Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

This SAND report provides system effectiveness analysis results for notional chemical facilities. Two facilities were analyzed in total, evaluating the effectiveness of the unique security systems in place at each location. Each analysis looked at a range of threat and response capabilities, specific target configurations, and task times to acquire target material in both theft and release scenarios. This report details results for both facilities.

The knowledge of long-term health and reliability of energy storage systems is still unknown, yet these systems are proliferating and are expected increasingly to assist in the maintenance of grid reliability. Understanding long-term reliability and performance characteristics to the degree of knowledge similar to that of traditional utility assets requires operational data. This guideline is intended to inform numerous stakeholders on what data are needed for given functions, how to prescribe access to those data and the considerations impacting data architecture design, as well as provide these stakeholders insight into the data and data systems necessary to ensure storage can meet growing expectations in a safe and cost-efficient manner. Understanding data needs, the systems required, relevant standards, and user needs early in a project conception aids greatly in ensuring that a project ultimately performs to expectations.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Motivation: Determine the length and opening of two lab-grown cracks, designated as LT-14 and LT-28, representative of stress corrosion cracks in spent nuclear fuel dry storage casks to supplement future testing of gas and aerosol transport. Problem: The extreme aspect ratio of the crack length to opening requires that imaging occurs in stages with the results merged before final analysis. Method: High magnification (1500x) optical images of both sides of the two plates were acquired. 20x stitched images with LSCM were acquired, fully stitched along the length, and leveled with newly developed PLATES Method in MATLAB®. Conclusion for LT-14: Side 1 is 47.25 mm long and has 366 separate crack features with an average length of 23.50 µm and an average opening of 8.27 µm. Side 2 is 69.44 mm long and has 550 separate crack features with an average length of 81.63 µm and an average opening of 67.70 µm. Conclusion for LT-28: Side 1 is 71.95 mm long and has 1,127 separate crack features with an average length of 42.27 µm and an average opening of 10.31 µm. Side 2 is 74.88 mm long and has 520 separate crack features with an average length of 98.13 µm and an average opening of 14.99 µm. The adjacent crack on side 1 is 18.95 mm long and has 37 separate crack features with an average length of 17.46 µm and an average opening of 10.42 µm. The adjacent crack on side 2 is 26.40 mm long and has 55 separate crack features with an average length of 87.26 µm and an average opening of 48.29 µm. Each adjacent crack is approximately 26 mm from the main crack.

Abstract not provided.

The Spent Fuel Waste Disposition (SFWD) program under the U.S. Department of Energy (DOE) is planning a seismic shake table test of full-scale dry storage systems of spent nuclear fuel (SNF) to close the gap related to the seismic loads on the fuel assemblies in dry storage systems. This test will allow for quantifying the strains and accelerations on surrogate fuel assembly during representative earthquakes. A concrete layer will be installed on the shake table before the test to simulate conditions representative of an ISFSI pad. In the shake table tests with the vertical cask, the cask will be free-standing because this is representative of all, except two, ISFSIs in the U.S. with vertical dry storage casks. The static and dynamic friction coefficients between the steel bottom of the cask and the concrete layer on the shake table are important parameters that will affect cask behavior during the test. These parameters must be known for the pre- and post-test modelling, data analysis, and model validation. The friction experiment was performed at the Engineering Department of the University of New Mexico (UNM) to determine the friction coefficients between a steel plate with the same finish as the bottom of the vertical cask manufactured for the test and different concrete surfaces. In this experiment the steel plate was fixed and the concrete sample was pulled over the plate with a constant displacement rate using an MTS machine. This allowed for collecting continuous horizontal force data over the length of the steel plate. Four displacement rates and three vertical loads were used. The tests were performed with four concrete blocks with different degrees of the surface roughness - light sandblast, light to medium sandblast, medium bush hammer, and heavy sandblast. The total number of tests was 48. The data were used to calculate static and dynamic friction coefficients.

This Section covers an introduction to the objectives and techniques used in this analysis. The objectives of the report are given in Subsection 1.1. An introduction to aqueous thermodynamics and how variance might propagate through the relevant thermodynamic equations is given in Subsection 1.2. An introduction to Bayesian inference and its application to thermodynamic modeling is given in Subsection 1.3.

Here we report on AlGaN high electron mobility transistor (HEMT)-based logic development, using combined enhancement- and depletion-mode transistors to fabricate inverters with operation from room temperature up to 500°C. Our development approach included: (a) characterizing temperature-dependent carrier transport for different AlGaN HEMT heterostructures, (b) developing a suitable gate metal scheme for use in high temperatures, and (c) over-temperature testing of discrete devices and inverters. Hall mobility data (from 30°C to 500°C) revealed the reference GaN-channel HEMT experienced a 6.9x reduction in mobility, whereas the AlGaN channel HEMTs experienced about a 3.1x reduction. Furthermore, a greater aluminum contrast between the barrier and channel enabled higher carrier densities in the two-dimensional electron gas for all temperatures. The combination of reduced variation in mobility with temperature and high sheet carrier concentration showed that an Al-rich AlGaN-channel HEMT with a high barrier-to-channel aluminum contrast is the best option for an extreme temperature HEMT design. Three gate metal stacks were selected for low resistivity, high melting point, low thermal expansion coefficient, and high expected barrier height. The impact of thermal cycling was examined through electrical characterization of samples measured before and after rapid thermal anneal. The 200-nm tungsten gate metallization was the top performer with minimal reduction in drain current, a slightly positive threshold voltage shift, and about an order of magnitude advantage over the other gates in on-to-off current ratio. After incorporating the tungsten gate metal stack in device fabrication, characterization of transistors and inverters from room temperature up to 500°C was performed. The enhancement-mode (e-mode) devices’ resistance started increasing at about 200°C, resulting in drain current degradation. This phenomenon was not observed in depletion-mode (d-mode) devices but highlights a challenge for inverters in an e-mode driver and d-mode load configuration.

The impact of 1.8 MeV proton irradiation on metalorganic chemical vapor deposition grown (010) β-Ga2O3 Schottky diodes is presented. It is found that after a 10.8 × 10 13 cm - 2 proton fluence the Schottky barrier height of (1.40 ± 0.05 eV) and the ideality factor of (1.05 ± 0.05) are unaffected. Capacitance-voltage extracted net ionized doping curves indicate a carrier removal rate of 268 ± 10 cm - 1. The defect states responsible for the observed carrier removal are studied through a combination of deep level transient and optical spectroscopies (DLTS/DLOS) as well as lighted capacitance-voltage (LCV) measurements. The dominating effect on the defect spectrum is due to the EC-2.0 eV defect state observed in DLOS and LCV. This state accounts for ∼ 75% of the total trap introduction rate and is the primary source of carrier removal from proton irradiation. Of the DLTS detected states, the EC-0.72 eV state dominated but had a comparably smaller contribution to the trap introduction. These two traps have previously been correlated with acceptor-like gallium vacancy-related defects. Several other trap states at EC-0.36, EC-0.63, and EC-1.09 eV were newly detected after proton irradiation, and two pre-existing states at EC-1.2 and EC-4.4 eV showed a slight increase in concentration after irradiation, together accounting for the remainder of trap introduction. However, a pre-existing trap at EC-0.40 eV was found to be insensitive to proton irradiation and, therefore, is likely of extrinsic origin. The comprehensive defect characterization of 1.8 MeV proton irradiation damage can aid the modeling and design for a range of radiation tolerant devices.

Ionizable polymers form dynamic networks with domains controlled by two distinct energy scales, ionic interactions and van der Waals forces; both evolve under elongational flows during their processing into viable materials. A molecular level insight of their nonlinear response, paramount to controlling their structure, is attained by fully atomistic molecular dynamics simulations of a model ionizable polymer, polystyrene sulfonate. As a function of increasing elongational flow rate, the systems display an initial elastic response, followed by an ionic fraction-dependent strain hardening, stress overshoot, and eventually strain-thinning. As the sulfonation fraction increases, the chain elongation becomes more heterogeneous. Finally, flow-driven ionic assembly dynamics that continuously break and reform control the response of the system.

Automation of rate-coefficient calculations for gas-phase organic species became possible in recent years and has transformed how we explore these complicated systems computationally. Kinetics workflow tools bring rigor and speed and eliminate a large fraction of manual labor and related error sources. In this paper we give an overview of this quickly evolving field and illustrate, through five detailed examples, the capabilities of our own automated tool, KinBot. We bring examples from combustion and atmospheric chemistry of C-, H-, O-, and N-atom-containing species that are relevant to molecular weight growth and autoxidation processes. The examples shed light on the capabilities of automation and also highlight particular challenges associated with the various chemical systems that need to be addressed in future work.

Vibrational spectroscopy is a nondestructive technique commonly used in chemical and physical analyses to determine atomic structures and associated properties. However, the evaluation and interpretation of spectroscopic profiles based on human-identifiable peaks can be difficult and convoluted. To address this challenge, we present a reliable protocol based on supervised manifold learning techniques meant to connect vibrational spectra to a variety of complex and diverse atomic structure configurations. As an illustration, we examined a large database of virtual vibrational spectroscopy profiles generated from atomistic simulations for silicon structures subjected to different stress, amorphization, and disordering states. We evaluated representative features in those spectra via various linear and nonlinear dimensionality reduction techniques and used the reduced representation of those features with decision trees to correlate them with structural information unavailable through classical human-identifiable peak analysis. We show that our trained model accurately (over 97% accuracy) and robustly (insensitive to noise) disentangles the contribution from the different material states, hence demonstrating a comprehensive decoding of spectroscopic profiles beyond classical (human-identifiable) peak analysis.

Calcite (CaCO₃) composition and properties are defined by the chemical environment in which CaCO₃ forms. However, a complete understanding of the relationship between aqueous chemistry during calcite precipitation and resulting chemical and physical CaCO₃ properties remains elusive; therefore, we present an investigation into the coupled effects of divalent cations Sr²⁺ and Mg²⁺ on CaCO₃ precipitation and subsequent crystal growth. Through chemical analysis of the aqueous phases and microscopy of the resulting calcite phases in compliment with density functional theory calculations, we elucidate the relationship between crystal growth and the resulting composition (elemental and isotopic) of calcite. The results of this experimental and modeling work suggest that Mg²⁺ and Sr²⁺ have cation-specific impacts that inhibit calcite crystal growth, including: (1) Sr²⁺ incorporates more readily into calcite than Mg²⁺ (D_Sr > D_Mg), and increasing [Sr²⁺]_t or [Mg²⁺]_t increases D_Sr; (2) the inclusion of Mg²⁺ into structure leads to a reduction in the calcite unit cell volume, whereas Sr²⁺ leads to an expansion; (3) the inclusion of both Mg²⁺ and Sr²⁺ results in a distribution of unit cell impacts based on the relative positions of the Sr²⁺ and Mg²⁺ in the lattice. These experiments were conducted at saturation indices of CaCO₃ of ~4.1, favoring rapid precipitation. This rapid precipitation resulted in observed Sr isotope fractionation confirming Sr isotopic fractionation is dependent upon the precipitation rate. We further note that the precipitation and growth of calcite favors the incorporation of the lighter ⁸⁶Sr isotope over the heavier ⁸⁷Sr isotope, regardless of the initial solution conditions, and the degree of fractionation increases with D_Sr. In sum, these results demonstrate the impact of solution environment to influence the incorporation behavior and crystal growth behavior of calcite. These factors are important to understand in order to effectively use geochemical signatures resulting from calcite precipitation or dissolution to gain specific information.

For computational physics simulations, code verification plays a major role in establishing the credibility of the results by assessing the correctness of the implementation of the underlying numerical methods. In computational electromagnetics, surface integral equations, such as the method-of-moments implementation of the magnetic-field integral equation, are frequently used to solve Maxwell's equations on the surfaces of electromagnetic scatterers. These electromagnetic surface integral equations yield many code-verification challenges due to the various sources of numerical error and their possible interactions. In this paper, we provide approaches to separately measure the numerical errors arising from these different error sources. We demonstrate the effectiveness of these approaches for cases with and without coding errors.

The Synchronic Web is a distributed network for securing data provenance on the World Wide Web. By enabling clients around the world to freely commit digital information into a single shared view of history, it provides a foundational basis of truth on which to build decentralized and scalable trust across the Internet. Its core cryptographical capability allows mutually distrusting parties to create and verify statements of the following form: “I commit to this information—and only this information—at this moment in time.” The backbone of the Synchronic Web infrastructure is a simple, small, and semantic-free blockchain that is accessible to any Internet-enabled entity. The infrastructure is maintained by a permissioned network of well-known servers, called notaries, and accessed by a permissionless group of clients, called journals. Through an evolving stack of flexible and composable semantic specifications, the parties cooperate to generate synchronic commitments over arbitrary data. When integrated with existing infrastructures, adapted to diverse domains, and scaled across the breadth of cyberspace, the Synchronic Web provides a ubiquitous mechanism to lock the world’s data into unique points in discrete time and digital space. This document provides a technical description of the core Synchronic Web system. The distinguishing innovation in our design—and the enabling mechanism behind the model—is the novel use of verifiable maps to place authenticated content into canonically defined locations off-chain. While concrete specifications and software implementations of the Synchronic Web continue to evolve, the information covered in the body of this document should remain stable. We aim to present this information clearly and concisely for technical non-experts to understand the essential functionality and value proposition of the network. In the interest of promoting discourse, we take some liberty in projecting the potential implications of the new model.

Bifurcations are commonly encountered during force controlled swept and stepped sine testing of nonlinear structures, which generally leads to the so-called jump-down or jump-up phenomena between stable solutions. There are various experimental closed-loop control algorithms, such as control-based continuation and phase-locked loop, to stabilize dynamical systems through these bifurcations, but they generally rely on specialized control algorithms that are not readily available with many commercial data acquisition software packages. A recent method was developed to experimentally apply sequential continuation using the shaker voltage that can be readily deployed using commercially available software. By utilizing the stabilizing effects of electrodynamic shakers and the force dropout phenomena in fixed frequency voltage control sine tests, this approach has been demonstrated to stabilize the unstable branch of a nonlinear system with three branches, allowing for three multivalued solutions to be identified within a specific frequency bandwidth near resonance. Recent testing on a strongly nonlinear system with vibro-impact nonlinearity has revealed jumping behavior when performing sequential continuation along the voltage parameter, like the jump phenomena seen during more traditional force controlled swept and stepped sine testing. Here, this paper investigates the stabilizing effects of an electrodynamic shaker on strongly nonlinear structures in fixed frequency voltage control tests using both numerical and experimental methods. The harmonic balance method is applied to the coupled shaker-structure system with an electromechanical model to simulate the fixed voltage control tests and predict the stabilization for different parameters of the model. The simulated results are leveraged to inform the design of a set of experiments to demonstrate the stabilization characteristics on a fixture-pylon assembly with a vibro-impact nonlinearity. Through numerical simulation and experimental testing on two different strongly nonlinear systems, the various parameters that influence the stability of the coupled shaker-structure are revealed to better understand the performance of fixed frequency voltage control tests.

Robust in situ power harvesting underlies all efforts to enable downhole autonomous sensors for real-time and long-term monitoring of CO₂ plume movement and permeance, wellbore health, and induced seismicity. This project evaluated the potential use of downhole thermopile arrays, known as thermoelectric generators (TEGs), as power sources to charge sensors for in situ real-time, long-term data capture and transmission. Real-time downhole monitoring will enable “Big Data” techniques and machine learning, using massive amounts of continuous data from embedded sensors, to quantify short- and long-term stability and safety of enhanced oil recovery and/or commercial-scale geologic CO₂ storage. This project evaluated possible placement of the TEGs at two different wellbore locations: on the outside of the casing; or on the production tubing. TEGs convert heat flux to electrical power, and in the borehole environment, would convert heat flux into or out of the borehole into power for downhole sensors. Such heat flux would be driven by pumping of cold or hot fluids into the borehole—for instance, injecting supercritical CO₂—creating a thermal pulse that could power the downhole sensors. Hence, wireless power generation could be accomplished with in situ TEG energy harvesting. This final report summarizes the project’s efforts that accomplished the creation of a fully operational thermopile field unit, including selection of materials, laboratory benchtop experiments and thermal-hydrologic modeling for design and optimization of the field-scale power generation test unit. Finally, the report describes the field unit that has been built and presents results of performance and survivability testing. The performance and survivability testing evaluated the following: 1) downhole power generation in response to a thermal gradient produced by pumping a heated fluid down a borehole and through the field unit; and 2) component survivability and operation at elevated temperature and pressure conditions representative of field conditions. The performance and survivability testing show that TEG arrays are viable for generating ample energy to power downhole sensors, although it is important to note that developing or connecting to sensors was beyond the scope of this project. This project’s accomplishments thus traversed from a low Technical Readiness Level (TRL) on fundamental concepts of the application and modeling to TRL-5 via testing of the fully integrated field unit for power generation in relevant environments. A fully issued United States Patent covers the wellbore power harvesting technology and applications developed by this project.

Hydrocarbon polymers are used in a wide variety of practical applications. In the field of dynamic compression at extreme pressures, these polymers are used at several high energy density (HED) experimental facilities. One of the most common polymers is poly(methyl methacrylate) or PMMA, also called Plexiglass® or Lucite®. Here, we present high-fidelity, hundreds of GPa range experimental shock compression data measured on Sandia's Z machine. We extend the principal shock Hugoniot for PMMA to more than threefold compression up to 650 GPa and re-shock Hugoniot states up to 1020 GPa in an off-Hugoniot regime, where experimental data are even sparser. These data can be used to put additional constraints on tabular equation of state (EOS) models. The present results provide clear evidence for the need to re-examine the existing tabular EOS models for PMMA above ∼120 GPa as well as perhaps revisit EOSs of similar hydrocarbon polymers commonly used in HED experiments investigating dynamic compression, hydrodynamics, or inertial confinement fusion.

Perovskite solar cells (PSCs) promise high efficiencies and low manufacturing costs. Most formulations, however, contain lead, which raises health and environmental concerns. In this review, we use a risk assessment approach to identify and evaluate the technology risks to the environment and human health. We analyze the risks by following the technology from production to transportation to installation to disposal and examine existing environmental and safety regulations in each context. We review published data from leaching and air emissions testing and highlight gaps in current knowledge and a need for more standardization. Methods to avoid lead release through introduction of absorbing materials or use of alternative PSC formulations are reviewed. We conclude with the recommendation to develop recycling programs for PSCs and further standardized testing to understand risks related to leaching and fires.

Improving the performance of inertial confinement fusion implosions requires physics models that can accurately predict the response to changes in the experimental inputs. Good predictive capability has been demonstrated for the fusion yield using a statistical mapping of simulated outcomes to experimental data [Gopalaswamy et al., Nature 565(771), 581–586 (2019)]. In this paper, a physics-based statistical mapping approach is used to extract and quantify all the major sources of degradation of fusion yield for direct-drive implosions on the OMEGA laser. Here, the yield is found to be dependent on the age of the deuterium tritium fill, the ℓ = 1 asymmetry in the implosion core, the laser beam-to-target size ratio, and parameters related to the hydrodynamic stability. A controlled set of experiments were carried out where only the target fill age was varied while keeping all other parameters constant. The measurements were found to be in excellent agreement with the fill age dependency inferred using the mapping model. In addition, a new implosion design was created, guided by the statistical mapping model by optimizing the trade-offs between increased laser energy coupling at larger target size and the degradations caused by the laser beam-to-target size ratio and hydrodynamic instabilities. When experimentally performed, an increased fusion yield was demonstrated in targets with larger diameters.

Accurately modeling the impact force used in the analysis of loosely constrained cantilevered pipes conveying fluid is imperative. If little information is known of the motion-limiting constraints used in experiments, the analysis of the system may yield inaccurate predictions. Here in this work, multiple forcing representations of the impact force are defined and analyzed for a cantilevered pipe that conveys fluid. Depending on the representation of the impact force, the dynamics of the pipe can vary greatly when only the stiffness of the constraints is known from experiments. Three gap sizes of the constraints are analyzed, and the representation of the impact force used to analyze the system is found to significantly affect the response of the pipe at each gap size. An investigation on the effects of the vibro-impact force representation is performed through using basin of attraction analysis and nonlinear characterization of the system’s response.

As the path towards Urban Air Mobility (UAM) continues to take shape, there are outstanding technical challenges to achieving safe and effective air transportation operations under this new paradigm. To inform and guide technology development for UAM, NASA is investigating the current state-of-the-art in key technology areas including traffic management, detect-and-avoid, and autonomy. In support of this effort, a new perception testbed was developed at NASA Ames Research Center to collect data from an array of sensing systems representative of those that could be found on a future UAM vehicle. This testbed, featuring a Light-Detection-and-Ranging (LIDAR) instrument, a long-wave infrared sensor, and a visible spectrum camera was deployed for a multiday test campaign in the Fog Chamber at Sandia National Laboratories (SNL), in Albuquerque, New Mexico. During the test campaign, fog conditions were created for tests with targets including a human, a resolution chart, and a small unmanned aerial vehicle (sUAV). Here, this paper describes in detail, the developed perception testbed, the experimental setup in the fog chamber, the resulting data, and presents an initial result from analysis of the data with the evaluation of methods to increase contrast through filtering techniques.

Cesium vapor thermionic converters are an attractive method of converting high-temperature heat directly to electricity, but theoretical descriptions of the systems have been difficult due to the multi-step ionization of Cs through inelastic electron-neutral collisions. This work presents particle-in-cell simulations of these converters, using a direct simulation Monte Carlo collision model to track 52 excited states of Cs. These simulations show the dominant role of multi-step ionization, which also varies significantly based on both the applied voltage bias and pressure. The electron energy distribution functions are shown to be highly non-Maxwellian in the cases analyzed here. A comparison with previous approaches is presented, and large differences are found in ionization rates due especially to the fact that previous approaches have assumed Maxwellian electron distributions. Finally, an open question regarding the nature of the plasma sheaths in the obstructed regime is discussed. The one-dimensional simulations did not produce stable obstructed regime operation and thereby do not support the double-sheath hypothesis.

Magnetized turbulence is ubiquitous in many astrophysical and terrestrial plasmas but no universal theory exists. Even the detailed energy dynamics in magnetohydrodynamic (MHD) turbulence are still not well understood. We present a suite of subsonic, super-Alfvénic, high plasma beta MHD turbulence simulations that only vary in their dynamical range, i.e., in their separation between the large-scale forcing and dissipation scales, and their dissipation mechanism (implicit large eddy simulation, ILES, and direct numerical simulation (DNS)). Using an energy transfer analysis framework we calculate the effective numerical viscosities and resistivities, and demonstrate that all ILES calculations of MHD turbulence are resolved and correspond to an equivalent visco-resistive MHD turbulence calculation. Increasing the number of grid points used in an ILES corresponds to lowering the dissipation coefficients, i.e., larger (kinetic and magnetic) Reynolds numbers for a constant forcing scale. Independently, we use this same framework to demonstrate that—contrary to hydrodynamic turbulence—the cross-scale energy fluxes are not constant in MHD turbulence. This applies both to different mediators (such as cascade processes or magnetic tension) for a given dynamical range as well as to a dependence on the dynamical range itself, which determines the physical properties of the flow. We do not observe any indication of convergence even at the highest resolution (largest Reynolds numbers) simulation at 2048³ cells, calling into question whether an asymptotic regime in MHD turbulence exists, and, if so, what it looks like.

Ammonia (NH₃) is an energy-dense chemical and a vital component of fertilizer. In addition, it is a carbon-neutral liquid fuel and a potential candidate for thermochemical energy storage for high-temperature concentrating solar power (CSP). Currently, NH₃ synthesis occurs via the Haber-Bosch process, which requires high pressures (15-25 MPa) and medium to high temperatures (400-500 °C). N₂ and H₂ are essential feedstocks for this NH₃ production process. H₂ is generally derived from methane via steam reforming; N₂ is sourced from air, after oxygen removal via combustion of hydrocarbons. Both processes consume hydrocarbons, resulting in the release of CO₂. In addition, hydrocarbon fuels are burned to produce the heat and mechanical energy required to perform the NH₃ reaction, further increasing CO₂ emissions. Overall, the production of ammonia via the Haber-Bosch (H-B) process is responsible for up to 1.4% of the world’s carbon emissions. The development of a renewable pathway to NH₃ synthesis, which utilizes concentrated solar irradiation as a process heat instead of fossil fuels and operates under low or ambient pressure, will result in a decrease (or elimination) of greenhouse gas emissions as well as avoid the cost, complexity, and safety issues inherent in high-pressure processes. Most current efforts to “green” ammonia production involve either electrolysis or simply replacing the energy source for H-B with renewable electricity, but otherwise leaving the process intact. The effort proposed here would create a new paradigm for the synthesis of NH₃ utilizing solar-thermal heat, water, and air as feedstocks, providing a truly green method of production. The overall objective of the STAP (Solar Thermal Ammonia Production) project was to develop a solar thermochemical looping technology to produce and store nitrogen (N₂) from air for the subsequent production of ammonia (NH₃) via an advanced two-stage process. The goal is a cost-effective and energy efficient technology for the renewable N₂ production and synthesis of NH₃ from H₂ (produced from H₂O) and air using solar-thermal energy from concentrating sunlight, under pressures an order of magnitude lower than H-B NH₃ production. Our process involves two looping cycles, which do not require catalysts and can be recycled. Over the course of the STAP project, we (1) developed and deeply characterized oxide materials for N₂ separation; (2) developed a method for the synthesis of metal nitrides, producing a series of quaternary compounds that have been heretofore unreported; (3) modeled, designed, and fabricated bench-scale tube and on-sun reactors for the N₂ production step and demonstrated the ability to separate N₂ over multiple cycles in the tube reactor; (4) designed and fabricated a bench-scale Ammonia Synthesis Reactor (ASR) and demonstrated the proof of concept of NH₃ synthesis via a novel looping process using metal nitrides over multiple cycles; and (5) completed a systems- and technoeconomic analysis showing the feasibility of ammonia production on a larger scale via the STAP process. The development of renewable, low-cost NH₃ will be of great interest to the chemicals industry, particularly agricultural sectors. The CSP industry should be both an important customer and potential end-user of this technology, as it affords the capability of synthesizing a promising thermochemical storage material on-site. Since the NH₃ synthesis step also requires H₂, there will exist a symbiotic relationship between this technology and solar-thermochemical water-splitting applications. Green ammonia synthesis will result in the decarbonization of a hydrocarbon-intensive industry, helping to meet the Administration goal of industrial decarbonization by 2050. The resulting decrease in CO₂ and related pollutants will improve health and well-being of society, particularly for those living in the vicinity of commercial production plants.

Fracture and short circuit in the Li₇La₃Zr₂O₁₂ (LLZO) solid electrolyte are two key issues that prevent its adoption in battery cells. In this paper, we utilize phase-field simulations that couple electrochemistry and fracture to evaluate the maximum electric potential that LLZO electrolytes can support as a function of crack density. In the case of a single crack, we find that the applied potential at the onset of crack propagation exhibits inverse square root scaling with respect to crack length, analogous to classical fracture mechanics. Here, we further find that the short-circuit potential scales linearly with crack length. In the realistic case where the solid electrolyte contains multiple cracks, we reveal that failure fits the Weibull model. The failure distributions shift to favor failure at lower overpotentials as areal crack density increases. Furthermore, when flawless interfacial buffers are placed between the applied potential and the bulk of the electrolyte, failure is mitigated. When constant currents are applied, current focuses in near-surface flaws, leading to crack propagation and short circuit. We find that buffered samples sustain larger currents without reaching unstable overpotentials and without failing. Our findings suggest several mitigation strategies for improving the ability of LLZO to support larger currents and improve operability.

We demonstrate a monolithic all-epitaxial resonant-cavity architecture for long-wave infrared photodetectors with substrate-side illumination. An nBn detector with an ultra-thin (t ≈ 350 nm) absorber layer is integrated into a leaky resonant cavity, formed using semi-transparent highly doped (n + +) epitaxial layers, and aligned to the anti-node of the cavity's standing wave. The devices are characterized electrically and optically and demonstrate an external quantum efficiency of ∼25% at T = 180 K in an architecture compatible with focal plane array configurations.

The interplay between hydrogen and dislocations (e.g., core and elastic energies, and dislocation–dislocation interactions) has implications on hydrogen embrittlement but is poorly understood. Continuum models of hydrogen enhanced local plasticity have not considered the effect of hydrogen on dislocation core energies. Energy minimization atomistic simulations can only resolve dislocation core energies in hydrogen-free systems because hydrogen motion is omitted so hydrogen atmosphere formation can’t occur. Additionally, previous studies focused more on face-centered-cubic than body-centered-cubic metals. Discrete dislocation dynamics studies of hydrogen–dislocation interactions assume isotropic elasticity, but the validity of this assumption isn’t understood. Here, we perform time-averaged molecular dynamics simulations to study the effect of hydrogen on dislocation energies in body-centered-cubic iron for several dislocation character angles. We see atmosphere formation and highly converged dislocation energies. We find that hydrogen reduces dislocation core energies but can increase or decrease elastic energies of isolated dislocations and dislocation–dislocation interaction energies depending on character angle. We also find that isotropic elasticity can be well fitted to dislocation energies obtained from simulations if the isotropic elastic constants are not constrained to their anisotropic counterparts. These results are relevant to ongoing efforts in understanding hydrogen embrittlement and provide a foundation for future work in this field.

In this work, we investigate the potential of liquid hydrogen storage (LH₂) on-board Class-8 heavy duty trucks to resolve many of the range, weight, volume, refueling time and cost issues associated with 350 or 700-bar compressed H₂ storage in Type-3 or Type-4 composite tanks. We present and discuss conceptual storage system configurations capable of supplying H₂ to fuel cells at 5-bar with or without on-board LH₂ pumps. Structural aspects of storing LH₂ in double walled, vacuum insulated, and low-pressure Type-1 tanks are investigated. Structural materials and insulation methods are discussed for service at cryogenic temperatures and mitigation of heat leak to prevent LH₂ boiloff. Failure modes of the liner and shell are identified and analyzed using the regulatory codes and detailed finite element (FE) methods. The conceptual systems are subjected to a Failure modes and effects analysis (FMEA) and a safety, codes, and standards (SCS) review to rank failures and identify safety gaps. The results indicate that the conceptual systems can reach 19.6% usable gravimetric capacity, 40.9 g-H₂/L usable volumetric capacity and $174-183/kg-H₂ cost (2016 USD) when manufactured 100,000 systems annually.

A challenge for TW-class accelerators, such as Sandia's Z machine, is efficient power coupling due to current loss in the final power feed. It is also important to understand how such losses will scale to larger next generation pulsed power (NGPP) facilities. While modeling is studying these power flow losses it is important to have diagnostic that can experimentally measure plasmas in these conditions and help inform simulations. The plasmas formed in the power flow region can be challenging to diagnose due to both limited lines of sight and being at significantly lower temperatures and densities than typical plasmas studied on Z. This necessitates special diagnostic development to accurately measure the power flow plasma on Z.

Despite state-of-the-art deep learning-based computer vision models achieving high accuracy on object recognition tasks, x-ray screening of baggage at checkpoints is largely performed by hand. Part of the challenge in automation of this task is the relatively small amount of available labeled training data. Furthermore, realistic threat objects may have forms or orientations that do not appear in any training data, and radiographs suffer from high amounts of occlusion. Using deep generative models, we explore data augmentation techniques to expand the intra-class variation of threat objects synthetically injected into baggage radiographs using openly available baggage x-ray datasets. We also benchmark the performance of object detection algorithms on raw and augmented data.

An inherited containment vessel design that has been used in the past to contain items in an environmental testing unit was brought to the Explosives Applications Lab to be analyzed and modified. The goal was to modify the vessel to contain an explosive event of 4g TNT equivalence at least once without failure or significant girth expansion while maintaining a seal. A total of ten energetic tests were performed on multiple vessels. In these tests, the 7075-T6 aluminum vessels were instrumented with thin-film resistive strain gages and both static and dynamic pressure gauges to study its ability to withstand an oversize explosive charge of 8g. Additionally, high precision girth (pi tape) measurements were taken before and after each test to measure the plastic growth of the vessel due to the event. Concurrent with this explosive testing, hydrocode modeling of the containment vessel and charge was performed. The modeling results were shown to agree with the results measured in the explosive field testing. Based on the data obtained during this testing, this vessel design can be safely used at least once to contain explosive detonations of 8g at the center of the chamber for a charge that will not result in damaging fragments.

Multiple Input Multiple Output (MIMO) vibration testing provides the capability to expose a system to a field environment in a laboratory setting, saving both time and money by mitigating the need to perform multiple and costly large-scale field tests. However, MIMO vibration test design is not straightforward oftentimes relying on engineering judgment and multiple test iterations to determine the proper selection of response Degree of Freedom (DOF) and input locations that yield a successful test. This work investigates two DOF selection techniques for MIMO vibration testing to assist with test design, an iterative algorithm introduced in previous work and an Optimal Experiment Design (OED) approach. The iterative-based approach downselects the control set by removing DOF that have the smallest impact on overall error given a target Cross Power Spectral Density matrix and laboratory Frequency Response Function (FRF) matrix. The Optimal Experiment Design (OED) approach is formulated with the laboratory FRF matrix as a convex optimization problem and solved with a gradient-based optimization algorithm that seeks a set of weighted measurement DOF that minimize a measure of model prediction uncertainty. The DOF selection approaches are used to design MIMO vibration tests using candidate finite element models and simulated target environments. The results are generalized and compared to exemplify the quality of the MIMO test using the selected DOF.

There is a global interest in decarbonizing the existing natural gas infrastructure by blending the natural gas with hydrogen. However, hydrogen is known to embrittle pipeline and pressure vessel steels used in gas transportation and storage applications. Thus, assessing the structural integrity of vintage pipeline (pre-1970s) in the presence of gaseous hydrogen is a critical step towards successful implementation of hydrogen blending into existing infrastructure. To this end, fatigue crack growth (FCG) behavior and fracture resistance of several vintage X52 pipeline steels were evaluated in high purity gaseous hydrogen environments at pressure of 210 bar (3,000 psi) and 34 bar (500 psi). The base metal and seam weld microstructures were characterized using optical microscopy, scanning electron microscopy (SEM) and Vickers hardness mapping. The base metals consisted of ferrite-pearlite banded microstructures, whereas the weld regions contained ferrite and martensite. In one case, a hook-like crack was observed in an electric resistance (seam) weld; whereas hard spots were observed near the bond line of a double-submerged arc (seam) weld. For a given hydrogen gas pressure, comparable FCG rates were observed for the different base metal and weld microstructures. Generally, the higher strength microstructures had lower fracture resistance in hydrogen. In particular, lower fracture resistance was measured when local hard spots were observed in the approximate region of the crack plane of the weld. Samples tested in lower H2 pressure (34 bar) exhibited lower FCG rates (in the lower ∆K regime) and greater fracture resistance when compared to the respective high-pressure (210 bar) hydrogen tests. The hydrogen-assisted fatigue and fracture surfaces were qualitatively characterized using SEM to rationalize the influence of microstructure on the dominant fracture mechanisms in gaseous hydrogen environment.

Physics-Based Reduced Order Models (ROMs) tend to rely on projection-based reduction. This family of approaches utilizes a series of responses of the full-order model to assemble a suitable basis, subsequently employed to formulate a set of equivalent, low-order equations through projection. However, in a nonlinear setting, physics-based ROMs require an additional approximation to circumvent the bottleneck of projecting and evaluating the nonlinear contributions on the reduced space. This scheme is termed hyper-reduction and enables substantial computational time reduction. The aforementioned hyper-reduction scheme implies a trade-off, relying on a necessary sacrifice on the accuracy of the nonlinear terms’ mapping to achieve rapid or even real-time evaluations of the ROM framework. Since time is essential, especially for digital twins representations in structural health monitoring applications, the hyper-reduction approximation serves as both a blessing and a curse. Our work scrutinizes the possibility of exploiting machine learning (ML) tools in place of hyper-reduction to derive more accurate surrogates of the nonlinear mapping. By retaining the POD-based reduction and introducing the machine learning-boosted surrogate(s) directly on the reduced coordinates, we aim to substitute the projection and update process of the nonlinear terms when integrating forward in time on the low-order dimension. Our approach explores a proof-of-concept case study based on a Nonlinear Auto-regressive neural network with eXogenous Inputs (NARX-NN), trying to potentially derive a superior physics-based ROM in terms of efficiency, suitable for (near) real-time evaluations. The proposed ML-boosted ROM (N3-pROM) is validated in a multi-degree of freedom shear frame under ground motion excitation featuring hysteretic nonlinearities.

As the electric grid becomes increasingly cyber-physical, it is important to characterize its inherent cyber-physical interdepedencies and explore how that characterization can be leveraged to improve grid operation. It is crucial to investigate what data features are transferred at the system boundaries, how disturbances cascade between the systems, and how planning and/or mitigation measures can leverage that information to increase grid resilience. In this paper, we explore several numerical analysis and graph decomposition techniques that may be suitable for modeling these cyber-physical system interdependencies and for understanding their significance. An augmented WSCC 9-bus cyber-physical system model is used as a small use-case to assess these techniques and their ability in characterizing different events within the cyber-physical system. These initial results are then analyzed to formulate a high-level approach for characterizing cyber-physical interdependencies.

The Sliding Scale of Cybersecurity is a framework for understanding the actions that contribute to cybersecurity. The model consists of five categories that provide varying value towards cybersecurity and incur varying implementation costs. These categories range from offensive cybersecurity measures providing the least value and incurring the greatest cost, to architecture providing the greatest value and incurring the least cost. This paper presents an application of the Sliding Scale of Cybersecurity to the Tiered Cybersecurity Analysis (TCA) of digital instrumentation and control systems for advanced reactors. The TCA consists of three tiers. Tier 1 is design and impact analysis. In Tier 1 it is assumed that the adversary has control over all digital systems, components, and networks in the plant, and that the adversary is only constrained by the physical limitations of the plant design. The plant’s safety design features are examined to determine whether the consequences of an attack by this cyber-enabled adversary are eliminated or mitigated. Accident sequences that are not eliminated or mitigated by security by design features are examined in Tier 2 analysis. In Tier 2, adversary access pathways are identified for the unmitigated accident sequences, and passive measures are implemented to deny system and network access to those pathways wherever feasible. Any systems with remaining susceptible access pathways are then examined in Tier 3. In Tier 3, active defensive cybersecurity architecture features and cybersecurity plan controls are applied to deny the adversary the ability to conduct the tasks needed to cause a severe consequence. Earlier application of the TCA in the design process provides greater opportunity for an efficient graded approach and defense-in-depth.

In this work, a modular and open-source platform has been developed for integrating hybrid battery energy storage systems that are intended for grid applications. Alongside integration, this platform will facilitate testing and optimal operation of hybrid storage technologies. Here, a hardware testbed and a control software have been designed, where the former comprises commercial Lithium-iron-phosphate (LiFePO4) and Lead Acid (Pb - acid) cells, custom built Dual Active Bridge (DAB) DC-DC converters, and a commercial DC-AC conversion system. In this testbed the batteries have an operating voltage range of 11-15V, the DC-AC conversion stage has a DC link voltage of 24V, and it connects to a 208V3-φ grid. The hardware testbed can be scaled up to higher voltages. The control software is developed in Python, and the firmware for all the hardware components is developed in C. This software implements hybrid charge/discharge protocols that are suitable for each battery technology for preventing cell degradation, and perform uninter-rupted quality checks on selected battery packs. The developed platform provides flexibility, modularity, safety and economic benefits to utility-scale storage integration.

Distribution systems may experience fast voltage swings in the matter of seconds from distributed energy resources, such as Wind Turbines Generators (WTG) and Photovoltaic (PV) inverters, due to their dependency on variable and intermittent wind speed and solar irradiance. This work proposes a WTG reactive power controller for fast voltage regulation. The controller is tested on a simulation model of a real distribution system. Real wind speed, solar irradiation, and load consumption data is used. The controller is based on a Reinforcement Learning Deep Deterministic Policy Gradient (DDPG) model that determines optimum control actions to avoid significant voltage deviations across the system. The controller has access to voltage measurements at all system buses. Results show that the proposed WTG reactive power controller significantly reduces system-wide voltage deviations across a large number of generation scenarios in order to comply with standardized voltage tolerances.

As the width and depth of quantum circuits implemented by state-of-the-art quantum processors rapidly increase, circuit analysis and assessment via classical simulation are becoming unfeasible. It is crucial, therefore, to develop new methods to identify significant error sources in large and complex quantum circuits. In this work, we present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most and thus helps to identify the most significant sources of error. The technique requires no classical verification of the circuit output and is thus a scalable tool for debugging large quantum programs in the form of circuits. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.

Creation of a Sandia internally developed, shock-hardened Recoverable Data Recorder (RDR) necessitated experimentation by ballistically-firing the device into water targets at velocities up to 5,000 ft/s. The resultant mechanical environments were very severe—routinely achieving peak accelerations in excess of 200 kG and changes in pseudo-velocity greater than 38,000 inch/s. High-quality projectile deceleration datasets were obtained though high-speed imaging during the impact events. The datasets were then used to calibrate and validate computational models in both CTH and EPIC. Hydrodynamic stability in these environments was confirmed to differ from aerodynamic stability; projectile stability is maintained through a phenomenon known as “tail-slapping” or impingement of the rear of the projectile on the cavitation vapor-water interface which envelopes the projectile. As the projectile slows the predominate forces undergo a transition which is outside the codes’ capabilities to calculate accurately, however, CTH and EPIC both predict the projectile trajectory well in the initial hypervelocity regime. Stable projectile designs and the achievable acceleration space are explored through a large parameter sweep of CTH simulations. Front face chamfer angle has the largest influence on stability with low angles being more stable.

Puerto Rico faced a double strike from hurricanes Irma and Maria in 2017. The resulting damage required a comprehensive rebuild of electric infrastructure. There are plans and pilot projects to rebuild with microgrids to increase resilience. This paper provides a techno-economic analysis technique and case study of a potential future community in Puerto Rico that combines probabilistic microgrid design analysis with tiered circuits in building energy modeling. Tiered circuits in buildings allow electric load reduction via remote disconnection of non-critiñl circuits during an emergency. When coupled to a microgrid, tiered circuitry can reduce the chances of a microgrid's storage and generation resources being depleted. The analysis technique is applied to show 1) Approximate cost savings due to a tiered circuit structure and 2) Approximate cost savings gained by simultaneously considering resilience and sustainability constraints in the microgrid optimization. The analysis technique uses a resistive capacitive thermal model with load profiles for four tiers (tier 1-3 and non-critical loads). Three analyses were conducted using: 1) open-source software called Tiered Energy in Buildings and 2) the Microgrid Design Toolkit. For a fossil fuel based microgrid 30% of the total microgrid costs of 1.18 million USD were calculated where the non-tiered case keeps all loads 99.9% available and the tiered case keeps tier 1 at 99.9%, tier 2 at 95%, tier 3 at 80% availability, with no requirement on non-critical loads. The same comparison for a sustainable microgrid showed 8% cost savings on a 5.10 million USD microgrid due to tiered circuits. The results also showed 6-7% cost savings when our analysis technique optimizes sustainability and resilience simultaneously in comparison to doing microgrid resilience analysis and renewables net present value analysis independently. Though highly specific to our case study, similar assessments using our analysis technique can elucidate value of tiered circuits and simultaneous consideration of sustainability and resilience in other locations.

Laser absorption spectroscopy (LAS) was used to measure temperature and XH2O at a rate of 500 kHz in post-detonation fireballs of solid explosives. A 25 g hemisphere of pentaerythritol tetranitrate (PETN) was initiated with an exploding-bridgewire detonator to produce a post-detonation fireball that traveled radially toward a hardened optical probe. The probe contained a pressure transducer and the near-infrared optics needed to measure H2O absorption transitions near 7185.6 cm-1 and 6806 cm-1 using peak-picking scanned-wavelength modulation-spectroscopy with first-harmonic-normalized second-harmonic detection (scanned-WMS-2f/1f). The two lasers were scanned across the peak of an absorption line at 500 kHz and modulated at either 35 MHz for the laser near 7185.6 cm-1 or 45.5 MHz for the laser near 6806 cm-1. This enabled measurements of temperature and XH2O at 500 kHz in the shock-heated air and trailing post-detonation fireball. Time histories of pressure, temperature, and XH2O were acquired at multiple standoff distances in order to quantify the temporal evolution of these quantities in the post-detonation environment produced by PETN.

Soft magnetic composites (SMCs) offer a promising alternative to electrical steels and soft ferrites in high performance motors and power electronics. They are ideal for incorporation into passive electronic components such as inductors and transformers, which require a non-permanent magnetic core to rapidly switch magnetization. As a result, there is a need for materials with the right combination of low coercivity, low magnetic remanence, high relative permeability, and high saturation magnetization to achieve these goals. Iron nitride is an attractive soft magnetic material for incorporation into an amine/epoxy resin matrix. This permits the synthesis of net-shaped SMCs using a “bottom-up” approach for overcoming the limitations of current state-of-the-art SMCs made via conventional powder metal processing techniques. In this work we present the fabrication of various net-shaped, iron nitride-based SMCs using two different amine/epoxy resin systems and their magnetic characterization. The maximum volume loading of iron nitride reached was ∼77% via hot pressing, which produced SMCs with a saturation magnetic polarization (Js) of ∼0.9 T, roughly 2–3 times the Js of soft ferrites.

Low loss silicon nitride ring resonator reflectors provide feedback to a III/V gain chip, achieving single-mode lasing at 772nm. The Si3N4 is fabricated in a CMOS foundry compatible process that achieves loss values of 0.036dB/cm.

The Information Harm Triangle (IHT) is a novel approach that aims to adapt intuitive engineering concepts to simplify defense in depth for instrumentation and control (I&C) systems at nuclear power plants. This approach combines digital harm, real-world harm, and unsafe control actions (UCAs) into a single graph named “Information Harm Triangle.” The IHT is based on the postulation that the consequences of cyberattacks targeting I&C systems can be expressed in terms of two orthogonal components: a component representing the magnitude of data harm (DH) (i.e., digital information harm) and a component representing physical information harm (PIH) (i.e., real-world harm, e.g., an inadvertent plant trip). The magnitude of the severity of the physical consequence is the aspect of risk that is of concern. The sum of these two components represents the total information harm. The IHT intuitively informs risk-informed cybersecurity strategies that employ independent measures that either act to prevent, reduce, or mitigate DH or PIH. Another aspect of the IHT is that the DH can result in cyber-initiated UCAs that result in severe physical consequences. The orthogonality of DH and PIH provides insights into designing effective defense in depth. The IHT can also represent cyberattacks that have the potential to impede, evade, or compromise countermeasures from taking appropriate action to reduce, stop, or mitigate the harm caused by such UCAs. Cyber-initiated UCAs transform DH to PIH.

Through atomistic simulations, we uncover the dynamic properties of the Cantor alloy under shock-loading conditions and characterize its equation-of-state over a wide range of densities and pressures along with spall strength at ultra-high strain rates. Simulation results reveal the role of local phase transformations during the development of the shock wave on the alloy's high spall strength. The simulated shock Hugoniot results are in remarkable agreement with experimental data, validating the predictability of the model. These mechanistic insights along with the quantification of dynamical properties can drive further advancements in various applications of this class of alloys under extreme environments.

Despite state-of-the-art deep learning-based computer vision models achieving high accuracy on object recognition tasks, x-ray screening of baggage at checkpoints is largely performed by hand. Part of the challenge in automation of this task is the relatively small amount of available labeled training data. Furthermore, realistic threat objects may have forms or orientations that do not appear in any training data, and radiographs suffer from high amounts of occlusion. Using deep generative models, we explore data augmentation techniques to expand the intra-class variation of threat objects synthetically injected into baggage radiographs using openly available baggage x-ray datasets. We also benchmark the performance of object detection algorithms on raw and augmented data.

Extreme meteorological events, such as hurricanes and floods, cause significant infrastructure damage and, as a result, prolonged grid outages. To mitigate the negative effect of these outages and enhance the resilience of communities, microgrids consisting of solar photovoltaics (PV), energy storage (ES) technologies, and backup diesel generation are being considered. Furthermore, it is necessary to take into account how the extreme event affects the systems' performance during the outage, often referred to as black-sky conditions. In this paper, an optimization model is introduced to properly size ES and PV technologies to meet various duration of grid outages for selected critical infrastructure while considering black-sky conditions. A case study of the municipality of Villalba, Puerto Rico is presented to identify the several potential microgrid configurations that increase the community's resilience. Sensitivity analyses are performed around the grid outage durations and black-sky conditions to better decide what factors should be considered when scoping potential microgrids for community resilience.

With the recent surge in big data analytics for hyperdimensional data, there is a renewed interest in dimensionality reduction techniques. In order for these methods to improve performance gains and understanding of the underlying data, a proper metric needs to be identified. This step is often overlooked, and metrics are typically chosen without consideration of the underlying geometry of the data. In this paper, we present a method for incorporating elastic metrics into the t-distributed stochastic neighbour embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). We apply our method to functional data, which is uniquely characterized by rotations, parameterization and scale. If these properties are ignored, they can lead to incorrect analysis and poor classification performance. Through our method, we demonstrate improved performance on shape identification tasks for three benchmark data sets (MPEG-7, Car data set and Plane data set of Thankoor), where we achieve 0.77, 0.95 and 1.00 F1 score, respectively.

Although enhancing permeability is vital for successful development of an Enhanced Geothermal System (EGS) reservoir, high-permeability pathways between injection and production wells can lead to short-circuiting of the flow, resulting in inefficient heat exchange with the reservoir rock. For this reason, the permeability of such excessively permeable paths needs to be reduced. Controlling the reservoir permeability away from wells, however, is challenging, because the injected materials need to form solid plugs only after they reach the target locations. To control the timing of the flow-diverter formation, we are developing a technology to deliver one or more components of the diverter-forming chemicals in microparticles (capsules) with a thin polymer shell. The material properties of the shell are designed so that it can withstand moderately high temperatures (up to ~200°C) of the injected fluid for a short period of time (up to ~30 minutes), but thermally degrades and releases the reactants at higher reservoir temperatures. A microfluidic system has been developed that can continuously produce reactant-encapsulating particles. The diameter of the produced particles is in the range of ~250-650 μm, which can be controlled by using capillary tubes with different diameters and by adjusting the flow rates of the encapsulated fluid and the UV-curable epoxy resin for the shell. Preliminary experiments have demonstrated that (1) microcapsules containing chemical activators for flow-diverter (silicate gel or metal silicate) formation can be produced, (2) the durability of the shell can be made to satisfy the required conditions, and (3) thermal degradation of the shell allows for release of the reaction activators and control of reaction kinetics in silica-based diverters.

Diffusion bonding of two immiscible, binary metallic systems, Cu-Ta and Cu-W was employed to make repeatable and predictable dual-layer impactors for shock-reshock experiments. The diffusion bonded impactors were characterized using ultrasonic imaging and optical microscopy to ensure bonding and the absence of excessive Cu grain coarsening. The diffusion bonded impactors were launched via a two-stage gas gun at [100] LiF windows instrumented with multiple interferometry probes spanning nearly the entire impactor area. Consistent interferometry data was obtained from all experiments with no evidence of release prior to recompression, indicating a uniform bond. Comparisons to hydrocode simulations show excellent agreement for all experiments, facilitating easy application of these impactors to future experiments.

Characterization of geologic heterogeneity at an enhanced geothermal system (EGS) is crucial for cost-effective stimulation planning and reliable heat production. With recent advances in computational power and sensor technology, large-scale fine-resolution simulations of coupled thermal-hydraulic-mechanical (THM) processes have been available. However, traditional large-scale inversion approaches have limited utility for sites with complex subsurface structures unless one can afford high, often computationally prohibitive, computations. Key computational burdens are predominantly associated with a number of large-scale coupled numerical simulations and large dense matrix multiplications derived from fine discretization of the field site domain and a large number of THM and chemical (THMC) measurements. In this work, we present deep-generative model-based Bayesian inversion methods for the computationally efficient and accurate characterization of EGS sites. Deep generative models are used to learn the approximate subsurface property (e.g., permeability, thermal conductivity, and elastic rock properties) distribution from multipoint geostatistics-derived training images or discrete fracture network models as a prior and accelerated stochastic inversion is performed on the low-dimensional latent space in a Bayesian framework. Numerical examples with synthetic permeability fields with fracture inclusions with THM data sets based on Utah FORGE geothermal site will be presented to test the accuracy, speed, and uncertainty quantification capability of our proposed joint data inversion method.

In this paper, the potential for time series classifiers to identify faults and their location in a DC Microgrid is explored. Two different classification algorithms are considered. First, a minimally random convolutional kernel transformation (MINIROCKET) is applied on the time series fault data. The transformed data is used to train a regularized linear classifier with stochastic gradient descent (SDG). Second, a continuous wavelet transform (CWT) is applied on the fault data and a convolutional neural network (CNN) is trained to learn the characteristic patterns in the CWT coefficients of the transformed data. The data used for training and testing the models are acquired from multiple fault simulations on a 750 VDC Microgrid modeled in PSCAD/EMTDC. The results from both classification algorithms are presented and compared. For an accurate classification of the fault location, the MINIROCKET and SGD Classifier model needed signals/features from several measurement nodes in the system. The CWT and CNN based model accurately identified the fault location with signals from a single measurement node in the system. By performing a self-learning monitoring and decision making analysis, protection relays equipped with time series classification algorithms can quickly detect the location of faults and isolate them to improve the protection operations on DC Microgrids.

TFLN/silicon photonic modulators featuring active silicon photonic components are reported with a Vπ of 3.6 Vcm. This hybrid architecture utilizes the bottom of the buried oxide as the bonding surface which features minimum topology.

Spent nuclear fuel repository simulations are currently not able to incorporate detailed fuel matrix degradation (FMD) process models due to their computational cost, especially when large numbers of waste packages breach. The current paper uses machine learning to develop artificial neural network and k-nearest neighbor regression surrogate models that approximate the detailed FMD process model while being computationally much faster to evaluate. Using fuel cask temperature, dose rate, and the environmental concentrations of CO32−, O2, Fe2+, and H2 as inputs, these surrogates show good agreement with the FMD process model predictions of the UO2 degradation rate for conditions within the range of the training data. A demonstration in a full-scale shale repository reference case simulation shows that the incorporation of the surrogate models captures local and temporal environmental effects on fuel degradation rates while retaining good computational efficiency.

Chemistry tabulation is a common approach in practical simulations of turbulent combustion at engineering scales. Linear interpolants have traditionally been used for accessing precomputed multidimensional tables but suffer from large memory requirements and discontinuous derivatives. Higher-degree interpolants address some of these restrictions but are similarly limited to relatively low-dimensional tabulation. Artificial neural networks (ANNs) can be used to overcome these limitations but cannot guarantee the same accuracy as interpolants and introduce challenges in reproducibility and reliable training. These challenges are enhanced as the physics complexity to be represented within the tabulation increases. In this manuscript, we assess the efficiency, accuracy, and memory requirements of Lagrange polynomials, tensor product B-splines, and ANNs as tabulation strategies. We analyze results in the context of nonadiabatic flamelet modeling where higher dimension counts are necessary. While ANNs do not require structuring of data, providing benefits for complex physics representation, interpolation approaches often rely on some structuring of the table. Interpolation using structured table inputs that are not directly related to the variables transported in a simulation can incur additional query costs. This is demonstrated in the present implementation of heat losses. We show that ANNs, despite being difficult to train and reproduce, can be advantageous for high-dimensional, unstructured datasets relevant to nonadiabatic flamelet models. We also demonstrate that Lagrange polynomials show significant speedup for similar accuracy compared to B-splines.

A high altitude electromagnetic pulse (HEMP) or other similar geomagnetic disturbance (GMD) has the potential to severely impact the operation of large-scale electric power grids. By introducing low-frequency common-mode (CM) currents, these events can impact the performance of key system components such as large power transformers. In this work, a solid-state transformer (SST) that can replace susceptible equipment and improve grid resiliency by safely absorbing these CM insults is described. An overview of the proposed SST power electronics and controls architecture is provided, a system model is developed, and the performance of the SST in response to a simulated CM insult is evaluated. Compared to a conventional magnetic transformer, the SST is found to recover quickly from the insult while maintaining nominal ac input/output behavior.

This paper presents a die-embedded glass interposer with minimum warpage for 5G/6G applications. The interposer performs high integration with low-loss interconnects by embedding multiple chips in the same glass substrate and interconnecting the chips through redistributive layers (RDL). Novel processes for cavity creation, multi-die embedding, carrier- less RDL build up and heat spreader attachment are proposed and demonstrated in this work. Performance of the interposer from 1 GHz to 110 GHz are evaluated. This work provides an advanced packaging solution for low-loss die-to-die and die-to-package interconnects, which is essential to high performance wireless system integration.

Computational simulations of high-speed flow play an important role in the design of hypersonic vehicles, for which experimental data are scarce; however, high-fidelity simulations of hypersonic flow are computationally expensive. Reduced order models (ROMs) have the potential to make many-query problems, such as design optimization and uncertainty quantification, tractable for this domain. Residual minimization-based ROMs, which formulate the projection onto a reduced basis as an optimization problem, are one promising candidate for model reduction of large-scale fluid problems. This work analyzes whether specific choices of norms and objective functions can improve the performance of ROMs of hypersonic flow. Specifically, we investigate the use of dimensionally consistent inner products and modifications designed for convective problems, including ℓ1 minimization and constrained optimization statements to enforce conservation laws. Particular attention is paid to accuracy for problems with strong shocks, which are common in hypersonic flow and challenging for projection-based ROMs. We demonstrate that these modifications can improve the predictability and efficiency of a ROM, though the impact of such formulations depends on the quantity of interest and problem considered.

Modern Industrial Control Systems (ICS) attacks evade existing tools by using knowledge of ICS processes to blend their activities with benign Supervisory Control and Data Acquisition (SCADA) operation, causing physical world damages. We present Scaphy to detect ICS attacks in SCADA by leveraging the unique execution phases of SCADA to identify the limited set of legitimate behaviors to control the physical world in different phases, which differentiates from attacker's activities. For example, it is typical for SCADA to setup ICS device objects during initialization, but anomalous during process-control. To extract unique behaviors of SCADA execution phases, Scaphy first leverages open ICS conventions to generate a novel physical process dependency and impact graph (PDIG) to identify disruptive physical states. Scaphy then uses PDIG to inform a physical process-aware dynamic analysis, whereby code paths of SCADA process-control execution is induced to reveal API call behaviors unique to legitimate process-control phases. Using this established behavior, Scaphy selectively monitors attacker's physical world-targeted activities that violates legitimate process-control behaviors. We evaluated Scaphy at a U.S. national lab ICS testbed environment. Using diverse ICS deployment scenarios and attacks across 4 ICS industries, Scaphy achieved 95% accuracy & 3.5% false positives (FP), compared to 47.5% accuracy and 25% FP of existing work. We analyze Scaphy's resilience to futuristic attacks where attacker knows our approach.

Terrain-relative autonomous navigation is a challenging task. In traditional approaches, an elevation map is carried onboard and compared to measurements of the terrain below the vehicle. These methods are computationally expensive, and it is impractical to store high-quality maps of large swaths of terrain. In this article, we generate position measurements using NeuroGrid, a recently-proposed algorithm for computing position information from terrain elevation measurements. We incorporate NeuroGrid into an inertial navigation scheme using a novel measurement rejection strategy and online covariance computation. Our results show that the NeuroGrid filter provides highly accurate state information over the course of a long trajectory.

Advanced finite-element discretizations and preconditioners for models of poroelasticity have attracted significant attention in recent years. The equations of poroelasticity offer significant challenges in both areas, due to the potentially strong coupling between unknowns in the system, saddle-point structure, and the need to account for wide ranges of parameter values, including limiting behavior such as incompressible elasticity. This paper was motivated by an attempt to develop monolithic multigrid preconditioners for the discretization developed in [C. Rodrigo et al., Comput. Methods App. Mech. Engrg, 341 (2018), pp. 467-484]; we show here why this is a difficult task and, as a result, we modify the discretization in [Rodrigo et al.] through the use of a reduced-quadrature approximation, yielding a more “solver-friendly” discretization. Local Fourier analysis is used to optimize parameters in the resulting monolithic multigrid method, allowing a fair comparison between the performance and costs of methods based on Vanka and Braess-Sarazin relaxation. Numerical results are presented to validate the local Fourier analysis predictions and demonstrate efficiency of the algorithms. Finally, a comparison to existing block-factorization preconditioners is also given.

The challenge of cyberattack detection can be illustrated by the complexity of the MITRE ATT&CKTM matrix, which catalogues >200 attack techniques (most with multiple sub-techniques). To reliably detect cyberattacks, we propose an evidence-based approach which fuses multiple cyber events over varying time periods to help differentiate normal from malicious behavior. We use Bayesian Networks (BNs) - probabilistic graphical models consisting of a set of variables and their conditional dependencies - for fusion/classification due to their interpretable nature, ability to tolerate sparse or imbalanced data, and resistance to overfitting. Our technique utilizes a small collection of expert-informed cyber intrusion indicators to create a hybrid detection system that combines data-driven training with expert knowledge to form a host-based intrusion detection system (HIDS). We demonstrate a software pipeline for efficiently generating and evaluating various BN classifier architectures for specific datasets and discuss explainability benefits thereof.

Phosphor thermometry has become an established remote sensing technique for acquiring the temperature of surfaces and gas-phase flows. Often, phosphors are excited by a light source (typically emitting in the UV region), and their temperature-sensitive emission is captured. Temperature can be inferred from shifts in the emission spectra or the radiative decay lifetime during relaxation. While recent work has shown that the emission of several phosphors remains thermographic during x-ray excitation, the radiative decay lifetime was not investigated. The focus of the present study is to characterize the lifetime decay of the phosphor Gd2O2S:Tb for temperature sensitivity after excitation from a pulsed x-ray source. These results are compared to the lifetime decays found for this phosphor when excited using a pulsed UV laser. Results show that the lifetime of this phosphor exhibits comparable sensitivity to temperature between both excitation sources for a temperature range between 21 °C to 140 °C in increments of 20 °C. This work introduces a novel method of thermometry for researchers to implement when employing x-rays for diagnostics.

Geomagnetic disturbances (GMDs) give rise to geomagnetically induced currents (GICs) on the earth's surface which find their way into power systems via grounded transformer neutrals. The quasi-dc nature of the GICs results in half-cycle saturation of the power grid transformers which in turn results in transformer failure, life reduction, and other adverse effects. Therefore, transformers need to be more resilient to dc excitation. This paper sets forth dc immunity metrics for transformers. Furthermore, this paper sets forth a novel transformer architecture and a design methodology which employs the dc immunity metrics to make it more resilient to dc excitation. This is demonstrated using a time-stepping 2D finite element analysis (FEA) simulation. It was found that a relatively small change in the core geometry significantly increases transformer resiliency with respect to dc excitation.

We consider the intersection between nonrepeating random FM (RFM) waveforms and practical forms of optimal mismatched filtering (MMF). Specifically, the spectrally-shaped inverse filter (SIF) is a well-known approximation to the least-squares (LS-MMF) that provides significant computational savings. Given that nonrepeating waveforms likewise require unique nonrepeating MMFs, this efficient form is an attractive option. Moreover, both RFM waveforms and the SIF rely on spectrum shaping, which establishes a relationship between the goodness of a particular waveform and the mismatch loss (MML) the corresponding filter can achieve. Both simulated and open-air experimental results are shown to demonstrate performance.

Quantum computing testbeds exhibit high-fidelity quantum control over small collections of qubits, enabling performance of precise, repeatable operations followed by measurements. Currently, these noisy intermediate-scale devices can support a sufficient number of sequential operations prior to decoherence such that near term algorithms can be performed with proximate accuracy (like chemical accuracy for quantum chemistry problems). While the results of these algorithms are imperfect, these imperfections can help bootstrap quantum computer testbed development. Demonstrations of these algorithms over the past few years, coupled with the idea that imperfect algorithm performance can be caused by several dominant noise sources in the quantum processor, which can be measured and calibrated during algorithm execution or in post-processing, has led to the use of noise mitigation to improve typical computational results. Conversely, benchmark algorithms coupled with noise mitigation can help diagnose the nature of the noise, whether systematic or purely random. Here, we outline the use of coherent noise mitigation techniques as a characterization tool in trapped-ion testbeds. We perform model-fitting of the noisy data to determine the noise source based on realistic physics focused noise models and demonstrate that systematic noise amplification coupled with error mitigation schemes provides useful data for noise model deduction. Further, in order to connect lower level noise model details with application specific performance of near term algorithms, we experimentally construct the loss landscape of a variational algorithm under various injected noise sources coupled with error mitigation techniques. This type of connection enables application-aware hardware code-sign, in which the most important noise sources in specific applications, like quantum chemistry, become foci of improvement in subsequent hardware generations.

Due to their increased levels of reliability, meshed low-voltage (LV) grid and spot networks are common topologies for supplying power to dense urban areas and critical customers. Protection schemes for LV networks often use highly sensitive reverse current trip settings to detect faults in the medium-voltage system. As a result, interconnecting even low levels of distributed energy resources (DERs) can impact the reliability of the protection system and cause nuisance tripping. This work analyzes the possibility of modifying the reverse current relay trip settings to increase the DER hosting capacity of LV networks without impacting fault detection performance. The results suggest that adjusting relay settings can significantly increase DER hosting capacity on LV networks without adverse effects, and that existing guidance on connecting DERs to secondary networks, such as that contained in IEEE Std 1547-2018, could potentially be modified to allow higher DER deployment levels.

This paper describes the methodology of designing a replacement blade tip and winglet for a wind turbine blade to demonstrate the potential of additive-manufacturing for wind energy. The team will later field-demonstrate this additive-manufactured, system-integrated tip (AMSIT) on a wind turbine. The blade tip aims to reduce the cost of wind energy by improving aerodynamic performance and reliability, while reducing transportation costs. This paper focuses on the design and modeling of a winglet for increased power production while maintaining acceptable structural loads of the original Vestas V27 blade design. A free-wake vortex model, WindDVE, was used for the winglet design analysis. A summary of the aerodynamic design process is presented along with a case study of a specific design.

This work developed a methodology for transmission line modeling of cable installations to predict the propagation of conducted high altitude electromagnetic pulses in a substation or generating plant. The methodology was applied to a termination cabinet example that was modeled with SPICE transmission line elements with information from electromagnetic field modeling and with validation using experimental data. The experimental results showed reasonable agreement to the modeled propagating pulse and can be applied to other installation structures in the future.

Abstract not provided.

Computational simulation allows scientists to explore, observe, and test physical regimes thought to be unattainable. Validation and uncertainty quantification play crucial roles in extrapolating the use of physics-based models. Bayesian analysis provides a natural framework for incorporating the uncertainties that undeniably exist in computational modeling. However, the ability to perform quality Bayesian and uncertainty analyses is often limited by the computational expense of first-principles physics models. In the absence of a reliable low-fidelity physics model, phenomenological surrogate or machine learned models can be used to mitigate this expense; however, these data-driven models may not adhere to known physics or properties. Furthermore, the interactions of complex physics in high-fidelity codes lead to dependencies between quantities of interest (QoIs) that are difficult to quantify and capture when individual surrogates are used for each observable. Although this is not always problematic, predicting multiple QoIs with a single surrogate preserves valuable insights regarding the correlated behavior of the target observables and maximizes the information gained from available data. A method of constructing a Gaussian Process (GP) that emulates multiple QoIs simultaneously is presented. As an exemplar, we consider Magnetized Liner Inertial Fusion, a fusion concept that relies on the direct compression of magnetized, laser-heated fuel by a metal liner to achieve thermonuclear ignition. Magneto-hydrodynamics (MHD) codes calculate diagnostics to infer the state of the fuel during experiments, which cannot be measured directly. The calibration of these diagnostic metrics is complicated by sparse experimental data and the expense of high-fidelity neutron transport models. The development of an appropriate surrogate raises long-standing issues in modeling and simulation, including calibration, validation, and uncertainty quantification. The performance of the proposed multi-output GP surrogate model, which preserves correlations between QoIs, is compared to the standard single-output GP for a 1D realization of the MagLIF experiment.

Springs play important roles in many mechanisms, including critical safety components employed by Sandia National Laboratories. Due to the nature of these safety component applications, serious concerns arise if their springs become damaged or unhook from their posts. Finite element analysis (FEA) is one technique employed to ensure such adverse scenarios do not occur. Ideally, a very fine spring mesh would be used to make the simulation as accurate as possible with respect to mesh convergence. While this method does yield the best results, it is also the most time consuming and therefore most computationally expensive process. In some situations, reduced order models (ROMs) can be adopted to lower this cost at the expense of some accuracy. This study quantifies the error present between a fine, solid element mesh and a reduced order spring beam model, with the aim of finding the best balance of a low computational cost and high accuracy analysis. Two types of analyses were performed, a quasi-static displacement-controlled pull and a haversine shock. The first used implicit methods to examine basic properties as the elastic limit of the spring material was reached. This analysis was also used to study the convergence and residual tolerance of the models. The second used explicit dynamics methods to investigate spring dynamics and stress/strain properties, as well as examine the impact of the chosen friction coefficient. Both the implicit displacement-controlled pull test and explicit haversine shock test showed good similarities between the hexahedral and beam meshes. The results were especially favorable when comparing reaction force and stress trends and maximums. However, the EQPS results were not quite as favorable. This could be due to differences in how the shear stress is calculated in both models, and future studies will need to investigate the exact causes. The data indicates that the beam model may be less likely to correctly predict spring failure, defined as inappropriate application of tension and/or compressive forces to a larger assembly. Additionally, this study was able to quantify the computational cost advantage of using a reduced order model beam mesh. In the transverse haversine shock case, the hexahedral mesh took over three days with 228 processors to solve, compared to under 10 hours for the ROM using just a single processor. Depending on the required use case for the results, using the beam mesh will significantly improve the speed of work flows, especially when integrated into larger safety component models. However, appropriate use of the ROM should carefully balance these optimized run times with its reduction in accuracy, especially when examining spring failure and outputting variables such as equivalent plastic strain. Current investigations are broadening the scope of this work to include a validation study comparing the beam ROM to physical testing data.

High reliability (Hi-Rel) electronics for mission critical applications are handled with extreme care; stress testing upon full assembly can increase a likelihood of degrading these systems before their deployment. Moreover, novel material parts, such as wide bandgap semiconductor devices, tend to have more complicated fabrication processing needs which could ultimately result in larger part variability or potential defects. Therefore, an intelligent screening and inspection technique for electronic parts, in particular gallium nitride (GaN) power transistors, is presented in this paper. We present a machine-learning-based non-intrusive technique that can enhance part-selection decisions to categorize the part samples to the population's expected electrical characteristics. This technique provides relevant information about GaN HEMT device characteristics without having to operate all of these devices at the high current region of the transfer and output characteristics, lowering the risk of damaging the parts prematurely. The proposed non-intrusive technique uses a small signal pulse width modulation (PWM) of various frequencies, ranging from 10 kHz to 500 kHz, injected into the transistor terminals and the corresponding output signals are observed and used as training dataset. Unsupervised clustering techniques with K-means and feature dimensional reduction through principal component analysis (PCA) have been used to correlate a population of GaN HEMT transistors to the expected mean of the devices' electrical characteristic performance.

Pulsed dielectric barrier discharges (DBD) in He-H2O and He-H2O-O2 mixtures are studied in near atmospheric conditions using temporally and spatially resolved quantitative 2D imaging of the hydroxyl radical (OH) and hydrogen peroxide (H2O2). The primary goal was to detect and quantify the production of these strongly oxidative species in water-laden helium discharges in a DBD jet configuration, which is of interest for biomedical applications such as disinfection of surfaces and treatment of biological samples. Hydroxyl profiles are obtained by laser-induced fluorescence (LIF) measurements using 282 nm laser excitation. Hydrogen peroxide profiles are measured by photo-fragmentation LIF (PF-LIF), which involves photo-dissociating H2O2 into OH with a 212.8 nm laser sheet and detecting the OH fragments by LIF. The H2O2 profiles are calibrated by measuring PF-LIF profiles in a reference mixture of He seeded with a known amount of H2O2. OH profiles are calibrated by measuring OH-radical decay times and comparing these with predictions from a chemical kinetics model. Two different burst discharge modes with five and ten pulses per burst are studied, both with a burst repetition rate of 50 Hz. In both cases, dynamics of OH and H2O2 distributions in the afterglow of the discharge are investigated. Gas temperatures determined from the OH-LIF spectra indicate that gas heating due to the plasma is insignificant. The addition of 5% O2 in the He admixture decreases the OH densities and increases the H2O2 densities. The increased coupled energy in the ten-pulse discharge increases OH and H2O2 mole fractions, except for the H2O2 in the He-H2O-O2 mixture which is relatively insensitive to the additional pulses.

Molten Salt Reactor (MSR) systems can be divided into two basic categories: liquid-fueled MSRs in which the fuel is dissolved in the salt, and solid-fueled systems such as the Fluoride-salt-cooled High-temperature Reactor (FHR). The molten salt provides an impediment to fission product release as actinides and many fission products are soluble in molten salt. Nonetheless, under accident conditions, some radionuclides may escape the salt by vaporization and aerosol formation, which may lead to release into the environment. We present recent enhancements to MELCOR to represent the transport of radionuclides in the salt and releases from the salt. Some soluble but volatile radionuclides may vaporize and subsequently condense to aerosol. Insoluble fission products can deposit on structures. Thermochimica, an open-source Gibbs Energy Minimization (GEM) code, has been integrated into MELCOR. With the appropriate thermochemical database, Thermochimica provides the solubility and vapor pressure of species as a function of temperature, pressure, and composition, which are needed to characterize the vaporization rate and the state of the salt with fission products. Since thermochemical databases are still under active development for molten salt systems, thermodynamic data for fission product solubility and vapor pressure may be user specified. This enables preliminary assessments of fission product transport in molten salt systems. In this paper, we discuss modeling of soluble and insoluble fission product releases in a MSR with Thermochimica incorporated into MELCOR. Separate-effects experiments performed as part of the Molten Salt Reactor Experiment in which radioactive aerosol was released are discussed as needed for determining the source term.

Most near-term quantum information processing devices will not be capable of implementing quantum error correction and the associated logical quantum gate set. Instead, quantum circuits will be implemented directly using the physical native gate set of the device. These native gates often have a parameterization (e.g., rotation angles) which provide the ability to perform a continuous range of operations. Verification of the correct operation of these gates across the allowable range of parameters is important for gaining confidence in the reliability of these devices. In this work, we demonstrate a procedure for sample-efficient verification of continuously-parameterized quantum gates for small quantum processors of up to approximately 10 qubits. This procedure involves generating random sequences of randomly-parameterized layers of gates chosen from the native gate set of the device, and then stochastically compiling an approximate inverse to this sequence such that executing the full sequence on the device should leave the system near its initial state. We show that fidelity estimates made via this technique have a lower variance than fidelity estimates made via cross-entropy benchmarking. This provides an experimentally-relevant advantage in sample efficiency when estimating the fidelity loss to some desired precision. We describe the experimental realization of this technique using continuously-parameterized quantum gate sets on a trapped-ion quantum processor from Sandia QSCOUT and a superconducting quantum processor from IBM Q, and we demonstrate the sample efficiency advantage of this technique both numerically and experimentally.

While significant investments have been made in the exploration of ethics in computation, recent advances in high performance computing (HPC) and artificial intelligence (AI) have reignited a discussion for more responsible and ethical computing with respect to the design and development of pervasive sociotechnical systems within the context of existing and evolving societal norms and cultures. The ubiquity of HPC in everyday life presents complex sociotechnical challenges for all who seek to practice responsible computing and ethical technological innovation. The present paper provides guidelines which scientists, researchers, educators, and practitioners alike, can employ to become more aware of one’s personal values system that may unconsciously shape one’s approach to computation and ethics.

The design of thermal protection systems (TPS), including heat shields for reentry vehicles, rely more and more on computational simulation tools for design optimization and uncertainty quantification. Since high-fidelity simulations are computationally expensive for full vehicle geometries, analysts primarily use reduced-physics models instead. Recent work has shown that projection-based reduced-order models (ROMs) can provide accurate approximations of high-fidelity models at a lower computational cost. ROMs are preferable to alternative approximation approaches for high-consequence applications due to the presence of rigorous error bounds. The following paper extends our previous work on projection-based ROMs for ablative TPS by considering hyperreduction methods which yield further reductions in computational cost and demonstrating the approach for simulations of a three-dimensional flight vehicle. We compare the accuracy and potential performance of several different hyperreduction methods and mesh sampling strategies. This paper shows that with the correct implementation, hyperreduction can make ROMs up to 1-3 orders of magnitude faster than the full order model by evaluating the residual at only a small fraction of the mesh nodes.

A large-scale numerical computation of five wind farms was performed as a part of the American WAKE experimeNt (AWAKEN). This high-fidelity computation used the ExaWind/AMR-Wind LES solver to simulate a 100 km × 100 km domain containing 541 turbines under unstable atmospheric conditions matching previous measurements. The turbines were represented by Joukowski and OpenFAST coupled actuator disk models. Results of this qualitative comparison illustrate the interactions of wind farms with large-scale ABL structures in the flow, as well as the extent of downstream wake penetration in the flow and blockage effects around wind farms.

This is an investigation on two experimental datasets of laminar hypersonic flows, over a double-cone geometry, acquired in Calspan—University at Buffalo Research Center’s Large Energy National Shock (LENS)-XX expansion tunnel. These datasets have yet to be modeled accurately. A previous paper suggested that this could partly be due to mis-specified inlet conditions. The authors of this paper solved a Bayesian inverse problem to infer the inlet conditions of the LENS-XX test section and found that in one case they lay outside the uncertainty bounds specified in the experimental dataset. However, the inference was performed using approximate surrogate models. In this paper, the experimental datasets are revisited and inversions for the tunnel test-section inlet conditions are performed with a Navier–Stokes simulator. The inversion is deterministic and can provide uncertainty bounds on the inlet conditions under a Gaussian assumption. It was found that deterministic inversion yields inlet conditions that do not agree with what was stated in the experiments. An a posteriori method is also presented to check the validity of the Gaussian assumption for the posterior distribution. This paper contributes to ongoing work on the assessment of datasets from challenging experiments conducted in extreme environments, where the experimental apparatus is pushed to the margins of its design and performance envelopes.

The DevOps movement, which aims to accelerate the continuous delivery of high-quality software, has taken a leading role in reshaping the software industry. Likewise, there is growing interest in applying DevOps tools and practices in the domains of computational science and engineering (CSE) to meet the ever-growing demand for scalable simulation and analysis. Translating insights from industry to research computing, however, remains an ongoing challenge; DevOps for science and engineering demands adaptation and innovation in those tools and practices. There is a need to better understand the challenges faced by DevOps practitioners in CSE contexts in bridging this divide. To that end, we conducted a participatory action research study to collect and analyze the experiences of DevOps practitioners at a major US national laboratory through the use of storytelling techniques. We share lessons learned and present opportunities for future investigation into DevOps practice in the CSE domain.

We demonstrate coherent anti-Stokes Raman scattering (CARS) detection of the CO and N2 molecules in the reaction layer of a graphite material sample exposed to the 5000-6000 K plume of an inductively-coupled plasma torch operating on air. CO is a dominant product in the surface oxidative reaction of graphite and lighter weight carbon-based thermalprotection-system materials. A standard nanosecond CARS approach using Nd:YAG and a single broadband dye laser with ~200 cm-1 spectral width is employed for demonstration measurements, with the CARS volume located less than 1-mm from an ablating graphite sample. Quantitative measurements of both temperature and the CO/N2 ratio are obtained from model fits to CARS spectra that have been averaged for 5 laser shots. The results indicate that CARS can be used for space- and time-resolved detection of CO in high-temperature ablation tests near atmospheric pressure.

Spectrally shaped forms of random frequency modulation (RFM) radar waveforms have been experimentally demonstrated for a variety of implementation approaches and applications. Of these, the continuous-wave (CW) perspective is particularly interesting because it enables the prospect of very high signal dimensionality and arbitrary receive processing from a range/Doppler perspective, while also mitigating range ambiguities by avoiding repetition. Here we leverage a modification to the constant-envelope orthogonal frequency division multiplexing (CE-OFDM) framework, which was originally proposed for power-efficient communications, to realize a nonrepeating FMCW radar signal that can be represented with a compact parameterization, thereby circumventing memory constraints that could arise for some applications. Experimental loopback and open-air measurements are used to demonstrate this waveform type.

Here we examine models for particle curtain dispersion using drag based formalisms and their connection to streamwise pressure difference closures. Focusing on drag models, we specifically demonstrate that scaling arguments developed in DeMauro et. al. [1] using early time drag modeling can be extended to include late time particle curtain dispersion behavior by weighting the dynamic portion of the drag relative velocity e.g. (Formula Presented) by the inverse of the particle volume fraction to the ¼th power. The additional parameter e.g. α introduced in this scaling is related to the model drag parameters by employing an early-time latetime matching argument. Comparison with the scaled measurements of DeMauro et. al. suggest that the proposed modification is an effective formalism. Next, the connection between drag-based models and streamwise pressure difference-based expressions is explored by formulating simple analytical models that verify an empirical (Daniel and Wagner [2]) upstream-downstream expression. Though simple, these models provide physics-based approached describing shock particle curtain interaction behavior.

Austenitic stainless steels are used in high-pressure hydrogen containment infrastructure for their resistance to hydrogen embrittlement. Applications for the use of austenitic stainless steels include pressure vessels, tubing, piping, valves, fittings and other piping components. Despite their resistance to brittle behavior in the presence of hydrogen, austenitic stainless steels can exhibit degraded fracture performance. The mechanisms of hydrogen-assisted fracture, however, remain elusive, which has motivated continued research on these alloys. There are two principal approaches to evaluate the influence of gaseous hydrogen on mechanical properties: internal and external hydrogen, respectively. The austenite phase has high solubility and low diffusivity of hydrogen at room temperature, which enables introduction of hydrogen into the material through thermal precharging at elevated temperature and pressure; a condition referred to as internal hydrogen. H-precharged material can subsequently be tested in ambient conditions. Alternatively, mechanical testing can be performed while test coupons are immersed in gaseous hydrogen thereby evaluating the effects of external hydrogen on property degradation. The slow diffusivity of hydrogen in austenite at room temperature can often be a limiting factor in external hydrogen tests and may not properly characterize lower bound fracture behavior in components exposed to hydrogen for long time periods. In this study, the differences between internal and external hydrogen environments are evaluated in the context of fracture resistance measurements. Fracture testing was performed on two different forged austenitic stainless steel alloys (304L and XM-11) in three different environments: 1) non-charged and tested in gaseous hydrogen at pressure of 1,000 bar (external H2), 2) hydrogen precharged and tested in air (internal H), 3) hydrogen precharged and tested in 1,000 bar H2 (internal H + external H2). For all environments, elastic-plastic fracture measurements were conducted to establish J-R curves following the methods of ASTM E1820. Following fracture testing, fracture surfaces were examined to reveal predominant fracture mechanisms for the different conditions and to characterize differences (and similarities) in the macroscale fracture processes associated with these environmental conditions.

Measurements of gas-phase temperature and pressure in hypersonic flows are important for understanding gas-phase fluctuations which can drive dynamic loading on model surfaces and to study fundamental compressible flow turbulence. To achieve this capability, femtosecond coherent anti-Stokes Raman scattering (fs CARS) is applied in Sandia National Laboratories’ cold-flow hypersonic wind tunnel facility. Measurements were performed for tunnel freestream temperatures of 42–58 K and pressures of 1.5–2.2 Torr. The CARS measurement volume was translated in the flow direction during a 30-second tunnel run using a single computer-controlled translation stage. After broadband femtosecond laser excitation, the rotational Raman coherence was probed twice, once at an early time where the collisional environment has not affected the Raman coherence, and another at a later time after the collisional environment has led to significant dephasing of the Raman coherent. The gas-phase temperature was obtained primarily from the early-probe CARS spectra, while the gas-phase pressure was obtained primarily from the late-probe CARS spectra. Challenges in implementing fs CARS in this facility such as changes in the nonresonant spectrum at different measurement location are discussed.

Lithium metal is an ideal anode for high energy density batteries, however the implementation of lithium metal anodes remains challenging. Beyond the development of highly efficient electrolytes, degradation processes restrict cycle life and reduce practical energy density. Herein lithium volumetric expansion and degradation pathways are studied in half cells through coupling electrochemical analysis with cross-sectional imaging of the intact electrode stack using a cryogenic laser plasma focused ion beam and scanning electron microscope. We find that the volumetric capacity is compromised as early as the first cycle, at best reaching values only half the theoretical capacity (1033 vs 2045 mAh cm−3). By the 101st electrodeposition, the practical volumetric capacity decreases to values ranging from 143 to 343 mAh cm−3

In recent years, high-altitude infrasound sensing has become more prolific, demonstrating an enormous value especially when utilized over regions inaccessible to traditional ground-based sensing. Similar to ground-based infrasound detectors, airborne sensors take advantage of the fact that impulsive atmospheric events such as explosions can generate low frequency acoustic waves, also known as infrasound. Due to negligible attenuation, infrasonic waves can travel over long distances, and provide important clues about their source. Here, we report infrasound detections of the Apollo detonation that was carried on 29 October 2020 as part of the Large Surface Explosion Coupling Experiment in Nevada, USA. Infrasound sensors attached to solar hot air balloons floating in the stratosphere detected the signals generated by the explosion at distances 170–210 km. Three distinct arrival phases seen in the signals are indicative of multipathing caused by the small-scale perturbations in the atmosphere. We also found that the local acoustic environment at these altitudes is more complex than previously thought.

With increasing penetration of variable renewable generation, battery energy storage systems (BESS) are becoming important for power system stability due to their operational flexibility. In this paper, we propose a method for determining the minimum BESS rated power that guarantees security constraints in a grid subject to disturbances induced by variable renewable generation. The proposed framework leverages sensitivity-based inverse uncertainty propagation where the dynamical responses of the states are parameterized with respect to random variables. Using this approach, the original nonlinear optimization problem for finding the security-constrained uncertainty interval may be formulated as a quadratically-constrained linear program. The resulting estimated uncertainty interval is utilized to find the BESS rated power required to satisfy grid stability constraints.

The current interest in hypersonic flows and the growing importance of plasma applications necessitate the development of diagnostics for high-enthalpy flow environments. Reliable and novel experimental data at relevant conditions will drive engineering and modeling efforts forward significantly. This study demonstrates the usage of nanosecond Coherent Anti-Stokes Raman Scattering (CARS) to measure temperature in an atmospheric, high-temperature (> 5500 K) air plasma. The experimental configuration is of interest as the plasma is close to thermodynamic equilibrium and the setup is a test-bed for heat shield materials. The determination of the non-resonant background at such high-temperatures is explored and rotational-vibrational equilibrium temperatures of the N2 ground state are determined via fits of the theory to measured spectra. Results show that the accuracy of the temperature measurements is affected by slow periodic variations in the plasma, causing sampling error. Moreover, depending on the experimental configuration, the measurements can be affected by two-beam interaction, which causes a bias towards lower temperatures, and stimulated Raman pumping, which causes a bias towards higher temperatures. The successful demonstration of CARS at the present conditions, and the exploration of its sensitivities, paves the way towards more complex measurements, e.g. close to interfaces in high-enthalpy plasma flows.

Machining-based deformation processing is used to produce metal foil and flat wire (strip) with suitable properties and quality for electrical power and renewable energy applications. In contrast to conventional multistage rolling, the strip is produced in a single-step and with much less process energy. Examples are presented from metal systems of varied workability, and strip product scale in terms of size and production rate. By utilizing the large-strain deformation intrinsic to cutting, bulk strip with ultrafine-grained microstructure, and crystallographic shear-texture favourable for formability, are achieved. Implications for production of commercial strip for electric motor applications and battery electrodes are discussed.

The widespread adoption of residential solar PV requires distribution system studies to ensure the addition of solar PV at a customer location does not violate the system constraints, which can be referred to as locational hosting capacity (HC). These model-based analyses are prone to error due to their dependencies on the accuracy of the system information. Model-free approaches to estimate the solar PV hosting capacity for a customer can be a good alternative to this approach as their accuracies do not depend on detailed system information. In this paper, an Adaptive Boosting (AdaBoost) algorithm is deployed to utilize the statistical properties (mean, minimum, maximum, and standard deviation) of the customer's historical data (real power, reactive power, voltage) as inputs to estimate the voltage-constrained PV HC for the customer. A baseline comparison approach is also built that utilizes just the maximum voltage of the customer to predict PV HC. The results show that the ensemble-based AdaBoost algorithm outperformed the proposed baseline approach. The developed methods are also compared and validated by existing state-of-the-art model-free PV HC estimation methods.

Scientific discovery increasingly relies on interoperable, multimodular workflows generating intermediate data. The complexity of managing intermediate data may cause performance losses or unexpected costs. This paper defines an approach to composing these scientific workflows on cloud services, focusing on workflow data orchestration, management, and scalability. We demonstrate the effectiveness of our approach with the SOMOSPIE scientific workflow that deploys machine learning (ML) models to predict high-resolution soil moisture using an HPC service (LSF) and an open-source cloud-native service (K8s) and object storage. Our approach enables scientists to scale from coarse-grained to fine-grained resolution and from a small to a larger region of interest. Using our empirical observations, we generate a cost model for the execution of workflows with hidden intermediate data on cloud services.

High-altitude electromagnetic pulse events are a growing concern for electric power grid vulnerability assessments and mitigation planning, and accurate modeling of surge arrester mitigations installed on the grid is necessary to predict pulse effects on existing equipment and to plan future mitigation. While some models of surge arresters at high frequency have been proposed, experimental backing for any given model has not been shown. This work examines a ZnO lightning surge arrester modeling approach previously developed for accurate prediction of nanosecond-scale pulse response. Four ZnO metal-oxide varistor pucks with different sizes and voltage ratings were tested for voltage and current response on a conducted electromagnetic pulse testbed. The measured clamping response was compared to SPICE circuit models to compare the electromagnetic pulse response and validate model accuracy. Results showed good agreement between simulation results and the experimental measurements, after accounting for stray testbed inductance between 100 and 250 nH.

Multiple rotors on single structures have long been proposed to increase wind turbine energy capture with no increase in rotor size, but at the cost of additional mechanical complexity in the yaw and tower designs. Standard turbines on their own very-closely-spaced towers avoid these disadvantages but create a significant disadvantage; for some wind directions the wake turbulence of a rotor enters the swept area of a very close downwind rotor causing low output, fatigue stress, and changes in wake recovery. Knowing how the performance of pairs of closely spaced rotors varies with wind direction is essential to design a layout that maximizes the useful directions and minimizes the losses and stress at other directions. In the current work, the high-fidelity large-eddy simulation (LES) code Exa-Wind/Nalu-Wind is used to simulate the wake interactions from paired-rotor configurations in a neutrally stratified atmospheric boundary layer to investigate performance and feasibility. Each rotor pair consists of two Vestas V27 turbines with hub-to-hub separation distances of 1.5 rotor diameters. The on-design wind direction results are consistent with previous literature. For an off-design wind direction of 26.6°, results indicate little change in power and far-wake recovery relative to the on-design case. At a direction of 45.0°, significant rotor-wake interactions produce an increase in power but also in far-wake velocity deficit and turbulence intensity. A severely off-design case is also considered.

We study both conforming and non-conforming versions of the practical DPG method for the convection-reaction problem. We determine that the most common approach for DPG stability analysis - construction of a local Fortin operator - is infeasible for the convection-reaction problem. We then develop a line of argument based on a direct proof of discrete stability; we find that employing a polynomial enrichment for the test space does not suffice for this purpose, motivating the introduction of a (two-element) subgrid mesh. The argument combines mathematical analysis with numerical experiments.

We report on a two-step technique for post-bond III-V substrate removal involving precision mechanical milling and selective chemical etching. We show results on GaAs, GaSb, InP, and InAs substrates and from mm-scale chips to wafers.

Kolmogorov's theory of turbulence assumes that the small-scale turbulent structures in the energy cascade are universal and are determined by the energy dissipation rate and the kinematic viscosity alone. However, thermal fluctuations, absent from the continuum description, terminate the energy cascade near the Kolmogorov length scale. Here, we propose a simple superposition model to account for the effects of thermal fluctuations on small-scale turbulence statistics. For compressible Taylor-Green vortex flow, we demonstrate that the superposition model in conjunction with data from direct numerical simulation of the Navier-Stokes equations yields spectra and structure functions that agree with the corresponding quantities computed from the direct simulation Monte Carlo method of molecular gas dynamics, verifying the importance of thermal fluctuations in the dissipation range.

This presentation describes a new effort to better understand insulator flashover in high current, high voltage pulsed power systems. Both experimental and modeling investigations are described. Particular emphasis is put upon understand flashover that initiate in the anode triple junction (anode-vacuum-dielectric).

A comprehensive control strategy is necessary to safely and effectively operate particle based concentrating solar power (CSP) technologies. Particle based CSP with thermal energy storage (TES) is an emerging technology with potential to decarbonize power and process heat applications. The high-temperature nature of particle based CSP technologies and daily solar transients present challenges for system control to prevent equipment damage and ensure operator safety. An operational controls strategy for a tower based particle CSP system during steady state and transient conditions with safety interlocks is described in this paper. Control of a solar heated particle recirculation loop, TES, and a supercritical carbon dioxide (sCO2) cooling loop designed to reject 1 MW of thermal power are considered and associated operational limitations and their influence on control strategy are discussed.

The structure-property linkage is one of the two most important relationships in materials science besides the process-structure linkage, especially for metals and polycrystalline alloys. The stochastic nature of microstructures begs for a robust approach to reliably address the linkage. As such, uncertainty quantification (UQ) plays an important role in this regard and cannot be ignored. To probe the structure-property linkage, many multi-scale integrated computational materials engineering (ICME) tools have been proposed and developed over the last decade to accelerate the material design process in the spirit of Material Genome Initiative (MGI), notably crystal plasticity finite element model (CPFEM) and phase-field simulations. Machine learning (ML) methods, including deep learning and physics-informed/-constrained approaches, can also be conveniently applied to approximate the computationally expensive ICME models, allowing one to efficiently navigate in both structure and property spaces effortlessly. Since UQ also plays a crucial role in verification and validation for both ICME and ML models, it is important to include UQ in the picture. In this paper, we summarize a few of our recent research efforts addressing UQ aspects of homogenized properties using CPFEM in a big picture context.

Detection and verification of underground nuclear explosions (UNEs) can be improved with a better understanding of the nature and extent of explosion-induced damage in rock and the effect of this damage on radionuclide migration. Much of the previous work in this area has focused on centimeterto meter-scale manifestations of damage, but to predict the effect of damage on permeability for radionuclide migration, observations at smaller scales are needed to determine deformation mechanisms. Based on studies of tectonic deformation in tuff, we expected that the heterogeneous tuff layers would manifest explosion-induced damage differently, with welded tuffs showing more fractures and nonwelded tuffs showing more deformation bands. In comparing post-UNE samples with lithologically matched pre-UNE equivalents, we observed damage in multiple lithologies of tuff through quantitative microfracture densities. We find that the texture (e.g., from deposition, welding, alteration, etc.) affects fracture densities, with stronger units fracturing more than weaker units. While we see no evidence of expected deformation bands in the nonwelded tuffs, we do observe, as expected, much larger microfracture densities at close range (<50 m) to the explosive source. We also observe a subtle increase in microfracture densities in post-UNE samples, relative to pre-UNE equivalents, in all lithologies and depths. The fractures that are interpreted to be UNE-induced are primarily transgranular and grain-boundary microfractures, with intragranular microfracture densities being largely similar to those of pre-UNE samples. This work has implications for models of explosion-induced damage and how that damage may affect flow pathways in the subsurface.

System provenance forensic analysis has been studied by a large body of research work. This area needs fine granularity data such as system calls along with event fields to track the dependencies of events. While prior work on security datasets has been proposed, we found a useful dataset of realistic attacks and details that can be used for provenance tracking is lacking. We created a new dataset of eleven vulnerable cases for system forensic analysis. It includes the full details of system calls including syscall parameters. Realistic attack scenarios with real software vulnerabilities and exploits are used. Also, we created two sets of benign and adversary scenarios which are manually labeled for supervised machine-learning analysis. We demonstrate the details of the dataset events and dependency analysis.

Subhourly changes in solar irradiance can lead to energy models being biased high if realistic distributions of irradiance values are not reflected in the resource data and model. This is particularly true in solar facility designs with high inverter loading ratios (ILRs). When resource data with sufficient temporal and spatial resolution is not available for a site, synthetic variability can be added to the data that is available in an attempt to address this issue. In this work, we demonstrate the use of anonymized commercial resource datasets with synthetic variability and compare results with previous estimates of model bias due to inverter clipping and increasing ILR.

As the path towardsUrban Air Mobility (UAM) continues to take shape, there are outstanding technical challenges to achieving safe and effective air transportation operations under this new paradigm. To inform and guide technology development for UAM, NASA is investigating the current state-of-the-art in key technology areas including traffic management, detect-and-avoid, and autonomy. In support of this effort, a new perception testbed was developed at NASA Ames Research Center to collect data from an array of sensing systems representative of those that could be found on a future UAM vehicle. This testbed, featuring a Light-Detection-and-Ranging (LIDAR) instrument, a long-wave infrared sensor, and a visible spectrum camera was deployed for a multiday test campaign in the Fog Chamber at Sandia National Laboratories (SNL), in Albuquerque, New Mexico. During the test campaign, fog conditions were created for tests with targets including a human, a resolution chart, and a small unmanned aerial vehicle (sUAV). This paper describes in detail, the developed perception testbed, the experimental setup in the fog chamber, the resulting data, and presents an initial result from analysis of the data with the evaluation of methods to increase contrast through filtering techniques.

Carbonaceous particulate produced by a diesel engine and turbojet engine combustor are analyzed by transmission electron microscopy (TEM) for differences in nanostructure before and after pulsed laser annealing. Soot is examined between low/high diesel engine torque and low/high turbojet engine thrust. Small differences in nascent nanostructure are magnified by the action of high-temperature annealing induced by pulsed laser heating. Lamellae length distributions show occurrence of graphitization while tortuosity analyses reveal lamellae straightening. Differences in internal particle structure (hollow shells versus internal graphitic ribbons) are interpreted as due to higher internal sp3 and O-atom content under the higher power conditions with hypothesized greater turbulence and resulting partial premixing. TEM in concert with fringe analyses reveal that a similar degree of annealing occurs in the primary particles in soot from both diesel engine and turbojet engine combustors—despite the aggregate and primary size differences between these sources. Implications of these results for source identification of the combustion particulate and for laser-induced incandescence (LII) measurements of concentration are discussed with inter-instrument comparison of soot mass from both diesel and turbojet soot sources.

We analyze the regression accuracy of convolutional neural networks assembled from encoders, decoders and skip connections and trained with multifidelity data. Besides requiring significantly less trainable parameters than equivalent fully connected networks, encoder, decoder, encoder-decoder or decoder-encoder architectures can learn the mapping between inputs to outputs of arbitrary dimensionality. We demonstrate their accuracy when trained on a few high-fidelity and many low-fidelity data generated from models ranging from one-dimensional functions to Poisson equation solvers in two-dimensions. We finally discuss a number of implementation choices that improve the reliability of the uncertainty estimates generated by Monte Carlo DropBlocks, and compare uncertainty estimates among low-, high- and multifidelity approaches.

We propose a set of benchmark tests for current-voltage (IV) curve fitting algorithms. Benchmark tests enable transparent and repeatable comparisons among algorithms, allowing for measuring algorithm improvement over time. An absence of such tests contributes to the proliferation of fitting methods and inhibits achieving consensus on best practices. Benchmarks include simulated curves with known parameter solutions, with and without simulated measurement error. We implement the reference tests on an automated scoring platform and invite algorithm submissions in an open competition for accurate and performant algorithms.

Complex angle theory can offer new fundamental insights into refraction at the absorptive interface. In this work we propose a new method to induce isofrequency opening via addition of scattering in the dual interface system.

High-pressure, ultra-zero air is being evaluated as a potential replacement to SF6 in a strategic focus to move away from environmentally damaging insulating gasses. There are a lot of unknowns about the dominant breakdown mechanisms of ultra-zero air in the high-pressure regime. The classical equations for Paschen curves appear to not be valid above 500 psig. In order to better understand the phenomena of gas breakdown in the high-pressure regime, Sandia National Laboratories is evaluating the basic gas physics breakdown using nonuniform-field electrode designs. Recent data has been collected at SNL to study the breakdown of this high-pressure regime in the range of 300 - 1500 psi with gaps on the order of 0.6 - 1 cm with different electrode designs. The self-breakdown voltages range from 200-900 kV with a pulse-charge rise times of 200-300 ns and discharge currents from 25-60 kA. This research investigates the phenomenon of high-pressure breakdown, highlights the data collected, and presents a few of the mechanisms that dominate in the high-pressure regime for electronegative gasses.

When exposed to mechanical environments such as shock and vibration, electrical connections may experience increased levels of contact resistance associated with the physical characteristics of the electrical interface. A phenomenon known as electrical chatter occurs when these vibrations are large enough to interrupt the electric signals. It is critical to understand the root causes behind these events because electrical chatter may result in unexpected performance or failure of the system. The root causes span a variety of fields, such as structural dynamics, contact mechanics, and tribology. Therefore, a wide range of analyses are required to fully explore the physical phenomenon. This paper intends to provide a better understanding of the relationship between structural dynamics and electrical chatter events. Specifically, electrical contact assembly composed of a cylindrical pin and bifurcated structure were studied using high fidelity simulations. Structural dynamic simulations will be performed with both linear and nonlinear reduced-order models (ROM) to replicate the relevant structural dynamics. Subsequent multi-physics simulations will be discussed to relate the contact mechanics associated with the dynamic interactions between the pin and receptacle to the chatter. Each simulation method was parametrized by data from a variety of dynamic experiments. Both structural dynamics and electrical continuity were observed in both the simulation and experimental approaches, so that the relationship between the two can be established.

A method is presented to detect clear-sky periods for plane-of-array, time-averaged irradiance data that is based on the algorithm originally described by Reno and Hansen. We show this new method improves the state-of-the-art by providing accurate detection at longer data intervals, and by detecting clear periods in plane-of-array data, which is novel. We illustrate how accurate determination of clear-sky conditions helps to eliminate data noise and bias in the assessment of long-term performance of PV plants.

The generalized Dryja-Smith-Widlund (GDSW) preconditioner is a two-level overlapping Schwarz domain decomposition (DD) preconditioner that couples a classical one-level overlapping Schwarz preconditioner with an energy-minimizing coarse space. When used to accelerate the convergence rate of Krylov subspace iterative methods, the GDSW preconditioner provides robustness and scalability for the solution of sparse linear systems arising from the discretization of a wide range of partial different equations. In this paper, we present FROSch (Fast and Robust Schwarz), a domain decomposition solver package which implements GDSW-type preconditioners for both CPU and GPU clusters. To improve the solver performance on GPUs, we use a novel decomposition to run multiple MPI processes on each GPU, reducing both solver's computational and storage costs and potentially improving the convergence rate. This allowed us to obtain competitive or faster performance using GPUs compared to using CPUs alone. We demonstrate the performance of FROSch on the Summit supercomputer with NVIDIA V100 GPUs, where we used NVIDIA Multi-Process Service (MPS) to implement our decomposition strategy.The solver has a wide variety of algorithmic and implementation choices, which poses both opportunities and challenges for its GPU implementation. We conduct a thorough experimental study with different solver options including the exact or inexact solution of the local overlapping subdomain problems on a GPU. We also discuss the effect of using the iterative variant of the incomplete LU factorization and sparse-triangular solve as the approximate local solver, and using lower precision for computing the whole FROSch preconditioner. Overall, the solve time was reduced by factors of about 2× using GPUs, while the GPU acceleration of the numerical setup time depend on the solver options and the local matrix sizes.

Power-flow studies on the 30-MA, 100-ns Z facility at Sandia National Labs have shown that plasmas in the facility's magnetically insulated transmission lines can result in a loss of current to the load.1 During the current pulse, electrode heating causes neutral surface contaminants (water, hydrogen, hydrocarbons, etc.) to desorb, ionize, and form plasmas in the anode-cathode gap.2 Shrinking typical electrode thicknesses (∼1 cm) to thin foils (5-200 μm) produces observable amounts of plasma on smaller pulsed power drivers <1 MA).3 We suspect that as electrode material bulk thickness decreases relative to the skin depth (50-100 μm for a 100-500-ns pulse in aluminum), the thermal energy delivered to the neutral surface contaminants increases, and thus desorb faster from the current carrying surface.

Two-dimensional (2D) layered oxides have recently attracted wide attention owing to the strong coupling among charges, spins, lattice, and strain, which allows great flexibility and opportunities in structure designs as well as multifunctionality exploration. In parallel, plasmonic hybrid nanostructures exhibit exotic localized surface plasmon resonance (LSPR) providing a broad range of applications in nanophotonic devices and sensors. A hybrid material platform combining the unique multifunctional 2D layered oxides and plasmonic nanostructures brings optical tuning into the new level. In this work, a novel self-assembled Bi2MoO6 (BMO) 2D layered oxide incorporated with plasmonic Au nanoinclusions has been demonstrated via one-step pulsed laser deposition (PLD) technique. Comprehensive microstructural characterizations, including scanning transmission electron microscopy (STEM), differential phase contrast imaging (DPC), and STEM tomography, have demonstrated the high epitaxial quality and particle-in-matrix morphology of the BMO-Au nanocomposite film. DPC-STEM imaging clarifies the magnetic domain structures of BMO matrix. Three different BMO structures including layered supercell (LSC) and superlattices have been revealed which is attributed to the variable strain states throughout the BMO-Au film. Owing to the combination of plasmonic Au and layered structure of BMO, the nanocomposite film exhibits a typical LSPR in visible wavelength region and strong anisotropy in terms of its optical and ferromagnetic properties. This study opens a new avenue for developing novel 2D layered complex oxides incorporated with plasmonic metal or semiconductor phases showing great potential for applications in multifunctional nanoelectronics devices. [Figure not available: see fulltext.]

Robot manipulation of the environment often uses force feedback control approaches such as impedance control. Impedance controllers can be designed to be passive and work well while coupled to a variety of dynamic environments. However, in the presence of a high gear ratio and compliance in manipulator links, non-passive system properties may result in force feedback instabilities when coupled to certain environments. This necessitates an approach that ensures stability when using impedance control methods to interact with a wide range of environments. We propose a method for improving stability and steady-state convergence of an impedance controller by using a deep neural network to map a damping impedance control parameter. In this paper, a dynamic model and impedance controlled simulated system are presented and used for analyzing the coupled dynamic behavior in worst case environments. This simulation environment is used for Nyquist analysis and closed-loop stability analysis to algorithmically determine updated impedance damping parameters that secures stability and desired performance. The deep neural network inputs utilized present impedance control parameters and environmental dynamic properties to determine an updated value of damping that improves performance. In a data set of 10,000 combinations of control parameters and environmental dynamics, 20.3% of all the cases result in instability or do not meet convergence criterion. Our deep neural network improves this and reduces instabilities and failed control performance to 2.29%. The design of the network architecture to achieve this improvement is presented and compared to other architectures with their respective performances.

While research in multiple-input/multiple-output (MIMO) random vibration testing techniques, control methods, and test design has been increasing in recent years, research into specifications for these types of tests has not kept pace. This is perhaps due to the very particular requirement for most MIMO random vibration control specifications – they must be narrowband, fully populated cross-power spectral density matrices. This requirement puts constraints on the specification derivation process and restricts the application of many of the traditional techniques used to define single-axis random vibration specifications, such as averaging or straight-lining. This requirement also restricts the applicability of MIMO testing by requiring a very specific and rich field test data set to serve as the basis for the MIMO test specification. Here, frequency-warping and channel averaging techniques are proposed to soften the requirements for MIMO specifications with the goal of expanding the applicability of MIMO random vibration testing and enabling tests to be run in the absence of the necessary field test data.

Due to their increased levels of reliability, meshed low-voltage (LV) grid and spot networks are common topologies for supplying power to dense urban areas and critical customers. Protection schemes for LV networks often use highly sensitive reverse current trip settings to detect faults in the medium-voltage system. As a result, interconnecting even low levels of distributed energy resources (DERs) can impact the reliability of the protection system and cause nuisance tripping. This work analyzes the possibility of modifying the reverse current relay trip settings to increase the DER hosting capacity of LV networks without impacting fault detection performance. The results suggest that adjusting relay settings can significantly increase DER hosting capacity on LV networks without adverse effects, and that existing guidance on connecting DERs to secondary networks, such as that contained in IEEE Std 1547-2018, could potentially be modified to allow higher DER deployment levels.

A medium-scale (30 cm diameter) methanol pool fire was simulated using Sandia National Laboratories’ Sierra/Fuego low-Mach number multi-physics turbulent reacting flow code. Large Eddy Simulation (LES) with subgrid turbulent kinetic energy closure was used as the turbulence model. Combustion was modeled using a strained laminar flamelet library approach. Radiative heat transfer was modeled using the gray-gas approximation. This paper details analysis done to support a validation study for the fire model. In this analysis, integral quantities were primarily examined. The radiant fraction was computed and used as a model calibration parameter. Integrated buoyancy flux was calculated and compared to an engineering correlation. Entrainment rate was computed with and without a mixture fraction threshold filter and compared to engineering correlations. Turbulent kinetic energy was computed and the effect of mesh size on the subgrid and total turbulent kinetic energy was examined. Flame height was calculated using an intermittency definition with two input parameters. A sensitivity study was then conducted to determine the sensitivity of the estimated flame height to the input parameters. This analysis aided in achieving the primary validation study objectives by providing model calibration and expanding the scope of the validation effort. In addition, the range of physics examined was increased, enhancing the understanding of the model's overall performance and of the relationship between phenomena.

Prescriptive approaches for the cybersecurity of digital nuclear instrumentation and control (I&C) systems can be cumbersome and costly. These considerations are of particular concern for advanced reactors that implement digital technologies for monitoring, diagnostics, and control. A risk-informed performance-based approach is needed to enable the efficient design of secure digital I&C systems for nuclear power plants. This paper presents a tiered cybersecurity analysis (TCA) methodology as a graded approach for cybersecurity design. The TCA is a sequence of analyses that align with the plant, system, and component stages of design. Earlier application of the TCA in the design process provides greater opportunity for an efficient graded approach and defense-in-depth. The TCA consists of three tiers. Tier 1 is design and impact analysis. In Tier 1 it is assumed that the adversary has control over all digital systems, components, and networks in the plant, and that the adversary is only constrained by the physical limitations of the plant design. The plant's safety design features are examined to determine whether the consequences of an attack by this cyber-enabled adversary are eliminated or mitigated. Accident sequences that are not eliminated or mitigated by security by design features are examined in Tier 2 analysis. In Tier 2, adversary access pathways are identified for the unmitigated accident sequences, and passive measures are implemented to deny system and network access to those pathways wherever feasible. Any systems with remaining susceptible access pathways are then examined in Tier 3. In Tier 3, active defensive cybersecurity architecture features and cybersecurity plan controls are applied to deny the adversary the ability to conduct the tasks needed to cause a severe consequence. Tier 3 is not performed in this analysis because of the design maturity required for this tier of analysis.

This study investigated the durability of four high temperature coatings for use as a Gardon gauge foil coating. Failure modes and effects analysis have identified Gardon gauge foil coating as a critical component for the development of a robust flux gauge for high intensity flux measurements. Degradation of coating optical properties and physical condition alters flux gauge sensitivity, resulting in flux measurement errors. In this paper, four coatings were exposed to solar and thermal cycles to simulate real-world aging. Solar simulator and box furnace facilities at the National Solar Thermal Test Facility (NSTTF) were utilized in separate test campaigns. Coating absorptance and emissivity properties were measured and combined into a figure of merit (FOM) to characterize the optical property stability of each coating, and physical coating degradation was assessed qualitatively using microscope images. Results suggest rapid high temperature cycling did not significantly impact coating optical properties and physical state. In contrast, prolonged exposure of coatings to high temperatures degraded coating optical properties and physical state. Coatings degraded after 1 hour of exposure at temperatures above 400 °C and stabilized after 6-24 hours of exposure. It is concluded that the combination of high temperatures and prolonged exposure provide the energy necessary to sustain coating surface reactions and alter optical and physical coating properties. Results also suggest flux gauge foil coatings could benefit from long duration high temperature curing (>400 °C) prior to sensor calibration to stabilize coating properties and increase measurement reliability in high flux and high temperature applications.

Analysis of radiation effects on electrical circuits requires computationally efficient compact radiation models. Currently, development of such models is dominated by analytic techniques that rely on empirical assumptions and physical approximations to render the governing equations solvable in closed form. In this paper we demonstrate an alternative numerical approach for the development of a compact delayed photocurrent model for a pn-junction device. Our approach combines a system identification step with a projection-based model order reduction step to obtain a small discrete time dynamical system describing the dynamics of the excess carriers in the device. Application of the model amounts to a few small matrix-vector multiplications having minimal computational cost. We demonstrate the model using a radiation pulse test for a synthetic pn-junction device.

The V31 containment vessel was procured by the US Army Recovered Chemical Material Directorate (RCMD) as a third-generation EDS containment vessel. It is the fifth EDS vessel to be fabricated under Code Case 2564 of the 2019 ASME Boiler and Pressure Vessel Code, which provides rules for the design of impulsively loaded vessels. The explosive rating for the vessel, based on the code case, is 24 lb (11 kg) TNT-equivalent for up to 1092 detonations. This report documents the results of explosive tests that were performed on the vessel at Sandia National Laboratories in Albuquerque, New Mexico to qualify the vessel for field operations use. There were three design basis configurations for qualification testing. Qualification test (1) consisted of a simulated M55 rocket motor and warhead assembly of 24 lb (11 kg) of Composition C-4 (30 lb [14 kg] TNT equivalent). This test was considered the maximum load case, based on modeling and simulation methods performed by Sandia prior to the vessel design phase. Qualification test (2) consisted of a regular, right circular cylinder, unitary charge, located central to the vessel interior of 19.2 lb (8.72 kg) of Composition C-4 (24 lb [11 kg] TNT equivalent). Qualification test (3) consisted of a 12-pack of regular, right circular cylinders of 2 lb (908 g) each, distributed evenly inside the vessel (totaling 19.2 lb [8.72 kg] of C-4, or 24 lb [11 kg] TNT equivalent). All vessel acceptance criteria were met.

Finding the maximum cut of a graph (MAXCUT) is a classic optimization problem that has motivated parallel algorithm development. While approximate algorithms to MAXCUT offer attractive theoretical guarantees and demonstrate compelling empirical performance, such approximation approaches can shift the dominant computational cost to the stochastic sampling operations. Neuromorphic computing, which uses the organizing principles of the nervous system to inspire new parallel computing architectures, offers a possible solution. One ubiquitous feature of natural brains is stochasticity: the individual elements of biological neural networks possess an intrinsic randomness that serves as a resource enabling their unique computational capacities. By designing circuits and algorithms that make use of randomness similarly to natural brains, we hypothesize that the intrinsic randomness in microelectronics devices could be turned into a valuable component of a neuromorphic architecture enabling more efficient computations. Here, we present neuromorphic circuits that transform the stochastic behavior of a pool of random devices into useful correlations that drive stochastic solutions to MAXCUT. We show that these circuits perform favorably in comparison to software solvers and argue that this neuromorphic hardware implementation provides a path for scaling advantages. This work demonstrates the utility of combining neuromorphic principles with intrinsic randomness as a computational resource for new computational architectures.

This paper introduces a new microprocessor-based system that is capable of detecting faults via the Traveling Wave (TW) generated from a fault event. The fault detection system is comprised of a commercially available Digital Signal Processing (DSP) board capable of accurately sampling signals at high speeds, performing the Discrete Wavelet Transform (DWT) decomposition to extract features from the TW, and a detection algorithm that makes use of the extracted features to determine the occurrence of a fault. Results show that this inexpensive fault detection system's performance is comparable to commercially available TW relays as accurate sampling and fault detection are achieved in a hundred and fifty microseconds. A detailed analysis of the execution times of each part of the process is provided.

Two relatively under-reported facets of fuel storage fire safety are examined in this work for a 250, 000 gallon two-tank storage system. Ignition probability is linked to the radiative flux from a presumed fire. First, based on observed features of existing designs, fires are expected to be largely contained within a designed footprint that will hold the full spilled contents of the fuel. The influence of the walls and the shape of the tanks on the magnitude of the fire is not a well-described aspect of conventional fire safety assessment utilities. Various resources are herein used to explore the potential hazard for a contained fire of this nature. Second, an explosive attack on the fuel storage has not been widely considered in prior work. This work explores some options for assessing this hazard. The various methods for assessing the constrained conventional fires are found to be within a reasonable degree of agreement. This agreement contrasts with the hazard from an explosive dispersal. Best available assessment techniques are used, which highlight some inadequacies in the existing toolsets for making predictions of this nature. This analysis, using the best available tools, suggests the offset distance for the ignition hazard from a fireball will be on the same order as the offset distance for the blast damage. This suggests the buy-down of risk by considering the fireball is minimal when considering the blast hazards. Assessment tools for the fireball predictions are not particularly mature, and ways to improve them for a higher-fidelity estimate are noted.

Plasma sprays can be used to melt particles, which may be deposited on an engineered surface to apply unique properties to the part. Because of the extreme temperatures (>>3000ºC) it is desirable to conduct the process in a way to avoid melting the parts to which the coatings are being applied. A jet of ambient gas is sometimes used to deflect the hot gases, while allowing the melted particles to impact and adhere to the substrate. This is known as a plume quench. While plume quenching is done in practice, to our knowledge there have not been any studies on how to apply a plume quench, and how it may affect the flows. We have recently adapted our fire simulation tool to simulate argon plasma sprays with a variety of metal particles. Two nozzle conditions are considered, with very different gas flow and power conditions. Two particle types are considered, Tantalum and Nickel. For the model, the k-epsilon turbulence model is compared to a more dynamic TFNS turbulence model. Limited data comparisons suggest the higher-fidelity TFNS model is significantly more accurate than the k-epsilon model. Additionally, the plume quench is found to have a noticeable effect for the low inlet flow case, but minimal effect on the high flow case. This suggests the effectiveness of a quench relates to the relative momentum of the intersecting gas jets.

This work proposes a method of designing adaptive controllers for reliable and stable operation of a Grid-Forming Inverter (GFI) during black-start. Here, the characteristic loci method has been primarily used for guiding the adaptation and tuning of the control parameters, based on a thorough sensitivity analysis of the system over a desired frequency bandwidth. The control hierarchy comprises active-reactive (P-Q) power support, voltage regulation, current control, and frequency recovery over the sequence of various events during black-starting. These events comprise energization of transformers and different types of loads, alongside post-fault recovery. The developed method has been tested in a 75 MVA inverter system, which is simulated in PSCAD®. The inverter energizes static and induction motor loads, besides transformers. This system has also been subjected to a line-ground fault for validating the robustness of the proposed adaptive control structure in post-fault recovery.

Aperture near-field microscopy and spectroscopy (a-SNOM) enables the direct experimental investigation of subwavelength-sized resonators by sampling highly confined local evanescent fields on the sample surface. Despite its success, the versatility and applicability of a-SNOM is limited by the sensitivity of the aperture probe, as well as the power and versatility of THz sources used to excite samples. Recently, perfectly absorbing photoconductive metasurfaces have been integrated into THz photoconductive antenna detectors, enhancing their efficiency and enabling high signal-to-noise ratio THz detection at significantly reduced optical pump powers. Here, we discuss how this technology can be applied to aperture near-field probes to improve both the sensitivity and potentially spatial resolution of a-SNOM systems. In addition, we explore the application of photoconductive metasurfaces also as near-field THz sources, providing the possibility of tailoring the beam profile, polarity and phase of THz excitation. Photoconductive metasurfaces therefore have the potential to broaden the application scope of aperture near-field microscopy to samples and material systems which currently require improved spatial resolution, signal-to-noise ratio, or more complex excitation conditions.

We present the SEU sensitivity and SEL results from proton and heavy ion testing performed on NVIDIA Xavier NX and AMD Ryzen V1605B GPU devices in both static and dynamic operation.

Abstract not provided.

In this study, we develop an end-to-end deep learning-based inverse design approach to determine the scatterer shape necessary to achieve a target acoustic field. This approach integrates non-uniform rational B-spline (NURBS) into a convolutional autoencoder (CAE) architecture while concurrently leveraging (in a weak sense) the governing physics of the acoustic problem. By utilizing prior physical knowledge and NURBS parameterization to regularize the ill-posed inverse problem, this method does not require enforcing any geometric constraint on the inverse design space, hence allowing the determination of scatterers with potentially any arbitrary shape (within the set allowed by NURBS). A numerical study is presented to showcase the ability of this approach to identify physically-consistent scatterer shapes capable of producing user-defined acoustic fields.

The error detection performance of cyclic redundancy check (CRC) codes combined with bit framing in digital serial communication systems is evaluated. Advantages and disadvantages of the combined method are treated in light of the probability of undetected errors. It is shown that bit framing can increase the burst error detection of the CRC but it can also adversely affect CRC random error detection performance. To quantify the effect of bit framing on CRC error detection the concept of error "exposure"is introduced. Our investigations lead us to propose resilient generator polynomials that, when combined with bit framing, can result in improved CRC error detection performance at no additional implementation cost. Example results are generated for short codewords showing that proper choice of CRC generator polynomial can improve error detection performance when combined with bit framing. The implication is that CRC combined with bit framing can reduce the probability of undetected errors even under high error rate conditions.

Unlike traditional base excitation vibration qualification testing, multi-axis vibration testing methods can be significantly faster and more accurate. Here, a 12-shaker multiple-input/multiple-output (MIMO) test method called intrinsic connection excitation (ICE) is developed and assessed for use on an example aerospace component. In this study, the ICE technique utilizes 12 shakers, 1 for each boundary condition attachment degree of freedom to the component, specially designed fixtures, and MIMO control to provide an accurate set of loads and boundary conditions during the test. Acceleration, force, and voltage control provide insight into the viability of this testing method. System field test and ICE test results are compared to traditional single degree of freedom specification development and testing. Results indicate the multi-shaker ICE test provided a much more accurate replication of system field test response compared with single degree of freedom testing.

This paper elaborates the results of the hardware implementation of a traveling wave (TW) protection device (PD) for DC microgrids. The proposed TWPD is implemented on a commercial digital signal processor (DSP) board. In the developed TWPD, first, the DSP board's Analog to Digital Converter (ADC) is used to sample the input at a 1 MHz sampling rate. The Analog Input card of DSP board measures the pole current at the TWPD location in DC microgrid. Then, a TW detection algorithm is applied on the output of the ADC to detect the fault occurrence instance. Once this instance is detected, multi-resolution analysis (MRA) is performed on a 128-sample data butter that is created around the fault instance. The MRA utilizes discrete wavelet transform (DWT) to extract the high-frequency signatures of measured pole current. To quantity the extracted TW features, the Parseval theorem is used to calculate the Parseval energy of reconstructed wavelet coefficients created by MRA. These Parseval energy values are later used as inputs to a polynomial linear regression tool to estimate the fault location. The performance of the created TWPD is verified using an experimental testbed.

This work investigates the low- and high-temperature ignition and combustion processes, applied to the Engine Combustion Network Spray A flame, combining advanced optical diagnostics and large-eddy simulations (LES). Simultaneous high-speed (50 kHz) formaldehyde (CH2O) planar laser-induced fluorescence (PLIF) and line-of-sight OH* chemiluminescence imaging were used to measure the low- and high-temperature flame, during ignition as well as during quasi-steady combustion. While tracking the cool flame at the laser sheet plane, the present experimental setup allows detection of distinct ignition spots and dynamic fluctuations of the lift-off length over time, which overcomes limitations for flame tracking when using schlieren imaging [Sim et al.Proc. Combust. Inst. 38 (4) (2021) 5713–5721]. After significant development to improve LES prediction of the low-and high-temperature flame position, both during the ignition processes and quasi-steady combustion, the simulations were analyzed to gain understanding of the mixture variance and how this variance affects formation/consumption of CH2O. Analysis of the high-temperature ignition period shows that a key improvement in the LES is the ability to predict heterogeneous ignition sites, not only in the head of the jet, but in shear layers at the jet edge close to the position where flame lift-off eventually stabilizes. The LES analysis also shows concentrated pockets of CH2O, in the center of jet and at 20 mm downstream of the injector (in regions where the equivalence ratio is greater than 6), that are of similar length scale and frequency as the experiment (approximately 5–6 kHz). The periodic oscillation of CH2O match the frequency of pressure waves generated during auto-ignition and reflected within the constant-volume vessel throughout injection. The ability of LES to capture the periodic appearance and destruction of CH2O is particularly important because these structures travel downstream and become rich premixed flames that affect soot production.

Event-based sensors are a novel sensing technology which capture the dynamics of a scene via pixel-level change detection. This technology operates with high speed (>10 kHz), low latency (10 µs), low power consumption (<1 W), and high dynamic range (120 dB). Compared to conventional, frame-based architectures that consistently report data for each pixel at a given frame rate, event-based sensor pixels only report data if a change in pixel intensity occurred. This affords the possibility of dramatically reducing the data reported in bandwidth-limited environments (e.g., remote sensing) and thus, the data needed to be processed while still recovering significant events. Degraded visual environments, such as those generated by fog, often hinder situational awareness by decreasing optical resolution and transmission range via random scattering of light. To respond to this challenge, we present the deployment of an event-based sensor in a controlled, experimentally generated, well-characterized degraded visual environment (a fog analogue), for detection of a modulated signal and comparison of data collected from an event-based sensor and from a traditional framing sensor.

A quantum-cascade-laser-absorption-spectroscopy (QCLAS) diagnostic was used to characterize post-detonation fireballs of RP-80 detonators via measurements of temperature, pressure, and CO column pressure at a repetition rate of 1 MHz. Scanned-wavelength direct-absorption spectroscopy was used to measure CO absorbance spectra near 2008.5 cm−1 which are dominated by the P(0,31), P(2,20), and P(3,14) transitions. Line-of-sight (LOS) measurements were acquired 51 and 91 mm above the detonator surface. Three strategies were employed to facilitate interpretation of the LAS measurements in this highly nonuniform environment and to evaluate the accuracy of four post-detonation fireball models: (1) High-energy transitions were used to deliberately bias the measurements to the high-temperature outer shell, (2) a novel dual-zone absorption model was used to extract temperature, pressure, and CO measurements in two distinct regions of the fireball at times where pressure variations along the LOS were pronounced, and (3) the LAS measurements were compared with synthetic LAS measurements produced using the simulated distributions of temperature, pressure, and gas composition predicted by reactive CFD modeling. The results indicate that the QCLAS diagnostic provides high-fidelity data for evaluating post-detonation fireball models, and that assumptions regarding thermochemical equilibrium and carbon freeze-out during expansion of detonation gases have a large impact on the predicted chemical composition of the fireball.

A comprehensive study of the mechanical response of a 316 stainless steel is presented. The split-Hopkinson bar technique was used to evaluate the mechanical behavior at dynamic strain rates of 500 s−1, 1500 s−1, and 3000 s−1 and temperatures of 22 °C and 300 °C under tension and compression loading, while the Drop-Hopkinson bar was used to characterize the tension behavior at an intermediate strain rate of 200 s−1. The experimental results show that the tension and compression flow stress are reasonably symmetric, exhibit positive strain rate sensitivity, and are inversely dependent on temperature. The true failure strain was determined by measuring the minimum diameter of the post-test tension specimen. The 316 stainless steel exhibited a ductile response, and the true failure strain increased with increasing temperature and decreased with increasing strain rate.

A high altitude electromagnetic pulse (HEMP) or other similar geomagnetic disturbance (GMD) has the potential to severely impact the operation of large-scale electric power grids. By introducing low-frequency common-mode (CM) currents, these events can impact the performance of key system components such as large power transformers. In this work, a solid-state transformer (SST) that can replace susceptible equipment and improve grid resiliency by safely absorbing these CM insults is described. An overview of the proposed SST power electronics and controls architecture is provided, a system model is developed, and the performance of the SST in response to a simulated CM insult is evaluated. Compared to a conventional magnetic transformer, the SST is found to recover quickly from the insult while maintaining nominal ac input/output behavior.

Laser-induced photoemission of electrons offers opportunities to trigger and control plasmas and discharges [1]. However, the underlying mechanisms are not sufficiently characterized to be fully utilized [2]. We present an investigation to characterize the effects of photoemission on plasma breakdown for different reduced electric fields, laser intensities, and photon energies. We perform Townsend breakdown experiments assisted by high-speed imaging and employ a quantum model of photoemission along with a 0D discharge model [3], [4] to interpret the experimental measurements.

We propose a set of benchmark tests for current-voltage (IV) curve fitting algorithms. Benchmark tests enable transparent and repeatable comparisons among algorithms, allowing for measuring algorithm improvement over time. An absence of such tests contributes to the proliferation of fitting methods and inhibits achieving consensus on best practices. Benchmarks include simulated curves with known parameter solutions, with and without simulated measurement error. We implement the reference tests on an automated scoring platform and invite algorithm submissions in an open competition for accurate and performant algorithms.

The Sliding Scale of Cybersecurity is a framework for understanding the actions that contribute to cybersecurity. The model consists of five categories that provide varying value towards cybersecurity and incur varying implementation costs. These categories range from offensive cybersecurity measures providing the least value and incurring the greatest cost, to architecture providing the greatest value and incurring the least cost. This paper presents an application of the Sliding Scale of Cybersecurity to the Tiered Cybersecurity Analysis (TCA) of digital instrumentation and control systems for advanced reactors. The TCA consists of three tiers. Tier 1 is design and impact analysis. In Tier 1 it is assumed that the adversary has control over all digital systems, components, and networks in the plant, and that the adversary is only constrained by the physical limitations of the plant design. The plant’s safety design features are examined to determine whether the consequences of an attack by this cyber-enabled adversary are eliminated or mitigated. Accident sequences that are not eliminated or mitigated by security by design features are examined in Tier 2 analysis. In Tier 2, adversary access pathways are identified for the unmitigated accident sequences, and passive measures are implemented to deny system and network access to those pathways wherever feasible. Any systems with remaining susceptible access pathways are then examined in Tier 3. In Tier 3, active defensive cybersecurity architecture features and cybersecurity plan controls are applied to deny the adversary the ability to conduct the tasks needed to cause a severe consequence. Earlier application of the TCA in the design process provides greater opportunity for an efficient graded approach and defense-in-depth.

Advancements in photonic quantum information systems (QIS) have driven the development of high-brightness, on-demand, and indistinguishable semiconductor epitaxial quantum dots (QDs) as single photon sources. Strain-free, monodisperse, and spatially sparse local-droplet-etched (LDE) QDs have recently been demonstrated as a superior alternative to traditional Stranski-Krastanov QDs. However, integration of LDE QDs into nanophotonic architectures with the ability to scale to many interacting QDs is yet to be demonstrated. We present a potential solution by embedding isolated LDE GaAs QDs within an Al0.4Ga0.6As Huygens’ metasurface with spectrally overlapping fundamental electric and magnetic dipolar resonances. We demonstrate for the first time a position- and size-independent, 1 order of magnitude increase in the collection efficiency and emission lifetime control for single-photon emission from LDE QDs embedded within the Huygens’ metasurfaces. Our results represent a significant step toward leveraging the advantages of LDE QDs within nanophotonic architectures to meet the scalability demands of photonic QIS.

Filamentous fungi can synthesize a variety of nanoparticles (NPs), a process referred to as mycosynthesis that requires little energy input, do not require the use of harsh chemicals, occurs at near neutral pH, and do not produce toxic byproducts. While NP synthesis involves reactions between metal ions and exudates produced by the fungi, the chemical and biochemical parameters underlying this process remain poorly understood. Here, the role of fungal species and precursor salt on the mycosynthesis of zinc oxide (ZnO) NPs is investigated. This data demonstrates that all five fungal species tested are able to produce ZnO structures that can be morphologically classified into i) well-defined NPs, ii) coalesced/dissolving NPs, and iii) micron-sized square plates. Further, species-dependent preferences for these morphologies are observed, suggesting potential differences in the profile or concentration of the biochemical constituents in their individual exudates. This data also demonstrates that mycosynthesis of ZnO NPs is independent of the anion species, with nitrate, sulfate, and chloride showing no effect on NP production. Finally, these results enhance the understanding of factors controlling the mycosynthesis of ceramic NPs, supporting future studies that can enable control over the physical and chemical properties of NPs formed through this “green” synthesis method.

In this work, we introduce and compare the results of several methods for determining the horizon profile at a PV site, and compare their use cases and limitations. The methods in this paper include horizon detection from time-series irradiance or performance data, modeling from GIS topology data, manual theodolite measurements, and camera-based horizon detection. We compare various combinations of these methods using data from 4 Regional Test Center sites in the US, and 3 World Bank sites in Nepal. The results show many differences between these methods, and we recommend the most practical solutions for various use-cases.

A microgrid is characterized by a high R/X ratio, making the voltage more sensitive to active power changes unlike in bulk power systems where voltage is mostly regulated by reactive power. Because of its sensitivity to active power, control approach should incorporate active power as well. Thus, the voltage control approach for microgrids is very different from conventional power systems. The energy costs associated with these power are different. Furthermore, because of diverse generation sources and different components such as distributed energy resources, energy storage systems, etc, model-based control approaches might not perform very well. This paper proposes a reinforcement learning-based voltage support framework for a microgrid where an agent learns control policy by interacting with the microgrid without requiring a mathematical model of the system. A MATLAB/Simulink simulation study on a test system from Cordova, Alaska shows that there is a large reduction in voltage deviation (about 2.5-4.5 times). This reduction in voltage deviation can improve the power quality of the microgrid: ensuring a reliable supply, longer equipment lifespan, and stable user operations.

Inverse problems constrained by partial differential equations (PDEs) play a critical role in model development and calibration. In many applications, there are multiple uncertain parameters in a model that must be estimated. However, high dimensionality of the parameters and computational complexity of the PDE solves make such problems challenging. A common approach is to reduce the dimension by fixing some parameters (which we will call auxiliary parameters) to a best estimate and use techniques from PDE-constrained optimization to estimate the other parameters. In this article, hyper-differential sensitivity analysis (HDSA) is used to assess the sensitivity of the solution of the PDE-constrained optimization problem to changes in the auxiliary parameters. Foundational assumptions for HDSA require satisfaction of the optimality conditions which are not always practically feasible as a result of ill-posedness in the inverse problem. We introduce novel theoretical and computational approaches to justify and enable HDSA for ill-posed inverse problems by projecting the sensitivities on likelihood informed subspaces and defining a posteriori updates. Our proposed framework is demonstrated on a nonlinear multiphysics inverse problem motivated by estimation of spatially heterogeneous material properties in the presence of spatially distributed parametric modeling uncertainties.

Surrogate construction is an essential component for all non-deterministic analyses in science and engineering. The efficient construction of easy and cheaper-to-run alternatives to a computationally expensive code paves the way for outer loop workflows for forward and inverse uncertainty quantification and optimization. Unfortunately, the accurate construction of a surrogate still remains a task that often requires a prohibitive number of computations, making the approach unattainable for large-scale and high-fidelity applications. Multifidelity approaches offer the possibility to lower the computational expense requirement on the highfidelity code by fusing data from additional sources. In this context, we have demonstrated that multifidelity Bayesian Networks (MFNets) can efficiently fuse information derived from models with an underlying complex dependency structure. In this contribution, we expand on our previous work by adopting a basis adaptation procedure for the selection of the linear model representing each data source. Our numerical results demonstrate that this procedure is computationally advantageous because it can maximize the use of limited data to learn and exploit the important structures shared among models. Two examples are considered to demonstrate the benefits of the proposed approach: an analytical problem and a nuclear fuel finite element assembly. From these two applications, a lower dependency of MFnets on the model graph has been also observed.

We demonstrate an InAs-based nonlinear dielectric metasurface, which can generate terahertz (THz) pulses with opposite phase in comparison to an unpatterned InAs layer. It enables binary phase THz metasurfaces for generation and focusing of THz pulses.

Previous research has provided strong evidence that CO2 and H2O gasification reactions can provide non-negligible contributions to the consumption rates of pulverized coal (pc) char during combustion, particularly in oxy-fuel environments. Fully quantifying the contribution of these gasification reactions has proven to be difficult, due to the dearth of knowledge of gasification rates at the elevated particle temperatures associated with typical pc char combustion processes, as well as the complex interaction of oxidation and gasification reactions. Gasification reactions tend to become more important at higher char particle temperatures (because of their high activation energy) and they tend to reduce pc oxidation due to their endothermicity (i.e. cooling effect). The work reported here attempts to quantify the influence of the gasification reaction of CO2 in a rigorous manner by combining experimental measurements of the particle temperatures and consumption rates of size-classified pc char particles in tailored oxy-fuel environments with simulations from a detailed reacting porous particle model. The results demonstrate that a specific gasification reaction rate relative to the oxidation rate (within an accuracy of approximately +/- 20% of the pre-exponential value), is consistent with the experimentally measured char particle temperatures and burnout rates in oxy-fuel combustion environments. Conversely, the results also show, in agreement with past calculations, that it is extremely difficult to construct a set of kinetics that does not substantially overpredict particle temperature increase in strongly oxygen-enriched N2 environments. This latter result is believed to result from deficiencies in standard oxidation mechanisms that fail to account for falloff in char oxidation rates at high temperatures.

Reinforcement learning (RL) may enable fixedwing unmanned aerial vehicle (UAV) guidance to achieve more agile and complex objectives than typical methods. However, RL has yet struggled to achieve even minimal success on this problem; fixed-wing flight with RL-based guidance has only been demonstrated in literature with reduced state and/or action spaces. In order to achieve full 6-DOF RL-based guidance, this study begins training with imitation learning from classical guidance, a method known as warm-staring (WS), before further training using Proximal Policy Optimization (PPO). We show that warm starting is critical to successful RL performance on this problem. PPO alone achieved a 2% success rate in our experiments. Warm-starting alone achieved 32% success. Warm-starting plus PPO achieved 57% success over all policies, with 40% of policies achieving 94% success.

High penetrations of residential solar PV can cause voltage issues on low-voltage (LV) secondary networks. Distribution utility planners often utilize model-based power flow solvers to address these voltage issues and accommodate more PV installations without disrupting the customers already connected to the system. These model-based results are computationally expensive and often prone to errors. In this paper, two novel deep learning-based model-free algorithms are proposed that can predict the change in voltages for PV installations without any inherent network information of the system. These algorithms will only use the real power (P), reactive power (Q), and voltage (V) data from Advanced Metering Infrastructure (AMI) to calculate the change in voltages for an additional PV installation for any customer location in the LV secondary network. Both algorithms are tested on three datasets of two feeders and compared to the conventional model-based methods and existing model-free methods. The proposed methods are also applied to estimate the locational PV hosting capacity for both feeders and have shown better accuracies compared to an existing model-free method. Results show that data filtering or pre-processing can improve the model performance if the testing data point exists in the training dataset used for that model.

The novel Hydromine harvests energy from flowing water with no external moving parts, resulting in a robust system with minimal environmental impact. Here two deployment scenarios are considered: an offshore floating platform configuration to capture energy from relatively steady ocean currents at megawatt-scale, and a river-based system at kilowatt-scale mounted on a pylon. Hydrodynamic and techno-economic models are developed. The hydrodynamic models are used to maximize the efficiency of the power conversion. The techno-economic models optimize the system size and layout and ultimately seek to minimize the levelized-cost-of-electricity produced. Parametric and sensitivity analyses are performed on the models to optimize performance and reduce costs.

Holography is an effective diagnostic for the three-dimensional imaging of multiphase and particle-laden flows. Traditional digital inline holography (DIH), however, is subject to distortions from phase delays caused by index-of-refraction changes. This prevents DIH from being implemented in extreme conditions where shockwaves and significant thermal gradients are present. To overcome this challenge, multiple techniques have been developed to correct for the phase distortions. In this work, several holography techniques for distortion removal are discussed, including digital off-axis holography, phase conjugate digital in-line holography, and electric field techniques. Then, a distortion cancelling off-axis holography configuration is implemented for distortion removal and a high-magnification phase conjugate system is evaluated. Finally, both diagnostics are applied to study extreme pyrotechnic igniter environments.

We demonstrate the use of low-temperature grown GaAs (LT-GaAs) metasurface as an ultrafast photoconductive switching element gated with 1550 nm laser pulses. The metasurface is designed to enhance a weak two-step photon absorption at 1550 nm, enabling THz pulse detection.

A high altitude electromagnetic pulse (HEMP) caused by a nuclear explosion has the potential to severely impact the operation of large-scale electric power grids. This paper presents a top-down mitigation design strategy that considers grid-wide dynamic behavior during a simulated HEMP event - and uses optimal control theory to determine the compensation signals required to protect critical grid assets. The approach is applied to both a standalone transformer system and a demonstrative 3-bus grid model. The performance of the top-down approach relative to conventional protection solutions is evaluated, and several optimal control objective functions are explored. Finally, directions for future research are proposed.

At the turn of the millennium the computational neuroscience community realized that neuroscience was in a software crisis: software development was no longer progressing as expected and reproducibility declined. The International Neuroinformatics Coordinating Facility (INCF) was inaugurated in 2007 as an initiative to improve this situation. The INCF has since pursued its mission to help the development of standards and best practices. In a community paper published this very same year, Brette et al. tried to assess the state of the field and to establish a scientific approach to simulation technology, addressing foundational topics, such as which simulation schemes are best suited for the types of models we see in neuroscience. In 2015, a Frontiers Research Topic “Python in neuroscience” by Muller et al. triggered and documented a revolution in the neuroscience community, namely in the usage of the scripting language Python as a common language for interfacing with simulation codes and connecting between applications. The review by Einevoll et al. documented that simulation tools have since further matured and become reliable research instruments used by many scientific groups for their respective questions. Open source and community standard simulators today allow research groups to focus on their scientific questions and leave the details of the computational work to the community of simulator developers. A parallel development has occurred, which has been barely visible in neuroscientific circles beyond the community of simulator developers: Supercomputers used for large and complex scientific calculations have increased their performance from ~10 TeraFLOPS (10¹³ floating point operations per second) in the early 2000s to above 1 ExaFLOPS (10¹⁸ floating point operations per second) in the year 2022. This represents a 100,000-fold increase in our computational capabilities, or almost 17 doublings of computational capability in 22 years. Moore's law (the observation that it is economically viable to double the number of transistors in an integrated circuit every other 18–24 months) explains a part of this; our ability and willingness to build and operate physically larger computers, explains another part. It should be clear, however, that such a technological advancement requires software adaptations and under the hood, simulators had to reinvent themselves and change substantially to embrace this technological opportunity. It actually is quite remarkable that—apart from the change in semantics for the parallelization—this has mostly happened without the users knowing. The current Research Topic was motivated by the wish to assemble an update on the state of neuroscientific software (mostly simulators) in 2022, to assess whether we can see more clearly which scientific questions can (or cannot) be asked due to our increased capability of simulation, and also to anticipate whether and for how long we can expect this increase of computational capabilities to continue.

This is an investigation on two experimental datasets of laminar hypersonic flows, over a double-cone geometry, acquired in Calspan—University at Buffalo Research Center’s Large Energy National Shock (LENS)-XX expansion tunnel. These datasets have yet to be modeled accurately. A previous paper suggested that this could partly be due to mis-specified inlet conditions. The authors of this paper solved a Bayesian inverse problem to infer the inlet conditions of the LENS-XX test section and found that in one case they lay outside the uncertainty bounds specified in the experimental dataset. However, the inference was performed using approximate surrogate models. In this paper, the experimental datasets are revisited and inversions for the tunnel test-section inlet conditions are performed with a Navier–Stokes simulator. The inversion is deterministic and can provide uncertainty bounds on the inlet conditions under a Gaussian assumption. It was found that deterministic inversion yields inlet conditions that do not agree with what was stated in the experiments. An a posteriori method is also presented to check the validity of the Gaussian assumption for the posterior distribution. This paper contributes to ongoing work on the assessment of datasets from challenging experiments conducted in extreme environments, where the experimental apparatus is pushed to the margins of its design and performance envelopes.

The International Electrotechnical Commission (IEC) Subcommittee SC45A has been active in development of cybersecurity standards and technical reports on the protection of Instrumentation and Control (I&C) and Electrical Power Systems (ES) that perform significant functions necessary for the safe and secure operation of Nuclear Power Plants (NPP). These international standards and reports advance and promote the implementation of good practices around the world. In recent years, there have been advances in NPP cybersecurity risk management nationally and internationally. For example, IAEA publications NSS 17-T [1] and NSS 33-T [2], propose a framework for computer security risk management that implements a risk management program at both the facility and individual system levels. These international approaches (i.e., IAEA), national approaches (e.g., Canada’s HTRA [3]) and technical methods (e.g., HAZCADS [4], Cyber Informed Engineering [5], France’s EBIOS [6]) have advanced risk management within NPP cybersecurity programmes that implement international and national standards. This paper summarizes key elements of the analysis that developed the new IEC Technical Report. The paper identifies the eleven challenges for applying ISO/IEC 27005:2018 [7]. cybersecurity risk management to I&C Systems and EPS of NPPs and a summary comparison of how national approaches address these challenges.

Experiments were conducted on a wave tank model of a bottom raised oscillating surge wave energy converter (OSWEC) model in regular waves. The OSWEC model shape was a thin rectangular flap, which was allowed to pitch in response to incident waves about a hinge located at the intersection of the flap and the top of the supporting foundation. Torsion springs were added to the hinge in order to position the pitch natural frequency at the center of the wave frequency range of the wave maker. The flap motion as well as the loads at the base of the foundation were measured. The OSWEC was modeled analytically using elliptic functions in order to obtain closed form expressions for added mass and radiation damping coefficients, along with the excitation force and torque. These formulations were derived and reported in a previous publication by the authors. While analytical predictions of the foundation loads agree very well with experiments, large discrepancies are seen in the pitch response close to resonance. These differences are analyzed by conducting a sensitivity study, in which system parameters, including damping and added mass values, are varied. The likely contributors to the differences between predictions and experiments are attributed to tank reflections, standing waves that can occur in long, narrow wave tanks, as well as the thin plate assumption employed in the analytical approach.

Two relatively under-reported facets of fuel storage fire safety are examined in this work for a 250, 000 gallon two-tank storage system. Ignition probability is linked to the radiative flux from a presumed fire. First, based on observed features of existing designs, fires are expected to be largely contained within a designed footprint that will hold the full spilled contents of the fuel. The influence of the walls and the shape of the tanks on the magnitude of the fire is not a well-described aspect of conventional fire safety assessment utilities. Various resources are herein used to explore the potential hazard for a contained fire of this nature. Second, an explosive attack on the fuel storage has not been widely considered in prior work. This work explores some options for assessing this hazard. The various methods for assessing the constrained conventional fires are found to be within a reasonable degree of agreement. This agreement contrasts with the hazard from an explosive dispersal. Best available assessment techniques are used, which highlight some inadequacies in the existing toolsets for making predictions of this nature. This analysis, using the best available tools, suggests the offset distance for the ignition hazard from a fireball will be on the same order as the offset distance for the blast damage. This suggests the buy-down of risk by considering the fireball is minimal when considering the blast hazards. Assessment tools for the fireball predictions are not particularly mature, and ways to improve them for a higher-fidelity estimate are noted.

The research investigates novel techniques to enhance supply chain security via addition of configuration management controls to protect Instrumentation and Control (I&C) systems of a Nuclear Power Plant (NPP). A secure element (SE) is integrated into a proof-of-concept testbed by means of a commercially available smart card, which provides tamper resistant key storage and a cryptographic coprocessor. The secure element simplifies setup and establishment of a secure communications channel between the configuration manager and verification system and the I&C system (running OpenPLC). This secure channel can be used to provide copies of commands and configuration changes of the I&C system for analysis.

Creation of a Sandia internally developed, shock-hardened Recoverable Data Recorder (RDR) necessitated experimentation by ballistically-firing the device into water targets at velocities up to 5,000 ft/s. The resultant mechanical environments were very severe—routinely achieving peak accelerations in excess of 200 kG and changes in pseudo-velocity greater than 38,000 inch/s. High-quality projectile deceleration datasets were obtained though high-speed imaging during the impact events. The datasets were then used to calibrate and validate computational models in both CTH and EPIC. Hydrodynamic stability in these environments was confirmed to differ from aerodynamic stability; projectile stability is maintained through a phenomenon known as “tail-slapping” or impingement of the rear of the projectile on the cavitation vapor-water interface which envelopes the projectile. As the projectile slows the predominate forces undergo a transition which is outside the codes’ capabilities to calculate accurately, however, CTH and EPIC both predict the projectile trajectory well in the initial hypervelocity regime. Stable projectile designs and the achievable acceleration space are explored through a large parameter sweep of CTH simulations. Front face chamfer angle has the largest influence on stability with low angles being more stable.

The focus of this study was to demonstrate the vaporphase halogenation of Si(100) and subsequently evaluate the inhibiting ability of the halogenated surfaces toward atomic layer deposition (ALD) of aluminum oxide (Al2O3). Hydrogen-terminated silicon ⟨100⟩ (H−Si(100)) was halogenated using N-chlorosuccinimide (NCS), N-bromosuccinimide (NBS), and N-iodosuccinimide (NIS) in a vacuum-based chemical process. The composition and physical properties of the prepared monolayers were analyzed by using X-ray photoelectron spectroscopy (XPS) and contact angle (CA) goniometry. These measurements confirmed that all three reagents were more effective in halogenating H−Si(100) over OH−Si(100) in the vapor phase. The stability of the modified surfaces in air was also tested, with the chlorinated surface showing the greatest resistance to monolayer degradation and silicon oxide (SiO2) generation within the first 24 h of exposure to air. XPS and atomic force microscopy (AFM) measurements showed that the succinimide-derived Hal-Si(100) surfaces exhibited blocking ability superior to that of H− Si(100), a commonly used ALD resist. This halogenation method provides a dry chemistry alternative for creating halogen-based ALD resists on Si(100) in near-ambient environments.

The novel Hydromine harvests energy from flowing water with no external moving parts, resulting in a robust system with minimal environmental impact. Here two deployment scenarios are considered: an offshore floating platform configuration to capture energy from relatively steady ocean currents at megawatt-scale, and a river-based system at kilowatt-scale mounted on a pylon. Hydrodynamic and techno-economic models are developed. The hydrodynamic models are used to maximize the efficiency of the power conversion. The techno-economic models optimize the system size and layout and ultimately seek to minimize the levelized-cost-of-electricity produced. Parametric and sensitivity analyses are performed on the models to optimize performance and reduce costs.

Researchers at Sandia National Laboratories, in conjunction with the Nuclear Energy Institute and Light Water Reactor Sustainability Programs, have conducted testing and analysis to reevaluate and redefine the minimum passible opening size through which a person can effectively pass and navigate. Physical testing with a representative population has been performed on both simple two-dimensional (rectangular and circular cross sections up to 91.4 cm in depth) and more complex three-dimensional (circular cross sections of longer lengths up to 9.1 m and changes in direction) opening configurations. The primary impact of this effort is to define the physical design in which an adversary could successfully pass through a potentially complex opening, as well as to define the designs in which an adversary would not be expected to successfully traverse a complex opening. These data can then be used to support risk-informed decision making.

A comprehensive study of the mechanical response of a 316 stainless steel is presented. The split-Hopkinson bar technique was used to evaluate the mechanical behavior at dynamic strain rates of 500 s⁻¹, 1500 s⁻¹, and 3000 s⁻¹ and temperatures of 22 °C and 300 °C under tension and compression loading, while the Drop-Hopkinson bar was used to characterize the tension behavior at an intermediate strain rate of 200 s⁻¹. The experimental results show that the tension and compression flow stress are reasonably symmetric, exhibit positive strain rate sensitivity, and are inversely dependent on temperature. The true failure strain was determined by measuring the minimum diameter of the post-test tension specimen. The 316 stainless steel exhibited a ductile response, and the true failure strain increased with increasing temperature and decreased with increasing strain rate.

Multiple rotors on single structures have long been proposed to increase wind turbine energy capture with no increase in rotor size, but at the cost of additional mechanical complexity in the yaw and tower designs. Standard turbines on their own very-closely-spaced towers avoid these disadvantages but create a significant disadvantage; for some wind directions the wake turbulence of a rotor enters the swept area of a very close downwind rotor causing low output, fatigue stress, and changes in wake recovery. Knowing how the performance of pairs of closely spaced rotors varies with wind direction is essential to design a layout that maximizes the useful directions and minimizes the losses and stress at other directions. In the current work, the high-fidelity large-eddy simulation (LES) code Exa-Wind/Nalu-Wind is used to simulate the wake interactions from paired-rotor configurations in a neutrally stratified atmospheric boundary layer to investigate performance and feasibility. Each rotor pair consists of two Vestas V27 turbines with hub-to-hub separation distances of 1.5 rotor diameters. The on-design wind direction results are consistent with previous literature. For an off-design wind direction of 26.6°, results indicate little change in power and far-wake recovery relative to the on-design case. At a direction of 45.0°, significant rotor-wake interactions produce an increase in power but also in far-wake velocity deficit and turbulence intensity. A severely off-design case is also considered.

Open Charge Point Protocol (OCPP) 1.6 is widely used in the electric vehicle (EV) charging industry to communicate between Charging System Management Services (CSMSs) and Electric Vehicle Supply Equipment (EVSE). Unlike OCPP 2.0.1, OCPP 1.6 uses unencrypted websocket communications to exchange information between EVSE devices and an on-premise or cloud-based CSMS. In this work, we demonstrate two machine-in-the-middle attacks on OCPP sessions to terminate charging sessions and gain root access to the EVSE equipment via remote code execution. Second, we demonstrate a malicious firmware update with a code injection payload to compromise an EVSE. Lastly, we demonstrate two methods to prevent availability of the EVSE or CSMS. One of these, originally reported by SaiFlow, prevents traffic to legitimate EVSE equipment using a DoS-like attack on CSMSs by repeatedly connecting and authenticating several CPs with the same identities as the legitimate CP. These vulnerabilities were demonstrated with proof-of-concept exploits in a virtualized Cyber Range at Wright State University and/or with a 350 kW Direct Current Fast Charger at Idaho National Laboratory. The team found that OCPP 1.6 could be protected from these attacks by adding secure shell tunnels to the protocol, if upgrading to OCPP 2.0.1 was not an option.

The increased complexity of high-consequence digital system designs with intricate interactions between numerous components has placed a greater need on ensuring that the design satisfies its intended requirements. This digital assurance can only come about through rigorous mathematical analysis of the design. This manuscript provides a detailed description of a formal language semantics that can be used for modeling and verification of systems. We use Event-B to build a formalized semantics that supports the construction of triggered enable statecharts with a run-to-completion scheduling. Rodin has previously been used to develop and analyse models using this semantics.

Visualization of mode shapes is a crucial step in modal analysis. However, the methods to create the test geometry, which typically require arduous hand measurements and approximations of rotation matrices, are crude. This leads to a lengthy test set-up process and a test geometry with potentially high measurement errors. Test and analysis delays can also be experienced if the orientation of an accelerometer is documented incorrectly, which happens more often than engineers would like to admit. To mitigate these issues, a methodology has been created to generate the test geometry (coordinates and rotation matrices) with probe data from a portable coordinate measurement machine (PCMM). This methodology has led to significant reductions in the test geometry measurement time, reductions in test geometry measurement errors, and even reduced test times. Simultaneously, a methodology has also been created to use the PCMM to easily identify desired measurement locations, as specified by a model. This paper will discuss the general framework of these methods and the realized benefits, using examples from actual tests.

Abstract not provided.

Polymers are widely used as damping materials in vibration and impact applications. Liquid crystal elastomers (LCEs) are a unique class of polymers that may offer the potential for enhanced energy absorption capacity under impact conditions over conventional polymers due to their ability to align the nematic phase during loading. Being a relatively new material, the high rate compressive properties of LCEs have been minimally studied. Here, we investigated the high strain rate compression behavior of different solid LCEs, including cast polydomain and 3D-printed, preferentially oriented monodomain samples. Direct ink write (DIW) 3D printed samples allow unique sample designs, namely, a specific orientation of mesogens with respect to the loading direction. Loading the sample in different orientations can induce mesogen rotation during mechanical loading and subsequently different stress-strain responses under impact. We also used a reference polymer, bisphenol-A (BPA) cross-linked resin, to contrast LCE behavior with conventional elastomer behavior.

Sustainable use of water resources continues to be a challenge across the globe. This is in part due to the complex set of physical and social behaviors that interact to influence water management from local to global scales. Analyses of water resources have been conducted using a variety of techniques, including qualitative evaluations of media narratives. This study aims to augment these methods by leveraging computational and quantitative techniques from the social sciences focused on text analyses. Specifically, we use natural language processing methods to investigate a large corpus (approx. 1.8M) of newspaper articles spanning approximately 35 years (1982–2017) for insights into human-nature interactions with water. Focusing on local and regional United States publications, our analysis demonstrates important dynamics in water-related dialogue about drinking water and pollution to other critical infrastructures, such as energy, across different parts of the country. Our assessment, which looks at water as a system, also highlights key actors and sentiments surrounding water. Extending these analytical methods could help us further improve our understanding of the complex roles of water in current society that should be considered in emerging activities to mitigate and respond to resource conflicts and climate change.