Phosphor thermometry has been successfully applied within several challenging environments. Typically, the thermographic phosphors are excited by an ultraviolet light source, and the temperature-dependent spectral or temporal response is measured. However, this is challenging or impossible in optically thick environments. In addition, emission from other sources (e.g., a flame) may interfere with the optical phosphor emission. A temperature dependent x-ray excitation/emission could alleviate these issues as x-rays could penetrate obscurants with no interference from flame luminosity. In addition, x-ray emission could allow for thermometry within solids while simultaneously x-ray imaging the structural evolution. In this study, select thermographic phosphors were excited via x-ray radiation, and their x-ray emission characteristics were measured at various temperatures. Several of the phosphors showed varying levels of temperature dependence with the strongest sensitivity occurring for YAG:Dy and ZnGa2O4:Mn. This approach opens a path for less intrusive temperature measurements, particularly in optically opaque multiphase and solid phase combustion environments.
The Spent Fuel & Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & Waste Disposition (SFWD) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). A high priority for SFWST disposal R&D is to develop a disposal system modeling and analysis capability for evaluating disposal system performance for nuclear waste in geologic media. This report describes fiscal year (FY) 2021 advances of the PFLOTRAN Development group of the SFWST Campaign. The mission of this group is to develop a geologic disposal system modeling capability for nuclear waste that can be used to probabilistically assess the performance of generic disposal concepts. In FY 2021, development proceeded along three main thrusts: software infrastructure, code performance, and process model advancement. Software infrastructure improvements included implementing an Agile software development framework and making improvements to the QA Test Suite. Code performance improvements included development of advanced linear and nonlinear solvers as well as design of flexible smoothing algorithms for capillary pressure functions. Process modeling advancements included the addition of flexible thermal conductivity function definitions and refinement of multi-continuum reactive transport to support Sandia’s participation in DECOVALEX
Villalba, Gara; Whelan, Mary; Montzka, Stephen A.; Cameron-Smith, Philip J.; Fischer, Marc; Zumkehr, Andrew; Hilton, Tim; Stinecipher, James; Baker, Ian; Bambha, Ray; Lafranchi, Brian W.; Estruch, Carme; Campbell, Elliott
Cities are implementing additional urban green as a means to capture CO2 and become more carbon neutral. However, cities are complex systems where anthropogenic and natural components of the CO2 budget interact with each other, and the ability to measure the efficacy of such measures is still not properly addressed. There is still a high degree of uncertainty in determining the contribution of the vegetation signal, which furthermore confounds the use of CO2 mole fraction measurements for inferring anthropogenic emissions of CO2. Carbonyl sulfide (OCS) is a tracer of photosynthesis which can aid in constraining the biosphere signal. This study explores the potential of using OCS to track the urban biosphere signal. We used the Sulfur Transport and dEposition Model (STEM) to simulate the OCS concentrations and the Carnegie Ames Stanford Approach ecosystem model to simulate global CO2 fluxes over the Bay Area of San Francisco during March 2015. Two observation towers provided measurements of OCS and CO2: The Sutro tower in San Francisco (upwind from the area of study providing background observations), and a tower located at Sandia National Laboratories in Livermore (downwind of the highly urbanized San Francisco region). Our results show that the STEM model works better under stable marine influence, and that the boundary layer height and entrainment are driving the diurnal changes in OCS and CO2 at the downwind Sandia site. However, the STEM model needs to better represent the transport and boundary layer variability, and improved estimates of gross primary productivity for characterizing the urban biosphere signal are needed.
Potts, Alexander M.; Bajaj, Sanyam; Daughton, David R.; Allerman, A.A.; Armstrong, Andrew A.; Razzak, Towhidur; Sohel, Shahadat H.; Rajan, Siddharth
In this work, ultrawide bandgap Al0.7Ga0.3N MESFETs with refractory Tungsten Schottky and Ohmic contacts are studied in 300–675 K environments. Variable-temperature dc electrical transport reveals large ON-state drain current densities for an AlGaN device: 209 mA/mm at 300 K and 156 mA/mm at 675 K in the ON-state (25% reduction). Drain and gate currents are only weakly temperature-dependent, suggesting potential for engineering temperature invariant operation. The ON-/ OFF-ratio is limited by OFF-state leakage through the gate, which is attributed to damage from sputter deposition. Future work using refractory metals with larger work functions that are deposited by electron beam deposition is proposed.
There is a growing interest in custom spatial accelerators for machine learning applications. These accelerators employ a spatial array of processing elements (PEs) interacting via custom buffer hierarchies and networks-on-chip. The efficiency of these accelerators comes from employing optimized dataflow (i.e., spatial/temporal partitioning of data across the PEs and fine-grained scheduling) strategies to optimize data reuse. The focus of this work is to evaluate these accelerator architectures using a tiled general matrix-matrix multiplication (GEMM) kernel. To do so, we develop a framework that finds optimized mappings (dataflow and tile sizes) for a tiled GEMM for a given spatial accelerator and workload combination, leveraging an analytical cost model for runtime and energy. Our evaluations over five spatial accelerators demonstrate that the tiled GEMM mappings systematically generated by our framework achieve high performance on various GEMM workloads and accelerators.
As the push towards exascale hardware has increased the diversity of system architectures, performance portability has become a critical aspect for scientific software. We describe the Kokkos Performance Portable Programming Model that allows developers to write single source applications for diverse high performance computing architectures. Kokkos provides key abstractions for both the compute and memory hierarchy of modern hardware. Here, we describe the novel abstractions that have been added to Kokkos recently such as hierarchical parallelism, containers, task graphs, and arbitrary-sized atomic operations. We demonstrate the performance of these new features with reproducible benchmarks on CPUs and GPUs.
The addition of a common amino acid, phenylalanine, to a Layer-by-Layer (LbL) deposited polyelectrolyte (PE) film on a nanoporous membrane can increase its ionic selectivity over a PE film without the added amino acid. The addition of phenylalanine is inspired by detailed knowledge of the structure of the channelrhodopsins family of protein ion channels, where phenylalanine plays an instrumental role in facilitating sodium ion transport. The normally deposited and crosslinked PE films increase the cationic selectivity of a support membrane in a controllable manner where higher selectivity is achieved with thicker PE coatings, which in turn also increases the ionic resistance of the membrane. The increased ionic selectivity is desired while the increased resistance is not. We show that through incorporation of phenylalanine during the LbL deposition process, in solutions of NaCl with concentrations ranging from 0.1 to 100 mM, the ionic selectivity can be increased independently of the membrane resistance. Specifically, the addition is shown to increase the cationic transference of the PE films from 81.4% to 86.4%, an increase on par with PE films that are nearly triple the thickness while exhibiting much lower resistance compared to the thicker coatings, where the phenylalanine incorporated PE films display an area specific resistance of 1.81 Ω cm2in 100 mM NaCl while much thicker PE membranes show a higher resistance of 2.75 Ω cm2in the same 100 mM NaCl solution.
Lithium-metal anodes can theoretically enable 10× higher gravimetric capacity than conventional graphite anodes. However, Li-metal anode cycling has proven difficult due to porous and dendritic morphologies, extensive parasitic solid electrolyte interphase reactions, and formation of dead Li. We systematically investigate the effects of applied interfacial pressure on Li-metal anode cycling performance and morphology in the recently developed and highly efficient 4 M lithium bis(fluorosulfonyl)imide in 1,2-dimethoxyethane electrolyte. We present cycling, morphology, and impedance data at a current density of 0.5 mA/cm2 and a capacity of 2 mAh/cm2 at applied interfacial pressures of 0, 0.01, 0.1, 1, and 10 MPa. Cryo-focused ion beam milling and cryo-scanning electron microscopy imaging in cross section reveal that increasing the applied pressure during Li deposition from 0 to 10 MPa leads to greater than a fivefold reduction in thickness (and therefore volume) of the deposited Li. This suggests that pressure during cycling can have a profound impact on the practical volumetric energy density for Li-metal anodes. A "goldilocks zone"of cell performance is observed at intermediate pressures of 0.1-1 MPa. Increasing pressure from 0 to 1 MPa generally improves cell-to-cell reproducibility, cycling stability, and Coulombic efficiency. However, the highest pressure (10 MPa) results in high cell overpotential and evidence of soft short circuits, which likely result from transport limitations associated with increased pressure causing local pore closure in the separator. All cells exhibit at least some signs of cycling instability after 50 cycles when cycled to 2 mAh/cm2 with thin 50 μm Li counter electrodes, though instability decreases with increasing pressure. In contrast, cells cycled to only 1 mAh/cm2 perform well for 50 cycles, indicating that capacity plays an important role in cycling stability.
The compound (Pb2MnSe3)0.6VSe2 was predicted to be kinetically stable based on density functional theory (DFT) calculations on an island of Pb2MnSe3 between layers of VSe2. This approach provides a high degree of freedom by not forcing interlayer lattice match, making it ideal to investigate the likelihood of formation of new incommensurate layer misfit structures. The free space around the island is critical, as it allows atoms to diffuse and hence exploring the local energy landscape around the initial configuration. (Pb2MnSe3)0.6VSe2 was synthesized via a near diffusionless reaction from precursors where a repeating sequence of elemental layers matches the local composition and layer sequence of the predicted compound. The VSe2 layer consists of a Se-V-Se trilayer with octahedral coordination of the V atoms. The Pb2MnSe3 layer consists of three rock-salt-like planes, with a MnSe layer between the planes of PbSe. The center MnSe plane stabilizes the puckering of the outer PbSe layers. Electrical properties indicate that (Pb2Mn1Se3)0.6VSe2 undergoes a charge density wave transition at ~100 K and orders ferromagnetically at 35 K. The combination of theory and experiment enables a faster convergence to new heterostructures than either approach in isolation.
This assessment analyzes ES&H occurrences and Non-Occurrence Trackable Events (NOTEs) from the first and second quarters (Q1 and Q2) of fiscal year (FY) 2021. For this report, assessors used three primary methods for categorizing occurrence and NOTE data: issue categorization, DOE reporting criteria groups, and DOE cause codes.
Efforts at Sandia National Laboratories have focused on fundamental experiments to understand the dispersal of dense particle distributions in high-speed compressible flow. The experiments are conducted in shock tube facilities where the flow conditions and the initial conditions of the particle distributions are well controlled and well characterized. An additional advantage of the shock tube is that it is more readily able to accommodate advanced measurement diagnostics in comparison to explosive field tests.
We present an adaptive algorithm for constructing surrogate models for integrated systems composed of a set of coupled components. With this goal we introduce ‘coupling’ variables with a priori unknown distributions that allow approximations of each component to be built independently. Once built, the surrogates of the components are combined and used to predict system-level quantities of interest (QoI) at a fraction of the cost of interrogating the full system model. We use a greedy experimental design procedure, based upon a modification of Multi-Index Stochastic Collocation (MISC), to minimize the error of the combined surrogate. This is achieved by refining each component surrogate in accordance with its relative contribution to error in the approximation of the system-level QoI. Our adaptation of MISC is a multi-fidelity procedure that can leverage ensembles of models of varying cost and accuracy, for one or more components, to produce estimates of system-level QoI. Several numerical examples demonstrate the efficacy of the proposed approach on systems involving feed-forward and feedback coupling. For a fixed computational budget, the proposed algorithm is able to produce approximations that are orders of magnitude more accurate than approximations that treat the integrated system as a black-box.
We present a surrogate modeling framework for conservatively estimating measures of risk from limited realizations of an expensive physical experiment or computational simulation. We adopt a probabilistic description of risk that assigns probabilities to consequences associated with an event and use risk measures, which combine objective evidence with the subjective values of decision makers, to quantify anticipated outcomes. Given a set of samples, we construct a surrogate model that produces estimates of risk measures that are always greater than their empirical estimates obtained from the training data. These surrogate models not only limit over-confidence in reliability and safety assessments, but produce estimates of risk measures that converge much faster to the true value than purely sample-based estimates. We first detail the construction of conservative surrogate models that can be tailored to the specific risk preferences of the stakeholder and then present an approach, based upon stochastic orders, for constructing surrogate models that are conservative with respect to families of risk measures. The surrogate models introduce a bias that allows them to conservatively estimate the target risk measures. We provide theoretical results that show that this bias decays at the same rate as the L2 error in the surrogate model. Our numerical examples confirm that risk-aware surrogate models do indeed over-estimate the target risk measures while converging at the expected rate.
Simulated previous quantum chemistry experiments using JAQAL (Just Another Quantum Assembly Language), matching expected results. Lays the groundwork for QSCOUT (Quantum Scientific Computing Open User Testbed) users to conduct their own quantum experiments, and tests the capabilities of the JAQAL language.
Vaidya, Sachin; Benalcazar, Wladimir A.; Cerjan, Alexander; Rechtsman, Mikael C.
We show that point defects in two-dimensional photonic crystals can support bound states in the continuum (BICs). The mechanism of confinement is a symmetry mismatch between the defect mode and the Bloch modes of the photonic crystal. These BICs occur in the absence of band gaps and therefore provide an alternative mechanism to confine light. Furthermore, we show that such BICs can propagate in a fiber geometry and exhibit arbitrarily small group velocity which could serve as a platform for enhancing nonlinear effects and light-matter interactions in structured fibers.
Potassium channels modulate various cellular functions through efficient and selective conduction of K+ions. The mechanism of ion conduction in potassium channels has recently emerged as a topic of debate. Crystal structures of potassium channels show four K+ions bound to adjacent binding sites in the selectivity filter, while chemical intuition and molecular modeling suggest that the direct ion contacts are unstable. Molecular dynamics (MD) simulations have been instrumental in the study of conduction and gating mechanisms of ion channels. Based on MD simulations, two hypotheses have been proposed, in which the four-ion configuration is an artifact due to either averaged structures or low temperature in crystallographic experiments. The two hypotheses have been supported or challenged by different experiments. Here, MD simulations with polarizable force fields validated byab initiocalculations were used to investigate the ion binding thermodynamics. Contrary to previous beliefs, the four-ion configuration was predicted to be thermodynamically stable after accounting for the complex electrostatic interactions and dielectric screening. Polarization plays a critical role in the thermodynamic stabilities. As a result, the ion conduction likely operates through a simple single-vacancy and water-free mechanism. The simulations explained crystal structures, ion binding experiments and recent controversial mutagenesis experiments. This work provides a clear view of the mechanism underlying the efficient ion conduction and demonstrates the importance of polarization in ion channel simulations.
We present a theoretical framework for designing and assessing the performance of algorithms executing in networks consisting of spiking artificial neurons. Although spiking neural networks (SNNs) are capable of general-purpose computation, few algorithmic results with rigorous asymptotic performance analysis are known. SNNs are exceptionally well-motivated practically, as neuromorphic computing systems with 100 million spiking neurons are available, and systems with a billion neurons are anticipated in the next few years. Beyond massive parallelism and scalability, neuromorphic computing systems offer energy consumption orders of magnitude lower than conventional high-performance computing systems. We employ our framework to design and analyze neuromorphic graph algorithms, focusing on shortest path problems. Our neuromorphic algorithms are message-passing algorithms relying critically on data movement for computation, and we develop data-movement lower bounds for conventional algorithms. A fair and rigorous comparison with conventional algorithms and architectures is challenging but paramount. We prove a polynomial-factor advantage even when we assume an SNN consisting of a simple grid-like network of neurons. To the best of our knowledge, this is one of the first examples of a provable asymptotic computational advantage for neuromorphic computing.
Actinide oxalates are chemical compounds important to nuclear industry, ranging from actinide separation in waste reprocessing, to production of specialty actinides, and to disposal of high level nuclear waste (HLW) and spent nuclear fuel (SNF). In this study, the solubility constants for Pr2(C2O4)3·10H2O and Nd2(C2O4)3·10H2O by performing solubility experiments in HNO3 and mixtures of HNO3 and H2C2O4 at 23.0 ± 0.2 °C have been determined. The targeted starting materials, Pr2(C2O4)3·10H2O and Nd2(C2O4)3·10H2O, were successfully synthesized at room temperature using PrCl3, NdCl3 and oxalic acid as the source metrials. Then, we utilized the targeted solubility-controlling phases to conduct solubility measurements. There was no phase change over the entire periods of experiments, demonstrating that Pr2(C2O4)3·10H2O and Nd2(C2O4)3·10H2O were the solubility-controlling phases in our respective experiments. Based on our experimental data, we have developed a thermodynamic model for Pr2(C2O4)3·10H2O and Nd2(C2O4)3·10H2O in the mixtures of HNO3 and H2C2O4 to high ionic strengths. The model for Pr2(C2O4)3·10H2O reproduces well the reported experimental data for Pu2(C2O4)3·10H2O, which are not utilized for the model development, demonstrating that Pr(III) is an excellent analog for Pu(III). Similarly, the model for Nd2(C2O4)3·10H2O reproduces the solubility of Am2(C2O4)3·10H2O and Cm2(C2O4)3·10H2O. The Pitzer model was used for the calculation of activity coefficients. Based on the published, well established model for dissociation constants for oxalic acid and stability constants for actinide-oxalate complexes [i.e., AmC2O4+, and Am(C2O4)2−] to high ionic strengths, we have obtained the solubility constants (log10K0) for the following reactions at 25 °C,Pr2(C2O4)3·10H2O ⇌ 2Pr3+ + 3C2O42− + 10H2O(l)Nd2(C2O4)3·10H2O ⇌ 2Nd3+ + 3C2O42− + 10H2O(l) to be −30.82 ± 0.30 (2σ), and −31.14 ± 0.35 (2σ), respectively. These values can be directly applied to Pu2(C2O4)3·10H2O, Am2(C2O4)3·10H2O and Cm2(C2O4)3·10H2O. The model established for actinide oxalates by this study provides the needed knowledge with regard to solubilities of actinide/REE oxalates at various ionic strengths, and is expected to find applications in many fields, including the geological disposal of nuclear waste and the mobility of REE under the surface conditions, as Pr2(C2O4)3·10H2O and Nd2(C2O4)3·10H2O can be regarded as the pure Pr and Nd end-members of deveroite, a recently discovered natural REE oxalate with the following stoichiometry, (Ce1.01Nd0.33La0.32Pr0.11Y0.11Sm0.01Pb0.04U0.03Th0.01Ca0.04)2.01(C2O4)2.99·9.99H2O. Regarding its importance in the geological disposal of nuclear waste, Am2(C2O4)3·10H2O/Pu2(C2O4)3·10H2O/Cm2(C2O4)3·10H2O can be the source-term phase for actinides, as demonstrated by the instance in the disposal in clay/shale formations. This is exemplified by the stability of Am2(C2O4)3·10H2O in comparison with Am(OH)3(am), Am(OH)3(s) and AmCO3(OH)(s) under the relevant geological repository conditions.
Understanding the impact of high-energy electron radiation on device characteristics remains critical for the expanding use of semiconductor electronics in space-borne applications and other radiation harsh environments. Here, we report on in situ measurements of high-energy electron radiation effects on the hole diffusion length in low threading dislocation density homoepitaxial bulk n-GaN Schottky diodes using electron beam induced current (EBIC) in high-voltage scanning electron microscopy mode. Despite the large interaction volume in this system, quantitative EBIC imaging is possible due to the sustained collimation of the incident electron beam. This approach enables direct measurement of electron radiation effects without having to thin the specimen. Using a combination of experimental EBIC measurements and Monte Carlo simulations of electron trajectories, we determine a hole diffusion length of 264 ± 11 nm for n-GaN. Irradiation with 200 kV electron beam with an accumulated dose of 24 × 1016 electrons cm−2 led to an approximate 35% decrease in the minority carrier diffusion length.
Dos Santos, Gonzalo; Meyer, Robert; Aparicio, Romina; Tranchida, Julien; Bringa, Eduardo M.; Urbassek, Herbert M.
Magnetization of clusters is often simulated using atomistic spin dynamics for a fixed lattice. Coupled spin-lattice dynamics simulations of the magnetization of nanoparticles have, to date, neglected the change in the size of the atomic magnetic moments near surfaces. We show that the introduction of variable magnetic moments leads to a better description of experimental data for the magnetization of small Fe nanoparticles. To this end, we divide atoms into a surface-near shell and a core with bulk properties. It is demonstrated that both the magnitude of the shell magnetic moment and the exchange interactions need to be modified to obtain a fair representation of the experimental data. This allows for a reasonable description of the average magnetic moment vs cluster size, and also the cluster magnetization vs temperature.
In the face of increasing natural disasters and an aging grid, utilities need to optimally choose investments to the existing infrastructure to promote resiliency. This paper presents a new investment decision optimization model to minimize unserved load over the recovery time and improve grid resilience to extreme weather event scenarios. Our optimization model includes a network power flow model which decides generator status and generator dispatch, optimal transmission switching (OTS) during the multi-time period recovery process, and an investment decision model subject to a given budget. Investment decisions include the hardening of transmission lines, generators, and substations. Our model uses a second order cone programming (SOCP) relaxation of the AC power flow model and is compared to the classic DC power flow approximation. A case study is provided on the 73-bus RTS-GMLC test system for various investment budgets and multiple hurricane scenarios to highlight the difference in optimal investment decisions between the SOCP model and the DC model, and demonstrate the advantages of OTS in resiliency settings. Results indicate that the network models yield different optimal investments, unit commitment, and OTS decisions, and an AC feasibility study indicates our SOCP resiliency model is more accurate than the DC model.
International Journal of High Performance Computing Applications
Benacchio, Tommaso; Bonaventura, Luca; Altenbernd, Mirco; Cantwell, Chris D.; Duben, Peter D.; Gillard, Mike; Giraud, Luc; Goddeke, Dominik; Raffin, Erwan; Teranishi, Keita; Wedi, Nils
Progress in numerical weather and climate prediction accuracy greatly depends on the growth of the available computing power. As the number of cores in top computing facilities pushes into the millions, increased average frequency of hardware and software failures forces users to review their algorithms and systems in order to protect simulations from breakdown. This report surveys hardware, application-level and algorithm-level resilience approaches of particular relevance to time-critical numerical weather and climate prediction systems. A selection of applicable existing strategies is analysed, featuring interpolation-restart and compressed checkpointing for the numerical schemes, in-memory checkpointing, user-level failure mitigation and backup-based methods for the systems. Numerical examples showcase the performance of the techniques in addressing faults, with particular emphasis on iterative solvers for linear systems, a staple of atmospheric fluid flow solvers. The potential impact of these strategies is discussed in relation to current development of numerical weather prediction algorithms and systems towards the exascale. Trade-offs between performance, efficiency and effectiveness of resiliency strategies are analysed and some recommendations outlined for future developments.
An integrated modeling approach has been developed to better understand the relative impacts of different expiratory and environmental factors on airborne pathogen transport and transmission, motivated by the recent COVID-19 pandemic. Computational fluid dynamics (CFD) modeling was used to simulate spatial-temporal aerosol concentrations and quantified risks of exposure as a function of separation distance, exposure duration, environmental conditions (e.g., airflow/ventilation), and face coverings. The CFD results were combined with infectivity models to determine probability of infection, which is a function of the spatial-temporal aerosol concentrations, viral load, infectivity rate, viral viability, lung-deposition probability, and inhalation rate. Uncertainty distributions were determined for these parameters from the literature. Probabilistic analyses were performed to determine cumulative distributions of infection probabilities and to determine the most important parameters impacting transmission. This modeling approach has relevance to both pathogen and pollutant dispersion from expelled aerosol plumes.
Glass–ceramics have received recent attention for use in glass–ceramic to metal hermetic seals. Due to their heterogeneous microstructure, these materials exhibit a number of advantageous responses over conventional glass based seals. Key amongst them is the possibility of a controllable thermal strain response and apparent coefficient of thermal expansion which may be used to minimize thermally induced residual stresses for aforementioned seals. These behaviors result from an inorganic glass matrix and variety of crystalline ceramic phases including silica polymorph(s) that may undergo reversible solid-to-solid transformations with associated inelastic strain. Correspondingly, these materials exhibit complex thermomechanical responses associated with multiple inelastic mechanisms (viscoelasticity and phase transformation). While modeling these behaviors is essential for developing and analyzing the corresponding applications, no such model exists. Therefore, in this work a three-dimensional continuum constitutive model for glass–ceramic materials combining these various inelastic mechanisms is developed via an internal state variable approach. A corresponding fully implicit three dimensional numerical formulation is also proposed and implemented. The model is used to simulate existing experiments and validate the proposed formalism. As an example, the simple seal problem of a glass–ceramic seal inside a concentric metal shell is explored. Finally, the impact of the cooling rates, viscoelastic shift factors, and inelastic strain on final residual stress state are all investigated and the differing contributions highlighted.
Direct Numerical Simulations (DNS) are performed to investigate the process of spontaneous ignition of hydrogen flames at laminar, turbulent, adiabatic and non-adiabatic conditions. Mixtures of hydrogen and vitiated air at temperatures representing gas-turbine reheat combustion are considered. Adiabatic spontaneous ignition processes are investigated first, providing a quantitative characterization of stable and unstable flames. Results indicate that, in hydrogen reheat combustion, compressibility effects play a key role in flame stability and that unstable ignition and combustion are consistently encountered for reactant temperatures close to the mixture's characteristic crossover temperature. Furthermore, it is also found that the characterization of the adiabatic processes is also valid in the presence of non-adiabaticity due to wall heat-loss. Finally, a quantitative characterization of the instantaneous fuel consumption rate within the reaction front is obtained and of its ability, at auto-ignitive conditions, to advance against the approaching turbulent flow of the reactants, for a range of different turbulence intensities, temperatures and pressure levels.
Nagasaka, Cocoro A.; Kozma, Karoly; Russo, Chris J.; Alam, Todd M.; Nyman, May
Removal of radioactive Cs from sodium-rich solutions is a technical challenge that goes back to post World War II nuclear waste storage and treatment; and interest in this topic was reinvigorated by the Fukushima-Daiichi nuclear power plant disaster, 10 years ago. Since the 1960′s there has been considerable focus on layered Zr phosphates as robust inorganic sorbents for separation of radionuclides such as Cs. Here we present synthesis and characterization, and direct comparison of Cs sorption capacity and selectivity of four related materials: 1) crystalline α-Zr phosphate and α-Hf phosphate, and 2) amorphous analogues of these. Powder X-ray diffraction, thermogravimetry, solid-state 31P magic angle spinning nuclear magnetic resonance (MAS-NMR) spectroscopy, and compositional analysis (inductively coupled plasma optical emission spectroscopy and mass spectroscopy, ICP OES and ICP MS) provided formulae; respectively M(HPO4)2⋅1H2O and M(HPO4)2⋅4H2O (M = Hf, Zr) for crystalline and amorphous analogues. Maximum Cs loading, competitive Cs-Na selectivity and maximum Cs-Na loading followed by the above characterizations plus 133Cs MAS-NMR spectroscopy revealed that amorphous analogues are considerably better Cs-sorbents (based on maximum Cs-loading and selectivity over Na) than the well-studied crystalline Zr-analogue. Additionally, crystalline α-Hf phosphate is better Cs-sorbent than crystalline α-Zr phosphate. All these studies consistently show that Hf phosphate is less crystallize than Zr phosphate, when obtained under similar or identical synthesis conditions. We attribute this to lower solubility of Hf phosphate compared to Zr phosphate, preventing ‘defect healing’ during the synthesis process.
Mechanical impact protection is an important consideration in many applications, ranging from product transportation to sports. Cellular materials are typically used due to their desirable energy absorption properties and light weight. However, their large deformation and rate dependent responses (especially of polymer foams) are challenging to consider in design. Additionally, the use of foams with uniform properties, such as uniform density and uniform stiffness, often restricts the designed foams to only be suitable for a narrow range of mechanical impact conditions whereas real applications commonly face unpredictable situations. 3D printing offers fabrication flexibility and thus opens the door to create foams with tailored properties. In this work, we investigate the feasibility of using 3D printing for functionally graded foams (FGFs) that are optimal over a broad range of mechanical environments. The foams are fabricated by the recently developed grayscale digital light processing (g-DLP) method which can print parts with locally designed properties. These foams are tested under drop test conditions and with slower displacement control. We also model the large deformation behavior of FGFs using finite element analysis in which we account for the different viscoelastic behaviors of the distinct grayscale regions. We then use the model to examine the impact mitigation capabilities of FGFs in different loading scenarios. Finally, we show how FGFs can be used to satisfy real-world design goals using the case study of a motorcycle helmet. In contrast to prior work, we investigate continuous, functionally graded foams of a single density that differ in their viscoelastic responses. This work provides further insight into the benefits of viscoelastic properties and modulus graded foams and presents a manufacturing approach that can be used to produce the next generation of flexible lattice foams as mechanical absorbers.
Surging interest in engineering quantum computers has stimulated significant and focused research on technologies needed to make them manufacturable and scalable. In the ion trap realm this has led to a transition from bulk three-dimensional macro-scale traps to chip-based ion traps and included important demonstrations of passive and active electronics, waveguides, detectors, and other integrated components. At the same time as these technologies are being developed the system sizes are demanding more ions to run noisy intermediate scale quantum (NISQ) algorithms, growing from around ten ions today to potentially a hundred or more in the near future. To realize the size and features needed for this growth, the geometric and material design space of microfabricated ion traps must expand. In this paper we describe present limitations and the approaches needed to overcome them, including how geometric complexity drives the number of metal levels, why routing congestion affects the size and location of shunting capacitors, and how RF power dissipation can limit the size of the trap array. We also give recommendations for future research needed to accommodate the demands of NISQ scale ion traps that are integrated with additional technologies.
We present the technology-aided computer design (TCAD) device simulation and modeling of a silicon p-i-n diode for detecting time-dependent X-ray radiation. We show that the simulated forward and reverse breakdown current-voltage characteristics agree well with the measured data under nonradiation environment by only calibrating carrier lifetimes for the forward bias case and avalanche model critical fields for the reverse bias condition. Using the calibrated parameters and other nominal material properties, we simulated the radiation responses of the p-i-n diode and compared with experimental data when the diode was exposed to X-ray radiation at Sandia's Saturn facility and the Idaho State University (ISU) TriMeV facility. For Saturn's Gaussian dose-rate pulses, we show three findings from TCAD simulations. First, the simulated photocurrents are in excellent agreement with the measured data for two dose-rate pulses with peak values of 1.16 times 10 -{10} and 1.88 times 10 -{10} rad(Si)/s. Second, the simulation results of high dose-rate pulses predict increased delayed photocurrents with longer time tails in the diode electrical responses due to excess carrier generation. Third, simulated peak values of diode radiation responses versus peak dose rates at different bias conditions provide useful guidance to determine the dose-rate range that the p-i-n diode can reliably detect in experiment. For TriMeV's non-Gaussian dose-rate pulse, our simulated diode response is in decent agreement with the measured data without further calibration. We also studied the effects of device geometry, recombination process, and dose-rate enhancement via TCAD simulations to understand the higher measured response in the time after the peak dose-rate radiation for the p-i-n diode exposed to TriMeV irradiation.
We present a combined molecular dynamics (MD) simulation and X-ray absorption fine structure (XAFS) spectroscopic investigation of aqueous iron adsorption on nanoconfined amorphous silica surfaces. The simulation models examine the effects of pore size, pH (surface charge), iron valency, and counter-ion (chloride or hydroxide). The simulation methods were validated by comparing the coordination environment of adsorbed iron with coordination numbers and bond lengths derived from XAFS. In the MD models, nanoconfinement effects on local iron coordination were investigated by comparing results for unconfined silica surfaces and in confined domains within 2 nm, 4 nm, and 8 nm pores. Experimentally, coordination environments of iron adsorbed onto mesoporous silica with 4 nm and 8 nm pores at pH 7.5 were investigated. The effect of pH in the MD models was included by simulating Fe(ii) adsorption onto negatively charged SiO2surfaces and Fe(iii) adsorption on neutral surfaces. The simulation results show that iron adsorption depends significantly on silica surface charge, as expected based on electrostatic interactions. Adsorption on a negatively charged surface is an order of magnitude greater than on the neutral surface, and simulated surface coverages are consistent with experimental results. Pore size effects from the MD simulations were most notable in the adsorption of Fe(ii) at deprotonated surface sites (SiO−), but adsorption trends varied with concentration and aqueous Fe speciation. The coordination environment of adsorbed iron varied significantly with the type of anion. Considerable ion pairing with hydroxide anions led to the formation of oligomeric surface complexes and aqueous species, resulting in larger iron hydroxide clusters at higher surface loadings.
Two events of magnitude (mb) 3.6-3.8 occurred in southern North Korea (NK) on 27 June 2019 and 11 May 2020. Although these events were located ~330-400 km from the known nuclear test site, the fact that they occurred within the territory of NK, a country with a recent history of underground nuclear tests, made them events of interest for the monitoring community. Weused P/Lg ratios from regional stations to categorize seismic events that occurred in NK from 2006 to May 2020, including these two recent events, the six declared NK nuclear tests, and the cavity collapse and triggered earthquakes that followed the 3 September 2017 nuclear explosion. We were able to separate the cavity collapse from the population of nuclear explosions. However, based on P/Lg ratios, the distinction between the earthquakes and the cavity collapse is ambiguous. The performed discriminant analyses suggest that combining Pg/Lg and Pn/Lg ratios results in improved discriminant power compared with any of the ratio types alone. We used the two ratio types jointly in a quadratic discriminant function and successfully classified the six declared nuclear tests and the triggered earthquakes that followed the September 2017 explosion. Our analyses also confirm that the recent southern events of June 2019 and May 2020 are both tectonic earthquakes that occurred naturally.
Despite their wide use in terahertz (THz) research and technology, the application spectra of photoconductive antenna (PCA) THz detectors are severely limited due to the relatively high optical gating power requirement. This originates from poor conversion efficiency of optical gate beam photons to photocurrent in materials with subpicosecond carrier lifetimes. Here we show that using an ultra-thin (160 nm), perfectly absorbing low-temperature grown GaAs metasurface as the photoconductive channel drastically improves the efficiency of THz PCA detectors. This is achieved through perfect absorption of the gate beam in a significantly reduced photoconductive volume, enabled by the metasurface. This Letter demonstrates that sensitive THz PCA detection is possible using optical gate powers as low as 5 μW-three orders of magnitude lower than gating powers used for conventionalPCAdetectors.We show that significantly higher optical gate powers are not necessary for optimal operation, as they do not improve the sensitivity to the THz field. This class of efficient PCA THz detectors opens doors for THz applications with low gate power requirements.
Advanced GaN power devices are promising for many applications in high power electronics but performance limitations due to material quality in etched-and-regrown junctions prevent their widespread use. Carrier diffusion length is a critical parameter that not only determines device performance but is also a diagnostic of material quality. Here we present the use of electron-beam induced current to measure carrier diffusion lengths in continuously grown and etched-and-regrown GaN pin diodes as models for interfaces in more complex devices. Variations in the quality of the etched-and-regrown junctions are observed and shown to be due to the degradation of the n-type material. We observe an etched-and-regrown junction with properties comparable to a continuously grown junction.
Non-uniqueness in groundwater model calibration is a primary source of uncertainty in groundwater flow and transport predictions. In this study, we investigate the ability of environmental tracer information to constrain groundwater model parameters. We utilize a pilot point calibration procedure conditioned to subsets of observed data including: liquid pressures, tritium (3H), chlorofluorocarbon-12 (CFC-12), and sulfur hexafluoride (SF6) concentrations; and groundwater apparent ages inferred from these environmental tracers, to quantify uncertainties in the heterogeneous permeability fields and infiltration rates of a steady-state 2-D synthetic aquifer and a transient 3-D model of a field site located near Riverton, Wyoming (USA). To identify the relative data worth of each observation data type, the post-calibration uncertainties of the optimal parameters for a given observation subset are compared to that from the full observation data set. Our results suggest that the calibration-constrained permeability field uncertainties are largest when liquid pressures are used as the sole calibration data set. We find significant reduction in permeability uncertainty and increased predictive accuracy when the environmental tracer concentrations, rather than apparent groundwater ages, are used as calibration targets in the synthetic model. Calibration of the Riverton field site model using environmental tracer concentrations directly produces infiltration rate estimates with the lowest uncertainties, however; permeability field uncertainties remain similar between the environmental tracer concentration and apparent groundwater age calibration scenarios. This work provides insight on the data worth of environmental tracer information to calibrate groundwater models and highlights potential benefits of directly assimilating environmental tracer concentrations into model parameter estimation procedures.
In this paper we investigate the utility of one-dimensional convolutional neural network (CNN) models in epidemiological forecasting. Deep learning models, in particular variants of recurrent neural networks (RNNs) have been studied for ILI (Influenza-Like Illness) forecasting, and have achieved a higher forecasting skill compared to conventional models such as ARIMA. In this study, we adapt two neural networks that employ one-dimensional temporal convolutional layers as a primary building block—temporal convolutional networks and simple neural attentive meta-learners—for epidemiological forecasting. We then test them with influenza data from the US collected over 2010-2019. We find that epidemiological forecasting with CNNs is feasible, and their forecasting skill is comparable to, and at times, superior to, plain RNNs. Thus CNNs and RNNs bring the power of nonlinear transformations to purely data-driven epidemiological models, a capability that heretofore has been limited to more elaborate mechanistic/compartmental disease models.
The Y-12 SIRP Quadrant 1, Quadrant 2, and Vehicle Barrier project involves the following summary elements. The perimeter intrusion detection and assessment system (PIDAS) is an existing system at the Y-12 National Security Complex, which is a government-owned facility located in Oak Ridge, Tennessee, and managed by Consolidated Nuclear Security, LLC (CNS) for the Department of Energy (DOE). National Technology and Engineering Solutions of Sandia, LLC (NTESS) is the engineering design agent and construction manager (CM) for the Y-12 SIRP effort. The Quadrants 1 and 2 portion of the project involves the replacement of the PIDAS, and the vehicle barrier portion of the project involves the installation of a continuous passive vehicle barrier alongside the inner PIDAS fence.
The costs associated with the increasing maintenance and surveillance needs of aging structures are rising at an unexpected rate. Multi-site fatigue damage, hidden cracks in hard-to-reach locations, disbonded joints, erosion, impact, and corrosion are among the major flaws encountered in today’s extensive fleet of aging aircraft and space vehicles. Aircraft maintenance and repairs represent about a quarter of a commercial fleet’s operating costs. The application of Structural Health Monitoring (SHM) systems using distributed sensor networks can reduce these costs by facilitating rapid and global assessments of structural integrity. The use of in-situ sensors for real-time health monitoring can overcome inspection impediments stemming from accessibility limitations, complex geometries, and the location and depth of hidden damage. Reliable, structural health monitoring systems can automatically process data, assess structural condition, and signal the need for human intervention. The ease of monitoring an entire on-board network of distributed sensors means that structural health assessments can occur more often, allowing operators to be even more vigilant with respect to flaw onset. SHM systems also allow for condition-based maintenance practices to be substituted for the current time-based or cycle-based maintenance approach thus optimizing maintenance labor. The Federal Aviation Administration has conducted a series of SHM validation and certification programs intended to comprehensively support the evolution and adoption of SHM practices into routine aircraft maintenance practices. This report presents one of those programs involving a Sandia Labs-aviation industry effort to move SHM into routine use for aircraft maintenance. The Airworthiness Assurance NDI Validation Center (AANC) at Sandia Labs, in conjunction with Sikorsky, Structural Monitoring Systems Ltd., Anodyne Electronics Manufacturing Corp., Acellent Technologies Inc., and the Federal Aviation Administration (FAA) carried out a trial validation and certification program to evaluate Comparative Vacuum Monitoring (CVM) and Piezoelectric Transducers (PZT) as a structural health monitoring solution to specific rotorcraft applications. Validation tasks were designed to address the SHM equipment, the health monitoring task, the resolution required, the sensor interrogation procedures, the conditions under which the monitoring will occur, the potential inspector population, adoption of CVM and PZT systems into rotorcraft maintenance programs and the document revisions necessary to allow for their routine use as an alternate means of performing periodic structural inspections. This program addressed formal SHM technology validation and certification issues so that the full spectrum of concerns, including design, deployment, performance and certification were appropriately considered. Sandia Labs designed, implemented, and analyzed the results from a focused and statistically relevant experimental effort to quantify the reliability of a CVM system applied to Sikorsky S-92 fuselage frame application and a PZT system applied to an S-92 main gearbox mount beam application. The applications included both local and global damage detection assessments. All factors that affect SHM sensitivity were included in this program: flaw size, shape, orientation and location relative to the sensors, as well as operational and environmental variables. Statistical methods were applied to performance data to derive Probability of Detection (POD) values for SHM sensors in a manner that agrees with current nondestructive inspection (NDI) validation requirements and is acceptable to both the aviation industry and regulatory bodies. The validation work completed in this program demonstrated the ability of both CVM and PZT SHM systems to detect cracks in rotorcraft components. It proved the ability to use final system response parameters to provide a Green Light/Red Light (“GO” – “NO GO”) decision on the presence of damage. In additional to quantifying the performance of each SHM system for the trial applications on the S-92 platform, this study also identified specific methods that can be used to optimize damage detection, guidance on deployment scenarios that can affect performance and considerations that must be made to properly apply CVM and PZT sensors. These results support the main goal of safely integrating SHM sensors into rotorcraft maintenance programs. Additional benefits from deploying rotorcraft Health and Usage Monitoring Systems (HUMS) may be realized when structural assessment data, collected by an SHM system, is also used to detect structural damage to compliment the operational environment monitoring. The use of in-situ sensors for health monitoring of rotorcraft structures can be a viable option for both flaw detection and maintenance planning activities. This formal SHM validation will allow aircraft manufacturers and airlines to confidently make informed decisions about the proper utilization of CVM and PZT technology. It will also streamline future regulatory actions and formal certification measures needed to assure the safe application of SHM solutions.
Accelerated growth of the additive manufacturing (AM) industry in recent years is accompanied by a rising need for methods to quickly assess quality at-scale. Current practices for quality inspection include nondestructive test methods and destructive testing of witness coupons, which are artifacts built alongside the actual part. However, these methods can be costly and time-consuming. Recognizing this need, the Additive Manufacturing Center of Excellence (AM CoE) initiated a project led by its partner, Auburn University, to develop rapid testing procedure using asbuilt samples tested in torsion to quantitatively assess build quality. The presented work developed a rapid testing procedure using as-built samples tested in torsion to quantify small variances for assessing build quality.
The software package developed by Sandia National Laboratories is intended to allow the integration of Simulink models into emulations of control networks. To accomplish this, three programs are included: Simulink S-Function, Data Broker, and End Point
Ignition and material response properties of aluminumized HMX heterogeneous explosive mixtures were explored in a series of planar impact experiments performed over multiple years. This work expands on previous work studying material response to impact in single-component HMX granular materials. The addition of nanometric aluminum is shown to affect the ignition sensitivity and growth to reaction from impact. The gas gun test results are presented here varying parameters of particle size, shock strength, and aluminum mass fraction.
The Primary Standards Lab employs guardbanding methods to reduce risk of false acceptance in calibration when test uncertainty ratios are low. Similarly, production agencies guardband their requirements to reduce false accept rates in product acceptance. The root-sum-square guardbanding method is recommended by PSL, but many other guardbanding methods have been proposed in literature or implemented in commercial software. This report analyzes the false accept and reject rates resulting from the most common guardbanding methods. It is shown that the root-sum-square method and the Dobbert Managed Guardband strategy are similar and both are suitable for calibration and product acceptance work in the NSE.
Foliage penetration (FOPEN) radar at lower frequencies (VHF, UHF) is a well-studied area with many contributions. However, there is growing interest in using higher Ku-band frequencies (12-18 GHz) for FOPEN. Specifically, the reduced wavelength sizes provide some key saliencies for developing more optimized detection solutions. The disadvantage is that exploiting Ku-band for FOPEN is complicated because higher frequencies have pronounced scattering effects due to their smaller wavelengths. A methodology h as been developed to model and simulate FOPEN problems that characterize the phenomenology of Ku-band electromagnetic ( EM ) wave transmissions through moderate foliage. The details of this research (i.e. the realistic tree models, simulation setup and results) are documented in multiple reports. The main focus of this report is to describe the preliminary validation and verification of Altair FEKO, the computational EM (CEM) software used for this research, as well as present a simplified symmetrical tree model and an introductory CAD tree model.
It used to think that is impossible to determine/measure electric field inside a physically isolated volume, especially inside an electrically shielded space, because a conventional electric-field sensor can only measure electric field at the location of the sensor, and when an electric-field source is screened by conductive materials, no leakage electric field can be detected. For first time, we experimentally demonstrated that electrically neutral particles, neutrons, can be used to measure/image electric field behind a physical barrier. This work enables a new measurement capability that can visualize electric-relevant properties inside a studied sample or detection target for scientific research and engineering applications.