The supercritical carbon dioxide (sCO2) Brayton cycle is a promising candidate for future nuclear reactors due to its ability to improve power cycle energy conversion efficiency. The sCO2 Brayton cycle can operate with an efficiency of 45-50% at operating temperatures of 550-700 C. One of the greatest hurdles currently faced by sCO2 Brayton cycles is the extreme corrosivity of sCO2. This affects the longevity of the power cycle and thus the levelized cost of electricity. Past studies have shown that sCO2 corrosion occurs through the formation of metal carbonates, oxide layers, and carburization, and alloys with Cr, Mo and Ni generally exhibit less corrosion. While stainless steels may offer sufficient corrosion resistance at the lower range of temperatures seen by the sCO2 Brayton cycles, more expensive alloys such as Inconel and Haynes are typically needed for the higher temperature regions. This study investigates the effects of corrosion on the Haynes 230 alloy, focusing on changes in the mechanical properties.
This paper explores the Systems Engineering structure, strategies and tools for real world scenarios involving work with accident response planning. A systems engineering approach must be taken by the technical teams to prepare for a successful response and design the technical systems in support of the operations. The scope of this project is focused on laying out the foundation of the systems engineering approach taken to help the teams develop an accident response strategy and identify new engineering designs in support of these operations for the black box systems. This Masters project involved several interdisciplinary teams & stakeholders. Identifying the proper tools to use was key to addressing the big picture needs of the multiple stakeholders. The integrated project work primarily took place over the course of eight weeks via integrated team meetings. Other work in support of this project was conducted off-line as needed by the project lead. Details on the prospective timeline, milestones, key dates and work scope can be referenced in this paper. Key systems engineering methodologies and tools used in support of this project included but is not limited to: Market Surveys and Interviews Project Charter Feasibility Study Swim Lane Diagram Knowledge Management Plan. A full suite of tools and the details regarding the application of these tools and results of this study is provided in this report.
Macrohomogeneous battery models are widely used to predict battery performance, necessarily relying on effective electrode properties, such as specific surface area, tortuosity, and electrical conductivity. While these properties are typically estimated using ideal effective medium theories, in practice they exhibit highly non-ideal behaviors arising from their complex mesostructures. In this paper, we computationally reconstruct electrodes from X-ray computed tomography of 16 nickel-manganese-cobalt-oxide electrodes, manufactured using various material recipes and calendering pressures. Due to imaging limitations, a synthetic conductive binder domain (CBD) consisting of binder and conductive carbon is added to the reconstructions using a binder bridge algorithm. Reconstructed particle surface areas are significantly smaller than standard approximations predicted, as the majority of the particle surface area is covered by CBD, affecting electrochemical reaction availability. Finite element effective property simulations are performed on 320 large electrode subdomains to analyze trends and heterogeneity across the electrodes. Significant anisotropy of up to 27% in tortuosity and 47% in effective conductivity is observed. Electrical conductivity increases up to 7.5× with particle lithiation. We compare the results to traditional Bruggeman approximations and offer improved alternatives for use in cellscale modeling, with Bruggeman exponents ranging from 1.62 to 1.72 rather than the theoretical value of 1.5. We also conclude that the CBD phase alone, rather than the entire solid phase, should be used to estimate effective electronic conductivity. This study provides insight into mesoscale transport phenomena and results in improved effective property approximations founded on realistic, image-based morphologies.
Since its introduction 25 years ago, the probe-fed U-slot patch antenna has remained popular. Recently, Characteristic Mode Analysis (CMA) revealed these devices are governed by Coupled Mode Theory (CMT). Although this principle is conceptually simple, achieving this understanding is only possible through a systematic analysis using CMA. This paper uses the U-slot patch to illustrate a general process for analyzing electrically small antennas using CMA with the software package FEKO.
Component coupling is a crucial part of climate models, such as DOE's E3SM (Caldwell et al., 2019). A common coupling strategy in climate models is for their components to exchange flux data from the previous time-step. This approach effectively performs a single step of an iterative solution method for the monolithic coupled system, which may lead to instabilities and loss of accuracy. In this paper we formulate an Interface-Flux-Recovery (IFR) coupling method which improves upon the conventional coupling techniques in climate models. IFR starts from a monolithic formulation of the coupled discrete problem and then uses a Schur complement to obtain an accurate approximation of the flux across the interface between the model components. This decouples the individual components and allows one to solve them independently by using schemes that are optimized for each component. To demonstrate the feasibility of the method, we apply IFR to a simplified ocean–atmosphere model for heat-exchange coupled through the so-called bulk condition, common in ocean–atmosphere systems. We then solve this model on matching and non-matching grids to estimate numerically the convergence rates of the IFR coupling scheme.
Functional variables are often used as predictors in regression problems. A commonly used parametric approach, called scalar-on-function regression, uses the L2 inner product to map functional predictors into scalar responses. This method can perform poorly when predictor functions contain undesired phase variability, causing phases to have disproportionately large influence on the response variable. One past solution has been to perform phase–amplitude separation (as a pre-processing step) and then use only the amplitudes in the regression model. Here we propose a more integrated approach, termed elastic functional regression model (EFRM), where phase-separation is performed inside the regression model, rather than as a pre-processing step. This approach generalizes the notion of phase in functional data, and is based on the norm-preserving time warping of predictors. Due to its invariance properties, this representation provides robustness to predictor phase variability and results in improved predictions of the response variable over traditional models. We demonstrate this framework using a number of datasets involving gait signals, NMR data, and stock market prices.
Kustas, Jessica K.; Hoffman, Jacob B.; Alonso, David; Reed, Julian H.; Gonsalves, Andrew E.; Oh, Junho; Hong, Sungmin; Jo, Kyoo D.; Dana, Catherine E.; Alleyne, Marianne; Cropek, Donald M.
Cicada wings exhibit several intriguing properties that arise from a combination of nanopillar structures and chemical constituents, including superhydrophobicity, as well as antimicrobial, antireflective, and self-cleaning functions. While the physical dimensions of the nanofeatures are relatively simple to characterize through microscopy, the chemicals that cover these pillars are more difficult to characterize due to the variety and complexity of the mixture. Here, we compared the extractable chemicals from the wing surfaces of two different cicada species using both gas chromatography time-of-flight mass spectrometry (GC-TOFMS) and two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS) platforms. Chemical extracts from Neotibicen pruinosus and Magicicada septendecim cicada wings were separated and analyzed. The GC × GC-TOFMS platform was able to isolate and identified roughly three times the number of constituents as the GC-TOFMS platform at a signal-to-noise ratio (SNR) ≥10.0 and spectral similarity ≥800. When comparing the two cicada species wing extracts, the two-dimensional platform was able to expose differences in the chemical composition that were undetectable by the one-dimensional technique. GC × GC-TOFMS revealed nearly four times the number of unique species-specific compounds as compared to the number identified by GC-TOFMS. Further, surface chemicals were identified that are likely xenobiotics and can pinpoint location and contamination from where the cicada was collected. While the advantages of GC × GC-TOFMS over GC-TOFMS have been documented in the past, our work presents a powerful biological application of GC × GC-TOFMS with promise to reveal both organism species-specific biomarkers while providing insight into the environmental conditions of individual organisms.
The study of thermal effects, both classical and quantum, at cryogenic temperatures requires the use of on-chip, local, high-sensitivity thermometry. Carbon-platinum composites fabricated using focused ion beam (FIB) assisted deposition form a granular structure which is shown in this study to be uniquely suited for this application. Carbon-platinum thermometers deposited using a 24 pA ion beam current have high sensitivities below 1 K, comparable to the best cryogenic thermometers. In addition, these thermometers can be accurately placed to within 10s of nanometers on the chip using a mask-free process. They also have a weak magnetic field dependence, < 3% change in resistance with applied magnetic fields from 0 to 8 T. Finally, these thermometers are integrable into a variety of nanoscale devices due to the existing wide spread use of FIB.
We demonstrate a Bayesian method for the “real-time” characterization and forecasting of partially observed COVID-19 epidemic. Characterization is the estimation of infection spread parameters using daily counts of symptomatic patients. The method is designed to help guide medical resource allocation in the early epoch of the outbreak. The estimation problem is posed as one of Bayesian inference and solved using a Markov chain Monte Carlo technique. The data used in this study was sourced before the arrival of the second wave of infection in July 2020. The proposed modeling approach, when applied at the country level, generally provides accurate forecasts at the regional, state and country level. The epidemiological model detected the flattening of the curve in California, after public health measures were instituted. The method also detected different disease dynamics when applied to specific regions of New Mexico.
X-ray phase contrast imaging (XPCI) is a nondestructive evaluation technique that enables high-contrast detection of low-attenuation materials that are largely transparent in traditional radiography. Extending a grating-based Talbot-Lau XPCI system to three-dimensional imaging with computed tomography (CT) imposes two motion requirements: the analyzer grating must translate transverse to the optical axis to capture image sets for XPCI reconstruction, and the sample must rotate to capture angular data for CT reconstruction. The acquisition algorithm choice determines the order of movement and positioning of the two stages. The choice of the image acquisition algorithm for XPCI CT is instrumental to collecting high fidelity data for reconstruction. We investigate how data acquisition influences XPCI CT by comparing two simple data acquisition algorithms and determine that capturing a full phase-stepping image set for a CT projection before rotating the sample results in higher quality data.
We present an effort to port the nonhydrostatic atmosphere dynamical core of the Energy Exascale Earth System Model (E3SM) to efficiently run on a variety of architectures, including conventional CPU, many-core CPU, and GPU. We specifically target cloud-resolving resolutions of 3 km and 1 km. To express on-node parallelism we use the C++ library Kokkos, which allows us to achieve a performance portable code in a largely architecture-independent way. Our C++ implementation is at least as fast as the original Fortran implementation on IBM Power9 and Intel Knights Landing processors, proving that the code refactor did not compromise the efficiency on CPU architectures. On the other hand, when using the GPUs, our implementation is able to achieve 0.97 Simulated Years Per Day, running on the full Summit supercomputer. To the best of our knowledge, this is the most achieved to date by any global atmosphere dynamical core running at such resolutions.
Ebrish, Mona A.; Anderson, Travis J.; Koehler, Andrew D.; Foster, Geoffrey M.; Gallagher, James C.; Kaplar, Robert K.; Gunning, Brendan P.; Hobart, Karl D.
GaN is a favorable martial for future efficient high voltage power switches. GaN has not dominated the power electronics market due to immature substrate, homoepitaxial growth, and immature processing technology. Understanding the impact of the substrate and homoepitaxial growth on the device performance is crucial for boosting the performance of GaN. In this work, we studied vertical GaN PiN diodes that were fabricated on non-homogenous Hydride Vapor Phase Epitaxy (HVPE) substrates from two different vendors. We show that defects which stemmed from growth techniques manifest themselves as leakage hubs. Different non-homogenous substrates showed different distribution of those defects spatially with the lesser quality substrates clustering those defects in clusters that causes pre-mature breakdown. Energetically these defects are mostly mid-gap around 1.8Ev with light emission spans from 450nm to 700nm. Photon emission spectrometry and hyperspectral electroluminescence were used to locate these defects spatially and energetically.
In shale gas production, gas composition may vary over time. To understand this phenomenon, we use molecular dynamics simulations to study the permeation of CH4, C2H6 and their mixture from a source container through a pyrophyllite nanopore driven by a pressure gradient. For a pure gas, the flow rate of CH4 is always higher than that of C2H6, regardless of pore size. For a 1:1 C2H6: CH4 mixture, however, C2H6:CH4 flow rate ratio is higher than the compositional ratio in the container (i.e., 1:1) when the pore size is smaller than ~1.8 nm. The selective transport is caused by the competitive adsorption of C2H6 over CH4 in the nanopore. The selectivity is also determined by the interplay between the surface diffusion of the adsorbed molecules and the viscous flow in the center of the pore, and it diminishes as the viscous flow becomes to dominate the surface diffusion when the pore size becomes larger than 1.8 nm. Our work shows that compositional differentiation of shale gas in production is a consequence of nanopore confinement and therefore a key characteristic of an unconventional reservoir. The related compositional information can potentially be used for monitoring the status of a production well such as its recovery rate.
Digital computing is nearing its physical limits as computing needs and energy consumption rapidly increase. Analogue-memory-based neuromorphic computing can be orders of magnitude more energy efficient at data-intensive tasks like deep neural networks, but has been limited by the inaccurate and unpredictable switching of analogue resistive memory. Filamentary resistive random access memory (RRAM) suffers from stochastic switching due to the random kinetic motion of discrete defects in the nanometer-sized filament. In this work, this stochasticity is overcome by incorporating a solid electrolyte interlayer, in this case, yttria-stabilized zirconia (YSZ), toward eliminating filaments. Filament-free, bulk-RRAM cells instead store analogue states using the bulk point defect concentration, yielding predictable switching because the statistical ensemble behavior of oxygen vacancy defects is deterministic even when individual defects are stochastic. Both experiments and modeling show bulk-RRAM devices using TiO2-X switching layers and YSZ electrolytes yield deterministic and linear analogue switching for efficient inference and training. Bulk-RRAM solves many outstanding issues with memristor unpredictability that have inhibited commercialization, and can, therefore, enable unprecedented new applications for energy-efficient neuromorphic computing. Beyond RRAM, this work shows how harnessing bulk point defects in ionic materials can be used to engineer deterministic nanoelectronic materials and devices.
Proceedings of INDIS 2020: Innovating the Network for Data-Intensive Science, Held in conjunction with SC 2020: The International Conference for High Performance Computing, Networking, Storage and Analysis
Priority-based Flow Control (PFC), RDMA over Converged Ethernet (RoCE) and Enhanced Transmission Selection (ETS) are three enhancements to Ethernet networks which allow increased performance and may make Ethernet attractive for systems supporting a diverse scientific workload. We constructed a 96-node testbed cluster with a 100 Gb/s Ethernet network configured as a tapered fat tree. Tests representing important network operating conditions were completed and we provide an analysis of these performance results. RoCE running over a PFC-enabled network was found to significantly increase performance for both bandwidth-sensitive and latency-sensitive applications when compared to TCP. Additionally, a case study of interfering applications showed that ETS can prevent starvation of network traffic for latency-sensitive applications running on congested networks. We did not encounter any notable performance limitations for our Ethernet testbed, but we found that practical disadvantages still tip the balance towards traditional HPC networks unless a system design is driven by additional external requirements.
Potential performance gains from optimal (non-causal) impedance-matching control of wave energy devices in irregular ocean waves are dependent on deterministic wave elevation prediction techniques that work well in practical applications. Although a number of devices are designed for operation in intermediate water depths, little work has been reported on deterministic wave prediction in such depths. Investigated in this paper is a deterministic wave-prediction technique based on an approximate propagation model that leads to an analytical formulation, which may be convenient to implement in practice. To improve accuracy, an approach to combine predictions based on multiple up-wave measurement points is evaluated. The overall method is tested using experimental time-series measurements recorded in the U.S. Navy MASK basin in Carderock, MD, USA. For comparison, an alternative prediction approach based on Fourier coefficients is also tested with the same data. Comparison of prediction approaches with direct measurements suggest room for improvement. Possible sources of error including tank reflections are estimated, and potential mitigation approaches are discussed.
Proceedings of PMBS 2020: Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems, Held in conjunction with SC 2020: The International Conference for High Performance Computing, Networking, Storage and Analysis
Dominguez-Trujillo, Jered; Haskins, Keira; Khouzani, Soheila J.; Leap, Christopher; Tashakkori, Sahba; Wofford, Quincy; Estrada, Trilce; Bridges, Patrick G.; Widener, Patrick W.
Performance variation deriving from hardware and software sources is common in modern scientific and data-intensive computing systems, and synchronization in parallel and distributed programs often exacerbates their impacts at scale. The decentralized and emergent effects of such variation are, unfortunately, also difficult to systematically measure, analyze, and predict; modeling assumptions which are stringent enough to make analysis tractable frequently cannot be guaranteed at meaningful application scales, and longitudinal methods at such scales can require the capture and manipulation of impractically large amounts of data. This paper describes a new, scalable, and statistically robust approach for effective modeling, measurement, and analysis of large-scale performance variation in HPC systems. Our approach avoids the need to reason about complex distributions of runtimes among large numbers of individual application processes by focusing instead on the maximum length of distributed workload intervals. We describe this approach and its implementation in MPI which makes it applicable to a diverse set of HPC workloads. We also present evaluations of these techniques for quantifying and predicting performance variation carried out on large-scale computing systems, and discuss the strengths and limitations of the underlying modeling assumptions.
Arm processors have been explored in HPC for several years, however there has not yet been a demonstration of viability for supporting large-scale production workloads. In this paper, we offer a retrospective on the process of bringing up Astra, the first Petascale supercomputer based on 64-bit Arm processors, and validating its ability to run production HPC applications. Through this process several immature technology gaps were addressed, including software stack enablement, Linux bugs at scale, thermal management issues, power management capabilities, and advanced container support. From this experience, several lessons learned are formulated that contributed to the successful deployment of Astra. These insights can be helpful to accelerate deploying and maturing other first-seen HPC technologies. With Astra now supporting many users running a diverse set of production applications at multi-thousand node scales, we believe this constitutes strong supporting evidence that Arm is a viable technology for even the largest-scale supercomputer deployments.
Understanding microstructural and strain evolutions induced by noble gas production in the nuclear fuel matrix or plasma-facing materials is crucial for designing next generation nuclear reactors, as they are responsible for volumetric swelling and catastrophic failure. We describe a multimodal approach combining synchrotron-based nanoscale X-ray imaging techniques with atomic-scale electron microscopy techniques for mapping chemical composition, morphology and lattice distortion in a single crystal W induced by Kr irradiation. We report that Kr-irradiated single crystal W undergoes surface deformation, forming Kr containing cavities. Furthermore, positive strain fields are observed in Kr-irradiated regions, which lead to compression of underlying W matrix.
Leung, L.R.; Bader, David C.; Taylor, Mark A.; Mccoy, Renata B.
Supported by the U.S. Department of Energy (DOE), the Energy Exascale Earth System Model (E3SM) project aims to optimize the use of DOE resources to address the grand challenge of actionable predictions of Earth system variability and change. This requires sustained advancement to (1) integrate model development with leading-edge computational advances toward ultra-high-resolution modeling; (2) represent the coupled human-Earth system to address energy sector vulnerability to variability and change; and (3) address uncertainty in model simulations and projections. Scientific development of the E3SM modeling system is driven by the simulation requirements in three overarching science areas centering on understanding the Earth's water cycle, biogeochemistry, and cryosphere systems and their future changes. This paper serves as an introduction to the E3SM special collection, which includes 50 papers published in several AGU journals. It provides an overview of the E3SM project, including its goals and science drivers. It also provides a brief history of the development of E3SM version 1 and highlights some key findings from papers included in the special collection.
Work performed under this one-year LDRD was concerned with estimating resource requirements for small quantum test beds that are expected to be available in the near future. This work represents a preliminary demonstration of our ability to leverage quantum hardware for solving small quantum simulation problems in areas of interest to the DOE. The algorithms enabling such studies are hybrid quantum-classical variational algorithms, in particular the widely-used variational quantum eigensolver (VQE). Employing this hybrid algorithm, in which the quantum computer complements the classical one, we implemented an end-to-end application-level toolchain that allows the user to specify a molecule of interest and compute the ground state energy using the VQE approach. We found significant limitations attributable to the classical portion of the hybrid system, including a greater than greater-than-quartic power scaling of the classical memory requirements with the system size. Current VQE approaches would require an exascale machine for solving any molecule with size greater than 150 nuclei. Our findings include several improvements that we implemented into the VQE toolchain, including a new classical optimizer that is decades old but hadn't been considered before in the VQE ecosystem. Our findings suggest limitations to variational hybrid approaches to simulation that further motivate the need for a gate-based fault-tolerant quantum processor that can implement larger problems using the fully digital quantum phase estimation algorithm.
An organic glass scintillator developed by Sandia National Laboratories was characterized in terms of its light output and pulse shape discrimination (PSD) properties and compared to commercial liquid (EJ-309) and plastic (EJ-276) organic scintillators. The electron light output was determined through relative comparison of the 137Cs Compton edge location. The proton light yield was measured using a double time-of-flight technique at the 88-Inch Cyclotron at Lawrence Berkeley National Laboratory. Using a tunable broad-spectrum neutron source and an array of pulse-shape-discriminating observation scintillators, a continuous measurement of the proton light yield was performed for EJ-309 (200 keV–3.2 MeV), EJ-276 (170 keV–4.9 MeV), and the organic glass (50 keV–20 MeV). Finally, the PSD properties of the organic glass, EJ-309, and EJ-276 were evaluated using an AmBe source and compared via a figure-of-merit metric. The organic glass exhibited a higher electron light output than both EJ-309 and EJ-276. Its proton light yield and PSD performance were comparable to EJ-309 and superior to that of EJ-276. With these performance characteristics, the organic glass scintillator is well poised to replace current state-of-the-art PSD-capable scintillators in a range of fast neutron detection applications.
Conathane EN-7 (referred to as EN-7) has been used for decades to pot electrical connectors, providing mechanical support for solder joints in cables. Unfortunately, the EN-7 formulation contains a suspect carcinogen and chemical sensitizer, toluene diisocyanate (TDI). Because of this, various groups have been formulating replacement materials, but all have come up short in final properties or in processing. We propose Arathane 5753 HVB as a replacement for EN-7. The properties compare very well with EN-7 and the processing has both advantages and disadvantages over EN-7 as discussed in detail below.
This report details the current benchmark results to verify, validate and demonstrate the capabilities of the in-house multi-physics phase-field modeling framework Mesoscale Multiphysics Phase Field Simulator (MEMPHIS) developed at the Center for Integrated Nanotechnologies (CINT). MEMPHIS is a general phase-field capability to model various nanoscience and materials science phenomena related to microstructure evolution. MEMPHIS has been benchmarked against a suite of reported classical phase-field benchmark problems to verify and validate the correctness, accuracy and precision of the models and numerical methods currently implemented into the code.
Atomic Force Microscopy (AFM), in conjunction with Peak Force Kelvin Probe Force Microscopy (PF-KPFM) and Peak Force Scanning Spreading Resistance Microscopy (PF-SSRM), was used to assess changes on thin metal films that underwent accelerated aging. The AFM technique provides a relatively easy, non-destructive methodology that does not require high-vacuum facilities to obtain nanometer-scale spatial resolution of surface chemistry changes. Surface morphology, roughness, contact potential difference, and spreading resistance were monitored to qualitatively identify effects of aging-morphology changes and oxidation of Au, Al, Cu thin film standards as well as diffusion of CuAu and AlAu thin film stacks at 65C under dried nitrogen flow conditions. AFM PF-KPFM and PF-SSRM modes have been exercised, refined and have proven to be viable and necessary early aging detection tools.
Secondary networks are used to supply power to loads that require very high reliability and are in use around the world, particularly in dense urban downtown areas. The protection of secondary network systems poses unique challenges. The addition of distributed energy resources (DERs) to secondary networks can compound these challenges, and deployment of microgrids on secondary networks will create a new set of challenges and opportunities. This report discusses secondary networks and their protection, the challenges associated with interconnecting DERs to a secondary network, issues expected to be associated with creating microgrids on secondary networks, standards that deal with these challenges and issues, and suggestions for research and development foci that would yield new means for addressing these challenges.
Lin, Yong; Scott, Bobby R.; Saxton, Bryanna; Chen, Wenshu; Belinsky, Steven; Potter, Charles A.
There are numerous self-shielded research irradiators used in various facilities throughout the United States. The irradiators employ radioactive sources containing either 137Cs or 60Co and the irradiators are used for a variety of radiobiological investigations involving cellular and/or animal models. A report from the National Academy of Sciences described security issues associated with particular radiation sources and the desire for their replacement with suitable X-ray irradiators. One possible replacement would be a 320 kV X-ray irradiator. The participants in this research successfully performed in vivo radiobiological studies involving mice exposed to filtered (HVL ≈ 4 mm Cu) 320 kV X rays. Two publications (one published and one submitted at the publishing of this report) documenting key findings are provided in Appendices A and B of this report. The 320 kV X rays were found suitable for in vivo (in mice) cell survival studies and are expected to be suitable for bone marrow transplantation studies using mice but this needs to be experimentally validated.
We present the results of the first Charged-Particle Transport Coefficient Code Comparison Workshop, which was held in Albuquerque, NM October 4–6, 2016. In this first workshop, scientists from eight institutions and four countries gathered to compare calculations of transport coefficients including thermal and electrical conduction, electron–ion coupling, inter-ion diffusion, ion viscosity, and charged particle stopping powers. Here, we give general background on Coulomb coupling and computational expense, review where some transport coefficients appear in hydrodynamic equations, and present the submitted data. Large variations are found when either the relevant Coulomb coupling parameter is large or computational expense causes difficulties. Understanding the general accuracy and uncertainty associated with such transport coefficients is important for quantifying errors in hydrodynamic simulations of inertial confinement fusion and high-energy density experiments.
Shale is characterized by the predominant presence of nanometer-scale (1-100 nm) pores. The behavior of fluids in those pores directly controls shale gas storage and release in shale matrix and ultimately the wellbore production in unconventional reservoirs. Recently, it has been recognized that a fluid confined in nanopores can behave dramatically differently from the corresponding bulk phase due to nanopore confinement. CO2 and H2O, either preexisting or introduced, are two major components that coexist with shale gas (predominately CH4) during hydrofracturing and gas extraction. Note that liquid or supercritical CO2 has been suggested as an alternative fluid for subsurface fracturing such that CO2 enhanced gas recovery can also serve as a CO2 sequestration process. Limited data indicate that CO2 may preferentially adsorb in nanopores (particularly those in kerogen) and therefore displace CH4 in shale. Similarly, the presence of water moisture seems able to displace or trap CH4 in shale matrix. Therefore, fundamental understanding of CH4-CO2-H2O behavior and their interactions in shale nanopores is of great importance for gas production and the related CO2 sequestration. This project focuses on the systematic study of CH4-CO2-H2O interactions in shale nanopores under high-pressure and high temperature reservoir conditions. The proposed work will help develop new stimulation strategies to enable efficient resource recovery from fewer and less environmentally impactful wells.
The goal of the ExaWind project is to enable predictive simulations of wind farms comprised of many megawatt-scale turbines situated in complex terrain. Predictive simulations will require computational fluid dynamics (CFD) simulations for which the mesh resolves the geometry of the turbines and captures the rotation and large deflections of blades. Whereas such simulations for a single turbine are arguably petascale class, multi-turbine wind farm simulations will require exascale-class resources. The primary physics codes in the ExaWind simulation environment are Nalu-Wind, an unstructured-grid solver for the acoustically incompressible Navier-Stokes equations, AMR-Wind, a block-structured-grid solver with adaptive mesh refinement capabilities, and OpenFAST, a wind-turbine structural dynamics solver. The Nalu-Wind model consists of the mass-continuity Poisson-type equation for pressure and Helmholtz-type equations for transport of momentum and other scalars. For such modeling approaches, simulation times are dominated by linear-system setup and solution for the continuity and momentum systems. For the ExaWind challenge problem, the moving meshes greatly affect overall solver costs as reinitialization of matrices and recomputation of preconditioners is required at every time step. The choice of overset-mesh methodology to model the moving and non-moving parts of the computational domain introduces constraint equations in the elliptic pressure-Poisson solver. The presence of constraints greatly affects the performance of algebraic multigrid preconditioners.
Quantum Monte Carlo (QMC) methods are useful for studies of strongly correlated materials because they are many body in nature and use the physical Hamiltonian. Typical calculations assume as a starting point a wave function constructed from single-particle orbitals obtained from one-body methods, e.g., density functional theory. However, mean-field-derived wave functions can sometimes lead to systematic QMC biases if the mean-field result poorly describes the true ground state. Here, we study the accuracy and flexibility of QMC trial wave functions using variational and fixed-node diffusion QMC estimates of the total spin density and lattice distortion of antiferromagnetic iron oxide (FeO) in the ground state B1 crystal structure. We found that for relatively simple wave functions the predicted lattice distortion was controlled by the choice of single-particle orbitals used to construct the wave function, rather than by subsequent wave function optimization techniques within QMC. By optimizing the orbitals with QMC, we then demonstrate starting-point independence of the trial wave function with respect to the method by which the orbitals were constructed by demonstrating convergence of the energy, spin density, and predicted lattice distortion for two qualitatively different sets of orbitals. The results suggest that orbital optimization is a promising method for accurate many-body calculations of strongly correlated condensed phases.
Coherent anti-Stokes Raman scattering (CARS) is a valuable spectroscopic tool for the measurement of temperature and species concentration. In recent years, multi-dimensional CARS has seen focused development and is especially important in reacting flows. An important aspect of multi-dimensional CARS is the phase-matching scheme used. Historically, collinear and BOXCARS phase-matching schemes have been used to achieve phase matching over a broad spectral range. For 1-D and 2-D CARS imaging, two-beam or counter-propagating beam arrangements are necessary. The two-beam arrangement offers many advantages, but introduces a phase mismatch which limits the spectral response of the measurement. This work explores the tradeoffs in spatial resolution, spectral bandwidth, and CARS intensity in 2-D CARS arrangements. Calculations are made for two-beam and counter-propagating beam CARS.
Mishra, Umakant; Gautam, Sagar; Riley, William J.; Hoffman, Forrest M.
Various approaches of differing mathematical complexities are being applied for spatial prediction of soil properties. Regression kriging is a widely used hybrid approach of spatial variation that combines correlation between soil properties and environmental factors with spatial autocorrelation between soil observations. In this study, we compared four machine learning approaches (gradient boosting machine, multinarrative adaptive regression spline, random forest, and support vector machine) with regression kriging to predict the spatial variation of surface (0–30 cm) soil organic carbon (SOC) stocks at 250-m spatial resolution across the northern circumpolar permafrost region. We combined 2,374 soil profile observations (calibration datasets) with georeferenced datasets of environmental factors (climate, topography, land cover, bedrock geology, and soil types) to predict the spatial variation of surface SOC stocks. We evaluated the prediction accuracy at randomly selected sites (validation datasets) across the study area. We found that different techniques inferred different numbers of environmental factors and their relative importance for prediction of SOC stocks. Regression kriging produced lower prediction errors in comparison to multinarrative adaptive regression spline and support vector machine, and comparable prediction accuracy to gradient boosting machine and random forest. However, the ensemble median prediction of SOC stocks obtained from all four machine learning techniques showed highest prediction accuracy. Although the use of different approaches in spatial prediction of soil properties will depend on the availability of soil and environmental datasets and computational resources, we conclude that the ensemble median prediction obtained from multiple machine learning approaches provides greater spatial details and produces the highest prediction accuracy. Thus an ensemble prediction approach can be a better choice than any single prediction technique for predicting the spatial variation of SOC stocks.
Time-resolved x-ray diffraction (XRD) was used to examine the behavior of Ce under shock loading to stress states up to 22 GPa that span the shock-melt transition. Experiments reported here observed Ce held at a steady state for ∼500 ns prior to being uniaxially released to ambient pressure. Time-resolved XRD shows a constant diffraction pattern over the duration of the steady state with rapid solidification occurring on release. Cerium was found to remain crystalline as Poisson's ratio (ν) increases in the α-phase with incipient melt observed in XRD once ν reaches 0.5. Diffraction results along with sound speed measurements limit melt completion to be between 12 and 14 GPa, significantly lower than previously expected. The XRD results add confidence to previous methods used to define incipient melt and help to define a method to constrain the melt region along the Hugoniot independent of a light source.
We describe a set of precise single-ion conducting polymers that form self-assembled percolated ionic aggregates in glassy polymer matrices and have decoupled transport of metal cations. These precise single-ion conductors (SICs), synthesized by a scalable ring-opening metathesis polymerization, consist of a polyethylene backbone with a sulfonated phenyl group pendant on every fifth carbon and are fully neutralized by a counterion X+ (Li+, Na+, or Cs+). Experimental X-ray scattering measurements and fully atomistic molecular dynamics (MD) simulations are in good agreement. The MD simulations show that the ionic groups nanophase separate from the polymer backbone to form percolating ionic aggregates. Using graph theory, we find that within the Li+- and Na+-neutralized polymers the percolated aggregates exhibit planar and ribbon-like configurations at intermediate length scales, while the percolated aggregates within the Cs+-neutralized polymers are more isotropic. Electrical impedance spectroscopy measurements show that the ionic conductivities exhibit Arrhenius behavior, with conductivities of 10-7 to 10-6 S/cm at 180 °C. In the MD simulations, the cations move between sulfonate groups in the percolated aggregates, larger ions travel further, and overall cations travel further than the polymer backbones, indicating a decoupled ion-transport mechanism. Thus, the percolated ionic aggregates in these polymers can serve as pathways to facilitate decoupled ion motion through a glassy polymer matrix.
The availability of repair garage infrastructure for hydrogen fuel cell vehicles is becoming increasingly important for future industry growth. Ventilation requirements for hydrogen fuel cell vehicles can affect both retrofitted and purpose-built repair garages and the costs associated with these requirements can be significant. A hazard and operability (HAZOP) study was performed to identify risk-significant scenarios related to light-duty hydrogen vehicles in a repair garage. Detailed simulations and modeling were performed using appropriate computational tools to estimate the location, behavior, and severity of hydrogen release based on key HAZOP scenarios. Here, this work compares current fire code requirements to an alternate ventilation strategy to further reduce potential hazardous conditions. Modeling shows that position, direction, and velocity of ventilation have a significant impact on the amount of instantaneous flammable mass in the domain.
Tensor decomposition is a well-known tool for multiway data analysis. This work proposes using stochastic gradients for efficient generalized canonical polyadic (GCP) tensor decomposition of large-scale tensors. GCP tensor decomposition is a recently proposed version of tensor decomposition that allows for a variety of loss functions such as Bernoulli loss for binary data or Huber loss for robust estimation. Here, the stochastic gradient is formed from randomly sampled elements of the tensor and is efficient because it can be computed using the sparse matricized-tensor times Khatri--Rao product tensor kernel. For dense tensors, we simply use uniform sampling. For sparse tensors, we propose two types of stratified sampling that give precedence to sampling nonzeros. Numerical results demonstrate the advantages of the proposed approach and its scalability to large-scale problems.
Spurgeon, Steven R.; Ophus, Colin; Jones, Lewys; Kalinin, Sergei V.; Olszta, Matthew J.; Dunin-Borkowski, Rafal E.; Salmon, Norman; Hattar, Khalid M.; Yang, Wei-Chang D.; Sharma, Renu; Du, Yingge; Chiaramonti, Ann; Zheng, Haimei; Buck, Edgar C.; Kovarik, Libor; Penn, R.L.; Li, Dongsheng; Zhang, Xin; Murayama, Mitsuhiro; Taheri, Mitra L.
The rapidly evolving field of electron microscopy touches nearly every aspect of mod- ern life, underpinning impactful materials discoveries in applications such as quan- tum information science, energy, and medicine. As the field enters a new decade, a paradigm has begun to emerge in which the convergence of advanced instrumenta- tion, robust in-situ platforms, and data-driven experimentation will help researchers distill observations of ever more complex systems into meaningful physical properties and mechanisms. Here we present the findings from the first in a series of work- shops gathering together scientists and technologists across academia, government laboratories, and industry, with the goal to develop a critical roadmap for next- generation transmission electron microscopy (NexTEM). We provide a perspective on the present and emerging state-of-the-art, highlighting progress and the crucial developments still needed to realize the materials of tomorrow.
A semantic understanding of the environment is needed to enable high level autonomy in robotic systems. Recent results have demonstrated rapid progress in underlying technology areas, but few results have been reported on end-to-end systems that enable effective autonomous perception in complex environments. In this paper, we describe an approach for rapidly and autonomously mapping unknown environments with integrated semantic and geometric information. We use surfel-based RGB-D SLAM techniques, with incremental object segmentation and classification methods to update the map in realtime. Information theoretic and heuristic measures are used to quickly plan sensor motion and drive down map uncertainty. Preliminary experimental results in simple and cluttered environments are reported.
A semantic understanding of the environment is needed to enable high level autonomy in robotic systems. Recent results have demonstrated rapid progress in underlying technology areas, but few results have been reported on end-to-end systems that enable effective autonomous perception in complex environments. In this paper, we describe an approach for rapidly and autonomously mapping unknown environments with integrated semantic and geometric information. We use surfel-based RGB-D SLAM techniques, with incremental object segmentation and classification methods to update the map in realtime. Information theoretic and heuristic measures are used to quickly plan sensor motion and drive down map uncertainty. Preliminary experimental results in simple and cluttered environments are reported.
In this paper, we present an optimization-based coupling method for local and nonlocal continuum models. Our approach couches the coupling of the models into a control problem where the states are the solutions of the nonlocal and local equations, the objective is to minimize their mismatch on the overlap of the local and nonlocal problem domains, and the virtual controls are the nonlocal volume constraint and the local boundary condition. We present the method in the context of Local-to-Nonlocal di usion coupling. Numerical examples illustrate the theoretical properties of the approach.
As self-sustained oscillators, lasers possess the unusual ability to spontaneously synchronize. These nonlinear dynamics are the basis for a simple yet powerful stabilization technique known as injection locking, in which a laser's frequency and phase can be controlled by an injected signal. Because of its inherent simplicity and favorable noise characteristics, injection locking has become a workhorse for coherent amplification and high-fidelity signal synthesis in applications ranging from precision atomic spectroscopy to distributed sensing. Within integrated photonics, however, these injection-locking dynamics remain relatively untapped - despite significant potential for technological and scientific impact. Here, we demonstrate injection locking in a silicon photonic Brillouin laser. Injection locking of this monolithic device is remarkably robust, allowing us to tune the laser emission by a significant fraction of the Brillouin gain bandwidth. Harnessing these dynamics, we demonstrate amplification of small signals by more than 23 dB. Moreover, we demonstrate that the injection-locking dynamics of this system are inherently nonreciprocal, yielding unidirectional control and backscatter immunity in an all-silicon system. This device physics opens the door to strategies for phase-noise reduction, low-noise amplification, and backscatter immunity in silicon photonics.
Here, we study a setting where a group of agents, each receiving partially informative private signals, seek to collaboratively learn the true underlying state of the world (from a finite set of hypotheses) that generates their joint observation profiles. To solve this problem, we propose a distributed learning rule that differs fundamentally from existing approaches, in that it does not employ any form of “belief-averaging”. Instead, agents update their beliefs based on a min-rule. Under standard assumptions on the observation model and the network structure, we establish that each agent learns the truth asymptotically almost surely. As our main contribution, we prove that with probability 1, each false hypothesis is ruled out by every agent exponentially fast, at a network-independent rate that is strictly larger than existing rates. We then develop a computationally-efficient variant of our learning rule that is provably resilient to agents who do not behave as expected (as represented by a Byzantine adversary model) and deliberately try to spread misinformation.
Marianno, C.M.; Ordonez, E.A.; King, J.W.; Suh, R.
Polyvinyl toluene (PVT) based detectors are used in radiation portal monitors (RPMs) worldwide to detect the trafficking of illicit nuclear material. PVT scintillators, which are normally optically clear, have been observed to suffer internal fogging throughout their volume due to prolonged exposure to varying environmental conditions. These changes could lead to reduced performance for RPMs that utilize plastic scintillators. In this research, a proof of concept system, consisting of different color light emitting diodes (LEDs) and a single optical sensor (OS), were used to examine the change in light transmission through a PVT scintillator. This Optical Monitoring System (OMS), coupled with an environmentally exposed PVT detector, was tested in an environmental chamber where it was subjected to changes in temperature and humidity ranging from 55 °C at 100% relative humidity to –20 °C at 40% relative humidity. At temperatures below 10 °C, light transmission was reduced by 81% ± 8% for blue LEDs, 84% ± 5% for yellow LEDs, and 49% ± 4% for green LEDs. Similar reductions in detected light were not recorded when the OMS was tested with only air between the LEDs and OS. Therefore, the significant reductions in transmitted light were attributed to changes occurring within the PVT scintillator. These results indicate that a significant reduction in PVT opacity occurs due to wide environmental changes. A device such as the OMS could be used to track these changes and provide users an early indication that a portal monitor is suffering from reduced performance.
Elinburg, Jessica K.; Hyre, Ariel S.; Mcneely, James; Alam, Todd M.; Klenner, Steffen; Pottgen, Rainer; Rheingold, Arnold L.; Arnold, Leon E.; Doerrer, Linda H.
The synthesis and characterization of a series of Sn(ii) and Sn(iv) complexes supported by the highly electron-withdrawing dianionic perfluoropinacolate (pinF) ligand are reported herein. Three analogs of [SnIV(pinF)3]2- with NEt3H+ (1), K+ (2), and {K(18C6)}+ (3) counter cations and two analogs of [SnII(pinF)2]2- with K+ (4) and {K(15C5)2}+ (5) counter cations were prepared and characterized by standard analytical methods, single-crystal X-ray diffraction, and 119Sn Mössbauer and NMR spectroscopies. The six-coordinate SnIV(pinF) complexes display 119Sn NMR resonances and 119Sn Mössbauer spectra similar to SnO2 (cassiterite). In contrast, the four-coordinate SnII(pinF) complexes, featuring a stereochemically-active lone pair, possess low 119Sn NMR chemical shifts and relatively high quadrupolar splitting. Furthermore, the Sn(ii) complexes are unreactive towards both Lewis bases (pyridine, NEt3) and acids (BX3, Et3NH+). Calculations confirm that the Sn(ii) lone pair is localized within the 5s orbital and reveal that the Sn 5px LUMO is energetically inaccessible, which effectively abates reactivity. This journal is
The impressive performance that deep neural networks demonstrate on a range of seismic monitoring tasks depends largely on the availability of event catalogs that have been manually curated over many years or decades. However, the quality, duration, and availability of seismic event catalogs vary significantly across the range of monitoring operations, regions, and objectives. Semisupervised learning (SSL) enables learning from both labeled and unlabeled data and provides a framework to leverage the abundance of unreviewed seismic data for training deep neural networks on a variety of target tasks. We apply two SSL algorithms (mean-teacher and virtual adversarial training) as well as a novel hybrid technique (exponential average adversarial training) to seismic event classification to examine how unlabeled data with SSL can enhance model performance. In general, we find that SSL can perform as well as supervised learning with fewer labels. We also observe in some scenarios that almost half of the benefits of SSL are the result of the meaningful regularization enforced through SSL techniques and may not be attributable to unlabeled data directly. Lastly, the benefits from unlabeled data scale with the difficulty of the predictive task when we evaluate the use of unlabeled data to characterize sources in new geographic regions. In geographic areas where supervised model performance is low, SSL significantly increases the accuracy of source-type classification using unlabeled data.
While elastic metasurfaces offer a remarkable and very effective approach to the subwavelength control of stress waves, their use in practical applications is severely hindered by intrinsically narrow band performance. In applications to electromagnetic and photonic metamaterials, some success in extending the operating dynamic range was obtained by using nonlocality. However, while electronic properties in natural materials can show significant nonlocal effects, even at the macroscales, in mechanics, nonlocality is a higher-order effect that becomes appreciable only at the microscales. This study introduces the concept of intentional nonlocality as a fundamental mechanism to design passive elastic metasurfaces capable of an exceptionally broadband operating range. The nonlocal behavior is achieved by exploiting nonlocal forces, conceptually akin to long-range interactions in nonlocal material microstructures, between subsets of resonant unit cells forming the metasurface. These long-range forces are obtained via carefully crafted flexible elements, whose specific geometry and local dynamics are designed to create remarkably complex transfer functions between multiple units. The resulting nonlocal coupling forces enable achieving phase-gradient profiles that are functions of the wavenumber of the incident wave. The identification of relevant design parameters and the assessment of their impact on performance are explored via a combination of semianalytical and numerical models. The nonlocal metasurface concept is tested, both numerically and experimentally, by embedding a total-internal-reflection design in a thin-plate waveguide. Results confirm the feasibility of the intentionally nonlocal design concept and its ability to achieve a fully passive and broadband wave control.
The goals of an electron beam-driven radiographic source are the focusing of high current at high voltage to a minimal spot size with excellent shot-to-shot reproducibility. The Self-Magnetic Pinch (SMP) diode makes use of such an intense electron beam impinging on a high-atomic weight (tantalum) converter, a counter-streaming ion beam to help minimize the spot size, and operation in a magnetic field-free diode region which further encourages small spot size. Through a series of diode development experiments, output voltages up to 12.5 MV and output currents up to 225 kA have been characterized, with resulting spot sizes below ~ few mm. Scaling studies with parameter variation, such as diode aspect ratio and anode-cathode (A-K) gap variation, give systematic validation to what has heretofore been noted anecdotally by other research groups. While the lack of an imbedded magnetic field helps minimize the SMP spot size, a secondary result may be the generation of beam instabilities which can terminate the radiation pulse. There is anecdotal evidence that in-situ DC heating of the diode region can help stabilize the beam pinch. Clear experimental evidence exists that DC heating/RF cleaning results in better control over the counter-streaming ion population. Expanded use of spatial dose-rate detection is shown to yield new insights into electron beam dynamics in the SMP diode. An attendant study of the SMP diode as a load for an Inductive Voltage Adder (IVA) driver leads to insights into the behavior of the IVA-SMP diode configuration, viewed as a total system, and yields constraints on the overall impedance behavior of the SMP diode load.
There is an emerging consciousness in India of the importance of nuclear security and safety. Motivated by a combination of rapid growth in its civil nuclear sector, heightened scrutiny of recent nuclear accidents around the world, and the deteriorating regional security environment, India has pushed to adopt measures to strengthen and enhance its nuclear security and safety governance structures. India recognizes that the various recent global nuclear security initiatives are in its own best interest and has been an enthusiastic participant in the Nuclear Security Summit process. Today, India demonstrates a greater willingness to showcase its nuclear security arrangements before the public and has undertaken many institutional, legal, and operational reforms to maintain international regime compliance. This study takes a comprehensive look at India's approach to nuclear security and critically examines the physical security measures the country has put in place. Particular focus is placed on the evolution and strengths, as well as weaknesses, of the country's nuclear security institutions, instruments, practices, and culture. Given that the strengthening of India's nuclear security governance is an ongoing endeavor, the paper also puts forward a number of policy recommendations.
Pulsed power drivers such as the Z generator of Sandia National Laboratories typically deliver high current (>20MA) to single experiments. This project is intended to develop and assess ways to simultaneously drive multiple targets on a single pulsed power driver (specifically a neutron and an x-ray producing target driven in a single experiment). The combined x-ray/neutron environment produced will then be used to investigate potential synergistic effects in integrated circuits. A pre-requisite for being able to design and study multiple targets on Z is first adapting simulation tools to be able to model them effectively. This will enable us to assess the tradeoffs between the different ways multiple targets can be combined, and to better understand how existing and future pulsed power machines can be used to generate combined testing environments. This report is limited to documenting the initial development of a parallel load modeling capability that is presently being applied to design experiments to produce combined neutron/x-ray environments on Z.
The objective of this study was to conduct a series of tests looking at the deposition and resuspension of aerosol particles deposited onto multiple representative substrate surfaces for a range of particle sizes under varying environmental conditions. The benefit of this study is to provide additional insight into the understanding of early time resuspension from different mechanisms and compare to existing literature. The resuspension methods utilized in this study were full-scale and the substrates were representative of real- world ground level conditions. Multiple experiments were conducted to assess the impact on resuspension from the varying substrates and mechanisms. The results of this study show variations in the size distribution of aerosol as a function of height from the source resuspension factors. Additionally, the aerosolized mass concentration and resuspension factor were evaluated. The maximum resuspension factor was found to be on the order of 10 -4 m -1 which is higher than most resuspension factors found in literature but represents idealized conditions due to the well constrained experimental setup.
The potential advantages of AM (e.g. weight reduction, novel geometries) are well understood within a systems context. However, adoption of AM at the system level has been slow due to the relative uncertainty in the final material properties, which leaves capabilities and/or performance gains unrealized. Utilizing remelt strategies it may be possible to expand the available process window to provide densities and microstructures beyond what is capable with standard scan strategies. This work explored remelting strategies for 316L stainless steel to tailor grain size and increase density. Twelve scan strategies were explored experimentally and computationally to understand the limitations of remelt strategies and the robustness of the current simulation package. Results show tailoring of grain size, density, and texture is achievable through remelting and several key lessons learned were made to improve the texture evaluation through simulation.
Natural events and human activity often generate acoustic waves capable of traveling tens to tens of thousands of kilometers across the globe. Ground-based acoustic sensors are limited to dry land and often suffer from wind noise. In contrast, balloon borne acoustic sensors can cross oceans, polar ice caps, and other inhospitable areas, greatly expanding sensor coverage. Since they move with the mean wind speed, their background noise levels are exceptionally low. In the last six years, such sensors have recorded sounds from colliding ocean waves, surface and buried chemical explosions, thunder, wind/mountain interactions, wind turbines, aircraft, and possibly meteors and the aurora. These results have led to new insights on acoustic heating of the upper atmosphere, the detectability of underground explosions, and directional sound fields generated by ocean waves.
Germanium–antimony–telluride has emerged as a nonvolatile phase change memory material due to the large resistivity contrast between amorphous and crystalline states, rapid crystallization, and cyclic endurance. Improving thermal phase stability, however, has necessitated further alloying with optional addition of a quaternary species (e.g., C). In this work, the thermal transport implications of this additional species are investigated using frequency-domain thermoreflectance in combination with structural characterization derived from x-ray diffraction and Raman spectroscopy. Specifically, the room temperature thermal conductivity and heat capacity of (Ge2Sb2Te5)1–xCx are reported as a function of carbon concentration (x ≤ 0:12) and anneal temperature (T ≤ 350 °C) with results assessed in reference to the measured phase, structure, and electronic resistivity. Phase stability imparted by the carbon comes with comparatively low thermal penalty as materials exhibiting similar levels of crystallinity have comparable thermal conductivity despite the addition of carbon. The additional thermal stability provided by the carbon does, however, necessitate higher anneal temperatures to achieve similar levels of structural order.
With inverter-based distributed energy resources (DERs) becoming more prevalent in grid-connected or islanded distribution feeders, a better understanding of the performance of these devices is needed. Increasing the amount of inverter-based generation, and therefore reducing conventional generation, i.e. rotating machines and synchronous generators, decreases generation sources with well-known characteristic responses for unbalanced and transient fault conditions. This paper experimentally tests the performance of commercial grid-forming inverters under fault and unbalanced conditions and provides a comparison between grid-forming inverters and their grid-following counterparts.
Changes in the Demand Profile and a growing role for renewable and distributed generation are leading to rapid evolution in the electric grid. These changes are beginning to considerably strain the transmission and distribution infrastructure. Utilities are increasingly recognizing that the integration of energy storage in the grid infrastructure will help manage intermittency and improve grid reliability. This recognition, coupled with the proliferation of state-level renewable portfolio standards and rapidly declining lithium-ion (Li-ion) battery costs, has led to a surge in the deployment of battery energy storage systems (BESSs). Additionally, although BESSs represented less than 1% of grid-scale energy storage in the United States in 2019, they are the preferred technology to meet growing demand because they are modular, scalable, and easy to deploy across diverse use cases and geographic locations.
The fatigue crack growth behavior of Ti–10V–2Fe–3Al in gaseous hydrogen (H2) was assessed through comparative experiments conducted in laboratory air and 8.3 MPa H2. The measured fatigue crack growth rate (da/dN) versus applied stress intensity factor range (ΔK) relationships and observed fracture morphologies for laboratory air and H2 were comparable up to ΔK ≈ 6.9 MPa√m, when tested at a load ratio of 0.1 and frequency of 10 Hz. At higher ΔK values, significant crack deflection and subsequent catastrophic failure occurred in the specimen tested in H2. This degradation was not observed in a specimen pre-exposed to 8.3 MPa H2 for 96 h and then immediately tested in laboratory air. X-ray diffraction of the failed H2-tested specimen revealed that the material remnants were predominantly composed of TiH2, suggesting that hydride formation was the catalyst for catastrophic failure in H2. The mechanistic implications of these results and their impact on current material compatibility assessments for Ti alloys in hydrogen service are then discussed.
Density functional perturbation theory (DFPT) calculations of the thermodynamic properties of metaschoepite, (UO2)8O2(OH)12·10H2O, are reported. Using a recently revised crystal structure of metaschoepite, the predicted molar entropy and isobaric heat capacity are overall significantly smaller than previous calculations using an earlier orthorhombic crystal structure model. The present DFPT calculations also show large differences between the thermodynamic functions of metaschoepite and schoepite, which might reflect the change in phonon properties upon removal of two H2O molecules per formula unit and alteration of the H-bonded interlayer water network from schoepite to metaschoepite.
Zhang, Tong; Price, Stephen F.; Hoffman, Matthew J.; Perego, Mauro P.; Asay-Davis, Xylar
Using a numerical ice flow model, we study changes in ice shelf buttressing and grounding-line flux due to localized ice thickness perturbations, a proxy for localized changes in sub-ice-shelf melting. From our experiments, applied to idealized (MISMIPC) and realistic (Larsen C) ice shelf domains, we identify a correlation between a locally derived buttressing number on the ice shelf, based on the first principal stress, and changes in the integrated grounding-line flux. The origin of this correlation, however, remains elusive from the perspective of a theoretical or physically based understanding. This and the fact that the correlation is generally much poorer when applied to realistic ice shelf domains motivate us to seek an alternative approach for predicting changes in grounding-line flux.We therefore propose an adjoint-based method for calculating the sensitivity of the integrated grounding-line flux to local changes in ice shelf geometry. We show that the adjoint-based sensitivity is identical to that deduced from pointwise, diagnostic model perturbation experiments. Based on its much wider applicability and the significant computational savings, we propose that the adjoint-based method is ideally suited for assessing grounding-line flux sensitivity to changes in sub-ice-shelf melting.
COVID 19 has been devastating the globe over the past year, and although our knowledge of how the disease progresses is constantly evolving, there is a significant amount we now about the mechanisms underlying COVID19. There is growing evidence that a 'cytokine storm' is a significant driver of mortality associated with COVID19, with studies showing high levels of hallmark inflammatory indicators in critically ill COVID19 patients. This essentially consists of the patient's immune system going awry and causing white blood cells to constantly release a large number of small molecules called cytokines. The result of this is further activation of the immune system, which ends up attacking not only infected but also healthy patient tissues, resulting in organ failure.
Nonlocal operators of fractional type are a popular modeling choice for applications that do not adhere to classical diffusive behavior; however, one major challenge in nonlocal simulations is the selection of model parameters. In this work we propose an optimization-based approach to parameter identification for fractional models with an optional truncation radius. We formulate the inference problem as an optimal control problem where the objective is to minimize the discrepancy between observed data and an approximate solution of the model, and the control variables are the fractional order and the truncation length. For the numerical solution of the minimization problem we propose a gradient-based approach, where we enhance the numerical performance by an approximation of the bilinear form of the state equation and its derivative with respect to the fractional order. Several numerical tests in one and two dimensions illustrate the theoretical results and show the robustness and applicability of our method.
In the last 20 years, biodiesel consumption in the United States has rapidly increased to ∼2 billion gallons per year as a renewable supplement to fossil fuel. However, further expansion of biodiesel use is currently limited in part by poor cold weather performance, which prevents year-round blending and necessitates blend walls ≤5% v/v. In order to provide a diesel fuel blendstock with improved cold weather performance (cloud point, pour point, and cold filter plug point), while at the same time maintaining other required fuel performance specifications, several biodiesel redox analogues were synthesized and tested. The best performing candidate fuels from this class showed improvement in the derived cetane number (29.3% shorter ignition delay), lower heating value (+4.7 MJ/kg), relative sooting tendency (-7.4 YSI/MJ), and cloud point (15 °C lower) when compared to a B100 biodiesel composed of an identical fatty acid profile. It was observed as a general trend that the reduced form of biodiesel, fatty alkyl ethers (FAEs), shows performance improvements in all fuel property metrics. The suite of improved properties provided by FAEs gives biodiesel producers the opportunity to diversify their portfolio of products derived from lipid and alcohol feedstocks to include long-chain alkyl ethers, a biodiesel alternative with particular applicability for winter weather conditions across the US.
Oh, Junho; Hoffman, Jacob B.; Hong, Sungmin; Jo, Kyoo D.; Kustas, Jessica K.; Reed, Julian H.; Dana, Catherine E.; Cropek, Donald M.; Alleyne, Marianne
Nanoimprinting lithography (NIL) is a next-generation nanofabrication method, capable of replicating nanostructures from original master surfaces. Here, we develop highly scalable, simple, and nondestructive NIL using a dissolvable template. Termed dissolvable template nanoimprinting lithography (DT-NIL), our method utilizes an economic thermoplastic resin to fabricate nanoimprinting templates, which can be easily dissolved in simple organic solvents. We used the DT-NIL method to replicate cicada wings which have surface nanofeatures of ∼100 nm in height. The master, template, and replica surfaces showed a >∼94% similarity based on the measured diameter and height of the nanofeatures. The versatility of DT-NIL was also demonstrated with the replication of re-entrant, multiscale, and hierarchical features on fly wings, as well as hard silicon wafer-based artificial nanostructures. The DT-NIL method can be performed under ambient conditions with inexpensive materials and equipment. Our work opens the door to opportunities for economical and high-throughput nanofabrication processes.
The color of light is a fundamental property of electromagnetic radiation; as such, control of the frequency is a cornerstone of modern optics. Nonlinear materials are typically used to generate new frequencies, however the use of time-variant systems provides an alternative approach. Utilizing a metasurface that supports a high-quality factor resonance, we demonstrate that a rapidly shifting refractive index will induce frequency conversion of light that is confined in the nanoresonator meta-atoms. We experimentally observe this frequency conversion and develop a time-dependent coupled mode theory model that well describes the system. The intersection of high quality-factor resonances, active materials, and ultrafast transient spectroscopy leads to the demonstration of metasurfaces operating in a time-variant regime that enables enhanced control over light-matter interaction.
Geophysical techniques are often implemented as quick and inexpensive ways to locate and characterize fractures in the subsurface, which is important for a number of geoscience fields. Seismic velocities are the most widely used proxies for identification of fractures, but the correlation is not always well-defined. In this study we present material property data: unconfined compressive strength (UCS), bulk density (ρ), Young's modulus (E), Poisson's ratio (ν), P wave velocity (Vp), and S wave velocity (Vs), in conjunction with microfracture densities measured on samples of granite collected before and after underground chemical explosions. Results indicate the relationship between fractures and material properties is complex, even in this single-lithology environment. We interpret that this complexity arises from varying fracture mechanisms (e.g. dilation-inducing fractures vs compression-inducing fractures) in different parts of the core, due to differences in stress conditions. Additional complexity may result from chemical interactions between the fresh fractures and the fluids in the area. Water content appears to have a significant, if not dominant, role in the unconfined compressive strength (UCS) of the samples. We suggest caution when using elastic property measurements as a proxy for fracturing in areas of explosion-induced damage, or in other areas where a variety of mechanisms induce fracturing.
Solid-state cold spraying (CS) of metals and respective blends is becoming increasingly attractive compared to conventional high temperature processes due to the unique properties such as increased yield strength, low ductility, and differences in tensile and compressive strengths that result from microstructural features due to the CS process. Here we report the results of plate impact experiments applied to CS deposits of tantalum (Ta), niobium (Nb), and a tantalum- niobium blend (TaNb). These methods allowed for definition of the Hugoniot for each material type and allowed for assessment of the Hugoniot Elastic Limit (HEL). Scanning electron microscopy was used on recovered samples to characterize the fracture mechanism during spallation.
We present a numerical modeling workflow based on machine learning (ML) which reproduces the the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible computational cost. Based on deep neural networks, our workflow yields the local density of states (LDOS) for a given atomic configuration. From the LDOS, spatially-resolved, energy-resolved, and integrated quantities can be calculated, including the DFT total free energy, which serves as the Born-Oppenheimer potential energy surface for the atoms. We demonstrate the efficacy of this approach for both solid and liquid metals and compare results between independent and unified machine-learning models for solid and liquid aluminum. Our machine-learning density functional theory framework opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms.
Gaussian process regression is a popular Bayesian framework for surrogate modeling of expensive data sources. As part of a larger effort in scientific machine learning, many recent works have incorporated physical constraints or other a priori information within Gaussian process regression to supplement limited data and regularize the behavior of the model. We provide an overview and survey of several classes of Gaussian process constraints, including positivity or bound constraints, monotonicity and convexity constraints, differential equation constraints provided by linear PDEs, and boundary condition constraints. We compare the strategies behind each approach as well as the differences in implementation, concluding with a discussion of the computational challenges introduced by constraints.
Robinson, Brandon; Da Costa, Leandro; Poirel, Dominique; Pettit, Chris; Khalil, Mohammad K.; Sarkar, Abhijit
This paper focuses on the derivation of an analytical model of the aeroelastic dynamics of an elastically mounted flexible wing. The equations of motion obtained serve to help understand the behaviour of the aeroelastic wind tunnel setup in question, which consists of a rectangular wing with a uniform NACA 0012 airfoil profile, whose base is free to rotate rigidly about a longitudinal axis. Of particular interest are the structural geometric nonlinearities primarily introduced by the coupling between the rigid body pitch degree-of-freedom and the continuous system. A coupled system of partial differential equations (PDEs) coupled with an ordinary differential equation (ODE) describing axial-bending-bending-torsion-pitch motion is derived using Hamilton's principle. A finite dimensional approximation of the system of coupled differential equations is obtained using the Galerkin method, leading to a system of coupled nonlinear ODEs. Subsequently, these nonlinear ODEs are solved numerically using Houbolt's method. The results that are obtained are verified by comparison with the results obtained by direct integration of the equations of motion using a finite difference scheme. Adopting a linear unsteady aerodynamic model, it is observed that the system undergoes coalescence flutter due to coupling between the rigid body pitch rotation dominated mode and the first flapwise bending dominated mode. The behaviour of the limit cycle oscillations is primarily influenced by the structural geometric nonlinear terms in the coupled system of PDEs and ODE.
Helium ion beam interactions with materials have important implications for magnetic confinement fusion, material modification, and helium ion microscopy. These interactions depend on the precise physics of how helium ions channel into the materials, which can vary greatly based on the local crystalline orientation. In this work, we performed a dedicated experiment to investigate helium ion channeling in a well-characterized tungsten single crystal. Time-of-flight impact-collision ion scattering spectroscopy was used to obtain multi-angle maps of the backscattering intensity for 3 keV He+ → W(111). We found that the backscattering intensity profile arising from helium ion channeling could be well described by a shadow cone analysis. This analysis revealed that subsurface W atoms as deep as the ninth monolayer contributed to the backscattering intensity profile. Binary collision approximation simulations were performed with MARLOWE to model the experimental maps with sufficient accuracy to allow for quantitative comparisons using reliability factors. These quantitative comparisons were applied to investigate how the W lattice structure and He-W interatomic potential affect the multi-angle maps.
Peters, Brandon L.; Salerno, K.M.; Ge, Ting; Perahia, Dvora; Grest, Gary S.
Polymer synthesis routes result in macromolecules with molecular weight dispersity M that depends on the polymerization mechanism. The lowest dispersity polymers are those made by anionic and atom-transfer radical polymerization, which exhibit narrow distributions M = Mw/Mn ∼1.02-1.04. Even for small dispersity, the chain length can vary by a factor of two from the average. The impact of chain length dispersity on the viscoelastic response remains an open question. Here, the effects of dispersity on stress relaxation and shear viscosity of entangled polyethylene melts are studied using molecular dynamics simulations. Melts with chain length dispersity, which follow a Schulz-Zimm (SZ) distribution with M = 1.0-1.16, are studied for times up to 800 μs, longer than the terminal time. These systems are compared to those with binary and ternary distributions. The stress relaxation functions are extracted from the Green-Kubo relation and from stress relaxation following a uniaxial extension. At short and intermediate time scales, both the mean squared displacement and the stress relaxation function G(t) are independent of M. At longer times, the terminal relaxation time decreases with increasing M. In this time range, the faster motion of the shorter chains results in constraint release for the longer chains.
In power electronic applications, reliability and power density are a few of the many important performance metrics that require continual improvement in order to meet the demand of today's complex electrical systems. However, due to the complexity of the synergy between various components, it is challenging to visualize and evaluate the effects of choosing one component over another and what certain design parameters impose on the overall reliability and lifetime of the system. Furthermore, many areas of electronics have realized remarkable innovation in the integration of new materials of passive and active components; wide-bandgap semiconductor devices and new magnetic materials allow higher operating temperature, blocking voltage, and switching frequency; all of which enable much more compact power converter designs. However, uncertainty remains in the overall electronics reliability in different design variations. Hence, in order to better understand the relationship between reliability and power density in a power electronic system, this paper utilizes a genetic algorithm (GA) to provide pareto optimal solution sets in a multi-variate trade space that relates the Mean Time Between Failures (MTBF) and volumetric power density for the design of a 5 kW synchronous boost converter. Different designs of the synchronous boost converter based on the variation of the electrical parameters and material types for the passive (input and output capacitors, the boost inductor, and the heatsink) and active components (switches) have been studied. A few candidate designs have been evaluated and verified through hardware experiments.
We present experimental results from the first systematic study of performance scaling with drive parameters for a magnetoinertial fusion concept. In magnetized liner inertial fusion experiments, the burn-averaged ion temperature doubles to 3.1 keV and the primary deuterium-deuterium neutron yield increases by more than an order of magnitude to 1.1×1013 (2 kJ deuterium-tritium equivalent) through a simultaneous increase in the applied magnetic field (from 10.4 to 15.9 T), laser preheat energy (from 0.46 to 1.2 kJ), and current coupling (from 16 to 20 MA). Individual parametric scans of the initial magnetic field and laser preheat energy show the expected trends, demonstrating the importance of magnetic insulation and the impact of the Nernst effect for this concept. A drive-current scan shows that present experiments operate close to the point where implosion stability is a limiting factor in performance, demonstrating the need to raise fuel pressure as drive current is increased. Simulations that capture these experimental trends indicate that another order of magnitude increase in yield on the Z facility is possible with additional increases of input parameters.
Large pulsed power accelerators deliver multi-MJ pulses of electrical energy to a variety of high energy density (HED) physics experiments that support stockpile science programs. Understanding the plasma formation mechanisms and resulting electrical power transport (or "power flow") in the vacuum magnetically insulated transmission lines (MITLs) is an important area of ongoing research, and could provide a means to improve the performance of today's pulsed power accelerators while improving confidence in the design options for next-generation pulsed power concepts. Power flow science has been studied for decades, but these studies have not provided a predictive understanding of plasma formation and expansion in MITL systems. Several recent factors in pulsed power system design have generated a renewed (and urgent) interest in developing validated, multi-physics power flow engineering models with increased scrutiny and understanding. Examples of these factors include (i) the use of high inductance experimental configurations that could increase current "loss", (ii) interest in long-pulse applications that require predictable pulse shapes, and (iii) the desire to develop a deeper understanding of how current loss phenomena scale to larger accelerator configurations. This work is directed to support the validation of multi-physics power flow engineering models required to realize pulsed power systems for the NNSA mission.
Malone, Fionn D.; Benali, Anouar; Morales, Miguel A.; Caffarel, Michel; Kent, Paul R.C.; Shulenburger, Luke N.
Quantum Monte Carlo (QMC) methods are some of the most accurate methods for simulating correlated electronic systems. We investigate the compatibility, strengths, and weaknesses of two such methods, namely, diffusion Monte Carlo (DMC) and auxiliary-field quantum Monte Carlo (AFQMC). The multideterminant trial wave functions employed in both approaches are generated using the configuration interaction using a perturbative selection made iteratively (CIPSI) technique. Complete basis-set full configuration interaction energies estimated with CIPSI are used as a reference in this comparative study between DMC and AFQMC. By focusing on a set of canonical finite-size solid-state systems, we show that both QMC methods can be made to systematically converge towards the same energy once basis-set effects and systematic biases have been removed. AFQMC shows a much smaller dependence on the trial wave function than DMC while simultaneously exhibiting a much larger basis-set dependence. We outline some of the remaining challenges and opportunities for improving these approaches.
To model and quantify the variability in plasticity and failure of additively manufactured metals due to imperfections in their microstructure, we have developed uncertainty quantification methodology based on pseudo marginal likelihood and embedded variability techniques. We account for both the porosity resolvable in computed tomography scans of the initial material and the sub-threshold distribution of voids through a physically motivated model. Calibration of the model indicates that the sub-threshold population of defects dominates the yield and failure response. Finally, the technique also allows us to quantify the distribution of material parameters connected to microstructural variability created by the manufacturing process, and, thereby, make assessments of material quality and process control.
The lack of a reliable method to evaluate the convergence of molecular dynamics simulations has contributed to discrepancies in different areas of molecular dynamics. In the present work, the method of information entropy is introduced to molecular dynamics for stationarity assessment. The Shannon information entropy formalism is used to monitor the convergence of the atom motion to a steady state in a continuous spatial domain and is also used to assess the stationarity of calculated multidimensional fields such as the temperature field in a discrete spatial domain. It is demonstrated in this work that monitoring the information entropy of the atom position matrix provides a clear indicator of reaching steady state in radiation damage simulations, non-equilibrium molecular dynamics thermal conductivity computations, and simulations of Poiseuille and Couette flow in nanochannels. A main advantage of the present technique is that it is non-local and relies on fundamental quantities available in all molecular dynamics simulations. Unlike monitoring average temperature, the technique is applicable to simulations that conserve total energy such as reverse non-equilibrium molecular dynamics thermal conductivity computations and to simulations where energy dissipates through a boundary as in radiation damage simulations. The method is applied to simulations of iron using the Tersoff/ZBL splined potential, silicon using the Stillinger-Weber potential, and to Lennard-Jones fluid. Its applicability to both solids and fluids shows that the technique has potential for generalization to other areas in molecular dynamics.
Mansoori, Ahmad; Addamane, Sadhvikas J.; Renteria, Emma J.; Shima, Darryl M.; Balakrishnan, Ganesh
The reduction of the threading dislocation density in metamorphic GaSb grown on GaAs substrates through the use of InGaSb defect filter layers has been investigated. More specifically, we study the effects of strain and thickness on the ability of a InGaSb defect filter layer to reduce threading dislocations in GaSb solar cells grown on GaAs substrates. The strain between the GaSb metamorphic layer on GaAs substrate (99.5% relaxed) and the InGaSb defect filter layer is varied by changing the indium composition in the InGaSb layer. Here, it is demonstrated that an InGaSb defect filter layer with 0.6% strain is more effective for blocking threading dislocations compared with higher-strain layers, resulting in improved short-circuit current (Jsc) and open-circuit voltage (Voc) for the metamorphic GaSb solar cell. The optimization of the defect filter layer involves varying the thickness of the layer to achieve the lowest possible threading dislocation density. This also takes into account the critical thickness of the InGaSb layer on GaSb to avoid generation of threading dislocations from the InGaSb layer itself. It is shown that adding an In0.11Ga0.89Sb defect filter layer with thickness of 250 nm and 0.6% strain beneath a GaSb solar cell grown on a GaAs substrate improves Voc from 0.1 V to 0.16 V and Jsc from 19.7 mA/cm2 to 24.7 mA/cm2.
Mageeney, Catherine M.; Mohammed, Hamidu T.; Dies, Marta; Anbari, Samira; Cudkevich, Netta; Chen, Yanyan; Buceta, Javier; Ware, Vassie C.
A diverse set of prophage-mediated mechanisms protecting bacterial hosts from infection has been recently uncovered within cluster N mycobacteriophages isolated on the host, Mycobacterium smegmatis mc2155. In that context, we unveil a novel defense mechanism in cluster N prophage Butters. By using bioinformatics analyses, phage plating efficiency experiments, microscopy, and immunoprecipitation assays, we show that Butters genes located in the central region of the genome play a key role in the defense against heterotypic viral attack. Our study suggests that a two-component system, articulated by interactions between protein products of genes 30 and 31, confers defense against heterotypic phage infection by PurpleHaze (cluster A/subcluster A3) or Alma (cluster A/subcluster A9) but is insufficient to confer defense against attack by the heterotypic phage Island3 (cluster I/subcluster I1). Therefore, based on heterotypic phage plating efficiencies on the Butters lysogen, additional prophage genes required for defense are implicated and further show specificity of prophage-encoded defense systems. IMPORTANCE: Many sequenced bacterial genomes, including those of pathogenic bacteria, contain prophages. Some prophages encode defense systems that protect their bacterial host against heterotypic viral attack. Understanding the mechanisms undergirding these defense systems is crucial to appreciate the scope of bacterial immunity against viral infections and will be critical for better implementation of phage therapy that would require evasion of these defenses. Furthermore, such knowledge of prophage-encoded defense mechanisms may be useful for developing novel genetic tools for engineering phage-resistant bacteria of industrial importance.
Bush, Hagan E.; Schrader, Andrew J.; Loutzenhiser, Peter G.
A novel method for pairing surface irradiation and volumetric absorption from Monte Carlo ray tracing to computational heat transfer models is presented. The method is well-suited to directionally and spatially complex concentrated radiative inputs (e.g., solar receivers and reactors). The method employs a generalized algorithm for directly mapping absorbed rays from a Monte Carlo ray tracing model to boundary or volumetric source terms in the computational mesh. The algorithm is compatible with unstructured, two and three-dimensional meshes with varying element shapes. Four case studies were performed on a directly irradiated, windowed solar thermochemical reactor model to validate the method. The method was shown to conserve energy and preserve spatial variation when mapping rays from a Monte Carlo ray tracing model to a computational heat transfer model in ansys fluent.
There is substantial demand for theoretical/computational tools that can produce correct predictions of the geometric structure and band gap to accelerate the design and screening of new materials with desirable electronic properties. DFT-based methods exist that reliably predict electronic structure given the correct geometry. Similarly, when good spectroscopic data are available, these same methods may, in principle, be used as input to the inverse problem of generating a good structural model. The same is generally true for gas-phase systems, for which the choice of method is different, but factors that guide its selection are known. Despite these successes, there are shortcomings associated with DFT for the prediction of materials' electronic structure. The present paper offers a perspective on these shortcomings. Fundamentally, the shortcomings associated with DFT stem from a lack of knowledge of the exact functional form of the exchange–correlation functional. Inaccuracies therefore arise from using an approximate functional. These inaccuracies can be reduced by judicious selection of the approximate functional. Other apparent shortcomings present due to misuse or improper application of the method. One of the most significant difficulties is the lack of a robust method for predicting electronic and geometric structure when only qualitative (connectivity) information is available about the system/material. Herein, some actual shortcomings of DFT are distinguished from merely common improper applications of the method. The role of the exchange functional in the predicted relationship between geometric structure and band gap is then explored, using fullerene, 2D polymorphs of elemental phosphorus and polyacetylene as case studies. The results suggest a potentially fruitful avenue of investigation by which some of the true shortcomings might be overcome, and serve as the basis for an appeal for high-accuracy experimental structure data to drive advances in theory.
Characterization of airborne transmission behavior for biological contaminants is a critical aspect in developing public health guidelines in the event of an outbreak. Guidelines such as safe separation distances and physical protections (mask and face-shields) are heavily influenced by the particular biological airborne transmission risk, which depends on a multitude of factors and is not easily or readily determined, therefore potentially lagging behind the time sensitive need to inform effective public health guidelines. Herein is proposed a means to leverage the substantial investment over the last three decades of the Nuclear Regulatory Commission in the MACCS code suite developed at Sandia National Laboratories. This work includes augmenting the MACCS code to represent biological rather than radioactive contaminants in the atmosphere, therefore characterizing the airborne transmission risk for such contaminants and helping to inform critical and time sensitive public health guidelines.
We implemented a vacuum field emission electron microscope (FEM) using the electron optics of a low-energy /photoemission electron microscope (LEEM/PEEM). Historically, there have been other FEM hardware platforms, and the distinctive feature of our method is that it integrates with the LEEM/PEEM and associated techniques, enabling a powerful multi-capability toolset for studying fundamental materials properties underpinning field emission (FE) and vacuum arc initiation. Typically, LEEM is used to image surface structure, which influences both work function and electric field distribution near a surface, while PEEM is used to map photoelectric work function across a surface. Our FEM adds the capability for spatially-correlated coincident-site measurements of FE currents to go-along with structure and work function. LEEM, PEEM, and our FEM implementation achieve nanoscale spatial resolution relevant for materials studies in nanoscience/engineering. Our approach requires a straightforward calibration of the electron optics to enable focused FEM imaging under intentional electric field variation. We demonstrate the FEM approach by imaging field emitter arrays relevant for vacuum nanoelectronics. We demonstrate submicron spatial resolution and dynamic measurement of FE versus applied electric field. We anticipate this capability will enable fundamental structure-function studies of FE and arc initiation.
The molecular level origins for symmetry breaking in the excited state of symmetrical quadrupolar molecules, particularly in polar solvents, was investigated using time-dependent density functional theory approaches. Molecules of the form ADA (A/D electron accepting/donating respectively), have been shown to break their symmetry upon excitation, producing an intramolecular charge transfer event and permanent dipole. Current research indicates that polar solvents stabilize the charge transfer event thereby producing asymmetrical solvent dynamics on opposite ends of the molecules. In this work key structural features of the molecule were identified including (1) incorporation of cyano groups, (2) rotation of grafted phenyl rings, and (3) the length of the conjugated R group chain. More specifically, incorporation of cyano groups appears to decrease the magnitude of the dipole in the excited state, thereby indicating that solvent interactions at these groups do not stabilize the charge transfer. While the rotation of the phenyl groups appears to be necessary to break the symmetry of the excited state in the molecule.
Oxiranes are a class of cyclic ethers formed in abundance during low-temperature combustion of hydrocarbons and biofuels, either via chain-propagating steps that occur from unimolecular decomposition of β-hydroperoxyalkyl radicals (β-˙QOOH) or from reactions of HÒO with alkenes. The cis- and trans-isomers of 2,3-dimethyloxirane are intermediates of n-butane oxidation, and while rate coefficients for β-˙QOOH → 2,3-dimethyloxirane + OH are reported extensively, subsequent reaction mechanisms of the cyclic ethers are not. As a result, chemical kinetics mechanisms commonly adopt simplified chemistry to describe the consumption of 2,3-dimethyloxirane by convoluting several elementary reactions into a single step, which may introduce mechanism truncation error—uncertainty derived from missing or incomplete chemistry. The present research examines the isomerdependence of 2,3-dimethyloxirane reaction mechanisms in support of ongoing efforts to minimize mechanism truncation error. Reaction mechanisms are inferred via the detection of products from Cl-initiated oxidation of both cis-2,3-dimethyloxirane and trans-2,3-dimethyloxirane using multiplexed photoionization mass spectrometry (MPIMS). The experiments were conducted at 10 Torr and temperatures of 650 K and 800 K. To complement the experiments, the enthalpies of stationary points on the ˙R + O2 surfaces were computed at the ccCA-PS3 level of theory. In total, 28 barrier heights were computed on the 2,3-dimethyloxiranylperoxy surfaces. Two notable aspects are low-lying pathways that form resonance-stabilized ketohydroperoxide-type radicals caused by ˙QOOH ring-opening when the unpaired electron is localized adjacent to the ether group, and cis-trans isomerization of ˙R and ˙QOOH radicals, via inversion, which enable reaction pathways otherwise restricted by stereochemistry. Several species were identified in the MPIMS experiments from ring opening of 2,3-dimethyloxiranyl radicals. Neither of the two conjugate alkene isomers prototypical of ˙R + O2 reactions were detected. Products were also identified from decomposition of ketohydroperoxide-type radicals. The present work provides the first analysis of 2,3-dimethyloxirane oxidation chemistry and reveals that consumption pathways are complex and require the expansion of submechanisms in chemical kinetics mechanisms.
Not long ago, it was shown that a discrete time crystal can be realized if a quantum system is periodically driven to a non-equilibrium state. Proof-of-concept experiments are reported by two groups using trapped ions and nitrogen-vacancy centers in diamond, respectively. The concept of discrete time crystals vividly demonstrates that the coherence time of a quantum system may be enhanced by driving the system out of equilibrium. In this project, we want to test this novel concept in another canonical quantum system, the quantum Hall system in a two-dimensional electron gas (2DEG). Compared to other systems, quantum Hall magnetism (QHM) in high quality, industry-compatible GaAs/AlGaAs heterostructures allows for detailed and quantitative studies in a particularly simple and clean environment. This detailed knowledge should help achieve longer coherence times in a driven QHM system. This report will detail the results from a recent study on the stability of the quantum Hall skyrmions (QHS) state at a Landau level filling close to ν = 1 by measuring its current-voltage (I-V) breakdown characteristics under radio-frequency (RF) radiations. We observe that the critical current increases visibly when the RF frequency is right at the Larmor frequency of 75As nuclei, where the hyperfine interaction between electron and nuclear spins perturbs the QHS state most significantly. We believe that this observation is consistent with the novel concept that the coherence time of a quantum system may be enhanced by driving the system out of equilibrium.
Years of work by 1350 and others has shown that the phenomenology behind EM penetration of joints and seams is a major driver in the shielding effectiveness of ND systems. Via analysis of a canonical cylindrical geometry and comparison against experimental data, we will provide evidence supporting the theory that proper treatment of contact phenomenology including joint deformation, asperity-induced contact impedance, and appropriate treatment of machining tolerance values is required to match electromagnetics modeling and simulation results to experimental data.
Diesel engines are an important technology for transportation of both people and goods. However, historically they have suffered a significant downside of high soot and nitrogen oxides (NOx) emissions. Recently, ducted fuel injection (DFI) has been demonstrated to attenuate soot formation in compression-ignition engines and combustion vessels by 50% to 100%. This allows for diesel engines to be run at low-NOx emissions that would have otherwise produced significantly more soot due to the soot/NOx tradeoff. Currently the root causes of this soot attenuation are not well understood. To be able to better optimize DFI for use across a variety of engines and conditions, it is important to understand clearly how it works. This study expands on the current understanding of DFI by using numerical modeling under nonreacting conditions to provide insights about the roles of entrainment and mixing that would have been much more challenging to obtain experimentally. This study found that DFI enhances charge gas entrainment upstream of the duct and blocks entrainment inside of the duct. Mixing is enhanced by the duct, which results in lower peak equivalence ratios at the exit of the duct.
In high-level radioactive waste disposal, a heat-generating waste canister is generally encased with a layer of bentonite-based buffer material acting as an engineered barrier to limit water percolation and radionuclide release. The low thermal conductivity of bentonite (~0.5 W/m∙K) combined with a high thermal loading waste package may result in a high surface temperature on the package that can potentially impact the structural integrity of the package itself as well as the surrounding buffer material. We show here that the thermal conductivity of bentonite can be effectively enhanced by embedding copper wires/meshes across the buffer layer to form fully connected high heat conduction pathways. A simple calculation based on Rayleigh’s model shows that a required thermal conductivity of 5 W/m∙K for effective heat dissipation can be achieved simply by adding ~1 vol % of copper wires/meshes into bentonite. As a result, the peak surface temperature on a large waste package such as a dual-purpose canister can be reduced by up to 300°C, leading to a significant reduction in the surface storage time for waste cooling and therefore the overall cost for direct disposal of such waste packages. Because of the ensured full thermal percolation across the buffer layer, copper wires/meshes turn out to be much more effective than any other materials currently suggested (such as graphene or graphite) in enhancing the thermal conductivity of buffer material. Furthermore, the embedded copper wires/meshes can help reinforce the mechanical strength of the buffer material, thus preventing the material from a potential erosion by a possible intrusion of dilute groundwater.
Adding small amounts of ring polymers to a matrix of their linear counterparts is known to increase the zero-shear-rate viscosity because of linear-ring threading. Uniaxial extensional rheology measurements show that, unlike its pure linear and ring constituents, the blend exhibits an overshoot in the stress growth coefficient. By combining these measurements with ex-situ small-angle neutron scattering and nonequilibrium molecular dynamics simulations, this overshoot is shown here to be driven by a transient threading–unthreading transition of rings embedded within the linear entanglement network. Prior to unthreading, embedded rings deform affinely with the linear entanglement network and produce a measurably stronger elongation of the linear chains in the blend compared to the pure linear melt. Thus, rings uniquely alter the mechanisms of transient elongation in linear polymers.
Graphs are a widely used abstraction for representing a variety of important real-world problems including emulating cyber networks for situational awareness, or studying social networks to understand human interactions or pandemic spread. Communication data is often converted into graphs to help understand social and technical patterns in the underlying communication data. However, prior to this project, little work had been performed analyzing how best to develop graphs from such data. Thus, many critical, national security problems were being performed against graph representations of questionable quality. Herein, we describe our analyses that were precursors to our final statistically grounded technique for creating static graph snapshots from a stream of communication events. The first analyzes the statistical distribution properties of a variety of real-world communication datasets generally fit best by Pareto, log normal, and extreme value distributions. The second derives graph properties that can be estimated given the expected statistical distribution for communication events and the communication interval to be viewed node observability, edge observability, and expected accuracy of node degree. Unfortunately, as that final process is under review for publication, we can't publish it here at this time.
Accurate knowledge of thermophysical properties of rock is vital to develop meaningful models of high level nuclear waste emplacement scenarios. The Israel Atomic Energy Commission is considering storing high level nuclear waste in the Ghareb formation, a porous kerogen bearing chalk. Sandia is supporting this effort with an evolving lab- based geomechanics testing program. We have completed measurements of thermal properties up to 275C and room temperature hydrostatic compaction measurements. We report thermal conductivity, thermal diffusivity, specific heat, and mass loss from our thermal measurements, and we report bulk moduli and porosity loss from our compaction measurements. These values are crucial for the numerical models to simulate heat transfer and formation compressibility around a heat generating repository.
Control of BO6 octahedral rotations at the heterointerfaces of dissimilar ABO3 perovskites has emerged as a powerful route for engineering novel physical properties. However, its impact length scale is constrained at 2–6 unit cells close to the interface and the octahedral rotations relax quickly into bulk tilt angles away from interface. Here, a long-range (up to 12 unit cells) suppression of MnO6 octahedral rotations in La0.9Ba0.1MnO3 through the formation of superlattices with SrTiO3 can be achieved. The suppressed MnO6 octahedral rotations strongly modify the magnetic and electronic properties of La0.9Ba0.1MnO3 and hence create a new ferromagnetic insulating state with enhanced Curie temperature of 235 K. The emergent properties in La0.9Ba0.1MnO3 arise from a preferential occupation of the out-of-plane Mn d3z2−r2 orbital and a reduced Mn eg bandwidth, induced by the suppressed octahedral rotations. The realization of long-range tuning of BO6 octahedra via superlattices can be applicable to other strongly correlated perovskites for exploring new emergent quantum phenomena.
Schambach, Jenna Y.; Finck, Anna M.; Kitin, Peter; Hanschen, Erik R.; Hunt, Christopher G.; Vogler, Brian; Starkenburg, Shawn R.; Barry, Amanda N.
Improving productivity and lipid concentration in microalgae is important for the economic success of both biofuel and microalgae coproducts production. Nannochloropsis spp. are marine microalgae currently being grown at large-scale for the production of biofuel and lipid coproducts. Here we demonstrate improvements of growth and omega-3 production in Nannochloropsis gaditana CCMP526 and Nannochloropsis oceanica CCAP849/10 with plant substrate addition, a potentially economical option for increasing microalgal productivity. We examine growth in the presence of corn stover, switchgrass, sugarcane bagasse, and yard waste. By examining the microbial ecology of N. gaditana cultures with and without plant substrate and with and without antibiotics, we discovered a potential bacterial interaction in these cultures, but its presence is not necessary for algal growth improvements in the presence of plant substrate. Analysis of plant substrate morphology by scanning electron microscopy (SEM) after cultivation in media with and without N. gaditana shows a degradation of specific plant structural features and a colonization of the plant phloem by N. gaditana. An examination of N. gaditana in a Congo red plate assay indicates potential cellulolytic activity, but a preliminary examination of the potential cellulase transcripts does not reveal differential expression of a candidate in the presence of plant. This evidence demonstrates potential raw plant utilization by a marine microalga for increased productivity and provides a potentially economical option for increasing coproduct concentration at industrial scale without genetic engineering.
Triangle counting is a fundamental building block in graph algorithms. In this paper, we propose a block-based triangle counting algorithm to reduce data movement during both sequential and parallel execution. Our block-based formulation makes the algorithm naturally suitable for heterogeneous architectures. The problem of partitioning the adjacency matrix of a graph is well-studied. Our task decomposition goes one step further: it partitions the set of triangles in the graph. By streaming these small tasks to compute resources, we can solve problems that do not fit on a device. We demonstrate the effectiveness of our approach by providing an implementation on a compute node with multiple sockets, cores and GPUs. The current state-of-the-art in triangle enumeration processes the Friendster graph in 2.1 seconds, not including data copy time between CPU and GPU. Using that metric, our approach is 20 percent faster. When copy times are included, our algorithm takes 3.2 seconds. This is 5.6 times faster than the fastest published CPU-only time.
Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) can be spread through close contact or through fomite mediated transmission. This study details the fabrication and analysis of a photocatalyst surface which can rapidly inactivate SARS-COV-2 to limit spread of the virus by fomite mediated transmission. The surface being developed at Sandia for this purpose is a minimally hazardous Ag-Ti0 2 nanomaterial which is engineered to have high photocatalytic activity. Initial results at Sandia California in a BSL-2 safe surrogate virus- Vesicular Stomatitis Virus (VSV) show a significant difference between the photocatalyst material under exposure to visible light than controls. Additionally, UV-A light (365 nm) was found to eliminate SARS-COV-2 after 9 hours on all tested surfaces with irradiance of 15 mW/cm 2 equivalent to direct circumsolar irradiance.
Electric power system planners utilize a variety of planning tools to inform decisions concerning generation and transmission additions to the electric grid, the need for operational changes, and to evaluate potential stressors on the system. Numerous factors contribute to the planning process including projected fuel and technology costs, policy and load profiles. There is also a growing recognition of the interdependency of the electric grid with other natural and engineered systems. Here we explore how future climate change and hydropower operability might influence decisions related to electricity capacity expansion planning and operations. To do so we assemble a multi-model framework. Specifically, water resource modeling is used to simulate climate impacts on future water supply for thermoelectric and hydropower generation. Separately, temperature impacts on electricity load are evaluated. Together, these climate factors spatially constrain a capacity expansion model that projects generation and transmission additions to the grid. The projected new capacity-builds are then evaluated on their operations, reliability, and cost under average and extreme climate conditions using production cost modeling. This coupled framework is demonstrated on the electric grid in the Western U.S., supporting capacity expansion planning by WECC, the North American Electric Reliability Corporation (NERC) regional entity responsible for reliability assurance of the Western Interconnection. This region was selected in part because the West is unique in that it has high potential for renewable penetrations and is experiencing large retirements/displacements of baseload resources, primarily coal, leading to possible operational challenges in terms of changing resource mix and the need for resource flexibility. Toward this challenge, planning scenarios encompass a range of alternative energy, climate and drought futures. In this context we explore answers to two strategic questions: 1) How does changing climate influence electricity expansion planning (generation and transmission) and future operations, including type and capacity of new builds, system reliability, cost and environmental impacts? 2) How does the representation of hydropower in the modeling framework influence the evaluation of bulk power system operations? Results indicate that climate has a measurable influence on recommendations concerning the capacity, type and location of new generation and transmission additions, with up to 17 GW additional capacity needed by 2038 to meet peak loads (~6.6% increase over capacity-builds based on historical climate). The extent of additional infrastructure needs is strongly influenced by future water availability for hydropower and the potential deployment of demand response technologies. Systems designed for future climate conditions were found to maintain high system reliability under a range of electricity and water availability scenarios (including significant drought), with minimal system curtailments. Additional capacity needs due to higher load tend to increase cumulative 20-year investment and operating costs by $\$$5-$\$$17 billion and generation costs increase by 9 to 19%. Finally, changing the representation of hydropower flexibility has a relatively small influence on capacity expansion in the Western Interconnection through 2038, but hydropower flexibility impacts generation costs to a similar extent as climate.
Metal borides have attracted the attention of researchers due to their useful physical properties and unique ability to form high hydrogen-capacity metal borohydrides. We demonstrate improved hydrogen storage properties of a nanoscale Mg-B material made by surfactant ball milling MgB2 in a mixture of heptane, oleic acid, and oleylamine. Transmission electron microscopy data show that Mg-B nanoplatelets are produced with sizes ranging from 5 to 50 nm, which agglomerate upon ethanol washing to produce an agglomerated nanoscale Mg-B material of micron-sized particles with some surfactant still remaining. X-ray diffraction measurements reveal a two-component material where 32% of the solid is a strained crystalline solid maintaining the hexagonal structure with the remainder being amorphous. Fourier transform infrared shows that the oleate binds in a "bridge-bonding"fashion preferentially to magnesium rather than boron, which is confirmed by density functional theory calculations. The Mg-B nanoscale material is deficient in boron relative to bulk MgB2 with a Mg-B ratio of ∼1:0.75. The nanoscale MgB0.75 material has a disrupted B-B ring network as indicated by X-ray absorption measurements. Hydrogenation experiments at 700 bar and 280 °C show that it partially hydrogenates at temperatures 100 °C below the threshold for bulk MgB2 hydrogenation. In addition, upon heating to 200 °C, the H-H bond-breaking ability increases ∼10-fold according to hydrogen-deuterium exchange experiments due to desorption of oleate at the surface. This behavior would make the nanoscale Mg-B material useful as an additive where rapid H-H bond breaking is needed.
The Spent Fuel and 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 highlevel 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) 2020 advances of the Geologic Disposal Safety Assessment (GDSA) Framework and PFLOTRAN development groups of the SFWST Campaign. The common mission of these groups is to develop a geologic disposal system modeling capability for nuclear waste that can be used to probabilistically assess the performance of disposal options and generic sites. The capability is a framework called GDSA Framework that employs high-performance computing (HPC) capable codes PFLOTRAN and Dakota.
Maritime trade accounts for approximately 80 percent of international commerce. The high volume of vessels traversing domestic and international ports makes port areas prime targets for terrorism as well as illegal trafficking of drugs and arms (conventional or nuclear). Port security is therefore a worldwide concern affecting global economies, freedom of movement, and national security. However, extensive port monitoring is inherently complex and time consuming — making it truly viable only via an automated framework that can detect potential illicit activity and alert authorities in a timely manner. The development of image processing algorithms for this purpose requires access to large, labeled datasets that cover the breadth of targets of interest as well as the environments that they are observed within. Curated and labeled datasets of this nature are of enormous value to Sandia's Defense Nuclear Nonproliferation and National Security Program portfolios, as well as to Sandia's machine learning/automatic target recognition (ML/ATR) algorithm development and R&D communities. The goal of this project is to create a commercial satellite imagery dataset of labeled maritime vessels in port areas to support the development of ML/ATR algorithms for port security nonproliferation purposes. This dataset — Port Security Nonproliferation Vessel Overhead Imagery Dataset (PSN VOID) — has the potential to support a variety of other ancillary missions, such as maritime domain awareness, domestic and international security, drug interdiction, and weapons trafficking.
Temperature checks for fever are extensively used for preliminary COVID screenings but are ineffective during the incubation stage of infection when a person is asymptotic. Researchers at the European Centre for Disease Prevention and Control concluded that approximately 75% of passengers infected with COVID-19 and traveling from affected Chinese cities would not be detected by early screening. Core body temperature is normally kept within a narrow range and has the smallest relative standard deviation of all vital signs. Heat in the body is prioritized around internal organs at the expense of the periphery by controlling blood flow. In fact, blood flow to the skin may vary by a factor of 100 depending on thermal conditions. This adaptation causes rapid temperature fluctuations in different skin regions from changes in cardiac output, metabolism, and likely cytokine diffusion during inflammation that would not be seen in average core body temperature. Current IR and thermal scanners used for temperature checks are not necessarily reflective of core body temperatures and require cautious interpretation as they frequently result in false positive and false negative diagnosis. Hand held thermometers measure average skin temperatures and can get readings that differ from core body temperature by as much as 7°. Rather than focusing on a core body temperature threshold assessment we believe that variability of temperature patterns using a novel wearable transdermal microneedle sensor will be more sensitive to infections in the incubation stage and propose to develop a wearable transdermal temperature sensor using established Sandia microneedle technology for pre symptomatic COVID screening that can additionally be used to monitor disease progression at later stages.
This report describes the efforts to characterize and model General Plastics TF6070 and EF4000 flexible polyurethane foams under room temperature, large deformation quasi-static cyclic mechanical loading conditions. Densities from three to fifteen pound per cubic foot (PCF) are examined, which correspond to relative densities of approximately 4 to 20%. These foams are open cell structured and flexible at room temperature with a glass transition transition less than -30°C, and they fully recover their original shape when unloaded (at room temperature). Uniaxial compression tests were conducted with accompanying lateral image series for Digital Image Correlation (DIC) analysis with the goal of extracting transverse strain responses. Due to difficulties with DIC analysis at large strains, lateral strains were instead extracted for each test via edge tracking. The experimental results exhibit a nonlinear elastic response and anisotropic material behavior (particularly for the lower densities). Some hysteresis is observed that is different between the first and subsequent cycles of deformation indicating both a small degree of permanent damage (reduced stiffness during reloading) and viscoelasticity. These inelastic mechanisms are not considered in the modeling and calibration in this report. This work considers only the rate independent, room temperature foam behavior. Individual foam densities were calibrated for loading in two directions, parallel and perpendicular to the foam bubble rise direction, since the mechanical behavior is different in these two directions. The Flex Foam constitutive model was used for all parameterizations despite the fact that the model is isotropic. A review of the constitutive model is given as well as necessary data reduction procedures to parameterize it for each foam density and orientation are discussed. Finally, two different parameterizations are developed that take the undeformed foam density as an input that span all densities considered. These two parameterized models represent foams loaded in the rise and transverse directions respectively. We summarize the assumptions and limitations of the parameterizations provided in this report to guide analysis choices with them. All parameterizations presented herein have the following traits, room temperature, rate independent, damage-free, and non-dissipative . Isotropy (even if they are representing anisotropic data). Supplied Sierra Solid Mechanics Flex Foam Model Inputs are in units: pounds, inches, Celsius, and seconds
Projection-based reduced-order models (ROMs) comprise a promising set of data-driven approaches for accelerating the simulation of high-fidelity numerical simulations. Standard projection-based ROM approaches, however, suffer from several drawbacks when applied to the complex nonlinear dynamical systems commonly encountered in science and engineering. These limitations include a lack of stability, accuracy, and sharp a posteriori error estimators. This work addresses these limitations by leveraging multiscale modeling, least-squares principles, and machine learning to develop novel reduced-order modeling approaches, along with data-driven a posteriori error estimators, for dynamical systems. Theoretical and numerical results demonstrate that the two ROM approaches developed in this work - namely the windowed least-squares method and the Adjoint Petrov - Galerkin method - yield substantial improvements over state-of-the-art approaches. Additionally, numerical results demonstrate the capability of the a posteriori error models developed in this work.
Once a Synthetic Aperture Radar (SAR) image is formed, the natural question then is, "Where is this image?" and/or "Where exactly is this feature displayed in the image?" Thus, geolocation is an important exploitation of the SAR image. Since SAR measures relative location to its own position, it is crucial to understand how the radars position and motion imp acts the ability to geolocate a feature in the SAR image. Furthermore, accuracy and precision of navigation aids like GPS directly impact the goodness of the geolocation solution. These relationships are developed and discussed.
An electro-magnetic pulse (EMP) event can induce large currents and voltages on electrical conductors such as electrical power transmission lines which span many kilometers and the shorter lines typically tens of meters in length used to monitor equipment controlling the power grid. The exact current and voltage induced on a conductor depends on many factors, such as line height, diameter and length as well as ground conductivity and the location of the EMP event relative to the conductor. The current work focus on the line location and orientation relative to the EMP source. A statistical, Monte-Carlo approach is used in sampling the line configuration and then calculating the induced current and voltage. Thousands of EMP events are simulated on the region of the Earth where the EMP event can couple to a given above-ground conductor and the resulting current and voltage is then calculated on that conductor. Through the many simulations, one can assemble statistics on the insult including the peak value, rise time and pulse width.
Nuclear power plants (NPPs) are considering flexible plant operations to take advantage of excess thermal and electrical energy. One option for NPPs is to pursue hydrogen production through high temperature electrolysis as an alternate revenue stream to remain economically viable. The intent of this study is to investigate the risk of a high temperature steam electrolysis hydrogen production facility (HTEF) in close proximity to an NPP. This analysis evaluates a postulated HTEF located 1 km from an NPP, including the likelihood of an accident and the associated consequence to critical NPP targets. This analysis shows that although the likelihood of a leak in an HTEF is not negligible, the consequence to critical NPP targets is not expected to lead to a failure at a distance of 1 km. Furthermore, the minimum separation distance of the HTEF is calculated based on the target fragility criteria of 1 psi defined in Regulatory Guide 1.91.
This report describes broadband measurements of transmission-scale transformers typical in the electric power grid. This work was performed as part of the EMP Resilient Grid LDRD project at Sandia National Laboratories to generate circuit models that can be used for high-altitude electromagnetic pulse (HEMP) coupling simulations and response predictions. The objective of the work was to obtain characterization data of substation yard equipment across a frequency range relevant to HEMP. Vector network analyzer measurements up to 100 MHz were performed on two power transformers at ABB-Hitachi and a single ITEC potential transformer. Custom cable breakouts were designed to interface with the transformer terminals and provide ground connections to the chassis at the base of the transformer bushings. The three-phase terminals of the power transformers were measured as a common mode impedance using a parallel resistive splitter, and the single-phase terminals of the potential transformer were measured directly. A vector fitting algorithm was used to empirically fit circuit models to the resulting two-port networks and input impedances of the measured objects. Simplified circuit representations of the input impedances were also generated to assess the degree of precision needed for high-altitude electromagnetic pulse response predictions, which were performed in Sandia's XYCE circuit simulator platform. HEMP coupling simulations using the transformer models showed significant reduction in the voltage peak and broadening in the pulse width seen at the power transformer compared to the traveling wave voltage. This indicated the importance of the load condition when defining the coupled insult in an electric power substation. Simplified circuit models showed a similar voltage at the transformer with a smoothed waveform. The presence of potential transformers in the simulation did not significantly change the simulated voltage at the power transformer. Single-port input impedance models were also developed to define load conditions when transfer characteristics were not necessary.
The Center for Disease Control has recommended that the public should wear cloth face coverings in public settings . Face coverings and face shields can be made by using Commonly Available Materials (CAMs). As part of the Sandia COVID - 19 LDRD effort (funded under the Materials Science Investment Area), the Sandia E - PiPEline task evaluated design options for face coverings and face shields considering their effectiveness, durability, build difficulty, build cost, and comfort. Observations from this investigation are presented here to provide guidelines for home construction of face coverings and face shields
Sandia Materials Science Investment Area contributed to the SARS-CoV-2 virus and COVID-19 disease which represent the most significant pandemic threat in over 100 years. We completed a series of 7, short duration projects to provide innovative materials science research and development in analytical techniques to aid the neutralization of COVID-19 on multiple surfaces, approaches to rapidly decontaminate personal protective equipment, and pareto assessment of construction materials for manufacturing personal protective equipment. The developed capabilities and processes through this research can help US medical personnel, government installations and assets, first responders, state and local governments, and multiple federal agencies address the COVID-19 Pandemic.
Some of the most stubborn and technologically critical problems in combustion are dominated by heterogeneous processes. While purely gas-phase combustion systems have been the subject of intense theoretical and experimental study, combustion phenomena occurring at interfaces are far less understood. This is partly caused by the lack of experimental approaches capable of probing locations very close to an interface, especially in the hostile environment of combustion. For laser-based optical techniques, measurements taken near interfaces are often complicated by laser scattering from the surface interfering with relatively weak signals. Further, for measurements intended to probe molecular species adsorbed at the interface between a gas-phase combustion reaction and a condensed phase material, signals are generally overwhelmed by contributions from the bulk phases, causing the small contribution from the interfacial molecular species to be undetectable. Our goal in this project has been to develop new optical tools for imaging chemical species, temperature, and surface species at and near surfaces or interfaces of relevance to combustion. We have placed focus on the development and refinement of ultrafast techniques such as femtosecond coherent Raman imaging and femtosecond/picosecond sum-frequency generation (SFG) scattering, as well as the models used to simulate such spectra under differing conditions of pressure and chemical speciation. The two physical phenomena initially targeted for study in this project were flamewall interactions, and the growth of particulates in flames.
This report documents a new approach to designing disease control policies that allocate scarce testing, contact tracing, and vaccination resources to better control community transmission of COVID19 or similar diseases. The Adaptive Recovery Model (ARM) combines a deterministic compartmental disease model with a stochastic network disease propagation model to enable us to simulate COVID-19 community spread through the lens of two complementary modeling motifs. ARM contact networks are derived from cell-phone location data that have been anonymized and interpreted as individual arrivals to specic public locations. Modeling disease spread over these networks allows us to identify locations within communities conducive to rapid disease spread. ARM applies this model- and data-derived abstractions of community transmission to evaluate the effectiveness of disease control measures including targeted social distancing, contact tracing, testing and vaccination. The architecture of ARM provides a unique capacity to help decision makers understand how best to deploy scarce testing, tracing and vaccination resources to minimize disease-spread potential in a community. This document details the novel mathematical formulations underlying ARM, presents a dynamical stability analysis of the deterministic model components, a sensitivity analysis of control parameters and network structure, and summarizes a process for deriving contact networks from cell-phone location data. An example use case steps through applying ARM to evaluate three targeted social distancing policies using Bernalillo County, New Mexico as an exemplar test locale. This step-by-step analysis demonstrates how ARM can be used to measure the relative performance of competing public health policies. Initial scenario tests of ARM shows that ARMs design focus on resource utilization rather than simple incidence prediction can provide decision makers with additional quantitative guidance for managing ongoing public health emergencies and planning future responses.
The response of a high - voltage (HV) transformer to fast rise time voltages, such as that from a electromagnetic pulse (EMP) can result in interruption of power distribution and possibly system failure. To help identify these potential occurrences, it is necessary to develop a transformer model that not only captures the input/output response of the transformer but also the internal behavior. The model constructed should cover the frequency band of interest while capturing the internal physical and electrical characteristics. This broad-band, high-fidelity model would enable the prediction of unwanted effects through simulation. A proposed modeling scheme for a HV transformer is described in Part 1 of this report. Part 2 of this report details assessments of internal voltage and electrical field holdoff testing of transformer insulation dielectric breakdown, including comparison of low frequency (DC/60 Hz) holdoff to rise times characteristic of lightning (1 s) and EMP E1 transients (10 - 30 ns). This initial project is a path toward establishing electrical grid transformer failure criteria for EMP voltage transients. We developed modeling methods and measured breakdown electrical field statistical distributions for direct current, 60 Hz, lightning and EMP characteristic voltage rise times. Methods of nanosecond-scale capacitive discharge unit high voltage source development, suggestions for derating of 60 Hz insulation maximum electrical fields for EMP nanosecond pulse voltage withstand rating, and potential methods for increasing transformer resilience to such fast rise time pulses are described.
TChem is an open source software library for solving complex computational chemistry problems and analyzing detailed chemical kinetic models. The software provides support for: complex kinetic models for gas-phase and surface chemistry; thermodynamic properties based on NASA polynomials; species production/consumption rates; stable time integrator for solving stiff time ordinary differential equations; and, reactor models such as homogenous gas-phase ignition (with analytical Jacobian matrices), continuously stirred tank reactor, plug-flow reactor. This toolkit builds upon earlier versions that were written in C and featured tools for gas-phase chemistry only. The current version of the software was completely refactored in C++, uses an object-oriented programming model, and adopts Kokkos as its portability layer to make it ready for the next generation computing architectures i.e., multi/many core computing platforms with GPU accelerators. We have expanded the range of kinetic models to include surface chemistry and have added examples pertaining to Continuously Stirred Tank Reactors (CSTR) and Plug Flow Reactor (PFR) models to complement the homogenous ignition examples present in the earlier versions. To exploit the massive parallelism available from modern computing platforms, the current software interface is designed to evaluate samples in parallel, which enables large scale parametric studies, e.g. for sensitivity analysis and model calibration.
In this report we investigate the utility of one-dimensional convolutional neural network (CNN) models in epidemiological forecasting. Deep learning models, especially variants of recurrent neural networks (RNNs) have been studied for influenza forecasting, and have achieved higher forecasting skill compared to conventional models such as ARIMA models. 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-learner for epidemiological forecasting and 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, 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.
Bigger is often said to be better, and the newest extreme-scale computers certainly are bigger, with millions of processing units. Moreover, the breadth of science performed on the U.S. Department of Energy (DOE) computing facilities is expanding, with new technology such as artificial intelligence emerging. These advances are exciting, creating new opportunities for scientific discovery; however, they also raise new questions for scientists who want to exploit these advances for tackling more complex problems. Will my simulation code be able to utilize the accelerators in extreme-scale computing systems? Can I take advantage of the deepening memory hierarchy in heterogeneous processors? Is there a way around bottlenecks caused by the widening ratio of peak floating-point operations per second to I/0 bandwidth? How can I manage my huge amounts of data effectively? Can I analyze data in situ, or must I transfer it to offline storage for later analysis? To address such questions, DOE announced that it is providing $57.5 million over the next five years for two multidisciplinary teams — FASTMath and RAPIDS2 — to develop new tools and techniques to harness supercomputers for scientific discovery. The teams, called SciDAC Institutes, are part of the Scientific Discovery through Advanced Computing program.
This paper explores the Systems Engineering structure, strategies and tools for real world scenarios involving work with accident response groups. A systems engineering approach must be taken by the technical teams to prepare for a successful response and design the technical systems in support of the operations. The scope of this project is focused on laying out the foundation of the systems engineering approach taken to help the teams develop an accident response strategy and identify new engineering designs in support of these operations for the black box systems. This Master’s project involves several interdisciplinary teams & stakeholders across different areas. Identifying the proper tools to use is key to addressing the big picture needs of the multiple stakeholders. This project explores some of the key tools used by the integrated team. The integrated project work will primarily take place over the course of 8 weeks via integrated team meetings. Other work in support of this project will take place off-line as needed by the project lead. Details on the prospective timeline, milestones, key dates and work scope can be referenced in other sections of this paper. Key systems engineering methodologies and tools used thus far in support of this project includes: 1) Market Surveys and Interviews, 2) Project Charter, 3) Feasibility Study, 4) Swim Lane Diagram, and 5) Knowledge Management Plan.
Currently the black box system is in the design and development phase. As the system design undergoes qualification & transitions to the production phase it will be key to evaluate, determine and integrate accident based requirements and capabilities into the design.
The SIERRA Low Mach Module: Fuego, henceforth referred to as Fuego, is the key element of the ASC fire environment simulation project. The fire environment simulation project is directed at characterizing both open large-scale pool fires and building enclosure fires. Fuego represents the turbulent, buoyantly-driven incompressible flow, heat transfer, mass transfer, combustion, soot, and absorption coefficient model portion of the simulation software. Using MPMD coupling, Scefire and Nalu handle the participating-media thermal radiation mechanics. This project is an integral part of the SIERRA multi-mechanics software development project. Fuego depends heavily upon the core architecture developments provided by SIERRA for massively parallel computing, solution adaptivity, and mechanics coupling on unstructured grids.
Presented in this document is a portion of the tests that exist in the Sierra Thermal/Fluids verification test suite. Each of these tests is run nightly with the Sierra/TF code suite and the results of the test checked under mesh refinement against the correct analytic result. For each of the tests presented in this document the test setup, derivation of the analytic solution, and comparison of the code results to the analytic solution is provided. This document can be used to confirm that a given code capability is verified or referenced as a compilation of example problems.
The SIERRA Low Mach Module: Fuego, henceforth referred to as Fuego, is the key element of the ASC fire environment simulation project. The fire environment simulation project is directed at characterizing both open large-scale pool fires and building enclosure fires. Fuego represents the turbulent, buoyantly-driven incompressible flow, heat transfer, mass transfer, combustion, soot, and absorption coefficient model portion of the simulation software. Using MPMD coupling, Scefire and Nalu handle the participating-media thermal radiation mechanics. This project is an integral part of the SIERRA multi-mechanics software development project. Fuego depends heavily upon the core architecture developments provided by SIERRA for massively parallel computing, solution adaptivity, and mechanics coupling on unstructured grids.
The SIERRA Low Mach Module: Fuego, henceforth referred to as Fuego, is the key element of the ASC fire environment simulation project. The fire environment simulation project is directed at characterizing both open large-scale pool fires and building enclosure fires. Fuego represents the turbulent, buoyantly-driven incompressible flow, heat transfer, mass transfer, combustion, soot, and absorption coefficient model portion of the simulation software. Using MPMD coupling, Scefire and Nalu handle the participating-media thermal radiation mechanics. This project is an integral part of the SIERRA multi-mechanics software development project. Fuego depends heavily upon the core architecture developments provided by SIERRA for massively parallel computing, solution adaptivity, and mechanics coupling on unstructured grids.
Aria is a Galerkin finite element based program for solving coupled-physics problems described by systems of PDEs and is capable of solving nonlinear, implicit, transient and direct-to-steady state problems in two and three dimensions on parallel architectures. The suite of physics currently supported by Aria includes thermal energy transport, species transport, and electrostatics as well as generalized scalar, vector and tensor transport equations. Additionally, Aria includes support for manufacturing process flows via the incompressible Navier-Stokes equations specialized to a low Reynolds number ( %3C 1 ) regime. Enhanced modeling support of manufacturing processing is made possible through use of either arbitrary Lagrangian-Eulerian (ALE) and level set based free and moving boundary tracking in conjunction with quasi-static nonlinear elastic solid mechanics for mesh control. Coupled physics problems are solved in several ways including fully-coupled Newtons method with analytic or numerical sensitivities, fully-coupled Newton-Krylov methods and a loosely-coupled nonlinear iteration about subsets of the system that are solved using combinations of the aforementioned methods. Error estimation, uniform and dynamic -adaptivity and dynamic load balancing are some of Arias more advanced capabilities.
Czaikowski, Oliver; Friedenberg, Larissa; Mueller-Hoeppe, Nina; Lerch, Christian; Eickemeier, Ralf; Laurich, Ben; Liu, Wenting; Zemke, Kornelia; Luedeling, Christoph; Popp, Till; Laros, James H.; Mills, Melissa M.; Reedlunn, Benjamin R.; Duesterloh, Uwe; Lerche, Svetlana; Zhao, Juan
In Germany, rock salt formations are a possible host rock taken into account for the safe disposal of heat-emitting radioactive waste. With respect to crushed salt will be used in the repository for backfilling of open cavitied (using dry material). With time, the crushed salt will be compacted by the convergence of the host rock and reaches porosities comparable with the rock salts. The compaction behaviour of crushed salt has been investigated within the last 40 years, however, its behaviour at low porosities and the resulting low permeabilities becomes relevant with the introduction of the approach of the containment providing rock zone. In the current state, the database and process understanding have some important gaps in knowledge referring the material behaviour, existing laboratory and numerical models, especially for the porosity range. The objective of this project was the development of methods and strategies for the reduction of deficits in the prediction of crushed salt compaction leading to an improvement of the prognosis quality. It includes the development of experimental methods for determining crushed salt properties in the range of low porosities, the enhancement of process understanding and the investigation and development of existing numerical models.
This report describes the testing of a model scale wave energy converter. This device, which uses two aps that pivot about a central platform when excited by waves, has a natural frequency within the range of the waves by which it is excited. The primary goal of this test was to assess the degree to which previously developed modeling, experimentation, and control design methods could be applied to a broad range of wave energy converter designs. Testing was conducted to identify a dynamic model for the impedance and excitation behavior of the device. Using these models, a series of closed loop tests were conducted using a causal impedance matching controller. This report provides a brief description of the results, as well as a summary of the device and ex- perimental design. The results show that the methods applied to this experimental device perform well and should be broadly applicable.
The Exascale Computing Project (ECP) Capability Assessment Report for Software Technologies at Sandia National Laboratories is provided. The projects are now aggregated to include Kokkos, Kokkos Kernels, VTK-m Operating Systems, and On-Node Runtime efforts. Key challenges and solution strategies are presented for each.
Direct coupling of early-time high-altitude electromagnetic pulse (HEMP) to substation control cables is simulated for cable layouts based on surveys of seven electrical substations in the United States. An analytic transmission line modeling code is used to estimate worst-case coupled current at the terminations of cable segments in or near the control shack. Where applicable, an induced voltage due to cable shield grounding is also estimated. Various configurations are simulated, including cables with different elevations, lengths, radii, and terminations. Plots of the coupled HEMP effects are given, and general relationships between these effects and the substations geometric and material parameters are highlighted and discussed.
The National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) to 1) align investments in a university capacity and workforce development with the NNSA mission to develop the needed skills and talent for NNSA’s enduring technical workforce at the laboratories and production plants, and 2) to enhance research and education at under-represented colleges and universities. Out of this effort, MSIPP launched a new consortium in early FY17 focused on Tribal Colleges and Universities (TCUs) known as the Advanced Manufacturing Network Initiative (AMNI). This consortium has been extended for FY20 and FY21. The following report summarizes the status update during this quarter.
This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users Guide. The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce. This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users Guide.
This manual describes the use of the Xyce Parallel Electronic Simulator. Xyce has been designed as a SPICE-compatible, high-performance analog circuit simulator, and has been written to support the simulation needs of the Sandia National Laboratories electrical designers. This development has focused on improving capability over the current state-of-the-art in the following areas: Capability to solve extremely large circuit problems by supporting large-scale parallel computing platforms (up to thousands of processors). This includes support for most popular parallel and serial computers. A differential-algebraic-equation (DAE) formulation, which better isolates the device model package from solver algorithms. This allows one to develop new types of analysis without requiring the implementation of analysis-specific device models. Device models that are specifically tailored to meet Sandias needs, including some radiation-aware devices (for Sandia users only). Object-oriented code design and implementation using modern coding practices. Xyce is a parallel code in the most general sense of the phrase - a message passing parallel implementation - which allows it to run efficiently a wide range of computing platforms. These include serial, shared-memory and distributed-memory parallel platforms. Attention has been paid to the specific nature of circuit-simulation problems to ensure that optimal parallel efficiency is achieved as the number of processors grows.
This manual describes the installation and use of the Xyce™ XDM Netlist Translator. XDM simplifies the translation of netlists generated by commercial circuit simulator tools into Xyce-compatible netlists. XDM currently supports translation from PSpice and HSPICE netlists into Xyce™ netlists.
This SAND Report provides an overview of AniMACCS, the animation software developed for the MELCOR Accident Consequence Code System (MACCS). It details what users need to know in order to successfully generate animations from MACCS results, to include information on the capabilities, requirements, testing, limitations, input settings, and problem reporting instructions for AniMACCS version 1.3. Supporting information is provided in the appendices, such as guidance on required input files using both WinMACCS and running MACCS from the command line. This page left blank
Bedded salt contains thin layers of clay, also known as clay seams, in-between far thicker layers of salt. These inhomogeneities are thought to have first-order effects on the closure of nearby drifts and potential roof collapses. Despite their importance, characterizations of the peak shear strength and residual shear strength of clay seams in salt are extremely rare in the published literature. A previous paper reported results from laboratory direct shear experiments on clay seam samples from the Permian Basin in New Mexico. These clay seams behaved similar to intact salt, which was attributed to the abundance of salt crystals intersecting the clay seams. None of those specimens contained a distinct ¼" - ½" (6 -12 mm) thick clay seam, as has been observed in drifts at the Waste Isolation Pilot Plant (WIPP). Due to the difficulty in obtaining WIPP samples with these types of clay seams, artificial seams of bentonite and brine sandwiched between sections of salt were created and shear tested. Eight 4" diameter samples were created with either a ¼" or ½" a thick seam and then consolidated at 3000 psi prior to shear testing. The direct shear tests on these samples were performed at nominal normal stresses representative of expected WIPP in-situ conditions (500 to 1500 psi). The resulting shear stress vs. shear displacement curves exhibited a peak followed by a gradual decay of shear strength. The shear stress never transitioned to a true residual shear stress plateau, so the final shear strength at the end of each test (0.75" of shear displacement) was analyzed instead. Both the peak shear strength and the final shear strength conformed to Mohr- Coulomb behavior with friction angles and cohesion strengths consistent with a saturated, highly consolidated, clay. These new artificial clay seam results and the previous clay-interspersed-with-salt results likely bound the expected shear behavior of WIPP clay seams.
Fontaine, A.A.; Straka, W.A.; Meyer, R.S.; Jonson, M.L.; Young, S.D.; Neary, Vincent S.
As hydrokinetic turbine technologies continue to advance towards commercialization, public datasets on the performance characteristics for these devices and their flow field effects are invaluable to advance our understanding of these technologies and to validate analytical and numerical models. The Applied Research Laboratory at The Pennsylvania State University (ARL Penn State) collaborated with Sandia National Laboratories and the University of California at Davis to design, fabricate (at a 1:8.7 scale), and experimentally test a novel hydrokinetic turbine rotor design to provide an open platform and dataset for further study and development. The water tunnel test of this three-bladed, horizontal-axis rotor recorded power production, blade loading, the near-wake flow, cavitation effects, and noise generation. These state-of-the-art measurements demonstrate much of the complex physics associated with the flow through an unducted, horizontal-axis turbine, and they elucidate the performance characteristics and flow field effects at an unprecedented fidelity, accuracy and resolution. Measurements of power coefficients (power, torque and thrust) as a function of tip-speed-ratio were performed. The dataset also includes unsteady measurements of driveshaft loading, blade strain, tower pressures, and radiated noise. Detailed flow mapping using laser Doppler velocimetry, and planar and stereo particle image velocimetry includes measurements of mean velocity and Reynolds stresses. Although the wake measurements are limited to less than half a diameter, they reveal the complex flow patterns in the near-wake structure of the rotor. The full database, available at the United States Department of Energy's marine and hydrokinetic data repository, includes tunnel and model Computer Aided Design geometry files and inflow data sufficient for a “Model-the-Test” computational Verification and Validation study.
We report electrically detected magnetic resonance (EDMR) results in metal-oxidesemiconductor field effect transistors before and after high field gate stressing. The measurements utilize EDMR detected through interface recombination currents. These interface recombination measurements provide information about one aspect of the stressing damage: The chemical and physical identity of trapping centers generated at and very near the interface. EDMR signal demonstrates that interface defects known as centers play important roles in the stress-induced damage.
Wang, Hui; Shao, Yuyan; Pan, Huilin; Feng, Xuefei; Chen, Ying; Liu, Yi S.; Walter, Eric D.; Engelhard, Mark H.; Han, Kee S.; Deng, Tao; Ren, Guoxi; Lu, Dongping; Lu, Xiaochuan; Xu, Wu; Wang, Chunsheng; Feng, Jun; Mueller, Karl T.; Guo, Jinghua; Zavadil, Kevin R.; Zhang, Ji G.
Lithium-sulfur (Li–S) battery is one of the most promising candidates for the next generation energy storage systems. However, several barriers, including polysulfide shuttle effect, the slow solid-solid surface reaction pathway in the lower discharge plateau, and corrosion of Li anode still limit its practical applications, especially under the lean electrolyte condition required for high energy density. Here, we propose a solution-mediated sulfur reduction pathway to improve the capacity and reversibility of the sulfur cathode. With this method, a high coulombic efficiency (99%) and stable cycle life over 100 cycles were achieved under application-relevant conditions (S loading: 6.2 mg cm−2; electrolyte to sulfur ratio: 3 mLE gs−1; sulfur weight ratio: 72 wt%). This result is enabled by a specially designed Li2S4-rich electrolyte, in which Li2S is formed through a chemical disproportionation reaction instead of electrochemical routes. A single diglyme solvent was used to obtain electrolytes with the optimum range of Li2S4 concentration. Operando X-ray absorption spectroscopy confirms the solution pathway in a practical Li–S cell. This solution pathway not only introduces a new electrolyte regime for practical Li–S batteries, but also provides a new perspective for bypassing the inefficient surface pathway for other electrochemical processes.
To address challenges in early detection of pond pests, we have extended a spectroradiometric monitoring method, initially demonstrated for measurement of pigment optical activity and biomass, to the detection of algal competitors and grazers. The method relies upon measurement and interpretation of pond reflectance spectra spanning from the visible into the near-infrared. Reflectance spectra are acquired every 5 min with a multi-channel, fiber-coupled spectroradiometer, providing monitoring of algal pond conditions with high temporal frequency. The spectra are interpreted via numerical inversion of a reflectance model, in which the above-water reflectance is expressed in terms of the absorption and backscatter coefficients of the cultured species, with additional terms accounting for the pigment fluorescence features and for the water-surface reflection of sunlight and skylight. With this method we demonstrate detection of diatoms and the predator Poteriochromonas in outdoor cultures of Nannochloropsis oceanica and Chlorella vulgaris, respectively. The relative strength of these signatures is compared to microscopy and sequencing analysis. Spectroradiometric detection of diatoms is then further assessed on beaker-contained mixtures of Microchloropsis salina with Phaeodactylum tricornutum, Thalassiosira weissflogii, and Thalassiosira pseudonana, respectively, providing an initial evaluation of the sensitivity and specificity of detecting pond competitors.
The field of visualization encompasses a wide range of techniques, from infographics to isosurfaces. An important subfield called "scientific visualization" is specifically dedicated to data sets with spatial components, i.e., (X, Y, Z) locations. Furthermore, this subfield's name is inspired by the fact that the data in question often come from the sciences, i.e., physics simulations or sensor networks.
The mechanical power and wake flow field of a 1:40 scale model of the US Department of Energy’s Reference Model 1 (RM1) dual rotor tidal energy converter are characterized in an open-channel flume to evaluate power performance and wake flow recovery. The NACA-63(4)-24 hydrofoil profile in the original RM1 design is replaced with a NACA-4415 profile to minimize the Reynolds dependency of lift and drag characteristics at the test chord Reynolds number. Precise blade angular position and torque measurements were synchronized with three acoustic Doppler velocimeters (ADV) aligned with each rotor centerline and the midpoint between the rotor axes. Flow conditions for each case were controlled to maintain a hub height velocity, uhub = 1.04 ms−1, a flow Reynolds number, ReD = 4.4 × 105, and a blade chord length Reynolds number, Rec = 3.1 × 105. Performance was measured for a range of tip-speed ratios by varying rotor angular velocity. Peak power coefficients, CP = 0.48 (right rotor) and CP = 0.43 (left rotor), were observed at a tip speed ratio, λ = 5.1. Vertical velocity profiles collected in the wake of each rotor between 1 and 10 rotor diameters are used to estimate the turbulent flow recovery in the wake, as well as the interaction of the counter-rotating rotor wakes. The observed performance characteristics of the dual rotor configuration in the present study are found to be similar to those for single rotor investigations in other studies. Similarities between dual and single rotor far-wake characteristics are also observed.
The solubility of RDX (hexahydro-1,3,5-tri-nitro-1,3,5-triazine) in TNT (2,4,6-trinitrotoluene) at elevated temperatures is required to accurately predict the response of Comp-B3 (60:40 RDX:TNT) during accidents involving fire. As the temperature increases, the TNT component melts, the RDX partially dissolves in the liquid TNT, and the remaining RDX melts (203 ∘C) as the Comp-B thermally ignites. In the current work, we used a differential scanning calorimeter (DSC) to estimate the solubility of RDX in TNT at the melting point of RDX. Most DSC measurements of Comp-B3 do not show an RDX melt endotherm. The absence of an endotherm associated with the RDX melt has been interpreted as RDX being completely dissolved in TNT before reaching the melting point. We have observed that the endotherm is not absent, but is masked by exothermic reactions occurring at these elevated temperatures. We have inhibited the exothermic reactions by venting our DSC samples and measuring the RDX melt endotherm in our Comp-B3 samples at about 203 ∘C. Using the measured heat flow associated with the RDX melt and the latent melting enthalpy of RDX, we have approximated the solubility of RDX in TNT to be roughly 50–100 g RDX per 100 g TNT. The broad range is based on corrections for exothermic reactions occurring as the RDX melts.
We have used scanning tunneling microscopy and density functional theory calculations to study molecular layers of coronene on Cu(111). The structure and stability of these layers is determined by the balance between coronene-substrate and coronene-coronene interactions. Here, we characterize this balance by measuring the maximum coverage before coronene dewets the substrate to form three-dimensional islands. We find that coronene molecules lie parallel to the substrate at the maximum coverage, in contrast to previous observations of tilted coronene on metal surfaces. We attribute this previously reported tilt to a metastability caused by an activation barrier to nucleate three-dimensional islands.
Refractory metals are favorable materials in applications where high strength and ductility are needed at elevated temperatures. In some cases, operating temperatures may be near the melting point of the material. However, as temperature drops, refractory metals typically undergo a significant mechanical response change - ductile-to-brittle transition. These materials may be subjected to high strain rate loading at an ambient temperature state, such as an impact or crash. Knowledge of the high rate material properties are essential for design as well as simulation of impact events. The high rate stress-strain behavior of brittle metallic materials at ambient temperature is rarely studied because of experimental challenges, particularly when failure is involved. Failure typically occurs within the non-gage section of the material, which invalidates any collected stress-strain information. In this study, a method to determine a specimen geometry which will produce failures in the gage section is presented. Pure tungsten in thin-sheet form was used as a trial material to select a specimen geometry for high rate Kolsky tension bar experiments. A finite element simulation was conducted to derive a strain correction for more accurate results. The room temperature stress-strain behavior of pure tungsten at a strain rate of 24 s−1 is presented. The outcome of this experimental technique can be applied to other brittle materials for dynamic tensile characterization.
In this work, we study reproducing kernel (RK) collocation method for peridynamic Navier equation. In the first part, we apply a linear RK approximation to both displacement and dilatation, and then back-substitute dilatation and solve the peridynamic Navier equation in a pure displacement form. The RK collocation scheme converges to the nonlocal limit for a fixed nonlocal interaction length and also to the local limit as nonlocal interactions vanish. The stability is shown by comparing the collocation scheme with the standard Galerkin scheme using Fourier analysis. In the second part, we apply the RK collocation to the quasi-discrete peridynamic Navier equation and show its convergence to the correct local limit when the ratio between the nonlocal length scale and the discretization parameter is fixed. The analysis is carried out on a special family of rectilinear Cartesian grids for the RK collocation method with a designated kernel with finite support. We assume the Lamé parameters satisfy λ≥μ to avoid extra assumptions on the nonlocal kernel. Finally, numerical experiments are conducted to validate the theoretical results.
Product designs from a wide range of industries such as aerospace, automotive, biomedical, and others can benefit from new metamaterials for mechanical energy dissipation. In this study, we explore a novel new class of metamaterials with unit cells that absorb energy via sliding Coulombic friction. Remarkably, even materials such as metals and ceramics, which typically have no intrinsic reversible energy dissipation, can be architected to provide dissipation akin to elastomers. The concept is demonstrated at different scales (centimeter to micrometer), with different materials (metal and polymer), and in different operating environments (high and low temperatures), all showing substantial dissipative improvements over conventional non-contacting lattice unit cells. Further, as with other ‘programmable’ metamaterials, the degree of Coulombic absorption can be tailored for a given application. An analytic expression is derived to allow rapid first-order optimization. This new class of Coulombic friction energy absorbers can apply broadly to many industrial sectors such as transportation (e.g. monolithic shock absorbers), biomedical (e.g. prosthetics), athletic equipment (e.g. skis, bicycles, etc.), defense (e.g. vibration tolerant structures), and energy (e.g. survivable electrical grid components).
We describe molecular dynamics simulations of helium implantation in geometries resembling tungsten nanotendrils observed in helium plasma exposure experiments. Helium atoms self-cluster and nucleate bubbles within the tendrillike geometries. However, helium retention in these geometries is lower than planar surfaces due to higher surface area to volume ratio which allows for continual bubble expansion and non-destructive release of helium atoms from the nanotendril. Limited diffusion of helium atoms deeper into the tendril was observed, and diffusion was enhanced with pre-existing, subsurface helium bubbles. Diffusion coefficients on the order of 10−12–10−11 m2s-1 were calculated. This suggests that while helium diffusion is low, it is still feasible that helium can diffuse to the base of a nanotendril to continue to drive fuzz growth.
The virtual bifiPV Workshop was held in July 2020 to provide the solar industry with a forum for sharing and discussing research into bifacial photovoltaic (PV) technology. This report outlines major insights from the workshop to give the reader an overview of the latest developments in bifacial PV technology worldwide, from the lab to the field. Citations are drawn from this workshop unless otherwise noted, with all proceedings available online at bifipvworkshop. com. Presentations for the bifiPV2020 Workshop focused on the following areas: bifacial power plant modeling and simulation, albedo improvements, the development of encapsulants, the durability and reliability of current bifacial technologies, performance comparisons between glass-glass and glass-transparent backsheet configurations, the future of passivated emitter and rear contact (PERC) solar cells, and the growing adoption of n-type solar cells. With 650 GW total PV installed worldwide and 1 TW to come very soon, PERC is now the standard PV cell type produced en masse. However, it is already reaching 23% efficiency, the upper limit for this type of technology. PV modules breaking the 0.5-kW barrier are starting to appear, and the costs of standard PERC technology are already below 0.2 USD/Wp. In 2019, five GW of bifacial PV were installed worldwide. In 2020, the majority of bifacial installations are expected to be located in the United States, China, and Middle East and North Africa (MENA) states. N-type bifacial technologies are becoming increasingly viable and have huge potential to dominate the market in the coming years. With bifacial technology mounted on horizontal single-axis trackers (HSAT), bids below 10 USD/MWh will soon be observed in the MENA region, and later in Chile and the United States. Factory audits and reliability testing can reduce field failures by helping buyers to select producers that follow rigorous quality assurance and quality control processes.