The Vanguard program informally began in January 2017 with the submission of a white paper entitled "Sandia's Vision for a 2019 Arm Testbed" to NNSA headquarters. The program proceeded in earnest in May 2017 with an announcement by Doug Wade (Director, Office of Advanced Simulation and Computing and Institutional R&D at NNSA) that Sandia National Laboratories (Sandia) would host the first Advanced Architecture Prototype platform based on the Arm architecture. In August 2017, Sandia formed a Tri-lab team chartered to develop a robust HPC software stack for Astra to support the Vanguard program goal of demonstrating the viability of Arm in supporting ASC production computing workloads.
Computational modeling and simulation are paramount to modern science. Computational models often replace physical experiments that are prohibitively expensive, dangerous, or occur at extreme scales. Thus, it is critical that these models accurately represent and can be used as replacements for reality. This paper provides an analysis of metrics that may be used to determine the validity of a computational model. While some metrics have a direct physical meaning and a long history of use, others, especially those that compare probabilistic data, are more difficult to interpret. Furthermore, the process of model validation is often application-specific, making the procedure itself challenging and the results difficult to defend. We therefore provide guidance and recommendations as to which validation metric to use, as well as how to use and decipher the results. An example is included that compares interpretations of various metrics and demonstrates the impact of model and experimental uncertainty on validation processes.
When making computational simulation predictions of multiphysics engineering systems, sources of uncertainty in the prediction need to be acknowledged and included in the analysis within the current paradigm of striving for simulation credibility. A thermal analysis of an aerospace geometry was performed at Sandia National Laboratories. For this analysis, a verification, validation, and uncertainty quantification (VVUQ) workflow provided structure for the analysis, resulting in the quantification of significant uncertainty sources including spatial numerical error and material property parametric uncertainty. It was hypothesized that the parametric uncertainty and numerical errors were independent and separable for this application. This hypothesis was supported by performing uncertainty quantification (UQ) simulations at multiple mesh resolutions, while being limited by resources to minimize the number of medium and high resolution simulations. Based on this supported hypothesis, a prediction including parametric uncertainty and a systematic mesh bias is used to make a margin assessment that avoids unnecessary uncertainty obscuring the results and optimizes use of computing resources.
“Heat waves” is a colloquial term used to describe convective currents in air formed when different objects in an area are at different temperatures. In the context of Digital Image Correlation (DIC) and other optical-based image processing techniques, imaging an object of interest through heat waves can significantly distort the apparent location and shape of the object. There are many potential heat sources in DIC experiments, including but not limited to lights, cameras, hot ovens, and sunlight, yet error caused by heat waves is often overlooked. This paper first briefly presents three practical situations in which heat waves contributed significant error to DIC measurements to motivate the investigation of heat waves in more detail. Then the theoretical background of how light is refracted through heat waves is presented, and the effects of heat waves on displacements and strains computed from DIC are characterized in detail. Finally, different filtering methods are investigated to reduce the displacement and strain errors caused by imaging through heat waves. The overarching conclusions from this work are that errors caused by heat waves are significantly higher than typical noise floors for DIC measurements, and that the errors are difficult to filter because the temporal and spatial frequencies of the errors are in the same range as those of typical signals of interest. Therefore, eliminating or mitigating the effects of heat sources in a DIC experiment is the best solution to minimizing errors caused by heat waves.
We present a direct numerical simulation of a temporal jet between n-dodecane and diluted air undergoing spontaneous ignition at conditions relevant to low-temperature diesel combustion. The jet thermochemical conditions were selected to result in two-stage ignition. Reaction rates were computed using a 35-species reduced mechanism which included both the low- and high-temperature reaction pathways. The aim of this study is to elucidate the mechanisms by which low-temperature reactions promote high-temperature ignition under turbulent, non-premixed conditions. We show that low-temperature heat release in slightly rich fuel regions initiates multiple cool flame kernels that propagate towards very rich fuel regions through a reaction-diffusion mechanism. Although low-temperature ignition is delayed by imperfect mixing, the propagation speed of the cool flames is high: as a consequence, high-temperature reactions in fuel-rich regions become active early during the ignition transient. Because of this early start, high-temperature ignition, which occurs in fuel-rich regions, is faster than homogeneous ignition. Following ignition, the high-temperature kernels expand and engulf the stoichiometric mixture-fraction iso-surface which in turn establish edge flames which propagate along the iso-surface. The present results indicate the preponderance of flame folding of existing burning surfaces, and that ignition due to edge-flame propagation is of lesser importance. Finally, a combustion mode analysis that extends an earlier classification [1] is proposed to conceptualize the multi-stage and multi-mode nature of diesel combustion and to provide a framework for reasoning about the effects of different ambient conditions on diesel combustion.
Freight transportation represents about 9.5% of GDP in the U.S., it is responsible for about 8% of greenhouse gas emissions, and supports the import and export of about 3.6 trillion in international trade. It is therefore important that the national freight transportation system is designed and operated efficiently. Hence, this paper develops a mathematical model to estimate international and domestic freight flows across ocean, rail, and truck modes, which can be used to study the impacts of changes in our infrastructure, as well as the imposition of new user fees and changes in operating policies. The model integrates a user equilibrium-based logit argument for path selection with a system optimal argument for rail network operations. This leads to the development of a unique solution procedure that is demonstrated in a large-scale analysis focused on all intercity freight and U.S export/import containerized freight. The model results are compared with the reported flow volumes. The model is applied to two case studies: (1) a disruption of the seaports of Los Angeles and Long Beach (LA and LB) similar to the impacts that would be felt in an earthquake; and (2) implementation of new user fees at the California ports.
Active participation in international R&D is crucial for achieving the Spent Fuel Waste Science & Technology (SFWST) long-term goals of conducting "experiments to fill data needs and confirm advanced modeling approaches" and of having a "robust modeling and experimental basis for evaluation of multiple disposal system options" (by 2020). DOE's Office of Nuclear Energy (NE) has developed a strategic plan to advance cooperation with international partners. The international collaboration on the evaluation of crystalline disposal media at Sandia National Laboratories (SNL) in FY18 focused on the collaboration through the Development of Coupled Models and their Validation against Experiments (DECOVALEX- 2019) project. The DECOVALEX project is an international research and model comparison collaboration, initiated in 1992, for advancing the understanding and modeling of coupled thermo-hydro-mechanicalchemical (THMC) processes in geological systems. SNL has been participating in three tasks of the DECOVALEX project: Task A. Modeling gas injection experiments (ENGINEER), Task C. Modeling groundwater recovery experiment in tunnel (GREET), and Task F. Fluid inclusion and movement in the tight rock (FINITO). FY18 work focused on Task C and preparing the interim reports for the three tasks SNL has been imvolved. The major accomplishments are summarized
MELCOR is being developed at Sandia National Laboratories for the U.S. Nuclear Regulatory Commission. MELCOR is a fully integrated code (encompassing the reactor coolant system and the containment building) that models the progression of postulated accidents in light water reactor power plants. It provides a capability for independently auditing analyses submitted by reactor manufacturers and utilities. In order to assess the adequacy of containment thermal - hydraulic modeling incorporated in the MELCOR code, a key containment test facility was analyzed. This report documents MELCOR code calculations for simulating steam - water blowdown tests performed in the Heissdampfreaktor ( HDR) de-commissioned containment facility located near Frankfurt , Germany . These tests are a series of blowdown experiments in a large scaled test facility ; including some tests with the addition of hydrogen release which are intended to simulate a variety of postulated break s inside large containment buildings. The key objectives of this MELCOR assessment are to study: (1) the expansion and transport of high energy steam - water releases, (2) heat and mass transfer to structural passive heat sinks, and (3) containment gas mixing and stratification. Moreover, MELCOR results are compared to the CONTAIN code for the same tests.
Applications of the severe accident analysis code MELCOR, developed for the U.S. Nuclear Regulatory Commission (NRC) by Sandia National Laboratories (SNL), have been supported by the graphical user-interface and post-processing suite Symbolic Nuclear Analysis Package (SNAP), developed for the NRC by Applied Programming Technology (APT). With the release of MELCOR 2.2, new user functionality and models have been introduced and an update to the SNAP MELCOR plugin user interface is necessary to access these new features. This document relates all new features introduced into MELCOR to the development team at APT as well as the NRC.
Material equation-of-state (EOS) models, generally providing the pressure and internal energy for a given density and temperature, are required to close the equations of hydrodynamics. As a result they are an essential piece of physics used to simulate inertial confinement fusion (ICF) implosions. Historically, EOS models based on different physical/chemical pictures of matter have been developed for ICF relevant materials such as the deuterium (D2) or deuterium-tritium (DT) fuel, as well as candidate ablator materials such as polystyrene (CH), glow-discharge polymer (GDP), beryllium (Be), carbon (C), and boron carbide (B4C). The accuracy of these EOS models can directly affect the reliability of ICF target design and understanding, as shock timing and material compressibility are essentially determined by what EOS models are used in ICF simulations. Systematic comparisons of current EOS models, benchmarking with experiments, not only help us to understand what the model differences are and why they occur, but also to identify the state-of-the-art EOS models for ICF target designers to use. For this purpose, the first Equation-of-State Workshop, supported by the US Department of Energy's ICF program, was held at the Laboratory for Laser Energetics (LLE), University of Rochester on 31 May–2nd June, 2017. This paper presents a detailed review on the findings from this workshop: (1) 5–10% model-model variations exist throughout the relevant parameter space, and can be much larger in regions where ionization and dissociation are occurring, (2) the D2 EOS is particularly uncertain, with no single model able to match the available experimental data, and this drives similar uncertainties in the CH EOS, and (3) new experimental capabilities such as Hugoniot measurements around 100 Mbar and high-quality temperature measurements are essential to reducing EOS uncertainty.
The On-Line Waste Library is a website that contains information regarding United States Department of Energy-managed high-level waste, spent nuclear fuel, and other wastes that are likely candidates for deep geologic disposal, with links to supporting documents for the data. This report provides supporting information for the data for which an already published source was not available.
In order to increase neutron yield in fusion experiments on the Magnetized Linear Inertial Fusion (MagLIF) platform, it is important to maximize the energy coupled to the fuel during the laser-preheat stage. However, laser-energy coupling is limited by laser—plasma instabilities (LPI). In this regard, the Pecos facility at Sandia National Laboratories uses the Z-beamlet laser to test and study the effects of LPI on MagLIF-relevant targets. In particular, stimulated Raman scattering (SRS) is measured in Pecos by using two photo-diodes and a near-beam imager. The measurements from the photo-diodes are processed using a synthetic spectrum based on a Gaussian model. With this relatively simple model, the mean wavelength and intensity of backscattered light can be deduced. Our measurements show similar trends as those given by time-resolved spectrometer data. Hence, this model provides a simple way to approximate time-resolved spectra for scattered light by SRS.
This is a work planning document that describes technical and programmatic goals for disposition of spent nuclear fuel (SNF) that is currently in dry storage in dual-purpose canisters (DPCs), or will be in the foreseeable future. It then describes how those goals can be promoted by a research and development (R&D) program. The needed R&D is compared to the ongoing work supported by the U.S. Department of Energy in FY18, and planned for FY19 and beyond. Some additional R&D activities are recommended, and plans are presented for technical integration activities that address the efficacy of the Direct Disposal of DPCs program (WBS 1.08.01.03.05), and integration with the overall Disposal Research program (WBS 1.08.01.03). The planned deliverable for this work package in FY19 (M2SF-1951\1010305051-Analysis of Solutions for DPC Disposal; 6/19/19) will be the product of this workplan. The deliverable will evaluate technical options for DPC direct disposal, taking into account the range ofpast and current DPC designs in the existing fleet. It will describe a set of goals for successful disposition of spent fuel in DPCs. It will analyze the scope and timing of needed R&D activities (R&D Plan), and discuss the uses of generic and site-specific analyses. Where appropriate, it will use alternative management cases to represent how DPC direct disposal could be incorporated in the overall geologic disposal program, given uncertainties in program direction and funding.
The Federal Radiological Monitoring and Assessment Center (FRMAC) relies on accurate and defensible analytical laboratory data to support its mission. FRMAC Laboratory Analysis personnel are responsible for (1) receiving samples, (2) managing samples, and (3) providing data quality assurance. Currently, the RadResponder software application does not meet all these needs. With some modifications, RadResponder could meet the needs for sample receiving functions, but it does not meet the needs of sample management and data quality assurance functions. The FRMAC Laboratory Analysis team has discussed and reviewed the following options moving forward: Option 1: Make minor revisions to RadResponder to improve sample receiving capability, purchase and configure a commercial laboratory information management system (LIMS) to perform sample management and data quality assurance, and build an interface between RadResponder and the commercial-off-the-shelf LIMS. Option 2: Make major revisions to RadResponder for all FRMAC Laboratory Analysis functions to support required sample management and data quality assurance activities. Option 3: Create a custom-built LIMS system to interface with RadResponder. Note: All three options will require the development of a Laboratory Analysis web portal and will require funding for ongoing maintenance and training. The FRMAC Laboratory Analysis team highly recommends Option 1 as the best and most efficient path forward. Commercial-off-the-shelf LIMS products have been proven successful in the laboratory community for decades. Option 1 leverages these proven technologies and takes advantage of RadResponder's current strengths.
This milestone presents a demonstration of an average surface mapping model that maps single-phase average wall temperatures from STAR-CCM+ to Cobra-TF using a multiplier that is linearly dependent on axial and azimuthal coordinates of the Cobra-TF mesh. The work presented herein lays the foundation for adding greater complexity to the average surface mapping model such as fluid property dependence. This average surface mapping model will be incorporated into the surface mapping model developed by Lindsay Gilkey to map fluctuations from the mean surface temperatures.
Objectives for Multi-Physics Simulations include: Provide a systematic framework for multi-process modeling — Conduct parallel model development efforts that cover the technical areas needed to support criticality consequence screening in performance assessment (PA) and that will be more closely integrated as development proceeds; Investigate separate effects — Allow partitioning of the overall waste package (WP) internal criticality multi-physics modeling effort during development activities, for study of specific processes that can later be coupled if warranted from interpretation of results; Study scaling and bounding approaches — Where possible, represent criticality consequences in PA using simplification of uncertain criticality event frequency and magnitude, bounding of consequences for screening purposes, and scaling of consequences to multiple WPs; and, Integration among participants — Multiple modeling teams (mainly SNL and ORNL, and their collaborators) will work on different parts of the in-package criticality phenomenology. Insights generated this way will be combined for more realistic coupled modeling, and for validation.
This SAND Report Guide offers support to authors, technical writers, principal investigators, and others involved in the process of creating, formatting, or refining a SAND Report. It details what you need to know before you begin compiling a SAND Report, directs you to the SAND Report templates, outlines the order of elements in a SAND Report, and explains what to do when your report is completed and ready for Review and Approval and subsequent distribution. Supporting information is provided in the appendix, such as guidance on styles, where to get technical assistance, trademarks, Microsoft Word, and equations.
Understanding performance is the key to risk management in energy storage project financing. Technical performance underlies both capital and operating costs, directly impacting the system's economic performance Since project development is an exercise in risk management, financing costs are the clearest view into how lenders' perceive a project's riskiness. Addressing this perception is the challenge facing the energy storage industry today. Growth in the early solar market was hindered until OEMs and project developers used verifiable performance to allay lenders' apprehension about the long-term viability of those projects. The energy storage industry is similarly laying the groundwork for sustained growth through better technical Standards and best practices. However, the storage industry remains far more complex than other markets, leading lenders to need better data, analytical tools, and performance metrics to invest not only to maximize returns, but also safely--through incorporating more precise performance metrics into the project's documents.
This document details the computational fluid dynamic and system-level modeling, including a mechanistic representation of a Terry turbopump. Until this recent effort, data and modeling results show that a Terry turbine, flowing air (or steam) at a certain rate, can develop the same power at two very different speeds, and has large implications with respect to understanding how a boiling water reactor's reactor core isolation cooling system or a pressurized water reactor turbine driven auxiliary feedwater system would respond to a loss of electrical power for Terry turbine speed governing. This work has provided insights in modeling uncertainties and provides confirmation for experimental efforts for the Terry turbopump expanded operating band being conducted at Texas A&M University.
This document summarizes research performed under the Laboratory Directed Research and Development (LDRD) project titled Developing Fugitive Emissions Sensor Networks: New Optimization Algorithms for Monitoring, Measurement and Verification. The purpose of this project is to develop methods and software to enhance detection programs through optimal design of the sensor network. This project includes both software development and field work. While this project is focused on methane emissions, the sensor placement optimization framework can be applied to a wide range of applications, including the placement of water quality sensors, surveillance cameras, fire and chemical detectors. This research has the potential to improve national security by improving the way sensors are deployed in the field.
The state of stress in the earth is complicated and it is difficult to determine all three components and directions of the stress. However, the state of stress affects all activities which take place in the earth, from causing earthquakes on critically stressed faults, to affecting production from hydraulically fractured shale reservoirs, to determining closure rates around a subterranean nuclear waste repository. Current state of the art methods commonly have errors in magnitude and direction of up to 40%. This is especially true for the intermediate principal stress. This project seeks to better understand the means which are used to determine the state of stress in the earth and improve upon current methods to decrease the uncertainty in the measurement. This is achieved by a multipronged experimental investigation which is closely coupled with advanced constitutive and numeric modeling.
A new technical basis on the mechanics of energetic materials at the individual particle scale has been developed. Despite these particles being in most of our Sandia non-nuclear explosive components, we have historically lacked any understanding of particle behavior. Through the novel application of nanoidentation methods to single crystal films and single particles of energetic materials with complex shapes, discovery data has been collected elucidating phenomena of particle strength, elastic and plastic deformation, and fracture. This work specifically developed the experimental techniques and analysis methodologies to distill data into relationships suitable for future integration into particle level simulations of particle reassembly. This project utilized experimental facilities at CINT and the Explosive Components Facility to perform ex-situ and in-situ nanoidentation experiments with simultaneous scanning electron microscope (SEM) imaging. Data collected by an applied axial compressive load in either force-control or displacement-control was well represented by Hertzian contact theory for linear elastic materials. Particle fracture phenomenology was effectively modeled by an empirical damage model.
Stochastic optimization is a fundamental field of research for machine learning. Stochastic gradient descent (SGD) and related methods provide a feasible means to train complicated prediction models over large datasets. SGD, however, does not explicitly address the problem of overfitting, which can lead to predictions that perform poorly on new data. This difference between loss performance on unseen testing data verses that of training data defines the generalization gap of a model. We introduce a new computational kernel called Stochastic Hessian Projection (SHP) that uses a maximum likelihood framework to simultaneously estimate gradient noise covariance and local curvature of the loss function. Our analysis illustrates that these quantities affect the evolution of parameter uncertainty and therefore generalizability. We show how these computations allow us to predict the generalization gap without requiring holdout data. Explicitly assessing this metric for generalizability during training may improve machine learning predictions when data is scarce and understanding prediction variability is critical.
Sierra/SolidMechanics (Sierra/SM) is a Lagrangian, three-dimensional code for finite element analysis of solids and structures. It provides capabilities for explicit dynamic, implicit quasistatic and dynamic analyses. The explicit dynamics capabilities allow for the efficient and robust solution of models with extensive contact subjected to large, suddenly applied loads. For implicit problems, Sierra/SM uses a multi-level iterative solver, which enables it to effectively solve problems with large deformations, nonlinear material behavior, and contact. Sierra/SM has a versatile library of continuum and structural elements, and a large library of material models. The code is written for parallel computing environments enabling scalable solutions of extremely large problems for both implicit and explicit analyses. It is built on the SIERRA Framework, which facilitates coupling with other SIERRA mechanics codes. This document describes the functionality and input syntax for Sierra/SM.
This document is a user's guide for capabilities that are not considered mature but are available in Sierra/SolidMechanics (Sierra/SM) for early adopters. The determination of maturity of a capability is determined by many aspects: having regression and verification level testing, documentation of functionality and syntax, and usability are such considerations. Capabilities in this document are lacking in one or many of these aspects.
In this work we examine approaches for using implementation diversity to disrupt or disable hardware trojans. We explore a variety of general frameworks for building diverse variants of circuits in voting architectures, and examine the impact of these on attackers and defenders mathematically and empirically. This work is augmented by analysis of a new majority voting technique. We also describe several automated approaches for generating diverse variants of a circuit and empirically study the overheads associated with these. We then describe a general technique for targeting functional circuit modifications to hardware trojans, present several specific implementations of this technique, and study the impact that they have on trojanized benchmark circuits.
In this article, we describe a prototype cosimulation framework using Xyce, GHDL and CocoTB that can be used to analyze digital hardware designs in out-of-nominal environments. We demonstrate current software methods and inspire future work via analysis of an open-source encryption core design. Note that this article is meant as a proof-of-concept to motivate integration of general cosimulation techniques with Xyce, an open-source circuit simulator.
The goal of the DOE OE Energy Storage System Safety Roadmap is to foster confidence in the safety and reliability of energy storage systems. There are three interrelated objectives to support the realization of that goal: research, codes and standards (C/S) and communication/coordination. The objective focused on C/S is "To apply research and development to support efforts that refocused on ensuring that codes and standards are available to enable the safe implementation of energy storage systems in a comprehensive, non-discriminatory and science-based manner."
The SNL Engineered Barrier System (EBS) International activities were focused on two main collaborative efforts for FY18: 1) Benchmarking semi-analytical codes used for thermal analysis, and 2) Benchmarking of reactive transport codes (including PFLOTRAN) used for chemical evolution of cementitious EBS components. The former topic, was completed over the course of FY18, while the latter has just begun in the latter half of FY18 under the aegis of additional appropriations and scoped as "Additional FY18 Activities". This report contains a complete summary of Item #1, as well as a status update on the progress of Item #2.
Photodetectors sensitive to the ultra-violet spectrum were demonstrated using an AlGaN high electron mobility transistor with an GaN nanodot optical floating gate. Peak responsivity of 2 x 109 A/W was achieved with a gain-bandwidth product > 1 GHz at a cut-on energy of 4.10 eV. Similar devices exhibited visible-blind rejection ratios > 106. The photodetection mechanism for $β$-Ga2O3 was also investigated. It was concluded that Schottky barrier lowering by self-trapped holes enables photodetector gain.
Cheap and efficient ion conducting separators are needed to improve efficiency and lifetime in fuel cells, batteries, and electrolyzers. Current state-of-the-art polymeric separators are made from Nafion, which is too expensive to be competitive with other technologies. Sandia has developed unique polymer separators that have lower cost and equivalent or superior ion transport compared to Nafion. These membranes consist of sulfonated Diels-Alder poly(phenylene) (SDAPP), a completely hydrocarbon polymer that conducts protons when hydrated. SDAPP membranes are thermally and chemically robust, with conductivities rivaling those of Nafion at high sulfonation levels. However, rational design of new separators requires molecular-level knowledge, currently unknown, of how polymer morphology affects transport. Here we describe the use of multiple computational and experimental techniques to understand the nanoscale morphology and water/proton transport properties in a series of sulfonated SDAPP membranes over a wide range of temperature, hydration, and sulfonation conditions.
Fibers doped with Yb3+ serve as optical amplification elements in many high-power amplification systems, and there is an interest in significantly extending the capabilities of rare-earth doped fiber amplifiers to space-based systems. We investigate the effects of gamma-radiation-induced photodarkening on the performance of such fibers, both for passive as well as active configurations. With an emphasis on low total ionizing doses, passive irradiations were found to show increased absorption across the visible and IR spectrum. Furthermore, continuous-pumping of an Yb3+ -doped fiber amplifier in a gamma radiation environment was found to exhibit significantly greater degradation than a similar intermittently-pumped irradiated amplifier for low total ionizing doses of under 10 krad(Si) [100 Gy(Si)]. We discuss the implications of the data which provide insight into energy-transfer mechanisms in the fibers and the relationship of gamma-radiation-induced photodarkening and pump-radiation-induced photodarkening associated with the observed fiber degradation.
The 2018 NRC HEAF tests were conducted in Chalfont Pennsylvania at KEMA High Power Laboratory during the week of September 10th. These scoping tests were executed to determine the most effective measurement methodologies for future tests. The goal of Sandia’s Photometrics group was to provide high-speed quantitative and qualitative imaging of the arcing fault tests for the Nuclear Regulatory Commission. The measurement methods included visible high-speed imaging, high-speed high-dynamic range visible imaging, thermal imaging, and quantitative flow imaging. In addition, data fusion products were generated to visualize instrumentation data and imaging measurements. All imaging has been time synchronized to the start of the arcing event.
This milestone presents a demonstration of a surface mapping model to map the surface temperature for single-phase STAR-CCM+ to Cobra-TF using average temperature data. This model can be used to generate high-resolution surface temperature data. This can be accomplished with linear equations or with an alternative non-linear model. Improvements and a path forward for the surface mapping model to be applied to two-phase temperature mappings is also laid out in this milestone report.
The overall goal of this work was to perform an in-depth analysis of resilience schemes adapted to the Asynchronous Many-Task (AMT) programming and execution model with the goal of informing the Sandia Advanced Simulation and Computing (ASC) program's application development strategy for next generation platforms (NGPs).
The overall goal of this work was to perform an in-depth analysis of resilience schemes adapted to the Asynchronous Many-Task (AMT) programming and execution model with the goal of informing the Sandia Advanced Simulation and Computing (ASC) program's application development strategy for next generation platforms (NGPs).
The overall goal of this work was to perform an in-depth analysis of resilience schemes adapted to the Asynchronous Many-Task (AMT) programming and execution model with the goal of informing the Sandia Advanced Simulation and Computing (ASC) program's application development strategy for next generation platforms (NGPs).
The biotransport of the intravascular nanoparticle (NP) is influenced by both the complex cellular flow environment and the NP characteristics. Being able to computationally simulate such intricate transport phenomenon with high efficiency is of far-reaching significance to the development of nanotherapeutics, yet challenging due to large length-scale discrepancies between NP and red blood cell (RBC) as well as the complexity of nanoscale particle dynamics. Recently, a lattice-Boltzmann (LB) based multiscale simulation method has been developed to capture both NP–scale and cell–level transport phenomenon at high efficiency. The basic components of this method include the LB treatment for the fluid phase, a spectrin-link method for RBCs, and a Langevin dynamics (LD) approach to capturing the motion of the suspended NPs. Comprehensive two-way coupling schemes are established to capture accurate interactions between each component. The accuracy and robustness of the LB–LD coupling method are demonstrated through the relaxation of a single NP with initial momentum and self-diffusion of NPs. This approach is then applied to study the migration of NPs in micro-vessels under physiological conditions. It is shown that Brownian motion is most significant for the NP distribution in 20μm venules. For 1 ∼ 100 nm particles, the Brownian diffusion is the dominant radial diffusive mechanism compared to the RBC-enhanced diffusion. For ∼ 500 nm particles, the Brownian diffusion and RBC-enhanced diffusion are comparable drivers for the particle radial diffusion process.
State of the art semiconductor processes have created high performance and low power consumption technologies. There is a drive to use these technologies for commercial space, defense, and infrastructure.
In October 2017, Sandia broke ground for a new computing center dedicated to High Performance Computing. The east expansion of Building 725 was entirely conceived of, designed, and built in less than 18 months and is a certified LEED Gold design building, the first of its kind for a data center in the State of New Mexico. This 15,000 square-foot building, with novel energy and water-saving technologies, will house Astra, the first in a new generation of Advanced Architecture Prototype Systems to be deployed by the NNSA and the first of many HPC systems in Building 725 East.
Demonstrating the thermodynamic efficiency of hydrogen conversion processes using various materials is a critical step in developing new technologies for storing concentrated solar energy, and is largely accomplished by using a thermodynamic model derived from experimental data. A main goal of this project is to calculate the uncertainty of the thermodynamic efficiency by calculating the uncertainty of the components that feed into the efficiency. Many different models and data sets were used to test the workflow. First, the models were fit to the data using a Bayesian Inference and a method called Markov Chain Monte Carlo (MCMC), which found the maximum a priori parameters, and a posterior probability distribution of the parameters. Next, the different models were compared to each other using model evidence values. It was found that for cleaner data sets, overfitting had not yet been reached, and the most complicated model was ideal, but on the noisier data sets, the less complex models were favored because the more complicated models resulted in overfitting. Next, forward propagation was used to calculate the enthalpy change and its associated uncertainty. A few variations on the models were tried, such as fitting in a different variable, producing negligible or negative effects on the fits of the models. Thus, the original models were used. A sensitivity analysis was performed, and used to calculate the model error. On the cleaner data sets, there was very minimal experimental noise, and thus, all resulting error was from the model. With consideration of the model error, the models fit the data very well, and the simpler model had a high model error, as expected. All these components will then be used to calculate the thermodynamic efficiency of the different materials.
We present novel stochastic optimization models to improve power systems resilience to extreme weather events. We consider proactive redispatch, transmission line hardening, and transmission line capacity increases as alternatives for mitigating expected load shed due to extreme weather. Our model is based on linearized or "DC" optimal power flow, similar to models in widespread use by independent system operators (ISOs) and regional transmission operators (RTOs). Our computational experiments indicate that proactive redispatch alone can reduce the expected load shed by as much as 25% relative to standard economic dispatch. This resiliency enhancement strategy requires no capital investments and is implementable by ISOs and RTOs solely through operational adjustments. We additionally demonstrate that transmission line hardening and increases in transmission capacity can, in limited quantities, be effective strategies to further enhance power grid resiliency, although at significant capital investment cost. We perform a cross validation analysis to demonstrate the robustness of proposed recommendations. Our proposed model can be augmented to incorporate a variety of other operational and investment resilience strategies, or combination of such strategies.
The scale and complexity of the quantum system to which real-space quantum Monte Carlo (QMC) can be applied in part depends on the representation and memory usage of the trial wavefunction. B-splines, the computationally most efficient basis set, can have memory requirements exceeding the capacity of a single computational node. This situation has traditionally forced a difficult choice of either using slow internode communication or a potentially less accurate but smaller basis set such as Gaussians. Here, we introduce a hybrid representation of the single particle orbitals that combine a localized atomic basis set around atomic cores and B-splines in the interstitial regions to reduce the memory usage while retaining the high speed of evaluation and either retaining or increasing overall accuracy. We present a benchmark calculation for NiO demonstrating a superior accuracy while using only one eighth of the memory required for conventional B-splines. The hybrid orbital representation therefore expands the overall range of systems that can be practically studied with QMC.
The temperature response of luminescent ionizable polymers confined into far from equilibrium nanoparticles without chemical links was studied using molecular dynamics simulations. These nanoparticles, often referred to as polydots, are emerging as a promising tool for nanomedicine. Incorporating ionizable groups into these polymers enables biofunctionality; however, they also affect the delicate balance of interactions that hold these nanoparticles together. Here polydots formed by a model polymer dialkyl p-phenylene ethynylene with varying number of carboxylate groups along the polymer backbone were probed. We find that increasing temperature affects neutral and charged polydots differently, where neutral polydots exhibit a transition above which their structure becomes dynamic and they unravel. The dependence of the transition temperature on the surface to volume ratio of these polydots is much stronger than what has previously been observed in polymeric thin films. Charged polydots become dynamic enabling migration of the ionizable groups toward the particle interface, while retaining the overall particle shape.
Wellbore integrity is of paramount importance to subsurface resource extraction, energy storage and hazardous waste disposal. We introduce a simple non-invasive technology for casing integrity screening, based on the continuity of electrical current flow. Applying low frequency current to a wellhead, with a distant return electrode, produces a casing current dependent on the properties and depth extent of the well casing as well as the background formation. These currents in-turn generate surface electrical fields that can be captured in a radial profile and be used to analyze properties of the well casing. Numerical modeling results reveal a strong relation of the electric field to the casing properties and depth extent of the well. A small breakage in the casing produces a profile coincident to a cased well with a completion depth above the break. A corroded patch, where the casing conductivity is reduced, also alters the field profiles and its depth may be estimated by comparing to the profile expected from the well completion diagrams. The electric field profiles are also strongly dependent on background resistivity distributions and on whether the well was drilled using water or oil-based drilling fluids. We validate the proof of concept in a field experiment, where we applied currents at the wellheads of two wells with different casing lengths. The two profiles were similar in appearance but offset in amplitude by more than a factor of 5, consistent with the theoretical analysis as well as the 3D modeling results. These results demonstrate that our proposed approach has promise for mapping the general casing condition without well intervention. This approach can be a practical and effective tool for rapidly screening a number of wells before expensive logging-based technologies are employed for casing inspection in detail.
To better understand the factors contributing to electromagentic (EM) observables in developed field sites, we examine in detail through finite element analysis the specific effects of casing completion design. The presense of steel casing has long been exploited for improved subsurface interrogation and there is growing interest in remote methods for assessing casing integrity accross a range of geophysical scenarios related to resource development and sequestration/storage activities. Accurate modeling of the casing response to EM stimulation is recognized as relevant, and a difficult computational challenge because of the casing's high conductivity contrast with geomaterials and its relatively small volume fraction over the field scale. We find that casing completion design can have a significant effect on the observed EM fields, especially at zero frequency. This effect appears to originate in the capacitive coupling between inner production casing and the outer surface casing. Furthermore we show that an equivalent “effective conductivity” for the combined surface/production casing system is inadequate for replicating this effect, regardless of whether the casings are grounded to one another or not. Lastly, we show that in situations where this coupling can be ignored and knowledge of casing currents is not required, simplifying the casing as a perfectly conducting line can be an effective strategy for reducing the computational burden in modeling field-scale response.
The prediction of heat flux over a sphere in hypersonic flow is an interesting and challenging problem. When using solutions to the Navier-Stokes equations to predict the heat flux, a common source of numerical error is a phenomenon known as 'grid imprinting'. Rather than the axisymmetric heat flux profile expected for a spherical surface, the topology of the grid can be seen in the surface heat flux distribution. We will examine the origin of this phenomenon; specifically the regions of the grid that contribute most. Comparisons will be made between second order finite volume and Discontinuous Galerkin (DG) results — the DG results will range from 2nd to 4th order. The Dual Weighted Residual (DWR) framework is used to obtain local error estimates for the heat flux integrated over the surface with respect to the grid for the DG results.
International collaborations on nuclear waste disposal R&D are an integral part of the Spent Fuel Waste Science and Technology (SFWST) campaign within the DOE Fuel Cycle and Technology (FCT) program. These partnerships with international repository R&D programs provide key opportunities to participate in experiments developing laboratory/field data (underground research laboratories (URL)) of engineered barrier system (EBS) interactions (e.g., near-field) and characterization of transport phenomena in the host rock (e.g., far-field). The results of these experiments are used in the evaluation of coupled processes and their representation via state-of-the-art simulation approaches to evaluate repository performance. During the thermal heating period, increases in temperature from radionuclide decay in the spent fuel (SF) waste canisters will increase temperature in the surrounding EBS driving chemical and transport processes in the near- and far-field domains of the repository. URL heater-tests for extended periods of times (e.g., years) provide key information and data on thermal effects affecting engineered barriers in response to temperature and water saturation levels. Groundwater interactions with cementitious barriers are also important to in-drift chemistry and EBS performance during post-closure. Descriptions of the various URL experiments for various disposal design concepts according to the host country repository program and relevance to the US program is given elsewhere (Birkholzer et al.,2017;Jové Col& et al., 2016). The DECOVALEX-2019 Task C involves collaboration with the GREET (Groundwater REcovery Experiment in Tunnel) at the Mizunami URL, Japan ,which targets the development of monitoring methodologies of groundwater in granitic rock with applications to THMC simulations (Iwatsuki et al., 2005;Iwatsuki et al.,2015,2017). Some of the goals of GREET is to conduct a facility-scale geochemical characterization study of short- and long-term effects of tunnel excavation activities, impacts on groundwater flow and transport, and influences on groundwater chemistry (Iwatsuki et al.,2015). The data obtained from these URL activities is then used in the development and evaluation of THC models to support post-closure safety and performance assessments of the repository environment.
Certification of successful completion of ASC CSSE Level 2 Milestone 6362: Local Failure Local Recovery (LFLR) Resiliency for Asynchronous Many Task (AMT) Programming and Execution Models
International collaborations on nuclear waste disposal is an integral part of the Spent Fuel Waste Science and Technology (SFWST) campaign within the DOE Fuel Cycle and Technology (FCT) program. These engagements with international repository R&D programs provide key opportunities to participate in experiments with international partners on research investigations developing laboratory/field (underground research laboratories (URL) experiments) data of engineered barrier system (EBS) components (e.g., near-field) and characterization of transport phenomena in the host rock (e.g., far-field). The results of these field and laboratory experiments are used in the evaluation of coupled processes and the development of state-of-the-art simulation approaches to evaluate repository performance. Thermal heating from radionuclide decay in the waste canisters will increase temperature in the surrounding EBS driving chemical and transport processes in the near- and far-field domains of the repository. URL heater-tests for extended periods of times (e.g., years) provide key information and data of thermal effects on barrier responses to temperature and water saturation levels.
Inconsistencies in high-pressure H2 adsorption data and a lack of comparative experiment-theory studies have made the evaluation of both new and existing metal-organic frameworks (MOFs) challenging in the context of hydrogen storage applications. In this work, we performed grand canonical Monte Carlo (GCMC) simulations in nearly 500 experimentally refined MOF structures to examine the variance in simulation results because of the equation of state, H2 potential, and the effect of density functional theory structural optimization. We find that hydrogen capacity at 77 K and 100 bar, as well as hydrogen 100-to-5 bar deliverable capacity, is correlated more strongly with the MOF pore volume than with the MOF surface area (the latter correlation is known as the Chahine's rule). The tested methodologies provide consistent rankings of materials. In addition, four prototypical MOFs (MOF-74, CuBTC, ZIF-8, and MOF-5) with a range of surface areas, pore structures, and surface chemistries, representative of promising adsorbents for hydrogen storage, are evaluated in detail with both GCMC simulations and experimental measurements. Simulations with a three-site classical potential for H2 agree best with our experimental data except in the case of MOF-5, in which H2 adsorption is best replicated with a five-site potential. However, for the purpose of ranking materials, these two choices for H2 potential make little difference. More significantly, 100 bar loading estimates based on more accurate equations of state for the vapor-liquid equilibrium yield the best comparisons with the experiment.
In this work, we study the interlayer interactions between sheets of blue phosphorus with quantum Monte Carlo (QMC) methods. We find that as previously observed in black phosphorus, interlayer binding of blue phosphorus cannot be described by van der Waals (vdW) interactions alone within the density functional theory framework. Specifically, while some vdW density functionals produced reasonable binding curves, none of them could provide a correct, even qualitatively, description of charge redistribution due to interlayer binding. We also show that small systematic errors in common practice QMC calculations, such as the choice of optimized geometry and finite-size corrections, are non-negligible given the energy and length scales of this problem. We mitigate some of the major sources of error and report QMC-optimized lattice constant, stacking, and interlayer binding energy for blue phosphorus. It is strongly suggested that these considerations are important and quite general in the modeling of two-dimensional phosphorus allotropes.
Techno-economic analyses of energy storage currently use constant-efficiency energy flow models. In practice, charge/discharge efficiency of energy storage varies as a function of state-of-charge, temperature, charge/discharge power. Therefore, using the constant-efficiency energy flow models will cause suboptimal results. This work focuses on incorporating nonlinear energy flow models based on nonlinear efficiency models in the revenue maximization problem of energy storage. Dynamic programming is used to solve the optimization problem. A case studies is conducted to maximize the revenue of a Vanadium Redox Flow Battery (VRFB) system in PJM's energy and frequency regulation market.
Through the use of advanced control techniques, wave energy converters have significantly improved energy absorption. The motion of the WEC device is a significant contribution to the energy absorbed by the device. Reactive control (complex conjugate control) maximizes the energy absorption due to the impedance matching. The issue with complex conjugate control is that the controller is non-causal, which requires prediction into the oncoming waves to the device. This paper explores the potential of using system identification (SID) techniques to build a causal transfer function that approximates the complex conjugate controller over a specific frequency band of interest. The resulting controller is stable, and the average efficiency of the power captured by the causal controller is 99%, when compared to the non-causal complex conjugate.
Placing a crystal under extreme pressure can sometimes change its structure from one form, or phase, to another. Determining exactly how crystals change phase under compression is an important area of materials physics research. The availability of x-ray diffraction at synchrotron facilities has allowed scientists to observe compression-driven phase changes in unprecedented detail. Most of the research in this field has focused on pressure-induced phase changes using slow (static) compression on the minute timescale. Now, utilizing x-ray diffraction at the APS, a multi-institution research team led by scientists at Sandia National Laboratories has observed, over nanosecond timescales, microstructural phase changes within a two-element (calcium fluoride) crystal subjected to extreme pressures. The high pressures were achieved both through high-velocity instantaneous shock compression and by statically squeezing the samples. The researchers expect that their real-time observations of phase transitions within calcium fluoride will provide a template for the phase transitions of similarly-structured compounds. More generally, it is anticipated that the experimental methods and results of this study will lead to improved modeling of phase transitions over nanosecond timescales, within a wide range of complex materials.
Chris Saunders and three technologists are in high demand from Sandia's deep learning teams, and they're kept busy by building new clusters of computer nodes for researchers who need the power of supercomputing on a smaller scale. Sandia researchers working on Laboratory Directed Research & Development (LDRD) projects, or innovative ideas for solutions on short timeframes, formulate new ideas on old themes and frequently rely on smaller cluster machines to help solve problems before introducing their code to larger HPC resources. These research teams need an agile hardware and software environment where nascent ideas can be tested and cultivated on a smaller scale. Saunders and his team at Sandia's Science and Engineering Computing Environments are successfully enabling this research by creating pipelines for emerging code—from Cloud, to containers, to virtual machines—that build the right environment quickly to help teams solve their problems in a matter of days rather than months. While the larger HPC sources are available, it's these smaller clusters that can rapidly build a foundation for teams to build on for later development on larger systems.
A new race is emerging among nuclear powers: the hypersonic weapon. Hypersonics are flight vehicles that travel at Mach 5 (five times the speed of sound) or faster. They can cruise in the atmosphere, unlike traditional exo-atmospheric ballistic missiles, allowing stealth and maneuverability during midflight. Faster, lower, and stealthier means the missiles can better evade adversary defense systems. The U.S. has experimented with hypersonics for years, but current investments by Russia and China into their own offensive hypersonic systems may render U.S. missile defense systems ineffective. For the U.S. to avoid obsolescence in this strategically significant technology arena, hypersonics—combined with autonomy—needs to be a force multiplier. Achieving an autonomous hypersonic missile, however, that can intelligently navigate, guide, and control itself and home-in on targets ranging from traditional stationary systems to targets that are themselves hypersonic vehicles—with all the maneuverability that this entails—may sound far-fetched. But to Sandia's Autonomy for Hypersonics (A4H) team, this dream is one step closer to reality.
Many modern applications have memory footprints that are increasingly large, driving system memory capacities higher and higher. Moreover, these systems are often organized where the bulk of the memory is collocated with the compute capability, which necessitates the need for message passing APIs to facilitate information sharing between compute nodes. Due to the diversity of applications that must run on High-Performance Computing (HPC) systems, the memory utilization can fluctuate wildly from one application to another. And, because memory is located in the node, maintenance can become problematic because each node must be taken offline and upgraded individually. To address these issues, vendors are exploring disaggregated, memory-centric, systems. In this type of organization, there are discrete nodes, reserved solely for memory, which are shared across many compute nodes. Due to their capacity, low-power, and non-volatility, Non-Volatile Memories (NVMs) are ideal candidates for these memory nodes. This report discusses a new component for the Structural Simulation Toolkit (SST), Opal, that can be used to study the impact of using NVMs in a disaggregated system in terms of performance, security, and memory management. This page intentionally left blank.
In August the team was able to get CUDA-9.2 up and running on white. Ride is not update to be the same as White, to support Trilinos testing. CUDA-9.2 builds are up on both. The team also expanded ATDM testing for SPARC and finished up the KNL build. Worked on ATDM Trilinos configuration to include the packages necessary for SPARC.
In this paper, we analyze a compact silicon photonic phase modulator at 1.55 μm using epsilon-near-zero transparent conducting oxide (TCO) films. The operating principle of the non-resonant phase modulator is field-effect carrier density modulation in a thin TCO film deposited on top of a passive silicon waveguide with a CMOS-compatible fabrication process. We compare phase modulator performance using both indium oxide (In2O3) and cadmium oxide (CdO) TCO materials. Our findings show that practical phase modulation can be achieved only when using high-mobility (i.e. low-loss) epsilon-near-zero materials such as CdO. The CdO-based phase modulator has a figure of merit of 17.1°/dB in a compact 5 μm length. This figure of merit can be increased further through the proper selection of high-mobility TCOs, opening a path for device miniaturization and increased phase shifts.
When metals plastically deform, the density of line defects called dislocations increases and the microstructure is continuously refined, leading to the strain hardening behavior. Using discrete dislocation dynamics simulations, we demonstrate the fundamental role of junction formation in connecting dislocation microstructure evolution and strain hardening in face-centered cubic (fcc) Cu. The dislocation network formed consists of line segments whose lengths closely follow an exponential distribution. This exponential distribution is a consequence of junction formation, which can be modeled as a one-dimensional Poisson process. According to the exponential distribution, two non-dimensional parameters control microstructure evolution, with the hardening rate dictated by the rate of stable junction formation. Among the types of junctions in fcc crystals, we find that glissile junctions make the dominant contribution to strain hardening.
In the published article (Xiong et al., 2017), there was an error for the reaction coefficient for the dissolution reaction of Ca2C10H12N2O8•7H2O(s) in the database used for the modeling. In the database for the modeling, the coefficient for water (i.e., 7H2O) was inadvertently omitted. Because of this omission, the results in Table 3 were affected. The authors wish to make the corrections to Table 3. The corrected values are tabulated in the revised Table 3. The corrected values reproduce the experimental data in MgCl2 solutions much better (see revised Fig. 5).
In this study, we demonstrate creation of electroforming-free TaOx memristive devices using focused ion beam irradiations to locally define conductive filaments in TaOx films. Electrical characterization shows that these irradiations directly create fully functional memristors without the need for electroforming. Finally, ion beam forming of conductive filaments combined with state-of-the-art nano-patterning presents a CMOS compatible approach to wafer level fabrication of fully formed and operational memristors.
DARMA (Distributed Asynchronous Resilient Models for Applications) is a runtime library developed as part of the the Sandia ATDM (Advanced Technology Development and Mitigation) program. DARMA supports applications within 2.2.5.03 ADNN03-ASC ATDM SNL Application, which includes a number of applications featuring dynamic physics which requires load balancing and asynchronous communication for high performance. We have implemented a modern C++ programming model that can enable dynamic, asynchronous communication on top of existing data structures from a serial or MPI code. DARMA development has occurred in parallel with a verification milestone for ATDM in FY18. For FY19, DARMA will impact ATDM by enabling the performance benefits of a dynamic runtime through only incremental changes to existing verified MPI codes. The results presented here demonstrate the DARMA development process for an MPI mini-app, showing a 3-4x improvement in performance for a challenging problem relative to the parent MPI code and coming within 25 percent of the theoretically optimal performance achievable from a perfect, fine-grained load balancer for most cases.
DARMA (Distributed Asynchronous Resilient Models for Applications) is a runtime library supporting the Sandia ATDM (Advanced Technology Development and Mitigation) program. The main application drivers fall within the ECP milestone 2.2.5.03 ADNN03-ASC ATDM SNL Application, which includes applications that require load balancing and asynchronous communication for high performance. The DARMA runtime infrastructure has been modified to be compatible with Kokkos/OpenMP parallelization within tasks, which is a critical requirement for high performance for the Sandia ATDM apps. DARMA development has occurred in parallel with a verification milestone for ATDM in FY18. For FY19, DARMA should impact ATDM by enabling dynamic load balancing and communication through only incremental changes to the existing verified MPI codes. DARMA can now support the intra-kernel thread parallelization in the parent MPI apps, allowing DARMA to be easily added without rewriting individual math kernels. The results presented here demonstrate the DARMA results for an MPI mini-app.
The DARMA many-task framework provides asynchronous communication and load balancing functionality. This functionality is embedded in standard, modern C++ through the use of the template wrapper classes similar to futures. DARMA codes previously could not interoperate with Kokkos or OpenMP since each runtime assumed sole ownership of thread resources. The most recent version now allows for the use of Kokkos/OpenMP within DARMA tasks, allowing for better performance through thread-level parallelism or use of accelerators.
The DARMA many-task framework provides asynchronous communication and load balancing functionality. This functionality is embedded in standard , modern C++ through the use of the template wrapper classes similar to futures. DARMA codes previously could not interoperate with MPI. A new C++ interface and extended semantics now allow quiescence of DARMA kernels and transfer of data ownership back into MPI, allowing 1) isolated DARMA kernels to be inserted in a larger MPI code or 2) reuse of MPI libraries like solvers within a DARMA application.
Simulating energetic materials with complex microstructure is a grand challenge, where until recently, an inherent gap in computational capabilities had existed in modelling grain-scale effects at the microscale. We have enabled a critical capability in modelling the multiscale nature of the energy release and propagation mechanisms in advanced energetic materials by implementing, in the widely used LAMMPS molecular dynamics (MD) package, several novel coarse-graining techniques that also treat chemical reactivity. Our innovative algorithmic developments rooted within the dissipative particle dynamics framework, along with performance optimisations and application of acceleration technologies, have enabled extensions in both the length and time scales far beyond those ever realised by atomistic reactive MD simulations. In this paper, we demonstrate these advances by modelling a shockwave propagating through a microstructured material and comparing performance with the state-of-the-art in atomistic reactive MD techniques. As a result of this work, unparalleled explorations in energetic materials research are now possible.
Power systems can become unstable under transient periods such as short-circuit faults, leading to equipment damage and large scale blackouts. Power system stabilizers (PSS) can be designed to improve the stability of generators by quickly regulating the exciter field voltage to damp the swings of generator rotor angle and speed. The stability achieved through exciter field voltage control can be further improved with a relatively small, fast responding energy storage system (ESS) connected at the terminals of the generator that enables electrical power damping. PSS are designed and studied using a single-machine infinite-bus (SMIB) model. In this paper, we present a comprehensive optimal-control design for a flexible ac synchronous generator PSS using both exciter field voltage and ESS control including estimation of unmeasurable states. The controller is designed to minimize disturbances in rotor frequency and angle, and thereby improve stability. The design process is based on a linear quadratic regulator of the SMIB model with a test system linearized about different operating frequencies in the range 10 Hz to 60 Hz. The optimal performance of the PSS is demonstrated along with the resulting stability improvement.
The increased penetration of renewable resources has made frequency regulation and generation control a growing concern. This has created an opportunity for Energy Storage Resource to participate in the frequency regulation market. This paper investigates the potential of Battery Energy Storage systems to participate in the German secondary frequency regulation market. A simulation model is developed to investigate the revenue opportunity of a 48 MWh Battery System participating in the secondary frequency regulation market.
Lipid-coated mesoporous silica nanoparticles (LC-MSNs) have recently emerged as a next-generation cargo delivery nanosystem combining the unique attributes of both the organic and inorganic components. The high surface area biodegradable inorganic mesoporous silica core can accommodate multiple classes of bio-relevant cargos in large amounts, while the supported lipid bilayer coating retains the cargo and increases the stability of the nanocarrier in bio-relevant media which should promote greater bio-accumulation of LC-MSNs in cancer sites. In this paper, we report on the optimization of various sol–gel synthesis (pH, stirring speed) and post-synthesis (hydrothermal treatment) procedures to enlarge the MSN pore size and tune the surface chemistry so as to enable loading and delivery of large biomolecules. Finally, the proof of concept of the dual cargo-loaded nanocarrier has been demonstrated in immortalized cervical cancer HeLa cells using MSNs of various fine-tuned pore sizes.
A wide-area controller to damp inter-area oscillations in the North American Western Interconnection (WI) by modulating power transfers in a HVDC link is used in this paper to investigate the effects that latencies in its feedback signals have on its performance. This controller uses two feedback measurements to perform its control action. The analysis show that the stabilizing effect of the controller in transient stability and small signal stability is compromised as the feedback measurements experience higher delays. The results show that one of the feedback signals can tolerate more delay than the other. The analysis was performed with Bode plots and time domain simulations on a reduced order model of the WI from which a linear version was obtained.
Power system utilities continue to strive for increased system resiliency. However, quantifying a baseline system resilience, and deciding the optimal investments to improve their resilience is challenging. This paper discusses a method to create scenarios, based on historical data, that represent the threats of severe weather events, their probability of occurrence, and the system wide consequences they generate. This paper also presents a mixed-integer stochastic nonlinear optimization model which uses the scenarios as an input to determine the optimal investments to reduce the system impacts from those scenarios. The optimization model utilizes a DC power flow to determine the loss of load during an event. Loss of load is the consequence that is minimized in this optimization model as the objective function. The results shown in this paper are from the IEEE RTS-96 three area reliability model. The scenario generation and optimization model have also been utilized on full utility models, but those results cannot be published.
A wide-area controller to damp inter-area oscillations in the North American Western Interconnection (WI) by modulating power transfers in a HVDC link is used in this paper to investigate the effects that latencies in its feedback signals have on its performance. This controller uses two feedback measurements to perform its control action. The analysis show that the stabilizing effect of the controller in transient stability and small signal stability is compromised as the feedback measurements experience higher delays. The results show that one of the feedback signals can tolerate more delay than the other. The analysis was performed with Bode plots and time domain simulations on a reduced order model of the WI from which a linear version was obtained.
Stochastic versions of the unit commitment problem have been advocated for addressing the uncertainty presented by high levels of wind power penetration. However, little work has been done to study trade-offs between computational complexity and the quality of solutions obtained as the number of probabilistic scenarios is varied. Here, we describe extensive experiments using real publicly available wind power data from the Bonneville Power Administration. Solution quality is measured by re-enacting day-ahead reliability unit commitment (which selects the thermal units that will be used each hour of the next day) and real-time economic dispatch (which determines generation levels) for an enhanced WECC-240 test system in the context of a production cost model simulator; outputs from the simulation, including cost, reliability, and computational performance metrics, are then analyzed. Unsurprisingly, we find that both solution quality and computational difficulty increase with the number of probabilistic scenarios considered. However, we find unexpected transitions in computational difficulty at a specific threshold in the number of scenarios, and report on key trends in solution performance characteristics. Our findings are novel in that we examine these tradeoffs using real-world wind power data in the context of an out-of-sample production cost model simulation, and are relevant for both practitioners interested in deploying and researchers interested in developing scalable solvers for stochastic unit commitment.
Power system utilities continue to strive for increased system resiliency. However, quantifying a baseline system resilience, and deciding the optimal investments to improve their resilience is challenging. This paper discusses a method to create scenarios, based on historical data, that represent the threats of severe weather events, their probability of occurrence, and the system wide consequences they generate. This paper also presents a mixed-integer stochastic nonlinear optimization model which uses the scenarios as an input to determine the optimal investments to reduce the system impacts from those scenarios. The optimization model utilizes a DC power flow to determine the loss of load during an event. Loss of load is the consequence that is minimized in this optimization model as the objective function. The results shown in this paper are from the IEEE RTS-96 three area reliability model. The scenario generation and optimization model have also been utilized on full utility models, but those results cannot be published.
Emerging applications of metal-organic frameworks (MOFs) in electronic devices will benefit from the design and synthesis of intrinsically, highly electronically conductive MOFs. However, very few are known to exist. It is a challenging task to search for electronically conductive MOFs within the tens of thousands of reported MOF structures. Using a new strategy (i.e., transfer learning) of combining machine learning techniques, statistical multivoting, and ab initio calculations, we screened 2932 MOFs and identified 6 MOF crystal structures that are metallic at the level of semilocal DFT band theory: Mn2[Re6X8(CN)6]4 (X = S, Se,Te), Mn[Re3Te4(CN)3], Hg[SCN]4Co[NCS]4, and CdC4. Five of these structures have been synthesized and reported in the literature, but their electrical characterization has not been reported. Our work demonstrates the potential power of machine learning in materials science to aid in down-selecting from large numbers of potential candidates and provides the information and guidance to accelerate the discovery of novel advanced materials.
Whitfield's laboratory notebook covers work from late 1960 into 1974. He used the notebook to capture ideas and to summarize what he was working on—including writing papers and traveling— as well as his research. It does not offer a detailed exploration of his thinking leading up to technical advances and it only rarely explicates experimental set-ups. It also has long gaps between some entries. It is nonetheless revealing of his approach, his focus, and his results.
Switched reluctance machines (SRMs) are low cost and fault tolerant alternatives for traction applications. Due to their relatively low torque density and high torque ripple, these machines have not been used in commercialized electrified power trains yet. The double-stator magnetic configuration, where a segmental rotor shared between two stators, is proven to have superior torque density, lower acoustic noise, and torque pulsation. The double-stator SRM (DSSRM) has a high potential of being the next generation of traction motors since it combines the advantages of permanent magnet-free machines, such as low cost and independence from supply chain issues associated with rare metals, while providing high torque and power densities. In this paper, we introduce the complete design process of a 100-kW DSSRM including electromagnetic, structural, thermal, and system level design. The performance of the developed DSSRM is verified with the experimental data. This paper presents overall study of DSSRM and describes merits of DSSRM drive system.
Shi, Chenyang; Billinge, Simon J.L.; Puma, Eric; Bang, Sun H.; Bean, Nathaniel J.H.; De Sugny, Jean C.; Gambee, Robert G.; Haskell, Richard C.; Hightower, Adrian; Monson, Todd M.
Barium titanate (BTO) nanoparticles (sizes 10-500 nm) exhibit a displacement of the Ti atom from the center of the perovskite unit cell as inferred from synchrotron x-ray diffraction patterns (XRD) analyzed using atomic pair distribution functions (PDFs). Fits to PDFs acquired at temperatures of 20 °C-220 °C indicate that these Ti displacements (∼0.1 Å) are comparable to or even greater than those in the bulk material. Moreover, these displacements persist at temperatures well above 120 °C, where the tetragonal-to-pseudocubic phase transition occurs in the bulk. Tetragonal Raman spectral lines were observed for all sizes of these BTO nanoparticles and confirm a distorted unit cell up to 120 °C. Above 120 °C, the small BTO nanoparticles (10, 50, 100 nm) continue to display tetragonal Raman lines, though with slowly decreasing amplitudes as the temperature rises. In contrast, the tetragonal Raman lines of large BTO nanoparticles (300, 400, 500 nm) disappear abruptly above 120 °C, suggestive of bulk material. Indeed, fits to large-particle x-ray PDFs over the range 20-60 Å reveal a sharp, long-range structural change toward a cubic lattice at 120 °C, again consistent with bulk material. This sharp, long-range structural change is absent in the small particles. In fact, laboratory XRD Bragg peak profiles for the small BTO particles appear to be singlets at 20 °C, indicating that significant long-range cubic order already exists at room temperature. As temperature rises, this long-range cubic order is gradually reinforced, as inferred from long-range fits of the small particle PDFs. By combining information from x-ray PDFs, Raman spectra, and Bragg peak profiles, we conclude that small BTO nanoparticles exhibit both short-range (unit-cell) distortion and long-range (mesoscale) cubic order from 20 °C to 220 °C, while the large nanoparticles behave as bulk material, differing from small particles only by exhibiting long-range tetragonal order below 120 °C and a mesoscale structural phase change at 120 °C.
The search for multivariate quadrature rules of minimal size with a specified polynomial accuracy has been the topic of many years of research. Finding such a rule allows accurate integration of moments, which play a central role in many aspects of scientific computing with complex models. The contribution of this paper is twofold. First, we provide novel mathematical analysis of the polynomial quadrature problem that provides a lower bound for the minimal possible number of nodes in a polynomial rule with specified accuracy. We give concrete but simplistic multivariate examples where a minimal quadrature rule can be designed that achieves this lower bound, along with situations that showcase when it is not possible to achieve this lower bound. Our second contribution is the formulation of an algorithm that is able to efficiently generate multivariate quadrature rules with positive weights on non-tensorial domains. Our tests show success of this procedure in up to 20 dimensions. We test our method on applications to dimension reduction and chemical kinetics problems, including comparisons against popular alternatives such as sparse grids, Monte Carlo and quasi Monte Carlo sequences, and Stroud rules. The quadrature rules computed in this paper outperform these alternatives in almost all scenarios.
Li, Shengxi; Teague, Mary T.; Doll, Gary L.; Schindelholz, Eric J.
This work aims to investigate the waterline corrosion of pure copper in 4 M NaCl solution. Three distinct regions are identified. Corrosion products formed at the waterline are characterized by micro-Raman spectroscopy, complemented by SEM/EDS, as a function of immersion time. The co-existence of four copper species is explained by thermodynamic equilibrium consideration. The composition of spreading region is determined and compared with the oxide film formation in different pH NaOH solutions. The anodic dissolution of copper exposed to the bulk electrolyte is investigated by electrochemical methods. The pH and potential profiles across three regions are proposed.
Rechargeable Zn/MnO2 alkaline batteries are a promising technology for grid storage applications since they are safe, low cost, and considered environmentally friendly. Here, a commercial ceramic sodium ion conductor which is impervious to zincate [Zn(OH)42−], a contributor to MnO2 cathode failure, is evaluated as the battery separator. As received, the ionic conductivity of this separator was measured with electrochemical impedance spectroscopy to be 3.5 mS cm−1, while its thickness is 1.0 mm, resulting in large total membrane resistance of 25.3 Ω. Reducing the thickness of the ceramic to 0.5 mm provided for a decreased resistance of 9.8 Ω. Crossover experiments conducted using inductively coupled plasma - mass spectrometry measurements failed to measure any Zn(OH)42− transport indicating a diffusion coefficient that is at least two orders of magnitude less than that for the commercial cellophane and Celgard separators. For 5% DOD at a C/5 rate, the cycle lifetime was increased by over 22% using the 0.5 mm thick ceramic separator compared to traditional Celgard and cellophane separators. Scanning electron microscopy/energy dispersive X-ray spectroscopy and X-ray diffraction characterization of cycled electrodes showed limited amounts of zinc species on the cathode utilizing the ceramic separator, consistent with its prevention of Zn(OH)42− transport.
Rechargeable Zn/MnO2 alkaline batteries are a promising technology for grid storage applications since they are safe, low cost, and considered environmentally friendly. Here, a commercial ceramic sodium ion conductor which is impervious to zincate [Zn(OH)42−], a contributor to MnO2 cathode failure, is evaluated as the battery separator. As received, the ionic conductivity of this separator was measured with electrochemical impedance spectroscopy to be 3.5 mS cm−1, while its thickness is 1.0 mm, resulting in large total membrane resistance of 25.3 Ω. Reducing the thickness of the ceramic to 0.5 mm provided for a decreased resistance of 9.8 Ω. Crossover experiments conducted using inductively coupled plasma - mass spectrometry measurements failed to measure any Zn(OH)42− transport indicating a diffusion coefficient that is at least two orders of magnitude less than that for the commercial cellophane and Celgard separators. For 5% DOD at a C/5 rate, the cycle lifetime was increased by over 22% using the 0.5 mm thick ceramic separator compared to traditional Celgard and cellophane separators. Scanning electron microscopy/energy dispersive X-ray spectroscopy and X-ray diffraction characterization of cycled electrodes showed limited amounts of zinc species on the cathode utilizing the ceramic separator, consistent with its prevention of Zn(OH)42− transport.
Predictive analysis of complex computational models, such as uncertainty quantification (UQ), must often rely on using an existing database of simulation runs. In this paper we consider the task of performing low-multilinear-rank regression on such a database. Specifically we develop and analyze an efficient gradient computation that enables gradient-based optimization procedures, including stochastic gradient descent and quasi-Newton methods, for learning the parameters of a functional tensor-train (FT). We compare our algorithms with 22 other nonparametric and parametric regression methods on 10 real-world data sets and show that for many physical systems, exploiting low-rank structure facilitates efficient construction of surrogate models. Here, we use a number of synthetic functions to build insight into behavior of our algorithms, including the rank adaptation and group-sparsity regularization procedures that we developed to reduce overfitting. Finally we conclude the paper by building a surrogate of a physical model of a propulsion plant on a naval vessel.
The Magnetized Liner Inertial Fusion (MagLIF) concept [Slutz et al. Phys. Plasmas 17, 056303 (2010); Gomez et al. Phys. Rev. Lett. 113, 155003 (2014)] is being studied on the Z facility at Sandia National Laboratories. MagLIF is a specific example of the more general Magnetized Inertial Fusion (MIF) approach to fusion. Numerical simulations indicate that yields approaching 100 kJ should be possible on the Z machine and much higher yields (10–1000 MJ) should be possible with pulsed power machines producing larger drive currents (45–60 MA) [Slutz et al. Phys. Plasmas 23, 022702 (2016)]. A significant advantage of MIF is that the implosions can be driven more slowly than conventional inertial fusion. In general, the efficiency of pulsed power machines increases with the current rise-time; however, we show by numerical simulation that the current and energy required to obtain a given fusion gain increase monotonically with the current rise-time over the range (10–500 ns). In conclusion, these results can be used to optimize the design of future accelerators to drive MIF concepts such as MagLIF. We also show that the required preheat energy increases strongly with current rise-time, which indicates that very long current rise-times are not desirable at least for MagLIF.
Epoxies and resins can require careful temperature sensing and control in order to monitor and prevent degradation. To sense the temperature inside a mold, it is desirable to utilize a small, wireless sensing element. In this paper, we describe a new architecture for wireless temperature sensing and closed-loop temperature control of exothermic polymers. This architecture is the first to utilize magnetic field estimates of the temperature of permanent magnets within a temperature feedback control loop. We further improve performance and applicability by demonstrating sensing performance at relevant temperatures, incorporating a cure estimator, and implementing a nonlinear temperature controller. This novel architecture enables unique experimental results featuring closed-loop control of an exothermic resin without any physical connection to the inside of the mold. In this paper we describe each of the unique features of this approach including magnetic field-based temperature sensing, Extended Kalman Filtering (EKF) for cure state estimation, and nonlinear feedback control over time-varying temperature trajectories. We use experimental results to demonstrate how low-cost permanent magnets can provide wireless temperature sensing up to ~90°C. In addition, we use a polymer curecontrol test-bed to illustrate how internal temperature sensing can provide improved temperature control over both short and long time-scales. In conclusion, this wireless temperature sensing and control architecture holds value for a range of manufacturing applications.
This article focuses on the finite element modeling of toroidal microinductors, employing first-of-its-kind nanocomposite magnetic core material and superparamagnetic iron nanoparticles covalently cross-linked in an epoxy network. Energy loss mechanisms in existing inductor core materials are covered as well as discussions on how this novel core material eliminates them providing a path toward realizing these low form factor devices. Designs for both a 2 μH output and a 500 nH input microinductor are created via the model for a high-performance buck converter. Both modeled inductors have 50 wire turns, less than 1 cm3 form factors, less than 1 Ω AC resistance, and quality factors, Q's, of 27 at 1 MHz. In addition, the output microinductor is calculated to have an average output power of 7 W and a power density of 3.9 kW/in3 by modeling with the 1st generation iron nanocomposite core material.
Significant reductions recently seen in the size of wide-bandgap power electronics have not been accompanied by a relative decrease in the size of the corresponding magnetic components. To achieve this, a new generation of materials with high magnetic saturation and permeability are needed. Here, we develop gram-scale syntheses of superparamagnetic Fe/FexOy core-shell nanoparticles and incorporate them as the magnetic component in a strongly magnetic nanocomposite. Nanocomposites are typically formed by the organization of nanoparticles within a polymeric matrix. However, this approach can lead to high organic fractions and phase separation; reducing the performance of the resulting material. Here, we form aminated nanoparticles that are then cross-linked using epoxy chemistry. The result is a magnetic nanoparticle component that is covalently linked and well separated. By using this 'matrix-free' approach, we can substantially increase the magnetic nanoparticle fraction, while still maintaining good separation, leading to a superparamagnetic nanocomposite with strong magnetic properties.
In Part I, equal channel angular extrusion (ECAE) was demonstrated as a novel, simple-shear deformation process for producing bulk forms of the low ductility Fe-Co-2V (Hiperco 50A®) soft ferromagnetic alloy with refined grain sizes. Microstructures and mechanical properties were discussed. In this Part II contribution, the crystallographic textures and quasi-static magnetic properties of ECAE-processed Hiperco were characterized. The textures were of a simple-shear character defined by partial {110} and (111) fibers inclined relative to the extrusion direction, in agreement with the expectations for simple-shear deformation textures of BCC metals. These textures were observed throughout all processing conditions and only slightly reduced in intensity by subsequent recrystallization heat treatments. Characterization of the magnetic properties revealed a lower coercivity and higher permeability for ECAE-processed Hiperco specimens relative to the conventionally processed and annealed Hiperco bar. The effects of the resultant microstructure and texture on the coercivity and permeability magnetic properties are discussed.
The growth of High Performance Computer (HPC) systems increases the complexity with respect to understanding resource utilization, system management, and performance issues. While raw performance data is increasingly exposed at the component level, the usefulness of the data is dependent on the ability to do meaningful analysis on actionable timescales. However, current system monitoring infrastructures largely focus on data collection, with analysis performed off-system in post-processing mode. This increases the time required to provide analysis and feedback to a variety of consumers. In this work, we enhance the architecture of a monitoring system used on large-scale computational platforms, to integrate streaming analysis capabilities at arbitrary locations within its data collection, transport, and aggregation facilities. We leverage the flexible communication topology of the monitoring system to enable placement of transformations based on overhead concerns, while still enabling low-latency exposure on node. Our design internally supports and exposes the raw and transformed data uniformly for both node level and off-system consumers. We show the viability of our implementation for a case with production-relevance: run-time determination of the relative per-node files system demands.
High-performance computing (HPC) systems are critically important to the objectives of universities, national laboratories, and commercial companies. Because of the cost of deploying and maintaining these systems ensuring their efficient use is imperative. Job scheduling and resource management are critically important to the efficient use of HPC systems. As a result, significant research has been conducted on how to effectively schedule user jobs on HPC systems. Developing and evaluating job scheduling algorithms, however, requires a detailed understanding of how users request resources on HPC systems. In this paper, we examine a corpus of job data that was collected on Trinity, a leadership-class supercomputer. During the stabilization period of its Intel Xeon Phi (Knights Landing) partition, it was made available to users outside of a classified environment for the Trinity Open Science Phase 2 campaign. We collected information from the resource manager about each user job that was run during this Open Science period. In this paper, we examine the jobs contained in this dataset. Our analysis reveals several important characteristics of the jobs submitted during the Open Science period and provides critical insight into the use of one of the most powerful supercomputers in existence. Specifically, these data provide important guidance for the design, development, and evaluation of job scheduling and resource management algorithms.
The review team convened at the University of Utah March 7-8, 2018, to review the Carbon Capture Multidisciplinary Science Center (CCMSC) funded by the 2nd Predictive Science ASC Alliance Program (PSAAP II). Center leadership and researchers made very clear and informative presentations, accurately portraying their work and successes while candidly discussing their concerns and known areas in need of improvement.
The performance critical path for MPI implementations relies on fast receive side operation, which in turn requires fast list traversal. The performance of list traversal is dependent on data-locality; whether the data is currently contained in a close-to-core cache due to its temporal locality or if its spacial locality allows for predictable pre-fetching. In this paper, we explore the effects of data locality on the MPI matching problem by examining both forms of locality. First, we explore spacial locality, by combining multiple entries into a single linked list element, we can control and modify this form of locality. Secondly, we explore temporal locality by utilizing a new technique called “hot caching”, a process that creates a thread to periodically access certain data, increasing its temporal locality. In this paper, we show that by increasing data locality, we can improve MPI performance on a variety of architectures up to 4x for micro-benchmarks and up to 2x for an application.
We discuss the multiple pursuer-based intercept of a threat unmanned aerial system (UAS) with stochastic dynamics via multiple pursuing UASs, using forward stochastic reachability and receding horizon control techniques. We formulate a stochastic model for the threat that can emulate the potentially adversarial behavior and is amenable to the existing scalable results in forward stochastic reachability literature. The optimal state for the intercept for each individual pursuer is obtained via a log-concave optimization problem, and the open-loop control paths are obtained via a convex optimization problem. With stochasticity modeled as a Gaussian process, we can approximate the optimization problem as a quadratic program, to enable real-time path planning. We also incorporate real-time sensing into the path planning by using a receding horizon controller, to improve the intercept probabilities. We validate the proposed framework via hardware experiments.
Recent work suggests that thermally stable nanocrystallinity in metals is achievable in several binary alloys by modifying grain boundary energies via solute segregation. The remarkable thermal stability of these alloys has been demonstrated in recent reports, with many alloys exhibiting negligible grain growth during prolonged exposure to near-melting temperatures. Pt–Au, a proposed stable alloy consisting of two noble metals, is shown to exhibit extraordinary resistance to wear. Ultralow wear rates, less than a monolayer of material removed per sliding pass, are measured for Pt–Au thin films at a maximum Hertz contact stress of up to 1.1 GPa. This is the first instance of an all-metallic material exhibiting a specific wear rate on the order of 10−9 mm3 N−1 m−1, comparable to diamond-like carbon (DLC) and sapphire. Remarkably, the wear rate of sapphire and silicon nitride probes used in wear experiments are either higher or comparable to that of the Pt–Au alloy, despite the substantially higher hardness of the ceramic probe materials. High-resolution microscopy shows negligible surface microstructural evolution in the wear tracks after 100k sliding passes. Mitigation of fatigue-driven delamination enables a transition to wear by atomic attrition, a regime previously limited to highly wear-resistant materials such as DLC.
We discuss the multiple pursuer-based intercept of a threat unmanned aerial system (UAS) with stochastic dynamics via multiple pursuing UASs, using forward stochastic reachability and receding horizon control techniques. We formulate a stochastic model for the threat that can emulate the potentially adversarial behavior and is amenable to the existing scalable results in forward stochastic reachability literature. The optimal state for the intercept for each individual pursuer is obtained via a log-concave optimization problem, and the open-loop control paths are obtained via a convex optimization problem. With stochasticity modeled as a Gaussian process, we can approximate the optimization problem as a quadratic program, to enable real-time path planning. We also incorporate real-time sensing into the path planning by using a receding horizon controller, to improve the intercept probabilities. We validate the proposed framework via hardware experiments.
This paper presents a control design methodology that addresses high penetration of variable generation or renewable energy sources and loads for networked AC /DC microgrid systems as an islanded subsystem or as part of larger electric power grid systems. High performance microgrid systems that contain large amounts of stochastic sources and loads is a major goal for the future of electric power systems. Alternatively, methods for controlling and analyzing AC/ DC microgrid systems will provide an understanding into the tradeoffs that can be made during the design phase. This method develops both a control design methodology and realizable hierarchical controllers that are based on the Hamiltonian Surface Shaping and Power Flow Control (HSSPFC) methodology that regulates renewable energy sources, varying loads and identifies energy storage requirements for a networked AC/DC microgrid system. Both static and dynamic stability conditions are derived. A renewable energy scenario is considered for a networked three DC microgrids tied into an AC ringbus configuration. Numerical simulation results are presented.
We present analysis and modeling of Al sputtering and ionization in attached, low-power L-mode plasmas near the outer divertor strike point of the DIII-D tokamak. Al serves as a useful proxy for Be, the low-Z main wall material for ITER and JET that will undergo significant divertor plasma contact upon migrating from the first wall to the divertor. Al is easily distinguishable from background sources in DIII-D (namely C and B), has a high physical sputtering yield similar to Be, and has a long ionization mean free path compared to its gyro radius ({λi} /rgyro ∼ 2.5). Using neutral Al emission imaging techniques, we measured a toroidal and radial asymmetry in the shape of the photo-emission plumes of sputtered neutral Al that was consistent with previously observed asymmetry in the distribution of redeposited Al in these experiments. We propose that the main cause of the emission and redeposition asymmetry is due to a sputtering anisotropy caused by near-grazing angle incident ions. The observed emission asymmetry was reproduced using a simple emission/ionization model that included full angular distributions of sputtering yield and energy calculated by SDTRIM.SP, but not when symmetric, mono-energetic cosine sputtering distributions were assumed. We used an ion orbit tracking model to calculate the distributions of ion impact energies through the potential gradient in the magnetic pre-sheath and Debye sheath. We found that with the magnetic field pitch angle (1.5°-2° with respect to the surface plane), the majority of ions strike the surface at <15° with respect to the surface plane, leading to angular sputtering yield and energy distributions with significant forward-scattering bias. We also observed surface microstructure consistent with directional sputtering and ion flux shadowing expected from the calculated ion incidence angles.
The High-Performance Computing of today is much different than the supercomputing resources a few decades ago. The supercomputers of the past were huge complexes of hundreds of interconnected computers that had large teams of specialists to keep them running. Today, while supercomputers like Summit (the world's most powerful supercomputer as of June 2018, installed at Oak Ridge National Laboratory) are still in high demand for extremely fast computational power (over 200,000 trillion calculations per second, or 200 petaflops), researchers at Sandia now have access to non-traditional computing resources in the way of very powerful graphics processing units (GPUs). These GPU systems have enabled an entirely new avenue of exploration for Sandia, allowing researchers to further tackle problems in energy, advanced materials, artificial intelligence, and nuclear weapons. Many teams at Sandia utilize these GPU clusters to build models through the use of machine learning. These models are faster representations of their code-driven counterparts and can often be leveraged from the exascale computing resources of traditionally larger systems with huge boosts in performance. We highlight two resources for these smaller systems: the team that builds the hardware, and the team that researches and builds the machine learning algorithms.
EmrE is a small, homodimeric membrane transporter that exploits the established electrochemical proton gradient across the Escherichia coli inner membrane to export toxic polyaromatic cations, prototypical of the wider small-multidrug resistance transporter family. While prior studies have established many fundamental aspects of the specificity and rate of substrate transport in EmrE, low resolution of available structures has hampered identification of the transport coupling mechanism. Here we present a complete, refined atomic structure of EmrE optimized against available cryo-electron microscopy (cryo-EM) data to delineate the critical interactions by which EmrE regulates its conformation during the transport process. With the model, we conduct molecular dynamics simulations of the transporter in explicit membranes to probe EmrE dynamics under different substrate loading and conformational states, representing different intermediates in the transport cycle. The refined model is stable under extended simulation. The water dynamics in simulation indicate that the hydrogen-bonding networks around a pair of solvent-exposed glutamate residues (E14) depend on the loading state of EmrE. One specific hydrogen bond from a tyrosine (Y60) on one monomer to a glutamate (E14) on the opposite monomer is especially critical, as it locks the protein conformation when the glutamate is deprotonated. The hydrogen bond provided by Y60 lowers the pKa of one glutamate relative to the other, suggesting both glutamates should be protonated for the hydrogen bond to break and a substrate-free transition to take place. These findings establish the molecular mechanism for the coupling between proton transfer reactions and protein conformation in this proton-coupled secondary transporter.
In an effort to generate single-source precursors for the production of metal-siloxide (MSiOx) materials, the tris(trimethylsilyl)silanol (H-SST or H-OSi(SiMe3)3 (1) ligand was reacted with a series of group 4 and 5 metal alkoxides. The group 4 products were crystallographically characterized as [Ti(SST)2(OR)2] (OR = OPri (2), OBut (3), ONep (4)); [Ti(SST)3(OBun)] (5); [Zr(SST)2(OBut)2(py)] (6); [Zr(SST)3(OR)] (OR = OBut (7), ONep, (8)); [Hf(SST)2(OBut)2] (9); and [Hf(SST)2(ONep)2(py)n] (n = 1 (10), n = 2 (10a)) where OPri = OCH(CH3)2, OBut = OC(CH3)3, OBun = O(CH2)3CH3, ONep = OCH2C(CH3)3, py = pyridine. The crystal structures revealed varied SST substitutions for: monomeric Ti species that adopted a tetrahedral (T-4) geometry; monomeric Zr compounds with coordination that varied from T-4 to trigonal bipyramidal (TBPY-5); and monomeric Hf complexes isolated in a TBPY-5 geometry. For the group 5 species, the following derivatives were structurally identified as [V(SST)3(py)2] (11), [Nb(SST)3(OEt)2] (12), [Nb(O)(SST)3(py)] (13), 2[H][(Nb(μ-O)2(SST))6(μ6-O)] (14), [Nb8O10(OEt)18(SST)2·1/5Na2O] (15), [Ta(SST)(μ-OEt)(OEt)3]2 (16), and [Ta(SST)3(OEt)2] (17) where OEt = OCH2CH3. The group 5 monomeric complexes were solved in a TBPY-5 arrangement, whereas the Ta of the dinculear 16 was solved in an octahedral coordination environment. Thermal analyses of these precursors revealed a stepwise loss of ligand, which indicated their potential utility for generating the MSiOx materials. The complexes were thermally processed (350-1100 °C, 4 h, ambient atmosphere), but instead of the desired MSiOx, transmission electron microscopy analyses revealed that fractions of the group 4 and group 5 precursors had formed unusual metal oxide silica architectures.
This work demonstrates that there is an impact on safety response with nanoscale silicon materials compared to graphite based anodes. Additionally, there appears to be a fundamental difference in abuse response based on more than just silicon content, particle size, and state of charge for the electrodes. Control of surface reactivity is essential to both control response homogeneity (for quantification) and understand the mechanisms during abuse conditions with silicon anodes.
Many of the BSAF participants provided source terms to be evaluated by Sandia National Laboratories by applying HYSPLIT [2,3,4,5] to treat atmospheric transport and dispersion (ATD). The objective was to estimate the deposition pattern that would have resulted from the predicted source term. For the participants who provided results for all three units, the overall deposition pattern can be compared with the observed deposition pattern; for the participants who submitted source terms for one or two units, the results can only be compared with each other. Atmospheric transport calculations were performed for a single isotope, Cs-137. It is the primary isotope of concern for long-term contamination and it is relatively easy to measure the strong gamma signal produced from its short-lived decay product, Ba-137m. All the atmospheric transport calculations used the actual location of each of the three units; the releases were not presumed to emanate from the same location. Also, when they were provided, release energies were accounted for in the analysis, so plume lofting was considered. Finally, aerosol size distribution data were considered for purposes of estimating deposition. In some cases, aerosol size distribution can significantly influence deposition patterns.
The role of Sandia National Laboratories to this project is to image in-cylinder soot and PAH under conditions where PAH and soot are on the threshold of formation due to dilution by excess nitrogen gas. The primary effect of dilution is to lower the combustion temperatures, and if sufficient dilution is provided, soot and/or PAH formation can be completely inhibited. Hence, these experimental data are useful for validation of CFD predictions of initial soot and PAH formation.
Considering the power constrained scaling of silicon complementary metal-oxide-semiconductor technology, the use of high mobility III-V compound semiconductors such as In0.53Ga0.47As in conjunction with high-κ dielectrics is becoming a promising option for future n-type metal-oxide-semiconductor field-effect-transistors. Development of low dissipation field-effect tunable III-V based photonic devices integrated with high-κ dielectrics is therefore very appealing from a technological perspective. In this work, we present an experimental realization of a monolithically integrable, field-effect-tunable, III-V hybrid metasurface operating at long-wave-infrared spectral bands. Our device relies on strong light-matter coupling between epsilon-near-zero (ENZ) modes of an ultra-thin In0.53Ga0.47As layer and the dipole resonances of a complementary plasmonic metasurface. The tuning mechanism of our device is based on field-effect modulation, where we modulate the coupling between the ENZ mode and the metasurface by modifying the carrier density in the ENZ layer using an external bias voltage. Modulating the bias voltage between ±2 V, we deplete and accumulate carriers in the ENZ layer, which result in spectrally tuning the eigenfrequency of the upper polariton branch at 13 μm by 480 nm and modulating the reflectance by 15%, all with leakage current densities less than 1 μA/cm2. Our wavelength scalable approach demonstrates the possibility of designing on-chip voltage-tunable filters compatible with III-V based focal plane arrays at mid- and long-wave-infrared wavelengths.
Global collectives (reductions/aggregations) are ubiquitous and feature in nearly every application of distributed high-performance computing (HPC). While it is advisable to devise algorithms by placing collectives off the critical path of execution, they are sometimes unavoidable for correctness, numerical convergence and analyses purposes. Scalable algorithms for distributed collectives are well studied and have become an integral part of MPI, but new and emerging distributed computing frameworks and paradigms such as Asynchronous Many-Task (AMT) models lack the same sophistication for distributed collectives. Since the central promise of AMT runtimes is that they automatically discover, and expose, task dependencies in the underlying program and can schedule work optimally to minimize idle time and hide data movement, a naively designed collectives protocol can completely offset any gains made from asynchronous execution. In this study we demonstrate that scalable distributed collectives are indispensable for performance in AMT models. We design, implement and test the performance of a scalable collective algorithm in Legion, an exemplar data-centric AMT programming model. Our results show that AMT systems contain the necessary primitives that allow for fully scalable collectives without breaking the transparent data movement abstractions. Scalability tests of an integrated Legion 1D stencil mini-application show the clear benefit of implementing scalable collectives and the performance degradation when a naïve collectives alternative is used instead.
We present a memory-scalable, parallel, sparse multifrontal solver for solving symmetric postive-definite systems arising in scientific and engineering applications. Factorizing sparse matrices requires memory for both the computed factors and the temporary workspaces for computing each frontal matrix - a data structure commonly used within multifrontal methods. To factorize multiple frontal matrices in parallel, the conventional approach is to allocate a uniform workspace for each hardware thread. In the manycore era, this results in increasing memory usage proportional to the number of hardware threads. We remedy this problem by using dynamic task parallelism with a scalable memory pool. Tasks are spawned while traversing an assembly tree and executed after their dependences are satisfied. We also use an idea to respawn the tasks when certain conditions are not met. Temporary workspace for frontal matrices in each task is allocated from a memory pool designed by us. If the requested memory space is not available in the memory pool, the task is respawned to yield the hardware thread to execute other tasks. The respawned task is executed after high priority tasks are executed. This approach allows to have robust parallel performance within a bounded memory space. Experimental results demonstrate the merits of our implementation on Intel multicore and manycore architectures.
Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2018
Herault, Thomas; Robert, Yves; Bouteiller, Aurelien; Arnold, Dorian; Ferreira, Kurt B.; Bosilca, George; Dongarra, Jack
In high-performance computing environments, input/output (I/O) from various sources often contend for scarce available bandwidth. Adding to the I/O operations inherent to the failure-free execution of an application, I/O from checkpoint/restart (CR) operations (used to ensure progress in the presence of failures) place an additional burden as it increase I/O contention, leading to degraded performance. In this work, we consider a cooperative scheduling policy that optimizes the overall performance of concurrently executing CR-based applications which share valuable I/O resources. First, we provide a theoretical model and then derive a set of necessary constraints needed to minimize the global waste on the platform. Our results demonstrate that the optimal checkpoint interval, as defined by Young/Daly, despite providing a sensible metric for a single application, is not sufficient to optimally address resource contention at the platform scale. We therefore show that combining optimal checkpointing periods with I/O scheduling strategies can provide a significant improvement on the overall application performance, thereby maximizing platform throughput. Overall, these results provide critical analysis and direct guidance on checkpointing large-scale workloads in the presence of competing I/O while minimizing the impact on application performance.
Large-scale HPC systems increasingly incorporate sophisticated power management control mechanisms. While these mechanisms are potentially useful for performing energy and/or power-aware job scheduling and resource management (EPA JSRM), greater understanding of their operation and performance impact on real-world applications is required before they can be applied effectively in practice. In this paper, we compare static p-state control to static node-level power cap control on a Cray XC system. Empirical experiments are performed to evaluate node-to-node performance and power usage variability for the two mechanisms. We find that static p-state control produces more predictable and higher performance characteristics than static node-level power cap control at a given power level. However, this performance benefit is at the cost of less predictable power usage. Static node-level power cap control produces predictable power usage but with more variable performance characteristics. Our results are not intended to show that one mechanism is better than the other. Rather, our results demonstrate that the mechanisms are complementary to one another and highlight their potential for combined use in achieving effective EPA JSRM solutions.
The dragonfly network topology has attracted attention in recent years owing to its high radix and constant diameter. However, the influence of job allocation on communication time in dragonfly networks is not fully understood. Recent studies have shown that random allocation is better at balancing the network traffic, while compact allocation is better at harnessing the locality in dragonfly groups. Based on these observations, this paper introduces a novel allocation policy called Level-Spread for dragonfly networks. This policy spreads jobs within the smallest network level that a given job can fit in at the time of its allocation. In this way, it simultaneously harnesses node adjacency and balances link congestion. To evaluate the performance of Level-Spread, we run packet-level network simulations using a diverse set of application communication patterns, job sizes, and communication intensities. We also explore the impact of network properties such as the number of groups, number of routers per group, machine utilization level, and global link bandwidth. Level-Spread reduces the communication overhead by 16% on average (and up to 71%) compared to the state-of-The-Art allocation policies.
Here, a numerical scheme is presented for approximating fractional order Poisson problems in two and three dimensions. The scheme is based on reformulating the original problem posed over $\Omega$ on the extruded domain $\mathcal{C}=\Omega\times[0,\infty)$ following. The resulting degenerate elliptic integer order PDE is then approximated using a hybrid FEM-spectral scheme. Finite elements are used in the direction parallel to the problem domain $\Omega$, and an appropriate spectral method is used in the extruded direction. The spectral part of the scheme requires that we approximate the true eigenvalues of the integer order Laplacian over $\Omega$. We derive an a priori error estimate which takes account of the error arising from using an approximation in place of the true eigenvalues. We further present a strategy for choosing approximations of the eigenvalues based on Weyl's law and finite element discretizations of the eigenvalue problem. The system of linear algebraic equations arising from the hybrid FEM-spectral scheme is decomposed into blocks which can be solved effectively using standard iterative solvers such as multigrid and conjugate gradient. Numerical examples in two and three dimensions suggest that the approach is quasi-optimal in terms of complexity.
While nearly all theoretical and computational studies of entangled polymer melts have focused on uniform samples, polymer synthesis routes always result in some dispersity, albeit narrow, of distribution of molecular weights (Crossed D signM=Mw/Mn∼1.02-1.04). Here, the effects of dispersity on chain mobility are studied for entangled, disperse melts using a coarse-grained model for polyethylene. Polymer melts with chain lengths set to follow a Schulz-Zimm distribution for the same average Mw=36 kg/mol with Crossed D signM=1.0 to 1.16, were studied for times of 600-800 μs using molecular dynamics simulations. This time frame is longer than the time required to reach the diffusive regime. We find that dispersity in this range does not affect the entanglement time or tube diameter. However, while there is negligible difference in the average mobility of chains for the uniform distribution Crossed D signM=1.0 and Crossed D signM=1.02, the shortest chains move significantly faster than the longest ones offering a constraint release pathway for the melts for larger Crossed D signM.
TiCl3 and TiF3 additives are known to facilitate hydrogenation and dehydrogenation in a variety of hydrogen storage materials, yet the associated mechanism remains under debate. Here, experimental and computational studies are reported for the reactivity with hydrogen gas of bulk and ball-milled TiCl3 and TiF3 at the temperatures and pressures for which these additives are observed to accelerate reactions when added to hydrogen storage materials. TiCl3, in either the α or δ polymorphic forms and of varying crystallite size ranging from ∼5 to 95 nm, shows no detectable reaction with prolonged exposure to hydrogen gas at elevated pressures (∼120 bar) and temperatures (up to 200 °C). Similarly, TiF3 with varying crystallite size from ∼4 to 25 nm exhibits no detectable reaction with hydrogen gas. Post-exposure vibrational and electronic structure investigations using Fourier transform infrared spectroscopy and x-ray absorption spectroscopy confirm this analysis. Moreover, there is no significant promotion of H2 dissociation at either interior or exterior surfaces, as demonstrated by H2/D2 exchange studies on pure TiF3. The computed energy landscape confirms that dissociative adsorption of H2 on TiF3 surfaces is thermodynamically inhibited. However, Ti-based additives could potentially promote H2 dissociation at interfaces where structural and compositional varieties are expected, or else by way of subsequent chemical transformations. At interfaces, metallic states could be formed intrinsically or extrinsically, possibly enabling hydrogen-coupled electronic transfer by donating electrons.
In this study, we focus on a hydrogeological inverse problem specifically targeting monitoring soil moisture variations using tomographic ground penetrating radar (GPR) travel time data. Technical challenges exist in the inversion of GPR tomographic data for handling non-uniqueness, nonlinearity and high-dimensionality of unknowns. We have developed a new method for estimating soil moisture fields from crosshole GPR data. It uses a pilot-point method to provide a low-dimensional representation of the relative dielectric permittivity field of the soil, which is the primary object of inference: the field can be converted to soil moisture using a petrophysical model. We integrate a multi-chain Markov chain Monte Carlo (MCMC)–Bayesian inversion framework with the pilot point concept, a curved-ray GPR travel time model, and a sequential Gaussian simulation algorithm, for estimating the dielectric permittivity at pilot point locations distributed within the tomogram, as well as the corresponding geostatistical parameters (i.e., spatial correlation range). We infer the dielectric permittivity as a probability density function, thus capturing the uncertainty in the inference. The multi-chain MCMC enables addressing high-dimensional inverse problems as required in the inversion setup. The method is scalable in terms of number of chains and processors, and is useful for computationally demanding Bayesian model calibration in scientific and engineering problems. The proposed inversion approach can successfully approximate the posterior density distributions of the pilot points, and capture the true values. The computational efficiency, accuracy, and convergence behaviors of the inversion approach were also systematically evaluated, by comparing the inversion results obtained with different levels of noises in the observations, increased observational data, as well as increased number of pilot points.
This work outlines the development of a two-fluid molten salt reactor process and safeguards model. The model is split into two parts consisting of a process model and a safeguards model. The process model is based on a design by Flibe Energy, the Liquid-Fluoride Thorium Reactor, which is a two-fluid molten salt reactor that performs full salt processing on-site to remove fission products and re-fuel the reactor. The model simulates feed and consumption rates of the reactor fuel and blanket salts. The process model includes the reactor core and salt processing loops. The reactor core model has a robust architecture that allows for integration with other tools and data sets as they become available. A majority of the effort to date has been focused on the process model, and the safeguards model will be developed in detail in future work. A preliminary safeguards analysis was performed based on actinide inventories, and a preliminary materials accountancy approach was initialized. The results of this analysis are presented along with a description of the model development.
This work provides a detailed explanation of the MELCOR Plot File format. MELCOR is a fully integrated, engineering-level computer code that models the progression of severe accidents in light water reactor nuclear power plants, and it primarily exports its simulation data to a binary file in the Plot File format. This work documents the Plot File's overall structure and the data types of exported information. The process of interpreting and associating time series information with MELCOR's Plot Variables for complete time histories is also presented. The format and meaning of time-independent information, called Special information, is also discussed.
Developed by the U.S. Nuclear Regulatory Commission for assessment of reactor accidents, Response Technical Manual (RTM) is a paper-based report which contains simple methods for estimating possible accident scenarios and relevant consequences for different kinds of radiological events. Based on RTM, a software called Response Technical Tools (RTT) was developed by Sandia National Laboratories to convert the paper manual into an automated and easy-to-use code. In particular, the RTT focuses on the nuclear power plant severe accidents and is informed by state-of-the-art analyses and software programs, such as MELCOR and MAAP. RTT evaluations can be used to track and predict, at a very coarse level, the progression of a severe accident in nuclear power plant. The RTT allows a user to track the progress of an accident in a nuclear power plant from the point of initiation through the point of containment breach and release to the environment.
Sandia National Laboratories performed analysis to develop conservative hazard guidelines regarding firefighters working near photovoltaic (PV) arrays. Assuming implementation of NFPA 70 system shutdown requirements, the analysis focused on DC hazards only. Several different PV variables were considered, including system grounding and DC voltage classes. The hazard scenarios considered the contact conditions, current paths through the body, and PPE. Guidelines for the hazard definitions for men and women were based on the IEC TS 60479-1 guidelines. The importance of PPE was illustrated in the results.
We describe our work to embed a Python interpreter in S3D, a highly scalable parallel direct numerical simulation reacting flow solver written in Fortran. Although S3D had no in-situ capability when we began, embedding the interpreter was surprisingly easy, and the result is an extremely flexible platform for conducting machine-learning experiments in-situ.
A critical component of the Underground Nuclear Explosion Signatures Experiment (UNESE) program is a realistic understanding of the post-detonation processes and changes in the environment that produce observable physical and radio-chemical signatures. Rock and fracture properties are essential parameters for any UNESE test bed. In response to the need for accurate modeling scenarios of these observations, an experimental program to determine the permeability and direct shear fracture properties of Barnwell core was developed. Room temperature gas permeability measurements of Barnwell core dried at 50degC yield permeability ranging from 6.24E-02 Darcys to 6.98E-08 Darcys. Friction angles from the direct shear tests vary from 28.1deg to 44.4deg for residual shear strength and average 47.9deg for peak shear strength. Cohesion averaged 3.2 psi and 13.3 psi for residual and peak shear strength values respectively. The work presented herein is the initial determination of an ongoing broader material characterization effort.
The Rapid Sample Insertion/Extraction System for Gamma Irradiation, otherwise known as the "rabbit" system, was a four-week long project which included many different aspects such as coding an Arduino, building PVC piping, and 3-D printing the "rabbit" capsules. The "rabbit" system is a system of PVC piping that allows a quick and efficient transfer of materials into and out of one of the irradiation chambers in the Gamma Irradiation Facility (GIF) with the use of a 3-D printed "rabbit." This "rabbit" encapsulates material to be irradiated and carries it from a position outside of the irradiation chamber to the basket inside of the chamber. The main purpose of this system is to save time and provide more exact data without any delays that normally occur when a person has to enter the chamber, retrieve data, and then analyze the data. This system should take measurements and retrieve the data instantaneously. The way in which the "rabbit" is sent through the PVC piping is with an advanced bi-directional, high-throughput pneumatic system, or a shop vacuum cleaner. When the vacuum is set to blow or suck then the "rabbit" will be pulled or pushed through the PVC piping to its intended destination and will hit sensors along the sides of the tubing when it reaches the end of the piping. These sensors tell the Arduino that the "rabbit" is finished moving throughout the tubing and stops a timer. Another timer is used to see how long the "rabbit" is being irradiated so when the "rabbit" reaches the sensors in the basket in the irradiation chamber another timer is started and it ends when the sensors no longer detect the "rabbit," which means that it has begun its journey back to the starting point. These times, as well as the temperatures, are the data necessary for the project and must be extremely accurate.
This chapter describes the processes that Members of the Workforce (MOW) follow to control Accountable Sealed Radioactive Sources (ASRSs) and Radioisotope Thermoelectric Generators (RTGs). It also describes the Sandia National Laboratories (SNL) program for implementing the ASRS requirements in 10 CFR 835.1202 and the ASRS and RTG requirements in DOE 0 231.1B.
This calculation documents the methodology used to determine Hazard Category (HC) II and III threshold quantities for select radioisotopes in the ISMS Software Nuclide Threshold Table.
Sandia National Laboratories has tested and evaluated three seismometers, the CMG-3V, manufactured by Guralp. These seismometers measure a single axes of broadband ground velocity in a borehole package. The purpose of the seismometer evaluation was to determine a measured sensitivity, response, passband, self-noise, dynamic range, and self-calibration ability. The Guralp CMG-3V seismometers are being evaluated for potential use in U.S. Air Force seismic monitoring systems as part of their Next Generation Qualification effort.
Remote Direct Memory Access (RDMA) is expected to be an integral communication mechanism for future exascale systems - enabling asynchronous data transfers, so that applications may fully utilize CPU resources while simultaneously sharing data amongst remote nodes. In this work we examine Network-induced Memory Contention (NiMC) on Infiniband networks. We expose the interactions between RDMA, main-memory and cache, when applications and out-of-band services compete for memory resources. We then explore NiMC's resulting impact on application-level performance. For a range of hardware technologies and HPC workloads, we quantify NiMC and show that NiMC's impact grows with scale resulting in up to 3X performance degradation at scales as small as 8K processes even in applications that previously have been shown to be performance resilient in the presence of noise. Additionally, this work examines the problem of predicting NiMC's impact on applications by leveraging machine learning and easily accessible performance counters. This approach provides additional insights about the root cause of NiMC and facilitates dynamic selection of potential solutions. Lastly, we evaluated three potential techniques to reduce NiMC's impact, namely hardware offloading, core reservation and software-based network throttling.
Cubic zirconium tungstate (α-ZrW2O8), a well-known negative thermal expansion material, has been investigated within the framework of density functional perturbation theory (DFPT), combined with experimental characterization to assess and validate computational results. Using combined Fourier transform infrared measurements and DFPT calculations, new and extensive assignments were made for the far-infrared (<400 cm−1) spectrum of α-ZrW2O8. A systematic comparison of DFPT-simulated infrared, Raman, and phonon density-of-state spectra with Fourier transform far-/mid-infrared and Raman data collected in this study, as well as with available inelastic neutron scattering measurements, shows the superior accuracy of the PBEsol exchange-correlation functional over standard PBE calculations for studying the spectroscopic properties of this material.
NoiseSpotter is currently designed for a 2-week deployment. This timeline will likely be problematic for MHK developers. Developers will want a robust/proven system that they can deploy and not worry about for longer than 2 weeks (especially for the continued monitoring of a site).
MFiX, a general-purpose Fortran-based suite, simulates the complex flow in fluidized bed applications via BiCGStab and GMRES methods along with plane relaxation preconditioners. Trilinos, an object-oriented framework, contains various first- and second-generation Krylov subspace solvers and preconditioners. We developed a framework to integrate MFiX with Trilinos as MFiX does not possess advanced linear methods. The framework allows MFiX to access advanced linear solvers and preconditioners in Trilinos. The integrated solver is called MFiX–Trilinos, here after. In the present work, we study the performance of variants of GMRES and CGS methods in MFiX–Trilinos and BiCGStab and GMRES solvers in MFiX for a 3D gas–solid fluidized bed problem. Two right preconditioners employed along with various solvers in MFiX–Trilinos are Jacobi and smoothed aggregation. The flow from MFiX–Trilinos is validated against the same from MFiX for BiCGStab and GMRES methods. And, the effect of the preconditioning on the iterative solvers in MFiX–Trilinos is also analyzed. In addition, the effect of left and right smoothed aggregation preconditioning on the solvers is studied. The performance of the first- and second-generation solver stacks in MFiX–Trilinos is studied as well for two different problem sizes.
The search for multivariate quadrature rules of minimal size with a specified polynomial accuracy has been the topic of many years of research. Finding such a rule allows accurate integration of moments, which play a central role in many aspects of scientific computing with complex models. The contribution of this paper is twofold. First, we provide novel mathematical analysis of the polynomial quadrature problem that provides a lower bound for the minimal possible number of nodes in a polynomial rule with specified accuracy. We give concrete but simplistic multivariate examples where a minimal quadrature rule can be designed that achieves this lower bound, along with situations that showcase when it is not possible to achieve this lower bound. Our second contribution is the formulation of an algorithm that is able to efficiently generate multivariate quadrature rules with positive weights on non-tensorial domains. Our tests show success of this procedure in up to 20 dimensions. We test our method on applications to dimension reduction and chemical kinetics problems, including comparisons against popular alternatives such as sparse grids, Monte Carlo and quasi Monte Carlo sequences, and Stroud rules. The quadrature rules computed in this paper outperform these alternatives in almost all scenarios.
Many existing shape memory alloy (SMA) devices consist of slender beams and frames. To better understand SMA beam behavior, we experimentally examined the isothermal, room temperature response of superelastic NiTi rods and tubes, of similar outer diameters, subjected to four different modes of loading. Pure tension, pure compression, and pure bending experiments were first performed to establish and compare the baseline uniaxial and bending behaviors of rods and tubes. Column buckling experiments were then performed on rod and tube columns of several slenderness ratios to investigate their mechanical responses, phase transformation kinetics under combined uniaxial and bending deformation, and the interaction between material and structural instabilities. In all experiments, stereo digital image correlation measured local displacement fields in order to capture phenomena such as strain localization and propagating phase boundaries. Superelastic mechanical behavior and the nature of stress-induced phase transformation were found to be strongly affected by specimen geometry and the deformation mode. Under uniaxial tension, both the rod and tube had well-defined loading and unloading plateaus in their superelastic responses, during which stress-induced phase transformation propagated along the length of the specimen in the form of a high/low strain front. Due to the dependence of strain localization on kinematic compatibility, the high/low strain front morphologies differed between the rod and tube: for the rod, the high/low strain front consisted of a diffuse “neck”, while the high/low strain front in the tube consisted of distinct, criss-crossing “fingers.” During uniaxial compression, both cross-sectional forms exhibited higher transformation stresses and smaller transformation strains than uniaxial tension, highlighting the now well-known tension-compression asymmetry of SMAs. Additionally, phase transformation localization and propagation were absent under compressive loading. During pure bending, the moment-curvature response of both forms exhibited plateaus and strain localization during forward and reverse transformations. Rod specimens developed localized, high-curvature regions that propagated along the specimen axis and caused shear strain near the high/low curvature interface; whereas, the tube specimens exhibited finger/wedge-like high strain regions over the tensile side of the tube which caused nonlinear strain profiles through the thickness of the specimen that did not propagate. It was therefore found that classical beam theory assumptions did not hold in the presence of phase transformation localization (although, the assumptions did hold on average for the tube). During column buckling, the structures were loaded into the post-buckling regime yet recovered nearly-straight forms upon unloading. Strain localization was observed only for high aspect ratio (slender) tubes, but the mechanical responses were similar to that of rods of the same slenderness ratio. Also, an interesting “unbuckling” phenomenon was discovered in certain low aspect ratio (stout) columns, where late post-buckling straightening was observed despite continuous monotonic loading. Thus, these behaviors are some of the challenging phenomena which must be captured when developing SMA constitutive models and executing structural simulations.
Injection of large amounts of fluid into the subsurface alters the states of pore pressure and stress in the formation, potentially inducing earthquakes. Increase in the seismicity rate after shut-in is often observed at fluid-injection operation sites, but mechanistic study of the rate surge has not been investigated thoroughly. Considering full poroelastic coupling of pore pressure and stress, the earthquake occurrence after shut-in can be driven by two mechanisms: (1) post shut-in diffusion of pore pressure into distant faults and (2) poroelastic stressing caused by fluid injection. Interactions of these mechanisms can depend on fault geometry, hydraulic and mechanical properties of the formation, and injection operation. In this work, a 2D aerial view of the target reservoir intersected by strike-slip basement faults is used to evaluate the impact of injection-induced pressure buildup on seismicity rate surge. A series of sensitivity tests are performed by considering the variation in (1) permeability of the fault zone, (2) locations and the number of faults with respect to the injector, and (3) well operations with time-dependent injection rates. Lower permeability faults have higher seismicity rates than more permeable faults after shut-in due to delayed diffusion and poroelastic stressing. Hydraulic barriers, depending on their relative location to injection, can either stabilize or weaken a conductive fault via poroelastic stresses. Gradual reduction of the injection rate minimizes the coulomb stress change and the least seismicity rates are predicted due to slower relaxation of coupling-induced compression as well as pore-pressure dissipation.
AlSi10Mg tensile bars were additively manufactured using the powder-bed selective laser melting process. Samples were subjected to stress relief annealing and hot isostatic pressing. Tensile samples built using fresh, stored, and reused powder feedstock were characterized for microstructure, porosity, and mechanical properties. Fresh powder exhibited the best mechanical properties and lowest porosity while stored and reused powder exhibited inferior mechanical properties and higher porosity. The microstructure of stress relieved samples was fine and exhibited (001) texture in the z-build direction. Microstructure for hot isostatic pressed samples was coarsened with fainter (001) texture. To investigate surface and interior defects, scanning electron microscopy, optical fractography, and laser scanning microscopy techniques were employed. Hot isostatic pressing eliminated internal pores and reduced the size of surface porosity associated with the selective laser melting process. Hot isostatic pressing tended to increase ductility at the expense of decreasing strength. However, scatter in ductility of hot isostatic pressed parts suggests that the presence of unclosed surface porosity facilitated fracture with crack propagation inward from the surface of the part.
The Federal Radiological Monitoring and Assessment Center (FRMAC) has two major assets it uses to perform it responsibilities for responding to a radiological emergency. These are the National Atmospheric Release Advisory Capability (NARAC) and Turbo FRMAC. Recently NARAC updated their deposition model to the state of the art Petroff and Zhang model leading to a significant discrepancy between these two assets in regards to deposition modeling. This report describes the investigation into an appropriate deposition model for Turbo FRMAC to bring the two assets back into line. The ultimate conclusion is that Petroff and Zhang is too complicated for Turbo FRMAC, but the model of Feng is not and is equal to Petroff and Zhang in predictive capability.
Foley, Brian M.; Wallace, Margeaux; Gaskins, John T.; Paisley, Elizabeth A.; Johnson, Raegan L.; Kim, Jong W.; Ryan, Philip J.; Trolier-Mckinstry, Susan; Hopkins, Patrick E.; Ihlefeld, Jon F.
Ferroelastic domain walls in ferroelectric materials possess two properties that are known to affect phonon transport: a change in crystallographic orientation and a lattice strain. Changing populations and spacing of nanoscale-spaced ferroelastic domain walls lead to the manipulation of phonon-scattering rates, enabling the control of thermal conduction at ambient temperatures. In the present work, lead zirconate titanate (PZT) thin-film membrane structures were fabricated to reduce mechanical clamping to the substrate and enable a subsequent increase in the ferroelastic domain wall mobility. Under application of an electric field, the thermal conductivity of PZT increases abruptly at ∼100 kV/cm by ∼13% owing to a reduction in the number of phonon-scattering domain walls in the thermal conduction path. The thermal conductivity modulation is rapid, repeatable, and discrete, resulting in a bistable state or a "digital" modulation scheme. The modulation of thermal conductivity due to changes in domain wall configuration is supported by polarization-field, mechanical stiffness, and in situ microdiffraction experiments. This work opens a path toward a new means to control phonons and phonon-mediated energy in a digital manner at room temperature using only an electric field.
Most inverters for use in distribution-connected distributed energy resource applications (distributed generation and energy storage) are tested and certified to detect and cease to energize unintentional islands on the electric grid. The requirements for the performance of islanding detection methods are specified in IEEE 1547-2018, and specified conditions for certification- type testing of islanding detection are defined in IEEE 1547.1. Such certification-type testing is designed to ensure a minimum level of confidence that these inverters will not island in field applications. However, individual inverter certification tests do not address interactions between dissimilar inverters or between inverter and synchronous machines that may occur in the field. This work investigates the performance of different inverter island detection methods for these two circumstances that are not addressed by the type testing: 1) combinations of different inverters using different types of islanding detection methods, and 2) combinations of inverters and synchronous generators. The analysis took into consideration voltage and frequency ride- through requirements as specified in IEEE 1547-2018, but did not consider grid support functionality such as voltage or frequency response. While the risk of islanding is low even in these cases, it is often difficult to deal with these scenarios in a simplified interconnection screening process. This type of analysis could provide a basis to establish a practical anti- islanding screening methodology for these complex scenarios, with the goal of reducing the number of required detailed studies. Eight generic Groups of islanding detection behavior are defined, and examples of each are used in the simulations. The results indicate that islanding detection methods lose effectiveness at significantly different rates as the composition of the distributed energy resources (DERs) varies, with some methods remaining highly effective over a wide range of conditions.
This report describes the mechanical characterization of six types of woven composites that Sandia National Laboratories are interested in. These six composites have various combinations of two types of fibers (Carbon-IM7 and Glass-S2) and three types of resins (UF-3362, TC275-1, TC350-1). In this work, two sets of experiments were conducted: quasi-static loading with displacement rate of 2 mm/min (1.3x10^(-3) in/s) and high rate loading with displacement of 5.08 m/s (200 in/s). Quasi-static experiments were performed at three loading orientations of 0°, 45°, 90° for all the six composites to fully characterize their mechanical properties. The elastic properties Young's modulus and Poisson's ratio, as well as ultimate stress and strain were obtained from the quasi-static experiments. The high strain rate experiments were performed only on glass fiber composites along 0° angle of loading. The high rate experiments were mainly to study how the strain rate affects the ultimate stress of the glass-fiber composites with different resins.
Efficiently performing predictive studies of irradiated particle-laden turbulent flows has the potential of providing significant contributions towards better understanding and optimizing, for example, concentrated solar power systems. As there are many uncertainties inherent in such flows, conducting uncertainty quantification analyses is fundamental to improve the predictive capabilities of the numerical simulations. For largescale, multi-physics problems exhibiting high-dimensional uncertainty, characterizing the stochastic solution presents a significant computational challenge as many methods require a large number of high-fidelity, forward model solves. This requirement results in the need for a possibly infeasible number of simulations when a typical converged high-fidelity simulation requires intensive computational resources. To reduce the cost of quantifying high-dimensional uncertainties, we investigate the application of a non-intrusive, bi-fidelity approximation to estimate statistics of quantities of interest associated with an irradiated particle-laden turbulent flow. This method relies on exploiting the low-rank structure of the solution to accelerate the stochastic sampling and approximation processes by means of cheaper-to-run, lower fidelity representations. The application of this bi-fidelity approximation results in accurate estimates of the QoI statistics while requiring a small number of high-fidelity model evaluations. It also enables efficient computation of sensitivity analyses which highlight that epistemic uncertainty plays an important role in the solution of irradiated, particle-laden turbulent flow.
This work is to characterize the mechanical properties of the selected composites along both on- and off- fiber axes at the ambient loading condition (+25°C), as well as at the cold (-54°C), and high temperatures (+71°C). A series of tensile experiments were conducted at different material orientations of 0°, 22.5°, 45°, 67.5°, 90° to measure the ultimate strength and strain $σ_{f}, ϵ_{f}$, and material engineering constants, including Young's modulus Ε and Poisson's ratio ν. The composite materials in this study were one carbon composite carbon (AS4C/UF3662) and one E-galss (E-glass/UF3662) composite. They both had the same resin of UF 3362, but with different fibers of carbon AS4C and E-glass. The mechanical loading in this study was limited to the quasi-static loading of 2 mm/min (1.3x10^(-3) in/s), which was equivalent to 5x10(-4) strain rate. These experimental data of the mechanical properties of composites at different loading directions and temperatures were summarized and compared. These experimental results provided database for design engineers to optimize structures through ply angle modifications and for analysts to better predict the component performance.
Subsurface seals and wellbores are central to oil and gas production, as well as the containment of subsurface fluids (e.g. methane or CO2 storage). Studying the evolution of cement-geomaterial interfaces of such systems is important to further our understanding of the fundamental physics and chemistry that underpins catastrophic wellbore seal failure. The objective of this study is to characterize cementitious and geomaterials through pore structure analysis and geochemical modeling. A variety of methods exist to characterize the pore structures and mineralogy of porous systems like cements and subsurface host rocks. This study will utilize traditional porosimetry techniques such as BET and IP, as well as more advanced methods using electron image analysis, to gain a more accurate understanding of pore geometries. The results of this study can help further the understanding of how cementitious materials will evolve, and can be used as inputs to field scale models used to predict wellbore behavior over time.
Mutual vulnerability to strategic forces seems to remain the de facto foundation for strategic stability across the U.S.-Russia and U.S.-China dyadic relationships; This work has suggested the bomber force tasked with delivering the LRSO is characterized by relatively long flight times and rich signature sets which make its use inconsistent with the requirements for a disarming first-strike. Therefore, the LRSO would not be expected to disrupt mutual vulnerability by making a disarming strike more possible or attractive; Even if a stealthy air-launched cruise missile is paired with a stealth bomber aircraft, the signatures associated with bomber generation and aerial refueling from non-stealth tanker aircraft make it unlikely the LRSO could be launched against a peer or near-peer nation-state without advance warning; To the extent a nuclear armed air-launched cruise missile deters would-be U.S. adversaries from nuclear use, maintaining a survivable weapon system is crucial for maintaining that stable deterrent effect. A modern stand-off weapon and stealth delivery platform increase the probability this capability will be maintained in the future against other nations' increasingly capable A2/AD systems; The LRSO has been touted as a flexible option to deter, or conduct should deterrence fail, limited nuclear strikes pursuant to Russia's reported "escalate-to-deescalate" doctrine. The concept of limited nuclear use is still intensely debated, and there is no guarantee that escalation could be controlled even with tailored LRSO employment; The challenge of warhead discrimination has not historically led to a nuclear response to a cruise missile launch, but there is no guarantee that cannot change. Having accurate military intelligence coupled with discerning analysis of the context in which cruise missiles are employed (e.g., how escalated is the conflict, how many missiles have been launched, have there been signatures of strategic force mobilization, or has the nuclear threshold been crossed?) will likely be essential for reducing the danger of misperception. Developing norms and/or communication channels in the aforementioned dyadic relationships may also further these ends. In sum, this work has identified and analyzed many of the major arguments in the debate regarding the LRSO's impact on strategic stability. During this study and survey of other nation's conceptions of strategic stability, it became clear that the LRSO is neither inherently stabilizing or destabilizing; rather, it is one instrument in addition to unambiguous U.S. policy, clear messaging, and signaling of intent that may promote stability by reducing the risk of miscalculation and unintended escalation.
Goal: create an efficient computational tool capable of predicting the complex, nonlinear response of truss lattices containing extremely Iarge numbers of beams and nodes.