The purpose of this short contribution is to report on the development of a Spectral Neighbor Analysis Potential (SNAP) for tungsten. We have focused on the characterization of elastic and defect properties of the pure material in order to support molecular dynamics simulations of plasma-facing materials in fusion reactors. A parallel genetic algorithm approach was used to efficiently search for fitting parameters optimized against a large number of objective functions. In addition, we have shown that this many-body tungsten potential can be used in conjunction with a simple helium pair potential1 to produce accurate defect formation energies for the W-He binary system.
A survey was designed to query former Biorisk management (BRM) trainees in the East Africa region about their practices post-training and their perceived future training needs. A subset of those surveyed had been trained as BRM trainers. The survey was conducted to obtain a baseline of BRM practices that can serve as a benchmark for performance monitoring, to identify priorities for future BRM training and to gauge local BRM trainers' abilities to deliver effective training. The survey revealed that less than 50% of the respondents could identify evidence of a BRM system in their institute. Coaching and mentoring by BRM experts was identified as being of highest benefit to enable success as BRM practitioners. Local trainers reached 1538 trainees in the previous year and reported that trainings positively correlated with desired BRM behavior. Acknowledgements The authors wish to sincerely thank all of the former biorisk management trainees in East Africa who agreed to participate in this survey. Their candid and honest input was extremely insightful. We also thank Lora Grainger (06826) and Ben Brodsky (Manager, 06824) for careful and critical review of the report. We are grateful for the financial support of the Defense Threat Reduction Agency, Cooperative Biological Engagement Program.
The primary purpose of the preclosure radiological safety assessment (that this document supports) is to identify risk factors for disposal operations, to aid in design for the deep borehole field test (DBFT) engineering demonstration.
This goal of this project is to address the current inability to assess the overall error and uncertainty of data products developed and distributed by DOE’s Consequence Management (CM) Program.
Ge, Ting; Kalathi, Jagannathan T.; Halverson, Jonathan D.; Grest, Gary S.; Rubinstein, Michael
The motion of nanoparticles (NPs) in entangled melts of linear polymers and nonconcatenated ring polymers are compared by large-scale molecular dynamics simulations. The comparison provides a paradigm for the effects of polymer architecture on the dynamical coupling between NPs and polymers in nanocomposites. Strongly suppressed motion of NPs with diameter d larger than the entanglement spacing a is observed in a melt of linear polymers before the onset of Fickian NP diffusion. This strong suppression of NP motion occurs progressively as d exceeds a and is related to the hopping diffusion of NPs in the entanglement network. In contrast to the NP motion in linear polymers, the motion of NPs with d > a in ring polymers is not as strongly suppressed prior to Fickian diffusion. The diffusion coefficient D decreases with increasing d much slower in entangled rings than in entangled linear chains. NP motion in entangled nonconcatenated ring polymers is understood through a scaling analysis of the coupling between NP motion and the self-similar entangled dynamics of ring polymers.
Complex systems are comprised of technical, social, political and environmental factors as well as the programmatic factors of cost, schedule and risk. Testing these systems for enhanced security requires expert knowledge in many different fields. It is important to test these systems to ensure effectiveness, but testing is limited to due cost, schedule, safety, feasibility and a myriad of other reasons. Without an effective decision framework for Test and Evaluation (T&E) planning that can take into consideration technical as well as programmatic factors and leverage expert knowledge, security in complex systems may not be assessed effectively. Therefore, this paper covers the identification of the current T&E planning problem and an approach to include the full variety of factors and leverage expert knowledge in T&E planning through the use of Bayesian Networks (BN).
Whether structural or electronic, all solder joints must provide the necessary level of reliability for the application. The Part 1 report examined the effects of filler metal properties and the soldering process on joint reliability. Filler metal solderability and mechanical properties, as well as the extents of base material dissolution and interface reaction that occur during the soldering process, were shown to affect reliability performance. The continuation of this discussion is presented in this Part 2 report, which highlights those factors that directly affect solder joint reliability. There is the growth of an intermetallic compound (IMC) reaction layer at the solder/base material interface by means of solid-state diffusion processes. In terms of mechanical response by the solder joint, fatigue remains as the foremost concern for long-term performance. Thermal mechanical fatigue (TMF), a form of low-cycle fatigue (LCF), occurs when temperature cycling is combined with mismatched values of the coefficient of thermal expansion (CTE) between materials comprising the solder joint “system.” Vibration environments give rise to high-cycle fatigue (HCF) degradation. Although accelerated aging studies provide valuable empirical data, too many variants of filler metals, base materials, joint geometries, and service environments are forcing design engineers to embrace computational modeling to predict the long-term reliability of solder joints.
The prediction of formation and early growth of microstructurally small fatigue cracks requires use of constitutive models that accurately estimate local states of stress, strain, and cyclic plastic strain. However, few research efforts have attempted to systematically consider the sensitivity of overall cyclic stress-strain hysteresis and higher order mean stress relaxation and plastic strain ratcheting responses introduced by the slip system back-stress formulation in crystal plasticity, even for face centered cubic (FCC) crystal systems. This paper explores the performance of two slip system level kinematic hardening models using a finite element crystal plasticity implementation as a User Material Subroutine (UMAT) within ABAQUS (Abaqus unified FEA, 2016) [1], with fully implicit numerical integration. The two kinematic hardening formulations aim to reproduce the cyclic deformation of polycrystalline Al 7075-T6 in terms of both macroscopic cyclic stress-strain hysteresis loop shape, as well as ratcheting and mean stress relaxation under strain- or stress-controlled loading with mean strain or stress, respectively. The first formulation is an Armstrong-Frederick type hardening-dynamic recovery law for evolution of the back stress [2]. This approach is capable of reproducing observed deformation under completely reversed uniaxial loading conditions, but overpredicts the rate of cyclic ratcheting and associated mean stress relaxation. The second formulation corresponds to a multiple back stress Ohno-Wang type hardening law [3] with nonlinear dynamic recovery. The adoption of this back stress evolution law greatly improves the capability to model experimental results for polycrystalline specimens subjected to cycling with mean stress or strain. The relation of such nonlinear dynamic recovery effects are related to slip system interactions with dislocation substructures.
Thermal spray deposited WC-CoCr coatings are extensively used for surface protection of wear prone components in a variety of applications. Although the primary purpose of the coating is wear and corrosion protection, many of the coated components are structural systems (aero landing gear, hydraulic cylinders, drive shafts etc.) and as such experience cyclic loading during service and are potentially prone to fatigue failure. It is of interest to ensure that the coating and the application process does not deleteriously affect the fatigue strength of the parent structural metal. It has long been appreciated that the relative fatigue life of a thermal sprayed component can be affected by the residual stresses arising from coating deposition. The magnitude of these stresses can be managed by torch processing parameters and can also be influenced by deposition effects, particularly the deposition temperature. In this study, the effect of both torch operating parameters (particle states) and deposition conditions (notably substrate temperature) were investigated through rotating bending fatigue studies. The results indicate a strong influence of process parameters on relative fatigue life, including credit or debit to the substrate's fatigue life measured via rotating bend beam studies. Damage progression within the substrate was further explored by stripping the coating off part way through fatigue testing, revealing a delay in the onset of substrate damage with more fatigue resistant coatings but no benefit with coatings with inadequate properties. Finally, the results indicate that compressive residual stress and adequate load bearing capability of the coating (both controlled by torch and deposition parameters) delay onset of substrate damage, enabling fatigue credit of the coated component.
ERPs are a powerful tool for the study of reading, as they are both temporally precise and functionally specific. These are essential characteristics for studying a process that unfolds rapidly and consists of multiple, interactive subprocesses. In work with adults, clear, specific models exist linking components of the ERP with individual subprocesses of reading including orthographic decoding, phonological processing, and semantic access (e.g., Grainger & Holcomb, 2009). The relationships between ERP components and reading subprocesses are less clear in development; here, we address two questions regarding these relationships. First, we ask whether there are ERP markers that predict future reading behaviors across a longitudinal year. Second, we ask whether any relationships observed between ERP components and reading behavior across time map onto the better-established relationships between ERPs and reading subprocesses in adults. To address these questions, we acquired ERPs from children engaging in a silent reading task and then, a year later, collected behavioral assessments of their reading ability. Finally, we find that ERPs collected in Year 1 do predict reading behaviors a year later. Further, we find that these relationships do conform, at least to some extent, to relationships between ERP components and reading subprocesses observed in adults, with, for example, N250 amplitude in Year 1 predicting phonological awareness in Year 2, and N400 amplitude in Year 1 predicting vocabulary in Year 2.
The reaction between CH3O2 and OH radicals has been shown to be fast and to play an appreciable role for the removal of CH3O2 radials in remote environments such as the marine boundary layer. Two different experimental techniques have been used here to determine the products of this reaction. The HO2 yield has been obtained from simultaneous time-resolved measurements of the absolute concentration of CH3O2, OH, and HO2 radicals by cw-CRDS. The possible formation of a Criegee intermediate has been measured by broadband cavity enhanced UV absorption. A yield of ηHO2 = (0.8 ± 0.2) and an upper limit for ηCriegee = 0.05 has been determined for this reaction, suggesting a minor yield of methanol or stabilized trioxide as a product. The impact of this reaction on the composition of the remote marine boundary layer has been determined by implementing these findings into a box model utilizing the Master Chemical Mechanism v3.2, and constraining the model for conditions found at the Cape Verde Atmospheric Observatory in the remote tropical Atlantic Ocean. Inclusion of the CH3O2+OH reaction into the model results in up to 30% decrease in the CH3O2 radical concentration while the HO2 concentration increased by up to 20%. Production and destruction of O3 are also influenced by these changes, and the model indicates that taking into account the reaction between CH3O2 and OH leads to a 6% decrease of O3.
The brain is capable of massively parallel information processing while consuming only ~1-100 fJ per synaptic event1,2. Inspired by the efficiency of the brain, CMOS-based neural architectures3 and memristors4,5 are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach. Here we describe an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors. ENODeswitches at lowvoltage and energy (<10 pJ for 103 μm2 devices), displays >500 distinct, non-volatile conductance states within a~1V range, and achieves high classification accuracy when implemented in neural network simulations. Plastic ENODes are also fabricated on flexible substrates enabling the integration of neuromorphic functionality in stretchable electronic systems6,7. Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.
Pentaerythritol tetranitrate (PETN) is a common secondary explosive and has been used extensively to study shock initiation and energy propagation in energetic materials. We report 2D IR measurements of PETN thin films that resolve vibrational energy transfer and relaxation mechanisms. Ultrafast anisotropy measurements reveal a sub-500 fs reorientation of transition dipoles in thin films of vapor-deposited PETN that is absent in solution measurements, consistent with intermolecular energy transfer. The anisotropy is frequency dependent, suggesting spectrally heterogeneous vibrational relaxation. Cross peaks are observed in 2D IR spectra that resolve a specific energy transfer pathway with a 2 ps time scale. Transition dipole coupling calculations of the nitrate ester groups in the crystal lattice predict that the intermolecular couplings are as large or larger than the intramolecular couplings. The calculations match well with the experimental frequencies and the anisotropy, leading us to conclude that the observed cross peak is measuring energy transfer between two eigenstates that are extended over multiple PETN molecules. Measurements of the transition dipole strength indicate that these vibrational modes are coherently delocalized over at least 15-30 molecules. We discuss the implications of vibrational relaxation between coherently delocalized eigenstates for mechanisms relevant to explosives.
A robust molecular-dynamics simulation method for calculating dislocation core energies has been developed. This method has unique advantages: It does not require artificial boundary conditions, is applicable for mixed dislocations, and can yield converged results regardless of the atomistic system size. Utilizing a high-fidelity bond order potential, we have applied this method in aluminium to calculate the dislocation core energy as a function of the angle β between the dislocation line and the Burgers vector. These calculations show that, for the face-centered-cubic aluminium explored, the dislocation core energy follows the same functional dependence on β as the dislocation elastic energy: Ec=Asin2β+Bcos2β, and this dependence is independent of temperature between 100 and 300 K. By further analyzing the energetics of an extended dislocation core, we elucidate the relationship between the core energy and the core radius of a perfect versus an extended dislocation. With our methodology, the dislocation core energy can accurately be accounted for in models of dislocation-mediated plasticity.
Quantum information processors promise fast algorithms for problems inaccessible to classical computers. But since qubits are noisy and error-prone, they will depend on fault-tolerant quantum error correction (FTQEC) to compute reliably. Quantum error correction can protect against general noise if - and only if - the error in each physical qubit operation is smaller than a certain threshold. The threshold for general errors is quantified by their diamond norm. Until now, qubits have been assessed primarily by randomized benchmarking, which reports a different error rate that is not sensitive to all errors, and cannot be compared directly to diamond norm thresholds. Here we use gate set tomography to completely characterize operations on a trapped-Yb+-ion qubit and demonstrate with greater than 95% confidence that they satisfy a rigorous threshold for FTQEC (diamond norm ≤6.7 × 10-4).
Directing the formation of nanostructures that serve as building blocks of membranes presents an immense step towards engineering controlled polymeric ion transport systems. Using the exquisite atomic detail captured by molecular dynamics simulations, we follow the assembly of a co-polymer that consists of polystyrene sulfonate tethered symmetrically to hydrophobic blocks, realizing a new type of long lived solvent-responsive soft nanoparticle.
We performed atomistic simulations on a series of sulfonated polyphenylenes systematically varying the degree of sulfonation and water content to determine their effect on the nanoscale structure, particularly for the hydrophilic domains formed by the ionic groups and water molecules. We found that the local structure around the ionic groups depended on the sulfonation and hydration levels, with the sulfonate groups and hydronium ions less strongly coupled at higher water contents. In addition, we characterized the morphology of the ionic domains employing two complementary clustering algorithms. At low sulfonation and hydration levels, clusters were more elongated in shape and poorly connected throughout the system. As the degree of sulfonation and water content were increased, the clusters became more spherical, and a fully percolated ionic domain was formed. These structural details have important implications for ion transport.
We demonstrate electrical tuning of the spectral response of a Mie-resonant dielectric metasurface consisting of silicon nanodisks embedded into liquid crystals. We use the reorientation of nematic liquid crystals in a moderate applied electric field to alter the anisotropic permittivity tensor around the metasurface. By switching a control voltage “on” and “off,” we induce a large spectral shift of the metasurface resonances, resulting in an absolute transmission modulation of up to 75%. Our experimental demonstration of voltage control of dielectric metasurfaces paves the way for new types of electrically tunable metadevices, including dynamic displays and holograms.
Pyroelectric coefficients were measured for 20 nm thick crystalline hafnium zirconium oxide (Hf1-xZrxO2) thin films across a composition range of 0 ≤ x ≤ 1. Pyroelectric currents were collected near room temperature under zero applied bias and a sinusoidal oscillating temperature profile to separate the influence of non-pyroelectric currents. The pyroelectric coefficient was observed to correlate with zirconium content, increased orthorhombic/tetragonal phase content, and maximum polarization response. The largest measured absolute value was 48 μCm−2 K−1 for a composition with x = 0.64, while no pyroelectric response was measured for compositions which displayed no remanent polarization (x = 0, 0.91, and 1).
Nickel-doped α-MnO2 nanowires (Ni-α-MnO2) were prepared with 3.4% or 4.9% Ni using a hydrothermal method. A comparison of the electrocatalytic data for the oxygen reduction reaction (ORR) in alkaline electrolyte versus that obtained with α-MnO2 or Cu-α-MnO2 is provided. In general, Ni-α-MnO2 (e.g., Ni-4.9%) had higher n values (n = 3.6), faster kinetics (k = 0.015 cm s-1), and lower charge transfer resistance (RCT = 2264 Ω at half-wave) values than MnO2 (n = 3.0, k = 0.006 cm s-1, RCT = 6104 Ω at half-wave) or Cu-α-MnO2 (Cu-2.9%, n = 3.5, k = 0.015 cm s-1, RCT = 3412 Ω at half-wave), and the overall activity for Ni-α-MnO2 trended with increasing Ni content, i.e., Ni-4.9% > Ni-3.4%. As observed for Cu-α-MnO2, the increase in ORR activity correlates with the amount of Mn3+ at the surface of the Ni-α-MnO2 nanowire. Examining the activity for both Ni-α-MnO2 and Cu-α-MnO2 materials indicates that the Mn3+ at the surface of the electrocatalysts dictates the activity trends within the overall series. Single nanowire resistance measurements conducted on 47 nanowire devices (15 of α-MnO2, 16 of Cu-α-MnO2-2.9%, and 16 of Ni-α-MnO2-4.9%) demonstrated that Cu-doping leads to a slightly lower resistance value than Ni-doping, although both were considerably improved relative to the undoped α-MnO2. The data also suggest that the ORR charge transfer resistance value, as determined by electrochemical impedance spectroscopy, is a better indicator of the cation-doping effect on ORR catalysis than the electrical resistance of the nanowire. (Figure Presented).
We provide the first demonstration that molecular-level methods based on gas kinetic theory and molecular chaos can simulate turbulence and its decay. The direct simulation Monte Carlo (DSMC) method, a molecular-level technique for simulating gas flows that resolves phenomena from molecular to hydrodynamic (continuum) length scales, is applied to simulate the Taylor-Green vortex flow. The DSMC simulations reproduce the Kolmogorov -5/3 law and agree well with the turbulent kinetic energy and energy dissipation rate obtained from direct numerical simulation of the Navier-Stokes equations using a spectral method. This agreement provides strong evidence that molecular-level methods for gases can be used to investigate turbulent flows quantitatively.
We report lasing from nonpolar p-i-n InGaN/GaN multi-quantum well core-shell single-nanowire lasers by optical pumping at room temperature. The nanowire lasers were fabricated using a hybrid approach consisting of a top-down two-step etch process followed by a bottom-up regrowth process, enabling precise geometrical control and high material gain and optical confinement. The modal gain spectra and the gain curves of the core-shell nanowire lasers were measured using micro-photoluminescence and analyzed using the Hakki-Paoli method. Significantly lower lasing thresholds due to high optical gain were measured compared to previously reported semipolar InGaN/GaN core-shell nanowires, despite significantly shorter cavity lengths and reduced active region volume. Mode simulations show that due to the core-shell architecture, annular-shaped modes have higher optical confinement than solid transverse modes. The results show the viability of this p-i-n nonpolar core-shell nanowire architecture, previously investigated for next-generation light-emitting diodes, as low-threshold, coherent UV-visible nanoscale light emitters, and open a route toward monolithic, integrable, electrically injected single-nanowire lasers operating at room temperature.
Wood, Brandon C.; Stavila, Vitalie; Poonyayant, Natchapol; Heo, Tae W.; Ray, Keith G.; Klebanoff, Leonard E.; Udovic, Terrence J.; Lee, Jonathan R.I.; Angboonpong, Natee; Pakawatpanurut, Pasit
Internal interfaces in the Li3N/[LiNH2 + 2LiH] solid-state hydrogen storage system alter the hydrogenation and dehydrogenation reaction pathways upon nanosizing, suppressing undesirable intermediate phases to dramatically improve kinetics and reversibility. Finally, the key role of solid interfaces in determining thermodynamics and kinetics suggests a new paradigm for optimizing complex hydrides for solid-state hydrogen storage by engineering internal microstructure.
International Journal of Computational Fluid Dynamics
Gel, Aytekin; Hu, Jonathan J.; El Ould-Ahmed-Vall, Moustapha; Kalinkin, Alexander A.
Legacy codes remain a crucial element of today's simulation-based engineering ecosystem due to the extensive validation process and investment in such software. The rapid evolution of high-performance computing architectures necessitates the modernization of these codes. One approach to modernization is a complete overhaul of the code. However, this could require extensive investments, such as rewriting in modern languages, new data constructs, etc., which will necessitate systematic verification and validation to re-establish the credibility of the computational models. The current study advocates using a more incremental approach and is a culmination of several modernization efforts of the legacy code MFIX, which is an open-source computational fluid dynamics code that has evolved over several decades, widely used in multiphase flows and still being developed by the National Energy Technology Laboratory. Two different modernization approaches,‘bottom-up’ and ‘top-down’, are illustrated. Preliminary results show up to 8.5x improvement at the selected kernel level with the first approach, and up to 50% improvement in total simulated time with the latter were achieved for the demonstration cases and target HPC systems employed.
Terahertz (THz) double-metal plasmonic resonators enable enhanced light-matter coupling by exploiting strong field confinement. The double-metal design however restricts access to the internal fields. We propose and demonstrate a method for spatial mapping and spectroscopic analysis of the internal electromagnetic fields in double-metal plasmonic resonators. We use the concept of image charges and aperture-type scanning near-field THz time-domain microscopy to probe the fields confined within the closed resonator. The experimental method opens doors to studies of light-matter coupling in deeply sub-wavelength volumes at THz frequencies.
Membrane distillation is a water purification technology which uses a porous hydrophobic membrane. Liquid water cannot penetrate the membrane at operational pressures but vapor flows through the membrane if there is a vapor pressure difference across the membrane. Many configurations for membrane distillation have been investigated over the last several decades. In this modeling effort, two successful direct contact membrane model development using steady-state control volume balances on energy and mass are presented. Verification and validation of the models is applied to the extent necessary to use the models for comparative design purposes. Significant errors between modeling and experimental membrane distillation data are argued to be due to uncertainty in membrane material property measurements. A second effort to model a vacuum membrane distillation system designed by Memsys® is still progressing. Two efforts have not yet produced output mass flow comparable to the literature. Even so, much of the framework needed to model the Memsys® system is complete.
The familiar story of Moore's law is actually inaccurate. This article corrects the story, leading to different projections for the future. Moore's law is a fluid idea whose definition changes over time. It thus doesn't have the ability to 'end,' as is popularly reported, but merely takes different forms as the semiconductor and computer industries evolve.
Elemental mapping at the atomic-scale by scanning transmission electron microscopy (STEM) using energy-dispersive X-ray spectroscopy (EDS) provides a powerful real-space approach to chemical characterization of crystal structures. However, applications of this powerful technique have been limited by inefficient X-ray emission and collection, which require long acquisition times. Recently, using a lattice-vector translation method, we have shown that rapid atomic-scale elemental mapping using STEM-EDS can be achieved. This method provides atomic-scale elemental maps averaged over crystal areas of ~ few 10 nm2 with the acquisition time of ~2 s or less. Here we report the details of this method, and, in particular, investigate the experimental conditions necessary for achieving it. It shows, that in addition to usual conditions required for atomic-scale imaging, a thin specimen is essential for the technique to be successful. Phenomenological modeling shows that the localization of X-ray signals to atomic columns is a key reason. The effect of specimen thickness on the signal delocalization is studied by multislice image simulations. The results show that the X-ray localization can be achieved by choosing a thin specimen, and the thickness of less than about 22 nm is preferred for SrTiO3 in [001] projection for 200 keV electrons.
Morel, Jim E.; Warsa, James S.; Franke, Brian C.; Prinja, Anil K.
We compare two methods for generating Galerkin quadratures. In method 1, the standard SN method is used to generate the moment-to-discrete matrix and the discrete-to-moment matrix is generated by inverting the moment-to-discrete matrix. This is a particular form of the original Galerkin quadrature method. In method 2, which we introduce here, the standard SN method is used to generate the discreteto-moment matrix and the moment-to-discrete matrix is generated by inverting the discrete-to-moment matrix. With an N-point quadrature, method 1 has the advantage that it preserves N eigenvalues and N eigenvectors of the scattering operator in a pointwise sense. With an N-point quadrature, method 2 has the advantage that it generates consistent angular moment equations from the corresponding SN equations while preserving N eigenvalues of the scattering operator. Our computational results indicate that these two methods are quite comparable for the test problem considered.
Remote temperature sensing is essential for applications in enclosed vessels, where feedthroughs or optical access points are not possible. A unique sensing method for measuring the temperature of multiple closely spaced points is proposed using permanent magnets and several three-axis magnetic field sensors. The magnetic field theory for multiple magnets is discussed and a solution technique is presented. Experimental calibration procedures, solution inversion considerations, and methods for optimizing the magnet orientations are described in order to obtain low-noise temperature estimates. The experimental setup and the properties of permanent magnets are shown. Finally, experiments were conducted to determine the temperature of nine magnets in different configurations over a temperature range of 5 °C to 60 °C and for a sensor-to-magnet distance of up to 35 mm. To show the possible applications of this sensing system for measuring temperatures through metal walls, additional experiments were conducted inside an opaque 304 stainless steel cylinder.
Grain boundaries often develop faceted morphologies in systems for which the interfacial free energy depends on the boundary inclination. Although the mesoscale thermodynamic basis for such morphological evolution has been extensively studied, the influence of line defects, such as secondary grain boundary dislocations, on the facet configurations has not been thoroughly explored. In this paper, through a combination of atomistic simulations and electron microscopic observations, we examine in detail the structure of an asymmetric Σ = 5 [001] grain boundary in well-annealed, body-centered cubic (BCC) Fe. The observed boundary forms with a hill-and-valley morphology composed of nanoscale {310} and {210} facets. Our analysis clarifies the atomic structure of the {310}/{210} facet junctions and identifies the presence of an array of secondary grain boundary dislocations that are localized to these junctions. Analysis of the Burgers vectors of the grain boundary dislocations, which are of type (1/5)<310> and (1/5)<120>, shows that the defect density is consistent with that required to accommodate a small observed angular deviation from the exact Σ = 5 orientation relationship. These observations and analysis suggest a crucial role for secondary grain boundary dislocations in dictating the length-scale of grain boundary facets, a consideration which has not been included in prior analyses of facet evolution and equilibrium facet length.
Interfacial crack fields and singularities in bimaterial interfaces (i.e., grain boundaries or dissimilar materials interfaces) are considered through a general formulation for two-dimensional (2-D) anisotropic elasticity while accounting for the interfacial structure by means of an interfacial elasticity paradigm. The interfacial elasticity formulation introduces boundary conditions that are effectively equivalent to those for a weakly bounded interface. This formalism considers the 2-D crack-tip elastic fields using complex variable techniques. While the consideration of the interfacial elasticity does not affect the order of the singularity, it modifies the oscillatory effects associated with problems involving interface cracks. Constructive or destructive “interferences” are directly affected by the interface structure and its elastic response. This general formulation provides an insight on the physical significance and the obvious coupling between the interface structure and the associated mechanical fields in the vicinity of the crack tip.
Linear mathematical models were applied to binary-discrimination tasks relevant to arms control verification measurements in which a host party wishes to convince a monitoring party that an item is or is not treaty accountable. These models process data in list-mode format and can compensate for the presence of variability in the source, such as uncertain object orientation and location. The Hotelling observer applies an optimal set of weights to binned detector data, yielding a test statistic that is thresholded to make a decision. The channelized Hotelling observer applies a channelizing matrix to the vectorized data, resulting in a lower dimensional vector available to the monitor to make decisions. We demonstrate how incorporating additional terms in this channelizing-matrix optimization offers benefits for treaty verification. We present two methods to increase shared information and trust between the host and monitor. The first method penalizes individual channel performance in order to maximize the information available to the monitor while maintaining optimal performance. Second, we present a method that penalizes predefined sensitive information while maintaining the capability to discriminate between binary choices. Data used in this study was generated using Monte Carlo simulations for fission neutrons, accomplished with the GEANT4 toolkit. Custom models for plutonium inspection objects were measured in simulation by a radiation imaging system. Model performance was evaluated and presented using the area under the receiver operating characteristic curve.
Drawn 304L stainless steel tubing was subjected to 42 different annealing heat treatments with the goal of initializing a microstructural model to select a heat treatment to soften the tubing from a hardness of 305 Knoop to 225–275 Knoop. The amount of recrystallization and grain size caused by 18 heat treatments were analyzed via optical microscopy and image analysis, revealing the full range of recrystallization from 0 to 100%. The formation of carbides during the longer duration and higher-temperature heat treatments was monitored via transmission electron microscope evaluation. The experimental results informed a model which includes recovery, recrystallization, and grain growth to predict microstructure and hardness. After initialization of the model, it was able to predict hardness with a R2 value of 0.95 and recrystallization with an R2 value of 0.99. The model was then utilized in the design and testing of a heat treatment to soften the tubing.
The ability to model nonlinear structures subject to random excitation is of key importance in designing hypersonic aircraft and other advanced aerospace vehicles. When a structure is linear, superposition can be used to construct its response to a known spectrum in terms of its linear modes. Superposition does not hold for a nonlinear system, but several works have shown that a system's dynamics can still be understood qualitatively in terms of its nonlinear normal modes (NNMs). This work investigates the connection between a structure's undamped nonlinear normal modes and the spectrum of its response to high amplitude random forcing. Two examples are investigated: a spring-mass system and a clamped-clamped beam modeled within a geometrically nonlinear finite element package. In both cases, an intimate connection is observed between the smeared peaks in the response spectrum and the frequency-energy dependence of the nonlinear normal modes. In order to understand the role of coupling between the underlying linear modes, reduced order models with and without modal coupling terms are used to separate the effect of each NNM's backbone from the nonlinear couplings that give rise to internal resonances. In the cases shown here, uncoupled, single-degree-of-freedom nonlinear models are found to predict major features in the response with reasonable accuracy; a highly inexpensive approximation such as this could be useful in design and optimization studies. More importantly, the results show that a reduced order model can be expected to give accurate results only if it is also capable of accurately predicting the frequency-energy dependence of the nonlinear modes that are excited.
For film cooling of combustor linings and turbine blades, it is critical to be able to accurately model jets-in-crossflow. Current Reynolds-averaged Navier-Stokes (RANS) models often give unsatisfactory predictions in these flows, due in large part to model form error, which cannot be resolved through calibration or tuning of model coefficients. The Boussinesq hypothesis, upon which most two-equation RANS models rely, posits the existence of a non-negative scalar eddy viscosity, which gives a linear relation between the Reynolds stresses and the mean strain rate. This model is rigorously analyzed in the context of a jet-in-crossflow using the high-fidelity large eddy simulation data of Ruiz et al. (2015, "Flow Topologies and Turbulence Scales in a Jet-in-Cross-Flow," Phys. Fluids, 27(4), p. 045101), as well as RANS k-ε results for the same flow. It is shown that the RANS models fail to accurately represent the Reynolds stress anisotropy in the injection hole, along the wall, and on the lee side of the jet. Machine learning methods are developed to provide improved predictions of the Reynolds stress anisotropy in this flow.
Time-lapse electrical resistivity tomography (ERT) is finding increased application for remotely monitoring processes occurring in the near subsurface in three-dimensions (i.e. 4D monitoring). However, there are few codes capable of simulating the evolution of subsurface resistivity and corresponding tomographic measurements arising from a particular process, particularly in parallel and with an open source license. Herein we describe and demonstrate an electrical resistivity tomography module for the PFLOTRAN subsurface flow and reactive transport simulation code, named PFLOTRAN-E4D. The PFLOTRAN-E4D module operates in parallel using a dedicated set of compute cores in a master-slave configuration. At each time step, the master processes receives subsurface states from PFLOTRAN, converts those states to bulk electrical conductivity, and instructs the slave processes to simulate a tomographic data set. The resulting multi-physics simulation capability enables accurate feasibility studies for ERT imaging, the identification of the ERT signatures that are unique to a given process, and facilitates the joint inversion of ERT data with hydrogeological data for subsurface characterization. PFLOTRAN-E4D is demonstrated herein using a field study of stage-driven groundwater/river water interaction ERT monitoring along the Columbia River, Washington, USA. Results demonstrate the complex nature of subsurface electrical conductivity changes, in both the saturated and unsaturated zones, arising from river stage fluctuations and associated river water intrusion into the aquifer. The results also demonstrate the sensitivity of surface based ERT measurements to those changes over time. PFLOTRAN-E4D is available with the PFLOTRAN development version with an open-source license at https://bitbucket.org/pflotran/pflotran-dev.
Concurrent sound associated with very bright meteors manifests as popping, hissing, and faint rustling sounds occurring simultaneously with the arrival of light from meteors. Numerous instances have been documented with â '11 to â '13 brightness. These sounds cannot be attributed to direct acoustic propagation from the upper atmosphere for which travel time would be several minutes. Concurrent sounds must be associated with some form of electromagnetic energy generated by the meteor, propagated to the vicinity of the observer, and transduced into acoustic waves. Previously, energy propagated from meteors was assumed to be RF emissions. This has not been well validated experimentally. Herein we describe experimental results and numerical models in support of photoacoustic coupling as the mechanism. Recent photometric measurements of fireballs reveal strong millisecond flares and significant brightness oscillations at frequencies ≥40 Hz. Strongly modulated light at these frequencies with sufficient intensity can create concurrent sounds through radiative heating of common dielectric materials like hair, clothing, and leaves. This heating produces small pressure oscillations in the air contacting the absorbers. Calculations show that â '12 brightness meteors can generate audible sound at ∼25 dB SPL. The photoacoustic hypothesis provides an alternative explanation for this longstanding mystery about generation of concurrent sounds by fireballs.
Time integration is a central component for most transient simulations. It coordinates many of the major parts of a simulation together, e.g., a residual calculation with a transient solver, solution with the output, various operator-split physics, and forward and adjoint solutions for inversion. Even though there is this variety in these transient simulations, there is still a common set of algorithms and procedures to progress transient solutions for ordinary-differential equations (ODEs) and differential-alegbraic equations (DAEs). Rythmos is a collection of these algorithms that can be used for the solution of transient simulations. It provides common time-integration methods, such as Backward and Forward Euler, Explicit and Implicit Runge-Kutta, and Backward-Difference Formulas. It can also provide sensitivities, and adjoint components for transient simulations. Rythmos is a package within Trilinos, and requires some other packages (e.g., Teuchos and Thrya) to provide basic time-integration capabilities. It also can be coupled with several other Trilinos packages to provide additional capabilities (e.g., AztecOO and Belos for linear solutions, and NOX for non-linear solutions). The documentation is broken down into three parts: Theory Manual, User's Manual, and Developer's Guide. The Theory Manual contains the basic theory of the time integrators, the nomenclature and mathematical structure utilized within Rythmos, and verification results demonstrating that the designed order of accuracy is achieved. The User's Manual provides information on how to use the Rythmos, description of input parameters through Teuchos Parameter Lists, and description of convergence test examples. The Developer's Guide is a high-level discussion of the design and structure of Rythmos to provide information to developers for the continued development of capabilities. Details of individual components can be found in the Doxygen webpages.
The purpose of this project was to develop sampling-based algorithms to discover hidden struc- ture in massive data sets. Inferring structure in large data sets is an increasingly common task in many critical national security applications. These data sets come from myriad sources, such as network traffic, sensor data, and data generated by large-scale simulations. They are often so large that traditional data mining techniques are time consuming or even infeasible. To address this problem, we focus on a class of algorithms that do not compute an exact answer, but instead use sampling to compute an approximate answer using fewer resources. The particular class of algorithms that we focus on are streaming algorithms , so called because they are designed to handle high-throughput streams of data. Streaming algorithms have only a small amount of working storage - much less than the size of the full data stream - so they must necessarily use sampling to approximate the correct answer. We present two results: * A streaming algorithm called HyperHeadTail , that estimates the degree distribution of a graph (i.e., the distribution of the number of connections for each node in a network). The degree distribution is a fundamental graph property, but prior work on estimating the degree distribution in a streaming setting was impractical for many real-world application. We improve upon prior work by developing an algorithm that can handle streams with repeated edges, and graph structures that evolve over time. * An algorithm for the task of maintaining a weighted subsample of items in a stream, when the items must be sampled according to their weight, and the weights are dynamically changing. To our knowledge, this is the first such algorithm designed for dynamically evolving weights. We expect it may be useful as a building block for other streaming algorithms on dynamic data sets.
Grid resilience is a concept related to a power system's ability to continue operating and delivering power even in the event that low probability, high-consequence disruptions such as hurricanes, earthquakes, and cyber-attacks occur. Grid resilience objectives focus on managing and, ideally, minimizing potential consequences that occur as a result of these disruptions. Currently, no formal grid resilience definitions, metrics, or analysis methods have been universally accepted. This document describes an effort to develop and describe grid resilience metrics and analysis methods. The metrics and methods described herein extend upon the Resilience Analysis Process (RAP) developed by Watson et al. for the 2015 Quadrennial Energy Review. The extension allows for both outputs from system models and for historical data to serve as the basis for creating grid resilience metrics and informing grid resilience planning and response decision-making. This document describes the grid resilience metrics and analysis methods. Demonstration of the metrics and methods is shown through a set of illustrative use cases.
Reducing the resource consumption and emissions of large institutions is an important step toward a sustainable future. Sandia National Laboratories' (SNL) Institutional Transformation (IX) project vision is to provide tools that enable planners to make well-informed decisions concerning sustainability, resource conservation, and emissions reduction across multiple sectors. The building sector has been the primary focus so far because it is the largest consumer of resources for SNL. The IX building module allows users to define the evolution of many buildings over time. The module has been created so that it can be generally applied to any set of DOE-2 ( http://doe2.com ) building models that have been altered to include parameters and expressions required by energy conservation measures (ECM). Once building models have been appropriately prepared, they are checked into a Microsoft Access (r) database. Each building can be represented by many models. This enables the capability to keep a continuous record of models in the past, which are replaced with different models as changes occur to the building. In addition to this, the building module has the capability to apply climate scenarios through applying different weather files to each simulation year. Once the database has been configured, a user interface in Microsoft Excel (r) is used to create scenarios with one or more ECMs. The capability to include central utility buildings (CUBs) that service more than one building with chilled water has been developed. A utility has been created that joins multiple building models into a single model. After using the utility, several manual steps are required to complete the process. Once this CUB model has been created, the individual contributions of each building are still tracked through meters. Currently, 120 building models from SNL's New Mexico and California campuses have been created. This includes all buildings at SNL greater than 10,000 sq. ft., representing 80% of the energy consumption at SNL. SNL has been able to leverage this model to estimate energy savings potential of many competing ECMs. The results helped high level decision makers to create energy reduction goals for SNL. These resources also have multiple applications for use of the models as individual buildings. In addition to the building module, a solar module built in Powersim Studio (r) allows planners to evaluate the potential photovoltaic (PV) energy generation potential for flat plate PV, concentrating solar PV, and concentration solar thermal technologies at multiple sites across SNL's New Mexico campus. Development of the IX modeling framework was a unique collaborative effort among planners and engineers in SNL's facilities division; scientists and computer modelers in SNL's research and development division; faculty from Arizona State University; and energy modelers from Bridger and Paxton Consulting Engineers Incorporated.
In recent years, the concept of Zero Knowledge Protocols (ZKP) as a useful approach to nuclear warhead verification has become increasingly popular. Several implementations of ZKP have been proposed, driving technology development toward proof of concept demonstrations. Whereas proposed implementations seem to fall within the general class of template-based techniques, all physical implementations of ZKPs proposed to date have a complication: once the instrumentation is prepared, it is no longer authenticatable; the instrument physically contains sensitive information. In this work we explore three different concepts that may offer more authenticatable and practical ZKP implementations and evaluate the sensitive information that may be at risk when doing so: sharing a subset of detector counts in a preloaded image (with spatial information removed), real-time image subtraction, and a new concept, CONfirmation using a Fast-neutron Imaging Detector with Anti-image NULL-positive Time Encoding (CONFIDANTE). CONFIDANTE promises to offer an almost ideal implementation of ZKP: a positive result is indicated by a constant rate at all times enabling the monitoring party the possibility of full access to the instrument before, during, and after confirmation. A prototype of CONFIDANTE was designed, built, and its performance evaluated in a series of measurements of several objects including a set of plutonium dioxide Hemispheres. Very encouraging results proving feasibility are presented. 1 Rebecca is currently a graduate student in Nuclear Engineering at UC Berkeley
During the initial phase of this SubTER project, we conducted a series of high resolution seismic imaging campaigns designed to characterize induced fractures. Fractures were emplaced using a novel explosive source, designed at Sandia National Laboratories, that limits damage to the borehole. This work provided evidence that fracture locations could be imaged at inch scales using high-frequency seismic tomography but left many fracture properties (i.e. permeability) unresolved. We present here the results of the second phase of the project, where we developed and demonstrated emerging seismic and electrical geophysical imaging technologies that characterize 1) the 3D extent and distribution of fractures stimulated from the explosive source, 2) 3D fluid transport within the stimulated fracture network through use of a contrasting tracer, and 3) fracture attributes through advanced data analysis. Focus was placed upon advancing these technologies toward near real-time acquisition and processing in order to help provide the feedback mechanism necessary to understand and control fracture stimulation and fluid flow. Results from this study include a comprehensive set of 4D cross-hole seismic and electrical data that take advantage of change detection methodologies allowing for perturbations associated with the fracture emplacement and particulate tracer to be isolated. During the testing the team also demonstrated near real-time 4D electrical resistivity tomography imaging and 4D seismic tomography using the CASSM approach with a temporal resolution approaching 1 minute. All of the data collected were used to develop methods of estimating fracture attributes from seismic data, develop methods of assimilating disparate and transient data sets to improve fracture network imaging resolution, and advance capabilities for near real-time inversion of cross-hole tomographic data. These results are illustrated here. Advancements in these areas are relevant to all situations where fracture emplacement is used for reservoir stimulation (e.g. Enhanced Geothermal Systems (EGS) and tight shale gases).
Approximate Nearest Neighbor (ANN) algorithms are increasingly important in machine learning, data mining, and image processing applications. There is a large family of space- partitioning ANN algorithms, such as randomized KD-Trees, that work well in practice but are limited by an exponential increase in similarity comparisons required to optimize recall. Additionally, they only support a small set of similarity metrics. We present Local Area Fo- cused Search (LAFS), a method that enhances the way queries are performed using an existing ANN index. Instead of a single query, LAFS performs a number of smaller (fewer similarity comparisons) queries and focuses on a local neighborhood which is refined as candidates are identified. We show that our technique improves performance on several well known datasets and is easily extended to general similarity metrics using kernel projection techniques.