMPS. Our potential not only fits well to properties of a variety of elemental and compound configurations (with coordination varying from 1 to 12) including small clusters, bulk lattices, defects, and surfaces, but also passes stringent molecular dynamics simulation tests that sample chaotic configurations. Careful studies verified that this Al-Cu-H potential predicts structural property trends close to experimental results and quantum-mechanical calculations; in addition, it properly captures Al-Cu, Al-H, and Cu-H phase diagrams and enables simulations of H2 dissociation, chemisorption, and absorption on Al-Cu surfaces.
This study details a complimentary testing and finite element analysis effort to model threaded fasteners subjected to multiple loadings and loading rates while identifying modeling sensitivities that impact this process. NAS1352-06-6P fasteners were tested in tension at quasistatic loading rates and tension and shear at dynamic loading rates. The quasistatic tension tests provided calibration and validation data for constitutive model fitting, but this process was complicated by the difference in the conventional (global) and novel (local) displacement measurements. The consequences of these differences are investigated in detail by obtaining calibrated models from both displacement measurements and assessing their performance when extended to the dynamic tension and shear applications. Common quantities of interest are explored, including failure load, time-to-failure, and displacement-at-failure. Finally, the mesh sensitivities of both dynamic analysis models are investigated to assess robustness and inform modeling fidelity. This study is performed in the context of applying these fastener models into large-scale, full system finite element analyses of complex structures, and therefore the models chosen are relatively basic to accommodate this desire and reflect typical modeling approaches. The quasistatic tension results reveal the sensitivity and importance of displacement measurement techniques in the testing procedure, especially when performing experiments involving multiple components that inhibit local specimen measurements. Additional compliance from test fixturing and load frames have an increasingly significant effect on displacement data as the measurement becomes more global, and models must necessarily capture these effects to accurately reproduce the test data. Analysis difficulties were also discovered in the modeling of shear loadings, as the results were very sensitive to mesh discretization, further complicating the ability to analyze joints subjected to diverse loadings. These variables can significantly contribute to the error and uncertainty associated with the model, and this study begins to quantify this behavior and provide guidance on mitigating these effects. When attempting to capture multiple loadings and loading rates in fasteners through simulation, it becomes necessary to thoroughly exercise and explore test and analysis procedures to ensure the final model is appropriate for the desired application.
Fluctuating boundary layer pressure fluctuations are an important loading component for high speed reentry vehicles. Characterization of the unsteady time series requires access to longitudinal and lateral coherence expressions as well spatial correlation and frequency power-spectral density models. Coherence, spatial correlation and frequency power spectral density are related as through their cross-spectral density definitions. However the frequency PSD and the spatial correlation are often based upon measurements or approximate models which may introduce bias in the associated derived coherence function. Here, we examine the effect of measurement and model form associated with frequency spectrum and correlation on the longitudinal and lateral coherence for supersonic pressure fluctuation flow fields. The widely utilized Corcos separable coherence model functional form has been employed in this study. The associated integral equations which relate coherence and correlation are solved using a simple iterative approach. To minimize distortion in results due to computational issues a high accuracy numerical integration procedure is utilized. Despite a more robust computational approach, solution accuracy is limited for some problems by the functional form of the longitudinal coherence model. These limitations are discussed in detail. This overall approach is applied to Mach 5 and Mach 8 seven degree sharp cone pressure fluctuation measurements. Estimates for the parameters associated with the Corcos coherence expressions are typically larger than more traditional values especially for the longitudinal coherence. These larger values suggest that fluctuations streamwise correlation length is small. Limited longitudinal correlation can be associated with shock influence and is explored as a possible cause.
Park, Michael A.; Barral, Nicolas; Ibanez, Daniel A.; Kamenetskiy, Dmitry S.; Krakos, Joshua A.; Michal, Todd; Loseille, Adrien
Unstructured grid adaptation is a tool to control Computational Fluid Dynamics (CFD) discretization error. However, adaptive grid techniques have made limited impact on production analysis workflows where the control of discretization error is critical to obtaining reliable simulation results. Issues that prevent the use of adaptive grid methods are identified by applying unstructured grid adaptation methods to a series of benchmark cases. Once identified, these challenges to existing adaptive workflows can be addressed. Unstructured grid adaptation is evaluated for test cases described on the Turbulence Modeling Resource (TMR) web site, which documents uniform grid refinement of multiple schemes. The cases are turbulent flow over a Hemisphere Cylinder and an ONERA M6 Wing. Adaptive grid force and moment trajectories are shown for three integrated grid adaptation processes with Mach interpolation control and output error based metrics. The integrated grid adaptation process with a finite element (FE) discretization produced results consistent with uniform grid refinement of fixed grids. The integrated grid adaptation processes with finite volume schemes were slower to converge to the reference solution than the FE method. Metric conformity is documented on grid/metric snapshots for five grid adaptation mechanics implementations. These tools produce anisotropic boundary conforming grids requested by the adaptation process.
The International Monitoring System (IMS) infrasound network has been designed to acquire the necessary data to detect and locate explosions in the atmosphere with a yield equivalent to 1 kiloton of TNT anywhere on Earth. A major associated challenge is the task of automatically processing data from all IMS infrasound stations to identify possible nuclear tests for subsequent review by analysts. This paper is the first attempt to quantify the false alarm rate (FAR) of the IMS network, and in particular to assess how the FAR is affected by the numbers and distributions of detections at each infrasound station. To ensure that the results are sufficiently general, and not dependent entirely on one detection algorithm, the assessment is based on two detection algorithms that can be thought of as end members in their approach to the trade-offbetween missed detections and false alarms. The results show that the FAR for events formed at only two arrays is extremely high (ranging from 10s to 100s of false events per day across the IMS network, depending on the detector tuning). It is further shown that the FAR for events formed at three or more IMS arrays is driven by ocean-generated waves (microbaroms), despite efforts within both detection algorithms for avoiding these signals, indicating that further research into this issue is merited. Overall, the results highlight the challenge of processing data from a globally sparse network of stations to detect and form events. The results suggest that more work is required to reduce false alarms caused by the detection of microbarom signals.
Desirable outcomes for geologic carbon storage include maximizing storage efficiency, preserving injectivity, and avoiding unwanted consequences such as caprock or wellbore leakage or induced seismicity during and post injection. To achieve these outcomes, three control measures are evident including pore pressure, injectate chemistry, and knowledge and prudent use of geologic heterogeneity. Field, experimental, and modeling examples are presented that demonstrate controllable GCS via these three measures. Observed changes in reservoir response accompanying CO2 injection at the Cranfield (Mississippi, USA) site, along with lab testing, show potential for use of injectate chemistry as a means to alter fracture permeability (with concomitant improvements for sweep and storage efficiency). Further control of reservoir sweep attends brine extraction from reservoirs, with benefit for pressure control, mitigation of reservoir and wellbore damage, and water use. State-of-the-art validated models predict the extent of damage and deformation associated with pore pressure hazards in reservoirs, timing and location of networks of fractures, and development of localized leakage pathways. Experimentally validated geomechanics models show where wellbore failure is likely to occur during injection, and efficiency of repair methods. Use of heterogeneity as a control measure includes where best to inject, and where to avoid attempts at storage. An example is use of waste zones or leaky seals to both reduce pore pressure hazards and enhance residual CO2 trapping.
To reduce the levelized cost of wind energy, wind plant controllers are being developed to improve overall performance by increasing energy capture. Previous work has shown that increased energy capture is possible by steering the wake around downstream turbines; however, the impact this steering action has on the loading of the turbines continues to need further investigation with operational data to determine overall benefit. In this work, rotor loading data from a wind turbine operating a wake steering wind plant controller at the DOE/Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) Facility is evaluated. Rotor loading was estimated from fiber optic strain sensors acquired with a state-of-the-art Micron Optics Hyperion interrogator mounted within the rotor and synchronized to the open-source SWiFT controller. A variety of ground and operational calibrations were performed to produce accurate measurements of rotor blade root strains. Time- and rotational-domain signal processing methods were used to estimate bending moment at the root of the rotor blade. Results indicate a correlation of wake steering angle with: one-perrevolution thrust moment amplitude, two-per-revolution torque phase, and three-perrevolution torque amplitude and phase. Future work is needed to fully explain the correlations observed in this work and study additional multi-variable relationships that may also exist.
Fluid-structure interactions were studied on a 7◦ half-angle cone in the Sandia Hypersonic Wind Tunnel at Mach 5 and 8 and in the Purdue Boeing/AFOSR Mach 6 Quiet Tunnel. A thin composite panel was integrated into the cone and the response to boundary-layer disturbances was characterized by accelerometers on the backside of the panel. Under quiet-flow conditions at Mach 6, the cone boundary layer remained laminar. Artificially generated turbulent spots excited a directionally dependent panel response which would last much longer than the spot duration. When the spot generation frequency matched a structural natural frequency of the panel, resonance would occur and responses over 200 times greater than under a laminar boundary layer were obtained. At Mach 5 and 8 under noisy flow conditions, natural transition driven by the wind-tunnel acoustic noise dominated the panel response. An elevated vibrational response was observed during transition at frequencies corresponding to the distribution of turbulent spots in the transitional flow. Once turbulent flow developed, the structural response dropped because the intermittent forcing from the spots no longer drove panel vibration.
Wang, Peng; Sternberg, Andrew L.; Kozub, John A.; Zhang, En X.; Dodds, Nathaniel A.; Jordan, Scott L.; Fleetwood, Daniel M.; Reed, Robert A.; Schrimpf, Ronald D.
Two-photon absorption (TPA) pulsed-laser testing is used to analyze the TPA-induced single-event latchup sensitive-area of a specially designed test structure. This method takes into account the existence of an onset region in which the probability of triggering latchup transits between 0 and 1 as the laser pulse energy increases. This variability is attributed to a combination of laser pulse-to-pulse variability and variations in local carrier density and temperature. For each spatial position, the latchup probability associated with a given energy is calculated. Calculation of latchup cross section at lower laser energies, relative to onset, is improved significantly by taking into account the full probability distribution. The transition from low probability of latchup to high probability is more abrupt near the source contacts than for surrounding areas.
Wind resource assessments are used to estimate a wind farm's power production during the planning process. It is important that these estimates are accurate, as they can impact financing agreements, transmission planning, and environmental targets. Here, we analyze the challenges in wind power estimation for onshore farms. Turbine wake effects are a strong determinant of farm power production. With given input wind conditions, wake losses typically cause downstream turbines to produce significantly less power than upstream turbines. These losses have been modeled extensively and are well understood under certain conditions. Most notably, validation of different model types has favored offshore farms. Models that capture the dynamics of offshore wind conditions do not necessarily perform equally as well for onshore wind farms. We analyze the capabilities of several different methods for estimating wind farm power production in 2 onshore farms with non-uniform layouts. We compare the Jensen model to a number of statistical models, to meteorological downscaling techniques, and to using no model at all. We show that the complexities of some onshore farms result in wind conditions that are not accurately modeled by the Jensen wake decay techniques and that statistical methods have some strong advantages in practice.
Silicon is a promising material candidate for qubits due to the combination of worldwide infrastructure in silicon microelectronics fabrication and the capability to drastically reduce decohering noise channels via chemical purification and isotopic enhancement. However, a variety of challenges in fabrication, control, and measurement leaves unclear the best strategy for fully realizing this material’s future potential. In this article, we survey three basic qubit types: those based on substitutional donors, on metal-oxide-semiconductor (MOS) structures, and on Si/SiGe heterostructures. We also discuss the multiple schema used to define and control Si qubits, which may exploit the manipulation and detection of a single electron charge, the state of a single electron spin, or the collective states of multiple spins. Far from being comprehensive, this article provides a brief orientation to the rapidly evolving field of silicon qubit technology and is intended as an approachable entry point for a researcher new to this field.
The development of multi-dimensional statistical methods has been demonstrated on variable contact time (VCT) 29Si{1H} cross-polarization magic angle spinning (CP/MAS) data sets collected using Carr-Purcell-Meiboom-Gill (CPMG) type acquisition. These methods utilize the transformation of the collected 2D VCT data set into a 3D data set and use tensor-rank decomposition to extract the spectral components that vary as a function of transverse relaxation time (T2) and CP contact time. The result is a data dense spectral set that can be used to reconstruct CP/MAS spectra at any contact time with a high signal to noise ratio and with an excellent agreement to 29Si{1H} CP/MAS spectra collected using conventional acquisition. These CPMG data can be collected in a fraction of time that would be required to collect a conventional VCT data set. We demonstrate the method on samples of functionalized mesoporous silica materials and show that the method can provide valuable surface specific information about their functional chemistry.
The hydrogen absorption properties of metal closo-borate/metal hydride composites, M2B10H10-8MH and M2B12H12-10MH, M = Li or Na, are studied under high hydrogen pressures to understand the formation mechanism of metal borohydrides. The hydrogen storage properties of the composites have been investigated by in situ synchrotron radiation powder X-ray diffraction at p(H2) = 400 bar and by ex situ hydrogen absorption measurements at p(H2) = 526 to 998 bar. The in situ experiments reveal the formation of crystalline intermediates before metal borohydrides (MBH4) are formed. On the contrary, the M2B12H12-10MH (M = Li and Na) systems show no formation of the metal borohydride at T = 400 °C and p(H2) = 537 to 970 bar. 11B MAS NMR of the M2B10H10-8MH composites reveal that the molar ratio of LiBH4 or NaBH4 and the remaining B species is 1:0.63 and 1:0.21, respectively. Solution and solid-state 11B NMR spectra reveal new intermediates with a B:H ratio close to 1:1. Our results indicate that the M2B10H10 (M = Li, Na) salts display a higher reactivity towards hydrogen in the presence of metal hydrides compared to the corresponding [B12H12]2- composites, which represents an important step towards understanding the factors that determine the stability and reversibility of high hydrogen capacity metal borohydrides for hydrogen storage.
At a low depth of discharge, the performance of rechargeable alkaline Zn/MnO2 batteries is determined by the concomitant processes of hydrogen ion insertion and electro-reduction in the solid phase of γ-MnO2. Ab initio computational methods based on density functional theory (DFT) were applied to study the mechanism of hydrogen ion insertion into the pyrolusite (β), ramsdellite (R), and nsutite (γ) MnO2 polymorphs. It was found that hydrogen ion insertion induced significant distortion in the crystal structures of MnO2 polymorphs. Calculations demonstrated that the hydrogen ions inserted into γ-MnO2 initially occupied the larger 2×1 ramsdellite tunnels. The protonated form of γ-MnO2 was found to be stable over the discharge range during which up to two hydrogen ions were inserted into each 2×1 tunnel. At the same time, the study showed that the insertion of hydrogen ions into the 1×1 pyrolusite tunnels of γ-MnO2 created instability leading to the structural breakdown of γ-MnO2. The results of this study explain the presence of groutite (α-MnOOH) and the absence of manganite (γ-MnOOH) among the reaction products of partially reduced γ-MnO2
Alexander, Christopher L.; Liu, Chao; Kelly, Robert G.; Carpenter, Jacob; Bryan, Charles
In this work, a rotating disk electrode was used to measure the cathodic kinetics on stainless steel as a function of diffusion layer thickness (6 to 60 μm) and chloride concentration (0.6 to 5.3 M NaCl). It was found that, while the cathodic kinetics followed the Levich equation for large diffusion layer thicknesses, the Levich equation overpredicts the mass-transfer limited current density for diffusion layer thicknesses less than 20 μm. Also, an unusual transitory response between the activation and mass-transfer controlled regions was observed for small diffusion layer thicknesses that was more apparent in lower concentration solutions. The presence and reduction of an oxide film and a transition in the oxygen reduction mechanism were identified as possible reasons for this response. The implications of these results on atmospheric corrosion kinetics under thin electrolyte layers is discussed.
The surface area dependence of the decomposition reaction between lithiated graphites and electrolytes for temperatures above 100◦C up to ~200◦C is explored through comparison of model predictions to published calorimetry data. The initial rate of the reaction is found to scale super-linearly with the particle surface area. Initial reaction rates are suggested to scale with edge area, which has also been measured to scale super-linearly with particle area. As in previous modeling studies, this work assumes that electron tunneling through the solid electrolyte interphase (SEI) limits the rate of the reaction between lithium and electrolyte. Comparison of model predictions to calorimetry data indicates that the development of the tunneling barrier is not linear with BET surface area; rather, the tunneling barrier correlates best with the square root of specific surface area. This result suggests that tunneling though the SEI may be controlled by defects with linear characteristics. The effect of activation energy on the tunneling-limited reaction is also investigated. The modified area dependence results in a model that predicts with reasonable accuracy the range of observed heat-release rates in the important temperature range from 100◦C to 200◦C where transition to thermal runaway typically occurs at the cell level.
Faegh, Ehsan; Omasta, Travis; Hull, Matthew; Ferrin, Sean; Shrestha, Sujan; Lechman, Jeremy B.; Bolintineanu, Dan S.; Zuraw, Michael; Mustain, William E.
The leading cause for safety vent rupture in alkaline batteries is the intrinsic instability of Zn in the highly alkaline reacting environment. Zn and aqueous KOH react in a parasitic process to generate hydrogen gas, which can rupture the seal and vent the hydrogen along with small amounts of electrolyte, and thus, damage consumer devices. Abusive conditions, particularly deep discharge, are known to accelerate this “gassing” phenomena. In order to understand the fundamental drivers and mechanisms for such gassing behavior, the results from multiphysics modeling, ex-situ microscopy and operando measurements of cell potential, pressure and visualization have been combined. Operando measurements were enabled by the development a new research platform that enables a cross-sectional view of a cylindrical Zn-MnO2 primary alkaline battery throughout its discharge and recovery. A second version of this cell can actively measure the in-cell pressure during the discharge. It is shown that steep concentration gradients emerge during the cell discharge through a redox electrolyte mechanism, leading to the formation of high surface area Zn deposits that experience rapid corrosion when the cell rests to its open circuit voltage. Such corrosion is paired with the release of hydrogen and high cell pressure – eventually leading to cell rupture.
The initial oxidation products of methyl butyrate (MB) and ethyl butyrate (EB) are studied using a time- and energy-resolved photoionization mass spectrometer. Reactions are initiated with Cl radicals in an excess of oxygen at a temperature of 550 K and a pressure of 6 Torr. Ethyl crotonate is the sole isomeric product that is observed from concerted HO2-elimination from initial alkylperoxy radicals formed in the oxidation of EB. Analysis of the potential energy surface of each possible alkylperoxy radical shows that the CH3CH(OO)CH2C(O)OCH2CH3 (RγO2) and CH3CH2CH(OO)C(O)OCH2CH3 (RβO2) radicals are the isomers that could undergo this concerted HO2-elimination. Two lower-mass products (formaldehyde and acetaldehyde) are observed in both methyl and ethyl butyrate reactions. Secondary reactions of alkylperoxy radicals with HO2 radicals can decompose into the aforementioned products and smaller radicals. These pathways are the likely explanation for the formation of formaldehyde and acetaldehyde.
ASME 2018 12th International Conference on Energy Sustainability, ES 2018, collocated with the ASME 2018 Power Conference and the ASME 2018 Nuclear Forum
Prior 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. Previous test results and models of the bladed receiver showed a receiver efficiency increase over a flat receiver panel of ~ 5-7% over a range of average irradiances, while showing that the receiver tubes can withstand temperatures > 800 °C with no issues. The bladed receiver is being tested at various peak heat fluxes ranging 75-150 kW/m2 under transient conditions using Air as a heat transfer fluid at inlet pressure ~250 kPa (~36 psi) using a regulating flow loop. The flow loop was designed and tested to maintain a steady mass flow rate for ~15 minutes using pressurized bottles as gas supply. Due to the limited flow-time available, a novel transient methodology to evaluate the thermal efficiencies is presented in this work. Computational fluid dynamics (CFD) models are used to predict the temperature distribution and the resulting transient receiver efficiencies. The CFD simulations results using air as heat transfer fluid have been validated experimentally at the National Solar Thermal Test Facility in Sandia National Labs.
Architecture simulation can aid in predicting and understanding application performance, particularly for proposed hardware or large system designs that do not exist. In network design studies for high-performance computing, most simulators focus on the dominant message passing (MPI) model. Currently, many simulators build and maintain their own simulator-specific implementations of MPI. This approach has several drawbacks. Rather than reusing an existing MPI library, simulator developers must implement all semantics, collectives, and protocols. Additionally, alternative runtimes like GASNet cannot be simulated without again building a simulator-specific version. It would be far more sustainable and flexible to maintain lower-level layers like uGNI or IB-verbs and reuse the production runtime code. Directly building and running production communication runtimes inside a simulator poses technical challenges, however. We discuss these challenges and show how they are overcome via the macroscale components for the Structural Simulation Toolkit (SST), leveraging a basic source-to-source tool to automatically adapt production code for simulation. SST is able to encapsulate and virtualize thousands of MPI ranks in a single simulator process, providing a “supercomputer in a laptop” environment. We demonstrate the approach for the production GASNet runtime over uGNI running inside SST. We then discuss the capabilities enabled, including investigating performance with tunable delays, deterministic debugging of race conditions, and distributed debugging with serial debuggers.
Directional drilling can be used to enable multi-lateral completions from a single well pad to improve well productivity and decrease environmental impact. Downhole rotation is typically developed with a motor in the Bottom Hole Assembly (BHA) that develops drilling power necessary to rotate the bit apart from the rotation developed by the surface rig. Historically, wellbore deviation has been introduced by a “bent-sub” that introduces a small angular deviation to allow the bit to drill off-axis with orientation of the BHA controlled via surface rotation. The geothermal drilling industry has not realized the benefit of Rotary Steerable Systems, and struggles with conventional downhole rotation systems that use bent-subs for directional control due to shortcomings with downhole motors. Commercially-available Positive Displacement Motors are limited to approximately 350 F (177C) and introduce lateral vibration to the bottom hole assembly contributing to hardware failures and compromising directional drilling objectives. Mud turbines operate at higher temperatures but do not have the low-speed, high torque performance envelope for use with conventional geothermal drill bits. Development of a fit-for purpose downhole motor would enable geothermal directional drilling. Sandia National Laboratories is developing technology for a downhole piston motor to enable directional drilling in high temperature, high strength rock. Application of conventional hydraulic piston motor power cycles using drilling fluids is detailed. Work is described regarding conceiving downhole piston motor power sections; modeling and analysis of potential solutions; and development and laboratory testing of prototype hardware. These developments will lead to more reliable access to geothermal resources and allow preferential wellbore trajectories resulting in improved resource recovery, decreased environmental impact and enhanced well construction economics.
This study evaluates the applicability of the Octane Index (OI) framework under conventional spark ignition (SI) and “beyond Research Octane Number (RON)” conditions using nine fuels operated under stoichiometric, knock-limited conditions in a direct injection spark ignition (DISI) engine, supported by Monte Carlo-type simulations which interrogate the effects of measurement uncertainty. Of the nine tested fuels, three fuels are “Tier III” fuel blends, meaning that they are blends of molecules which have passed two levels of screening, and have been evaluated to be ready for tests in research engines. These molecules have been blended into a four-component gasoline surrogate at varying volume fractions in order to achieve a RON rating of 98. The molecules under consideration are isobutanol, 2-butanol, and diisobutylene (which is a mixture of two isomers of octene). The remaining six fuels were research-grade gasolines of varying formulations. The DISI research engine was used to measure knock limits at heated and unheated intake temperature conditions, as well as throttled and boosted intake pressures, all at an engine speed of 1400 rpm. The tested knock-limited operating conditions conceptually exist both between the Motor Octane Number (MON) and RON conditions, as well as “beyond RON” conditions (conditions which are conceptually at lower temperatures, higher pressures, or longer residence times than the RON condition). In addition to directly assessing the performance of the Tier III blends relative to other gasolines, the OI framework was evaluated with considerations of experimental uncertainty in the knock-limited combustion phasing (KL-CA50) measurements, as well as RON and MON test uncertainties. The OI was found to hold to the first order, explaining more than 80% of the knock-limited behavior, although the remaining variation in fuel performance from OI behavior was found to be beyond the likely experimental uncertainties. This indicates that the effects of specific fuel components on knock which are not captured by RON and MON ratings, and complicating the assessment of a given fuel by RON and MON ratings alone.
Methyl vinyl ketone (MVK) and methacrolein (MACR) are important intermediate products in atmospheric degradation of volatile organic compounds, especially of isoprene. This work investigates the reactions of the smallest Criegee intermediate, CH2OO, with its co-products from isoprene ozonolysis, MVK and MACR, using multiplexed photoionization mass spectrometry (MPIMS), with either tunable synchrotron radiation from the Advanced Light Source or Lyman-α (10.2 eV) radiation for photoionization. CH2OO was produced via pulsed laser photolysis of CH2I2 in the presence of excess O2. Time-resolved measurements of reactant disappearance and of product formation were performed to monitor reaction progress; first order rate coefficients were obtained from exponential fits to the CH2OO decays. The bimolecular reaction rate coefficients at 300 K and 4 Torr are k(CH2OO + MVK) = (5.0 ± 0.4) × 10-13 cm3 s-1 and k(CH2OO + MACR) = (4.4 ± 1.0) × 10-13 cm3 s-1, where the stated ±2σ uncertainties are statistical uncertainties. Adduct formation is observed for both reactions and is attributed to the formation of a secondary ozonides (1,2,4-trioxolanes), supported by master equation calculations of the kinetics and the agreement between measured and calculated adiabatic ionization energies. Kinetics measurements were also performed for a possible bimolecular CH2OO + CO reaction and for the reaction of CH2OO with CF3CHCH2 at 300 K and 4 Torr. For CH2OO + CO, no reaction is observed and an upper limit is determined: k(CH2OO + CO) < 2 × 10-16 cm3 s-1. For CH2OO + CF3CHCH2, an upper limit of k(CH2OO + CF3CHCH2) < 2 × 10-14 cm3 s-1 is obtained.
We use machine learning (ML) to infer stress and plastic flow rules using data from representative polycrystalline simulations. In particular, we use so-called deep (multilayer) neural networks (NN) to represent the two response functions. The ML process does not choose appropriate inputs or outputs, rather it is trained on selected inputs and output. Likewise, its discrimination of features is crucially connected to the chosen input-output map. Hence, we draw upon classical constitutive modeling to select inputs and enforce well-accepted symmetries and other properties. In the context of the results of numerous simulations, we discuss the design, stability and accuracy of constitutive NNs trained on typical experimental data. With these developments, we enable rapid model building in real-time with experiments, and guide data collection and feature discovery.
Inter-area oscillation is one of the main concerns in power system small signal stability. It involves wide area in power system, therefore identifying the causes and damping these oscillations are challenging. Undamped inter-area oscillations may cause severe problems in power systems including large-scale blackouts. Designing a proper controller for power systems also is a challenging problem due to the complexity of the system. Moreover, for a large-scale system it is impractical to collect all system information in one location to design a centralized controller. Decentralized controller will be more desirable for large scale systems to minimize the inter area oscillations by using local information. In this paper, we consider a large-scale power system consisting of three areas. After decomposing the system into three subsystems, each subsystem is modeled with a lower order system. Finally, a decentralized controller is designed for each subsystem to maintain the large-scale system frequency at the desired level even in the presence of disturbances.
Three-dimensional (3D) seismic wave propagation is simulated in the newly-developed Marmousi2 elastic model, using both acoustic and elastic finite-difference (FD) algorithms. Although acoustic and elastic ocean-bottom particle velocity seismograms display distinct differences, only subtle variations are discernable in pressure seismograms recorded in the marine water layer.
The plan is based on various implementation plans and strategies that define ongoing activities. If significant updates, such as changes or additions to goals and messaging occur, Sandia will submit an updated plan to the Contracting Officer for approval.
The photovoltaics industry is in dire need of a cheap, robust, reliable arc fault detector that is sensitive enough to detect arc faults before they can develop into a fire while robust enough to noise to limit unwanted tripping. Management Sciences has developed an arc fault detector that is housed in a standard PV connector, which will disconnect the PV array when it detects the surge current from an arc fault. Sandia National Labs, an industry leader in the detection, characterization, and mitigation of arc faults in PV arrays, will work with Management Sciences to characterize, demonstrate, and develop their arc fault detection/connector technology.
This Sandia National Laboratories, New Mexico Environmental Restoration Operations (ER) Consolidated Quarterly Report (ER Quarterly Report) fulfills all quarterly reporting requirements set forth in the Compliance Order on Consent. The 12 sites in the corrective action process are listed in Table I-1. This ER Quarterly Report presents activities and data.
Bacterial pathogens have numerous processes by which their genomic DNA is acquired or rearranged as part of their normal physiology (e.g., exchange of plasmids through conjugation) or by bacteriophage that parasitize bacteria and often insert into the bacterial genome as prophages. These processes occur with relatively high probability/frequency, and may lead to sudden changes in virulence, as new genetic material is added to the chromosome, or structural changes in the chromosome affect gene expression. We set out to devise methods to measure the rates of these processes in bacteria using next generation DNA sequencing. Using very deep sequencing on genomes we had assembled, using library preparation methods and bioinformatics tools designed to help find mobile elements and signs of their insertion, we were able to find numerous examples of attempted novel genome arrangements, revealing data that can be used to calculate rates of different mechanisms of genome change.
Trichloroethene (TCE) and nitrate have been identified as constituents of concern in groundwater at the Sandia National Laboratories, New Mexico (SNL/NM) Technical Area (TA)-V Groundwater (TAVG) Area of Concern (AOC) based on detections above the U.S. Environmental Protection Agency (EPA) maximum contaminant level (MCL) in samples collected from monitoring wells. The EPA MCLs and the State of New Mexico drinking water standards for TCE and nitrate are 5 micrograms per liter and 10 milligrams per liter (as nitrogen), respectively. A phased Treatability Study/Interim Measure (TS/IM) of in-situ bioremediation (ISB) will be implemented to evaluate the effectiveness of ISB as a potential technology to treat the groundwater contamination at TAVG AOC (New Mexico Environment Department [NMED] April 2016). The NMED Hazardous Waste Bureau (HWB) approved the Revised Treatability Study Work Plan (TSWP) (SNL/NM March 2016) in May 2016 (NMED May 2016). The SNL/NM Environmental Restoration Operations (ER) personnel are responsible for implementing the TS/IM of ISB at TAVG AOC in accordance with the Revised TSWP.
Sandia National Laboratories is a multimission laboratory managed by National Technology & Engineering Solutions of Sandia, LLC a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration. Sandia has a long history of pioneering AM technology development. In the mid 1990s, Sandia developed laser-engineered net shaping (LENS), one of the first direct metal AM technologies, which was commercialized by Optomec. Robocast, an extrusion-based direct-write process for 3D ceramic parts, is another technology developed at Sandia. It was commercialized by Robocast Enterprises, LLC. As of January, 2019, Sandia was conducting AM R&D projects valued at about $25 million, with an emphasis on: 1) analysis-driven design, 2) materials reliability, and 3) multi-material AM.
This document will outline the test plans for the Hill AFB Mk 84 aging studies. The goal of the test series is to measure early case expansion velocities, sample the fragment field at various locations, and measure the overall shockwave and large fragment trajectories. This will be accomplished with 3 imaging systems as outlined in the sections below.
The US currently has about 80,000 metric tons of uranium in 279,000 used fuel assemblies from commercial spent nuclear fuel (SNF), most of which is stored “on-site” at or near power plants where it was produced. On-site storage facilities were not designed to provide a permanent solution for the disposal of spent nuclear fuel, and building a permanent disposal facility will likely take decades. Therefore, it is important to consider how the US public views options for constructing one or more storage facilities for safely consolidating and storing SNF in the interim. The 2017 iteration of the Energy and Environment survey (EE17) by the Center for Energy, Security, & Society (CES&S) included a battery of questions that measure public views about SNF storage and disposal options. The questions gauge general support for continued on-site storage, interim storage, and permanent disposal. EE17 also measured support for several of the specific sites under consideration, including the two private initiatives for interim storage of SNF in New Mexico and Texas. In addition, EE17 respondents provide insight into the factors likely to affect broader public support for these initiatives as the siting process unfolds, including public views about the importance of support for a prospective facility by host communities and host state residents.
Thermochemical energy storage (TCES) offers the potential for greatly increased storage density relative to sensible-only energy storage. Moreover, heat may be stored indefinitely in the form of chemical bonds via TCES, accessed upon demand, and converted to heat at temperatures significantly higher than current solar thermal electricity production technology and is therefore well-suited to more efficient high-temperature power cycles. However, this potential has yet to be realized as no current TCES system satisfies all requirements. This project involves the design, development, and demonstration of a robust and innovative storage cycle based on redox-active metal oxides that are Mixed Ionic-Electronic Conductors (MIECs). We will develop, characterize, and demonstrate a first of its kind 100kWth particle-based TCES system for direct integration with combined-cycle Air Brayton based on the endothermic reduction and exothermic reoxidation of MIECs. Air Brayton cycles require temperatures in the range of 1000-1230 °C for smaller axial flow turbines and are therefore inaccessible to all but the most robust storage solutions such as metal oxides. The choice of MIECs, with exceptional tunability and stability over the specified operating conditions allows us to optimally target this high impact cycle and to introduce the innovation of directly driving the turbine with the reacting/heat recovery fluid. The potential for high temperature thermal storage has direct bearing on next-gen CSP, and an appropriate investment for SETO.
The collapse or merging of individual plumes of direct-injection gasoline injectors is of fundamental importance to engine performance because of its impact on fuel-air mixing. However, the mechanisms of spray collapse are not fully understood. The purpose of this work is to study the effects of injection duration and multiple injections on the interaction and/or collapse of multi-plume GDI sprays. High-speed (100 kHz) Particle Image Velocimetry (PIV) is applied along a plane between plumes to observe the full temporal evolution of plume-interaction and potential collapse, resolved for individual injection events. Supporting information along a line of sight is obtained using Diffused Back Illumination (DBI). Experiments are performed under simulated engine conditions using a symmetric 8-hole injector in a high-temperature, high-pressure vessel at the "Spray G" operating conditions of the Engine Combustion Network (ECN). Longer injection duration is found to promote plume collapse, while staging fuel delivery with multiple, shorter injections is resistant to plume collapse.
Our goal is to develop a general theoretical basis for quantifying uncertainty in supervised machine learning models. Current machine learning accuracy-based validation metrics indicate how well a classifier performs on a given data set as a whole. However, these metrics do not tell us a model's efficacy in predicting particular samples. We quantify uncertainty by constructing probability distributions of the predictions made by an ensemble of classifiers. This report details our initial investigations into uncertainty quantification for supervised machine learning. We apply an uncertainty analysis to the problem of malicious website detection. Machine learning models can be trained to find suspicious characteristics in the text of a website's Uniform Resource Locator (URL). However, given the vast numbers of URLs and the ever changing tactics of malicious actors, it will always be possible to find sets of websites which are outliers with respect to a model's hypothesis. Therefore, we seek to understand a model's per-sample reliability when classifying URL data. Acknowledgements This work was funded by the Sandia National Laboratories Laboratory Directed Research and Development (LDRD) program.
The Sandia Cable Tester is an automated cable testing solution capable of quickly testing connectivity and isolation of arbitrary cables. This manual describes how to operate the unit including system setup and how to read out results. This manual also goes into detail on the design and theory behind the system.
The purpose of this paper is to review what a ‘dirty bomb” is and what the challenges are to actually developing and utilizing one. Additionally, it will review whether there are other radiological options for the non-state actor desiring to utilize them as a WMD. Integrated in this study and analysis is to determine if a non-state actor has the potential for better access to, and the ability to utilize, certain radiological WMDs.
Polymer foam encapsulants provide mechanical, electrical, and thermal isolation in engineered systems. It can be advantageous to surround objects of interest, such as electronics, with foams in a hermetically sealed container to protect the electronics from hostile environments, such as a crash that produces a fire. However, in fire environments, gas pressure from thermal decomposition of foams can cause mechanical failure of the sealed system. In this work, a detailed study of thermally decomposing polymeric methylene diisocyanate (PMDI)-polyether-polyol based polyurethane foam in a sealed container is presented. Both experimental and computational work is discussed. Three models of increasing physics fidelity are presented: No Flow, Porous Media, and Porous Media with VLE. Each model us described in detail, compared to experiment, and uncertainty quantification is performed. While the Porous Media with VLE model matches has the best agreement with experiment, it also requires the most computational resources.
Sparse Matrix-Matrix multiplication is a key kernel that has applications in several domains such as scientific computing and graph analysis. Several algorithms have been studied in the past for this foundational kernel. In this paper, we develop parallel algorithms for sparse matrix- matrix multiplication with a focus on performance portability across different high performance computing architectures. The performance of these algorithms depend on the data structures used in them. We compare different types of accumulators in these algorithms and demonstrate the performance difference between these data structures. Furthermore, we develop a meta-algorithm, kkSpGEMM, to choose the right algorithm and data structure based on the characteristics of the problem. We show performance comparisons on three architectures and demonstrate the need for the community to develop two phase sparse matrix-matrix multiplication implementations for efficient reuse of the data structures involved.
In August 2017, Sandia convened five workshops to explore the future of advanced technologies and global peace and security through the lenses of deterrence, information, innovation, nonproliferation, and population and Earth systems.
The 2018 Annual Terrestrial Sampling Plan for Sandia National Laboratories/New Mexico on Kirtland Air Force Base has been prepared in accordance with the “Letter of Agreement Between Department of Energy, National Nuclear Security Administration, Sandia Field Office (DOE/NNSA/SFO) and 377th Air Base Wing (ABW), Kirtland Air Force Base (KAFB) for Terrestrial Sampling” (signed January 2017), Sandia National Laboratories, New Mexico (SNL/NM). The Letter of Agreement requires submittal of an annual terrestrial sampling plan.
The Carolina Infrasound package, added as a piggyback to the 2016 ULDB ight, recorded unique acoustic signals such as the ocean microbarom and a large meteor. These data both yielded unique insights into the acoustic energy transfer from the lower to the upper atmosphere as well as highlighted the vast array of signals whose origins remain unknown. Now, the opportunity to y a payload across the north Atlantic offers an opportunity to sample one of the most active ocean microbarom sources on Earth. Improvements in payload capabilities should result in characterization of the higher frequency range of the stratospheric infrasound spectrum as well. Finally, numerous large mining and munitions disposal explosions in the region may provide \ground truth" events for assessing the detection capability of infrasound microphones in the stratosphere. The flight will include three different types of infrasound sensors. One type is a pair of polarity reversed InfraBSU microphones (standard for high altitude flights since 2016), another is a highly sensitive Chaparral 60 modified for a very low corner period, and the final sensor is a lightweight, low power Gem infrasound package. By evaluating these configurations against each other on the same flight, we will be able to optimize future campaigns with different sensitivity and mass constraints.
Recent high-performance computing (HPC) platforms such as the Trinity Advanced Technology System (ATS-1) feature burst buffer resources that can have a dramatic impact on an application’s I/O performance. While these non-volatile memory (NVM) resources provide a new tier in the storage hierarchy, developers must find the right way to incorporate the technology into their applications in order to reap the benefits. Similar to other laboratories, Sandia is actively investigating ways in which these resources can be incorporated into our existing libraries and workflows without burdening our application developers with excessive, platform-specific details. This FY18Q1 milestone summaries our progress in adapting the Sandia Parallel Aerodynamics and Reentry Code (SPARC) in Sandia’s ATDM program to leverage Trinity’s burst buffers for checkpoint/restart operations. We investigated four different approaches with varying tradeoffs in this work: (1) simply updating job script to use stage-in/stage out burst buffer directives, (2) modifying SPARC to use LANL’s hierarchical I/O (HIO) library to store/retrieve checkpoints, (3) updating Sandia’s IOSS library to incorporate the burst buffer in all meshing I/O operations, and (4) modifying SPARC to use our Kelpie distributed memory library to store/retrieve checkpoints. Team members were successful in generating initial implementation for all four approaches, but were unable to obtain performance numbers in time for this report (reasons: initial problem sizes were not large enough to stress I/O, and SPARC refactor will require changes to our code). When we presented our work to the SPARC team, they expressed the most interest in the second and third approaches. The HIO work was favored because it is lightweight, unobtrusive, and should be portable to ATS-2. The IOSS work is seen as a long-term solution, and is favored because all I/O work (including checkpoints) can be deferred to a single library.
The goal of this project is to develop and execute methods for characterizing uncertainty in data products that are deve loped and distributed by the DOE Consequence Management (CM) Program. A global approach to this problem is necessary because multiple sources of error and uncertainty from across the CM skill sets contribute to the ultimate p roduction of CM data products. This report presents the methods used to develop a probabilistic framework to characterize this uncertainty and provides results for an uncertainty analysis for a study scenario analyzed using this framework.
Evlyukhin, Egor; Kim, Eunja; Goldberger, David; Cifligu, Petrika; Weck, Philippe F.; Pravica, Michael
X-ray radiation induced damage has been known for decades and has largely been viewed as a tremendous nuisance; e.g., most X-ray-related studies of organic and inorganic materials suffer X-ray damage to varying degrees. Although, recent theoretical and experimental investigation of the response of simple chemical systems to X-rays offered better understanding of the mechanistic details of X-ray induced damage, the question about useful applicability of this technique is still unclear. Furthermore we experimentally demonstrate that by tuning pressure and X-ray energy, the radiation induced damage can be controlled and used for synthesis of novel materials.
Through long-term investments in computing, algorithms, facilities, and instrumentation, DOE is an established leader in massive-scale, high-fidelity simulations, as well as science-leading experimentation. In both cases, DOE is generating more data than it can analyze and the problem is intensifying quickly. The need for advanced algorithms that can automatically convert the abundance of data into a wealth of useful information by discovering hidden structures is well recognized. Such efforts however, are hindered by the massive volume of the data and its high velocity. Here, the challenge is developing unsupervised learning methods to discover hidden structure in high-volume, high-velocity data.
Zero emission hydrogen fuel cell technology has the potential to drastically reduce total “well-to-waves” maritime emissions. Through realistic design studies of five commercially-relevant passenger vessels, this study examines the most cost-effective entry points in the US fleet for deploying today’s available technology, and includes analysis of resulting well-to-waves emission profiles. The results show that per-passenger mile vessel energy use is directly correlated to increased emissions, capital costs, and operating costs. As a consequence, low speed, large capacity vessels offer a cost-effective starting place today. Increases in vessel efficiency through such measures as hull design and light-weighting can have large impacts in reducing cost and emissions of these systems. Overall this work showed all five vessel types to be feasible with today’s hydrogen fuel cell technology and presents more options to fleets that are committed to reducing maritime emissions in cost effective ways.
This paper documents the history of the TRU program at Sandia, previous and current activities associated with TRU material and waste, interfaces with other TRU waste generator sites and the Waste Isolation Pilot Plan (WIPP), and paths forward for TRU material and waste. This document is a snapshot in time of the TRU program and should be updated as necessary, or when significant changes have occurred in the Sandia TRU program or in the TRU regulatory environment. This paper should serve as a roadmap to capture past TRU work so that efforts are not repeated and ground is not lost due to future inactivity and personnel changes.
Safety basis analysts throughout the U.S. Department of Energy (DOE) complex rely heavily on the information provided in the DOE Handbook, DOE-HDBK-3010, Airborne Release Fractions/Rates and Respirable Fractions for Nonreactor Nuclear Facilities, to determine radionuclide source terms from postulated accident scenarios. In calculating source terms, analysts tend to use the DOE Handbook’s bounding values on airborne release fractions (ARFs) and respirable fractions (RFs) for various categories of insults (representing potential accident release categories). This is typically due to both time constraints and the avoidance of regulatory critique. Unfortunately, these bounding ARFs/RFs represent extremely conservative values. Moreover, they were derived from very limited small-scale bench/laboratory experiments and/or from engineered judgment. Thus, the basis for the data may not be representative of the actual unique accident conditions and configurations being evaluated. The goal of this research is to develop a more accurate and defensible method to determine bounding values for the DOE Handbook using state-of-art multi-physics-based computer codes.
Diesel on-road vehicles with a Gross Vehicle Weight Rating (GVWR) greater than 14,000 pounds that operate in California are subject to the On-Road Diesel-Vehicle Regulation. SNL/CA operates eight diesel vehicles that are subject to this regulation; four of the vehicles are classified as low-use vehicles and four of the vehicles have Particulate Matter Filters installed as original equipment. This regulation requires that all fleets using flexibility options, such as the low-use exemption, report annually by January 31 in order to extend the exemption for that year. CA ARB encourages facilities to complete the reporting requirements using the TRUCRS on-line reporting tool.
This report describes tests conducted using a full-size rail cask, the ENSA ENUN 32P, involving handling of the cask and transport of the cask via truck, ships, and rail. The purpose of the tests was to measure strains and accelerations on surrogate pressurized water reactor fuel rods when the fuel assemblies were subjected to Normal Conditions of Transport within the rail cask. In addition, accelerations were measured on the transport platform, the cask cradle, the cask, and the basket within the cask holding the assemblies. These tests were an international collaboration that included Equipos Nucleares S.A., Sandia National Laboratories, Pacific Northwest National Laboratory, Coordinadora Internacional de Cargas S.A., the Transportation Technology Center, Inc., the Korea Radioactive Waste Agency, and the Korea Atomic Energy Research Institute. All test results in this report are PRELIMINARY – complete analyses of test data will be completed and reported in FY18. However, preliminarily: The strains were exceedingly low on the surrogate fuel rods during the rail-cask tests for all the transport and handling modes. The test results provide a compelling technical basis for the safe transport of spent fuel.
A common problem associated with the effort to better assess potential behaviors of various individuals within different countries is the shear difficulty in comprehending the dynamic nature of populations, particularly over time and considering feedback effects. This paper discusses a theory-based analytical capability designed to enable analysts to better assess the influence of events on individuals interacting within a country or region. These events can include changes in policy, man-made or natural disasters, migration, war, or other changes in environmental/economic conditions. In addition, this paper describes potential extensions of this type of research to enable more timely and accurate assessments.
Energetic materials (i.e. explosives, propellants, and pyrotechnics) have complex mesoscale features that influence their dynamic response. Direct measurement of the complex mechanical, thermal, and chemical response of energetic materials is critical for improving computational models and enabling predictive capabilities. Many of the physical phenomena of interest in energetic materials cover time and length scales spanning several orders of magnitude. Examples include chemical interactions in the reaction zone, the distribution and evolution of temperature fields, mesoscale deformation in heterogeneous systems, and phase transitions. This is particularly true for spontaneous phenomena, like thermal cook-off. The ability for MaRIE to capture multiple length scales and stochastic phenomena can significantly advance our understanding of energetic materials and yield more realistic, predictive models.
The significantly higher buoyancy of hydrogen compared to natural gas means that hazardous zones defined in the IGF code may be inaccurate if applied to hydrogen. This could place undue burden on ship design or could lead to situations that are unknowingly unsafe. We present dispersion analyses to examine three vessel case studies: (1) abnormal external vents of full blowdown of a liquid hydrogen tank due to a failed relief device in still air and with crosswind; (2) vents due to naturally-occurring boil-off of liquid within the tank; and (3) a leak from the pipes leading into the fuel cell room. The size of the hydrogen plumes resulting from a blowdown of the tank depend greatly on the wind conditions. It was also found that for normal operations releasing a small amount of "boil- off" gas to regulate the pressure in the tank does not create flammable concentrations.
Slycat™ is a web-based system for performing data analysis and visualization of potentially large quantities of remote, high-dimensional data. Slycat™ specializes in working with ensemble data. An ensemble is a group of related data sets, which typically consists of a set of simulation runs exploring the same problem space. An ensemble can be thought of as a set of samples within a multi-variate domain, where each sample is a vector whose value defines a point in high-dimensional space. To understand and describe the underlying problem being modeled in the simulations, ensemble analysis looks for shared behaviors and common features across the group of runs. Additionally, ensemble analysis tries to quantify differences found in any members that deviate from the rest of the group. The Slycat™ system integrates data management, scalable analysis, and visualization. Results are viewed remotely on a user’s desktop via commodity web clients using a multi-tiered hierarchy of computation and data storage, as shown in Figure 1. Our goal is to operate on data as close to the source as possible, thereby reducing time and storage costs associated with data movement. Consequently, we are working to develop parallel analysis capabilities that operate on High Performance Computing (HPC) platforms, to explore approaches for reducing data size, and to implement strategies for staging computation across the Slycat™ hierarchy. Within Slycat™, data and visual analysis are organized around projects, which are shared by a project team. Project members are explicitly added, each with a designated set of permissions. Although users sign-in to access Slycat™, individual accounts are not maintained. Instead, authentication is used to determine project access. Within projects, Slycat™ models capture analysis results and enable data exploration through various visual representations. Although for scientists each simulation run is a model of real-world phenomena given certain conditions, we use the term model to refer to our modeling of the ensemble data, not the physics. Different model types often provide complementary perspectives on data features when analyzing the same data set. Each model visualizes data at several levels of abstraction, allowing the user to range from viewing the ensemble holistically to accessing numeric parameter values for a single run. Bookmarks provide a mechanism for sharing results, enabling interesting model states to be labeled and saved.
