We present two-dimensional temperature measurements of magnetized and unmagnetized plasma experiments performed at Z relevant to the preheat stage in magnetized liner inertial fusion. The deuterium gas fill was doped with a trace amount of argon for spectroscopy purposes, and time-integrated spatially resolved spectra and narrow-band images were collected in both experiments. The spectrum and image data were included in two separate multiobjective analysis methods to extract the electron temperature spatial distribution Te(r,z). The results indicate that the magnetic field increases Te, the axial extent of the laser heating, and the magnitude of the radial temperature gradients. Comparisons with simulations reveal that the simulations overpredict the extent of the laser heating and underpredict the temperature. Temperature gradient scale lengths extracted from the measurements also permit an assessment of the importance of nonlocal heat transport.
The marine and hydrokinetic (MHK) industry is at an early stage of development and has the potential to play a significant role in diversifying the U.S. energy portfolio and reducing the U.S. carbon footprint. Wave energy is the largest among all the U.S. MHK energy resources, which include wave energy, ocean current, tidal-instream, ocean thermal energy conversion, and river-instream. Wave resource characterization is an essential step for regional wave energy assessments, Wave Energy Converter (WEC) project development, site selection and WEC design. The present paper provides an overview of a joint modelling effort by the Pacific Northwest National Laboratory and Sandia National Laboratories on high-resolution wave hindcasts to support the U.S. Department of Energy’s Water Power Technologies Office’s program of wave resource characterization, assessment and classifications in all US coastal regions. Topics covered include the modelling approach, model input requirements, model validation strategies, high performance computing resource requirements, model outputs and data management strategies. Examples of model setup and validation for different regions are provided along with application to development of classification systems, and analysis of regional wave climates. Lessons learned and technical challenges of the long-term, high-resolution regional wave hindcast are discussed.
We present a classical approximation algorithm for the MAX-2-Local Hamiltonian problem. This is a maximization version of the QMA-complete 2-Local Hamiltonian problem in quantum computing, with the additional assumption that each local term is positive semidefinite. The MAX-2-Local Hamiltonian problem generalizes NP-hard constraint satisfaction problems, and our results may be viewed as generalizations of approximation approaches for the MAX-2-CSP problem. We work in the product state space and extend the framework of Goemans and Williamson for approximating MAX-2-CSPs. The key difference is that in the product state setting, a solution consists of a set of normalized 3-dimensional vectors rather than boolean numbers, and we leverage approximation results for rank-constrained Grothendieck inequalities. For MAX-2-Local Hamiltonian we achieve an approximation ratio of 0.328. This is the first example of an approximation algorithm beating the random quantum assignment ratio of 0.25 by a constant factor.
Multiple experimental configurations for performing nanoscale orientation mapping are compared to determine their fidelity to the true microstructure of a sample. Transmission Kikuchi diffraction (TKD) experiments in a scanning electron microscope (SEM) and nanobeam diffraction (NBD) experiments in a transmission electron microscope (TEM) were performed on thin electrodeposited hard Au films with two different microstructures. The Au samples either had a grain size that is >50 or <20 nm. The same regions of the samples were measured with TKD apparatuses at 30 kV in an SEM with detectors in the horizontal and vertical configurations and in the TEM at 300 kV. Under the proper conditions, we demonstrate that all three configurations can produce data of equivalent quality. Each method has strengths and challenges associated with its application and representation of the true microstructure. The conditions needed to obtain high-quality data for each acquisition method and the challenges associated with each are discussed.
Logan, N.C.; Park, J.K.; Hu, Q.; Paz-Soldan, C.; Markovic, T.; Wang, H.; In, Y.; Piron, L.; Piovesan, P.; Myers, Clayton E.; Maraschek, M.; Wolfe, S.M.; Strait, E.J.; Munaretto, S.
This paper presents a multi-machine, multi-parameter scaling law for the n = 2 core resonant error field threshold that leads to field penetration, locked modes, and disruptions. Here, n is the toroidal harmonic of the non-axisymmetric error field (EF). While density scalings have been reported by individual tokamaks in the past, this work performs a regression across a comprehensive range of densities, toroidal fields, and pressures accessible across three devices using a common metric to quantify the EF in each device. The metric used is the amount of overlap between an EF and the spectrum that drives the largest linear ideal MHD resonance, known as the "dominant mode overlap". This metric, which takes into account both the external field and plasma response, is scaled against experimental parameters known to be important for the inner layer physics. These scalings validate non-linear MHD simulation scalings, which are used to elucidate the dominant inner layer physics. Both experiments and simulations show that core penetration thresholds for EFs with toroidal mode number n = 2 are of the same order as the n = 1 thresholds that are considered most dangerous on current devices. Both n = 1 and n = 2 thresholds scale to values within the ITER design tolerances, but data from additional devices with a range of sizes are needed in order to increase confidence in quantitative extrapolations of n = 2 thresholds to ITER.
The ability to train deep neural networks on large data sets have made significant impacts onto artificial intelligence, but consume significant amounts of energy due to the need to move information from memory to logic units. In-memory "neuromorphic" computing presents an alternative framework that processes information directly on memory elements. In-memory computing has been limited by the poor performance of the analogue information storage element, often phase-change memory or memristors. To solve this problem, we developed two types of "redox transistors" using TiO2 (anatase) which stores analogue information states through the electrochemical concentration of dopants in the crystal. The first type of redox transistor uses lithium as the electrochemical dopant ion, and its key advantage is low operating voltage. The second uses oxygen vacancies as the dopant, which is CMOS compatible and can retain state even when scaled to nanosized dimensions. Both devices offer significant advantages in terms of predictable analogue switching over conventional filamentary-based devices, and provide a significant advance in developing materials and devices for neuromorphic computing.
In this report, we evaluate a novel method for modeling the spread of COVID-19 pandemic. In this new approach we leverage methods and algorithms developed for fully-kinetic plasma physics simulations using Particle-In-Cell (PIC) Direct Simulation Monte-Carlo (DSMC) models. This approach then leverages Sandia-unique simulation capabilities, and High-Performance Computer (HPC) resources and expertise in particle-particle interactions using stochastic processes. Our hypothesis is that this approach would provide a more efficient platform with assumptions based on physical data that would then enable the user to assess the impact of mitigation strategies and forecast different phases of infection. This work addresses key scientific questions related to the assumptions this new approach must make to model the interactions of people using algorithms typically used for modeling particle interactions in physics codes (kinetic plasma, gas dynamics). The model developed uses rational/physical inputs while also providing critical insight; the results could serve as inputs to, or alternatives for, existing models. The model work presented was developed over a four-week time frame, thus far showing promising results and many ways in which this model/approach could be improved. This work is aimed at providing a proof-of-concept for this new pandemic modeling approach, which could have an immediate impact on the COVID-19 pandemic modeling, while laying a basis to model future pandemic scenarios in a manner that is timely and efficient. Additionally, this new approach provides new visualization tools to help epidemiologists comprehend and articulate the spread of this and other pandemics as well as a more general tool to determine key parameters needed in order to better predict pandemic modeling in the future. In the report we describe our model for pandemic modeling, apply this model to COVID-19 data for New York City (NYC), assess model sensitivities to different inputs and parameters and , finally, propagate the model forward under different conditions to assess the effects of mitigation and associated timing. Finally, our approach will help understand the role of asymptomatic cases, and could be extended to elucidate the role of recovered individuals in the second round of the infection, which is currently being ignored.
Compression-induced solidification has been observed in cerium on nanosecond timescales. A series of experiments was conducted in Sandia National Laboratories' Z Facility in which cerium was shock melted and subsequently shocklessly, or ramp, loaded across the melt line inducing solidification. The signature of solidification manifested in the recovery of material strength and the propagation of waves at the local elastic sound velocity. Density functional theory simulations of cerium along the experimental phase-space path exhibit spontaneous freezing to a tetragonal phase at the same pressure and closely predict the observed physical properties of solid and liquid cerium near melt.
Significant effort is placed on tuning the internal parameters of fuzzers to explore the state space, measured as coverage, of binaries. In this work, we investigate the effects of the external environment on the resulting coverage after fuzzing two binaries with AFL for 24 hours. Parameters such as scaling to multiple nodes, node saturation, and parallel file system type on HPC resources are controlled in order to maximize coverage. It will be shown that employing a parallel file system such as IBM's General Parallel File System offers an advantage for fuzzing operations, since it contains enhancements for performance optimization. When combined with scaling to two and four nodes, while simultaneously restricting the number of coordinated AFL tasks per node on the low end (10-50% of available physical cores), coverage may be enhanced within a shorter period of time. Thus, controlling the external environment is a useful effort.
Researchers from Sandia National Laboratories (Sandia) and the University of Texas at Austin (UT) conducted this study to explore the effectiveness of commercial artificial neural network (ANN) software to improve insider threat detection and mitigation (ITDM). This study hypothesized that ANNs could be "trainee to learn patterns of organizational behaviors, detect off-normal (or anomalous) deviations from these patterns, and alert when certain types, frequencies, or quantities of deviations emerge. The ReconaSense ANN system was installed at UT's Nuclear Engineering Teaching Laboratory (NETL) and collected 13,653 access control data points and 694 intrusion sensor data points over a three-month period. Preliminary analysis of this baseline data demonstrated regularized patterns of life in the facility, and that off-normal behaviors are detectable under certain situations -- even for a facility with anticipated highly non-routine, operational behaviors. Completion of this pilot study demonstrated how the ReconaSense ANN could be used to identify expected operational patterns and detect unexpected anomalous behaviors in support of a data-analytic approach to ITDM. While additional studies are needed to fully understand and characterize this system, the results of this initial study are overall very promising for demonstrating a new framework for ITDM utilizing ANNs and data analysis techniques.
Since even before its establishment as an independent national security laboratory in 1949, Sandia has been devoted to an overarching mission of developing advanced technologies for global peace. These technologies have taken a variety of forms, and they exist in and must address an ever-changing global security environment. An understanding of that global security environment and its possible or likely evolution is therefore critical to ensuring that Sandia can maintain its focus on strategic technology investments that will benefit the nation in the next 20- 30 years. Sandia sustains multiple Systems Analysis organizations whose responsibility includes maintaining an understanding of the global security environment as it applies across multiple mission domains. The topics below include two from Sandia's emerging threats and biodefense mission, three with relevance to Sandia's cyber defense mission, and four of particular but not exclusive relevance to Sandia's nuclear deterrence mission. All are intended to spur independent academic thought that could assist Sandia as well as the broader national security community in anticipating and adapting to a continually changing world. Sandia anticipates periodic interactions between Sandia Systems Analysis staff and SciPol Scholars Program faculty and students who choose to expand upon these topics in order to provide opportunities for feedback and communication throughout 2020-2021.
The ECP/VTK-m project is providing the core capabilities to perform scientific visualization on Exascale architectures. The ECP/VTK-m project fills the critical feature gap of performing visualization and analysis on processors like graphics-based processors. The results of this project will be delivered in tools like ParaView, Vislt, and Ascent as well as in stand-alone form. Moreover, these projects are depending on this ECP effort to be able to make effective use of ECP architectures. One of the biggest recent changes in high-performance computing is the increasing use of accelerators. Accelerators contain processing cores that independently are inferior to a core in a typical CPU, but these cores are replicated and grouped such that their aggregate execution provides a very high computation rate at a much lower power. Current and future CPU processors also require much more explicit parallelism. Each successive version of the hardware packs more cores into each processor, and technologies like hyper threading and vector operations require even more parallel processing to leverage each core's full potential. VTK-m is a toolkit of scientific visualization algorithms for emerging processor architectures. VTK-m supports the fine-grained concurrency for data analysis and visualization algorithms required to drive extreme scale computing by providing abstract models for data and execution that can be applied to a variety of algorithms across many different processor architectures. The ECP/VTK-m project is building up the VTK-m codebase with the necessary visualization algorithm implementations that run across the varied hardware platforms to be leveraged at the Exascale. We will be working with other ECP projects, such as ALPINE, to integrate the new VTK-m code into production software to enable visualization on our HPC systems.
On June 30, 2020, an inadvertent reaction occurred during pressing of the energetic material pentaerythritol tetranitrate (PETN). The location of the event was the energetic component Rapid Prototype Facility (RPF), where similar operations performed on a variety of energetic materials are routinely provided for customers throughout Sandia National Laboratories (SNL). A background on pressing of energetic materials is provided to enhance clarity in the description of the event. This background includes a description of the equipment, materials, and tooling present during the event.
GentenMPl is a toolkit of sparse canonical polyadic (CP) tensor decomposition algorithms that is designed to run effectively on distributed-memory high-performance computers. Its use of distributed-memory parallelism enables it to efficiently decompose tensors that are too large for a single compute node's memory. GentenMPl leverages Sandia's decades-long investment in the Trilinos solver framework for much of its parallel-computation capability. Trilinos contains numerical algorithms and linear algebra classes that have been optimized for parallel simulation of complex physical phenomena. This work applies these tools to the data science problem of sparse tensor decomposition. In this report, we describe the use of Trilinos in GentenMPl, extensions needed for sparse tensor decomposition, and implementations of the CP-ALS (CP via alternating least squares) and GCP-SGD (generalized CP via stochastic gradient descent) sparse tensor decomposition algorithms. We show that GentenMPl can decompose sparse tensors of extreme size, e.g., a 12.6-terabyte tensor on 8192 computer cores. We demonstrate that the Trilinos backbone provides good strong and weak scaling of the tensor decomposition algorithms.
In 2016/2017, the field of High-Performance Computing (HPC) entered a new era driven by fundamental physics challenges to produce ever more energy and cost-efficient processors. Since the convergence on the Message-Passing Interface (MPI) standard in the mid-1990s, application developers enjoyed a seemingly static view of the underlying machine — that of a distributed collection of homogeneous nodes executing in collaboration. However, after almost two decades of dominance, the sole use of MPI to derive parallelism acted as a limiter to improved future performance. While MPI is widely expected to continue to function as the basic mechanism for communication between compute nodes for the immediate future, additional parallelism is required on the computing node itself if high performance and efficiency goals are to be realized. When reviewing the architectures of the top HPC systems today, the change in paradigm is clear: the compute nodes of the leading machines in the world are either powered by many-core chips with a few dozen cores each, or use heterogeneous designs, where traditional CPUs marshal work to massively parallel compute accelerators which has as many as 200,000 processing threads in flight simultaneously. Complicating matters further for application developers, each processor vendor has its own preferred way of writing code for their architecture.The Kokkos EcoSystem was released by Sandia in 2017 to address this new era in HPC system design by providing a vendor independent performance portable programming system for scientific, engineering, and mathematical software applications written in the C++ programming language. Using Kokkos, application developers can be more productive because they will not have to create and maintain separate versions of their software for each architecture, nor will they have to be experts in each architecture's peculiar requirements. Instead, they will have a single method of programming for the diverse set of modern HPC architectures. While Kokkos started in 2011 as a programming model only, it soon became clear that complex applications needed more. It is also critical to have a portable mathematical functions and developers need tools to debug their applications, gain insight into the performance characteristics of their codes and tune algorithm performance parameters through automated processes. The Kokkos EcoSystem addresses those needs through its three main components: the Kokkos Core programming model, the Kokkos Kernels math library, and the Kokkos Tools project.
The component that is powered by the battery pack being monitored is a valuable asset and must be in working condition at all times. Battery chemistry and characteristics have a major role in how to evaluate the state of the battery. The battery monitoring system has many parts that lead to an accurate battery reading. The components consist of a coulomb counting device, end of life voltage detection, a consideration of use for a real-time clock (RTC), temperature sensor, and non-volatile random-access memory (NVRAM). The combination of these elements allows the monitoring system to be highly reliant. Moving forward a better implementation of the ideas in this paper and further testing should ensure a high-quality battery monitoring system.
The Sandia National Laboratories' (SNL) Corporate Behavioral Health Program is a workplace- based program that: 1) provides Employee Assistance Program (EAP) services including early identification and resolution of personal concerns which may impact job performance, 2) assists managers and the organization in addressing productivity issues, and 3) supports the SNL commitment to provide a safe and healthful work environment. The program is offered to approximately 13,500 employees in New Mexico. The Behavioral Health Program is a corporate program combining services in NM and CA. It is integrated with other occupational health and clinical services including disability, disease management and preventive health programs. In addition, Sandia's Behavioral Health Program provides critical management consultation and psychological assessment services for external organizations including Human Resources, the Department of Energy and Security through the Clinical Evaluation (CE) process, Human Reliability Program (HRP), Protective Force Program, Workplace Violence/Threat Assessment Team (TAT), and Insider Threat Working Group programs. The program supports Sandia National Laboratories' mission to safeguard national security, the environment, and the public; it is a proactive approach to early identification, intervention and assessment. Importantly, it reduces barriers to accessing mental health services and assists with reducing health care costs attributed to illness or injuries related to unhealthy lifestyles and behaviors. The team is comprised of a professional staff including a licensed Clinical Psychologist, a licensed professional clinical counselor (LPCC) and a licensed Marriage and Family counselor (MFT) who is also a Certified Employee Assistance Professional and who holds a doctorate in counseling psychology.
The formation of a stress corrosion crack (SCC) in the canister wall of a dry cask storage system (DCSS) has been identified as a potential issue for the long-term storage of spent nuclear fuel. The presence of an SCC in a storage system could represent a through-wall flow path from the canister interior to the environment. Modern, vertical DCSSs are of particular interest due to the significant backfill pressurization of the canister, up to approximately 800 kPa. This pressure differential offers a relatively high driving potential for blowdown of any particulates that might be present in the canister. In this study, the carrier gas flow rates and aerosol transmission properties were evaluated for an engineered microchannel with characteristic dimensions similar to those of an SCC. The microchannel was formed by mating two gage blocks with a slot orifice measuring 28.9 μm (0.0011 in.) tall by 12.7 mm (0.500 in.) wide by 8.86 mm (0.349 in.) long (flow length). Surrogate aerosols of cerium oxide, Ce02, were seeded and mixed inside a pressurized tank. The aerosol characteristics were measured immediately upstream and downstream of the simulated SCC at elevated and ambient pressures, respectively. These data sets are intended to demonstrate a new capability to characterize SCCs under well-controlled boundary conditions. Separate modeling efforts are also underway that will be validated using these data. The test apparatus and procedures developed in this study can be easily modified for the evaluation of more complex SCC-like geometries including laboratory-grown SCC samples.
This report summarizes the 2020 fiscal year (FY20) status of the borehole heater test in salt funded by the US Department of Energy Office of Nuclear Energy (DOE-NE) Spent Fuel and Waste Science & Technology (SFWST) campaign. This report satisfies SFWST level-two milestone number M2SF-20SNO10303032. This report is an update of an August 2019 level-three milestone report to present the final as-built description of the test and the first phase of operational data (BATS la, January to March 2020) from the Brine Availability Test in Salt (BATS) field test.
A variety of issues and occurrences are reported at Sandia National Laboratories every year. The challenge is to filter through these occurrences and determine whether there are any notable trends related to work planning and control processes and whether some of the common problems can be mitigated or prevented. The hope is that some of the more common issues can be improved through training, communication, and/or documentation. These causal trends may also help to improve current training and work planning and control processes by revealing some opportunities for improvement.
The U.S. military has been exploring pathways to reduce the logistical burden of fuel on virtually all their missions globally. Energy harvesting of local resources such as wind and solar can help increase the resilience and operational effectiveness of military units, especially at the most forward operating bases where the fuel logistics are most challenging. This report considers the potential benefits of wind energy provided by deployable wind turbines as measured by a reduction in fuel consumption and supply convoys to a hypothetical network of Army Infantry Brigade Combat Team bases. Two modeling and simulation tools are used to represent the bases and their operations and quantify the impacts of system design variables that include wind turbine technologies, battery storage, number of turbines, and wind resource quality. The System of Systems Analysis Toolkit Joint Operational Energy Model serves as a baseline scenario for comparison. The Hybrid Optimization of Multiple Energy Resources simulation tool is used to optimize a single base within the larger Joint Operational Energy Model. The results of both tools show that wind turbines can provide significant benefits to contingency bases in terms of reduced fuel use and number of convoy trips to resupply the base. The match between the turbine design and wind resource, which is statistically low across most of the global land area, is a critical design consideration. The addition of battery storage can enhance the benefits of wind turbines, especially in systems with more wind turbines and higher wind resources. Wind turbines may also provide additional benefits to other metrics such as resilience that may be important but not fully considered in the current analysis. ACKNOWLEDGEMENTS The authors would like to thank the following individuals for their helpful support, feedback and review to improve this report: U.S. Department of Energy Wind Energy Technologies Office, Patrick Gilman and Bret Barker; Idaho National Laboratory, Jake Gentle and Bradley Whipple; The National Renewable Energy Laboratory, Robert Preus and Tony Jimenez; Sandia National Laboratories, Alan Nanco, Dennis Anderson, and Hai Le. In addition, numerous discussions with military and industry stakeholders over the year were invaluable in focusing the efforts represented in this report.
A literature search of the most prominent and widely available additive manufacturing technologies was done to understand the current developments in this area. The first section provides the introduction and scope for this report, followed by a very detailed second section on the different types of additive manufacturing technologies, how they work, the materials used, advantages and disadvantages of each technology, and manufacturer information. For comparative purposes, a third section on the most widely used subtractive technology, Computer Numerical Control (CNC) machining, is presented with information about the parameters used and the benefits and limitations of this technology. A final section with a summary and conclusions is presented with information comparing the power and utility of the different additive manufacturing technologies compared to traditional manufacturing.
The Generation 3 Concentrating Solar Power (Gen3CSP) supercritical carbon dioxide (sCO2) coolant loop, typically referred to here as the `sCO2loop,' is designed to continuously remove heat from a primary heat exchanger (PHX) subsystem through a flow of sCO2 as a substitute for a sCO2 Brayton power cycle as shown in Figure 1-1. This system is designed to function as a pumped coolant loop operating at a high baseline pressure with a high degree of flexibility, stability, and autonomy to simplify operation of a Gen3CSP Topic 1 team Phase 3 pilot plant. The complete system includes a dedicated inventory management module to fill the main flow loop with CO2 and recovery CO2 during heating and venting operations to minimize the delivery of CO2 to the site.
Engine run days in the Diesel Combustion and Fuel Effects Lab are hectic. The long mental lists that must be kept by engine operators, paired with the tight time constraints between experiments, can cause operational issues that may be dangerous to personnel and/or cause damage to test equipment. Until now, a paper sign has been used to warn operators not to motor the engine when a foreign object has been placed inside of it. Unfortunately, this simple administrative control has failed in the past, motivating this effort to develop an improved system. The lockout system described in this document introduces an engineering control that, when activated, actually prevents the engine from being motored. The new system consists of a primary and a secondary control panel. Prior to an operator placing a foreign object into the cylinder, they press a button on the secondary control panel near the engine. This breaks the interlock circuit for the engine dynamometer and activates LEDs on both control panels to notify operators that a foreign object is present within the engine cylinder. Once the work is done and all foreign objects have been removed from the combustion chamber, two operators must be present to disable the system by simultaneously pressing the buttons on the primary and secondary control panels. Requiring a second operator to disable the system increases accountability and reduces the likelihood of potentially costly mistakes.
Thermal sprayed metal coatings are used in many industrial applications, and characterizing the structure and performance of these materials is vital to understanding their behavior in the field. X-ray Computed Tomography (CT) machines enable volumetric, nondestructive imaging of these materials, but precise segmentation of this grayscale image data into discrete material phases is necessary to calculate quantities of interest related to material structure. In this work, we present a methodology to automate the CT segmentation process as well as quantify uncertainty in segmentations via deep learning. Neural networks (NNs) are shown to accurately segment full resolution CT scans of thermal sprayed materials and provide maps of uncertainty that conservatively bound the predicted geometry. These bounds are propagated through calculations of material properties such as porosity that may provide an understanding of anticipated behavior in the field.
Battery based energy storage systems are becoming a critical part of a modernized, resilient power system. However, batteries have a unique combination of hazards that can make design and engineering of battery systems difficult. This report presents a systematic hazard analysis of a hypothetical, grid scale lithium-ion battery powerplant to produce sociotechnical "design objectives" for system safety. We applied system's theoretic process analysis (STPA) for the hazard analysis which is broken into four steps: purpose definition, modeling the safety control structure, identifying unsafe control actions, and identifying loss scenarios. The purpose of the analysis was defined as to prevent event outcomes that can result in loss of battery assets due to fires and explosions, loss of health or life due to battery fires and explosions, and loss of energy storage services due to non- operational battery assets. The STPA analysis resulted in identification of six loss scenarios, and their constituent unsafe control actions, which were used to define a series of design objectives that can be applied to reduce the likelihood and severity of thermal events in battery systems. These design objectives, in all or any subset, can be utilized by utilities and other industry stakeholders as "design requirements" in their storage request for proposals (RFPs) and for evaluation of proposals. Further, these design objectives can help to protect firefighters and bring a system back to full functionality after a thermal event. We also comment on the hazards of flow battery technologies.
A new in transit Data Service is presented and compared to the traditional file-based workflow and the newly refactored in situ Catalyst workflow. Each workflow is enabled by the IOSS mesh interface equipped with data management layers for Exodus and CGNS (file-based), Catalyst (in situ), and FAODEL (in transit). FAODEL is a distributed object store that can transmit data across MPI allocations. Catalyst is a Para View-based visualization capability developed as part of the CSSE Data Services effort. The workflows considered here take SPARC data into Catalyst for visualization post-processing. Although still in unoptimized form, we show that the in transit approach is a viable alternative to file-based and in situ workflows and offers several advantages to both simulation and post-processing developers. Since IOSS is a mature interface with wide adoption across Sandia and externally, each workflow can be reconfigured to use different simulations that generate mesh data and post-processing tools that consume it.
Often the Radar Cross-Section (RCS) of a target is incorrectly assumed to be a single number by those unfamiliar with electromagnetic scattering. In actuality, a target's RCS depends on many factors. These factors include radar signal frequency, radar observation angle, as well as target orientation. Another possible parameter (often not considered) is time. The RCS of targets may change over time due to movement, environmental changes, etc. In order to accurately represent the dynamic RCS of a target in a time-stepped analysis, the ability to interface with large RCS datasets efficiently is desired. To this end, a file format and API (written in C++) were developed and are described in this report.
Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.
Haynes, Casey D.; Gudmunson, Shadron E.; Baca, Roman G.R.; Aldinger, Elijah L.; Liebrock, Lorie M.
The New Mexico Cybersecurity Center 2020 Summer Institute with Sandia National Laboratories was the rst ever for the New Mexico Cybersecurity Centers. Due to the occurrence of the COVID-19 global pandemic during the summer of 2020 some unforeseen challenges had to be faced in running the institute, such as the fact that all students had to work remotely. This document details the students involved in the institute and their contributions to Sandia projects over the summer of 2020, as well as an analysis on how to successfully run a summer institute via distance.
Vadlamani, Sri K.; Agarwal, Sapan A.; Limmer, David T.; Louie, Steven G.; Fischer, Felix R.; Yablonovitch, Eli
In tunnel field-effect transistors (tFETs), the preferred mechanism for switching occurs by alignment (on) or misalignment (off) of two energy levels or band edges. Unfortunately, energy levels are never perfectly sharp. When a quantum dot interacts with a wire, its energy is broadened. Its actual spectral shape controls the current/voltage response of such transistor switches, from on (aligned) to off (misaligned). The most common model of spectral line shape is the Lorentzian, which falls off as reciprocal energy offset squared. Unfortunately, this is too slow a turnoff, algebraically, to be useful as a transistor switch. Electronic switches generally demand an on/off ratio of at least a million. Steep exponentially falling spectral tails would be needed for rapid off-state switching. This requires a new electronic feature, not previously recognized: narrowband, heavy-effective mass, quantum wire electrical contacts, to the tunneling quantum states. These are a necessity for spectrally sharp switching.
The wide adoption of deep neural networks has been accompanied by ever-increasing energy and performance demands due to the expensive nature of training them. Numerous special-purpose architectures have been proposed to accelerate training: both digital and hybrid digital-analog using resistive RAM (ReRAM) crossbars. ReRAM-based accelerators have demonstrated the effectiveness of ReRAM crossbars at performing matrix-vector multiplication operations that are prevalent in training. However, they still suffer from inefficiency due to the use of serial reads and writes for performing the weight gradient and update step. A few works have demonstrated the possibility of performing outer products in crossbars, which can be used to realize the weight gradient and update step without the use of serial reads and writes. However, these works have been limited to low precision operations which are not sufficient for typical training workloads. Moreover, they have been confined to a limited set of training algorithms for fully-connected layers only. To address these limitations, we propose a bit-slicing technique for enhancing the precision of ReRAM-based outer products, which is substantially different from bit-slicing for matrix-vector multiplication only. We incorporate this technique into a crossbar architecture with three variants catered to different training algorithms. To evaluate our design on different types of layers in neural networks (fully-connected, convolutional, etc.) and training algorithms, we develop PANTHER, an ISA-programmable training accelerator with compiler support. Our design can also be integrated into other accelerators in the literature to enhance their efficiency. Our evaluation shows that PANTHER achieves up to 8.02×, 54.21×, and 103× energy reductions as well as 7.16×, 4.02×, and 16× execution time reductions compared to digital accelerators, ReRAM-based accelerators, and GPUs, respectively.
Microbial production of iron (oxyhydr)oxides on polysaccharide rich biopolymers occurs on such a vast scale that it impacts the global iron cycle and has been responsible for major biogeochemical events. Yet the physiochemical controls these biopolymers exert on iron (oxyhydr)oxide formation are poorly understood. Here we used dynamic force spectroscopy to directly probe binding between complex, model and natural microbial polysaccharides and common iron (oxyhydr)oxides. Applying nucleation theory to our results demonstrates that if there is a strong attractive interaction between biopolymers and iron (oxyhydr)oxides, the biopolymers decrease the nucleation barriers, thus promoting mineral nucleation. These results are also supported by nucleation studies and density functional theory. Spectroscopic and thermogravimetric data provide insight into the subsequent growth dynamics and show that the degree and strength of water association with the polymers can explain the influence on iron (oxyhydr)oxide transformation rates. Combined, our results provide a mechanistic basis for understanding how polymer-mineral-water interactions alter iron (oxyhydr)oxides nucleation and growth dynamics and pave the way for an improved understanding of the consequences of polymer induced mineralization in natural systems. This journal is
The objective of the Photovoltaic Collaborative to Advance Multi-climate and Performance Research (PVCAMPER) is to create a multi-climate research platform similar to the US DOE Regional Test Center (RTC) program. Overall, the goal is to foster collaborative research and to build an international organization dedicated to sharing data and exchanging best practices related to PV performance.
The Sandia National Laboratories (SNL) staff is meeting the requirements of Nuclear Fuel Cycle and Supply Chain (NFCSC) Quality Assurance Program Document (QAPD). Each of the NFCSC FY 19 packages for SNL were reviewed. Minor errors were identified with regard to one package for the previous version of this report. Those are being rectified and future instances will indicate the appropriate entries. Aside from the minor discrepancies noted, there were no quality assurance findings this fiscal year for the NFCSC program. In particular, all of the Quality Rigor Level (QRL) level assignments were confirmed to be appropriate.
The Sandia National Laboratories (SNL) staff is meeting the requirements of the Nuclear Fuel Cycle and Supply Chain (NFCSC) Quality Assurance Program Document (QAPD)1. Each of the 46 SFWD FY 19 packages for SNL were reviewed. Six of the 46 packages had incorrect QRL categories, but technical reviews were always found to be appropriate. No major corrective actions are assigned, but recommendations have been made to adjust the identified QRL items. Additional and minor PICS:NE checkbox errors are recognized. Future training will be geared to ensure proper QRL categorizations and other check box entries in future cases
The Office of Radiological Security (ORS) In-Device Delay (IDD) program has undertaken a project to research and develop a novel protection system for industrial irradiators that contain high-activity Co-60 sources. Based on adversary testing conducted by ORS, it is was determined that to successfully accomplish the theft of the target material, the adversary will require visual contact of the sources and source rack located at the bottom of the pool. Therefore, if a means of obscuring or visually hiding the sources in the pool can be achieved (while adhering to facility operations, safety, and regulatory requirements), then illicit source theft will be significantly hindered. This project aims to develop a low-cost, non-propriety obscurant that, when an adversary action is detected, the obscurant will be deployed into the pool quickly, rendering visual observation of the source problematic; however, this obscurant will not otherwise disturb the sources, source rack, and filtration system. The obscurant will remain in the pool until removed by another process.
Recent progress in the development of dynamic strength experimental platforms is allowing for unprecedented insight into the assumptions used to construct constitutive models operating in extreme conditions. In this work, we make a quantitative assessment of how tantalum strength scales with its shear modulus to pressures of hundreds of gigapascals through a cross-platform examination of three dynamic strength experiments. Specifically, we make use of Split-Hopkinson pressure bar and Richtmyer-Meshkov instability experiments to assess the low-pressure strain and strain rate dependence. Concurrent examination of magnetically driven ramp-release experiments up to pressures of 350 GPa allows us to examine the pressure dependence. Using a modern description of the shear modulus, validated against both ab initio theory and experimental measurements, we then assess how the experimentally measured pressure dependence scales with shear modulus. We find that the common assumption of scaling strength linearly with the shear modulus is too soft at high pressures and offer discussion as to how descriptions of slip mediated plasticity could result in an alternative scaling that is consistent with the data.
In this study, I present experimental results on the equilibrium between boracite [Mg3B7O13Cl(cr)] and kurnakovite [chemical formula, Mg2B6O11.15H2O(cr); structural formula, MgB3O3(OH)5.5H2O(cr)] at 22.5 ± 0.5 °C from a long-term experiment up to 1629 days, approaching equilibrium from the direction of supersaturation, Mg3B7O13Cl(cr) + H+ + 2B(OH)4 + 18H2O(1) . 3MgB3O3(OH)5.5H2O(cr) + Cl . Based on solubility measurements, the 10-based logarithm of the equilibrium constant for the above reaction at 25 °C is determined to be 12.83 ± 0.08 (2s). Based on the equilibrium constant for dissolution of boracite, Mg3B7O13Cl(cr) + 15H2O(l) = 3Mg2+ + 7B(OH)4 + Cl + 2H+ at 25 °C measured previously (Xiong et al. 2018) and that for the reaction between boracite and kurnakovite determined here, the equilibrium constant for dissolution of kurnakovite, MgB3O3(OH)5.5H2O(cr) = Mg2+ + 3B(OH)4 + H+ + H2O(1) is derived as 14.11 ± 0.40 (2s). Using the equilibrium constant for dissolution of kurnakovite obtained in this study and the experimental enthalpy of formation for kurnakovite from the literature, a set of thermodynamic properties for kurnakovite at 25 °C and 1 bar is recommended as follows: ΔH0f = 4813.24 ± 4.92 kJ/mol, .G0f = 4232.0 ± 2.3 kJ/mol, and S0 = 414.3 ± 0.9 J/(mol.K). Among them, the Gibbs energy of formation is based on the equilibrium constant for kurnakovite determined in this study; the enthalpy of formation is from the literature (Li et al. 1997), and the standard entropy is calculated internally with the Gibbs-Helmholtz equation in this work. The thermodynamic properties of kurnakovite estimated using the group contribution method for borate minerals based on the sums of contributions from the cations, borate polyanions, and structural water to the thermodynamic properties from the literature (Li et al. 2000) are consistent, within their uncertainties, with the values listed above. Since kurnakovite usually forms in salt lakes rich in sulfate, studying the interactions of borate with sulfate is important to modeling kurnakovite in salt lakes. For this purpose, I have re-calibrated our previous model (Xiong et al. 2013) describing the interactions of borate with sulfate based on the new solubility data for borax in Na2SO4 solutions presented here.
The design and synthesis of artificial materials that mimic the structures, mechanical properties, and ultimately functionalities of biological cells remains a current holy grail of materials science. Here, based on a silica cell bioreplication approach, we report the design and construction of synthetic rebuilt red blood cells (RRBCs) that fully mimic the broad properties of native RBCs: Size, biconcave shape, deformability, oxygen-carrying capacity, and long circulation time. Four successive nanoscale processing steps (RBC bioreplication, layer-by-layer polymer deposition, and precision silica etching, followed by RBC ghost membrane vesicle fusion) are employed for RRBC construction. A panel of physicochemical analyses including zeta-potential measurement, fluorescence microscopy, and antibody-mediated agglutination assay proved the recapitulation of RBC shape, size, and membrane structure. Flow-based deformation studies carried out in a microfluidic blood capillary model confirmed the ability of RRBCs to deform and pass through small slits and reconstitute themselves in a manner comparable to native RBCs. Circulation studies of RRBCs conducted ex ovo in a chick embryo and in vivo in a mouse model demonstrated the requirement of both deformability and native cell membrane surface to achieve long-term circulation. To confer additional non-native functionalities to RRBCs, we developed modular procedures with which to load functional cargos such as hemoglobin, drugs, magnetic nanoparticles, and ATP biosensors within the RRBC interior to enable various functions, including oxygen delivery, therapeutic drug delivery, magnetic manipulation, and toxin biosensing and detection. Taken together, RRBCs represent a class of long-circulating RBC-inspired artificial hybrid materials with a broad range of potential applications.
MACCS (MELCOR Accident Consequence Code System), WinMACCS, and MelMACCS now facilitate a multi-unit consequence analysis. MACCS evaluates the consequences of an atmospheric release of radioactive gases and aerosols into the atmosphere and is most commonly used to perform probabilistic safety assessments (PSAs) and related consequence analyses for nuclear power plants (NPPs). WinMACCS is a user-friendly preprocessor for MACCS. MelMACCS extracts source-term information from a MELCOR plot file. The current development can combine an arbitrary number of source terms, representing simultaneous releases from a multi-unit facility, into a single consequence analysis. The development supports different release signatures, fission product inventories, and accident initiation times for each unit. The treatment is completely general except that the model is currently limited to collocated units. A major practical consideration for performing a multi-unit PSA is that a comprehensive treatment for more than two units may involve an intractable number of combinations of source terms. This paper proposes and evaluates an approach for reducing the number of calculations to be tractable, even for sites with eight or ten units. The approximation error introduced by the approach is acceptable and is considerably less than other errors and uncertainties inherent in a Level 3 PSA.
While dragonflies are well-known for their high success rates when hunting prey, how the underlying neural circuitry generates the prey-interception trajectories used by dragonflies to hunt remains an open question. I present a model of dragonfly prey interception that uses a neural network to calculate motor commands for prey-interception. The model uses the motor outputs of the neural network to internally generate a forward model of prey-image translation resulting from the dragonfly's own turning that can then serve as a feedback guidance signal, resulting in trajectories with final approaches very similar to proportional navigation. The neural network is biologically-plausible and can therefore can be compared against in vivo neural responses in the biological dragonfly, yet parsimonious enough that the algorithm can be implemented without requiring specialized hardware.
The widely parallel, spiking neural networks of neuromorphic processors can enable computationally powerful formulations. While recent interest has focused on primarily machine learning tasks, the space of appropriate applications is wide and continually expanding. Here, we leverage the parallel and event-driven structure to solve a steady state heat equation using a random walk method. The random walk can be executed fully within a spiking neural network using stochastic neuron behavior, and we provide results from both IBM TrueNorth and Intel Loihi implementations. Additionally, we position this algorithm as a potential scalable benchmark for neuromorphic systems.
Deep learning networks have become a vital tool for image and data processing tasks for deployed and edge applications. Resource constraints, particularly low power budgets, have motivated methods and devices for efficient on-edge inference. Two promising methods are reduced precision communication networks (e.g. binary activation spiking neural networks) and weight pruning. In this paper, we provide a preliminary exploration for combining these two methods, specifically in-training weight pruning of whetstone networks, to achieve deep networks with both sparse weights and binary activations.
Jiang, Y.; Wang, J.; Zhao, T.; Dun, Z.L.; Huang, Q.; Wu, X.S.; Mourigal, M.; Pan, Wei P.; Ozerov, M.; Smirnov, D.
For materials near the phase boundary between weak and strong topological insulators (TIs), their band topology depends on the band alignment, with the inverted (normal) band corresponding to the strong (weak) TI phase. Here, taking the anisotropic transition-metal pentatelluride ZrTe5 as an example, we show that the band inversion manifests itself as a second extremum (band gap) in the layer stacking direction, which can be probed experimentally via magnetoinfrared spectroscopy. Specifically, we find that the band anisotropy of ZrTe5 features a slow dispersion in the layer stacking direction, along with an additional set of optical transitions from a band gap next to the Brillouin zone center. Our work identifies ZrTe5 as a strong TI at liquid helium temperature and provides a new perspective in determining band inversion in layered topological materials.
Two material types identified by Sew-EZ were tested in various configurations, and under various conditions, by Sandia National Laboratories (SNL). The primary focus of this study was to assess the filtration performance of these two materials and identify if they perform similarly to certified N95 respirators. Testing was conducted on two systems which use distinctly different techniques to characterize the aerosol penetration characteristics of materials: a) R&D Filtration System: A large-scale R&D filtration system was used with testing parameters that mimicked NIOSH guidelines, where possible. Efficiency data as a function of particle size was attained using NaC1 as the test aerosol and a Scanning Mobility Particle Sizer (SMPS) for measurements. A more detailed system description can be found in Omana et al. 2020. b) Automated Tester: A commercial, automated filter tester (100Xs, Air Techniques International) was used to provide penetration/efficiency data for Sew EZ materials. The 100Xs aerosolizes a polydisperse NaC1 aerosol with a consistent concentration and size profile. The 100Xs manual (Air Techniques International 2018) states, "The aerosol particle size and distribution are designed to meet all requirements as defined in the relevant sections of NIOSH 42 CFR, Part 84 (pg. 32)."
On June 30, 2020, a Notice of Violation (NOV) was issued by the City of Albuquerque (COA) Environmental Health Department, Air Quality Program. The NOV identified two violations of New Mexico Administrative Code (NMAC) 20.11.20, Fugitive Dust Control, stemming from an August 30, 2019 inspection of the construction site at Sandia/New Mexico (SNL/NM) Building 812. After the August 30 inspection, a Post-Instruction Notification (PIN) was issued to the SNL Construction Facilities Manager. The PIN was acknowledged by Department 4722 and sent to the COA on September 13, 2020 via email. The PIN "Comply by" date was September 13, 2019 was transmitted by National Nuclear Security Administration/ Sandia Field Office (NNSA/SFO) to COA offices on November 12, 2019. The PIN response was 48 business days past due for various reasons which were explored during the causal analysis.
Here in this paper, we present an efficient approach to compute the integral of monomials and polynomials over polyhedra and regions defined by parametric curved boundary surfaces. We use Euler's theorem for homogeneous functions in combination with Stokes's theorem to reduce the integration of a monomial over a three-dimensional solid to its boundary. If the solid is a polytope, through a recursive application of these theorems, the integral is further reduced to just the evaluation of the monomial and its derivatives at the vertices of the polytope. The present approach is simpler than existing techniques that rely on repeated use of the divergence theorem, which require the antiderivative of the monomials and the projection of these functions onto hyperplanes. For convex and nonconvex polytopes, our approach does not introduce any approximation for the integration of monomials. For curved solid regions bounded by surfaces that admit a parameterization, the same approach yields simplified formulas to compute the integral of any homogeneous function, including monomials. For surfaces parameterized by polynomial surfaces (such as Bezier surface triangles and B-spline patches), the method yields machine-precision accuracy for the volumetric integration of monomials with an appropriate quadrature rule. Numerical examples over regions bounded by polynomial surfaces and rational surfaces are presented to establish the accuracy and efficiency of the method.
In May-June, 2020, a study was conducted to characterize the outgassing properties of a series PEKK (Poly(ether ketone ketone)) samples using cryo-GC/MS headspace analysis. Three sets of samples were interrogated: sample group 1 consisted of 2 additively manufactured PEKK samples (PEKK "ole and "New") prepared by KCNSC from powder material from Solvay Specialty Polymers USA, LLC. Sample groups 2 and 3 consist of 5 PEKK powder types (used as feedstock for additive manufacturing processes) and 4 additively-manufactured PEKK material lots, respectively. Contrary to expectations, all samples of PEKK material were observed to outgas sulfur-containing compounds. Other analyses (EDS/EMA, GC-TOF/MS of PEKK sample extractions) confirmed the presence of sulfur in the PEKK bulk material. Specifically, Diphenyl sulfone (used as a reagent or high-temperature solvent in the synthesis of Polyaryletherketone or PAEK polymers) was observed in three of the powders and in both the PEKK "Old" and "New" samples, suggesting that the source of the sulfur can be traced to impurities in the material left over from the synthesis process.
Recent investigations like the second and third Sandia Fracture Challenges have characterized and demonstrated the performance of a variety of failure techniques and models. These surveys have considered a wide breadth of models encapsulating both general failure criteria as well as those focusing on pore nucleation and growth. Extensive reviews exist on both topics. The former category generally consists of classic models like the Johnson-Cook or Wilkins criteria. These models were recently added to modular plasticity models in the Library of Advanced Materials for Engineering (LAME) as criteria for use with element death capabilities. The latter category was not treated in that effort. There exists a large class of failure models based on predicting the evolution of pores and failure associated with such microstructures. While the exact mechanisms and corresponding impact on the macroscale behavior remain an active area of research, a large suite of formulations have been proposed combining different features of both pore nucleation and subsequent growth. The most famous of these are based on the popular Gurson model of pore growth derived via micromechanical analysis assuming a plastically incompressible matrix. Numerous other models exist for both growth and nucleation and the Cocks-Ashby growth and Horstemeyer-Gokhale nucleation models have been used successfully in recent Sandia Fracture Challenges. This specific combination is colloquially referred to as the "BCJ-failure model as it has been frequently used with the Bammann-Chisea-Johnson plasticity model.
DiPietro, Kelsey L.; Paquin-Lefebvre, Frederic; Bin XuBin; Lindsay, Alan E.; Jilkine, Alexandra
Reaction-diffusion systems have been widely used to study spatio-temporal phenomena in cell biology, such as cell polarization. Coupled bulk-surface models naturally include compartmentalization of cytosolic and membrane-bound polarity molecules. Here we study the distribution of the polarity protein Cdc42 in a mass-conserved membrane-bulk model, and explore the effects of diffusion and spatial dimensionality on spatio-temporal pattern formation. We first analyze a one-dimensional (1-D) model for Cdc42 oscillations in fission yeast, consisting of two diffusion equations in the bulk domain coupled to nonlinear ODEs for binding kinetics at each end of the cell. In 1-D, our analysis reveals the existence of symmetric and asymmetric steady states, as well as anti-phase relaxation oscillations typical of slow-fast systems. We then extend our analysis to a two-dimensional (2-D) model with circular bulk geometry, for which species can either diffuse inside the cell or become bound to the membrane and undergo a nonlinear reaction-diffusion process. We also consider a nonlocal system of PDEs approximating the dynamics of the 2-D membrane-bulk model in the limit of fast bulk diffusion. In all three model variants we find that mass conservation selects perturbations of spatial modes that simply redistribute mass. In 1-D, only anti-phase oscillations between the two ends of the cell can occur, and in-phase oscillations are excluded. In higher dimensions, no radially symmetric oscillations are observed. Instead, the only instabilities are symmetry-breaking, either corresponding to stationary Turing instabilities, leading to the formation of stationary patterns, or to oscillatory Turing instabilities, leading to traveling and standing waves. Codimension-two Bogdanov–Takens bifurcations occur when the two distinct instabilities coincide, causing traveling waves to slow down and to eventually become stationary patterns. Our work clarifies the effect of geometry and dimensionality on behaviors observed in mass-conserved cell polarity models.
Different machine learning (ML) methods were explored for the prediction of self-diffusion in Lennard-Jones (LJ) fluids. Using a database of diffusion constants obtained from the molecular dynamics simulation literature, multiple Random Forest (RF) and Artificial Neural Net (ANN) regression models were developed and characterized. The role and improved performance of feature engineering coupled to the RF model development was also addressed. The performance of these different ML models was evaluated by comparing the prediction error to an existing empirical relationship used to describe LJ fluid diffusion. It was found that the ANN regression models provided superior prediction of diffusion in comparison to the existing empirical relationships.
Composition dependence of second harmonic generation, refractive index, extinction coefficient, and optical bandgap in 20 nm thick crystalline Hf1-xZrxO2 (0 ≤ x ≤ 1) thin films is reported. The refractive index exhibits a general increase with increasing ZrO2 content with all values within the range of 1.98-2.14 from 880 nm to 400 nm wavelengths. A composition dependence of the indirect optical bandgap is observed, decreasing from 5.81 eV for HfO2 to 5.17 eV for Hf0.4Zr0.6O2. The bandgap increases for compositions with x > 0.6, reaching 5.31 eV for Hf0.1Zr0.9O2. Second harmonic signals are measured for 880 nm incident light. The magnitude of the second harmonic signal scales with the magnitude of the remanant polarization in the composition series. Film compositions that display near zero remanent polarizations exhibit minimal second harmonic generation while those with maximum remanent polarization also display the largest second harmonic signal. The results are discussed in the context of ferroelectric phase assemblage in the hafnium zirconium oxide films and demonstrate a path toward a silicon-compatible integrated nonlinear optical material.
Dattelbaum, Dana M.; Ionita, Axinte; Patterson, Brian M.; Branch, Brittany A.; Kuettner, Lindsey
The advent of additive manufacturing (AM) has enabled topological control of structures at the micrometer scale, transforming the properties of polymers for a variety of applications. Examples include tailored mechanical responses, acoustic properties, and thermal properties. Porous polymer materials are a class of materials used for shock and blast mitigation, yet they frequently possess a lack of structural order and are largely developed and evaluated via trial-and-error. Here, we demonstrate control of shockwave dissipation through interface-dominated structures prepared by AM using 2-photon polymerization. A fractal structure with voids, or free surfaces, arranged less than 100 μm apart, allows for rarefaction interactions on the timescale of the shockwave loading. Simulations and dynamic x-ray phase contrast imaging experiments show that fractal structures with interfaces assembled within a “critical” volume reduce shockwave stress and wave velocity by over an order of magnitude within the first unit cell.
A sensor to determine water layer (WL) thickness, ranging from 0-5 mm, in salt-spray testing is presented. WL thickness is based on electrical resistivity and sensor design was guided by Finite Element Modeling with validation under known WL thicknesses. WLs were measured in continuous salt spray testing and angle of exposure played the largest role in thicknesses. At angles greater than 20' from vertical, semi-periodic run-off decreased WLs up to 80 %. Finally, exposure angle determines if thin-film conditions are achieved, likely influencing corrosion rate and morphology. Allowances for sample angle in testing standards pose a potentially large source of variability.
Magnetically driven implosions are susceptible to magnetohydrodynamic instabilities, including the magneto-Rayleigh-Taylor instability (MRTI). To reduce MRTI growth in solid-metal liner implosions, the use of a dynamic screw pinch (DSP) has been proposed [P. F. Schmit et al., Phys. Rev. Lett. 117, 205001 (2016)PRLTAO0031-900710.1103/PhysRevLett.117.205001]. In a DSP configuration, a helical return-current structure surrounds the liner, resulting in a helical magnetic field that drives the implosion. Here, we present the first experimental tests of a solid-metal liner implosion driven by a DSP. Using the 1-MA, 100-200-ns COBRA pulsed-power driver, we tested three DSP cases (with peak axial magnetic fields of 2 T, 14 T, and 20 T) and a standard z-pinch (SZP) case (with a straight return-current structure and thus zero axial field). The liners had an initial radius of 3.2 mm and were made from 650-nm-thick aluminum foil. Images collected during the experiments reveal that helical MRTI modes developed in the DSP cases, while nonhelical (azimuthally symmetric) MRTI modes developed in the SZP case. Additionally, the MRTI amplitudes for the 14-T and 20-T DSP cases were smaller than in the SZP case. Specifically, when the liner had imploded to half of its initial radius, the MRTI amplitudes for the SZP case and for the 14-T and 20-T DSP cases were, respectively, 1.1±0.3 mm, 0.7±0.2 mm, and 0.3±0.1 mm. Relative to the SZP, the stabilization obtained using the DSP agrees reasonably well with theoretical estimates.
Doubrawa Moreira, Paula; Quon, Eliot; Martinez; Tossas, Luis (Tony) M.; Shaler, Kelsey; Debnath, Mithu; Hamilton, Nicholas; Herges, Thomas H.; Maniaci, David C.; Kelley, Christopher L.; Laros, James H.; Blaylock, Myra L.; Van Der Laan, Paul; Andersen, Soren J.; Krueger, Sonja; Cathelain, Marie; Schlez, Wolfgang; Jonkman, Jason; Branlard, Emmanuel; Steinfeld, Gerald; Schmidt, Sascha; Blondel, Frederic; Lukassen, Laura J.; Moriarty, Patrick
Previous research has revealed the need for a validation study that considers several wake quantities and code types so that decisions on the trade-off between accuracy and computational cost can be well informed and appropriate to the intended application. In addition to guiding code choice and setup, rigorous model validation exercises are needed to identify weaknesses and strengths of specific models and guide future improvements. Here, we consider 13 approaches to simulating wakes observed with a nacelle-mounted lidar at the Scaled Wind Technology Facility (SWiFT) under varying atmospheric conditions. We find that some of the main challenges in wind turbine wake modeling are related to simulating the inflow. In the neutral benchmark, model performance tracked as expected with model fidelity, with large-eddy simulations performing the best. In the more challenging stable case, steady-state Reynolds-averaged Navier–Stokes simulations were found to outperform other model alternatives because they provide the ability to more easily prescribe noncanonical inflows and their low cost allows for simulations to be repeated as needed. Dynamic measurements were only available for the unstable benchmark at a single downstream distance. These dynamic analyses revealed that differences in the performance of time-stepping models come largely from differences in wake meandering. This highlights the need for more validation exercises that take into account wake dynamics and are able to identify where these differences come from: mesh setup, inflow, turbulence models, or wake-meandering parameterizations. In addition to model validation findings, we summarize lessons learned and provide recommendations for future benchmark exercises.
The objectives of this project are as follows: 1. Reduce uncertainty in PV performance models by developing and validating new and improved models and submodes. 2. Create and manage an open source repository of modeling functions and data. 3. Build and grow the PV Performance Modeling Collaborative; and, 4. Represent the US in the IEA PVPS Task 13 Working group.
This project has four main technical objectives. Develop and improve bifacial performance models by adding the capability to evaluate electrical behavior and performance of bifacial modules and arrays under realistic field conditions including irradiance variability caused by racking, module frame, and position in the array. Instrument and monitor performance of fielded bifacial systems to validate performance models and to measure, analyze and publish on bifacial energy gain. These should include both research and commercial bifacial systems and cover a variety of deployment applications. Evaluate optimal bifacial system designs using simulations leveraging high performance computing, and also using full sized and miniaturized experimental field deployments. Establish and contribute to international test standards for bifacial system performance, testing, and safety, and work with the community to establish installation and siting best practices.
The DOE R&D program under the Spent Fuel Waste Science Technology (SFWST) campaign has made key progress in modeling and experimental approaches towards the characterization of chemical and physical phenomena that could impact the long-term safety assessment of heat-generating nuclear waste disposition in deep clay/shale/argillaceous rock. International collaboration activities such as heater tests and postmortem analysis of samples recovered from these have elucidated key information regarding changes in the engineered barrier system (EBS) material exposed to years of thermal loads. Chemical and structural analyses of sampled bentonite material from such tests has as well as experiments conducted on these are key to the characterization of thermal effects affecting bentonite clay barrier performance and the extent of sacrificial zones in the EBS during the thermal period. Thermal, hydrologic, and chemical data collected from heater tests and laboratory experiments has been used in the development, validation, and calibration of THMC simulators to model near-field coupled processes. This information leads to the development of simulation approaches (e.g., continuum vs. discrete) to tackle issues related to flow and transport at various scales of the host-rock and EBS design concept. Consideration of direct disposal of large capacity dual-purpose canisters (DPCs) as part of the back-end SNF waste disposition strategy has generated interest in improving our understanding of the effects of elevated temperatures on the EBS design. This is particularly important for backfilled repository concepts where temperature plays a key role in the EBS behavior and long-term performance. This report describes multiple R&D efforts on disposal in argillaceous geologic media through development and application of coupled THMC process models, experimental studies on clay/metal/cement barrier and host-rock (argillite) material interactions, molecular dynamic (MD) simulations of water transport during (swelling) clay dehydration, first-principles studies of metaschoepite (UO2 corrosion product) stability, and advances in thermodynamic plus surface complexation database development. Drift-scale URL experiments provides key data for testing hydrological-chemical (HC) model involving strong couplings of fluid mixing and barrier material chemical interactions. The THM modeling focuses on heater test experiments in argillite rock and gas migration in bentonite as part of international collaboration activities at underground research laboratories (URLs). In addition, field testing at an URL involves in situ analysis of fault slip behavior and fault permeability. Pore-scale modeling of gas bubble migration is also being investigated within the gas migration modeling effort. Interaction experiments on bentonite samples from heater test under ambient and elevated temperatures permit the evaluation of ion exchange, phase stability, and mineral transformation changes that could impact clay swelling. Advances in the development, testing, and implementation of a spent nuclear fuel (SNF) degradation model coupled with canister corrosion focus on the effects of hydrogen gas generation and its integration with Geologic Disposal Safety Assessment (GDSA). GDSA integration activities includes evaluation of groundwater chemistries in shale formations.
Nonlocal models provide accurate representations of physical phenomena ranging from fracture mechanics to complex subsurface flows, settings in which traditional partial differential equation models fail to capture effects caused by long-range forces at the microscale and mesoscale. However, the application of nonlocal models to problems involving interfaces, such as multimaterial simulations and fluid-structure interaction, is hampered by the lack of a physically consistent interface theory which is needed to support numerical developments and, among other features, reduces to classical models in the limit as the extent of nonlocal interactions vanish. In this paper, we use an energy-based approach to develop a formulation of a nonlocal interface problem which provides a physically consistent extension of the classical perfect interface formulation for partial differential equations. Numerical examples in one and two dimensions validate the proposed framework and demonstrate the scope of our theory.
We report on the availability and chemical utility of primary amines within metal-organic frameworks (MOFs) for cell targeting. Primary amine groups represent one of the most versatile chemical moieties for conjugation to biologically relevant molecules, including antibodies and enzymes. Specifically, we used two different chemical conjugations schemes, utilizing the amino functionality on the organic linker: first, carbodiimide chemistry was used to link the primary amine to available carboxyl groups on the protein neutravidin; second, sulfhydryl cross-linking chemistry was used via Traut's reagent scheme. Importantly, this is the first report that documents this methodology implemented with MOF systems. Finally, the ability of the EpCAM antibody targeted MOFs to bind to a human epithelial cell line (A549), a common target for imaging studies, was confirmed with confocal microscopy.
Changes in the nature, intensity, and frequency of climate-related extreme events have imposed a higher risk of failure on energy systems, especially those at the community level. Furthermore, the evolving energy demand patterns and transition towards renewable and localised energy supply can affect energy system resilience. How can an energy system be planned and reconfigured to address these challenges without compromising the system's resilience against chronic stresses and extreme events? Unlike energy system reliability, resilience is neither a common nor an explicit consideration in energy master planning at the community level. In addition, there is no universally agreed-upon method or metrics for measuring or estimating resilience and defining mitigation strategies. This paper introduces a multi-layered energy resilience framework and set of metrics for energy master planning of communities, including the new generation of district energy systems. The potential system disturbances and their short and long-term impacts on various components of the energy system are discussed for commonly expected and extreme events. Three layers of energy resilience are discussed: engineering-designed resilience, operational resilience, and community-societal resilience. A starting set of energy resilience metrics to support engineering design and energy master planning for communities is identified. Implications for future research and practice are noted.
Rodriguez-Gallegos, Carlos D.; Liu, Haohui; Singh, Jai P.; Krishnamurthy, Vijay; Kumar, Abhishek; Stein, Joshua S.; Wang, Shitao; Li, Li; Reindl, Thomas
This work presents a worldwide analysis on the yield potential and cost effectiveness of photovoltaic farms composed of monofacial fixed-tilt and single/dual (1T/2T) tracker installations, as well as their bifacial counterparts. Our approach starts by estimating the irradiance reaching both the front and rear surfaces of the modules for the different system designs (validated based on data from real photovoltaic systems and results from the literature) to estimate their energy production. Subsequently, the overall system cost during their 25-year lifetime is factored in, and the levelized cost of electricity (LCOE) is obtained. The results reveal that bifacial-1T installations increase energy yield by 35% and reach the lowest LCOE for the majority of the world (93.1% of the land area). Although dual-axis trackers achieve the highest energy generation, their costs are still too high and are therefore not as cost effective. Sensitivity analyses are also provided to show the general robustness of our findings. This work performs a comprehensive techno-economic analysis worldwide for photovoltaic systems using a combination of bifacial modules and single- and dual-axis trackers. We find that single-axis trackers with bifacial modules achieve the lowest LCOE in the majority of locations (16% reduction on average). Yield is boosted by 35% by using bifacial modules with single-axis trackers and by 40% in combination with dual-axis trackers. Energy production of photovoltaic (PV) modules can be increased not only by solar cells that are more efficient but also by innovative system concepts. In this study, we explore two such concepts in combination: tracking and bifacial modules. A tracking setup increases energy production by moving a PV module over the course of a day, so that it always faces the sun. Bifacial modules use special solar cells and a transparent cover to collect light not only from the front but also from the rear. Through recent advances, both concepts have seen price reductions that enable them to produce electricity cheaper than conventional PV systems. Here, we analyze the technical and economic aspects of combinations of these two concepts worldwide. We find that a combination of bifacial modules with one-axis trackers produces the cheapest electricity (LCOE 16% lower than conventional systems) by significantly boosting energy production (35% more than conventional systems).
A simple linear configuration for multi-line femtosecond laser electronic excitation tagging (FLEET) velocimetry is used for the first time, to the best of our knowledge, to image an overexpanded unsteady supersonic jet. The FLEET lines are spaced 0.5-1.0mmapart, and up to six lines can be used simultaneously to visualize the flowfield. These lines are created using periodic masks, despite the mask blocking 25%-30%of the 10 mJ incident beam.Maps of mean singlecomponent velocity in the direction along the principal flow axis, and turbulence intensity in that same direction, are created using multi-line FLEET, and computed velocities agree well with those obtained from single-line (traditional) FLEET. Compared to traditional FLEET, multi-line FLEET offers increased simultaneous spatial coverage and the ability to produce spatial correlations in the streamwise direction. This FLEET permutation is especially well suited for short-duration test facilities.
We present real-space quantum Monte Carlo (QMC) calculations of the scandate LaScO3 that proved to be challenging for traditional electronic structure approaches due to strong correlation effects resulting in inaccurate band gaps from DFT and GW methods when compared with existing experimental data. Besides calculating an accurate QMC band gap corrected for supercell size biases and in agreement with numerous experiments, we also predict the cohesive energy of the crystal using the standard fixed-node QMC without any empirical or nonvariational parameters. We show that promotion (optical) gap and fundamental gap agree with each other illustrating a clear absence of significant excitonic effects in the ideal crystal. We obtained these results in perfect consistency in two independent tracks that employ different basis sets (plane wave versus localized Gaussians), different codes for generating orbitals (quantum espresso versus crystal), different QMC codes (qmcpack versus qwalk) and different high-accuracy pseudopotentials (ccECPs versus Troullier-Martins) presenting the maturity and consistency of QMC methodology and tools for studies of strongly correlated problems.
Chemical security can be described as the practice of protecting chemicals from people. This editorial introduces the Special Issue on Chemical Security. Herein, we present a concise history of the use of chemicals as weapons and briefly describe consolidated global approaches needed to decrease the security risks from chemicals. We briefly introduce the contributions to this Special Issue from scientists and educators around the world on the topic of chemical security. Given recent high-profile global events that involved chemicals as weapons, improving chemical security awareness and competency is a critical need. We hope this Special Issue continues to generate conversation and collaboration on this important topic and enables educators to teach chemical security principles in their classrooms and laboratories.
Herein, 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 m2 s-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 thermodynamic stability and melting point of magnesium borohydride were probed under hydrogen pressures up to 1000 bar (100 MPa) and temperatures up to 400 °C. At 400 °C, Mg(BH4)2 was found to be chemically stable between 700 and 1000 bar H2, whereas under 350 bar H2 or lower pressures, the bulk material partially decomposed into MgH2 and MgB12H12. The melting point of solvent-free Mg(BH4)2 was estimated to be 367-375 °C, which was above previously reported values by 40-90 °C. Our results indicated that a high hydrogen backpressure is needed to prevent the decomposition of Mg(BH4)2 before measuring the melting point and that molten Mg(BH4)2 can exist as a stable liquid phase between 367 and 400 °C under hydrogen overpressures of 700 bar or above. The occurrence of a pure molten Mg(BH4)2 phase enabled efficient melt-infiltration of Mg(BH4)2 into the pores of porous templated carbons (CMK-3 and CMK-8) and graphene aerogels. Both transmission electron microscopy and small-angle X-ray scattering confirmed efficient incorporation of the borohydride into the carbon pores. The Mg(BH4)2@carbon samples exhibited comparable hydrogen capacities to bulk Mg(BH4)2 upon desorption up to 390 °C based on the mass of the active component; the onset of hydrogen release was reduced by 15-25 °C compared to the bulk. Importantly, melt-infiltration under hydrogen pressure was shown to be an efficient way to introduce metal borohydrides into the pores of carbon-based materials, helping to prevent particle agglomeration and formation of stable closo-polyborate byproducts.
Hamann, Danielle M.; Bardgett, Dylan; Sr., Bauers; Tw, Kasel; Am, Mroz; Ch, Hendon; Medlin, Douglas L.; Dc, Johnson
A series of [(SnSe)1+Î][TiSe2]q heterostructures with systematic changes in the number of TiSe2 layers in the repeating unit were synthesized, and both the structure and electronic-transport properties were characterized. The c-axis lattice parameter increased linearly as q increased, and the slope was consistent with the thickness of a TiSe2 layer. In-plane lattice constants for SnSe and TiSe2 were independent of q. Temperature-dependent resistivity and Hall coefficient data varied systematically as q was increased. The low-temperature electrical data was modeled assuming that only electrons were involved, and the data was fit to a variable range hopping mechanism. The number of carriers involved in this low-temperature transport decreased as q increased, indicating that approximately 1/10th of an electron per SnSe bilayer was transferred to the TiSe2. Calculations also indicated that there was charge donation from the SnSe layer to the TiSe2 layer, resulting in an ionic bond between the layers, which aided in stabilizing the heterostructures. The charge donation created a TiSe2-SnSe-TiSe2 block with the properties distinct from the constituent bulk properties. At high temperatures in large q samples, the transport data required holes to be activated across a band gap to be successfully modeled. This high-temperature transport scales with the number of TiSe2 layers that are not adjacent to SnSe. Using a consistent model across all of the samples significantly constrained the adjustable parameters. The charge transfer between the two constituents results in the stabilization of the heterostructure by an ionic interaction and the formation of a conducting TiSe2-SnSe-TiSe2 block. This is consistent with prior reports, where interactions between two-dimensional (2D) layers and their surroundings (i.e., adjacent layers, substrate, or atmosphere) have been shown to strongly influence the properties.
Chemical risk management is a process to control safety and security risks associated with hazardous chemicals. Chemical risk management includes the management of both chemical safety and chemical security. It is generally accepted that there are five pillars that make up chemical security management. Each of the five pillars are key components to the implementation of a chemical security risk management system. In this work, we will review the "Material Control and Accountability"pillar and how an academic institution can implement this principle using a chemical inventory management system (CIMS). A robust CIMS will improve the quality and efficiency of research, reduce time and resources associated with laboratory management, and reduce both the safety and security risks associated with chemicals.
Payne, Maurice K.; Nelson, Andrew W.; Hotchkiss, Peter; Humphrey, Walter R.; Straut, Christine M.
Comprehensive inventory management is central to the efficient operation of any facility that uses and stores chemicals. There are numerous software packages available that perform this function satisfactorily. Most commercially available inventory management software is regulatory or procurement focused and usually comes with upfront or monthly fees. This report describes freely downloadable software developed at Sandia National Laboratories that delivers an effective inventory management system with an additional focus on chemical security.
To date, chemical security education practices in postsecondary institutions are poorly understood. The purpose of this study is to provide an initial understanding of the practices, attitudes, and barriers toward chemical security education for undergraduate and graduate programs in the United States (US) by surveying representatives of American Chemical Society (ACS)-approved programs. All programs with ACS-approved undergraduate chemistry programs (n = 691) were contacted for participation: 21% (n = 148) fully completed and 6% (n = 41) partially completed the survey for a combined total of 27% complete and/or partially complete surveys (n = 189). We observed that most programs currently teach chemical safety (undergraduate >99%, graduate 73%); however, only about one-third of programs teach chemical security at any education level (undergraduate 32%, graduate 34%). We also observed that safety education is provided more frequently than security education. Further, ACS-approved programs reported that their chemical safety culture was stronger than chemical security culture and felt that safety should be taught differently than security. The overwhelming majority of respondents (96%) indicated that chemical safety should be mandatory at some level, while only about half of respondents (57%) indicated that chemical security should be mandatory at some level. More efforts are needed by the chemistry community to raise awareness of the importance of chemical security education so that more institutions commit to training their faculty and students on the topic. The authors suggest that adoption of chemical security education could be increased if ACS were to advocate for chemical security by including it in its guidelines for educational program approval.
Here, we introduce a novel chance-constrained stochastic unit commitment model to address uncertainty in renewables' production uncertainty in power systems operation. For most thermal generators,underlying technical constraints that are universally treated as "hard" by deterministic unit commitment models are in fact based on engineering judgments, such that system operators can periodically request operation outside these limits in non-nominal situations, e.g., to ensure reliability. We incorporate this practical consideration into a chance-constrained stochastic unit commitment model, specifically by in-frequently allowing minor deviations from the minimum and maximum thermal generator power output levels. We demonstrate that an extensive form of our model is computationally tractable for medium-sized power systems given modest numbers of scenarios for renewables' production. We show that the model is able to potentially save significant annual production costs by allowing infrequent and controlled violation of the traditionally hard bounds imposed on thermal generator production limits. Finally, we conduct a sensitivity analysis of optimal solutions to our model under two restricted regimes and observe similar qualitative results.
A central challenge in developing quantum computers and long-range quantum networks is the distribution of entanglement across many individually controllable qubits1. Colour centres in diamond have emerged as leading solid-state ‘artificial atom’ qubits2,3 because they enable on-demand remote entanglement4, coherent control of over ten ancillae qubits with minute-long coherence times5 and memory-enhanced quantum communication6. A critical next step is to integrate large numbers of artificial atoms with photonic architectures to enable large-scale quantum information processing systems. So far, these efforts have been stymied by qubit inhomogeneities, low device yield and complex device requirements. Here we introduce a process for the high-yield heterogeneous integration of ‘quantum microchiplets’—diamond waveguide arrays containing highly coherent colour centres—on a photonic integrated circuit (PIC). We use this process to realize a 128-channel, defect-free array of germanium-vacancy and silicon-vacancy colour centres in an aluminium nitride PIC. Photoluminescence spectroscopy reveals long-term, stable and narrow average optical linewidths of 54 megahertz (146 megahertz) for germanium-vacancy (silicon-vacancy) emitters, close to the lifetime-limited linewidth of 32 megahertz (93 megahertz). We show that inhomogeneities of individual colour centre optical transitions can be compensated in situ by integrated tuning over 50 gigahertz without linewidth degradation. The ability to assemble large numbers of nearly indistinguishable and tunable artificial atoms into phase-stable PICs marks a key step towards multiplexed quantum repeaters7,8 and general-purpose quantum processors9–12.
Geologic reservoirs containing gas hydrate occur beneath permafrost environments and within marine continental slope sediments, representing a potentially vast natural gas source. Numerical simulators provide scientists and engineers with tools for understanding how production efficiency depends on the numerous, interdependent (coupled) processes associated with potential production strategies for these gas hydrate reservoirs. Confidence in the modeling and forecasting abilities of these gas hydrate reservoir simulators (GHRSs) grows with successful comparisons against laboratory and field test results, but such results are rare, particularly in natural settings. The hydrate community recognized another approach to building confidence in the GHRS: comparing simulation results between independently developed and executed computer codes on structured problems specifically tailored to the interdependent processes relevant for gas hydrate-bearing systems. The United States Department of Energy, National Energy Technology Laboratory, (DOE/NETL), sponsored the first international gas hydrate code comparison study, IGHCCS1, in the early 2000s. IGHCCS1 focused on coupled thermal and hydrologic processes associated with producing gas hydrates from geologic reservoirs via depressurization and thermal stimulation. Subsequently, GHRSs have advanced to model more complex production technologies and incorporate geomechanical processes into the existing framework of coupled thermal and hydrologic modeling.
Part of the Presidential Policy Directive 21 (PPD-21) (PPD 2013) mandate includes evaluating safety, security, and safeguards (or nonproliferation) mechanisms traditionally implemented within the nuclear reactors, materials, and waste sector of critical infrastructure—including a complex, dynamic set of risks and threats within an all-hazards approach. In response, research out of Sandia National Laboratories (Sandia) explores the ability of systems theory principles (hierarchy and emergence) and complex systems engineering concepts (multidomain interdependence) to better understand and address these risks and threats. Herein, this Sandia research explores the safety, safeguards, and security risks of three different nuclear sector-related activities—spent nuclear fuel transportation, small modular reactors, and portable nuclear power reactors—to investigate the complex and dynamic risk related to the PPD-21-mandated all-hazards approach. This research showed that a systems-theoretic approach can better identify inter-dependencies, conflicts, gaps, and leverage points across traditional safety, security, and safeguards hazard mitigation strategies in the nuclear reactors, materials, and waste sector. Resulting from this, mitigation strategies from applying systems theoretic principles and complex systems engineering concepts can be (1) designed to better capture interdependencies, (2) implemented to better align with real-world operational uncertainties, and (3) evaluated as a systems-level whole to better identify, characterize, and manage PPD-21's all hazards strategies.
Temperature is a difficult thermodynamic variable to measure in dynamic compression experiments. Optical pyrometry is a general-purpose technique for measuring temperature from a radiant surface, but that surface is often the interface between distinct materials with temperatures that vary spatially along the loading direction. This leads to a fundamental problem: how does the measured interface temperature relate to this temperature profile along the compression axis? Numerical analysis of loading history and thermal diffusion at these interfaces shows that seemingly subtle changes in experiment geometry can lead to very different temperature profiles. We compare these results to laboratory temperature measurements of shock-compressed tin.
The European Association on Antennas and Propagation Software Working Group has found significant discrepancy between computer model and measurement of the RangeStar Ultima™ 'World GSM' antenna. This work shows good agreement between our model and the Working Group model as well as with our measurement. It briefly explores several possible sources of error in the Working Group measurements.
The impedance bandwidth of a microstrip patch antenna may be increased by additional resonances in the antenna structure. This work uses Characteristic Mode Analysis to show that the E-shaped patch operates in this manner and that its operation is well-modeled by Coupled Mode Theory.
Emerging classical and quantum applications require computational electromagnetics methods that can efficiently analyze complex structures over wide bandwidths, including down to very low frequencies. This work begins to address these needs by presenting a type of charge and current integral equation that has been formulated in the time domain and is applicable to dielectric regions. This system introduces charge densities as unknowns in addition to the current densities, resulting in a system that does not exhibit a low frequency breakdown. An appropriate marching-on-in-time discretization scheme is discussed so that stable and accurate results can be achieved down to very low frequencies. Numerical results are shown to verify the accuracy and stability of this formulation.
The impedance bandwidth of a microstrip patch antenna may be increased by additional resonances in the antenna structure. This work uses Characteristic Mode Analysis to show that a classic stacked patch design from the literature operates in this manner and that Coupled Mode Theory governs its operation.
The impedance bandwidth of a microstrip patch antenna may be increased by additional resonances in the antenna structure. This work uses Characteristic Mode Analysis to show that the E-shaped patch operates in this manner and that its operation is well-modeled by Coupled Mode Theory.
This paper implemented an approximate direct inverse for the surface integral equation including multilevel fast-multipole method. We apply it as a preconditioner to two examples suffering convergence problem with an iterative solver.
Chhantyal-Pun, Rabi; Khan, M.A.H.; Taatjes, Craig A.; Percival, Carl J.; Orr-Ewing, Andrew J.; Shallcross, Dudley E.
In the context of tropospheric chemistry, Criegee intermediates denote carbonyl oxides with biradical/zwitterionic character (R1R2COO) that form during the ozonolysis of alkenes. First discovered almost 70 years ago, stabilised versions of Criegee intermediates formed via collisional removal of excess energy have interesting kinetic and mechanistic properties. The direct production and detection of these intermediates were not reported in the literature until 2008. However, recent advances in their generation through the ultraviolet irradiation of the corresponding diiodoalkanes in excess O2 and detection by various spectroscopic techniques (photoionisation, ultraviolet, infrared, microwave and mass spectrometry) have shown that these species can react rapidly with closed-shell molecules, in many cases at or exceeding the classical gas-kinetic limit, via multiple reaction pathways. These reactions can be complex, and laboratory measurements of products and the temperature and pressure dependence of the reaction kinetics have also revealed unusual behaviour. The potential role of these intermediates in atmospheric chemistry is significant, altering models of the oxidising capacity of the Earth's atmosphere and the rate of generation of secondary organic aerosol.
Advances in printed electronics are predicated on the integration of sophisticated printing technologies with functional materials. Although scalable manufacturing methods, such as letterpress and flexographic printing, have significant history in graphic arts printing, functional applications require sophisticated control and understanding of nanoscale transfer of fluid inks. In this paper, a versatile platform is introduced to study and engineer printing forms, exploiting a microscale additive manufacturing process to design micro-architected materials with controllable porosity and deformation. Building on this technology, controlled ink transfer for submicron functional films is demonstrated. The design freedom and high-resolution 3D control afforded by this method provide a rich framework for studying mechanics of fluid transfer for advanced manufacturing processes.
In finite element simulations, not all of the data are of equal importance. In fact, the primary purpose of a numerical study is often to accurately assess only one or two engineering output quantities that can be expressed as functionals. Adjoint-based error estimation provides a means to approximate the discretization error in functional quantities and mesh adaptation provides the ability to control this discretization error by locally modifying the finite element mesh. In the past, adjoint-based error estimation has only been accessible to expert practitioners in the field of solid mechanics. In this work, we present an approach to automate the process of adjoint-based error estimation and mesh adaptation on parallel machines. This process is intended to lower the barrier of entry to adjoint-based error estimation and mesh adaptation for solid mechanics practitioners. We demonstrate that this approach is effective for example problems in Poisson’s equation, nonlinear elasticity, and thermomechanical elastoplasticity.
Many social networks contain sensitive relational information. One approach to protect the sensitive relational information while offering flexibility for social network research and analysis is to release synthetic social networks at a pre-specified privacy risk level, given the original observed network. We propose the DP-ERGM procedure that synthesizes networks that satisfy the differential privacy (DP) via the exponential random graph model (EGRM). We apply DP-ERGM to a college student friendship network and compare its original network information preservation in the generated private networks with two other approaches: differentially private DyadWise Randomized Response (DWRR) and Sanitization of the Conditional probability of Edge given Attribute classes (SCEA). The results suggest that DP-EGRM preserves the original information significantly better than DWRR and SCEA in both network statistics and inferences from ERGMs and latent space models. In addition, DP-ERGM satisfies the node DP, a stronger notion of privacy than the edge DP that DWRR and SCEA satisfy.
Bentz, Brian Z.; Mahalingam, Sakkarapalayam M.; Ysselstein, Daniel; Montenegro Larrea, Paola C.; Cannon, Jason R.; Rochet, Jean C.; Low, Philip S.; Webb, Kevin
Imaging fluorescence through millimeters or centimeters of tissue has important in vivo applications, such as guiding surgery and studying the brain. Often, the important information is the location of one of more optical reporters, rather than the specifics of the local geometry, motivating the need for a localization method that provides this information. We present an optimization approach based on a diffusion model for the fast localization of fluorescent inhomogeneities in deep tissue with expanded beam illumination that simplifies the experiment and the reconstruction. We show that the position of a fluorescent inhomogeneity can be estimated while assuming homogeneous tissue parameters and without having to model the excitation profile, reducing the computational burden and improving the utility of the method. We perform two experiments as a demonstration. First, a tumor in a mouse is localized using a near infrared folate-targeted fluorescent agent (OTL38). This result shows that localization can quickly provide tumor depth information, which could reduce damage to healthy tissue during fluorescence-guided surgery. Second, another near infrared fluorescent agent (ATTO647N) is injected into the brain of a rat, and localized through the intact skull and surface tissue. This result will enable studies of protein aggregation and neuron signaling.
We report an experimental implementation for neutron transverse polarization analysis that is capable of detecting a small angular change (≪10-3 rad) in neutron spin orientation. This approach is demonstrated for monochromatic beams, and we show that it could be extended to polychromatic neutron beams. Our approach employs a 3He spin filter inside a solenoid with an analyzing direction perpendicular to the incident neutron polarization direction. The method was tested with polarized neutron beams and a spin rotator placed inside a μ-metal shield just upstream of the analyzer. No cryogenic superconducting shields or additional neutron spin manipulations are needed. With a counting detector, we experimentally show that the angular resolution δθ=1/(PnA√N) rad is only determined by the counting statistics for the total counts N and the product of the neutron polarization Pn and the analyzing power A. With a high-flux neutron beam, 10-6 rad angular sensitivity is feasible within a day. This simple, classical-quantum-limited transverse polarization analysis scheme may reduce the overall complexity of experimental implementation for applications requiring sensitive neutron polarimetry and improve the precision in fundamental science studies and polarized neutron imaging.
Existing security models are highly linear and fail to capture the rich interactions that occur across security technology, infrastructure, cybersecurity, and human/organizational components. In this work, we will leverage insights from resilience science, complex system theory, and network theory to develop a next-generation security model based on these interactions to address challenges in complex, nonlinear risk environments and against innovative and disruptive technologies. Developing such a model is a key step forward toward a dynamic security paradigm (e.g., shifting from detection to anticipation) and establishing the foundation for designing next-generation physical security systems against evolving threats in uncontrolled or contested operational environments.
Neural network (NN) inference is an essential part of modern systems and is found at the heart of numerous applications ranging from image recognition to natural language processing. In situ NN accelerators can efficiently perform NN inference using resistive crossbars, which makes them a promising solution to the data movement challenges faced by conventional architectures. Although such accelerators demonstrate significant potential for dense NNs, they often do not benefit from sparse NNs, which contain relatively few non-zero weights. Processing sparse NNs on in situ accelerators results in wasted energy to charge the entire crossbar where most elements are zeros. To address this limitation, this letter proposes Granular Matrix Reordering (GMR): a preprocessing technique that enables an energy-efficient computation of sparse NNs on in situ accelerators. GMR reorders the rows and columns of sparse weight matrices to maximize the crossbars' utilization and minimize the total number of crossbars needed to be charged. The reordering process does not rely on sparsity patterns and incurs no accuracy loss. Overall, GMR achieves an average of 28 percent and up to 34 percent reduction in energy consumption over seven pruned NNs across four different pruning methods and network architectures.
It is widely believed that one's peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the "pattern" by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game decides among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make a similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information.
This paper proposes an optimal relay placement approach for microgrids. The proposed approach considers both grid-connected and islanded microgrid modes. The algorithm separately calculates the System Average Interruption Frequency Index (SAIFI) of a microgrid in each operating mode. Then, two weighting factors corresponding to different operating modes are used to calculate the overall SAIFI of the microgrid. The objective is to find the optimal relay locations such that the microgrid overall SAIFI is minimized. The power electronics interfaces associated with distributed energy resources may be classified as grid following or grid forming. As opposed to grid-following distributed energy resources (DERs) such as typical solar inverters, grid-forming inverters are able to control the microgrid voltage and frequency at the point of their interconnection. Therefore, these DERs can facilitate the formation of sub-islands in the microgrid when the protective relays isolate a portion of the microgrid. If there is at least one grid-forming DER available in a sub-island, that sub-island can continue supplying its local load. The exchange market algorithm (EMA) is used for optimizing functions. The effectiveness of the proposed optimal relay placement approach is verified using an 18-bus microgrid and IEEE 123-bus test system.
As more renewable generation connects to distribution systems, it is imminent that existing distribution feeders will be converted to microgrids-systems that offer resilience by providing the flexibility of supporting the grid in normal operation and operating as self-sustained islands when the grid is disconnected. However, inverter control and feeder protection will need to be tuned to the operating modes of the microgrid. This paper offers an insight into the issues involved by taking a case study of a real-world feeder located in the southwestern US that was converted to a microgrid with three solar PV units connecting to the feeder. Different inverter control configurations and adaptive protection using different settings for different operating conditions are proposed for safe operation of this microgrid. The solution also helps to create a framework for protection and coordination of other similar microgrids.
The transient drag force exerted by mobile solutes on a moving dislocation is computed using continuum theory. These mobile solutes form so-called Cottrell atmospheres around dislocations during static and dynamic strain aging. We evaluate the evolution of the drag force exerted by the atmosphere under two velocity time-histories: impulsive acceleration to a chosen velocity and a constant acceleration rate. A particular focus is on the conditions under which the stationary limit assumed by theories of dynamic strain aging is obeyed. According to our results, two conditions—one on the dislocation velocity and one on the acceleration rate—must be satisfied for the stationary limit to hold. Using the Orowan relation and a line tension model, we obtain estimates for the temperature, stress, strain rate, and dislocation density regimes where the stationary limit is valid, and compare these results with experiments for a few material systems.
Although common practice for estimating photovoltaic (PV) degradation rate (RD) assumes a linear behavior, field data have shown that degradation rates are frequently nonlinear. This article presents a new methodology to detect and calculate nonlinear RD based on PV performance time-series from nine different systems over an eight-year period. Prior to performing the analysis and in order to adjust model parameters to reflect actual PV operation, synthetic datasets were utilized for calibration purposes. A change-point analysis is then applied to detect changes in the slopes of PV trends, which are extracted from constructed performance ratio (PR) time-series. Once the number and location of change points is found, the ordinary least squares method is applied to the different segments to compute the corresponding rates. The obtained results verified that the extracted trends from the PR time-series may not always be linear and therefore, 'nonconventional' models need to be applied. All thin-film technologies demonstrated nonlinear behavior whereas nonlinearity detected in the crystalline silicon systems is thought to be due to a maintenance event. A comparative analysis between the new methodology and other conventional methods demonstrated levelized cost of energy differences of up to 6.14%, highlighting the importance of considering nonlinear degradation behavior.
A proof-of-principle CR-39 based neutron-recoil-spectrometer was built and fielded on the Z facility. Data from this experiment match indium activation yields within a factor of 2 using simplified instrument response function models. The data also demonstrate the need for neutron shielding in order to infer liner areal densities. A new shielded design has been developed. The spectrometer is expected to achieve signal-to-background greater than 2 for the down-scattered neutron signal and greater than 30 for the primary signal.
Accurate modeling of photovoltaic (PV) performance requires the precise calculation of module temperature. Currently, most temperature models rely on steady-state assumptions that do not account for the transient climatic conditions and thermal mass of the module. On the other hand, complex physics-based transient models are computationally expensive and difficult to parameterize. In order to address this, a new approach to transient thermal modeling was developed, in which the steady-state predictions from previous timesteps are weighted and averaged to accurately predict the module temperature at finer time scales. This model is informed by 3-D finite-element analyses, which are used to calculate the effect of wind speed and module unit mass on module temperature. The model, in application, serves as an added filter over existing steady-state models that smooths out erroneous values that are a result of intermittency in solar resource. Validation of this moving-Average model has shown that it can improve the overall PV energy performance model accuracy by as much as 0.58% over steady-state models based on mean absolute error improvements and can significantly reduce the variability between the model predictions and measured temperature times series data.
Hansen, Nils H.; Adamson, Brian A.; Skeen, Scott A.; Ahmed, Musahid
The irreversible dimerization of polycyclic aromatic hydrocarbons (PAHs)-typically pyrene (C16H10) dimerization-is widely used in combustion chemistry models to describe the soot particle inception step. This paper concerns itself with the detection and identification of dimers of flame-synthesized PAH radicals and closed-shell molecules and an experimental assessment of the role of these PAH dimers for the nucleation of soot. To this end, flame-generated species were extracted from an inverse co-flow flame of ethylene at atmospheric pressure and immediately diluted with excess nitrogen before the mixture was analyzed using flame-sampling tandem mass spectrometry with collision-induced fragmentation. Signal at m/z = 404.157 (C32H20) and m/z = 452.157 (C36H20) were detected and identified as dimers of closed-shell C16H10 and C18H10 monomers, respectively. A complex between a C13H9 radical and a C24H12 closed-shell PAH was observed at m/z = 465.164 (C37H21). However, a rigorous analysis of the flame-sampled mass spectra as a function of the dilution ratio, defined as the ratio of the flow rates of the diluent nitrogen to the sampled gases, indicates that the observed dimers are not flame-born, but are produced in the sampling line. In agreement with theoretical considerations, this paper provides experimental evidence that pyrene dimers cannot be a key intermediate in particle inception at elevated flame temperatures.
Grid operators are now considering using distributed energy resources (DERs) to provide distribution voltage regulation rather than installing costly voltage regulation hardware. DER devices include multiple adjustable reactive power control functions, so grid operators have the difficult decision of selecting the best operating mode and settings for the DER. In this work, we develop a novel state estimation-based particle swarm optimization (PSO) for distribution voltage regulation using DER-reactive power setpoints and establish a methodology to validate and compare it against alternative DER control technologies (volt-VAR (VV), extremum seeking control (ESC)) in increasingly higher fidelity environments. Distribution system real-time simulations with virtualized and power hardware-in-the-loop (PHIL)-interfaced DER equipment were run to evaluate the implementations and select the best voltage regulation technique. Each method improved the distribution system voltage profile; VV did not reach the global optimum but the PSO and ESC methods optimized the reactive power contributions of multiple DER devices to approach the optimal solution.
Copper is a commonly used interconnect metal in microelectronic interconnects due to its exceptional electrical and thermal properties. Particularly in applications of the 2.5 and 3D integration, Cu is utilized in through-silicon-vias (TSVs) and flip chip interconnects between microelectronic chips for providing miniaturization, lower power and higher performance than current 2D packaging approaches. SnAg capped Cu pillars are a common high-density interconnect technology for flip chip bonding. For these interconnects, specific properties of the Cu surface, such as roughness and cleanliness, are an important factor in the process to ensure quality solder bumps. During electroplating, tight processing parameters must be met so that defects are avoided, and high bump uniformity is achieved. An understanding of the interactions at the solder and Cu pillar interface is needed, based on the electroplating parameters, to determine the best method for populating solder on the wafer surface. In this study, surface treatment techniques such as oxygen plasma cleaning were performed on the Cu surfaces and the SnAg plating chemistry for depositing the solder were evaluated through hull cell testing to qualitatively determine the range of current densities to investigate. It was observed that current density while plating played a large role in solder bump deposition morphology. At the higher current densities greater than 60 mA/cm2, bump height non-uniformity and dendritic growth are observed and at lower current densities, less than or equal to 60 mA/cm2, uniform, continuous bump height occurred.
IFCS-ISAF 2020 - Joint Conference of the IEEE International Frequency Control Symposium and IEEE International Symposium on Applications of Ferroelectrics, Proceedings
A true series resonance oscillator has been developed for use with a wide-range of 1-port resonance-based sensors and devices. The oscillator effectively removes the shunt capacitance Co, allowing the true series resonance to be monitored, providing the optimum sensitivity across a wide range of frequencies (i.e. kilohertz to gigahertz), shunt capacitances, and quality factors (Q) for the first time. It is well-known that non-zero shunt capacitance alters the impedance by shifting the location of the impedance minimum and the zero-phase crossing while causing significant impedance distortion. We have developed an active shunt capacitance cancelling oscillator (ASSCO) that removes any shunt capacitance across the resonator by supplying the circuit an equal 'dummy' capacitance using a cancelling current. The oscillator does not require automatic gain control (AGC) and the resonator can be grounded to reduce parasitic contributions.
Atomic precision advanced manufacturing (APAM) offers creation of donor devices in an atomically thin layer doped beyond the solid solubility limit, enabling unique device physics. This presents an opportunity to use APAM as a pathfinding platform to investigate digital electronics at the atomic limit. Scaling to smaller transistors is increasingly difficult and expensive, necessitating the investigation of alternative fabrication paths that extend to the atomic scale. APAM donor devices can be created using a scanning tunneling microscope (STM). However, these devices are not currently compatible with industry standard fabrication processes. There exists a tradeoff between low thermal budget (LT) processes to limit dopant diffusion and high thermal budget (HT) processes to grow defect-free layers of epitaxial Si and gate oxide. To this end, we have developed an LT epitaxial Si cap and LT deposited Al2O3 gate oxide integrated with an atomically precise single-electron transistor (SET) that we use as an electrometer to characterize the quality of the gate stack. The surface-gated SET exhibits the expected Coulomb blockade behavior. However, the gate’s leverage over the SET is limited by defects in the layers above the SET, including interfaces between the Si and oxide, and structural and chemical defects in the Si cap. We propose a more sophisticated gate stack and process flow that is predicted to improve performance in future atomic precision devices.
This report is a functional review of the radionuclide containment strategies of fluoride-salt-cooled high temperature reactor (FHR), molten salt reactor (IVISR) and high temperature gas reactor (HTGR) systems. This analysis serves as a starting point for further, more in-depth analyses geared towards identifying phenomenological gaps that still exist, preventing the creation of a mechanistic source term for these reactor types. As background information to this review, an overview of how a mechanistic source term is created and used for consequence assessment necessary for licensing is provided. How mechanistic source term is used within the LMP is also provided. Third, the characteristics of non-LWR mechanistic source terms are examined This report does not assess the viability of any software system for use with advanced reactor designs, but instead covers system function requirements. Future work within the Nuclear Energy Advanced Modeling and Simulations (NEAMS) program will address such gaps.