Publications

Surface acoustic wave devices have been fabricated on a GaAs 100 substrate to demonstrate the capability of 2D Raman microscopy as an imaging technique for acoustic waves on the surface of a piezoelectric substrate. Surface acoustic waves are generated using a two-port interdigitated transducer platform, which is modified to produce surface standing waves. We have derived an analytical model to relate Raman peak broadening to the near-surface strain field of the GaAs surface produced by the surface acoustic waves. Atomic force microscopy is used to confirm the presence of a standing acoustic wave, resolving a total vertical displacement of 3 nm at the antinode of the standing wave. Stress calculations are performed for both imaging techniques and are in good agreement, demonstrating the potential of this Raman analysis.

Abstract not provided.

Metallic plasmonic hybrid nanostructures have attracted enormous research interest due to the combined physical properties coming from different material components and the broad range of applications in nanophotonic and electronic devices. However, the high loss and narrow range of property tunability of the metallic hybrid materials have limited their practical applications. In this study, a metallic alloy-based self-assembled plasmonic hybrid nanostructure, i.e., a BaTiO₃–Au_xAg_1–x (BTO) vertically aligned nanocomposite, has been integrated by a templated growth method for low-loss plasmonic systems. Comprehensive microstructural characterizations including high-resolution scanning transmission electron microscopy (HRSTEM), energy-dispersive X-ray spectroscopy (EDS), and three-dimensional (3D) electron tomography demonstrate the formation of an ordered “nano-domino-like” morphology with Au_0.4Ag_0.6 nanopillars as cylindrical cores and BTO as square shells. By comparing with the BTO–Au hybrid thin film, the BTO–Au_0.4Ag_0.6 alloyed film exhibits much broader plasmon resonance, hyperbolic dispersion, low-loss, and thermally robust features in the UV–vis–NIR wavelength region. This study provides a feasible platform for a complex alloyed plasmonic hybrid material design with low-loss and highly tunable optical properties toward all-optical integrated devices.

Finite element solutions are only valid if the information inputted into the models is a valid assumption. This is why it is important to determine if the model behaves as expected especially in the material response. Material models that consider deformation in the elastic and plastic regions are desirable. As a material deforms plastically, hardening will occur until the ultimate strength is reached due to the reduction of the cross sectional area in a uniaxial tensile test environment. A phenomenological approximation of this behavior which is known as power law hardening (plh) can be expressed as σ = σ_y + K $ϵ^{n}_{p}$ where K and n are empirically determined constants, and σ_y is the proportional limit of the material. The first objective of this study is to verify the behavior for a calibrated plh material model for selective deviatoric hexahedral elements when applied to a different material stock. The Sierra SM input for this calibrated model can be found in the appendix. The second objective of this study is to determine the differences in failure characteristics for considerably large shell elements versus that of fine hexahedral elements.

High density interconnects are required for increased input/output for microelectronics applications, incentivizing the development of Cu electrochemical deposition (ECD) processes for high aspect ratio through-silicon vias (TSVs). This work outlines Cu ECD processes for 62.5 μm diameter TSVs, etched into a 625 μm thick silicon substrate, a 10:1 aspect ratio. Cu ECD in high aspect ratio features relies on a delicate balance of electrolyte composition, solution replenishment, and applied voltage. Implementing a CuSO4-H2SO4 electrolyte, which contains suppressor and a low chloride concentration, allows for a tunable relationship between applied voltage and localized deposition in the vias. A stepped potential waveform was applied to move the Cu growth front from the bottom of the via to the top. Sample characterization was performed through mechanical cross-sections and X-ray computed tomography (CT) scans. The CT scans revealed small seam voids in the Cu electrodeposit, and process parameters were tuned accordingly to produce void-free Cu features. During the voltage-controlled experiments, measured current data showed a characteristic current minimum, which was identified as an endpoint detection method for Cu deposition in these vias. We believe this is the first report of this novel endpoint detection method for TSV filling.

Diborane (B2H6) is a promising molecular precursor for atomic precision p-type doping of silicon that has recently been experimentally demonstrated [ Škereň et al. Nat. Electron. 2020 ]. We use density functional theory (DFT) calculations to determine the reaction pathway for diborane dissociating into a species that will incorporate as electrically active substitutional boron after adsorbing onto the Si(100)-2×1 surface. Our calculations indicate that diborane must overcome an energy barrier to adsorb, explaining the experimentally observed low sticking coefficient (<1 × 10-4 at room temperature) and suggesting that heating can be used to increase the adsorption rate. Upon sticking, diborane has an ≈50% chance of splitting into two BH3 fragments versus merely losing hydrogen to form a dimer such as B2H4. As boron dimers are likely electrically inactive, whether this latter reaction occurs is shown to be predictive of the incorporation rate. The dissociation process proceeds with significant energy barriers, necessitating the use of high temperatures for incorporation. Using the barriers calculated from DFT, we parameterize a Kinetic Monte Carlo model that predicts the incorporation statistics of boron as a function of the initial depassivation geometry, dose, and anneal temperature. Our results suggest that the dimer nature of diborane inherently limits its doping density as an acceptor precursor and furthermore that heating the boron dimers to split before exposure to silicon can lead to poor selectivity on hydrogen and halogen resists. This suggests that, while diborane works as an atomic precision acceptor precursor, other non-dimerized acceptor precursors may lead to higher incorporation rates at lower temperatures.

Optimizing the performance of the Magnetized Liner Inertial Fusion (MagLIF) platform on the Z pulsed power facility requires coupling greater than 2 kJ of preheat energy to an underdense fuel in the presence of an applied axial magnetic field ranging from 10 to 30 T. Achieving the suggested optimal preheat energies has not been experimentally achieved so far. In this work, we explore the preheat design space for cryogenically cooled MagLIF targets, which represent a viable candidate for increasing preheat energies. Using 2D and 3D HYDRA MHD simulations, we first discuss the various physical effects that occur during laser preheat, such as laser energy deposition, self-focusing, and filamentation. After identifying the changes that different phase plates, gas-fill densities, and magnetic fields bring to the aforementioned physical effects, we, then, consider higher laser energies that are achievable with modest upgrades to the Z Beamlet laser. Lastly, with a 6.0-kJ upgraded laser, 3D calculations suggest that it is possible to deliver 4.25 kJ into the MagLIF fuel, resulting in an expected deuterium neutron yield of Y_DD ≃ 1.5 × 10¹⁴, or roughly 50 kJ of DT equivalent yield, at 20-MA current drive. This represents a 10-fold increase in the currently achieved yields for MagLIF.

In-situ transmission electron microscopy (TEM) provides an avenue to explore time-dependent nanoscale material changes induced by a wide range of environmental conditions that govern material performance and degradation. The In-situ Ion Irradiation TEM (I³TEM) at Sandia National Laboratories is a JEOL 2100 microscope that has been highly modified with an array of hardware and software that makes it particularly well suited to explore fundamental mechanisms that arise from coupled extreme conditions. Here, examples pertaining to multibeam ion irradiation, rapid thermal cycling, and nanomechanical testing on the I³TEM are highlighted, along with prospective advancements in the field of in-situ microscopy.

Physical vapor deposition of organic explosives enables growth of polycrystalline films with a unique microstructure and morphology compared to the bulk material. This study demonstrates the ability to control crystal orientation and porosity in pentaerythritol tetranitrate films by varying the interfacial energy between the substrate and the vapor-deposited explosive. Variation in density, porosity, surface roughness, and optical properties is achieved in the explosive film, with significant implications for initiation sensitivity and detonation performance of the explosive material. Various surface science techniques, including angle-resolved X-ray photoelectron spectroscopy and multiliquid contact angle analysis, are utilized to characterize interfacial characteristics between the substrate and explosive film. Optical microscopy and scanning electron microscopy of pentaerythritol tetranitrate surfaces and fracture cross sections illustrate the difference in morphology evolution and the microstructure achieved through surface energy modification. X-ray diffraction studies with the Tilt-A-Whirl three-dimensional pole figure rendering and texture analysis software suite reveal that high surface energy substrates result in a preferred (110) out-of-plane orientation of pentaerythritol tetranitrate crystallites and denser films. Low surface energy substrates create more randomly textured pentaerythritol tetranitrate and lead to nanoscale porosity and lower density films. This work furthers the scientific basis for interfacial engineering of polycrystalline organic explosive films through control of surface energy, enabling future study of dynamic and reactive detonative phenomena at the microscale. Results of this study also have potential applications to active pharmaceutical ingredients, stimuli-responsive polymer films, organic thin film transistors, and other areas.

Mie-resonant dielectric metasurfaces are excellent candidates for both fundamental studies related to light-matter interactions and for numerous applications ranging from holography to sensing to nonlinear optics. To date, however, most applications using Mie metasurfaces utilize only weak light-matter interaction. Here, we go beyond the weak coupling regime and demonstrate for the first time strong polaritonic coupling between Mie photonic modes and intersubband (ISB) transitions in semiconductor heterostructures. Furthermore, along with demonstrating ISB polaritons with Rabi splitting as large as 10%, we also demonstrate the ability to tailor the strength of strong coupling by engineering either the semiconductor heterostructure or the photonic mode of the resonators. Unlike previous plasmonic-based works, our new all-dielectric metasurface approach to generate ISB polaritons is free from ohmic losses and has high optical damage thresholds, thereby making it ideal for creating novel and compact mid-infrared light sources based on nonlinear optics.

Programmable accelerators have become commonplace in modern computing systems. Advances in programming models and the availability of unprecedented amounts of data have created a space for massively parallel accelerators capable of maintaining context for thousands of concurrent threads resident on-chip. These threads are grouped and interleaved on a cycle-by-cycle basis among several massively parallel computing cores. One path for the design of future supercomputers relies on an ability to model the performance of these massively parallel cores at scale. The SST framework has been proven to scale up to run simulations containing tens of thousands of nodes. A previous report described the initial integration of the open-source, execution-driven GPU simulator, GPGPU-Sim, into the SST framework. This report discusses the results of the integration and how to use the new GPU component in SST. It also provides examples of what it can be used to analyze and a correlation study showing how closely the execution matches that of a Nvidia V100 GPU when running kernels and mini-apps.

We analyze the well-posedness of an anisotropic, nonlocal diffusion equation. Establishing an equivalence between weighted and unweighted anisotropic nonlocal diffusion operators in the vein of unified nonlocal vector calculus, we apply our analysis to a class of fractional-order operators and present rigorous estimates for the solution of the corresponding anisotropic anomalous diffusion equation. Furthermore, we extend our analysis to the anisotropic diffusion-advection equation and prove well-posedness for fractional orders s ∊ [0.5, 1). We also present an application of the advection-diffusion equation to anomalous transport of solutes.

The NetFlow Dynamics (NFD) model was developed for estimating the availability of a commodity supplied by a national- or regional-scale infrastructure following unexpected disruption of one or more of its components. The large scope of the disruptions of interest produce changes in availability lasting days to weeks. Consequently, the model does not resolve daily variations in system state and does not include the long-term processes that cause infrastructures to evolve as assets are added and removed according to owners ’planning decisions. NFD simulates fluid flow, including petroleum and other incompressible fluids, as well as natural gas and other compressible fluids, through pipeline networks characterized by limits on transmission capacity and storage. It was designed to enable efficient exploration of possible transmission system responses to large-scale disruptions lasting for days or longer. The model formulation reflects constraints on transmission and storage capacity imposed by the physical system assets. Those capacity limits are input parameters and are not derived from more basic system properties such as pipeline diameters and compressor power. A system’s response to a large disruption is controlled by operational decisions as well as damage to physical assets. The NFD model formulation allows users to efficiently consider alternative scenarios about the way remaining capacity might be used so that the analysis result appropriately reflects uncertainties about operator response.

In this work, we present a new Li source for focused ion beam applications. Based on an AuSi eutectic alloy, Li is added as an impurity to minimize effects from degradation when exposed to air. We show the source is stable over the course of an hour and spot sizes ≲10 nm can be achieved. The Li beam can achieve hundreds of nanometer ranges in semiconductors with minimal damage being generated along the path length. The source performance is evaluated through a high-resolution ion beam induced charge collection experiment on an Si-based detector. Further application of the source for ion beam analysis is numerically explored; the example investigated is based on probing a semiconductor heterostructure through a Rutherford backscattering experiment, where the Li beam can reveal information that is inaccessible with either low energy or high energy He projectiles used as probes.

Ferrites have potential for use as active materials in solar-thermochemical cycles because of their versatile redox chemistry. Such cycles utilize solar-thermal energy for the production of hydrogen from water and carbon monoxide from carbon dioxide. Although ferrites offer the potential for deep levels of reduction (e.g., stoichiometric conversion of magnetite to wüstite) and correspondingly large per-cycle product yields, in practice reactions are limited to surface regions made smaller by rapid sintering and agglomeration. Combining ferrites with zirconia or yttria-stabilized zirconia (YSZ) greatly improves the cyclability of the ferrites and enables a move away from powder to monolithic systems. We have studied the behavior of iron oxides composited with YSZ using thermogravimetric analysis under operando conditions. Samples in which the iron was fully dissolved within the YSZ matrix showed greater overall extent of thermochemical redox and higher rate of reaction than samples with equal iron loading but in which the iron was only partially dissolved, with the rest existing as agglomerates of iron oxide within the ceramic matrix. Varying the yttria content of the YSZ revealed a maximum thermochemical capacity (yield per cycle) for 6 mol% Y2O3 in YSZ. The first thermochemical redox cycle performed for each sample resulted in a net mass loss that was proportional to the iron oxide loading in the material and was stoichiometrically consistent with complete reduction of Fe2O3 to Fe3O4 and further partial reduction of the Fe3O4 to FeO. Mass gains upon reaction with CO2 were consistent with re-oxidation of the FeO fraction back to Fe3O4. The Fe dissolved in the YSZ matrix, however, is capable of cycling stoichiometrically between Fe3+ and Fe2+. Varying the re-oxidation temperature between 1000 and 1200 °C highlighted the trade-off between re-oxidation rate and equilibrium limitations. This journal is

The rudiments of a particle-based single-fluid two-temperature magnetohydrodynamic (MHD) algorithm have been outlined in Thoma et al. (2013). The extension of this algorithm to include the effect of Hall physics is described in this paper. An implicit leapfrog version of the algorithm, which allows timesteps large compared to the resistive decay time and other relevant timescales, has recently been added to a hybrid particle-in-cell code. In standard MHD the Hall term in the generalized Ohm’s law can often be neglected when the Hall parameter is small. This term must, however, be retained in regimes where it is non-negligible. The retention of displacement current in Maxwell’s equations avoids the numerical difficulties associated with the whistler mode, which are encountered in standard explicit Hall-MHD codes, and allows the algorithm to be incorporated into hybrid particle-in-cell codes, for which particles may migrate from a kinetic to fluid to MHD description based upon local ambient plasma conditions. A highly-coupled implicit Hall-MHD formalism is presented, in which displacement current can either be retained or neglected. Even when displacement current is neglected, the highly-coupled implicit formalism avoids the restrictive timesteps for the whistler mode in explicit Hall-MHD codes. A comparison of numerical and analytic dispersion analysis demonstrates the feasibility of this approach and establishes relevant constraints to assure numerical stability. The implementation of the algorithm is described, and test simulation results in 1D and 2D in both linear and nonlinear regimes are presented.

The phase-field method is a powerful and versatile computational approach for modeling the evolution of microstructures and associated properties for a wide variety of physical, chemical, and biological systems. However, existing high-fidelity phase-field models are inherently computationally expensive, requiring high-performance computing resources and sophisticated numerical integration schemes to achieve a useful degree of accuracy. In this paper, we present a computationally inexpensive, accurate, data-driven surrogate model that directly learns the microstructural evolution of targeted systems by combining phase-field and history-dependent machine-learning techniques. We integrate a statistically representative, low-dimensional description of the microstructure, obtained directly from phase-field simulations, with either a time-series multivariate adaptive regression splines autoregressive algorithm or a long short-term memory neural network. The neural-network-trained surrogate model shows the best performance and accurately predicts the nonlinear microstructure evolution of a two-phase mixture during spinodal decomposition in seconds, without the need for “on-the-fly” solutions of the phase-field equations of motion. We also show that the predictions from our machine-learned surrogate model can be fed directly as an input into a classical high-fidelity phase-field model in order to accelerate the high-fidelity phase-field simulations by leaping in time. Such machine-learned phase-field framework opens a promising path forward to use accelerated phase-field simulations for discovering, understanding, and predicting processing–microstructure–performance relationships.

Abstract not provided.

Six degree of freedom shaker tests are becoming more popular as they save testing time because they test a component in multiple directions in one test rather than executing multiple tests in one direction at a time. However, there are several difficulties in conducting a component six degree of freedom shaker test in a way that adequately replicates the component field stress. One difficulty is knowing if a classical rigid test fixture will produce component modes that span the displacement space of the component in the field environment. If the modes of the component while attached to a rigid fixture do not span the space of the component in the field environment, then the test will be unable to replicate that motion and corresponding stresses. This paper will examine the motion of the Removable Component of the BARC hardware in an field assembly and calculate the modal projection error expected by executing a six degree of freedom shaker test on a rigid fixture. The paper will conclude by examining the data and comparing it to the pre-test predictions of error calculated by the modal projection error.

Gradient free regime identification (GFRI) is applied to 1D Raman/Rayleigh/LIF measurements of temperature and major species from the intermediate velocity case of the Sydney piloted inhomogeneous jet flame series to better understand the structure of reaction zones and the downstream evolution of multi-regime characteristics. The GFRI approach allows local reaction zones to be detected and characterized as premixed, dominantly premixed, multi-regime, dominantly non-premixed, or non-premixed flame structures, based on flame markers (mixture fraction, chemical mode, and heat release rate) derived from the experimental data. The statistics of chemical mode zero-crossings, which mark premixed reaction zones, and the relative populations of flame structures are shown to be sensitive to the state of mixing in the near field of the flame and to the level of local extinction farther downstream. Multi-regime structures, where premixed and non-premixed reaction zones occur in close proximity and both contribute to overall heat release, account for nearly half the total population at streamwise locations within the first several jet diameters. There is a rapid transition within the near field whereby the relative population of non-premixed and dominantly non-premixed structures grows from 0.05 to nearly 0.5, and the population of premixed and dominantly premixed structures decreases correspondingly as fluid entering the reaction zone becomes progressively fuel-rich. Local extinction and re-ignition bring a resurgence in premixed-type structures, many of which occur at fuel-lean conditions. There are also modest populations of multi-regime structures, having chemical mode zero-crossings at lean conditions, which would not exist in a fully burning jet flame.

This paper develops a novel limited-memory method to solve dynamic optimization problems. The memory requirements for such problems often present a major obstacle, particularly for problems with PDE constraints such as optimal flow control, full waveform inversion, and optical tomography. In these problems, PDE constraints uniquely determine the state of a physical system for a given control; the goal is to find the value of the control that minimizes an objective. While the control is often low dimensional, the state is typically more expensive to store. This paper suggests using randomized matrix approximation to compress the state as it is generated and shows how to use the compressed state to reliably solve the original dynamic optimization problem. Concretely, the compressed state is used to compute approximate gradients and to apply the Hessian to vectors. The approximation error in these quantities is controlled by the target rank of the sketch. This approximate first- and second-order information can readily be used in any optimization algorithm. As an example, we develop a sketched trust-region method that adaptively chooses the target rank using a posteriori error information and provably converges to a stationary point of the original problem. Numerical experiments with the sketched trust-region method show promising performance on challenging problems such as the optimal control of an advection-reaction-diffusion equation and the optimal control of fluid flow past a cylinder.

Abstract not provided.

This work compared the effects of modeling grain structure in hypervelocity impact simulations. Comparisons of strain rate at failure (fragment size) and material temperature were made between a suite of simulations performed with the standard bulk modeling structure and one in which individual grains were modeled. Smaller fragments or higher temperatures are needed to match EO/IR signatures from observed impacts. Results from the various studies described herein indicate that strain rate at failure is influenced primarily by projectile size, impact velocity, and material porosity. Material temperature is predominantly influenced by impact velocity and porosity; not by projectile size. Changes to the material properties within grains tended to affect lower strain rates only, but material interfaces (here, manifested as material porosity) drastically increased strain rate at failure and material temperatures. Higher strain rates are likely to produce smaller debris fragments, which, along with hot debris may help provide evidence supporting the generation of sub-micron fragments currently required by many EO/IR predictive models to successfully compare with observed hypervelocity impacts. Future work will focus on extending the study to three dimensions, assessing more realistic grain aspect ratios, and simulating other types of interfaces such as inclusions and dislocations.

Reproducing kernel (RK) approximations are meshfree methods that construct shape functions from sets of scattered data. We present an asymptotically compatible (AC) RK collocation method for nonlocal diffusion models with Dirichlet boundary condition. The numerical scheme is shown to be convergent to both nonlocal diffusion and its corresponding local limit as nonlocal interaction vanishes. The analysis is carried out on a special family of rectilinear Cartesian grids for a linear RK method with designed kernel support. The key idea for the stability of the RK collocation scheme is to compare the collocation scheme with the standard Galerkin scheme, which is stable. In addition, assembling the stiffness matrix of the nonlocal problem requires costly computational resources because high-order Gaussian quadrature is necessary to evaluate the integral. We thus provide a remedy to the problem by introducing a quasi-discrete nonlocal diffusion operator for which no numerical quadrature is further needed after applying the RK collocation scheme. The quasi-discrete nonlocal diffusion operator combined with RK collocation is shown to be convergent to the correct local diffusion problem by taking the limits of nonlocal interaction and spatial resolution simultaneously. The theoretical results are then validated with numerical experiments. We additionally illustrate a connection between the proposed technique and an existing optimization based approach based on generalized moving least squares.

In this work we evaluated the effects that equations of state and strength models have on SCJ development using the Sandia National Laboratories multiphysics shock code, ALEGRA. Results were quantified using a Lagrangian tracer particle following liner collapse, passing through the compression zone, and flowing into the jet tip. We found consistent results among several EOS: 3320, 3331, and 3337. The 3325 EOS generated a measurable low density and hollow region near the jet tip which appears to be reflected in a lower internal energy of the jet. At this time, we cannot tell, experimentally, if such a hollow region exists. The 3337 EOS is recent, well documented [6], and produces results similar to 3320 [3]. The various strength models produced more noticeable differences. In terms of internal energy and temperature, SGL had the largest values followed by PTW, ZA, and finally JC and MTS, which were quite similar to each other. We looked at melt conditions in the SGL and JC models using the 3337 EOS. The SGL model reported a liquid region along the jet axis all the way to the tip-seemingly consistent with experiment-while the JC model does not indicate any phase transition. None of the other yield models indicated melt along the jet axis. For all EOS and strength models, we found similar results for the velocity history of the jet tip as measured against experiment using photon Dopper velocimetry.

The main objective of this letter is to consolidate the knowledge about the dynamics and control of oscillating-body wave energy converters (WECs). A number of studies have shown that control systems strongly affect power absorption; however, there remains a need for a concise and integrated explanation of the theoretical and practical implications that control can have on both performance and the broader WEC design process. This short letter attempts to fill this gap by presenting a discussion on the key practical aspects concerning the dynamics and control of oscillating-body WEC. In particular, the focus is on the choice of control models and a simple causal control scheme suitable for real-time implementation. Finally, consideration is given to the effect of the power takeoff (PTO) on the maximization of electrical power, thus leading to the derivation of useful conditions for the control co-design of the PTO system.

Simple electro-mechanical models of electrodynamic shakers are useful for predicting shaker electrical requirements in vibration testing. A lumped parameter, multiple degree-of-freedom model can sufficiently capture most of the shaker electrical and mechanical features of interest. While several model parameters can be measured directly or obtained from a specifications sheet, others must be inferred from an electrical impedance measurement. Here, shaker model parameters are determined from electrical impedance measurements of a shaker driving a mass. Then, parameter sensitivity is explored to determine a model calibration procedure where model parameters are determined using manual and automated selection methods. The model predictions are then compared to test measurements. The model calibration procedure described in this work provides a simple, practical approach to developing predictive shaker electromechanical models which can then be used in test design and assessment simulations.

In this study, we explore the interaction of electron wind force (EWF) with defects originating from ion irradiation in-situ inside a transmission electron microscope. Nanocrystalline gold specimens were self-ion irradiated to a dose of 5 × 1015 ions/cm2 (45 displacement per atom) to generate a high density of displacement damage. We also developed a molecular dynamics simulation model to understand the associated atomic scale mechanisms. Both experiments and simulations show that the EWF can impart significant defect mobility even at low temperatures, resulting in the migration and elimination of defects in a few minutes. We propose that the EWF interacts with defects to create highly glissile Shockley partial dislocations, which makes the fast and low temperature defect annihilation possible.

Polynomial preconditioning can improve the convergence of the Arnoldi method for computing eigenvalues. Such preconditioning significantly reduces the cost of orthogonalization; for difficult problems, it can also reduce the number of matrix-vector products. Parallel computations can particularly benefit from the reduction of communication-intensive operations. The GMRES algorithm provides a simple and effective way of generating the preconditioning polynomial. For some problems high degree polynomials are especially effective, but they can lead to stability problems that must be mitigated. A two-level "double polynomial preconditioning"strategy provides an effective way to generate high-degree preconditioners.

We have completed a series of vented and sealed cookoff experiments of the ε-polymorph of CL-20 in our Sandia Instrumented Thermal Ignition (SITI) apparatus using both powder and pressed pellets at nominal densities of 313 ± 8 kg/m3 and 1030 ± 4 kg/m3, respectively. The boundary temperature of our aluminum confinement cylinder was ramped in 10 minutes from room temperature to a prescribed set-point temperature ranging between 448 nd 468 K and held at the set-point temperature until ignition. A universal cookoff model (UCM) has been calibrated using the ε-CL-20 SITI data to predict pressurization and thermal ignition of ε-CL-20. The ignition model was validated by using one-dimensional time-to-explosion (ODTX) ignition data from a different laboratory. We found that a thirtyfold increase in the reaction rates due to liquefaction at 520 K could explain the high temperature ODTX cookoff data. The model gives a plausible explanation of why melting is important in fast cookoff events involving CL-20. Our model also gives support to 520 K as the liquefaction point of CL-20, which has different values in the literature.

We propose a learning algorithm for discovering unknown parameterized dynamical systems by using observational data of the state variables. Our method is built upon and extends the recent work of discovering unknown dynamical systems, in particular those using a deep neural network (DNN). We propose a DNN structure, largely based upon the residual network (ResNet), to not only learn the unknown form of the governing equation but also to take into account the random effect embedded in the system, which is generated by the random parameters. Once the DNN model is successfully constructed, it is able to produce system prediction over a longer term and for arbitrary parameter values. For uncertainty quantification, it allows us to conduct uncertainty analysis by evaluating solution statistics over the parameter space.

There is a wide variety of applications that subject systems to mechanical shock and vibration environments. How to best characterize those environments and generate the necessary system and component test specifications varies according to the nature of the underlying environment. The purpose of this paper is to provide the reader with an overview of some commonly used analysis techniques for a range of field environments including transportation and handling, aircraft carriage, and missile flight. The paper will also address statistical methods for defining the Maximum Predicted Environment and test control methods as they pertain to achieving the best possible system and component laboratory simulations.

Design of multi-shaker tests relies on locating shakers on the structure such that the desired vibration response is obtained within the shaker force, acceleration, voltage, and current requirements. While shaker electro-mechanical models can be used to relate the shaker force and acceleration to voltage and current requirements, they need to be integrated with a structural dynamics model of the device under test. This connection of a shaker to a structure is a substructuring problem, with the structure representing one component and the shaker representing a second component. Here, frequency based substructuring is used to connect a shaker electro-mechanical model to a model of device under test. This provides a straightforward methodology for predicting shaker requirements given a target vibration response in a multi-shaker test. Predictions of the coupled shaker-structure model yield the shaker force, acceleration, voltage and current requirements which can be compared with the shaker capabilities to choose optimal shaker locations.

Researchers have shown that the dynamic field environment for a component may not be represented well by a component level single Degree-of-Freedom shaker environmental test. Here we demonstrate for a base mounted component, a controlled six Degree-of-Freedom component level shaker test. The field response power spectral densities are well simulated by the component response on the six Degree-of-Freedom shaker. The component is the Removable Component from the boundary condition challenge problem. The field environment was established with the component mounted in the AWE Modal Analysis Test Vehicle during an acoustic test. Interesting mileposts during the process of achieving the controlled component response are discussed.

Microstructure reconstruction is a long-standing problem in experimental and computational materials science, for which numerous attempts have been made to solve. However, the majority of approaches often treats microstructure as discrete phases, which, in turn, reduces the quality of the resulting microstructures and limits its usage to the computational level of fidelity, but not the experimental level of fidelity. In this work, we applied our previously proposed approach [41] to generate synthetic microstructure images at the experimental level of fidelity for the UltraHigh Carbon Steel DataBase (UHCSDB) [13].

Determining process–structure–property linkages is one of the key objectives in material science, and uncertainty quantification plays a critical role in understanding both process–structure and structure–property linkages. In this work, we seek to learn a distribution of microstructure parameters that are consistent in the sense that the forward propagation of this distribution through a crystal plasticity finite element model matches a target distribution on materials properties. This stochastic inversion formulation infers a distribution of acceptable/consistent microstructures, as opposed to a deterministic solution, which expands the range of feasible designs in a probabilistic manner. To solve this stochastic inverse problem, we employ a recently developed uncertainty quantification framework based on push-forward probability measures, which combines techniques from measure theory and Bayes’ rule to define a unique and numerically stable solution. This approach requires making an initial prediction using an initial guess for the distribution on model inputs and solving a stochastic forward problem. To reduce the computational burden in solving both stochastic forward and stochastic inverse problems, we combine this approach with a machine learning Bayesian regression model based on Gaussian processes and demonstrate the proposed methodology on two representative case studies in structure–property linkages.

The ability of particulate matter index (PMI) to describe the sooting behavior of various gasoline formulations in a stratified-charge (SC) spark-ignition engine was studied. The engine was operated at 2000 rpm with an intake pressure of 130 kPa where soot formation is known to primarily occur in the bulk gases. Exhaust soot emissions were measured for nine test fuels at various exhaust gas recirculation levels. A comparison between measured soot levels and PMI showed that PMI is a relatively poor predictor of the sooting tendency of the tested fuels under lean SC combustion. Among the fuels, the diisobutylene blend, high olefin, and E30 fuels exhibited measured soot behavior opposite of that predicted by PMI. Optical diagnostics were utilized to further investigate the in-cylinder phenomena for these three fuels. Analysis of natural luminosity and diffused back-illumination extinction imaging indicated that fuel-induced differences in the amount of soot formed are responsible for a majority of the discrepancy in measured versus predicted sooting tendency. Fuel-induced differences in soot oxidation and spray development seem to play minor roles. Because the combustion and air-fuel mixing processes for lean SC combustion are different from conventional stoichiometric operation, it was hypothesized that the PMI correlation needs to be modified to account for differences in stoichiometric air-fuel ratio and level of oxygenation between fuels. Furthermore, the role of fuel volatility in PMI possibly needs to be de-emphasized for SC operation with fuel injection into compression-heated gases.

High-speed aerospace engineering applications rely heavily on computational fluid dynamics (CFD) models for design and analysis. This reliance on CFD models necessitates performing accurate and reliable uncertainty quantification (UQ) of the CFD models, which can be very expensive for hypersonic flows. Additionally, UQ approaches are many-query problems requiring many runs with a wide range of input parameters. One way to enable computationally expensive models to be used in such many-query problems is to employ projection-based reduced-order models (ROMs) in lieu of the (high-fidelity) full-order model (FOM). In particular, the least-squares Petrov–Galerkin (LSPG) ROM (equipped with hyper-reduction) has demonstrated the ability to significantly reduce simulation costs while retaining high levels of accuracy on a range of problems, including subsonic CFD applications. This allows LSPG ROM simulations to replace the FOM simulations in UQ studies, making UQ tractable even for large-scale CFD models. This work presents the first application of LSPG to a hypersonic CFD application, the Hypersonic International Flight Research Experimentation 1 (HIFiRE-1) in a three-dimensional, turbulent Mach 7.1 flow. This paper shows the ability of the ROM to significantly reduce computational costs while maintaining high levels of accuracy in computed quantities of interest.

Drop shock machines are commonly used to create a single sided shock pulse that is characterized by an amplitude and a pulse length. While the amplitude of the pulse input is critical in determining a majority of the stresses found in a test article, the pulse length determines the frequency content excited by the shock and can also have an effect on stress. Current simulation methods to model the drop shock machine environment typically use an experimentally measured acceleration on the surface of the drop tower carriage as the input. This measurement assumes that the surface of the drop table is rigid through the shock event, due to a lack of knowledge about the true input force on the drop table during the shock event. The purpose of this work is to test this rigid assumption and reconstruct the input force to better characterize the shock event seen by a test article. Results from laboratory modal and drop tests, force reconstruction using SWAT, and FEM analysis are presented along with a brief background into the drop shock machine environment and the SWAT method.

The optimal use of atmospheric pressure plasma jets (APPJs) for treatment of surfaces-inorganic, organic and liquid-depends on being able to control the flow of plasma-generated reactive species onto the surface. The typical APPJ is a rare gas mixture (RGM) flowed through a tube to which voltage is applied, producing an RGM plasma plume that extends into the ambient air. The RGM plasma plume is guided by a surrounding shroud of air due to the higher electric field required for an ionization wave (IW) to propagate into the air. The mixing of the ambient air with the RGM plasma plume then determines the production of reactive oxygen and nitrogen species (RONS). The APPJ is usually oriented perpendicular to the surface being treated. However, the angle of the APPJ with respect to the surface may be a method to control the production of reactive species to the surface due to the change in APPJ propagation properties and the resulting gas dynamics. In this paper, we discuss results from computational and experimental investigations addressing two points-propagation of IWs in APPJs with and without a guiding gas shroud as a function of angle of the APPJ with respect to the surface; and the use of this angle to control plasma activation of thin water layers. We found that APPJs propagating out of the plasma tube into a same-gas environment lack any of the directional properties of shroud-guided jets, and largely follow electric field lines as the angle of the plasma tube is changed. Guided APPJs propagate coaxially with the tube as the angle is changed, and turn perpendicularly towards the surface only a few mm above the surface. The angle of the APPJ produces different gas dynamic distributions, which enable some degree of control over the content of RONS transferred to thin water layers.

A flamelet analysis of a highly resolved direct numerical simulation (DNS) of a multi-injection flame with both auto-ignition and ignition induced by flame-flame interaction was conducted. A novel method was proposed to identify the different combustion modes of ignition processes using generalized flamelet equations. A state-of-the-art DNS database for a multi-injection n-dodecane flame in a diesel engine environment was investigated. Three-dimensional flamelets were extracted from the DNS at different time instants with a focus on auto-ignition and interaction-ignition processes. The influences of mixture field interactions and the scalar dissipation rate on the ignition process were examined by varying the species composition boundary conditions of the transient flamelet equations. Results showed that auto-ignition is delayed if the burned products are added to the oxidizer side of the flamelet, and the ignition delay time is sensitive to the scalar dissipation rate. The significance of mass diffusion in the flame-normal direction is reduced due to the existence of burned products in the oxidizer stream. Budget analyses of the generalized flamelet equations revealed that the transport along the mixture fraction iso-surface is insignificant during the auto-ignition process, but becomes important when interaction-ignition occurs, which is further confirmed through a flamelet regime classification method.

In the present work, three-dimensional turbulent non-premixed oblique slot-jet flames impinging at a wall were investigated using direct numerical simulation (DNS). Two cases are considered with the Damköhler number (Da) of case A being twice that of case B. A 17 species and 73-step mechanism for methane combustion was employed in the simulations. It was found that flame extinction in case B is more prominent compared to case A. Reignition in the lower branch of combustion for case A occurs when the scalar dissipation rate relaxes, while no reignition occurs in the lower branch for case B due to excessive scalar dissipation rate. A method was proposed to identify the flame quenching edges of turbulent non-premixed flames in wall-bounded flows based on the intersections of mixture fraction and OH mass fraction iso-surfaces. The flame/wall interactions were examined in terms of the quenching distance and the wall heat flux along the quenching edges. There is essentially no flame/wall interaction in case B due to the extinction caused by excessive turbulent mixing. In contrast, significant interactions between flames and the wall are observed in case A. The quenching distance is found to be negatively correlated with wall heat flux as previously reported in turbulent premixed flames. The influence of chemical reactions and wall on flow topologies was identified. The FS/U and FC/U topologies are found near flame edges, and the NNN/U topology appears when reignition occurs. The vortex-dominant topologies, FC/U and FS/S, play an increasingly important role as the jet turbulence develops.

There are many synthesis methods through phase space to produce nanostructures in metals. Condensation methods with rapid solidification are extensively explored from the gas or liquid phase. In particular, electrodeposition using pulsed currents favors continuous nucleation in the processing of structures to produce free-standing sheets as well as protective coatings for surfaces. An analysis approach used to develop the method for refining the structure and surface finish for nanocrystalline gold-copper alloy coatings relates the energy in each deposition pulse to the constituent grain size that forms during growth. Application is now pursued to evaluate a select determination of the grain size for nanocrystalline nickel foils synthesized by pulsed electrodeposition. The mechanical behaviors of hardness and rate-sensitivity of strength are assessed as function of grain size.

Measurements are presented of the aero-optic distortion produced by a Mach 8 turbulent boundary layer in the Sandia Hypersonic Wind Tunnel. Flat optical inserts installed in the test section walls enabled a double-pass arrangement of a collimated laser beam. The distortion of this beam was imaged by a high-speed Shack-Hartmann sensor at a sampling rate of up to 1 MHz. Analysis is performed using two processing methods to extract the aero-optic distortion from the data. A novel de-aliasing algorithm is proposed to extract convective-only spectra and is demonstrated to correctly quantify the physical spectra even in case of relatively low sampling rates. The results are compared with an existing theoretical model, and it is shown that this model under-predicts the experimentally measured distortions regardless of the processing method used. Possible explanations for this discrepancy are presented. The presented results represent to-date the highest Mach number for which aero-optic boundary layer distortion measurements are available.

This paper discusses the findings from an EPRI industry survey mapping the state, current practices, and future needs of distribution load modeling in the U.S. and internationally. The paper provides a benchmark for distribution utilities and a view of the current industry state and future needs for researchers and other readers. The survey found the parameters and measurements available and utilized for load modeling to vary widely between the utilities and data types. Loads were found to be largely modeled based on different load allocation methods. While distribution planning was found to focus on assessing peak load conditions, some utilities evaluate other time instances and/or explore time-series assessments. Simple grid edge and voltage sensitivity models were found common. The identified future needs include access for additional data, as well as methods to process and utilize the increasing data, handle masked load, and perform time-series load modeling.

The study of heat transfer and ablation plays an important role in many problems of scientific and engineering interest. As with the computational simulation of any physical phenomenon, the first step toward establishing credibility in ablation simulations involves code verification. Code verification is typically performed using exact and manufactured solutions. However, manufactured solutions generally require the invasive introduction of an artificial forcing term within the source code such that the code solves a modified problem for which the solution is known. In this paper, we present a nonintrusive method for manufacturing solutions for a non-decomposing ablation code, which does not require the addition of a source term.

The study of heat transfer and ablation plays an important role in many problems of scientific and engineering interest. As with the computational simulation of any physical phenomenon, the first step toward establishing credibility in ablation simulations involves code verification. Code verification is typically performed using exact and manufactured solutions. However, manufactured solutions generally require the invasive introduction of an artificial forcing term within the source code such that the code solves a modified problem for which the solution is known. In this paper, we present a nonintrusive method for manufacturing solutions for a non-decomposing ablation code, which does not require the addition of a source term.

In Bayesian model calibration, evaluation of the likelihood function usually involves finding the inverse and determinant of a covariance matrix. When Markov Chain Monte Carlo (MCMC) methods are used to sample from the posterior, hundreds of thousands of likelihood evaluations may be required. In this paper, we demonstrate that the structure of the covariance matrix can be exploited, leading to substantial time savings in practice. We also derive two simple equations for approximating the inverse of the covariance matrix in this setting, which can be computed in near-quadratic time. The practical implications of these strategies are demonstrated using a simple numerical case study and the "quack"R package. For a covariance matrix with 1000 rows, application of these strategies for a million likelihood evaluations leads to a speedup of roughly 4000 compared to the naive implementation.

The spatial and temporal progression of two-stage autoignition of a diesel-fuel surrogate, n-heptane, in a lean-premixed charge of synthetic natural-gas (NG) and air were studied in an optically accessible heavy-duty diesel engine. The lean-premixed charge of NG was prepared by fumigation upstream of the engine intake manifold. Optical diagnostics used high-speed (15 kfps) cool-flame chemiluminescence imaging as an indicator of low-temperature heat-release (LTHR) and OH* chemiluminescence imaging as an indicator of high-temperature heat-release (HTHR). NG prolonged the ignition delay of the pilot fuel and increased the combustion duration. Zero-dimensional chemical-kinetics simulations predicted that LTHR initiated most likely on the air streamlines before transitioning to HTHR, either on fuel-streamlines or on air-streamlines in regions of near-constant Φ Due to the relatively short pilot-fuel injection-durations, the transient increase in entrainment near the end of injection (entrainment wave) was important for quickly creating auto-ignitable mixtures. The desired combustion characteristics, e.g., multiple ignition-kernels and favorable combustion phasing and location (e.g., for reducing wall heat-transfer or optimizing charge stratification) and adjusting injection parameters can be achieved by tailor mixing trajectories to offset changes in fuel ignition chemistry.

Heat waves have catastrophic effects causing mortality, air quality loss, grid failures, infrastructure damage, and increases in electricity consumption. The literature indicates that heat waves are growing in intensity, duration, and frequency. This paper documents a heat wave study of the Sandia National Laboratories (SNL) California site. The analysis involves: 1) projection of a heat wave based on historical data and NEX-DCP30 climate projections, 2) Classification of peak electricity load points that represent the site on workdays, Fridays, and weekends 3) Regression of the peak load data to produce confidence bounds for the analysis, and 4) Calibration and projection of building energy models (BEMs) to the heat wave scenario. This approach worked well for the previous NM site analysis of meter data and 97 representative BEM's. For the CA site, the BEM calibration procedure was unsuccessful without individual BEM calibrations. Many of the 23 California BEM's required calibration at the building level rather than for the entire site. This was found to be due to many of the BEM's having significantly different electric demand profiles than their meter data whereas the NM BEM's were much more accurate. Unlike the NM site, the CA site did not distinguish Friday operations clearly and the associated K-mean cluster algorithm that worked for the NM site did not add value for the CA site. The regression analyses produced estimates of site-wide increases to daily peak loads with 95% confidence bounds that were much wider than the NM analysis. The CA site was found to have higher average peak load sensitivity of 1.07%/0C (0.59%/0F) in comparison to the NM site with 0.61%/0C (0.34%/0F). Even so, the larger sensitivity is counteracted by a milder projection for future heat waves from NEX-DCP30 downscaled climate projections. The expected heat wave maximum temperature of 45.10C (113.20F) did not even break the current record of 46.10C (115.00F) in Livermore, CA and only had total heating energy of 280C·day (510F·day) from baseline 2019 weather in comparison to NM's 380C·day (680F·day). This work emphasizes issues that can aid development of future guidelines for application of BEM and meter data to heat-wave scenarios.

Fluctuating boundary layer pressure fluctuations are an important loading component for reentry bodies. Characterization of these loads is often described through cross-spectral density-based definitions, such as, longitudinal and lateral coherence, spatial correlation and frequency power spectral density. The widely utilized Corcos separable coherence model functional form has been employed in this study. While the classical Corcos D xD style model using a self-similar velocity-spacing variable e.g. (here the subscript denotes a dimensional U vaiable) has been effectively used for low speed simulations, high speed problems often require a model that involves both the self-similar variable and the sensor spacing D Here we examine longitudinal coherence formulations that include explicit D behavior as well as the self-similar variable. Examination of an analytical model/synthetic pressure fluctuation correlation function developed here clearly demonstrate that the self-similar form may need to be supplement by non-similar information. Using the synthetic space-time correlation expression, a coherence model which uses self-similar variables and explicit (but continuous) spatial information is proposed. Estimates for the parameters in the coherence model are derived using asymptotic arguments available from the synthetic result. Further, relationships are derived to estimate coherence model parameters and their connection to longitudinal correlation behavior assuming exponential auto-spectral density models. Comparison of these expressions with wind tunnel test and DNS simulation shows good comparison. Measurements from flight tests which deviate greatly from the classical self-similar form can be successfully described using the extended model although the coherence model parameters must be modified. In summary, an extended coherence model is developed which provides good explanations of longitudinal coherence and correlation behavior.

Accurate knowledge of post-detonation fireball temperatures is important for understanding device performance and for validation of numerical models. Such measurements are difficult to make even under controlled laboratory conditions. In this work temperature measurements were performed in the fireball of a commercial detonator (RP-80, Teledyne RISI). The explosion and fragments were contained in a plastic enclosure with glass windows for optical access. A hybrid femtosecond-picosecond (fs-ps) rotational coherent anti-Stokes Raman scattering (CARS) instrument was used to perform gas-phase thermometry along a one-dimensional measurement volume in a single laser shot. The 13-mm-thick windows on the explosive-containment housing introduced significant nonlinear chirp on the fs lasers pulses, which reduced the Raman excitation bandwidth and did not allow for efficient excitation of high-J Raman transitions populated at flame temperatures. To overcome this, distinct pump and Stokes pulses were used in conjunction with spectral focusing, achieved by varying the relative timing between the pump and Stokes pulses to preferentially excite Raman transitions relevant to flame thermometry. Light scattering from particulate matter and solid fragments was a significant challenge and was mitigated using a new polarization scheme to isolate the CARS signal. Fireball temperatures were measured 35-40 mm above the detonator, 12-25 mm radially outward from the detonator centerline, and at 18 and 28 μs after initiation. At these locations and times, significant mixing between the detonation products and ambient air had occurred thus increasing the nitrogen-based CARS thermometry signal. Initial measurements show a distribution of fireball temperatures in the range 300-2000 K with higher temperatures occurring 28 μs after detonation.

A novel method based on combining Direct Simulation Monte Carlo (DSMC) and Discrete Velocity Method (DVM) representations of the velocity distribution functions in velocity space is applied to rarefied ionized gas flows in order to study its efficiency and accuracy. The objective is to improve the efficiency of modeling of flows where trace populations have a significant effect on the flow physics. Numerical results are obtained for a 0-dimensional flow of a Ar/Ar+ /e− mixture and compared with the BOLSIG+ solver.

When faced with uncertainty regarding potential failure contingencies, prioritizing system resilience through optimal control of exciter reference voltage and mechanical torque can be arduous due to the scope of potential failure contingencies. Optimal control schemes can be generated through a two-stage stochastic optimization model by anticipating a set of contingencies with associated probabilities of occurrence, followed by the optimal recourse action once the contingency has been realized. The first stage, common across all contingency scenarios, co-optimally positions the grid for the set of possible contingencies. The second stage dynamically assesses the impact of each contingency and allows for emergency control response. By unifying the optimal control scheme prior and post the failure contingency, a singular policy can be constructed to maximize system resilience.

This paper describes how the performance of motion primitive based planning algorithms can be improved using reinforcement learning. Specifically, we describe and evaluate a framework for policy improvement via the discovery of new motion primitives. Our approach combines the predictable behavior of deterministic planning methods with the exploration capability of reinforcement learning. The framework consists of three phases: evaluation, exploration, and extraction. This framework can be iterated continuously to provide successive improvement. The evaluation step scores the performance of a motion primitive library using value iteration to create a cost map. A local difference metric is then used to identify regions that need improvement. The exploration step utilizes reinforcement learning to examine new trajectories in the regions of greatest need. The extraction step encodes the agent’s experiences into new primitives. The framework is tested on a point-to-point navigation task using a 6DOF nonlinear F-16 model. One iteration of the framework discovered 17 new primitives and provided a maximum planning time reduction of 96.91%. After 3 full iterations, 123 primitives were added with a maximum time reduction of 97.39%. The proposed framework is easily extensible to a range of vehicles, environments, and cost functions.

Understanding phase transformations in 2D materials can unlock unprecedented developments in nanotechnology, since their unique properties can be dramatically modified by external fields that control the phase change. Here, experiments and simulations are used to investigate the mechanical properties of a 2D diamond boron nitride (BN) phase induced by applying local pressure on atomically thin h-BN on a SiO2 substrate, at room temperature, and without chemical functionalization. Molecular dynamics (MD) simulations show a metastable local rearrangement of the h-BN atoms into diamond crystal clusters when increasing the indentation pressure. Raman spectroscopy experiments confirm the presence of a pressure-induced cubic BN phase, and its metastability upon release of pressure. Å-indentation experiments and simulations show that at pressures of 2–4 GPa, the indentation stiffness of monolayer h-BN on SiO2 is the same of bare SiO2, whereas for two- and three-layer-thick h-BN on SiO2 the stiffness increases of up to 50% compared to bare SiO2, and then it decreases when increasing the number of layers. Up to 4 GPa, the reduced strain in the layers closer to the substrate decreases the probability of the sp2-to-sp3 phase transition, explaining the lower stiffness observed in thicker h-BN.

This study seeks to simplify the optical requirements for multi-line FLEET (Femtosecond Laser Electronic Excitation Tagging) generation by focusing the image of a periodic slit-mask with a cylindrical and spherical lens. Geometry effects on the signal were analyzed over fifteen mask iterations. The signal for each mask was found to vary with mask standoff from the focusing optics, which was optimized based on maximizing the Signal-to-Noise Ratio (SNR) for each mask. The number of generated lines was found to decrease with slit spacing while the separation of the lines increased. FLEET line spacing was determined by a constant magnification value of the imaged masks’ slit spacing. From the geometry study, two masks that produced three to five lines spaced at 0.8–1 mm apart with SNR > 4 were chosen to demonstrate the multi-line technique in a supersonic free-jet. Velocity calculations from this data showed good agreement with schlieren imaging of compressible flow structures.

Neural networks (NNs) are known as universal function approximators and can interpolate nonlinear functions between observed data points. However, when the target domain for deployment shifts from the training domain and NNs must extrapolate, the results are notoriously poor. Prior work Martinez et al. (2019) has shown that NN uncertainty estimates can be used to correct binary predictions in shifted domains without retraining the model. We hypothesize that this approach can be extended to correct real-valued time series predictions. As an exemplar, we consider two mechanical systems with nonlinear dynamics. The first system consists of a spring-mass system where the stiffness changes abruptly, and the second is a real experimental system with a frictional joint that is an open challenge for structural dynamicists to model efficiently. Our experiments will test whether 1) NN uncertainty estimates can identify when the input domain has shifted from the training domain and 2) whether the information used to calculate uncertainty estimates can be used to correct the NN’s time series predictions. While the method as proposed did not significantly improve predictions, our results did show potential for modifications that could improve models’ predictions and play a role in structural health monitoring systems that directly impact public safety.

Multi-phase, pseudospectral optimization is employed in a variety of applications, but many of the world-class optimization libraries are closed-source. In this paper we formulate an open-source, object-oriented framework for dynamic optimization using the Pyomo modeling language. This strategy supports the reuse of common code for rapid, error-free model development. Flexibility of our framework is demonstrated on a series of dynamic optimization problems, including multi-phase trajectory optimization using highly accurate pseudospectral methods and controller gain optimization in the presence of stability margin constraints. We employ numerical procedures to improve convergence rates and solution accuracy. We validate our framework using GPOPS-II, a commercial, MATLAB-based optimization program, for a vehicle ascent problem. The trajectory results show close alignment with this state-of-the-art optimization suite.

When analyzing and predicting stochastic variability in a population of devices or systems, it is important to segregate epistemic lack-of-knowledge uncertainties and aleatory uncertainties due to stochastic variation in the population. This traditionally requires dual-loop Monte Carlo (MC) uncertainty propagation where the outer loop samples the epistemic uncertainties and for each realization, an inner loop samples and propagates the aleatory uncertainties. This results in various realizations of what the aleatory distribution of population response variability might be. Under certain conditions, the various possible realizations can be represented in a concise manner by approximate upper and lower bounding distributions of the same shape, composing a “Level 1” approximate probability box (L1 APbox). These are usually sufficient for model validation purposes, for example, and can be formed with substantially reduced computational cost and complication in propagating the aleatory and epistemic uncertainties (compared to dual-loop MC). Propagation cost can be further reduced by constructing and sampling response surface models that approximate the variation of physics-model output responses over the uncertainty parameter space. A simple dimension-and order-adaptive polynomial response surface approach is demonstrated for propagating the aleatory and epistemic uncertainties in a L1 APbox and for estimating the error contributed by using the surrogate model. Sensitivity analysis is also performed to quantify which uncertainty sources contribute most to the total aleatory-epistemic uncertainty in predicted response. The methodology is demonstrated as part of a model validation assessment involving thermal-chemical-mechanical response and weld breach failure of sealed canisters weakened by high temperatures and pressurized by heat-induced pyrolysis of foam.

Aeroengines ingest foreign object debris such as sand, which eventually erode components through repeated impacts. Due to the wide feature space, modeling and simulations are needed to rapidly assess the erosion behavior of materials such as composites. Peridynamic simulations were performed to analyze erosion of SiC/SiC composite due to sand impacts, which gives direct insight into the impact erosion mechanism and amounts. The erosion data was strongly correlated to impact velocity and angle, providing predictive equations.

Environmental Barrier Coatings (EBC) protect ceramic matrix composites from exposure to high temperature moisture present in turbine operation through their dense top coats. However, moisture is able to diffuse and oxidize the Si bond coat to form the Thermally Grown Oxide (TGO), a layer of SiO2 where the incorporation of O causes swelling and stress. At sufficient TGO-based swelling, the EBC will fail due to increased damage such as delamination. A multiscale simulation framework has been developed to link operating conditions of a high-performance turbine to the failure modes of the EBC. Computational fluid dynamics (CFD) simulations of the E3 turbine were performed and compared to prior literature data to demonstrate the fidelity of the Loci/CHEM software to determine the flow conditions on the turbine blade surface. Boundary condition data of pressure and heat flux were then determined with the CFD simulations, providing the temperature at the bond coat. Peridynamics was used to model the microscale TGO growth. A swelling model that links moisture concentration to strain at the TGO due to the volume increase from oxidation was demonstrated, coupling moisture transport to localized strain and directly observing TGO growth and the corresponding damage. This framework is generalized and can be adapted to a range of EBC microstructures and operating conditions.

Dielectric–metallic hybrid metamaterials exhibit extraordinary optical properties due to the light–matter interactions at the dielectric–metallic interfaces. The ability in precision control of the light–matter interactions in nanoscale is key to tailor the optical properties of hybrid metamaterials. In this work, a complex 3D framework of multilayered self-assembled BaTiO3(BTO)-Au hybrid thin films is demonstrated with such precision control of the light–matter interaction in nanoscale. Unique “bamboo-like” Au nanostructures are formed via the bilayer and trilayer stacking of BTO-Au hybrid layers with interlayers of SrTiO3, CeO2, or MgO. Different film strain states introduced by the three interlayers result in variable diameter and density of Au nanopillars. Both simulated and experimental optical data demonstrate the localized surface plasmon resonance change and hyperbolic dispersion wavelength shift in visible to near-infrared because of the effective tuning of the Au nanopillar aspect ratio and free electron density. The highly tunable optical properties along with the ferroelectric behavior and thermal robustness of the 3D hybrid film enable it to be a great candidate for multifunctional applications. This study demonstrates a unique 3D approach for precision optical property tuning and combined functionalities in oxide–metal metamaterial systems toward future integrated photonic and electronic devices.

Satellites and launch vehicles are subject to pyroshock events that come from the actuation of separation devices. The shocks are high frequency transients that decay quickly—within 5-20 ms—and can be damaging events for satellites and their components. The damage risk can be reduced by good design practice, taking advantage of the attenuating properties of structural features in the load path. NASA and MIL handbooks provide general guidelines for estimating the attenuating effects of distance, joints, and other structural features in the load path between the shock source and the shock sensitive component. One of the challenges is adequately modeling the dissipative mechanisms in structural features to better understand the risk to shock sensitive components. Previously, we examined the modeling of pyroshock attenuation in a cylindrical structure and used peak acceleration to evaluate how much shocks are attenuated by distance and structural features in a cylindrical structure. In this work, we investigated different quantities to gain more insight into how and why pyroshocks get attenuated by a bulkhead. We found that the bulkhead affects the SRS peak more than the SRS ramp and that approximately 30% of the structural intensity of the pyroshock flows into the bulkhead regardless of the thickness.

Accurate modeling of mixing in large-eddy simulation (LES)/transported probability density function (PDF) modeling of turbulent combustion remains an outstanding issue. The issue is particularly salient in turbulent premixed combustion under extreme conditions such as high-Karlovitz number Ka. The present study addresses this issue by conducting an a priori analysis of a power-law scaling based mixing timescale model for the transported PDF model. A recently produced DNS dataset of a high-Ka turbulent jet flame is used for the analysis. A power-law scaling is observed for a scaling factor used to model the sub-filter scale mixing timescale in this high-Ka turbulent premixed DNS flame when the LES filter size is much greater than the characteristic thermal thickness of a laminar premixed flame. The sensitivity of the observed power-law scaling to the different viewpoints (local or global) and to the different scalars for the data analysis is examined and the dependence of the model parameters on the dimensionless numbers Ka and Re (the Reynolds number) is investigated. Different model formulations for the mixing timescale are then constructed and assessed in the DNS flame. The proposed model is found to be able to reproduce the mixing timescale informed by the high-Ka DNS flame significantly better than a previous model.

We show that machine learning can improve the accuracy of simulations of stress waves in one-dimensional composite materials. We propose a data-driven technique to learn nonlocal constitutive laws for stress wave propagation models. The method is an optimization-based technique in which the nonlocal kernel function is approximated via Bernstein polynomials. The kernel, including both its functional form and parameters, is derived so that when used in a nonlocal solver, it generates solutions that closely match high-fidelity data. The optimal kernel therefore acts as a homogenized nonlocal continuum model that accurately reproduces wave motion in a smaller-scale, more detailed model that can include multiple materials. We apply this technique to wave propagation within a heterogeneous bar with a periodic microstructure. Several one-dimensional numerical tests illustrate the accuracy of our algorithm. The optimal kernel is demonstrated to reproduce high-fidelity data for a composite material in applications that are substantially different from the problems used as training data.

Thermal protection system designers rely heavily on computational simulation tools for design optimization and uncertainty quantification. Because high-fidelity analysis tools are computationally expensive, analysts primarily use low-fidelity or surrogate models instead. In this work, we explore an alternative approach wherein projection-based reduced-order models (ROMs) are used to approximate the computationally infeasible high-fidelity model. ROMs are preferable to alternative approximation approaches for high-consequence applications due to the presence of rigorous error bounds. This work presents the first application of ROMs to ablation systems. In particular, we present results for Galerkin and least-squares Petrov-Galerkin ROMs of 1D and 2D ablation system models.

The well-developed theory of Lorentz plasma that is dominated by electron–ion interactions is used to calculate the PETN arc characteristics. The spark-gap discharge current is a ramp with 10 to 25 ns rise time to peak and remaining constant subsequently. The approximate formulas for the arc channel conductivity, arc temperature, arc radius, and shock pressure from the arc are obtained from a system of nonlinear ordinary differential equations, which is the similarity solution of hydrodynamic equations similar to the Braginskii approximation. These arc parameters are given for the peak current ranging from 100 A to 1000 A and with different rise times. Representative cases are compared to the nonlinear ordinary differential equation code results. The shock pressures at the peak current are comparable to those from a typical commercial EBW bridgewire burst reported in the literature; the arc radius at the peak current is comparable to a typical bridgewire diameter of 0.0375 mm (e.g., RISI detonators, RP-1, and RP-80). The relevant Pop-Plot for low-density PETN is converted into an empirical detonation criterion, which is applicable to explosives subject to shocks of variable pressure. This criterion is then used to determine the detonation thresholds, which are comparable with test data obtained by Tucker, et al.

Aluminum particle combustion is a critical component in solid propellant operation. Understanding these processes is essential for improving specific impulse and other performance metrics. Prior studies of aluminum particle combustion in the literature have focused on spatial and temperature statistics for a single propellant strand size, which is typically significantly smaller than the full grain size used in aerospace and defense applications. In this work, we aim to determine the effect of increasing propellant strand size on several key properties of aluminum particle combustion at atmospheric pressure. To accomplish this, we use simultaneous high speed holography and imaging pyrometry to obtain temporally resolved spatial and temperature information. Here, we discuss how agglomerate size, velocity, and temperature statistics vary as a function of propellant strand size from 6 mm up to 19 mm in diameter. By understanding how the statistics scale as a function of strand size, we can determine how to extrapolate lab-scale experimental data to full-scale propellant burns.

Thermal runaway of Li-ion batteries is a risk that is magnified when stacks of lithium-ion cells are used for large scale energy storage. When limits of propagation can be identified so that systems can be designed to prevent large scale cascading failure even if a failure does occur, these systems will be safer. The prediction of cell-to-cell failure propagation and the propagation limits in lithium-ion cell stacks were studied to better understand and identify safe designs. A thermal-runaway model was considered based on recent developments in thermochemical source terms. Propagating failure was characterized by temperatures above which calorimetry data is available. Results showed high temperature propagating failure predictions are too rapid unless an intra-particle diffusion limit is included, introducing a Damköhler number limiter into the rate expression. This new model form was evaluated against cell-to-cell failure propagation where the end cell of a stack is forced into thermal runaway through a nail-induced short circuit. Limits of propagation for this configuration are identified. Results showed cell-to-cell propagation predictions are consistent with measurements over a range of cell states of charge and with the introduction of metal plates between cells to add system heat capacity representative of structural members. This consistency extends from scenarios where propagation occurs through scenarios where propagation is prevented.

Problems in optimal control may exhibit a bang-bang or singular control structure. These qualities pose challenges with indirect solution methods when the control law is discontinuous or indefinite. Recent efforts in control regularization strategies have sought to overcome these difficulties. These methods approximate a smoothed mapping of the constrained multi-stage Hamiltonian boundary value problem, resolving the singular/bang arcs into a single-stage problem. This work investigates the use of control saturation functions for error-control regularization. A key feature of the new approach is to eliminate ambiguity of the control law derived from the necessary conditions for optimality. The method is shown to have improved stability in numerical continuation due to the removal of small error terms from the control law. A well-known classical problem with analytical solutions is studied, as well as a more applied problem involving atmospheric flight of a maneuvering reentry vehicle.

Laser-induced incandescence (LII) is a widely used technique for measuring soot concentrations. For flame applications LII is frequently deployed as a planar diagnostic to measure the two-dimensional soot field. However, when the laser sheet is focused, as is typical to reach the requisite laser fluence level and achieve good spatial resolution, the complex laser power dependence of the LII signal generation process can introduce a large variation in LII signal sensitivity across an LII image. In this work, this effect is quantified for the first time as a function of laser pulse fluence, using a typical planar LII excitation scheme with a clipped Gaussian YAG laser beam focused with a 1 m focal length lens. Furthermore, the cross-sectional energy distribution in the laser sheet was measured across the image plane, to relate the details of the laser sheet focal properties with the resultant LII behavior. The results show that a unique laser fluence level (referenced to the focal plane) exists whereby there is essentially no dependence of LII signal on position relative to the focal plane. However, at lower or higher fluences, the radial signals either decrease (low fluence) or increase (high fluence) rapidly with increasing distance away from the focal point. For measurements using an LII 'plateau' laser fluence level, as is usual in environments with significant optical depth (i.e. sufficiently strong soot levels), the LII signals are found to be 2.5X larger 40 mm away from the focal point. An analysis conducted by combining a previously measured LII fluence dependence for a top-hat laser profile with the laser sheet cross-sections measured in this work shows general agreement with the measured results for LII signal variation. Further, the sensitivity of LII signals at high fluences to the laser beam spatial profile, particularly away from the sheet focus, is highlighted.

This study employs nonlinear ultrasonic techniques to track microstructural changes in additively manufactured metals. The second harmonic generation technique based on the transmission of Rayleigh surface waves is used to measure the acoustic nonlinearity parameter, β. Stainless steel specimens are made through three procedures: traditional wrought manufacturing, laser-powder bed fusion, and laser engineered net shaping. The β parameter is measured through successive steps of an annealing heat treatment intended to decrease dislocation density. Dislocation density is known to be sensitive to manufacturing variables. In agreement with fundamental material models for the dislocation-acoustic nonlinearity relationship in the second harmonic generation, β drops in each specimen throughout the heat treatment before recrystallization. Geometrically necessary dislocations (GNDs) are measured from electron back-scatter diffraction as a quantitative indicator of dislocations; average GND density and β are found to have a statistical correlation coefficient of 0.852 showing the sensitivity of β to dislocations in additively manufactured metals. Moreover, β shows an excellent correlation with hardness, which is a measure of the macroscopic effect of dislocations.

We present a method for learning dynamics of complex physical processes described by time-dependent nonlinear partial differential equations (PDEs). Our particular interest lies in extrapolating solutions in time beyond the range of temporal domain used in training. Our choice for a baseline method is physics-informed neural network (PINN) because the method parameterizes not only the solutions, but also the equations that describe the dynamics of physical processes. We demonstrate that PINN performs poorly on extrapolation tasks in many benchmark problems. To address this, we propose a novel method for better training PINN and demonstrate that our newly enhanced PINNs can accurately extrapolate solutions in time. Our method shows up to 72% smaller errors than existing methods in terms of the standard L2-norm metric.

This paper addresses two challenges in Bayesian calibration: (1) computational speed of existing sampling algorithms and (2) calibration with spatiotemporal responses. The commonly used Markov chain Monte Carlo (MCMC) approaches require many sequential model evaluations making the computational expense prohibitive. This paper proposes an efficient sampling algorithm: iterative importance sampling with genetic algorithm (IISGA). While iterative importance sampling enables computational efficiency, the genetic algorithm enables robustness by preventing sample degeneration and avoids getting stuck in multimodal search spaces. An inflated likelihood further enables robustness in high-dimensional parameter spaces by enlarging the target distribution. Spatiotemporal data complicate both surrogate modeling, which is necessary for expensive computational models, and the likelihood estimation. In this work, singular value decomposition is investigated for reducing the high-dimensional field data to a lower-dimensional space prior to Bayesian calibration. Then the likelihood is formulated and Bayesian inference is performed in the lower-dimension, latent space. An illustrative example is provided to demonstrate IISGA relative to existing sampling methods, and then IISGA is employed to calibrate a thermal battery model with 26 uncertain calibration parameters and spatiotemporal response data.

Multi-shaker testing is used to represent the response of a structure to a complex operational load in a laboratory setting. One promising method of multi-shaker testing is Impedance Matched Multi-Axis Testing (IMMAT). IMMAT targets responses at discrete measurement points to control the multiple shaker input excitations, resulting in a laboratory response representative of the expected operational response at the controlled measurement points. However, the relationship between full-field operational responses and the full-field IMMAT response has not been thoroughly explored. Poorly chosen excitation positions may match operational responses at the control points, but over or under excite uncontrolled regions of the structure. Additionally, the effectiveness of the IMMAT method on the whole test structure could depend on the type of operational excitation. Spatially distributed excitations, such as acoustic loading, may be difficult to reproduce over the whole test structure in a lab setting using the point force IMMAT excitations. This work will simulate operational and IMMAT responses of a lab-scale structure to analyze the accuracy of IMMAT at uncontrolled regions of the structure. Determination of the effect of control locations and operational locations on the IMMAT method will lead to better test design and improved predictive capabilities.

The study of heat transfer and ablation plays an important role in many problems of scientific and engineering interest. As with the computational simulation of any physical phenomenon, the first step towards establishing credibility in ablation simulations involves code verification. Code verification is typically performed using exact and manufactured solutions. However, manufactured solutions generally require the invasive introduction of an artificial forcing term within the source code, such that the code solves a modified problem for which the solution is known. In this paper, we present a nonintrusive method for manufacturing solutions for a non-decomposing ablation code, which does not require the addition of a source term.

We consider the integral definition of the fractional Laplacian and analyze a linearquadratic optimal control problem for the so-called fractional heat equation; control constraints are also considered. We derive existence and uniqueness results, first order optimality conditions, and regularity estimates for the optimal variables. To discretize the state equation we propose a fully discrete scheme that relies on an implicit finite difference discretization in time combined with a piecewise linear finite element discretization in space. We derive stability results and a novel L2(0, T;L2(Ω)) a priori error estimate. On the basis of the aforementioned solution technique, we propose a fully discrete scheme for our optimal control problem that discretizes the control variable with piecewise constant functions, and we derive a priori error estimates for it. We illustrate the theory with one- and two-dimensional numerical experiments.

Environmental contours of extreme sea states are often utilized for the purposes of reliability-based offshore design. Many methods have been proposed to estimate environmental contours of extreme sea states, including, but not limited to, the traditional inverse first-order reliability method (I-FORM) and subsequent modifications, copula methods, and Monte Carlo methods. These methods differ in terms of both the methodology selected for defining the joint distribution of sea state parameters and in the method used to construct the environmental contour from the joint distribution. It is often difficult to compare the results of proposed methods to determine which method should be used for a particular application or geographical region. The comparison of the predictions from various contour methods at a single site and across many sites is important to making environmental contours of extreme sea states useful in practice. The goal of this paper is to develop a comparison framework for evaluating methods for developing environmental contours of extreme sea states. This paper develops generalized metrics for comparing the performance of contour methods to one another across a collection of study sites, and applies these metrics and methods to develop conclusions about trends in the wave resource across geographic locations, as demonstrated for a pilot dataset. These proposed metrics and methods are intended to judge the environmental contours themselves relative to other contour methods, and are thus agnostic to a specific device, structure, or field of application. The metrics developed and applied in this paper include measures of predictive accuracy, physical validity, and aggregated temporal performance that can be used to both assess contour methods and provide recommendations for the use of certain methods in various geographical regions. The application and aggregation of the metrics proposed in this paper outline a comparison framework for environmental contour methods that can be applied to support design analysis workflows for offshore structures. This comparison framework could be extended in future work to include additional metrics of interest, potentially including those to address issues pertinent to a specific application area or analysis discipline, such as metrics related to structural response across contour methods or additional physics-based metrics based on wave dynamics.

The effects of heat release on interactions between vorticity (ω) and strain rate (s) in turbulent premixed CH4/O2/N2 counterflow flames are investigated using simultaneous OH laser-induced fluorescence (LIF) and tomographic particle image velocimetry (TPIV) measurements. A comparison between the flames and a corresponding turbulent non-reacting variable density N2-vs-products counterflow reveals the impact of heat release on vorticity-strain rate alignment statistics. Vorticity and strain rate statistics in the flames and non-reacting flow are conditioned on distance from the local flame front and gas mixing layer interface (GMLI) contours, respectively. The magnitude, alignment, and spatial distribution of the vorticity and principal strain rates (s1, s2, s3) are rather different when heat release is present. Density variations without heat release enhance the ω-s2 alignment while significantly reducing the ω-s3 alignment and modestly reducing the ω-s1 alignment. In contrast, heat release at the flame front further reduces the ω-s1 alignment but increases the ω-s3 alignment and suppresses the preferential ω-s2 alignment. Furthermore, increasing turbulence diminishes the effect of heat release on this preferential alignment. In regions with the largest vorticities, both the reacting and non-reacting counterflows show an increase in the probability of ω-s2 alignment. All counterflow cases have a net positive vortex-stretching contribution to the enstrophy production with a peak production rate at the flame front or GMLI, but the peak values depend on the density variation, heat release, and turbulence level. Elucidation of the complex interplay between these factors contributes to the understanding of the dynamics of turbulence-flame interactions.

In this work, the covalent attachment of an amine functionalized metal-organic framework (UiO-66-NH2 = Zr6O4(OH)4(bdc-NH2)6; bdc-NH2 = 2-amino-1,4-benzenedicarboxylate) (UiO-Universitetet i Oslo) to the basal-plane of carboxylate functionalized graphene (graphene acid = GA) via amide bonds is reported. The resultant GA@UiO-66-NH2 hybrid displayed a large specific surface area, hierarchical pores and an interconnected conductive network. The electrochemical characterizations demonstrated that the hybrid GA@UiO-66-NH2 acts as an effective charge storing material with a capacitance of up to 651 F g−1, significantly higher than traditional graphene-based materials. The results suggest that the amide linkage plays a key role in the formation of a π-conjugated structure, which facilitates charge transfer and consequently offers good capacitance and cycling stability. Furthermore, to realize the practical feasibility, an asymmetric supercapacitor using a GA@UiO-66-NH2 positive electrode with Ti3C2TX MXene as the opposing electrode has been constructed. The cell is able to deliver a power density of up to 16 kW kg−1 and an energy density of up to 73 Wh kg−1, which are comparable to several commercial devices such as Pb-acid and Ni/MH batteries. Under an intermediate level of loading, the device retained 88% of its initial capacitance after 10 000 cycles.

The industrial approach to nonlinearities in structural dynamics is still very conservative, particularly from an experimental point of view. A demo aluminum aircraft has been equipped with discrete nonlinear elements designed to replicate real-world engine pylon subassemblies to increase awareness on the effects of nonlinearities in design, and understand how these effects can be positively exploited, if properly understood. After some preliminary experiments aimed at understanding the coupled behavior of the aircraft-pylon mockup, it became clear that more in-depth numerical and experimental analyses are required on the pylon subassembly alone. For this paper, experimental data is collected to analyze the nonlinear dynamic behavior of the pylon, leading to better understanding of the subassembly once it connects to the aircraft. The pylon element has three main sources of nonlinearities: (1) geometric nonlinearities of the connecting beam, (2) contact as the beam presses into the tapered block surface and (3) friction in the bolted connections. Backbone curves are generated, which map the evolution of natural frequency and damping ratio with excitation amplitude. Using the experimental data, a low-order nonlinear model is identified to replicate the backbone characteristics and response of the pylon.

The main point of mechanical environment testing is to prove that designs can withstand the loads imparted on them while being exposed to in-service conditions. This is dependent not only on the test article construction, but also the loads imparted through its boundary conditions. Current practices for developing environment test specification are typically based on inadequate information reduced to single input point control with large uncertainty as compared to the field environment. Yet the test specifications are considered conservative, with the assumption that most of the adjustment for uncertainty is conservatism. For base mounted components, a modal model is presented that can be used to generate specifications with much lower uncertainty and with guaranteed quantifiable conservatism. In this method, the modal energies in the fixed base modes of the article due to the in-service loads are determined. Using the fixed base modes of the test article as a basis, the test specification is derived by determining what fixture motion is required to emulate the in-service environment. The specification method accounts for frequency shifts between the in-service and test configurations. Variability in nominal test articles can be included in the derivation of the test specifications. Real hardware under in-service environment loads and in a ground test fixture and loading configuration are considered.

The aim of this study is to determine the threshold wave energy converter (WEC) type and size to charge a fleet of U.S. Navy autonomous underwater vehicles (AUVs) in various geographic locations of interest. The U.S. Navy deploys AUVs in locations around the world that must be charged manually, decreasing their operational endurance and creating operational limitations. Ocean waves are a potential power source that can be converted into electricity using a WEC and stored using a battery. It would be beneficial to develop a WEC that could autonomously charge AUVs offshore. Numerous locations were analyzed to determine the minimum size of a WEC capable of providing sufficient charging power and offering a strategic advantage. By predicting the WEC efficiency (based on empirical equations) and wave resource (based on available data), electrical power generation across numerous WEC types and locations was compared in MATLAB. The generalized process developed here could be used to determine the required size and type of WECs to charge a fleet of AUVs in different locations around the world.

Here we review recent work by the authors to revisit the concept of extrapolating thermodynamic properties of classical systems using statistical mechanical principles. Specifically, we discuss how the combination of these principles with biased sampling techniques enables the prediction of free energy landscapes and other detailed information, such as structural properties, of the system in question. Remarkably accurate estimates of physical properties across a broad range of conditions have been achieved using this approach, greatly reducing the number of simulations needed to explore a given system's behaviour. While approximate, these extrapolations significantly amplify the amount of reasonably accurate information that can be extracted from simulations enabling a small set of them to feed data-intensive regression algorithms such as neural networks. Thus, this extrapolation methodology represents a useful tool for performing tasks such as high-throughput screening of physical properties, optimising force field parameters, exploring equilibrium phase behaviour, and enabling theory-guided data science for these systems.

To develop a fundamental understanding of dynamic strain aging, discovery experiments were designed and completed to inform the development of a dislocation based micromechanical constitutive model that will ultimately tie to continuum level plasticity and failure models. Dynamic strain aging occurs when dislocation motion is hindered by the repetitive interaction of solute atoms, most frequently interstitials, with dislocation cores. Initially, the solute atmospheres pin the dislocation core until the virtual force on the dislocation is high enough to allow glissile motion. At temperatures where the interstitials are mobile enough, the atmospheres can repeatedly reform, lock, and release dislocations producing a characteristic serrated flow curve. This phenomenon can produce unusual mechanical behavior of materials and changes in the strain rate and temperature responses. Detrimental effects such as loss of ductility often accompany these altered responses.

Physical aging of polymers is a thermodynamic phenomenon that occurs in the glassy regime. Upon cooling, the thermal contraction is restricted by a lack of adequate free volume within the polymer structure. This leaves the polymer in a state of thermodynamic non-equilibrium which relieves itself over long timescales. Time temperature superposition is typically used to accelerate this aging process to achieve validation of properties over the service life of the material. The shift factors determine the degree to which the material time is accelerated in an isothermal environment at elevated temperature. This is typically achieved with dynamic mechanical thermal analysis (DMTA). This method works well for neat polymers but fiber reinforced polymer composites (FRPC) have significantly higher stiffnesses and typical DMTA testing is limited to under 20 N of force. Due to the large unit cell for a fabric composite and geometrical limitations in the thickness of a ply, a higher force method would be more useful. In this study, an electrodynamic test frame was used to determine the shift factors for a glass fiber reinforced polymer (GFRP) composite which has a thermoset matrix. The goal is to determine whether the shift factors differ for different orientations of the composite. For an orthotropic material, directional dependent shift factors would increase material model complexity significantly.

Rendezvous algorithms encode a communication pattern that is useful when processors sending data do not know who the receiving processors should be, or vice versa. The idea is to define an intermediate decomposition where datums from different sending processors can ”rendezvous” to perform a computation, in a manner that both the senders and eventual receivers of the results can identify the appropriate rendezvous processor. Originally designed for interpolating between overlaid grids with independent parallel decompositions (Plimpton et al., 2004), we have recently found rendezvous algorithms useful for a variety of operations in particle- or grid-based simulation codes when running large problems on large numbers of processors. In particular, we show they can perform well when a load-balanced intermediate decomposition is randomized and not spatial, requiring all-to-all communication to move data between processors. In this case rendezvous algorithms leverage the large bisection communication bandwidths which parallel machines provide. We describe how rendezvous algorithms work in a scientific computing context and give specific examples for molecular dynamics and Direct Simulation Monte Carlo codes which result in dramatic performance improvements versus simpler algorithms which do not scale as well. We explain how a generic rendezvous algorithm can be implemented, and also point out similarities with the MapReduce paradigm popularized by Google and Hadoop.

In the present study, commercially available detonators with pentaerythritol tetranitrate (PETN) were subjected to elevated temperatures. The detonators were thermally ignited over a range of heating rates to measure ignition delay time and assess detonator violence. The violence of the detonator was quantified by measuring the velocity of the detonator closure disc (or "flyer"). The maximum flyer velocity of a thermally ignited detonator was comparable in magnitude to that obtained by initiating a room temperature pristine detonator with an exploding bridge wire (under the same confinement); however, the high flyer velocity was not an indication of deflagration to detonation transition (DDT) in the thermally ignited detonator. The detonator responded more violently than a thermally ignited detonator when initiated at 95% of the ignition delay time. Inoperability thresholds were also measured by varying the detonator temperature and the threshold was found to be sensitive at detonator temperatures below the melting point of PETN.

Protecting high consequence facilities (HCF) from malicious attacks is challenged by today’s increasingly complex, multi-faceted, and interdependent operational environments and threat domains. Building on current approaches, insights from complex systems and network science can better incorporate multidomain interactions observed in HCF security operations. These observations and qualitative HCF security expert data support invoking a multilayer modeling approach for HCF security to shift from a “reactive” to a “proactive” paradigm that better explores HCF security dynamics and resilience not captured in traditional approaches. After exploring these multi-domain interactions, this paper introduces how systems theory and network science insights can be leveraged to describe HCF security as complex, interdependent multilayer directed networks. A hypothetical example then demonstrates the utility of such an approach, followed by a discussion on key insights and implications of incorporating multilayer network analytical performance measures into HCF security.

The dissociation of aligned, electronically excited H2 (E,F (Formula presented.)), followed by the ionisation of the produced H atom, is analysed via the velocity mapped imaging technique. The dissociation and ionisation processes are accomplished, respectively, by a two- and a one-photon absorption from a single 532-nm laser pulse, while the alignment is induced by a separate 1064-nm laser pulse. The velocity of the produced H+ photofragments shows a weak perpendicular alignment at low alignment laser field values, evolving to strongly parallel for larger fields. We modelled this alignment behaviour with a simple two-state model involving the Stark mixing of the initially-prepared J = 0 with the J = 2 rotational state. This model is able to reproduce all of the observed angular distribution and permits us to extract from the fit the polarisability anisotropy of H2 (E,F) electronic state. We determine this value to be (3.7 ± 1.2) × 103 a.u. As this value is extremely large in comparison to what one would expect from the pure H2 (E,F) electronic state, we hypothesise that this value comes from the 1064-nm laser beam mixing nearby electronic states with the initially laser prepared (E,F) state generating a mixed state (EF**) with an extremely large polarisability anisotropy.

Incident response is an area within cyber defense that is responsible for detecting, mitigating, and preventing threats within a given network. Like other areas of cyber security, incident response is experiencing a shortage of qualified workers which has led to technological development aimed at alleviating labor-related pressures on organizations. A cognitive task analysis was conducted with incident response experts to capture expertise requirements and used an existing construct to help prioritize development of new technology. Findings indicated that current software development incorporates factors such as analyst efficiency and consistency. Gaps were identified regarding communication and team navigation that are inherent to dynamic team environments. This research identified which expertise areas are needed at lower-tier levels of incident response and which of those areas current automation platforms are addressing. These gaps help focus future studies by bridging expertise research to development efforts.

Multi-phase, pseudospectral optimization is employed in a variety of applications, but many of the world-class optimization libraries are closed-source. In this paper we formulate an open-source, object-oriented framework for dynamic optimization using the Pyomo modeling language. This strategy supports the reuse of common code for rapid, error-free model development. Flexibility of our framework is demonstrated on a series of dynamic optimization problems, including multi-phase trajectory optimization using highly accurate pseudospectral methods and controller gain optimization in the presence of stability margin constraints. We employ numerical procedures to improve convergence rates and solution accuracy. We validate our framework using GPOPS-II, a commercial, MATLAB-based optimization program, for a vehicle ascent problem. The trajectory results show close alignment with this state-of-the-art optimization suite.

Second-order optimizers hold intriguing potential for deep learning, but suffer from increased cost and sensitivity to the non-convexity of the loss surface as compared to gradient-based approaches. We introduce a coordinate descent method to train deep neural networks for classification tasks that exploits global convexity of the cross-entropy loss in the weights of the linear layer. Our hybrid Newton/Gradient Descent (NGD) method is consistent with the interpretation of hidden layers as providing an adaptive basis and the linear layer as providing an optimal fit of the basis to data. By alternating between a second-order method to find globally optimal parameters for the linear layer and gradient descent to train the hidden layers, we ensure an optimal fit of the adaptive basis to data throughout training. The size of the Hessian in the second-order step scales only with the number weights in the linear layer and not the depth and width of the hidden layers; furthermore, the approach is applicable to arbitrary hidden layer architecture. Previous work applying this adaptive basis perspective to regression problems demonstrated significant improvements in accuracy at reduced training cost, and this work can be viewed as an extension of this approach to classification problems. We first prove that the resulting Hessian matrix is symmetric semi-definite, and that the Newton step realizes a global minimizer. By studying classification of manufactured two-dimensional point cloud data, we demonstrate both an improvement in validation error and a striking qualitative difference in the basis functions encoded in the hidden layer when trained using NGD. Application to image classification benchmarks for both dense and convolutional architectures reveals improved training accuracy, suggesting gains of second-order methods over gradient descent. A Tensorflow implementation of the algorithm is available at github.com/rgp62/.

It is widely anticipated that climate change will negatively affect both food security and diet diversity. Diet diversity is especially critical for children as it correlates with macro and micronutrient intake important for child development. Despite these anticipated links, little empirical evidence has demonstrated a relationship between diet diversity and climate change, especially across large datasets spanning multiple global regions and with more recent climate data. Here we use survey data from 19 countries and more than 107 000 children, coupled with 30 years of precipitation and temperature data, to explore the relationship of climate to child diet diversity while controlling for other agroecological, geographic, and socioeconomic factors. We find that higher long-term temperatures are associated with decreases in overall child diet diversity, while higher rainfall in the previous year, compared to the long-term average rainfall, is associated with greater diet diversity. Examining six regions (Asia, Central America, North Africa, South America, Southeast Africa, and West Africa) individually, we find that five have significant reductions in diet diversity associated with higher temperatures while three have significant increases in diet diversity associated with higher precipitation. In West Africa, increasing rainfall appears to counterbalance the effect of rising temperature impacts on diet diversity. In some regions, the statistical effect of climate on diet diversity is comparable to, or greater than, other common development efforts including those focused on education, improved water and toilets, and poverty reduction. These results suggest that warming temperatures and increasing rainfall variability could have profound short- and long-term impacts on child diet diversity, potentially undermining widespread development interventions aimed at improving food security.

Direct Numerical Simulations (DNS) data of Moderate or Intense Low-oxygen Dilution (MILD) combustion are analysed to identify the contributions of the autoignition and flame modes. This is performed using an extended Chemical Explosive Mode Analysis (CEMA) which accounts for diffusion effects allowing it to discriminate between deflagration and autoignition. This analysis indicates that in premixed MILD combustion conditions, the main combustion mode is ignition for all dilution and turbulence levels and for the two reactant temperature conditions considered. In non-premixed conditions, the preponderance of the ignition mode was observed to depend on the axial location and mixture fraction stratification. With a large mixture fraction lengthscale, ignition is more preponderant in the early part of the domain while the deflagrative mode increases further downstream. On the other hand, when the mixture fraction lengthscale is small, sequential autoignition is observed. Finally, the various combustion modes are observed to correlate strongly with mixture fraction where lean mixtures are more likely to autoignite while stoichiometric and rich mixtures are more likely to react as deflagrative structures.

The interaction of multiple injections in a diesel engine facilitates a complex interplay between freshly introduced fuel, previous combustion products, and overall combustion. To improve understanding of the relevant processes, high-speed Planar Laser-Induced Fluorescence (PLIF) with 355-nm excitation of formaldehyde and Polycyclic Aromatic Hydrocarbon (PAH) soot precursors is applied to multiple injections of n-dodecane from Engine Combustion Network Spray D, characterized by a converging 189-µm nozzle. High-speed schlieren imaging is applied simultaneously with 50-kHz PLIF excitation to visualize the spray structures, jet penetration, and ignition processes. For the first injection, formaldehyde (as an indicator of low-temperature chemistry) is first found in the jet periphery, after which it quickly propagates through the center of the jet, towards the jet head prior to high-temperature ignition. At second-stage ignition, downstream formaldehyde is consumed rapidly and upstream formaldehyde develops into a quasi-steady structure for as long as the momentum flux from the injector continues. Since the first injection in this work is relatively short, differences to a single long injection are readily observed, ultimately resulting in high-temperature combustion and PAH structures appearing farther upstream after the end of injection. For the second injection in this work, the first formaldehyde signal is significantly advanced because of the entrained high-temperature combustion products, and an obvious premixed burn event does not occur. The propensity for combustion recession after the end of the first injection changes significantly with ambient temperature, thereby affecting the level of interaction between the first- and second injection.

Distribution systems with high levels of solar PV may experience notable changes due to external conditions, such as temperature or solar irradiation. Fault detection methods must be developed in order to support these changes of conditions. This paper develops a method for fast detection, location, and classification of faults in a system with a high level of solar PV. The method uses the Continuous Wavelet Transform (CWT) technique to detect the traveling waves produced by fault events. The CWT coefficients of the current waveform at the traveling wave arrival time provide a fingerprint that is characteristic of each fault type and location. Two Convolutional Neural Networks are trained to classify any new fault event. The method relays of several protection devices and doesn't require communication between them. The results show that for multiple fault scenarios and solar PV conditions, high accuracy for both location and type classification can be obtained.

A computational simulation of various mixing laws for gaseous equations of state using planar traveling shocks for multiple mixtures in three dimensions is analyzed against nominal experimental data. Numerical simulations use the Sandia National Laboratories shock hydrodynamic code CTH and other codes including the thermochemical equilibrium code TIGER and the uncertainty qualification and sensitivity analysis code DAKOTA. The mixtures are 1:1 and a 1:3 molar mixtures of helium and sulfur hexafluoride. The mixing laws to be analyzed are the ideal-gas law, Amagat’s law, Dalton’s law, the Becker–Kistiakowsky–Wilson equation of state (EOS), the exponential 6 EOS, and the Jacobs-Cowperthwaite-Zwisler EOS. Examination of the experimental data with TIGER revealed that the shock strength should not be strong enough to turn the mixture nonideal because the compressibility factor z was essentially unity (z ≈ 1.02). Experimental results show that none of the equations of state are able to accurately predict the properties of the shocked mixture; similar discrepancies have been observed in previous works. Kinetic molecular theory appears to introduce a parameter that offers an explanation regarding the discrepancies. Implementation of the kinetic molecular theory parameter into the EOS is left for future work.

In the Nuclear Security Enterprise (NSE), many high reliability components must be stored for long periods of time before being called on to function a single time. During dormant storage, changes in the performance of these components may occur due to environmental exposures. These exposures may enhance the natural degradation of materials or result in shifts in the performance of electronics. Ongoing assessment of these components is necessary to inform the need for upgrades or replacements to ensure high reliability requirements are being maintained. This paper presents several assessment methodologies that are used and have been proposed for this problem. We also present methods that we believe to be most appropriate for the assessment of nuclear weapons components subjected to dormant storage.

The Source Physics Experiment (SPE) Phase I conducted six underground chemical explosions at the same experimental pad with the goal of characterizing underground explosions to enhance the United States (U.S.) ability to detect and discriminate underground nuclear explosions (UNEs). A fully polarimetric synthetic aperture RADAR (PolSAR) collected imagery in VideoSAR mode during the fifth and sixth explosions in the series (SPE-5 and SPE-6). Previously, we reported the prompt PolSAR surface changes cause by SPE-5 and SPE-6 explosions within seconds or minutes of the underground chemical explosions, including a drop of spatial coherence and polarimetric scattering changes. Therein it was hypothesized that surface changes occurred when surface particles experienced upward acceleration greater than 1 g. Because the SPE site was instrumented with surface accelerometers, we explore that hypothesis and report our findings in this article. We equate explosion-caused prompt surface expressions measured by PolSAR to the prompt surface movement measured by accelerometers. We tie these findings to UNE detection by comparing the PolSAR and accelerometer results to empirical ground motion predictions derived from accelerometer recordings of UNEs collected prior to cessation of U.S. nuclear testing. We find the single threshold greater than 1 g hypothesis is not correct for it does not explain the PolSAR results. Our findings show PolSAR surface coherence spatial extent is highly correlated with surface velocity, both measured and predicted, and the resulting surface deformation extent is corroborated by accelerometer records and the predicted lateral spall extent. PolSAR scattering changes measured during SPE-6 are created by the prompt surface displacement being larger than the spall gap.

Two key mechanical processes exist in the formation of powder compacts. These include the complex kinematics of particle rearrangement as the powder is densified and particle deformation leading to mechanical failure and fragmentation. Experiments measuring the time varying forces across a densifying powder bed have been performed in powders of microcrystalline cellulose with mean particle sizes between 0.4 and 1.2 mm. In these experiments, diagnostics measured the applied and transmitted loads and the bulk powder density. Any insight into the particle behavior must be inferred from deviations in the smoothly increasing stress-density compaction relationship. By incorporating a window in the compaction die body, simultaneous images of particle rearrangement and fracture at the confining window are captured. The images are post-processed in MATLAB® to track individual particle motion during compression. Complimentary discrete element method (DEM) simulations are presented and compared to experiment. The comparison provides insight into applying DEM methods for simulating large or permanent particle deformation and suggests areas for future study.

Abstract–Climate field reconstructions (CFRs) attempt to estimate spatiotemporal fields of climate variables in the past using climate proxies such as tree rings, ice cores, and corals. Data assimilation (DA) methods are a recent and promising new means of deriving CFRs that optimally fuse climate proxies with climate model output. Despite the growing application of DA-based CFRs, little is understood about how much the assimilated proxies change the statistical properties of the climate model data. To address this question, we propose a robust and computationally efficient method, based on functional data depth, to evaluate differences in the distributions of two spatiotemporal processes. We apply our test to study global and regional proxy influence in DA-based CFRs by comparing the background and analysis states, which are treated as two samples of spatiotemporal fields. We find that the analysis states are significantly altered from the climate-model-based background states due to the assimilation of proxies. Moreover, the difference between the analysis and background states increases with the number of proxies, even in regions far beyond proxy collection sites. Our approach allows us to characterize the added value of proxies, indicating where and when the analysis states are distinct from the background states. Supplementary materials for this article are available online.

Deep neural networks (DNNs) have achieved state-of-the-art performance across a variety of traditional machine learning tasks, e.g., speech recognition, image classification, and segmentation. The ability of DNNs to efficiently approximate high-dimensional functions has also motivated their use in scientific applications, e.g., to solve partial differential equations and to generate surrogate models. In this paper, we consider the supervised training of DNNs, which arises in many of the above applications. We focus on the central problem of optimizing the weights of the given DNN such that it accurately approximates the relation between observed input and target data. Devising effective solvers for this optimization problem is notoriously challenging due to the large number of weights, nonconvexity, data sparsity, and nontrivial choice of hyperparameters. To solve the optimization problem more efficiently, we propose the use of variable projection (VarPro), a method originally designed for separable nonlinear least-squares problems. Our main contribution is the Gauss–Newton VarPro method (GNvpro) that extends the reach of the VarPro idea to nonquadratic objective functions, most notably cross-entropy loss functions arising in classification. These extensions make GNvpro applicable to all training problems that involve a DNN whose last layer is an affine mapping, which is common in many state-of-the-art architectures. In our four numerical experiments from surrogate modeling, segmentation, and classification, GNvpro solves the optimization problem more efficiently than commonly used stochastic gradient descent (SGD) schemes. Also, GNvpro finds solutions that generalize well, and in all but one example better than well-tuned SGD methods, to unseen data points.

Linear elastic fracture mechanics theory predicts a parabolic crack opening profile. However, direct observation of crack tip shape in situ for brittle materials is challenging due to the small size of the active crack tip region. By leveraging advances in optical microscopy techniques and using a soft brittle hydrogel material, we can measure crack geometry on the micron scale. For glasses and ceramics, expected crack opening displacements are on the order of nanometers. However, for hydrogels, we can achieve crack opening displacements on the order of hundreds of microns or larger while maintaining brittle fracture behavior. Knowing the elastic properties, we can use crack geometry to calculate the stress intensity factor, K, and energy release rate, G, during propagation. Assuming the gel is hyperelastic, we can also approximate the size of the nonlinear region ahead of the crack tip. Geometric measurement of fracture properties eliminates the need to measure complex boundary and loading conditions, allowing us to explore new methods of inducing crack propagation. Further, this allows us to define measures of fracture resistance in materials that do not fit the traditionally defined theories of fracture mechanics.

Several recent workshops conducted by the DOE Advanced Scientific Computing Research program have established the fact that the complexity of developing applications and executing them on high-performance computing (HPC) systems is rising at a rate which will make it nearly impossible to continue to achieve higher levels of performance and scalability. Absent an alternative approach to managing this ever-growing complexity, HPC systems will become increasingly difficult to use. A more holistic approach to designing and developing applications and managing system resources is required. This paper outlines a research strategy for managing the increasing the complexity by providing the programming environment, software stack, and hardware capabilities needed for autonomous resource management of HPC systems. Developing portable applications for a variety of HPC systems of varying scale requires a paradigm shift from the current approach, where applications are painstakingly mapped to individual machine resources, to an approach where machine resources are automatically mapped and optimized to applications as they execute. Achieving such automated resource management for HPC systems is a daunting challenge that requires significant sustained investment in exploring new approaches and novel capabilities in software and hardware that span the spectrum from programming systems to device-level mechanisms. This paper provides an overview of the functionality needed to enable autonomous resource management and optimization and describes the components currently being explored at Sandia National Laboratories to help support this capability.

Physical vapor deposition (PVD) of high explosives can produce energetic samples with unique microstructure and morphology compared to traditional powder processing techniques, but challenges may exist in fabricating explosive films without defects. Deposition conditions and substrate material may promote microcracking and other defects in the explosive films. In this study, we investigate effects of engineered microscale defects (gaps) on detonation propagation and failure for pentaerythritol tetranitrate (PETN) films using ultra-high-speed refractive imaging and hydrocode modelling. Observations of the air shock above the gap reveal significant instabilities during gap crossing and re-ignition.

Oxiranes are a class of cyclic ethers formed in abundance during low-temperature combustion of hydrocarbons and biofuels, either via chain-propagating steps that occur from unimolecular decomposition of β-hydroperoxyalkyl radicals (β-̇QOOH) or from reactions of HOȮ with alkenes. Ethyloxirane is one of four alkyl-substituted cyclic ether isomers produced as an intermediate from n-butane oxidation. While rate coefficients for β-̇QOOH → ethyloxirane + ȮH are reported extensively, subsequent reaction mechanisms of the cyclic ether are not. As a result, chemical kinetics mechanisms commonly adopt simplified chemistry to describe ethyloxirane consumption by convoluting several elementary reactions into a single step, which may introduce mechanism truncation error—uncertainty derived from missing or incomplete chemistry. The present work provides fundamental insight on reaction mechanisms of ethyloxirane in support of ongoing efforts to minimize mechanism truncation error. Reaction mechanisms are inferred from the detection of products during chlorine atom-initiated oxidation experiments using multiplexed photoionization mass spectrometry conducted at 10 Torr and temperatures of 650 K and 800 K. To complement the experiments, calculations of stationary point energies were conducted using the ccCA-PS3 composite method on ̇R + O2 potential energy surfaces for the four ethyloxiranyl radical isomers, which produced barrier heights for 24 reaction pathways. In addition to products from ̇QOOH → cyclic ether + ȮH and ̇R + O2 → conjugate alkene + HOȮ, both of which were significant pathways and are prototypical to alkane oxidation, other species were identified from ring-opening of both ethyloxiranyl and ̇QOOH radicals. The latter occurs when the unpaired electron is localized on the ether group, causing the initial ̇QOOH structure to ring-open and form a resonance-stabilized ketohydroperoxide-type radical. The present work provides the first analysis of ethyloxirane oxidation chemistry, which reveals that consumption pathways are complex and may require an expansion of submechanisms to increase the fidelity of chemical kinetics mechanisms.

Neuromorphic computing with spintronic devices has been of interest due to the limitations of CMOS-driven von Neumann computing. Domain wall-magnetic tunnel junction (DW-MTJ) devices have been shown to be able to intrinsically capture biological neuron behavior. Edgy-relaxed behavior, where a frequently firing neuron experiences a lower action potential threshold, may provide additional artificial neuronal functionality when executing repeated tasks. In this letter, we demonstrate that this behavior can be implemented in DW-MTJ artificial neurons via three alternative mechanisms: shape anisotropy, magnetic field, and current-driven soft reset. Using micromagnetics and analytical device modeling to classify the Optdigits handwritten digit dataset, we show that edgy-relaxed behavior improves both classification accuracy and classification rate for ordered datasets while sacrificing little to no accuracy for a randomized dataset. This letter establishes methods by which artificial spintronic neurons can be flexibly adapted to datasets.

The intentional spread of disinformation is not a new challenge for the scientific world. We have seen it perpetuate the idea of a flat earth, convince communities that vaccines are more dangerous than helpful, and even suggest a connection between the “5G” communication infrastructure and COVID-19.1 Nor is disinformation a new phenomenon in the weapons inspection arena. Weapons inspectors themselves are often forced to sift through alternative narratives of events and inconsistent reporting, and they regularly see their credibility and conclusions questioned in the face of government politics or public biases. But certain recent disinformation campaigns have become so overwhelmingly comprehensive and effective that they constitute a new kind of threat. By preventing accountability for clear violations of international law, these campaigns have created a challenge to the survival of arms control treaties themselves. If weapons inspectors cannot regain the trust of the international community in the face of this challenge, it will be increasingly difficult to ensure compliance with arms control and disarmament treaties going forward. In this essay, I will briefly discuss one of the most comprehensive disinformation efforts of the past decade: the disinformation campaign used to prevent accountability for Syria's repeated use of chemical weapons. After this discussion, I will propose one possible approach to help protect the credibility of disarmament experts and weapons inspectors in the face of pervasive disinformation. This approach will require a concerted effort to connect and support compliance experts and to understand and explain their expertise across cultural, political, national, economic, and religious divides.

This paper offers new insights into a partial fuel stratification (PFS) combustion strategy that has proven to be effective at stabilizing overall lean combustion in direct injection spark ignition engines. To this aim, high spatial and temporal resolution optical diagnostics were applied in an optically accessible engine working in PFS mode for two fuels and two different durations of pilot injection at the time of spark: 210 μs and 330 μs for E30 (gasoline blended with ethanol by 30% volume fraction) and gasoline, respectively. In both conditions, early injections during the intake stroke were used to generate a well-mixed lean background. The results were compared to rich, stoichiometric and lean well-mixed combustion with different spark timings. In the PFS combustion process, it was possible to detect a non-spherical and highly wrinkled blue flame, coupled with yellow diffusive flames due to the combustion of rich zones near the spark plug. The initial flame spread for both PFS cases was faster compared to any of the well-mixed cases (lean, stoichiometric and rich), suggesting that the flame propagation for PFS is enhanced by both enrichment and enhanced local turbulence caused by the pilot injection. Different spray evolutions for the two pilot injection durations were found to strongly influence the flame kernel inception and propagation. PFS with pilot durations of 210 μs and 330 μs showed some differences in terms of shapes of the flame front and in terms of extension of diffusive flames. Yet, both cases were highly repeatable.

Solving dense systems of linear equations is essential in applications encountered in physics, mathematics, and engineering. This paper describes our current efforts toward the development of the ADELUS package for current and next generation distributed, accelerator-based, high-performance computing platforms. The package solves dense linear systems using partial pivoting LU factorization on distributed-memory systems with CPUs/GPUs. The matrix is block-mapped onto distributed memory on CPUs/GPUs and is solved as if it was torus-wrapped for an optimal balance of computation and communication. A permutation operation is performed to restore the results so the torus-wrap distribution is transparent to the user. This package targets performance portability by leveraging the abstractions provided in the Kokkos and Kokkos Kernels libraries. Comparison of the performance gains versus the state-of-the-art SLATE and DPLASMA GESV functionalities on the Summit supercomputer are provided. Preliminary performance results from large-scale electromagnetic simulations using ADELUS are also presented. The solver achieves 7.7 Petaflops on 7600 GPUs of the Sierra supercomputer translating to 16.9% efficiency.

We analyze the coupling into a slotted cylindrical cavity operating at fundamental cavity modal frequencies overlapping with the slot’s first resonance frequency through an unmatched formulation that accounts for the slot’s absorption and radiation processes. The model is validated through full-wave simulations and experimental data. We then couple the unmatched formulation to a perturbation theory model to investigate an absorber within the cavity to reduce the interior field strength, also validated with full-wave simulations and experiments. These models are pivotal to understanding the physical processes involved in the electromagnetic penetration through slots, and may constitute design tools to mitigate electromagnetic interference effects within cavities.

Abstract not provided.

The accurate construction of a surrogate model is an effective and efficient strategy for performing Uncertainty Quantification (UQ) analyses of expensive and complex engineering systems. Surrogate models are especially powerful whenever the UQ analysis requires the computation of statistics which are difficult and prohibitively expensive to obtain via a direct sampling of the model, e.g. high-order moments and probability density functions. In this paper, we discuss the construction of a polynomial chaos expansion (PCE) surrogate model for radiation transport problems for which quantities of interest are obtained via Monte Carlo simulations. In this context, it is imperative to account for the statistical variability of the simulator as well as the variability associated with the uncertain parameter inputs. More formally, in this paper we focus on understanding the impact of the Monte Carlo transport variability on the recovery of the PCE coefficients. We are able to identify the contribution of both the number of uncertain parameter samples and the number of particle histories simulated per sample in the PCE coefficient recovery. Our theoretical results indicate an accuracy improvement when using few Monte Carlo histories per random sample with respect to configurations with an equivalent computational cost. These theoretical results are numerically illustrated for a simple synthetic example and two configurations of a one-dimensional radiation transport problem in which a slab is represented by means of materials with uncertain cross sections.

Abstract not provided.

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Explosively driven ferroelectric generators (FEG) are used as pulsed power sources in many applications that require a compact design that delivers a short high-voltage and high-current pulse. A mechanical shock applied to ferroelectrics releases bound electrical charge through a combination of piezoelectric, domain reorientation, and phase transformation effects. Lead-zirconate-titanate (PZT) 95/5 lies near the ferroelectric (FE)-antiferroelectric (AF) phase boundary and readily transforms to AF phase under compression because AF has a smaller unit volume. This makes it a popular choice for FEGs as the FE-AF transformation completely releases all the stored dipole charge. The complexity of piezoelectric, domain reorientation, and phase transformation behaviors under high deviatoric stress makes modeling this FE to AF transformation and the accompanying charge release challenging. The mode and direction of domain reorientation and phase transformation varies significantly with different deviatoric and hydrostatic stress states. Microstructure changes due to domain reorientation and phase alter the piezoelectric properties of the material. Inaccuracies in modeling any one of these phenomena can result in inaccurate electrical response. This work demonstrates a material model that accurately captures the linear piezoelectric, domain reorientation and phase transformation phenomena by using a micromechanical approach to approximate the changes in domain-structure.

Abstract not provided.

To meet the extreme compute demands for deep learning across commercial and scientific applications, dataflow accelerators are becoming increasingly popular. While these “domain-specific” accelerators are not fully programmable like CPUs and GPUs, they retain varying levels of flexibility with respect to data orchestration, i.e., dataflow and tiling optimizations to enhance efficiency. There are several challenges when designing new algorithms and mapping approaches to execute the algorithms for a target problem on new hardware. Previous works have addressed these challenges individually. To address this challenge as a whole, in this work, we present a HW-SW codesign ecosystem for spatial accelerators called Union within the popular MLIR compiler infrastructure. Our framework allows exploring different algorithms and their mappings on several accelerator cost models. Union also includes a plug-and-play library of accelerator cost models and mappers which can easily be extended. The algorithms and accelerator cost models are connected via a novel mapping abstraction that captures the map space of spatial accelerators which can be systematically pruned based on constraints from the hardware, workload, and mapper. We demonstrate the value of Union for the community with several case studies which examine offloading different tensor operations (CONV/GEMM/Tensor Contraction) on diverse accelerator architectures using different mapping schemes.

In this study, we present spectral equivalence results for high-order tensor product edge- and face-based finite elements for the H(curl) and H(div) function spaces. Specifically, we show for certain choices of shape functions that the mass and stiffness matrices of the high-order elements are spectrally equivalent to those for an assembly of low-order elements on the associated Gauss-Lobatto-Legendre mesh. Based on this equivalence, efficient preconditioners can be designed with favorable computational complexity. Numerical results are presented which confirm the theory and demonstrate the benefits of the equivalence results for overlapping Schwarz preconditioners.

The Quantum Scientific Computing Open User Testbed (QSCOUT) at Sandia National Laboratories is a trapped-ion qubit system designed to evaluate the potential of near-term quantum hardware in scientific computing applications for the U.S. Department of Energy and its Advanced Scientific Computing Research program. Similar to commercially available platforms, it offers quantum hardware that researchers can use to perform quantum algorithms, investigate noise properties unique to quantum systems, and test novel ideas that will be useful for larger and more powerful systems in the future. However, unlike most other quantum computing testbeds, the QSCOUT allows both quantum circuit and low-level pulse control access to study new modes of programming and optimization. The purpose of this article is to provide users and the general community with details of the QSCOUT hardware and its interface, enabling them to take maximum advantage of its capabilities.

In the Republic of Uganda, it is estimated that nearly 28 million people lack access to clean and safe drinking water [1]. The authors developed a model by performing multiple linear regression using predictors air temperature, irradiance, and wind speeds in two rural areas to determine the suitability of a proposed system to assist. The system consists of a commercial grade vertical axis wind turbine (VAWT) with embedded solar cells capable of providing water pumping and small-scale electricity generation. A suite of equations was used alongside the regression models to determine how to adjust the mechanical properties of the turbine such that the solar and wind energy function as mutually redundant drivers for both a small-scale electricity generator and water pumping system. The results are consistent with the following: solar output variation of 20% (from 4.5 to 5.5 W/m2) [2], [3]; 3.7 m/s to 6m/sec require to operate the turbine; and water pumped at the rate of 3748.54 ft-lbs to 42314.72 ft-lbs per hour. The primary researcher for this project has applied for and received a provisional patent to advance the VAWT/SES technology. This year a utility patent was filed to move the complete energy renewal system toward commercialization.

Abstract not provided.

This paper reports on the development of a self-synchronizing underwater acoustic network developed for remote monitoring of mooring loads in Wave Energy Converters (WECs). This network uses Time Division Multiple Access and operates self-contained with the ability for users to remotely transmit commands to the network as needed. Each node is a self-contained unit, consisting of a protocol adaptor board, an FAU-DPAM underwater acoustic modem and a battery pack. A node can be connected to a load cell, to a topside user or to the WEC. Every node is swapable. The protocol adaptor board, named Protocol Adaptor for Digital LOad Cell (PADLOC) supports a variety of digital load cell message formats (CAN, MODBUS, custom ASCII) and underwater acoustic modem serial formats. PADLOC enables topside users to connect to separate load cells through a user-specific command. This is especially important if the user is monitoring multiple load cells during deployment or maintenance, when the primary data system may be offline. Each PADLOC board handles formatting, buffering and has a one-on-one serial connection with each pair (node) of a digital load cell and acoustic modem. In addition, each PADLOC board handles the timekeeping and power saving features for each node. The only limitation is the data bit rate and delay limitations associated with the underwater acoustic modem. A four node self-synchronizing network has been developed to demonstrate the load cell monitoring capability using the PADLOC technology on the CalWave WEC.

Both the data science and scientific computing communities are embracing GPU acceleration for their most demanding workloads. For scientific computing applications, the massive volume of code and diversity of hardware platforms at supercomputing centers has motivated a strong effort toward performance portability. This property of a program, denoting its ability to perform well on multiple architectures and varied datasets, is heavily dependent on the choice of parallel programming model and which features of the programming model are used. In this paper, we evaluate performance portability in the context of a data science workload in contrast to a scientific computing workload, evaluating the same sparse matrix kernel on both. Among our implementations of the kernel in different performance-portable programming models, we find that many struggle to consistently achieve performance improvements using the GPU compared to simple one-line OpenMP parallelization on high-end multicore CPUs. We show one that does, and its performance approaches and sometimes even matches that of vendor-provided GPU math libraries.

Gate set tomography (GST) is a protocol for detailed, predictive characterization of logic operations (gates) on quantum computing processors. Early versions of GST emerged around 2012-13, and since then it has been refined, demonstrated, and used in a large number of experiments. This paper presents the foundations of GST in comprehensive detail. The most important feature of GST, compared to older state and process tomography protocols, is that it is calibration-free. GST does not rely on pre-calibrated state preparations and measurements. Instead, it characterizes all the operations in a gate set simultaneously and self-consistently, relative to each other. Long sequence GST can estimate gates with very high precision and efficiency, achieving Heisenberg scaling in regimes of practical interest. In this paper, we cover GST’s intellectual history, the techniques and experiments used to achieve its intended purpose, data analysis, gauge freedom and fixing, error bars, and the interpretation of gauge-fixed estimates of gate sets. Our focus is fundamental mathematical aspects of GST, rather than implementation details, but we touch on some of the foundational algorithmic tricks used in the pyGSTi implementation.

On high-performance computing (HPC) systems, job allocation strategies control the placement of a job among available nodes. As the placement changes a job's communication performance, allocation can significantly affects execution times of many HPC applications. Existing allocation strategies typically make decisions based on resource limit, network topology, communication patterns, etc. However, system network performance at runtime is seldom consulted in allocation, even though it significantly affects job execution times.In this work, we demonstrate using monitoring data to improve HPC systems' performance by proposing a NetworkData-Driven (NeDD) job allocation framework, which monitors the network performance of an HPC system at runtime and allocates resources based on both network performance and job characteristics. NeDD characterizes system network performance by collecting the network traffic statistics on each router link, and it characterizes a job's sensitivity to network congestion by collecting Message Passing Interface (MPI) statistics. During allocation, NeDD pairs network-sensitive (network-insensitive) jobs with nodes whose parent routers have low (high) network traffic. Through experiments on a large HPC system, we demonstrate that NeDD reduces the execution time of parallel applications by 11% on average and up to 34%.

The ability to handle data is critical at all stages of marine energy development. The Marine and Hydrokinetic Toolkit (MHKiT) is an open-source marine energy software, which includes modules for ingesting, applying quality control, processing, visualizing, and managing data. MHKiT-Python and MHKiT-MATLAB provide robust and verified functions that are needed by the marine energy community to standardize data processing. Calculations and visualizations adhere to International Electrotechnical Commission technical specifications and other guidelines. A resource assessment of National Data Buoy Center buoy 46050 near PACWAVE is performed using MHKiT and we discuss comparisons to the resource assessment provided performed by Dunkle et al. (2020).

The Domain Name System (DNS) has been frequently abused for distributed denial-of-service (DDoS) attacks and cache poisoning because it relies on the User Datagram Protocol (UDP). Since UDP is connection-less, it is trivial for an attacker to spoof the source of a DNS query or response. While other secure transport mechanisms provide identity management, such as the Transmission Control Protocol (TCP) and DNS Cookies, there is currently no method for a client to state that they only use a given protocol. This paper presents a new method to allow protocol enforcement: DNS Protocol Advertisement Records (DPAR). Advertisement records allow Internet Protocol (IP) address subnets to post a public record in the reverse DNS zone stating which DNS mechanisms are used by their clients. DNS servers may then look up this record and require a client to use the stated mechanism, in turn preventing an attacker from sending spoofed messages over UDP. In this paper, we define the specification for DNS Protocol Advertisement Records, considerations that were made, and comparisons to alternative approaches. We additionally estimate the effectiveness of advertisements in preventing DDoS attacks and the expected burden to DNS servers.

Industrial and security communities leverage x-ray computed tomography for several applications in non-destructive evaluation such as material detection and metrology. Many of these applications ultimately reach a limit as most x-ray systems have a nonlinear mathematical operator due to the Bremsstrahlung radiation emitted from the x-ray source. This work proposes a design of a multi-metal pattered anode coupled with a hyperspectral X-ray detector to improve spatial resolution, absorption signal, and overall data quality for various quantitative. The union of a multi-metal pattered anode x-ray source with an energy-resolved photon counting detector permits the generation and detection of a preferential set of X-ray energy peaks. When photons about the peaks are detected, while rejecting photons outside this neighborhood, the overall quality of the image is improved by linearizing the operator that defines the image formation. Additionally, the effective X-ray focal spot size allows for further improvement of the image quality by increasing resolution. Previous works use machine learning techniques to analyze the hyperspectral computed tomography signal and reliably identify and discriminate a wide range of materials based on a material's composition, improving data quality through a multi-material pattern anode will further enhance these identification and classification methods. This work presents initial investigations of a multi-metal patterned anode along with a hyperspectral detector using a general-purpose Monte Carlo particle transport code known as PHITS version 3.24. If successful, these results will have tremendous impact on several nondestructive evaluation applications in industry, security, and medicine.

This study analyzes the ability of various processing techniques to reduce leakage current in vertical GaN MOS devices. Careful analysis is required to determine suitable gate dielectric materials in vertical GaN MOSFET devices since they are largely responsible for determination of threshold voltage, gate leakage reduction, and semiconductor/dielectric interface traps. SiO2, Al2 O3, and HfO2 films were deposited by Atomic Layer Deposition (ALD) and subjected to treatments nominally identical to those in a vertical GaN MOSFET fabrication sequence. This work determines mechanisms for reducing gate leakage by reduction of surface contaminants and interface traps using pre-deposition cleans, elevated temperature depositions, and post-deposition anneals. Breakdown measurements indicate that ALD Al2O3 is an ideal candidate for a MOSFET gate dielectric, with a breakdown electric field near 7.5 MV/cm with no high temperature annealing required to increase breakdown strength. SiO2 ALD films treated with a post deposition anneal at 850 °C for 30 minutes show significant reduction in leakage current while maintaining breakdown at 5.5 MV/cm. HfO2 films show breakdown nominally identical to annealed SiO2 films, but with significantly higher leakage. Additionally, HfO2 films show more sensitivity to high temperature annealing suggesting that more research into surface cleans is necessary to improving these films for MOSFET gate applications.

This paper focuses on the role of the Marine Renewable Energy (MRE) Software Knowledge Hub on the Portal and Repository for Information on Marine Renewable Energy (PRIMRE). The MRE Software Knowledge Hub provides online services for MRE software users and developers, and seeks to develop assessments and recommendations for improving MRE software in the future. Online software discovery platforms, known as the Code Hub and the Code Catalog, are provided. The Code Hub is a collection of open-source MRE software that includes a landing page with search functionality, linked to files hosted on the MRE Code Hub GitHub organization. The Code Catalog is a searchable online platform for discovery of useful (open-source or commercial) software packages, tools, codes, and other software products. To gather information about the existing MRE software landscape, a software survey is being performed, the preliminary results of which are presented herein. Initially, the data collected in the MRE software survey will be used to populate the MRE Software knowledge hub on PRIMRE, and future work will use data from the survey to perform a gap analysis and develop a vision for future software development. Additionally, as one of PRIMRE’s roles is to support development of MRE software within project partners, a silo of knowledge relating to best practices has been gathered. An early draft of new guidance developed from this knowledge is presented.

Typical Type-4 wind turbines use DC-link inverters to couple the electrical machine to the power grid. Each wind turbine has two power conversion steps. Therefore, an N-turbine farm will have 2N power converters. This work presents a DC bus collection system for a type-4 wind farm that reduces the overall required number of converters and minimizes the energy storage system (ESS) requirements. This approach requires one conversion step per turbine, one converter for the ESS and a single grid coupling converter, which leads to N + 2 converters for the wind farm which will result in significant cost savings. However, one of the trade-offs for a DC collection system is the need for increased energy storage to filter the power variations and improve power quality to the grid. This paper presents a novel approach to an effective DC bus collection system design. The DC collection for the wind farm implements a power phasing control method between turbines that filter the variations and improves power quality while minimizing the need for added energy storage system hardware and improved power quality. The phasing control takes advantage of a novel power packet network concept with nonlinear power flow control design techniques that guarantees both stable and enhanced dynamic performance. This paper presents the theoretical design of the DC collection and phasing control. To demonstrate the efficacy of this approach detailed numerical simulation examples are presented.

This work provides the first demonstration of vertical GaN Junction Barrier Schottky (JBS) rectifiers fabricated by etch and regrowth of p-GaN. A reverse blocking voltage near 1500 V was achieved at 1 mA reverse leakage, with a sub 1 V turn-on and a specific on-resistance of 10 mΩ-cm2. This result is compared to other reported JBS devices in the literature and our device demonstrates the lowest leakage slope at high reverse bias. A large initial leakage current is present near zero-bias which is attributed to a combination of inadequate etch-damage removal and passivation induced leakage current.

This paper presents an isolated bidirectional dc/dc converter for battery energy storage applications. Two main features of the proposed circuit topology are high voltage-conversion ratio and reduced battery current ripple. The primary side circuit is a quasi-switched-capacitor circuit with reduced voltage stress on switching devices and a 3:1 voltage step down ratio, which reduces the turns ratio of the transformer to 6:1:1. The secondary side circuit has an interleaved operation by utilizing the split magnetizing inductance of the transformer, which not only helps to increase the step down ratio but also reduces the battery current ripple. Similar to the dual-active-bridge circuit, the phase shift control is implemented to regulate the operation power of the circuit. A 1-kW, 300-kHz, 380-420 V/20-33 V GaN-based circuit prototype is currently under fabrication. The preliminary test results are presented.

This study presents a numerical model of a WEC array. The model will be used in subsequent work to study the ability of data assimilation to support power prediction from WEC arrays and WEC array design. In this study, we focus on design, modeling, and control of the WEC array. A case study is performed for a small remote Alaskan town. Using an efficient method for modeling the linear interactions within a homogeneous array, we produce a model and predictionless feedback controllers for the devices within the array. The model is applied to study the effects of spectral wave forecast errors on power output. The results of this analysis show that the power performance of the WEC array will be most strongly affected by errors in prediction of the spectral period, but that reductions in performance can realistically be limited to less than 10% based on typical data assimilation based spectral forecasting accuracy levels.

Ultrasonic waves can be used to transfer power and data to electronic devices in sealed metallic enclosures. Two piezoelectric transducers are used to transmit and receive elastic waves that propagate through the metal. For an efficient power transfer, both transducers are typically bonded to the metal or coupled with a gel which limits the device portability. We present an ultrasonic power transfer system with a detachable transmitter that uses a dry elastic layer and a magnetic joint for efficient coupling. We show that the system can deliver more than 2 W of power to an electric load with 50% efficiency.

For systems that require complete metallic enclosures, it is impossible to power and communicate with interior electronics using conventional electromagnetic techniques. Instead, pairs of ultrasonic transducers can be used to send and receive elastic waves through the enclosure, forming an equivalent electrical transmission line that bypasses the Faraday cage effect. These mechanical communication systems introduce the possibility for electromechanical crosstalk between channels on the same barrier, in which receivers output erroneous electrical signals due to ultrasonic guided waves generated by transmitters in adjacent communication channels. To minimize this crosstalk, this work investigates the use of a phononic crystal/metamaterial machined into the barrier via periodic grooving. Barriers with simultaneous ultrasonic power and data transfer are fabricated and tested to measure the effect of grooving on crosstalk between channels.

Both the data science and scientific computing communities are embracing GPU acceleration for their most demanding workloads. For scientific computing applications, the massive volume of code and diversity of hardware platforms at supercomputing centers has motivated a strong effort toward performance portability. This property of a program, denoting its ability to perform well on multiple architectures and varied datasets, is heavily dependent on the choice of parallel programming model and which features of the programming model are used. In this paper, we evaluate performance portability in the context of a data science workload in contrast to a scientific computing workload, evaluating the same sparse matrix kernel on both. Among our implementations of the kernel in different performance-portable programming models, we find that many struggle to consistently achieve performance improvements using the GPU compared to simple one-line OpenMP parallelization on high-end multicore CPUs. We show one that does, and its performance approaches and sometimes even matches that of vendor-provided GPU math libraries.

Poisson Tensor Factorization (PTF) is an important data analysis method for analyzing patterns and relationships in multiway count data. In this work, we consider several algorithms for computing a low-rank PTF of tensors with sparse count data values via maximum likelihood estimation. Such an approach reduces to solving a nonlinear, non-convex optimization problem, which can leverage considerable parallel computation due to the structure of the problem. However, since the maximum likelihood estimator corresponds to the global minimizer of this optimization problem, it is important to consider how effective methods are at both leveraging this inherent parallelism as well as computing a good approximation to the global minimizer. In this work we present comparisons of multiple methods for PTF that illustrate the tradeoffs in computational efficiency and accurately computing the maximum likelihood estimator. We present results using synthetic and real-world data tensors to demonstrate some of the challenges when choosing a method for a given tensor.

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. A method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improved decision-making in the field and to further discern technology performance during post-drilling evaluations. Drill rig parameter data were acquired by drilling contractor Frontier Drilling and evaluated for two wells drilled at the DOE-sponsored site, Utah Frontier Observatory for Research in Geothermal Energy (FORGE). The subject wells include: 1) FORGE 16A(78)-32, a directional well with vertical depth to a kick-off point at 5892 ft and a 65 degree tangent to a measured depth of 10987 ft and, 2) FORGE 56-32, a vertical monitoring well to a measured depth of 9145 ft. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) along with other model constraints in computational algorithms. The method is used to evaluate overall bit performance, develop rock strength approximations, determine bit aggressiveness, characterize frictional energy losses, evaluate bit wear rates, and detect the presence of drillstring vibrations contributing to bit failure; comparisons are made to observations of bit wear and damage. Analyses are also presented to correlate performance to bit run cost drivers to provide guidance on the relative tradeoff between bit penetration rate and life. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs.

This paper reports results from an ongoing investigation on potential ways to utilize small wave energy devices that can be transported in, and deployed from, torpedo tubes. The devices are designed to perform designated ocean measurement operations and thus need to convert enough energy to power onboard sensors, while storing any excess energy to support vehicle recharging operations. Examined in this paper is a traditional tubular oscillating water column device, and particular interest here is in designs that lead to optimization of power converted from shorter wind sea waves. A two step design procedure is investigated here, wherein a more approximate two-degree-of-freedom model is first used to identify relative dimensions (of device elements) that optimize power conversion from relative oscillations between the device elements. A more rigorous mathematical model based on the hydrodynamics of oscillating pressure distributions within solid oscillators is then used to provide the hydrodynamic coefficients, forces, and flow rates for the device. These results provide a quick but rigorous way to estimate the energy conversion performance of the device in various wave climates, while enabling more accurate design of the power takeoff and energy storage systems.

The Sandia Critical Experiments (SCX) Program provides a specialized facility for performing water moderated and reflected critical experiments with UO₂ fuel rod arrays. A history of safe reactor operations and flexibility in reactor core configuration has resulted in the completion of several benchmark critical experiment evaluations that are published in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook. The LEUCOMP-THERM-078 and LEU-COMP-THERM-080 evaluations from the handbook provide similar cases for reference. The set of experiments described here were performed using the Seven Percent Critical Experiment (7uPCX) fuel to measure the effects of decreasing the fuel-to-water volume ratio on the critical array size. This was accomplished by using fuel loading patterns to effectively increase the pitch of the fuel arrays in the assembly. The fuel rod pitch variations provided assembly configurations that ranged from strongly undermoderated to slightly overmoderated.

In this work, we show that reduced communication algorithms for distributed stochastic gradient descent improve the time per epoch and strong scaling for the Generalized Canonical Polyadic (GCP) tensor decomposition, but with a cost, achieving convergence becomes more difficult. The implementation, based on MPI, shows that while one-sided algorithms offer a path to asynchronous execution, the performance benefits of optimized allreduce are difficult to best.

On high-performance computing (HPC) systems, job allocation strategies control the placement of a job among available nodes. As the placement changes a job's communication performance, allocation can significantly affects execution times of many HPC applications. Existing allocation strategies typically make decisions based on resource limit, network topology, communication patterns, etc. However, system network performance at runtime is seldom consulted in allocation, even though it significantly affects job execution times.In this work, we demonstrate using monitoring data to improve HPC systems' performance by proposing a NetworkData-Driven (NeDD) job allocation framework, which monitors the network performance of an HPC system at runtime and allocates resources based on both network performance and job characteristics. NeDD characterizes system network performance by collecting the network traffic statistics on each router link, and it characterizes a job's sensitivity to network congestion by collecting Message Passing Interface (MPI) statistics. During allocation, NeDD pairs network-sensitive (network-insensitive) jobs with nodes whose parent routers have low (high) network traffic. Through experiments on a large HPC system, we demonstrate that NeDD reduces the execution time of parallel applications by 11% on average and up to 34%.

We present StressBench, a network benchmarking framework written for testing MPI operations and file I/O concurrently. It is designed specifically to execute MPI communication and file access patterns that are representative of real-world scientific applications. Existing tools consider either the worst case congestion with small abstract patterns or peak performance with simplistic patterns. StressBench allows for a richer study of congestion by allowing orchestration of network load scenarios that are representative of those typically seen at HPC centres, something that is difficult to achieve with existing tools. We demonstrate the versatility of the framework from micro benchmarks through to finely controlled congested runs across a cluster. Validation of the results using four proxy application communication schemes within StressBench against parent applications shows a maximum difference of 15%. Using the I/O modeling capabilities of StressBench, we are able to quantify the impact of file I/O on application traffic showing how it can be used in procurement and performance studies.

Resilience has been defined as a priority for the US critical infrastructure. This paper presents a process for incorporating resiliency-derived metrics into security system evaluations. To support this analysis, we used a multi-layer network model (MLN) reflecting the defined security system of a hypothetical nuclear power plant to define what metrics would be useful in understanding a system's ability to absorb perturbation (i.e., system resilience). We defined measures focusing on the system's criticality, rapidity, diversity, and confidence at each network layer, simulated adversary path, and the system as a basis for understanding the system's resilience. For this hypothetical system, our metrics indicated the importance of physical infrastructure to overall system criticality, the relative confidence of physical sensors, and the lack of diversity in assessment activities (i.e., dependence on human evaluations). Refined model design and data outputs will enable more nuanced evaluations into temporal, geospatial, and human behavior considerations. Future studies can also extend these methodologies to capture respond and recover aspects of resilience, further supporting the protection of critical infrastructure.

The Domain Name System (DNS) has been frequently abused for Distributed Denial of Service (DDoS) attacks and cache poisoning because it relies on the User Datagram Protocol (UDP). Since UDP is connection-less, it is trivial for an attacker to spoof the source of a DNS query or response. DNS Cookies, a protocol standardized in 2016, add pseudo-random values to DNS packets to provide identity management and prevent spoofing attacks. In this paper, we present the first study measuring the deployment of DNS Cookies in nearly all aspects of the DNS architecture. We also provide an analysis of the current benefits of DNS Cookies and the next steps for stricter deployment. Our findings show that cookie use is limited to less than 30% of servers and 10% of recursive clients. We also find several configuration issues that could lead to substantial problems if cookies were strictly required. Overall, DNS Cookies provide limited benefit in a majority of situations, and, given current deployment, do not prevent DDoS or cache poisoning attacks.

We present a novel graph convolutional layer that is conceptually simple, fast, and provides high accuracy with reduced overfitting. Based on pseudo-differential operators, our layer operates on graphs with relative position information available for each pair of connected nodes. Our layer represents a generalization of parameterized differential operators (previously shown effective for shape correspondence, image segmentation, and dimensionality reduction tasks) to a larger class of graphs. We evaluate our method on a variety of supervised learning tasks, including 2D graph classification using the MNIST and CIFAR-100 datasets and 3D node correspondence using the FAUST dataset. We also introduce a superpixel graph version of the lesion classification task using the ISIC 2016 challenge dataset and evaluate our layer versus other state-of-the-art graph convolutional network architectures.The new layer outperforms multiple recent architectures on graph classification tasks using the MNIST and CIFAR-100 superpixel datasets. For the ISIC dataset, we outperform all other graph neural networks examined as well as all of the submissions to the original ISIC challenge despite the best of those models having more than 200 times as many parameters as our model.

Rechargeable alkaline Zn–MnO2 (RAM) batteries are a promising candidate for grid-scale energy storage owing to their high theoretical energy density rivaling lithium-ion systems (∼400 Wh/L), relatively safe aqueous electrolyte, established supply chain, and projected costs below $100/kWh at scale. In practice, however, many fundamental chemical and physical processes at both electrodes make it difficult to achieve commercially competitive energy density and cycle life. This review presents a detailed and timely analysis of the constituent materials, current commercial status, electrode processes, and performance-limiting factors of RAM batteries. We also examine recently reported strategies in RAM and related systems to address these issues through additives and modifications to the electrode materials and electrolyte, special ion-selective separators and/or coatings, and unconventional cycling protocols. We conclude with a critical summary of these developments and discussion of how future studies should be focused toward the goal of energy-dense, scalable, and cost-effective RAM systems.

Dimension reduction techniques have frequently been used to summarize information from high dimensional hyperspectral data, usually done in effort to classify or visualize the materials contained in the hyperspectral image. The main challenge in applying these techniques to Hyperspectral Computed Tomography (HCT) data is that if the materials in the field of view are of similar composition then it can be difficult for a visualization of the hyperspectral image to differentiate between the materials. We propose novel alternative methods of preprocessing and summarizing HCT data in a single colorized image and novel measures to assess desired qualities in the resultant colored image, such as the contrast between different materials and the consistency of color within the same object. Proposed processes in this work include a new majority-voting method for multi-level thresholding, binary erosion, median filters, PAM clustering for grouping pixels into objects (of homogeneous materials) and mean/median assignment along the spectral dimension for representing the underlying signature, UMAP or GLMs to assign colors, and quantitative coloring assessment with developed measures. Strengths and weaknesses of various combinations of methods are discussed. These results have the potential to create more robust material identification methods from HCT data that has wide use in industrial, medical, and security-based applications for detection and quantification, including visualization methods to assist with rapid human interpretability of these complex hyperspectral signatures.

Pyomo and Dakota are openly available software packages developed by Sandia National Labs. In this tutorial, methods for automating the optimization of controller parameters for a nonlinear cart-pole system are presented. Two approaches are described and demonstrated on the cart-pole example problem for tuning a linear quadratic regulator and also a partial feedback linearization controller. First the problem is formulated as a pseudospectral optimization problem under an open box methodology utilizing Pyomo, where the plant model is fully known to the optimizer. In the next approach, a black-box approach utilizing Dakota in concert with a MATLAB or Simulink plant model is discussed, where the plant model is unknown to the optimizer. A comparison of the two approaches provides the end user the advantages and shortcomings of each method in order to pick the right tool for their problem. We find that complex system models and objectives are easily incorporated in the Dakota-based approach with minimal setup time, while the Pyomo-based approach provides rapid solutions once the system model has been developed.

Special protection schemes (SPSs) safeguard the grid by detecting predefined abnormal conditions and deploying predefined corrective actions. Utilities leverage SPSs to maintain stability, acceptable voltages, and loading limits during disturbances. However, traditional SPSs cannot defend against unpredictable disturbances. Events such as cyber attacks, extreme weather, and electromagnetic pulses have unpredictable trajectories and require adaptive response. Therefore, we propose a harmonized automatic relay mitigation of nefarious intentional events (HARMONIE)-SPS that learns system conditions, mitigates cyber-physical consequences, and preserves grid operation during both predictable and unpredictable disturbances. In this paper, we define the HARMONIE-SPS approach, detail progress on its development, and provide initial results using a WSCC 9-bus system.

I/O performance in a multi-user environment is difficult to predict. Users do not know what I/O performance to expect when running and tuning applications. We propose to use the IO500 benchmark as a way to guide user expectations on their application's performance and to aid identifying root causes of their I/O problems that might come from the system. Our experiments describe how we manage user expectation with IO500 and provide a mechanism for system fault identification. This work also provides us with information of the tail latency problem that needs to be addressed and granular information about the impact of I/O technique choices (POSIX and MPI-IO).

In high-performance computing (HPC), scientific applications often manage a massive amount of data using I/O libraries. These libraries provide convenient data model abstractions, help ensure data portability, and, most important, empower end users to improve I/O performance by tuning configurations across multiple layers of the HPC I/O stack. We propose SCTuner, an autotuner integrated within the I/O library itself to dynamically tune both the I/O library and the underlying I/O stack at application runtime. To this end, we introduce a statistical benchmarking method to profile the behaviors of individual supercomputer I/O subsystems with varied configurations across I/O layers. We use the benchmarking results as the built-in knowledge in SCTuner, implement an I/O pattern extractor, and plan to implement an online performance tuner as the SCTuner runtime. We conducted a benchmarking analysis on the Summit supercomputer and its GPFS file system Alpine. The preliminary results show that our method can effectively extract the consistent I/O behaviors of the target system under production load, building the base for I/O autotuning at application runtime.

Conditional Point Sampling (CoPS) is a recently developed stochastic media transport algorithm that has demonstrated a high degree of accuracy in 1D and 3D simulations implemented for the CPU in Python. However, it is increasingly important that modern, production-level transport codes like CoPS be adapted for use on next-generation computing architectures. In this project, we describe the creation of a fast and accurate variant of CoPS implemented for the GPU in C++. As an initial test, we performed a code-to-code verification using single-history cohorts, which showed that the GPU implementation matched the original CPU implementation to within statistical uncertainty, while improving the speed by over a factor of 4000. We then tested the GPU implementation for cohorts up to size 64 and compared three variants of CoPS based on how the particle histories are grouped into cohorts: successive, simultaneous, and a successive-simultaneous hybrid. We examined the accuracy-efficiency tradeoff of each variant for 9 different benchmarks, measuring the reflectance and transmittance in a cubic geometry with reflecting boundary conditions on the four non-transmissive or reflective faces. Successive cohorts were found to be far more accurate than simultaneous cohorts for both reflectance (4.3 times) and transmittance (5.9 times), although simultaneous cohorts run more than twice as fast as successive cohorts, especially for larger cohorts. The hybrid cohorts demonstrated speed and accuracy behavior most similar to that of simultaneous cohorts. Overall, successive cohorts were found to be more suitable for the GPU due to their greater accuracy and reproducibility, although simultaneous and hybrid cohorts present an enticing prospect for future research.

Performance measures commonly used in systems security engineering tend to be static, linear, and have limited utility in addressing challenges to security performance from increasingly complex risk environments, adversary innovation, and disruptive technologies. Leveraging key concepts from resilience science offers an opportunity to advance next-generation systems security engineering to better describe the complexities, dynamism, and non-linearity observed in security performance—particularly in response to these challenges. This article introduces a multilayer network model and modified Continuous Time Markov Chain model that explicitly captures interdependencies in systems security engineering. The results and insights from a multilayer network model of security for a hypothetical nuclear power plant introduce how network-based metrics can incorporate resilience concepts into performance metrics for next generation systems security engineering.

Network modeling is a powerful tool to enable rapid analysis of complex systems that can be challenging to study directly using physical testing. Two approaches are considered: emulation and simulation. The former runs real software on virtualized hardware, while the latter mimics the behavior of network components and their interactions in software. Although emulation provides an accurate representation of physical networks, this approach alone cannot guarantee the characterization of the system under realistic operative conditions. Operative conditions for physical networks are often characterized by intrinsic variability (payload size, packet latency, etc.) or a lack of precise knowledge regarding the network configuration (bandwidth, delays, etc.); therefore uncertainty quantification (UQ) strategies should be also employed. UQ strategies require multiple evaluations of the system with a number of evaluation instances that roughly increases with the problem dimensionality, i.e., the number of uncertain parameters. It follows that a typical UQ workflow for network modeling based on emulation can easily become unattainable due to its prohibitive computational cost. In this paper, a multifidelity sampling approach is discussed and applied to network modeling problems. The main idea is to optimally fuse information coming from simulations, which are a low-fidelity version of the emulation problem of interest, in order to decrease the estimator variance. By reducing the estimator variance in a sampling approach it is usually possible to obtain more reliable statistics and therefore a more reliable system characterization. Several network problems of increasing difficulty are presented. For each of them, the performance of the multifidelity estimator is compared with respect to the single fidelity counterpart, namely, Monte Carlo sampling. For all the test problems studied in this work, the multifidelity estimator demonstrated an increased efficiency with respect to MC.

The US DOE Office of Electricity's Energy Storage Program's joint RD work with the Cordova Electric Cooperative (CEC) has deployed several advanced grid technologies that are providing benefits today to Cordova Alaska's electricity users. Advanced grid technologies deployed through DoE co-funded RD include a 1MW Battery Energy Storage System (BESS), and enhanced monitoring including Phasor Measurement Units (PMU's) to help better understand the operational impacts of the added BESS. This paper will highlight key accomplishments to-date in deploying and using advanced grid technologies, and then outline the next phase of work that will use these technologies to implement an operating scheme to reconfigure the utility's distribution system and utility resources including BESS to provide emergency back-up power to a critical load: The Cordova Community Medical Center (CCMC). This paper will include additional insights on the use of utility resources to support critical loads via case study examples by the National Rural Electric Cooperative Assoc. (NRECA).

Work on radiation transport in stochastic media has tended to focus on binary mixing with Markovian mixing statistics. However, although some real-world applications involve only two materials, others involve three or more. Therefore, we seek to provide a foundation for ongoing theoretical and numerical work with “N-ary” stochastic media comprised of discrete material phases with spatially homogenous Markovian mixing statistics. To accomplish this goal, we first describe a set of parameters and relationships that are useful to characterize such media. In doing so, we make a noteworthy observation: media that are frequently called Poisson media only comprise a subset of those that have Markovian mixing statistics. Since the concept of correlation length (as it has been used in stochastic media transport literature) and the hyperplane realization generation method are both tied to the Poisson property of the media, we argue that not all media with Markovian mixing statistics have a correlation length in this sense or are realizable with the traditional hyperplane generation method. Second, we describe methods for generating realizations of N-ary media with Markovian mixing. We generalize the chord- and hyperplane-based sampling methods from binary to N-ary mixing and propose a novel recursive hyperplane method that can generate a broader class of material structures than the traditional, non-recursive hyperplane method. Finally, we perform numerical studies that provide validation to the proposed N-ary relationships and generation methods in which statistical quantities are observed from realizations of ternary and quaternary media and are shown to agree with predicted values.

We extend the Monte Carlo Chord Length Sampling (CLS) and Local Realization Preserving (LRP) algorithms to the N-ary stochastic medium case using two recently developed uniform and volume fraction models that follow a Markov-chain process for N-ary problems in one-dimensional, Markovian-mixed media. We use the Lawrence Livermore National Laboratory Mercury Monte Carlo particle transport code to compute CLS and LRP reflection and transmission leakage values and material scalar flux distributions for one-dimensional, Markovian-mixed quaternary stochastic media based on the two N-ary stochastic medium models. We conduct accuracy comparisons against benchmark results produced with the Sandia National Laboratories PlaybookMC stochastic media transport research code. We show that CLS and LRP produce exact results for purely absorbing N-ary stochastic medium problems and find that LRP is generally more accurate than CLS for problems with scattering.

Sobol' sensitivity indices (SI) provide robust and accurate measures of how much uncertainty in output quantities is caused by different uncertain input parameters. These allow analysts to prioritize future work to either reduce or better quantify the effects of the most important uncertain parameters. One of the most common approaches to computing SI requires Monte Carlo (MC) sampling of uncertain parameters and full physics code runs to compute the response for each of these samples. In the case that the physics code is a MC radiation transport code, this traditional approach to computing SI presents a workflow in which the MC transport calculation must be sufficiently resolved for each MC uncertain parameter sample. This process can be prohibitively expensive, especially since thousands or more particle histories are often required on each of thousands or so uncertain parameter samples. We propose a process for computing SI in which only a few MC radiation transport histories are simulated before sampling new uncertain parameter values. We use Embedded Variance Deconvolution (EVADE) to parse the desired parametric variance from the MC transport variance on each uncertain parameter sample. To provide a relevant benchmark, we propose a new radiation transport benchmark problem and derive analytic solutions for its outputs, including SI. The new EVADE-based approach is found to converge with MC convergence behavior and be at least an order of magnitude more precise for the same computational cost than the traditional approach for several SI on our test problem.

In this paper, we introduce and analyze a new class of optimal control problems constrained by elliptic equations with uncertain fractional exponents. We utilize risk measures to formulate the resulting optimization problem. We develop a functional analytic framework, study the existence of solution, and rigorously derive the first-order optimality conditions. Additionally, we employ a sample-based approximation for the uncertain exponent and the finite element method to discretize in space. We prove the rate of convergence for the optimal risk neutral controls when using quadrature approximation for the uncertain exponent and conclude with illustrative examples.

Container escapes enable the adversary to execute code on the host from inside an isolated container. These high severity escape vulnerabilities originate from three sources: (1) container profile misconfigurations, (2) Linux kernel bugs, and (3) container runtime vulnerabilities. While the first two cases have been studied in the literature, no works have investigated the impact of container runtime vulnerabilities. In this paper, to fill this gap, we study 59 CVEs for 11 different container runtimes. As a result of our study, we found that five of the 11 runtimes had nine publicly available PoC container escape exploits covering 13 CVEs. Our further analysis revealed all nine exploits are the result of a host component leaked into the container. We apply a user namespace container defense to prevent the adversary from leveraging leaked host components and demonstrate that the defense stops seven of the nine container escape exploits.

Estimated parameters in Battery Energy Storage Systems (BESSs) may be vulnerable to cyber-attacks such as False Data Injection Attacks (FDIAs). FDIAs, which typically evade bad data detectors, could damage or degrade Battery Energy Storage Systems (BESSs). This paper will investigate methods to detect small magnitude FDIA using battery equivalent circuit models, an Extended Kalman Filter (EKF), and a Cumulative Sum (CUSUM) algorithm. A priori error residual data estimated by the EKF was used in the CUSUM algorithm to find the lowest detectable FDIA for this battery equivalent model. The algorithm described in this paper was able to detect attacks as low as 1 mV, with no false positives. The CUSUM algorithm was compared to a chi-squared based FDIA detector. In this study the CUSUM was found to detect attacks of smaller magnitudes than the conventional chi-squared detector.

This paper describes a co-simulation environment used to investigate how high penetrations of electric vehicles (EV s) impact a distribution feeder during a resilience event. As EV adoption and EV supply equipment (EVSE) technology advance, possible impacts to the electric grid increase. Additionally, as weather related resilience events become more common, the need to understand possible challenges associated with EV charging during such events becomes more important. Software designed to simulate vehicle travel patterns, EV charging characteristics, and the associated electric demand can be integrated with power system software using co-simulation to provide more realistic results. The work in progress described here will simulate varying EV loading and location over time to provide insights about EVSE characteristics for maximum benefit and allow for general sizing of possible micro grids to supply EVs and critical loads.

Advanced solar PV inverter control settings may not be reported to utilities or may be changed without notice. This paper develops an estimation method for determining a fixed power factor control setting of a behind-the-meter (BTM) solar PV smart inverter. The estimation is achieved using linear regression methods with historical net load advanced metering infrastructure (AMI) data. Notably, the BTM PV power factor setting may be unknown or uncertain to a distribution engineer, and cannot be trivially estimated from the historical AMI data due to the influence of the native load on the measurements. To solve this, we use a simple percentile-based approach for filtering the measurements. A physics-based linear sensitivity model is then used to determine the fixed power factor control setting from the sensitivity in the complex power plane. This sensitivity parameter characterizes the control setting hidden in the aggregate data. We compare several loss functions, and verify the models developed by conducting experiments on 250 datasets based on real smart meter data. The data are augmented with synthetic quasi-static-timeseries (QSTS) simulations of BTM PV that simulate utility-observed aggregate measurements at the load. The simulations demonstrate the reactive power sensitivity of a BTM PV smart inverter can be recovered efficiently from the net load data after applying the filtering approach.

Due to the existence of DC-DC converters, fast-tripping fault location in DC power systems is of particular importance to ensure the reliable operation of DC systems. Traveling wave (TW) protection is one of the promising approaches to accommodate fast detection and location of faults in DC systems. This paper proposes a numerical approach for a DC system fault location using the concept of TWs. The proposed approach is based on multiresolution analysis to calculate the TW signal's wavelet coefficients for different frequency ranges, and then, the Parseval theorem is used to calculate the energy of wavelet coefficients. A curve-fitting approach is used to find the best curve that fits the Parseval energy as a function of fault location for a set of curve-fitting datapoints. The identified Parseval energy curves are then utilized to estimate the fault location when a new fault is applied on a DC cable. A DC test system simulated in PSCAD/EMTDC is used to verify the performance of the proposed fault location algorithm.

This paper discusses a solar photovoltaic (PV) DC microgrid system consisting of a PV array, a battery, DC-DC converters, and a load, where all these elements are simulated in MATLAB/Simulink environment. The design and testing entail the functions of a boost converter and a bidirectional converter and how they work together to maintain stable control of the DC bus voltage and its energy management. Furthermore, the boost converter operates under Maximum Power Point Tracking (MPPT) settings to maximize the power that the PV array can output. The control algorithm can successfully maintain the output power of the PV array at its maximum point and can respond well to changes in input irradiance. This is shown in detail in the results section.

Once Synthetic Aperture Radar (SAR) images are formed, they typically need to be stored in some file format which might restrict the dynamic range of what can be represented. Thereafter, for exploitation by human observers, the images might need to be displayed in a manner to reveal the subtle scene reflectivity characteristics the observer seeks, which generally requires further manipulation of dynamic range. Proper image scaling, for both storage and for display, to maximize the perceived dynamic range of interest to an observer depends on many factors, and an understanding of underlying data characteristics. While SAR images are typically rendered with grayscale, or at least monochromatic intensity variations, color might also be usefully employed in some cases. We analyze these and other issues pertaining to SAR image scaling, dynamic range, radiometric calibration, and display.

Distribution system topology identification has historically been accomplished by unencrypting the information that is received from the smart meters and then running a topology identification algorithm. Unencrypted smart meter data introduces privacy and security issues for utility companies and their customers. This paper introduces security aware machine learning algorithms to alleviate the privacy and security issues raised with un-encrypted smart meter data. The security aware machine learning algorithms use the information received from the Advanced Metering Infrastructure (AMI) and identifies the distribution systems topology without unencrypting the AMI data by using fully homomorphic NTRU and CKKS encryption. The encrypted smart meter data is then used by Linear Discriminant Analysis, Convolution Neural Network, and Support Vector Machine algorithms to predict the distribution systems real time topology. This method can leverage noisy voltage magnitude readings from smart meters to accurately identify distribution system reconfiguration between radial topologies during operation under changing loads.

This paper presents a new method for modeling rarefied gas flows based on hybridization of direct simulation Monte Carlo (DSMC) and discrete velocity method (DVM)-based quasi-particle representations of the velocity distribution function. It is aimed at improving the resolution of the tails of the distribution function (compared with DSMC) and computational efficiency (compared with DVM). Details of the method, such as the collision algorithm and the particle merging scheme, are discussed. The hybrid approach is applied to the study of noise in a Maxwellian distribution, computation of electron-impact ionization rate coefficient, as well as numerical simulation of a supersonic Couette flow. The hybrid-based solver is compared with pure DSMC and DVM approaches in terms of accuracy, computational speed, and memory use. It is shown that such a hybrid approach can provide a lower computational cost than a pure DVM approach, while being able to retain accuracy in modeling high-velocity tails of the distribution function. For problems where trace species have a significant impact on the flow physics, the proposed method is shown to be capable of providing better computational efficiency and accuracy compared with standard fixed-weight DSMC.

The American distributed energy resource (DER) interconnection standard, IEEE Std. 1547, was updated in 2018 to include standardized interoperability functionality. As state regulators begin ratifying these requirements, all DER - such as photovoltaic (PV) inverters, energy storage systems (ESSs), and synchronous generators - in those jurisdictions must include a standardized SunSpec Modbus, IEEE 2030.5, or IEEE 1815 (DNP3) communication interface. Utilities and authorized third parties will interact with these DER interfaces to read nameplate information, power measurements, and alarms as well as configure the DER settings and grid-support functionality. In 2020, the certification standard IEEE 1547.1 was revised with test procedures for evaluating the IEEE 1547-2018 interoperability requirements. In this work, we present an open-source framework to evaluate DER interoperability. To demonstrate this capability, we used four test devices: a SunSpec DER Simulator with a SunSpec Modbus interface, an EPRI-developed DER simulator with an IEEE 1815 interface, a Kitu Systems DER simulator with an IEEE 2030.5 interface, and an EPRI IEEE 2030.5-to-Modbus converter. By making this test platform openly available, DER vendors can validate their implementations, utilities can spot check communications to DER equipment, certification laboratories can conduct type testing, and research institutions can more easily research DER interoperability and cybersecurity. We indicate several limitations and ambiguities in the communication protocols, information models, and the IEEE 1547.1-2020 test protocol which were exposed in these evaluations in anticipation that the standards-development organizations will address these issues in the future.

Partitioned point-To-point communication and persistent collective communication were both recently standardized in MPI-4.0. Each offers performance and scalability advantages over MPI-3.1-based communication when planned transfers are feasible in an MPI application. Their merger into a generalized, persistent collective communication with partitions is a logical next step, with significant advantages for performance portability. Non-Trivial decisions about the syntax and semantics of such operations need to be addressed, including scope of knowledge of partitioning choices by members of the communicator's group(s). This paper introduces and motivates proposed interfaces for partitioned collective communication. Partitioned collectives will be particularly useful for multithreaded, accelerator-offloaded, and/or hardware-collective-enhanced MPI implementations driving suitable applications, as well as for pipelined collective communication (e.g., partitioned allreduce) with single consumers and producers per MPI process. These operations also provide load imbalance mitigation. Halo exchange codes arising from regular and irregular grid/mesh applications are a key candidate class of applications for this functionality. Generalizations of lightweight notification procedures MPI-Parrived and MPI-Pready are considered. Generalization of MPIX-Pbuf-prepare, a procedure proposed for MPI-4.1 for point-To-point partitioned communication, are also considered, shown in context of supporting ready-mode send semantics for the operations. The option of providing local and incomplete modes for initialization procedures is mentioned (which could also apply to persistent collective operations); these semantics interact with the MPIX-Pbuf-prepare concept and the progress rule. Last, future work is outlined, indicating prerequisites for formal consideration for the MPI-5 standard.

Compression analytics have gained recent interest for application in malware classification and digital forensics. This interest is due to the fact that compression analytics rely on measured similarity between byte sequences in datasets without requiring prior feature extraction; in other words, these methods are featureless. Being featureless makes compression analytics particularly appealing for computer security applications, where good static features are either unknown or easy to circumvent by adversaries. However, previous classification methods based on compression analytics relied on algorithms that scaled with the size of each labeled class and the number of classes. In this work, we introduce an approach that, in addition to being featureless, can perform fast and accurate inference that is independent of the size of each labeled class. Our method is based on calculating a representative sample, the Fréchet mean, for each labeled class and using it at inference time. We introduce a greedy algorithm for calculating the Fréchet mean and evaluate its utility for classification across a variety of computer security applications, including authorship attribution of source code, file fragment type detection, and malware classification.

Boolean functions and binary arithmetic operations are central to standard computing paradigms. Accordingly, many advances in computing have focused upon how to make these operations more efficient as well as exploring what they can compute. To best leverage the advantages of novel computing paradigms it is important to consider what unique computing approaches they offer. However, for any special-purpose co-processor, Boolean functions and binary arithmetic operations are useful for, among other things, avoiding unnecessary I/O on-and-off the co-processor by pre- and post-processing data on-device. This is especially true for spiking neuromorphic architectures where these basic operations are not fundamental low-level operations. Instead, these functions require specific implementation. Here we discuss the implications of an advantageous streaming binary encoding method as well as a handful of circuits designed to exactly compute elementary Boolean and binary operations.

As climate change and human migration accelerate globally, decision-makers are seeking tools that can deepen their understanding of the complex nexus between climate change and human migration. These tools can help to identify populations under pressure to migrate, and to explore proactive policy options and adaptive measures. Given the complexity of factors influencing migration, this article presents a system dynamics-based model that couples migration decision making and behavior with the interacting dynamics of economy, labor, population, violence, governance, water, food, and disease. The regional model is applied here to the test case of migration within and beyond Mali. The study explores potential systems impacts of a range of proactive policy solutions and shows that improving the effectiveness of governance and increasing foreign aid to urban areas have the highest potential of those investigated to reduce the necessity to migrate in the face of climate change.

We present recent results toward the quantification of spray characteristics at engine conditions for an eight-hole counter-bored (stepped) GDI injector – Spray G in the ECN denomination. This computational study is characterized by two novel features: the detailed description of a real injector's internal surfaces via tomographic reconstruction; and a general equation of state that represents the thermodynamic properties of homogeneous liquid-vapor mixtures. The combined level-set moment-of-fluid approach, coupled to an embedded boundary formulation for moving solid walls, makes it possible to seamlessly connect the injector's internal flow to the spray. The Large Eddy Simulation (LES) discussed here presents evidence of partial hydraulic flipping and, during the closing transient, string cavitation. Results are validated by measurements of spray density profiles and droplet size distribution.

The next generation of supercomputing resources is expected to greatly expand the scope of HPC environments, both in terms of more diverse workloads and user bases, as well as the integration of edge computing infrastructures. This will likely require new mechanisms and approaches at the Operating System level to support these broader classes of workloads along with their different security requirements. We claim that a key mechanism needed for these workloads is the ability to securely compartmentalize the system software executing on a given node. In this paper, we present initial efforts in exploring the integration of secure and trusted computing capabilities into an HPC system software stack. As part of this work we have ported the Kitten Lightweight Kernel (LWK) to the ARM64 architecture and integrated it with the Hafnium hypervisor, a reference implementation of a secure partition manager (SPM) that provides security isolation for virtual machines. By integrating Kitten with Hafnium, we are able to replace the commodity oriented Linux based resource management infrastructure and reduce the overheads introduced by using a full weight kernel (FWK) as the node-level resource scheduler. While our results are very preliminary, we are able to demonstrate measurable performance improvements on small scale ARM based SOC platforms.

We describe efforts in generating synthetic malware samples that have specified behaviors that can then be used to train a machine learning (ML) algorithm to detect behaviors in malware. The idea behind detecting behaviors is that a set of core behaviors exists that are often shared in many malware variants and that being able to detect behaviors will improve the detection of novel malware. However, empirically the multi-label task of detecting behaviors is significantly more difficult than malware classification, only achieving on average 84% accuracy across all behaviors as opposed to the greater than 95% multi-class or binary accuracy reported in many malware detection studies. One of the difficulties in identifying behaviors is that while there are ample malware samples, most data sources do not include behavioral labels, which means that generally there is insufficient training data for behavior identification. Inspired by the success of generative models in improving image processing techniques, we examine and extend a 1) conditional variational auto-encoder and 2) a flow-based generative model for malware generation with behavior labels. Initial experiments indicate that synthetic data is able to capture behavioral information and increase the recall of behaviors in novel malware from 32% to 45% without increasing false positives and to 52% with increased false positives.

Many individuals' mobility can be characterized by strong patterns of regular movements and is influenced by social relationships. Social networks are also often organized into overlapping communities which are associated in time or space. We develop a model that can generate the structure of a social network and attribute purpose to individuals' movements, based solely on records of individuals' locations over time. This model distinguishes the attributed purpose of check-ins based on temporal and spatial patterns in check-in data. Because a location-based social network dataset with authoritative ground-truth to test our entire model does not exist, we generate large scale datasets containing social networks and individual check-in data to test our model. We find that our model reliably assigns community purpose to social check-in data, and is robust over a variety of different situations.

This work reports an on-wafer study of avalanche behavior and failure analysis of in-house fabricated 1.3 kV GaN-on-GaN P-N diodes. DC breakdown is measured at different temperatures to confirm avalanche behavior. Diode's avalanche ruggedness is measured directly on-wafer using a modified unclamped inductive switching (UIS) test set-up with an integrated thermal chuck and high-speed CCD for real-time imaging during the test. The avalanche ruggedness of the GaN P-N diode is evaluated and compared with a commercial SiC Schottky diode of similar voltage and current rating. Failure analysis is done using SEM and optical microscopy to gain insight into the diode's failure mechanism during avalanche operation.

We propose primal-dual mesh optimization algorithms that overcome shortcomings of the standard algorithm while retaining some of its desirable features. “Hodge-Optimized Triangulations” defines the “HOT energy” as a bound on the discretization error of the diagonalized Delaunay Hodge star operator. HOT energy is a natural choice for an objective function, but unstable for both mathematical and algorithmic reasons: it has minima for collapsed edges, and its extrapolation to non-regular triangulations is inaccurate and has unbounded minima. We propose a different extrapolation with a stronger theoretical foundation. We propose new objectives, based on normalizations of the HOT energy, with barriers to edge collapses and other undesirable configurations. We propose mesh improvement algorithms coupling these. When HOT optimization nearly collapses an edge, we actually collapse the edge. Otherwise, we use the barrier objective to update positions and weights. By combining discrete connectivity changes with continuous optimization, we more fully explore the space of possible meshes and obtain higher quality solutions.

The reactions of carbonyl oxide Criegee intermediates with acids proceed predominantly by an insertion mechanism. We characterise the products from one of the simplest reactions of carbonyl oxides with inorganic acids, CH2OO + hydrogen chloride, which occurs via a 1,2-insertion in the H–Cl bond. Reactions of both HCl and DCl isotopologues yield product signal at the mass of the insertion product chloro(hydroperoxy)methane and a dissociative ionisation peak at the mass of the protonated (or deuteronated) Criegee intermediate. The isotopic composition of the insertion product has been measured for reaction mixtures where both HCl isotopologues are present, and the H/D ratio of the product is consistently higher (by a factor of 1.6 ± 0.3) than that of the reactants. This isotope selectivity in the products has smaller uncertainty than the ratio of measured rate coefficients and suggests a normal (k H > k D) kinetic isotope effect in the reaction. Theoretical kinetics calculations predict a small normal kinetic isotope effect for the overall reaction (k H / k D = 1.35 at 20 Torr N2 and k H / k D = 1.2 at 1 atm N2) but predict a substantial inverse kinetic isotope effect (k D > k H) for the stabilisation fraction, in disagreement with the experimental observation.

Global thinning of integrated circuits is a technique that enables backside failure analysis and radiation testing. Prior work also shows increased thresholds for single-event latchup and upset in thinned devices. We present impacts of global thinning on device performance and reliability of 28 nm node field programmable gate arrays (FPGA). Devices are thinned to values of 50, 10, and 3 microns using a micromachining and polishing method. Lattice damage, in the form of dislocations, extend about 1 micron below the machined surface. The damage layer is removed after polishing with colloidal SiO2 slurry. We create a 2D finite-element model with liner elasticity equations and flip-chip packaged device geometry to show that thinning increases compressive global stress in the Si, while C4 bumps increase stress locally. Measurements of stress using Raman spectroscopy qualitatively agree with our stress model but also reveal the need for more complex structural models to account for nonlinear effects occurring in devices thinned to 3 microns and after temperature cycling to 125 °C. Thermal imaging shows that increased local heating occurs with increased thinning but the maximum temperature difference across the 3-micron die is less than 2 °C. Ring oscillators (ROs) programmed throughout the FPGA fabric slow about 0.5% after thinning compared to full thickness values. Temperature cycling the devices to 125 °C further decreases RO frequency about 0.5%, which we attribute to stress changes in the Si.

In this work, we analyze the second and third harmonic signal from a dielectric metasurface in conjunction with polarization selection rules to unambiguously demonstrate the occurrence of cascaded second-order nonlinearities.

Abstract not provided.

We present an empirical methodology for thermally characterizing and determining absorption and scattering losses in released ring whisper gallery mode optical resonators. We used the methodology to deduce absorption and scattering contributions in Q = 308,000 silicon nitride resonators coupled to on-chip waveguides.

We present an optical wavelength division multiplexer enabled by a ring resonator tuned by MEMS electrostatic actuation. Analytical analysis, simulation and fabrication are discussed leading to results showing controlled tuning greater than one FSR.

We measure the photoelastic constants of piezo-optomechanical photonic integrated circuits incorporating a specially formulated, silicon-depleted silicon nitride thin films using a laser doppler vibrometer to calibrate the strain produced by the integrated piezoelectric actuators.

In spite of several advantages of SiC JFETs over enhancement mode SiC MOSFETs, the intrinsic normally-ON characteristic of the JFETs can be undesirable for many industrial power conversion applications due to the negative turn-OFF voltage requirement. This prevents normally-ON JFETs from being widely accepted in industry. However, a cascode configuration, which uses a low voltage (LV) Si MOSFET can be used to enable a normally-OFF behavior, making this approach an attractive solution to utilize the benefits of SiC JFETs. For medium-, and high-voltage applications that require larger blocking voltage than the rating of each JFET, additional devices can be connected in series to increase the overall blocking voltage capability, creating a super-cascode configuration. This paper provides a review of several super-cascode topology variations and presents a comprehensive comparative study, evaluating similarities and differences in operating principles, equivalent circuits, and design considerations and limitations.

The prevalence effect is the observation that, in visual search tasks as the signal (target) to noise (non-target) ratio becomes smaller, humans are more likely to miss the target when it does occur. Studied extensively in the basic literature [e.g., 1, 2], this effect has implications for real-world settings such as security guards monitoring physical facilities for attacks. Importantly, what seems to drive the effect is the development of a response bias based on learned sensitivity to the statistical likelihood of a target [e.g., 3-5]. This paper presents results from two experiments aimed at understanding how the target prevalence impacts the ability for individuals to detect a target on the 1,000th trial of a series of 1000 trials. The first experiment employed the traditional prevalence effect paradigm. This paradigm involves search for a perfect capital letter T amidst imperfect Ts. In a between-subjects design, our subjects experienced target prevalence rates of 50/50, 1/10, 1/100, or 1/1000. In all conditions, the final trial was always a target. The second (ongoing) experiment replicates this design using a notional physical facility in a mod/sim environment. This simulation enables triggering different intrusion detection sensors by simulated characters and events (e.g., people, animals, weather). In this experiment, subjects viewed 1000 “alarm” events and were asked to characterize each as either a nuisance alarm (e.g., set off by an animal) or an attack. As with the basic visual search study, the final trial was always an attack.

Dimension reduction techniques have frequently been used to summarize information from high dimensional hyperspectral data, usually done in effort to classify or visualize the materials contained in the hyperspectral image. The main challenge in applying these techniques to Hyperspectral Computed Tomography (HCT) data is that if the materials in the field of view are of similar composition then it can be difficult for a visualization of the hyperspectral image to differentiate between the materials. We propose novel alternative methods of preprocessing and summarizing HCT data in a single colorized image and novel measures to assess desired qualities in the resultant colored image, such as the contrast between different materials and the consistency of color within the same object. Proposed processes in this work include a new majority-voting method for multi-level thresholding, binary erosion, median filters, PAM clustering for grouping pixels into objects (of homogeneous materials) and mean/median assignment along the spectral dimension for representing the underlying signature, UMAP or GLMs to assign colors, and quantitative coloring assessment with developed measures. Strengths and weaknesses of various combinations of methods are discussed. These results have the potential to create more robust material identification methods from HCT data that has wide use in industrial, medical, and security-based applications for detection and quantification, including visualization methods to assist with rapid human interpretability of these complex hyperspectral signatures.

Pyomo and Dakota are openly available software packages developed by Sandia National Labs. In this tutorial, methods for automating the optimization of controller parameters for a nonlinear cart-pole system are presented. Two approaches are described and demonstrated on the cart-pole example problem for tuning a linear quadratic regulator and also a partial feedback linearization controller. First the problem is formulated as a pseudospectral optimization problem under an open box methodology utilizing Pyomo, where the plant model is fully known to the optimizer. In the next approach, a black-box approach utilizing Dakota in concert with a MATLAB or Simulink plant model is discussed, where the plant model is unknown to the optimizer. A comparison of the two approaches provides the end user the advantages and shortcomings of each method in order to pick the right tool for their problem. We find that complex system models and objectives are easily incorporated in the Dakota-based approach with minimal setup time, while the Pyomo-based approach provides rapid solutions once the system model has been developed.

The rising penetration levels of photovoltaic (PV) systems within distribution networks has driven considerable interest in the implementation of advanced inverter functions, like autonomous Volt- Var, to provide grid support in response to adverse conditions. Quasi-static time-series (QSTS) analyses are increasingly being utilized to evaluate advanced inverter functions on their potential benefits to the grid and to quantify the magnitude of PV power curtailment they may induce. However, these analyses require additional modeling efforts to appropriately capture the time-varying behavior of circuit elements like loads and PV systems. The contribution of this paper is to study QSTS-based curtailment evaluations with different load allocation and PV modeling practices under a variety of assumptions and data limitations. A total of 24 combinations of PV and load modeling scenarios were tested on a realistic test circuit with 1,379 loads and 701 PV systems. The results revealed that the average annual curtailment varied from the baseline value of 0.47% by an absolute difference of +0.55% to -0.43 % based on the modeling scenario.

The impact of machine learning (ML) explanations and different attributes of explanations on human performance was investigated in a simulated spam detection task. Participants decided whether the metadata presented about an email indicated that it was spam or benign. The task was completed with the aid of a ML model. The ML model’s prediction was displayed on every trial. The inclusion of an explanation and, if an explanation was presented, attributes of the explanation were manipulated within subjects: the number of model input features (3, 7) and visualization of feature importance values (graph, table), as was trial type (i.e., hit, false alarm). Overall model accuracy (50% vs 88%) was manipulated between subjects, and user trust in the model was measured as an individual difference metric. Results suggest that a user’s trust in the model had the largest impact on the decision process. The users showed better performance with a more accurate model, but no differences in accuracy based on number of input features or visualization condition. Rather, users were more likely to detect false alarms made by the more accurate model; they were also more likely to comply with a model “miss” when more model explanation was provided. Finally, response times were longer in individuals reporting low model trust, especially when they did not comply with the model’s prediction. Our findings suggest that the factors impacting the efficacy of ML explanations depends, minimally, on the task, the overall model accuracy, the likelihood of different model errors, and user trust.

Early light emission provides information about the dominant mechanisms culminating in vacuum surface flashover (anode-initiated vs. cathode-initiated) for particular geometries. From experimental evidence gathered elsewhere, for the case of an insulator oriented at 45° with respect to the anode, anode-initiated flashover is believed to dominate since the field at the anode triple point is roughly three times that of the cathode. Similar to previous work performed on cathode-initiated flashover, light emission from the voltage rise through the impedance collapse is collected into two optical fibers focused on light emanating from the insulator in regions near the anode and cathode. The optical fibers are either connected to PMTs for spectrally integrated localized light intensity information or to a spectrograph used in conjunction with an ICCD camera. Challenges associated with localizing the flashover for optical diagnostics and incorporating the optical diagnostics into the high-field environment are discussed. Initial results for cross-linked polystyrene (Rexolite 1422) support the premise that flashover is initiated from the anode for these geometries, as early light from the anode leads cathode light up to photocathode saturation. Early spectroscopy results show promise for future characterization of the spatio-temporal development of emission from desorbed gas species across the insulator surface and identification of bulk insulator involvement if it occurs.

Abstract not provided.

Ultra-wide-bandgap aluminum gallium nitride (AlGaN) possesses several material properties that make it attractive for use in a variety of applications. This chapter focuses on power switching and radio-frequency (RF) devices based on Al-rich AlGaN heterostructures. The relevant figures of merit for both power switching and RF devices are discussed as motivation for the use of AlGaN heterostructures in such applications. The key physical parameters impacting these figures of merit include critical electric field, channel mobility, channel carrier density, and carrier saturation velocity, and the factors influencing these and the trade-offs between them are discussed. Surveys of both power switching and RF devices are given and their performance is described including in special operating regimes such as at high temperatures. Challenges to be overcome, such as the formation of low-resistivity Ohmic contacts, are presented. Finally, an overview of processing-related challenges, especially related to surfaces and interfaces, concludes the chapter.