Aria is a Galerkin finite element based program for solving coupled-physics problems described by systems of PDEs and is capable of solving nonlinear, implicit, transient and direct-to-steady state problems in two and three dimensions on parallel architectures. The suite of physics currently supported by Aria includes thermal energy transport, species transport, and electrostatics as well as generalized scalar, vector and tensor transport equations. Additionally, Aria includes support for manufacturing process flows via the incompressible Navier-Stokes equations specialized to a low Reynolds number (Re < 1) regime. Enhanced modeling support of manufacturing processing is made possible through use of either arbitrary Lagrangian-Eulerian (ALE) and level set based free and moving boundary tracking in conjunction with quasi-static nonlinear elastic solid mechanics for mesh control. Coupled physics problems are solved in several ways including fully-coupled Newtons method with analytic or numerical sensitivities, fully-coupled Newton-Krylov methods and a loosely-coupled nonlinear iteration about subsets of the system that are solved using combinations of the aforementioned methods. Error estimation, uniform and dynamic-adaptivity and dynamic load balancing are some of Arias more advanced capabilities.
SIERRA/Aero is a compressible fluid dynamics program intended to solve a wide variety compressible fluid flows including transonic and hypersonic problems. This document describes the commands for assembling a fluid model for analysis with this module, henceforth referred to simply as Aero for brevity. Aero is an application developed using the SIERRA Toolkit (STK). The intent of STK is to provide a set of tools for handling common tasks that programmers encounter when developing a code for numerical simulation. For example, components of STK provide field allocation and management, and parallel input/output of field and mesh data. These services also allow the development of coupled mechanics analysis software for a massively parallel computing environment.
SIERRA/Aero is a compressible fluid dynamics program intended to solve a wide variety compressible fluid flows including transonic and hypersonic problems. This document describes the commands for assembling a fluid model for analysis with this module, henceforth referred to simply as Aero for brevity. Aero is an application developed using the SIERRA Toolkit (STK). The intent of STK is to provide a set of tools for handling common tasks that programmers encounter when developing a code for numerical simulation. For example, components of STK provide field allocation and management, and parallel input/output of field and mesh data. These services also allow the development of coupled mechanics analysis software for a massively parallel computing environment.
The SNL Sierra Mechanics code suite is designed to enable simulation of complex multiphysics scenarios. The code suite is composed of several specialized applications which can operate either in standalone mode or coupled with each other. Arpeggio is a supported utility that enables loose coupling of the various Sierra Mechanics applications by providing access to Framework services that facilitate the coupling. More importantly Arpeggio orchestrates the execution of applications that participate in the coupling. This document describes the various components of Arpeggio and their operability. The intent of the document is to provide a fast path for analysts interested in coupled applications via simple examples of its usage.
Brigner, Wesley; Hassan, Naimul X.; Friedman, Joseph S.; Velasquez, Alvaro; Garcia-Sanchez, Felipe X.; Akinola, Otitoaleke; Incorvia, Jean A.; Bennett, Christopher H.; Marinella, Matthew J.
The U.S. Department of Energy Office of Spent Fuel Waste Disposition (SFWD) established in fiscal year 2010 (FY10) the Spent Fuel Waste Science & Technology (SFWST) Program (formerly the Used Fuel Disposition Campaign - UFDC) program to conduct the research and development (R&D) activities related to storage, transportation and disposal of used nuclear fuel and high level nuclear waste. The Mission of the SFWST is: To identify alternatives and conduct scientific research and technology development to enable storage, transportation and disposal of used nuclear fuel and wastes generated by existing and future nuclear fuel cycles. Significant progress has been made in FY20 in both experimental and modeling arenas in evaluation of used fuel disposal in crystalline rocks, especially in model demonstration using field data. The work covers a wide range of research topics identified in the R&D plan.
On June 30, 2020, a 0.87 gram PETN charge being pressed in the Rapid Prototyping Facility (RPF), unexpectedly initiated, resulting in destruction of the pressing fixture but no injuries or facility damage. In response, the Safety Review Board (SRB) met on Aug. 13, 2020 and Oct. 1, 2020 to review information collected following the incident, consider likely direct causes, and form recommendations.
Sandia National Laboratories (SNL) personnel operate a low power research reactor (the Annular Core Research Reactor, or ACRR), and a zero-power critical experiment assembly referred to as CX. In accordance with 10 CFR 830, Subpart B, Appendix A, the acceptable methodology for developing a Documented Safety Analysis (DSA) for DOE nuclear reactors is the Nuclear Regulatory Commission’s (NRC’s) Regulatory Guide 1.70 (RG 1.70). RG 1.70 does not address certain areas required by 10 CFR 830 and expected by DOE (e.g., full facility hazard analysis).Thus, the current DSAs for SNL’s reactor nuclear facilities are based on RG 1.70, but also of necessity supplemented by DOE-STD-3009-94 methods. SNL personnel, in consultation with the National Nuclear Security Administration (NNSA) Sandia Field Office (SFO), have concluded that an alternate methodology is preferred to RG 1.70. The details of the proposal, and the reasons motivating its development, are discussed in the order described below. The proposed alternate methodology will be applicable to the ACRR and the CX (i.e., it will be applicable to nuclear facilities in which a reactor and/or a critical assembly will be operated).
This report has been created to give an in-depth walkthrough of the functionality of the Total Cost of Operation webtool. The webtool was created to give users the ability to calculate the total cost of owning a vehicle over from the year they bought the vehicle to the end of the vehicles lifetime. This tool was developed using a combination of front-end and back-end technologies. To create the front-end HTML, CSS, and JavaScript were utilized. On the backend PHP is used as a scripting language with a database powered by MySQL. Through a combination of these technologies, a fully featured well developed webtool was created allowing users to view a cost breakdown of vehicle ownership over the lifetime of that vehicle.
Krishnamoorthy, Siddharth; Bowman, Daniel B.; Komjathy, Attila; Pauken, Michael T.; Cutts, James A.
High-altitude monitoring of low-frequency acoustic waves (infrasound) on Earth has regained prominence in recent years, primarily driven by improvements in light-weight sensor technology and advances in scientific ballooning techniques. Balloon-borne infrasound monitoring is also being proposed as a remote sensing technique for planetary exploration. Contrary to ground-based infrasound monitoring, the infrasound noise background in the stratosphere as measured by a balloon remains uncharacterized and the efficacy of wind noise mitigation filters has not been investigated. In this study, an analysis of pressure data collected using infrasound microbarometers during the flight of a long-duration zero pressure balloon is presented. A dramatic reduction of background noise in the stratosphere is demonstrated and it is shown that wind noise mitigation filters are not effective at reducing wind noise under these conditions. Results from this study demonstrate stratospheric balloons as a low-noise platform for infrasound monitoring and motivate the development of improved noise mitigation tools.
We propose a novel approach to evaluate the relaxation time of vapor bubble growth in the context of the flash boiling of a superheated liquid. In alternative to the empirical correlation derived from superheated water experiments almost fifty years ago, the new model describes the thermally-dominated growth of vapor bubbles in terms that are dependent on the local Jakob number (the ratio of sensible heat to latent heat during phase change) and the number density of vapor bubbles. The model is tested by plugging the resulting relaxation time into the Homogenous Relaxation Model (HRM). Flash-boiling simulations carried out with HRM are compared with n-pentane (C5H12) injection and boil-off experiments conducted with a real-size, axial-hole, transparent gasoline injector discharging into a constant-pressure vessel. The long-distance microscopy images from the experiments, processed to derive the projected liquid volume (PLV) of the spray, provide a unique set of time-resolved validation data for direct fuel injection simulations. At conditions ranging from flaring to mild and minimal flash boiling, we show that switching to the new relaxation time improves the agreement with the measured PLV profiles with respect to the standard empirical model. Particularly at flaring conditions, the predicted increase in gas cooling caused by rapid vapor production is shown to be more consistent with the observed boil-off.
In this article, we examine the coupling into an electrically short azimuthal slot on a cylindrical cavity operating at fundamental cavity modal frequencies. We first develop a matched bound formulation through which we can gather information for maximum achievable levels of interior cavity fields. Actual field levels are below this matched bound; therefore, we also develop an unmatched formulation for frequencies below the slot resonance to achieve a better insight on the physics of this coupling. Good agreement is observed between the unmatched formulation, full-wave simulations, and experimental data, providing a validation of our analytical models. We then extend the unmatched formulation to treat an array of slots, found again in good agreement with full-wave simulations. These analytical models can be used to investigate ways to mitigate electromagnetic interference and electromagnetic compatibility effects within cavities.
PyGSTi is a Python software package for assessing and characterizing the performance of quantum computing processors. It can be used as a standalone application, or as a library, to perform a wide variety of quantum characterization, verification, and validation (QCVV) protocols on as-built quantum processors. We outline pyGSTi's structure, and what it can do, using multiple examples. We cover its main characterization protocols with end-to-end implementations. These include gate set tomography, randomized benchmarking on one or many qubits, and several specialized techniques. We also discuss and demonstrate how power users can customize pyGSTi and leverage its components to create specialized QCVV protocols and solve user-specific problems.
This work presents the design of nonlinear stabilization techniques for the finite element discretization of Euler equations in both steady and transient form. Implicit time integration is used in the case of the transient form. A differentiable local bounds preserving method has been developed, which combines a Rusanov artificial diffusion operator and a differentiable shock detector. Nonlinear stabilization schemes are usually stiff and highly nonlinear. This issue is mitigated by the differentiability properties of the proposed method. Moreover, in order to further improve the nonlinear convergence, we also propose a continuation method for a subset of the stabilization parameters. The resulting method has been successfully applied to steady and transient problems with complex shock patterns. Numerical experiments show that it is able to provide sharp and well resolved shocks. The importance of the differentiability is assessed by comparing the new scheme with its non-differentiable counterpart. Numerical experiments suggest that, for up to moderate nonlinear tolerances, the method exhibits improved robustness and nonlinear convergence behavior for steady problems. In the case of transient problem, we also observe a reduction in the computational cost.
Additively manufactured metamaterials such as lattices offer unique physical properties such as high specific strengths and stiffnesses. However, additively manufactured parts, including lattices, exhibit a higher variability in their mechanical properties than wrought materials, placing more stringent demands on inspection, part quality verification, and product qualification. Previous research on anomaly detection has primarily focused on using in-situ monitoring of the additive manufacturing process or post-process (ex-situ) x-ray computed tomography. In this work, we show that convolutional neural networks (CNN), a machine learning algorithm, can directly predict the energy required to compressively deform gyroid and octet truss metamaterials using only optical images. Using the tiled nature of engineered lattices, the relatively small data set (43 to 48 lattices) can be augmented by systematically subdividing the original image into many smaller sub-images. During testing of the CNN, the prediction from these sub-images can be combined using an ensemble-like technique to predict the deformation work of the entire lattice. This approach provides a fast and inexpensive screening tool for predicting properties of 3D printed lattices. Importantly, this artificial intelligence strategy goes beyond ‘inspection’, since it accurately estimates product performance metrics, not just the existence of defects.
In this paper we introduce a method to compare sets of full-field data using Alpert tree-wavelet transforms. The Alpert tree-wavelet methods transform the data into a spectral space allowing the comparison of all points in the fields by comparing spectral amplitudes. The methods are insensitive to translation, scale and discretization and can be applied to arbitrary geometries. This makes them especially well suited for comparison of field data sets coming from two different sources such as when comparing simulation field data to experimental field data. We have developed both global and local error metrics to quantify the error between two fields. We verify the methods on two-dimensional and three-dimensional discretizations of analytical functions. We then deploy the methods to compare full-field strain data from a simulation of elastomeric syntactic foam.
Oya, Y.; Sun, F.; Yamauchi, Y.; Nobuta, Y.; Shimada, M.; Taylor, C.N.; Nakata MNakata; Garrison, L.M.; Hatano, Y.; Wampler, William R.
Single crystalline W (tungsten) samples irradiated at 633, 963 and 1073 K by neutrons to a damage level of 0.1 dpa were exposed to a high-flux D (deuterium) plasma at 673, 873 and 973 K, respectively, in TPE (Tritium Plasma Experiment) at INL (Idaho National Laboratory). Deuterium desorption was analyzed by TDS (Thermal Desorption Spectroscopy), and D depth profiles were determined by NRA (Nuclear Reaction Analysis) at SNL (Sandia National Laboratories). HIDT (Hydrogen Isotope Diffusion and Trapping) simulation code was applied to evaluate D behavior for neutron-damaged W at higher temperature. The D retention at depths up to 3 μm for the neutron-damaged sample at 673 K was two orders of magnitude larger than that for undamaged tungsten, and its D desorption spectrum had a single broad stage at around 900 K. As the neutron irradiation/plasma exposure temperature increased, D retention was largely reduced, and the desorption temperature was shifted to higher temperatures above 1100 K. The D depth profiles by NRA also showed D migration toward bulk by higher temperature irradiation, compared to undamaged W. The HIDT simulation indicated that the major binding energy of D was changed from 1.43 eV to 2.07 eV at higher neutron irradiation and plasma exposure temperatures, suggesting that some vacancies and small vacancy clusters would aggregate to form larger voids, or depopulation of weak traps at high D plasma exposure temperatures. It can be said that more stable trapping sites played dominant roles in the D retention at higher neutron irradiation and plasma exposure temperature. The binding energy by HIDT simulation was almost consistent with the reported value by TMAP, but the consideration of not only total D retention measured by TDS but also D depth profile by NRA led to the more accurate D behavior in neutron-damaged W.
The optical properties of InAs quantum dashes (QDashes) grown on InP and InAs quantum dots (QDots) grown on GaAs in a dashes- or dots-in-a-well (DWELL) configuration are comparatively investigated using temperature-dependent photoluminescence (PL) measurements. The trends in PL characteristics such as exciton energy, spectral bandwidth and integrated intensity with respect to temperature are found to be distinctly dissimilar between the two systems. A rate-equation model involving exciton recombination and thermal transfer in a localized-state ensemble is used to quantitively interpret the experimental data. These results suggest that QDashes in this configuration exhibit PL properties more consistent with a lower degree of carrier localization compared to QDots. A preliminary structural analysis highlighting the shape/size differences between the two nanostructures is also presented.
We report on the measurement results of the superconducting-ferromagnetic transistors (SFTs). The devices were made at Northwestern University and Hypres (SeeQC), Inc. (Nevirkovets et al., 2014; 2015). SFT is a multiterminal device with the SISFIFS (or SFIFSIS) structure (where S, I, and F denote a superconductor, an insulator, and a ferromagnetic material, respectively) exploiting intense quasiparticle injection in order to modify the nonlinear I-V curve of a superconducting tunnel junction. SFT is capable of providing voltage, current, and power amplification while having good input/output isolation. We characterized the devices using different measurement techniques. We measured S parameters of the single- and double-acceptor devices at frequencies up to 5 MHz. Importantly, we confirmed that the isolation between the input and output of the device is quite good. However, the techniques typically employed to characterize semiconductor devices do not allow for revealing the full potential of our low-resistive SFT devices, especially those having two acceptors. In the latter case, we also tested the devices using the battery-powered current sources with floating grounds. Analyzing double-acceptor I-V curves recorded at different levels of injection currents, for an optimal load, we deduced a small-signal voltage gain of 33 and a power gain of 2.4. We suggest that further improvement of the SFT device parameters is possible in optimized devices, so that the device potentially may serve as a preamplifier for readout of output signals of cryogenic detectors and be useful as an element of other superconductor-based circuits. In addition, we used scanning transmission electron microscopy to identify some problems in the fabrication of the devices without any planarization.
This review analyses the fundamental thermodynamic theory of the crude oil-brine-rock (COBR) interface and the underlying rock-brine and oil-brine interactions. The available data are then reviewed to outline potential mechanisms responsible for increased oil recovery from low salinity waterflooding (LSWF). We propose an approach to studying LSWF and identify the key missing links that are needed to explain observations at multiple length scales. The synergistic effect of LSWF on other chemical enhanced oil recovery methods such as surfactant, alkaline, nanoparticle and polymer flooding are also outlined. We specifically highlight key uncertainties that must be overcome to fully implement the technique in the field.
Sierra/SD provides a massively parallel implementation of structural dynamics finite element analysis, required for high-fidelity, validated models used in modal, vibration, static and shock analysis of weapons systems. This document provides a user's guide to the input for Sierra/SD . Details of input specifications for the different solution types, output options, element types and parameters are included. The appendices contain detailed examples, and instructions for running the software on parallel platforms.
This report describes research and development (R&D) activities conducted during fiscal year 2020 (FY20) specifically related to the Engineered Barrier System (EBS) R&D Work Package in the Spent Fuel and Waste Science and Technology (SFWST) Campaign supported by the United States (U.S.) Department of Energy (DOE). The R&D activities focus on understanding EBS component evolution and interactions within the EBS, as well as interactions between the host media and the EBS. A primary goal is to advance the development of process models that can be implemented directly within the Generic Disposal System Analysis (GDSA) platform or that can contribute to the safety case in some manner such as building confidence, providing further insight into the processes being modeled, establishing better constraints on barrier performance, etc. The FY20 EBS activities involved not only modeling and analysis work, but experimental work as well. Despite delays to some planned activities due to COVID-19 precautions, progress was made during FY20 in multiple research areas and documented in this report as follows: (1) EBS Task Force: Task 9/FEBEX Modeling Final Report: Thermo-Hydrological Modeling with PFLOTRAN, (2) preliminary sensitivity analysis for the FEBEX in-situ heater test, (3) cement-carbonate rock interaction under saturated conditions: from laboratory to modeling, (4) hydrothermal experiments, (5) progress on investigating the high temperature behavior of the uranyl-carbonate complexes, (6) in-situ and electrochemical work for model validation, (7) investigation of the impact of high temperature on EBS bentonite with THMC modeling, (8) sorption and diffusion experiments on bentonite, (9) chemical controls on montmorillonite structure and swelling pressure, (10) microscopic origins of coupled transport processes in bentonite, (11) understanding the THMC evolution of bentonite in FEBEX-DP—coupled THMC modeling, (12) modeling in support of HotBENT, an experiment studying the effects of high temperatures on clay buffers/near-field, and (13) high temperature heating and hydration column test on bentonite.
The two pieces, "Ten Research Challenge Areas in Data Science" by Jeannette M. Wing and “Challenges and Opportunities in Statistics and Data Science: Ten Research Areas” by Xuming He and Xihong Lin, provide an impressively complete list of data science challenges from luminaries in the field of data science. They have done an extraordinary job, so this response offers a complementary viewpoint from a mathematical perspective and evangelizes advanced mathematics as a key tool for meeting the challenges they have laid out. Notably, we pick up the themes of scientific understanding of machine learning and deep learning, computational considerations such as cloud computing and scalability, balancing computational and statistical considerations, and inference with limited data. We propose that mathematics is an important key to establishing rigor in the field of data science and as such has an essential role to play in its future.
Motivated by the need to improve visualizations of Earth's complex water cycle, this team embarked on non-trivial tasks to push from traditional methods of viewing data from simulations as static line graphs and contour plots into new realms with multiple dimensions (three spatial dimensions and the time component) viewable at once. To do this, we chose to feature the extremes in the general circulation of the atmosphere because these Earth system elements are short-lived, but impactful events. We used simulation data produced by the Department of Energy's (DOE) Energy Exascale Earth System Model (E3SM),which is designed to answer DOE science questions using DOE computing facilities. The E3SM project includes three sets of simulation experiments: cryosphere, bio-geochemical cycles, and the water cycle. The water cycle experiment campaign includes a set of simulations designed to understand how the water cycle will change in coming decades under a changing climate.
Goals for this project are to: numerically simulate wave propagation in layered crustal models with laterally-varying structure; Investigate seismic phases Pg and Pn; help understand the variability and complexity of these phases in recorded seismic data; and Use wavefield movies and "wavepaths" to suggest improvements for SALSA3D crustal tomography. In this presentation I will outline progress that has been made to date in an effort to modify a community research modeling code ("simulator")into a tool which can be used by GNDD researchers to study the effect of varying crustal velocity on wavefield propagation.
Critical components, such as detonators, in Sandia's stockpile contain heterogeneous materials whose performance and reliability depend on accurate, predictive models of coupled, complex phenomena to predict their synthesis, processing, and operation. Ongoing research in energetic materials has shown that microstructural properties, such as density, pore-size, morphology, and specific surface area are correlated to their initiation threshold and detonation behavior. However, experiments to study these specific characteristics of energetic materials are challenging and time consuming. Therefore, in this work, we turn to mesoscale modeling methods that may be capable of reproducing some observed phenomena to refine and predict outcomes beforehand. Even so, we have no physics-based modeling capability to predict how the microstructure of an energetic material will evolve over near- and long-term time scales. Thus, the goal of this work is to (i) identify any knowledge gaps in how the underlying microstructure forms and evolves during the synthesis process, and (ii) develop and test a mesoscale phase-field model for vapor deposition to capture critical mechanisms of microstructure formation, evolution, and variability in vapor-deposited energetic materials, such as processing conditions, material properties, and substrate interactions. Based on this work, the phase-field method is shown to be a valuable tool for developing the necessary models containing coupled, complex phenomena to investigate and understand the synthesis and processing of energetic materials.
This work examines how we may cast machine learning within a complete Bayesian framework to quantify and suppress explanatory complexity from first principles. Our investigation into both the philosophy and mathematics of rational belief leads us to emphasize the critical role of Bayesian inference in learning well-justified predictions within a rigorous and complete extended logic. The Bayesian framework allows us to coherently account for evidence in the learned plausibility of potential explanations. As an extended logic, the Bayesian paradigm regards probability as a notion of degrees of truth. In order to satisfy critical properties of probability as a coherent measure, as well as maintain consistency with binary propositional logic, we arrive at Bayes' Theorem as the only justifiable mechanism to update our beliefs to account for empiracle evidence. Yet, in the machine learning paradigm, where explanations are unconstrained algorithmic abstractions, we arrive at a critical challenge: Bayesian inference requires prior belief. Conventional approaches fail to yield a consistent framework in which we could compare prior plausibility among the infinities of potential choices in learning architectures. The difficulty of articulating well-justified prior belief over abstract models is the provinence of memorization in traditional machine learning training practices. This becomes exceptionally problematic in the context of limited datasets, when we wish to learn justifiable predictions from only a small amount of data.
Fu, Wenxin; Li, Jiachen; Bustamante, Jacqueline; Lien, Thanh; Adams, Ralph W.; Teat, Simon J.; Stokes, Benjamin J.; Alam, Todd M.; Yang, Weitao; Liu, Yi; Lu, Jennifer Q.
We report that an agile eight-membered cycloalkane can be stabilized by fusing a benzene ring on each side, substituted with proper functional groups. The conformational change of dibenzocycloocta-1,5-diene (DBCOD), a rigid−flexible−rigid organic moiety, from its Boat to Chair conformation requires an activation energy of 42 kJ/mol, which is substantially lower than those of existing submolecular shape-changing units. Experimental data corroborated by theoretical calculations demonstrate that intramolecular hydrogen bonding can stabilize Boat, whereas electron repulsive interaction from opposing ester substituents favors Chair. Intramolecular hydrogen bonding formed by 1,10-diamide substitution stabilizes Boat, spiking the temperature at which Boat and Chair can readily interchange from −60 to 60 °C. Concomitantly this intramolecular attraction raises the energy barrier from 42 kJ/mol for unsubstituted DBCOD to 68 kJ/mol for diamide-substituted DBCOD. Remarkably, this value falls within the range of the activation energy of highly efficient enzyme-catalyzed biological reactions. With shape changes once considered only possible with high energy, our work reveals a potential pathway exemplified by a specific submolecular structure to achieve low-energy-driven shape changes for the first time. The intrinsic cycle stability and high-energy output systems that would incur damage under high-energy stimuli could particularly benefit from this new kind of low-energy-driven shape-changing mechanism. This work has laid the basis to construct systems for low-energy-driven stimuli-responsive applications, hitherto a challenge to overcome.
The US Department of Energy (DOE) Nuclear Energy Research Initiative funded the design and construction of the Seven Percent Critical Experiment (7uPCX) at Sandia National Laboratories. The start-up of the experiment facility and the execution of the experiments described here were funded by the DOE Nuclear Criticality Safety Program. The 7uPCX is designed to investigate critical systems with fuel for light water reactors in the enrichment range above 5 % 235U. The 7uPCX assembly is a water-moderated and -reflected array of aluminum-clad square-pitched U(6.90 %)O2 fuel rods. Other critical experiments performed in the 7uPCX assembly are documented in LEU-COMP-THERM-078, LEU-COMP-THERM-080, LEU-COMPTHERM- 096, LEU-COMP-THERM-097, and LEU-COMP-THERM-101. The fuel used in these experiments was fabricated using unirradiated 6.90 % enriched UO2 fuel pellets from fuel elements designed to be used in the internal nuclear superheater section of the Pathfinder boiling water reactor operated in South Dakota by the Northern States Power Company in the 1960s. The fuel elements were obtained from The Pennsylvania State University where they had been stored for many years. The fuel pellets in those fuel elements were removed from the original Incoloy cladding and reclad in 3003 aluminum tubes and end caps for use in the experiments reported here. The purpose of these experiments was to measure the effects of decreasing the fuel-to-water volume ratio on the critical array size. This was accomplished by removing fuel rods from fully fueled configurations, effectively increasing the pitch of the fuel arrays in the assembly. The fuel rod pitch variations provided assembly configurations that ranged from strongly undermoderated to slightly overmoderated.
Sierra/SD provides a massively parallel implementation of structural dynamics finite element analysis, required for high fidelity, validated models used in modal, vibration, static and shock analysis of structural systems. This manual describes the theory behind many of the constructs in Sierra/SD. For a more detailed description of how to use Sierra/SD, we refer the reader to User's Manual. Many of the constructs in Sierra/SD are pulled directly from published material. Where possible, these materials are referenced herein. However, certain functions in Sierra/SD are specific to our implementation. We try to be far more complete in those areas. The theory manual was developed from several sources including general notes, a programmer notes manual, the user's notes and of course the material in the open literature.
The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy Office of Nuclear Energy, Office of Spent Fuel and Waste Disposition (SFWD), has been conducting research and development on generic deep geologic disposal systems (i.e., geologic repositories). This report describes specific activities in the Fiscal Year (FY) 2020 associated with the Geologic Disposal Safety Assessment (GDSA) Repository Systems Analysis (RSA) work package within the SFWST Campaign. The overall objective of the GDSA RSA work package is to develop generic deep geologic repository concepts and system performance assessment (PA) models in several host-rock environments, and to simulate and analyze these generic repository concepts and models using the GDSA Framework toolkit, and other tools as needed.
Talley, Samantha J.; Branch, Brittany A.; Welch, Cynthia F.; Park, Chi H.; Watt, John; Kuettner, Lindsey; Patterson, Brian; Dattelbaum, Dana M.; Lee, Kwan S.
Cellular silicone reinforced with silica filler prepared using additive manufacturing (AM) have been used widely for vibrational damping and shockwave mitigation. The two most commonly printed cellular silicone structures, simple cubic (SC) and face-centered tetragonal (FCT) display distinctly different static and dynamic mechanical responses dependent upon structure. In this work, the relationship between filler size and composition with mechanical response is investigated using polydimethylsiloxane-based silicones filled with aluminum oxide, graphite, or titanium dioxide. SC and FCT structures of porous, periodic silicone pads were printed using new direct ink write (DIW) resin formulations containing up to 25 wt% of functional filler (TiO2, Al2O3, or graphite). All AM pads were characterized using mechanical techniques (DMA, compression). Dynamic compression experiments coupled with time-resolved X-ray phase contrast imaging were performed to obtain insights into role of filler interactions in the in situ evolution of shockwave coupling in these functional, periodic porous polymers.
Software flaw detection using multimodal deep learning models has been demonstrated as a very competitive approach on benchmark problems. In this work, we demonstrate that even better performance can be achieved using neural architecture search (NAS) combined with multimodal learning models. We adapt a NAS framework aimed at investigating image classification to the problem of software flaw detection and demonstrate improved results on the Juliet Test Suite, a popular benchmarking data set for measuring performance of machine learning models in this problem domain.
Over the last 15 years, compressive sensing techniques have been developed which have the potential to greatly reduce the amount of data collected by systems while preserving the amount of information obtained. A cost of this efficiency is that a computationally-intensive optimization routine must be used to put the sensed data into a form that a person can interpret. At the same time, machine learning techniques have experienced tremendous growth as well. Machines have demonstrated the ability learn how to effectively perform tasks such as detection and classification at speeds much faster than humanly possible. Our goal in this project was to study the feasibility of using compressive sensing systems "at the edge." That is, how can compressive sensing sensors be deployed such that information is created at the remote sensor rather than sending raw data to a central processing location? Studies were performed to analyze whether machine learning could be done on the compressively sensed data in its raw form. If a machine is performing the task, is it possible to do so without putting the data into a human interpretable form? We show that this is possible for some systems, in particular a compressive sensing snapshot imaging spectrometer. Machine learning tasks were demonstrated to be more effective and more robust to noise when the machine learning algorithm worked on data in its raw form. This system is shown to outperform a traditional spectrometer. Techniques for reducing the complexity of the reconstruction routine were also analyzed. Techniques for such as data regularization, deep neural networks, and matrix completion were studied and shown to have benefits over traditional reconstruction techniques. In this project we showed that compressive sensing sensors are indeed feasible at the edge. As always, sensors and algorithms must be carefully tuned to work in the constrained environment. In this project we developed tools and techniques to enable those analyses.
Researchers at Sandia National Laboratories have recently conducted a series of experiments on novel cold spray deposited materials to understand dynamic material properties at high strain rates.
Autophagy is a natural, regulated cellular process that "cleans up" cellular debris by degrading and recycling dysfunctional proteins. There is a high potential impact of exploiting the benefits of autophagy to complement existing treatments, but little has been done to date on bacterial pathogens of defense concern such as Burkholderia pseudomallei, a highly virulent Select Agent pathogen that is intrinsically resistant to most classes of antibiotics. Assessment of autophagy in the context of infection typically requires use of multiple technologies in combination (e.g., Western analysis paired with microscopy or flow cytometry) as applied to heterogeneous populations of cells. To address this, we have developed a dual target reporter cell line (RAW264.7 LC3-BFP:mPlum, GFP-RelA) that enables concurrent visualization of infection and autophagy induction. We assessed the effect of clinically approved small molecule inducers of autophagy on infection by Burkholderia thailendensis, a closely related but less virulent surrogate for B. pseudomallei. The reporter cells were first infected with a B. thailendensis strain that constitutively expresses GFP, then treated with one of four known autophagy inducers (rapamycin, niclosamide, bromhexine HC1, or flubendazole) for 4 hours. Confocal fluorescence imaging was used to quantify autophagy stimulation at the single cell level. Autophagy maturation was observed as a decrease in BFP LC3 puncta with a concurrent increase in mPlum LC3 puncta. B. thailendensis infection was assessed by monitoring translocation of GFP-RelA (an NFkB subunit) into the nucleus and through quantitating the intracellular bacterial presence in single cells. Preliminary results indicate that bromhexine HC1 and niclosamide may hinder B. thailendensis' ability to replicate intracellularly and reduce overall bacterial survival.
Using continuously-differentiable (C1) interpolation methods previously developed for computer visualization and other applications, a new class of shape functions is constructed and applied to electrostatic (ES) Particle-In-Cell (PIC) simulations on simplex (triangle or tetrahedral) meshes. The resulting shape functions are non-negative and partition unity,and lead to a reduction of shot noise and aliasing associated with particle interpolation to and from the mesh. For an energy-conserving implementation as chosen, C1 interpolation of the potential leads to a continuous (C0) electric field, with associated improved fidelity of particle motion.
It is well established that the miniaturization of batteries has not kept pace with the miniaturization of electronics. Three-dimensional (3D) batteries, which were developed with the intent of improving microbattery performance, have had limited success because of fabrication challenges and material constraints. Solid-state, 3D batteries have been particularly susceptible to these shortcomings. In this paper, we demonstrate that the incorporation of a high-conductivity, solid electrolyte is the key to achieving a nonplanar solid-state battery with high areal capacity and high power density. The model 2.5D platform used in this study is a modification of the more typical 3D configuration in that it is comprised of a cathode array of pillars (3D) and a planar (two-dimensional, 2D) anode. This 2.5D geometry exploits the use of a high-conductivity, ionogel electrolyte (10-3 S cm-1), which interpenetrates the 3D electrode array. The 2.5D battery offers high areal energy densities from the post array, while the high-conductivity, solid electrolyte enables high power densities (3.7 mWh cm-2 at 2.8 mW cm-2). The reported solid-state 2.5D device exceeds the energy and power densities of any 3D solid-state system and the derived multiphysics model provides guidance for achieving significantly higher energy and power densities.
Kelley, Christopher R.; Lopp, Garret K.; Kauffman, Jeffrey L.
Modern turbomachinery blades have extremely low inherent damping, which can lead to high transient vibrations and failure through high-cycle fatigue. Smart materials enable vibration reduction while meeting strict blade requirements such as weight and aerodynamic efficiency. In particular, piezoelectric-based vibration reduction offers the potential to reduce vibration semi-actively while simultaneously harvesting sufficient energy to power the implementation. The placement and the size of the piezoelectric material is critical to the vibration reduction capabilities of the system. Furthermore, the implementation should target multiple vibration modes. In this study, we develop a procedure to optimize electromechanical coupling across multiple vibration modes for a representative turbomachinery blade with surface-mounted piezoelectric patches. Experimental validation demonstrates good coupling across three targeted modes with a single piezoelectric patch. Placing the piezoelectric material in regions of high signed strain energy for all targeted modes enables vibration reduction across all of the targeted modes.
A series of networks is introduced with systematically varied network heterogeneity and high overall values of average glass transition temperature (Tg), based on polymerization of rigid acrylate and aromatic thiol monomers. The curing behavior, chain dynamics, and microstructure of these networks were investigated through a combination of dynamic mechanical analysis and infrared spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and x-ray scattering, respectively. The maximum Tg achieved during cure can be related to the breadth of the mechanical loss tangent, as others have previously suggested, as well as the temperature dependence of the chain dynamics in the network as monitored by 1H NMR. In addition, the microstructures of the networks are characterized by periodic, fractal microgels with characteristic length scales of ca. 20–40 nm. Intriguingly, this structural motif persists in the more homogeneous networks exhibiting comparatively narrow glass transitions and chain dynamics, indicating that dynamically homogeneous networks can still exhibit significant compositional heterogeneity at the mesoscale.
Hahn, Nathan H.; Driscoll, Darren M.; Yu, Zhou; Sterbinsky, George E.; Cheng, Lei; Balasubramanian, Mahalingam; Zavadil, Kevin R.
The emergence of magnesium and calcium batteries as potential beyond Li ion energy storage technologies has generated significant interest into the fundamental aspects of alkaline earth metal cation coordination in multivalent electrolytes and the impact of coordination on application-critical electrolyte properties such as solubility, transport, and electrochemical stability. Understanding these details in calcium electrolytes is of immediate importance due to recent, unprecedented demonstrations of reversible calcium metal electrodeposition in a limited number of ethereal solvent-based systems. In this work, we provide insight connecting Ca2+ coordination tendencies to important calcium battery electrolyte properties. Our results demonstrate a clear solvent:Ca2+ coordination strength trend across a series of cyclic ether and linear glyme solvents that controls the extent of ion association in solutions of "weakly"coordinating salts. We apply understanding gained from these results to rationalize relative anion:Ca2+ coordination tendencies and attendant Ca2+ coordination structures using two oxidatively stable anions of particular interest for current battery electrolytes. Armed with this understanding of solvent and anion interactions with Ca2+, we demonstrate and interpret differences in electrochemical calcium deposition behavior across several electrolyte exemplars with varying solvent and anion coordination strengths. Our findings demonstrate that solvents exhibiting especially strong coordination to Ca2+, such as triglyme, can inhibit reversible calcium deposition despite effective elimination of anion:Ca2+ coordination while solvents exhibiting more modest coordination strength, such as 1,2-dimethoxyethane, may enable deposition provided anion:Ca2+ coordination is substantially limited. These results reveal that the strength of coordination of both anion and solvent should be considered in the design of electrolytes for calcium batteries.
Thermal runaway of lithium-ion batteries is a risk that is magnified when stacks of lithium-ion cells are used for large scale energy storage. When limits of propagation can be identified so that systems can be designed to prevent large scale cascading failure even if a failure does occur, these systems will be safer. This work addresses the prediction of cell-to-cell failure propagation and the propagation limits in lithium-ion cell stacks to better understand and identify safe designs. A thermal-runaway model is presented based on recent developments in thermochemical source terms. It is noted that propagating failure is characterized by temperatures above which calorimetry data is available. Results show high temperature propagating failure predictions are too rapid unless an intra-particle diffusion limit is included, introducing a Damköhler number limiter into the rate expression. This new model form is evaluated against cell-to-cell failure propagation where the end cell of a stack is forced into thermal runaway through a nail-induced short circuit. Limits of propagation for this configuration are identified. Results show cell-to-cell propagation predictions are consistent with measurements over a range of cell states of charge and with the introduction of metal plates between cells to add system heat capacity representative of structural members. This consistency extends from scenarios where propagation occurs through scenarios where propagation is prevented.
The constantly evolving nature of the grid is compelling the design process of Remedial Action Schemes (RAS) to keep up with the changes. This document proposes a flexible and computationally efficient approach to automatically determine RAS corrective actions that alleviate line overloading violations. Statistical and functional characteristics summarized from RAS implemented in real power systems are used to guide the design parameters. This report also leverages sensitivity-based techniques to determine corrective actions for specific contingencies quickly without repeated numerical simulations. Finally, future directions for implementing this approach for a fully automated, online RAS are discussed.
With many recent advances in interconnect technologies and memory interfaces, disaggregated memory systems are approaching industrial adoption. For instance, the recent Gen-Z consortium focuses on a new memory semantic protocol that enables fabric-attached memories (FAM), where the memory and other compute units can be directly attached to fabric interconnects. Decoupling of memory from compute units becomes a feasible option as the rate of data transfer increases due to the emergence of novel interconnect technologies, such as Silicon Photonic Interconnects. Disaggregated memories not only enable more efficient use of capacity (minimizes under-utilization) they also allow easy integration of evolving technologies. Additionally, they simplify the programming model at the same time allowing efficient sharing of data. However, the latency of accessing the data in these Fabric Attached disaggregated Memories (FAMs) is dependent on the latency imposed by the fabric interfaces. To reduce memory access latency and to improve the performance of FAM systems, in this paper, we explore techniques to prefetch data from FAMs to the local memory present in the node (PreFAM). We realize that since the memory access latency is high in FAMs, prefetching a cache block (64 bytes) from FAM can be inefficient, since the possibility of issuing demand requests before the completion of prefetch requests, to the same FAM locations, is high. Hence, we explore predicting and prefetching FAM blocks at a distance; prefetching blocks which are going to be accessed in future but not immediately. We show that, with prefetching, the performance of FAM architectures increases by 38.84%, while memory access latency is improved by 39.6%, with only 17.65% increase in the number of accesses to the FAM, on average. Further, by prefetching at a distance we show a performance improvement of 72.23%.
Early on in the COVID-19 pandemic, potential ventilator shortages were a critical issue identified by national health care providers. Capacity modeling at the time suggested patient demand may exceed ventilator supply. Thus, the challenge became finding an urgent interim solution to meet health care needs. Our initial hypothesis was that CPAP technology could be modified to provide similar functionality to a ventilator, relieving demand and allowing physicians to decide which patients need high end machines, ultimately saving lives. In conjunction with medical experts and pulmonologists, we were able to identify three key thrusts associated with this research problem: (1) modification of CPAP technology to allow for 02 input that would be capable of providing ventilation; (2) development of an alarming function that would provide real-time audible alarms to alert medical personnel to critical conditions, which would be used inline with CPAP technology; and (3) a method of sterilizing expiratory air from such a system in order to protect medical personnel from biohazard, since CPAPs vent to the atmosphere. We were unable to realize results for thrust 1 (CPAP modification for 02); we identified potential safety issues associated with utilizing medical grade oxygen with a common CPAP device. In order to characterize and mitigate these issues, we would need to partner closely with a device manufacturer; such a partnership could not be achieved in the timeframe needed for this rapid response work. However, we determined that some medical grade BiPAP devices do not need this modification and that the significant progress on thrusts 2 and 3 would be sufficient to buy down risk of a massive ventilator shortage. Our team built a prototype alarm system that can be utilized with any assistive respiratory device to alert on all key conditions identified by medical personnel (high pressure, low pressure, apnea, loss of power, low battery). Finally, our team made significant progress in the rapid prototyping and demonstration of an inline UV air purifier device. The device is cost efficient and can be manufactured at scale with both commercially available and additively manufactured parts. Initial tests with SARS-CoV-2 analog bacteriophage MS2 show 99% efficacy at reducing bioburden. Following a successful demonstration of the prototype device with medical personnel, we were able to obtain follow-on (non-LDRD) funding to provide additional device characterization, validation, and production in order to respond to an immediate regional need.
Miller, David A.; Samborsky, Daniel D.; Stoffels, Mark T.; Voth, Michael M.; Nunemaker, Jake D.; Newhouse, Kai J.; Hernandez-Sanchez, Bernadette A.
Marine and hydrokinetic (MHK) energy technologies convert the energy of waves, tides, and river and ocean currents into electricity. The primary structure of MHK energy devices have difficult and challenging environments for which fiber reinforced plastics are often considered. For cost benefits, glass fiber reinforced plastics (GFRP) are the most prevalent system under consideration. Montana State University and Sandia National Laboratories have performed research into the moisture effects on the stiffness, strength, and damage of GFRPs for many years. This paper presents a summary of a portion of this effort and also provides references to the thesis and conferences that contain the detailed information. The results include models of moisture absorption, effects of stress on moisture uptake, effects of moisture on damage modes and development, laminate stacking order effects, and culminates with moisture uptake effects on a large sample of industry collected coupons.
The Sandia Utilities form an RF toolkit extending the GNU Radio framework for straightforward interaction with bursty RF systems which can be cumbersome using built in capabilities of the framework or other open source extensions. Motivated by the abstract exercise of responding to a modulated burst of information with a unique modulated burst of information, the Sandia Utilities provide several dozen additional debug and signal processing blocks through four software modules. These blocks manifest their utility through a new general concept of operation for GNU Radio applications and extend many existing streaming capabilities to the PDU-based message passing API. Using these concepts and tools, it is straightforward to develop software capabilities to interact with existing and new bursty RF devices.
The 30 cm drop is the remaining NRC normal conditions of transport (NCT) regulatory requirement (10 CFR 71.71) for which there are no data on the actual surrogate fuel. While obtaining data on the actual fuel is not a direct requirement, it provides definitive information which aids in quantifying the risk of fuel breakage resulting from a cask drop from a height of 30 cm or less. The 30 cm drop test with the full-scale surrogate assembly conducted in May 2020 was the last step needed for quantifying the strains on the surrogate assembly rods under NCT. The full-scale surrogate assembly used in the 2020 30 cm drop test was built using a new 17x17 Pressurized Water Reactor (PWR) Westinghouse skeleton filled with the copper rods and 3 zircaloy rods from the full-scale surrogate assembly used in the Multi-Modal Transportation Test (MMTT). Felt pads were attached to the surrogate assembly bottom prior to the 30 cm drop to adequately represent the effects of the impact limiters and the cask. Note that felt "programming material" has been used extensively in past drop tests and is known to be a good material for programming a desired shock pulse. The felt pad configuration was determined during a previous series of tests reported in. The acceleration pulses observed on the surrogate assembly during the test were in good agreement with the expected pulses. This confirmed that during the 30 cm drop the surrogate assembly experienced the same conditions as it would if it was dropped in the cask with the impact limiters.
Singh, Manish K.; Ghosh, Chanchal; Miller, Benjamin; Kotula, Paul G.; Watt, John; Bakan, Gokhan; Silva, Helena; Carter, Clive B.
Ge2Sb2Te5 (GST-225) has been the most used active material in nonvolatile phase-change memory devices. Understanding the kinetics and dynamics involved in crystallization is critical for the optimization of materials and devices. A GST-225 thin film of 20 nm thickness was prepared by sputtering directly onto a Protochip and left uncapped and exposed to atmosphere for approximately 1 year. Early stages of crystallization and growth of the film have been studied inside the TEM from room temperature to 140 °C. The morphological and structural transformations have been studied by a Cs-corrected environmental TEM, and images have been recorded using a high-speed low electron dose camera (Gatan K3 IS). The amorphous to crystalline transformation has been observed at ~35 °C. From the large field, high-resolution images obtained using the Gatan K3 IS camera early crystallization can be detected and nucleation rates and growth velocities can be obtained.
Deciding on an imaging modality for terrain classification can be a challenging problem. For some terrain classes a given sensing modality may discriminate well, but may not have the same performance on other classes that a different sensor may be able to easily separate. The most effective terrain classification will utilize the abilities of multiple sensing modalities. The challenge of utilizing multiple sensing modalities is then determining how to combine the information in a meaningful and useful way. In this paper, we introduce a framework for effectively combining data from optical and polarimetric synthetic aperture radar sensing modalities. We demonstrate the fusion framework for two vegetation classes and two ground classes and show that fusing data from both imaging modalities has the potential to improve terrain classification from either modality, alone.
Deciding on an imaging modality for terrain classification can be a challenging problem. For some terrain classes a given sensing modality may discriminate well, but may not have the same performance on other classes that a different sensor may be able to easily separate. The most effective terrain classification will utilize the abilities of multiple sensing modalities. The challenge of utilizing multiple sensing modalities is then determining how to combine the information in a meaningful and useful way. In this paper, we introduce a framework for effectively combining data from optical and polarimetric synthetic aperture radar sensing modalities. We demonstrate the fusion framework for two vegetation classes and two ground classes and show that fusing data from both imaging modalities has the potential to improve terrain classification from either modality, alone.
The use of gradient-based data-driven models to solve a range of real-world remote sensing problems can in practice be limited by the uniformity of available data. Use of data from disparate sensor types, resolutions, and qualities typically requires compromises based on assumptions that are made prior to model training and may not necessarily be optimal given over-arching objectives. For example, while deep neural networks (NNs) are state-of-the-art in a variety of target detection problems, training them typically requires either limiting the training data to a subset over which uniformity can be enforced or training independent models which subsequently require additional score fusion. The method we introduce here seeks to leverage the benefits of both approaches by allowing correlated inputs from different data sources to co-influence preferred model solutions, while maintaining flexibility over missing and mismatching data. In this paper, we propose a new data fusion technique for gradient updated models based on entropy minimization and experimentally validate it on a hyperspectral target detection dataset. We demonstrate superior performance compared to currently available techniques and highlight the value of the proposed method for data regimes with missing data.
Sinuous antennas are capable of producing ultra-wideband radiation with polarization diversity in a low-profile form factor, making them a good fit for close-in sensing applications such as ground-penetrating radar (GPR). This work proposes an unconventional method of operating a four-port sinuous antenna - driving each arm independently and unbalanced - to achieve a quasi-monostatic antenna system capable of polarimetry while separating transmit and receive channels, as is common in GPR systems. The quasi-monostatic configuration of the antenna reduces system size as well as increasing sensitivity to near-surface targets by preventing extreme bistatic angles. A prototype four-port sinuous antenna is fabricated and integrated into a GPR testbed. The polarimetric data obtained with the antenna is then used to distinguish between buried target symmetries.
Here, the effects of heat release on interactions between vorticity (ω) and strain rate (s) in turbulent premixed CH4/O2/N2 counterflow flames are investigated using simultaneous OH laser-induced fluorescence (LIF) and tomographic particle image velocimetry (TPIV) measurements. A comparison between the flames and a corresponding turbulent non-reacting variable density N2-vs-products counterflow reveals the impact of heat release on vorticity-strain rate alignment statistics. Vorticity and strain rate statistics in the flames and non-reacting flow are conditioned on distance from the local flame front and gas mixing layer interface (GMLI) contours, respectively. The magnitude, alignment, and spatial distribution of the vorticity and principal strain rates (s1, s2, s3) are rather different when heat release is present. Density variations without heat release enhance the ω-s2 alignment while significantly reducing the ω-s3 alignment and modestly reducing the ω-s1 alignment. In contrast, heat release at the flame front further reduces the ω-s1 alignment but increases the ω-s3 alignment and suppresses the preferential ω-s2 alignment. Furthermore, increasing turbulence diminishes the effect of heat release on this preferential alignment. In regions with the largest vorticities, both the reacting and non-reacting counterflows show an increase in the probability of ω-s2 alignment. All counterflow cases have a net positive vortex-stretching contribution to the enstrophy production with a peak production rate at the flame front or GMLI, but the peak values depend on the density variation, heat release, and turbulence level. Elucidation of the complex interplay between these factors contributes to the understanding of the dynamics of turbulence-flame interactions.
Interception of a moving and potentially evading target can be a challenging problem, in particular for conditions in which the target may be moving at high speeds and difficult to detect. We have proposed to merge two Sandia LDRD efforts, the SPARR Spiking/Processing Array (neuromorphic event-driven sensing) and the Dragonfly-Inspired Algorithms for Intercept- Trajectory Planning (neural-inspired algorithms for interception) toward a unified system with direct application to national security. Neuromorphic systems demonstrate the most potential for speed and efficiency gains when communication is event-driven and computations are simple but parallelizable. Accordingly, we anticipate fully realizing potential benefits from a neuromorphic interception system if event-driven sensing is combined with processing and acting also implemented on event-driven (spiking) systems. We have successfully translated a neural-inspired interception algorithm to a neural network architecture for evaluation on neuromorphic hardware. Preliminary implementations of the neural network designed for implementation on the Loihi chip are still too immature for conclusive evaluation, but the results of this effort have demonstrated a viable path for a previously developed dragonfly-inspired interception algorithm to be implemented on neuromorphic hardware.
U.S. nuclear power facilities face increasing challenges in meeting evolving security requirements caused by evolving and expanding threats while keeping cost reasonable to make nuclear energy competitive. The addition of security features after a facility has been designed and without attention to optimization (the past approach) can lead to cost overruns. Incorporating security in the design process can provide robust, cost effective, and sufficient physical protection systems. The purpose of this work is to develop a framework for the integration of security into the design phase of Small Modular Reactors (SMRs) and the use of modeling and simulation tools to optimize the design of physical protection systems. This effort will intend to integrate security into the design phase of a model SMR that meets current NRC physical protection requirements and provide advanced solutions to improve physical protection and decrease costs. A suite of tools, including SCRIBE3D, PATHTRACE and Blender were used to model a hypothetical generic domestic SMR facility. Physical protection elements such as sensors, cameras, portal monitors, barriers, and guard forces were added to the model based on best practices for physical protection systems. One outsider sabotage scenario was examined with 4-8 adversaries to determine security metrics. This work will influence physical protection system designs and facility designs for U.S. domestic SMRs. The purpose of this project is to demonstrate how a series of experimental and modeling capabilities across the Department of Energy Complex can impact the design of U.S. domestic SMRs and the complete Safeguards and Security by Design (SSBD) for SMRs.
The U.S. Department of Energy (DOE) established a need to understand the thermal-hydraulic properties of dry storage systems for commercial spent nuclear fuel (SNF) in response to a shift towards the storage of high-burnup (HBU) fuel (> 45 gigawatt days per metric ton of uranium, or GWd/MTU). This shift raises concerns regarding cladding integrity, which faces increased risk at the higher temperatures within spent fuel assemblies present within HBU fuel compared to low-burnup fuel (≤ 45 GWd/MTU). A dry cask simulator (DCS) was built at Sandia National Laboratories (SNL) in Albuquerque, New Mexico to produce validation-quality data that can be used to test the accuracy of the modeling used to predict cladding temperatures. These temperatures are critical to evaluating cladding integrity throughout the storage cycle of commercial spent nuclear fuel. A model validation exercise was previously carried out for the DCS in a vertical configuration. Lessons learned during the previous validation exercise have been applied to a new, blind study using a horizontal dry cask simulator (HDCS). Three modeling institutions – the Nuclear Regulatory Commission (NRC), Pacific Northwest National Laboratory (PNNL), and Empresa Nacional del Uranio, S.A., S.M.E. (ENUSA) – were granted access to the input parameters from the DCS Handbook, SAND2017-13058R, and results from a limited data set from the horizontal BWR dry cask simulator tests reported in the HDCS update report, SAND2019-11688R. With this information, each institution was tasked to calculate peak cladding temperatures and air mass flow rates for ten HDCS test cases. Axial as well as vertical and horizontal transverse temperature profiles were also calculated. These calculations were done using modeling codes (ANSYS/Fluent, STAR-CCM+, or COBRA-SFS), each with their own unique combination of modeling assumptions and boundary conditions. For this validation study, the ten test cases of the horizontal dry cask simulator were defined by three independent variables – fuel assembly decay heat (0.5 kW, 1 kW, 2.5 W, and 5 kW), internal backfill pressure (100 kPa and 800 kPa), and backfill gas (helium and air). The plots provided in Chapter 3 of this report show the axial, vertical, and horizontal temperature profiles obtained from the dry cask simulator experiments in the horizontal configuration and the corresponding models used to describe the thermal-hydraulic behavior of this system. The tables provided in Chapter 3 illustrate the closeness of fit of the model data to the experiment data through root mean square (RMS) calculations of the error in peak cladding temperatures (PCTs), PCT axial locations, axial temperature profiles, vertical and horizontal temperature profiles at two different axial locations, and air mass flow rates for the ten test cases, normalized by the experimental results. The model results are assigned arbitrary model numbers to retain anonymity. Due to the relatively flat axial temperature profiles, small temperature gradients resulted in large deviations of all models’ PCT axial location from the experimental PCT axial location. When the PCT axial location error is excluded in the calculation of the combined RMS of the normalized errors that considers PCT, the temperature profiles, and the air mass flow rates, the model data fits the experimental data to within 5%. When the vault information is excluded, the model data fits the experimental data to within 2.5%. An error analysis was developed further for one model, using the model and experimental uncertainties in each validation parameter to calculate validation uncertainties. The uncertainties for each parameter were used to define quantifiable validation criteria. For this analysis, the model was considered validated for a given comparison metric if the normalized error in that metric divided by the validation uncertainty was less than or equal to 1. When considering the combined RMS of the normalized errors of all metrics divided by their validation uncertainties, the model was found to have satisfied the criterion for model validation.
The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Fuel Cycle Technology (FCT) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). Two high priorities for SFWST disposal R&D are design concept development and disposal system modeling. These priorities are directly addressed in the SFWST ''Geologic Disposal Safety Assessment'' (GDSA) control account, which is charged with developing a geologic repository system modeling and analysis capability, and the associated software, ''GDSA Framework'', for evaluating disposal system performance for nuclear waste in geologic media. ''GDSA Framework'' is supported by SFWST Campaign and its predecessor the Used Fuel Disposition (UFD) campaign. This report fulfills the GDSA Uncertainty and Sensitivity Analysis Methods work package (SF-20SN01030403) level 3 milestone — ''Advances in Uncertainty and Sensitivity Analysis Methods and Applications in GDSA Framework'' (M3SF-20SN010304032). It presents high level objectives and strategy for development of uncertainty and sensitivity analysis tools, demonstrates uncertainty quantification (UQ) and sensitivity analysis (SA) tools in GDSA Framework in FY20, and describes additional UQ/SA tools whose future implementation would enhance the UQ/SA capability of ''GDSA Framework''. This work was closely coordinated with the other Sandia National Laboratory GDSA work packages: the GDSA Framework Development work package (SF- 2051\101030404), the GDSA Repository Systems Analysis work package (SF-2051\101030405), and the GDSA PFLOTRAN Development work package (SF-20SN01030406). This report builds on developments reported in previous ''GDSA Framework'' milestones, particularly M2SF- 19SNO1030403.
Currently, spent nuclear fuel (SNF) is stored in onsite independent spent fuel storage facilities (ISFSIs), which is a dry storage facility, at 55 nuclear power plant sites. The majority of SNF in dry storage is in welded metal canisters (2,917 canisters at the end of 2019). The canisters are loaded for storage in storage overpacks (vertical casks or horizontal storage modules) and placed on outdoor concrete pads. Because the SNF will be stored at ISFSIs for an extended period of time, there is growing concern with regards to the behavior of the SNF within these dry storage systems during earthquakes. To address these concerns, the SFWST program is considering conducting an earthquake shaker table test. The goal of this test is to determine the strains and accelerations on fuel assembly hardware and cladding during earthquakes of different magnitudes to better quantify the potential damage an earthquake could inflict on spent nuclear fuel rods. The seismic integrity of the storage system has been addressed in the past by the US Nuclear Regulatory Commission and is not the focus of this potential test. Instead the DOE would benefit from knowing the condition of the fuel cladding from storage, transportation, to disposal so that it can ascertain repository performance for the fuel and packaging in its final state. A seismic event is part of the possible loading events that the fuel could experience in its lifetime. This report proposes several earthquake shaker table tests with different degrees of complexity. Alternative 1 was defined in the FY20 work scope. Alternatives 2 and 3 were recently developed to take advantage of the NUHOMS 32PTH dry storage canister that may be available in FY21 for this test at a minimum cost to the project. The selection of the alternative(s) will depend on the available budget and the SFWST program priorities for the near future.
The “how to” document is designed to help walk the analyst through difficult aspects of software usage. It should supplement both the User’s manual and the Theory document, by providing examples and detailed discussion that reduce learning time for complex set ups. These documents are intended to be used together. We will not formally list all parameters for an input here – see the User’s manual for this. All the examples in the “How To” document are part of the Sierra/SD test suite, and each will run with no modification. The nature of this document casts together a number of rather unrelated procedures. Grouping them is difficult. Please try to use the table of contents and the index as a guide in finding the analyses of interest.
Accurate and efficient constitutive modeling remains a cornerstone issue for solid mechanics analysis. Over the years, the LAMÉ advanced material model library has grown to address this challenge by implementing models capable of describing material systems spanning soft polymers to stiff ceramics including both isotropic and anisotropic responses. Inelastic behaviors including (visco)plasticity, damage, and fracture have all incorporated for use in various analyses. This multitude of options and flexibility, however, comes at the cost of many capabilities, features, and responses and the ensuing complexity in the resulting implementation. Therefore, to enhance confidence and enable the utilization of the LAMÉ library in application, this effort seeks to document and verify the various models in the LAMÉ library. Specifically, the broader strategy, organization, and interface of the library itself is first presented. The physical theory, numerical implementation, and user guide for a large set of models is then discussed. Importantly, a number of verification tests are performed with each model to not only have confidence in the model itself but also highlight some important response characteristics and features that may be of interest to end-users. Finally, in looking ahead to the future, approaches to add material models to this library and further expand the capabilities are presented.
We employ a fully charge self-consistent quantum transport formalism, together with a heuristic elastic scattering model, to study the local density of state (LDOS) and the conductive properties of Si:P δ-layer wires at the cryogenic temperature of 4 K. The simulations allow us to explain the origin of shallow conducting sub-bands, recently observed in high resolution angle-resolved photoemission spectroscopy experiments. Our LDOS analysis shows the free electrons are spatially separated in layers with different average kinetic energies, which, along with elastic scattering, must be accounted for to reproduce the sheet resistance values obtained over a wide range of the δ-layer donor densities.
Additive manufacturing via selective laser melting can result in variable levels of internal porosity both between build plates and within components from the same build. In this investigation, sample porosity levels were compared to tensile properties for 176 samples spanning eight different build plates. Sample porosity was measured both by Archimedes density, which provided an estimation of overall porosity, and by observation of voids in the fracture surface, which provided an estimation of the porosity at the failure plane. The porosity observed at the fracture surface consistently demonstrated higher porosity than that suggested by Archimedes density. The porosity values obtained from both methods were compared against the mechanical results. Sample porosity appears to have some correlation to the ultimate tensile strength, yield strength, and modulus, but the strongest relationship is observed between porosity and ductility. Three different models were used to relate the fracture surface porosity to the ductility. The first method was a simple linear regression analysis, while the other two models have been used to relate porosity to ductility in cast alloys. It is shown that all three models fit the data well over the observed porosity ranges, suggesting that the models taken from casting theory can extend to additively manufactured metals. Finally, it is proposed that the non-destructive Archimedes method could be used to estimate an approximate sample ductility through the use of correlations realized here. Such a relationship could prove useful for design and for a deeper understanding of the impact of pores on tensile behavior.
One big challenge of the emerging atomic precision advanced manufacturing (APAM) technology for microelectronics application is to realize APAM devices that operate at room temperature (RT). We demonstrate that semiclassical technology computer aided design (TCAD) device simulation tool can be employed to understand current leakage and improve APAM device design for RT operation. To establish the applicability of semiclassical simulation, we first show that a semiclassical impurity scattering model with the Fermi-Dirac statistics can explain the very low mobility in APAM devices quite well; we also show semiclassical TCAD reproduces measured sheet resistances when proper mobility values are used. We then apply semiclassical TCAD to simulate current leakage in realistic APAM wires. With insights from modeling, we were able to improve device design, fabricate Hall bars, and demonstrate RT operation for the very first time.
We present a Physics-Informed Graph Neural Network (pigNN) methodology for rapid and automated compact model development. It brings together the inherent strengths of data-driven machine learning, high-fidelity physics in TCAD simulations, and knowledge contained in existing compact models. In this work, we focus on developing a neural network (NN) based compact model for a non-ideal PN diode that represents one nonlinear edge in a pigNN graph. This model accurately captures the smooth transition between the exponential and quasi-linear response regions. By learning voltage dependent non-ideality factor using NN and employing an inverse response function in the NN loss function, the model also accurately captures the voltage dependent recombination effect. This NN compact model serves as basis model for a PN diode that can be a single device or represent an isolated diode in a complex device determined by topological data analysis (TDA) methods. The pigNN methodology is also applicable to derive reduced order models in other engineering areas.
Coherent manipulation of quantum states is at the core of quantum information science (QIS). Many state-of-the-art quantum systems rely on microwave fields for quantum operations. As such, the microwave electromagnetic fields serve as the ideal "quantum bus" to integrate different types of QIS systems into a hybrid quantum system. Superconducting metamaterials are artificial materials consisting of arrays of superconducting resonant microstructures with sizes much smaller than the microwave wavelengths of interest. Superconducting metamaterials are a strong candidate medium for the microwave quantum bus, because the effective impedance, field distributions, and frequency response can all be controlled by engineering the microstructures, electrical bias, and magnetic flux while maintaining extremely low loss. In this project, we investigate the fundamental unit of a superconducting metamaterial - a resonator with physical dimensions much smaller than the microwave wavelengths - using NbTiN as the working superconductor, whose high operating temperatures and magnetic fields are desirable attributes for compatibility with a wide variety of quantum systems. We first studied the properties of sputtered NbTiN thin films by correlating the film thickness with the normal state resistivity, superconducting transition temperature, and resonances of transmission line resonators made from these films. We developed a process flow and designed a coplanar waveguide platform for studying small resonators. The platform significantly shortens the turnaround times of the resonator fabrication and testing cycles. Several resonators with different designs were fabricated and tested at 4 Kelvin. Resonances were observed in some resonator testers. Potential paths for improvements and future directions are discussed.
Since extending the Autodetector to a convolutional neural network (CNN) machine learning classifier model, an effort has been executed to demonstrate its ability to distinguish not only a switching DC-DC converter as high voltage, but identify the make and model of a converter on which it was trained. This was achieved by collecting data in a noisy environment, pre-processing the time domain data to obtain composite images using a method that improves upon that of the prior research, then validating a trained CNN model to an accuracy of 100% on a selected candidate converter.
Canonical Polyadic tensor decomposition using alternate Poisson regression (CP-APR) is an effective analysis tool for large sparse count datasets. One of the variants using projected damped Newton optimization for row subproblems (PDNR) offers quadratic convergence and is amenable to parallelization. Despite its potential effectiveness, PDNR performance on modern high performance computing (HPC) systems is not well understood. To remedy this, we have developed a parallel implementation of PDNR using Kokkos, a performance portable parallel programming framework supporting efficient runtime of a single code base on multiple HPC systems. We demonstrate that the performance of parallel PDNR can be poor if load imbalance associated with the irregular distribution of nonzero entries in the tensor data is not addressed. Preliminary results using tensors from the FROSTT data set indicate that using multiple kernels to address this imbalance when solving the PDNR row subproblems in parallel can improve performance, with up to 80% speedup on CPUs and 10-fold speedup on NVIDIA GPUs.
Many factors that influence the effect of leading edge erosion on annual energy production are uncertain, such as the time to initiation, damage growth rate, the blade design, operational conditions, and atmospheric conditions. In this work, we explore how the uncertain parameters that drive leading edge erosion impact wind turbine power performance using a combination of uncertainty quantification and wind turbine modelling tools, at both low and medium fidelity. Results will include the predicted effect of erosion on several example wind plant sites for representative ranges of wind turbine designs, with a goal of helping wind plant operators better decide mitigation strategies.
Work has begun towards model validation of wake dynamics for the large-eddy simulation (LES) code Nalu-Wind in the context of research-scale wind turbines in a neutral atmospheric boundary layer (ABL). Interest is particularly directed at the structures and spectra which are influential for wake recovery and downstream turbine loading. This initial work is to determine the feasibility of using nacelle-mounted, continuous-wave lidars to measure and validate wake physics via comparisons of full actuator line simulation results with those obtained from a virtual lidar embedded within the computational domain. Analyses are conducted on the dominant large-scale flow structures via proper orthogonal decomposition (POD) and on the various scales of wake-added turbulence through spectral comparisons. The virtual lidar adequately reproduces spatial structures and energies compared to the full simulation results. Correction of the higher-frequency turbulence spectra for volume-averaging attenuation was most successful at locations where mean gradients were not severe. The results of this work will aid the design of experiments for validation of high-fidelity wake models.
An uncertainty quantification technique for nacelle-mounted lidar is developed that extends conventional error analyses to precisely account for residual uncertainty due to observed non-ideal features in processed Doppler lidar spectra. The technique is applied after quality assurance/quality control (QAQC) processing to quantify residual error, both bias and random, from solid-body interference, shot noise, and any additional uncertainty introduced to the data from the QAQC process itself. The approach follows from the one-time construction of a high-dimensional parametric database of synthetic lidar spectra and subsequent processing with an existing QAQC technique. A model of the correspondence between the spectral shape and the associated residual errors due to non-ideal features is then developed for quantities of interest (QOIs) including the geometric median and spectral standard deviation of line-of-sight velocity. The model is preliminarily implemented within a neural network framework that is then applied in post-processing to sample returns from a DTU SpinnerLidar. The initial analysis uncovers the effects of specific sources of uncertainty in the context of both individual spectra and full-field maps of the measurement domain. The technique is described in terms of application to continuous wave (CW) lidar, though it is also relevant to pulsed lidar.
Two-dimensional monolayers derived from 3D bulk structures remain a relatively unexplored class of materials because of the challenge of stabilizing nonepitaxial interfaces. Here, we report an unusual reconstruction during the deposition of precursors when targeting the synthesis of heterostructures with an odd number of PbSe monolayers. Multilayer elemental precursors of Pb|Se + V|Se were deposited to have the correct number of atoms to form [(PbSe)1+δ]q(VSe2)1 where q is the number of PbSe monolayers in the heterostructure. Structural analysis of the self-assembled precursor via X-ray reflectivity, X-ray diffraction, and HAADF-STEM suggests three different behaviors upon deposition. Precursors with q ≥ 7 and even values of q have the targeted nanoarchitectures after deposition, which are maintained as the products are self-assembled through a near diffusionless process. Significant lateral surface diffusion occurred during the deposition of precursors with q = 1, 3, and 5, resulting in the precursor to have a different nanoarchitecture than targeted. Additional perpendicular long-range diffusion occurs during self-assembly of these precursors, resulting in different final products than targeted. Density functional theory (DFT) calculations of PbSe blocks show that the odd-numbered layers are less stable than the even-numbered layers, which suggests an energetic driving force for the observed rearrangement. This work highlights the importance of understanding the reaction mechanism when attempting to prepare 2D layers of constituents with bulk 3D structures.
In this study, large eddy simulations (LES) of offshore boundary layers near the Nantucket coast are performed using Nalu-Wind. The marine boundary layer conditions are chosen to match the predominantly unstable and neutral conditions measured by the Cape Wind platform. The appropriate domain, resolution, and boundary condition settings required for the LES are established through this work. Differences between stable and unstable cases are found in the wind speed profiles, averaged statistics, and wind spectra, and explained in terms of stratification effects. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. SAND2020-5996C.
An uncertainty quantification technique for nacelle-mounted lidar is developed that extends conventional error analyses to precisely account for residual uncertainty due to observed non-ideal features in processed Doppler lidar spectra. The technique is applied after quality assurance/quality control (QAQC) processing to quantify residual error, both bias and random, from solid-body interference, shot noise, and any additional uncertainty introduced to the data from the QAQC process itself. The approach follows from the one-time construction of a high-dimensional parametric database of synthetic lidar spectra and subsequent processing with an existing QAQC technique. A model of the correspondence between the spectral shape and the associated residual errors due to non-ideal features is then developed for quantities of interest (QOIs) including the geometric median and spectral standard deviation of line-of-sight velocity. The model is preliminarily implemented within a neural network framework that is then applied in post-processing to sample returns from a DTU SpinnerLidar. The initial analysis uncovers the effects of specific sources of uncertainty in the context of both individual spectra and full-field maps of the measurement domain. The technique is described in terms of application to continuous wave (CW) lidar, though it is also relevant to pulsed lidar.
In the past, experimentally observed dislocations were often interpreted using an isolated dislocation assumption because the effect of background dislocation density was difficult to evaluate. Contrarily, dislocations caused by atomistic simulations under periodic boundary conditions can be better interpreted because linear elastic theory has been developed to address the effect of periodic dislocation array in the literature. However, this elastic theory has been developed only for perfect dislocations, but not for dissociated dislocations. The periodic boundary conditions may significantly change the dissociation energy of dislocations and stacking fault width, which in turn, change the deformation phenomena observed in simulations. To enable materials scientists to understand the dislocation behavior under the periodic boundary conditions, we use isotropic elastic theory to analyze the thermodynamics of dissociated periodic dislocations with an arbitrary dislocation character angle. Analytical expressions for force, stacking fault width, and energies are presented in the study. Results obtained from the periodic dislocation array were compared with those obtained from isolated dislocations to shed light on the interpretation of experimentally observed and simulated dislocations.
Tensor decomposition models play an increasingly important role in modern data science applications. One problem of particular interest is fitting a low-rank Canonical Polyadic (CP) tensor decomposition model when the tensor has sparse structure and the tensor elements are nonnegative count data. SparTen is a high-performance C++ library which computes a low-rank decomposition using different solvers: a first-order quasi-Newton or a second-order damped Newton method, along with the appropriate choice of runtime parameters. Since default parameters in SparTen are tuned to experimental results in prior published work on a single real-world dataset conducted using MATLAB implementations of these methods, it remains unclear if the parameter defaults in SparTen are appropriate for general tensor data. Furthermore, it is unknown how sensitive algorithm convergence is to changes in the input parameter values. This report addresses these unresolved issues with large-scale experimentation on three benchmark tensor data sets. Experiments were conducted on several different CPU architectures and replicated with many initial states to establish generalized profiles of algorithm convergence behavior.
Nalu-Wind simulations of the neutral inflow Scaled Wind Farm Technology (SWiFT) benchmark were used to analyze which quantities of interest within the wind turbine wake and surrounding control volume are important in performing a momentum deficit analysis of the wind turbine thrust force. The necessary quantities of interest to conduct a full Reynolds-Averaged Navier-Stokes (RANS) formulation analysis were extracted along the control volume surfaces within the Nalu simulation domain over a 10 minute period. The thrust force calculated within the wake from two to eight diameters downstream using the control volume surfaces and the full RANS approach matched the thrust force that the wind turbine applied to the flowfield. A simplified one-dimension momentum analysis was included to determine if the inflow and wake velocities typically acquired during field campaigns would be sufficient to perform a momentum deficit analysis within a wind turbine wake. The one-dimensional analysis resulted in a 70% difference relative to the coefficient of thrust (Ct ) determined by the full RANS method at 2D downstream and a 40% difference from 5D to 8D, where D is the diameter of the turbine. This suggests that the quantities typically captured during field campaigns are insufficient to perform an accurate momentum deficit analysis unless streamwise pressure distribution is acquired, which reduced the relative difference to less than 10% for this particular atmospheric inflow.
This is an addendum to the Sierra/SolidMechanics 4.58 User's Guide that documents additional capabilities available only in alternate versions of the Sierra/SolidMechanics (Sierra/SM) code. These alternate versions are enhanced to provide capabilities that are regulated under the U.S. Department of State's International Traffic in Arms Regulations (ITAR) export control rules. The ITAR regulated codes are only distributed to entities that comply with the ITAR export control requirements. The ITAR enhancements to Sierra/SM include material models with an energy-dependent pressure response (appropriate for very large deformations and strain rates) and capabilities for blast modeling. This document is an addendum only; the standard Sierra/SolidMechanics 4.58 User's Guide should be referenced for most general descriptions of code capability and use.
Port scanning is a commonly applied technique in the discovery phase of cyber attacks. As such, defending against them has long been the subject of many research and modeling efforts. Though modeling efforts can search large parameter spaces to find effective defensive parameter settings, confidence in modeling results can be hampered by limited or omitted validation efforts. In this paper, we introduce a novel, mathematical model that describes port scanning progress by an attacker and intrusion detection by a defender. The paper further describes a set of emulation experiments that we conducted with a virtual testbed and used to validate the model. Results are presented for two scanning strategies: a slow, stealthy approach and a fast, loud approach. Estimates from the model fall within 95% confidence intervals on the means estimated from the experiments. Consequently, the model's predictive capability provides confidence in its use for evaluation and development of defensive strategies against port scanning.
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, an d 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.
Inducing new phases in thick films via vertical lattice strain is one of the critical advantages of vertically aligned nanocomposites (VANs). In SrTiO3 (STO), the ground state is ferroelastic, and the ferroelectricity in STO is suppressed by the orthorhombic transition. Here, we explore whether vertical lattice strain in three-dimensional VANs can be used to induce new ferroelectric phases in SrTiO3:MgO (STO:MgO) VAN thin films. The STO:MgO system incorporates ordered, vertically aligned MgO nanopillars into a STO film matrix. Strong lattice coupling between STO and MgO imposes a large lattice strain in the STO film. We have investigated ferroelectricity in the STO phase, existing up to room temperature, using piezoresponse force microscopy, phase field simulation and second harmonic generation. We also serendipitously discovered the formation of metastable TiO nanocores in MgO nanopillars embedded in the STO film matrix. Our results emphasize the design of new phases via vertical epitaxial strain in VAN thin films. This journal is
Three-beam rotational coherent anti-Stokes Raman scattering (CARS) measurements performed in highly scattering environments are susceptible to contamination by two-beam CARS signals generated by the pump–probe and Stokes–probe interactions at the measurement volume. If this occurs, differences in the Raman excitation bandwidth between the two-beam and three-beam CARS signals can add significant errors to the spectral analysis. This interference, to the best of our knowledge, has not been acknowledged in previous three-beam rotational CARS experiments, but may introduce measurement errors up to 25% depending on the temperature, amount of scattering, and differences between the two-beam and three-beam Raman excitation bandwidths. In this work, the presence of two-beam CARS signal contamination was experimentally verified using a femtosecond–picosecond rotational CARS instrument in two scattering environments: (1) a fireball generated by a laboratory-scale explosion that contained particulate matter, metal fragments, and soot, and (2) a flow of air and small liquid droplets. A polarization scheme is presented to overcome this interference. By rotating the pump and Stokes polarizations +55◦ and −55◦ from the probe, respectively, the two-beam and three-beam CARS signals are orthogonally polarized and can be separated using a polarization analyzer. Using this polarization arrangement, the Raman-resonant three-beam CARS signal amplitude is reduced by a factor of 2.3 compared to the case where all polarizations are parallel. This method is successfully demonstrated in both scattering environments. A theoretical model is presented, and the temperature measurement error is studied for different experimental conditions. The criteria for when this interference may be present are discussed.
Fe-Co-2V is a popular metallic alloy used in electromagnetic applications. However, there is a lack of mechanical fatigue characterization of this alloy in the literature. In this work, Fe-Co-2V specimens with rectangular cross-sections were carefully prepared in accordance with standards. They were measured for surface roughness and then subjected to quasi-static monotonic testing, as well as fatigue testing at both 0.5 Hz and 1 Hz frequencies. Both Rockwell hardness and Vickers micro- hardness testing were performed. Additionally, scanning electron microscopy imaging of the fractured surfaces was done. The quasi-static testing revealed a flat yield region characteristic of Laders bands. Here, the fatigue results did not show significant differences or sensitivity to change in frequency, although the fatigue life was higher on average for the 0.5 Hz. However, the fatigue results differed from published work at 0.33 Hz. The fractography revealed purely brittle fracture, with clear chevron marks and fracture initiation always starting at the surface. Lastly, it was identified that the C, D, and F Rockwell hardness scales were appropriate for testing this material and that the grain size necessitated the use of the upper end of indentation force for Vickers micro-hardness testing.
Among the main challenges in shape optimization is the coupling of Finite Element Method (FEM) codes in a way that facilitates efficient computation of shape derivatives. This is particularly difficult with multi-physics problems involving legacy codes, where the costs of implementing and maintaining shape derivative capabilities are prohibitive. There are two mathematically equivalent approaches to computing the shape derivative: the volume method, and the boundary method. Each has a major drawback: the boundary method is less accurate, while the volume method is more invasive to the FEM code. Prior implementations of shape derivatives at Sandia have been based on the volume method. We introduce the strip method, which computes shape derivatives on a strip adjacent to the boundary. The strip method makes code coupling simple. Like the boundary method, it queries the state and adjoint solutions at quadrature nodes, but requires no knowledge of the FEM code implementations. At the same time, it exhibits the higher accuracy of the volume method. The development of the strip method also offers us the opportunity to share some lessons learned about implementing the volume method and boundary method, to show shape optimization results on problems of interest, and to begin addressing the other main challenges at hand: constraints on optimized shapes, and their interplay with optimization algorithms.
In model simulations of deep geologic repositories, UO2 fuel matrix degradation typically begins as soon as the waste package breaches and groundwater contacts the fuel surface. The initial degradation rate depends on the timing of these events, burnup of the fuel, temperature, and concentrations of dissolved reactants. Estimating the initial rate of degradation is fairly straightforward, but as UO2 corrosion products precipitate on the fuel surface and the movement of dissolved species between the fuel surface and environment is impeded by the precipitated solids, the rate is more difficult to quantify. At that point, calculating the degradation rate becomes a reactive-transport problem in which a large number of equations must be solved by iteration for a large number of grid cells at each time step. The consequence is that repository simulations, which are already expensive, become much more expensive, especially when hundreds or thousands of waste packages breach. The Fuel Matrix Degradation (FMD) model is the process model of the Spent Fuel and Waste Science and Technology (SFWST) campaign of the US Department of Energy (DOE). It calculates spent fuel degradation rates as a function of radiolysis, redox reactions, electrochemical reactions, alteration layer growth, and diffusion of reactants through the alteration layer. Like other similar fuel degradation process models, it is a complicated model requiring a large number of calculations and iterations at each time step.
The HERMES III accelerator is an 18-20 MeV linear induction accelerator constructed at Sandia National Laboratories in the late 1980s and which continues operation to this day. As part of recent modernization efforts, the laser triggering system on the accelerator has been replaced with a newly designed solid-state system. This system consists of ten Nd:YAG lasers, each having a nominal output energy of 40-45 mJ at a wavelength of 266 nm. The beam from each laser is split such that it triggers two of the Rimfire gas switches on the accelerator. Compared to the previous laser triggering system, this arrangement makes it possible to more readily tailor the final output pulse shape, and overall reliability for the accelerator's operation with these new lasers has increased. The design of this new laser triggering system is presented in this paper, along with details pertaining to the energy budgeting, optical beam paths, and electrical triggering of the lasers. Initial operational data from the HERMES III accelerator using this new triggering system is also presented.
Presented in this document is a small portion of the tests that exist in the Sierra/SolidMechanics (Sierra/SM) verification test suite. Most of these tests are run nightly with the Sierra/SM code suite, and the results of the test are checked versus the correct analytical result. For each of the tests presented in this document, the test setup, a description of the analytic solution, and comparison of the Sierra/SM code results to the analytic solution is provided. Mesh convergence is also checked on a nightly basis for several of these tests. This document can be used to confirm that a given code capability is verified or referenced as a compilation of example problems. Additional example problems are provided in the Sierra/SM Example Problems Manual. Note, many other verification tests exist in the Sierra/SM test suite, but have not yet been included in this manual.
Presented in this document are tests that exist in the Sierra/SolidMechanics example problem suite, which is a subset of the Sierra/SM regression and performance test suite. These examples showcase common and advanced code capabilities. A wide variety of other regression and verification tests exist in the Sierra/SM test suite that are not included in this manual.
Nearly every protocol used to analyze the performance of quantum information processors is based on an assumption that the errors experienced by the device during logical operations are constant in time and are insensitive to external contexts. This assumption is pervasive, rarely stated, and almost always wrong. Quantum devices that do behave this way are termed "Markovian:' but nearly every system we have ever probed has displayed drift or crosstalk or memory effects they are all non-Markovian. Strong non-Markovianity introduces spurious effects in characterization protocols and violates assumptions of the fault-tolerance threshold theorems. This SAND report details a three year laboratory-directed research and development (LDRD) project entitled, "Diagnosing and Destroying non-Markovian Noise in Quantum Information Processors." This program was initiated to build tools to study non-Markovian dynamics and quantum systems and develop robust methodologies for eliminating it. The program achieved a number of notable successes, including the first statistically rigorous protocol for identifying and characterizing drift in quantum systems, a formalism for modeling memory effects in quantum devices, and the successful suppression of drift in a Sandia trapped-ion quantum processor.
The Ecosystem for Open Science (eOS) initiative was established in 2019. Its objective is improving openness and sharing of data and information across Defense Nuclear Nonproliferation (DNN) Research and Development (R&D) activities. To support this initiative, the eOS team at Sandia National Laboratories (SNL) developed metadata and data standards and proposed a machine-readable metadata schema. The nuclear explosion monitoring field was selected as a focus area due to its the wide range of pertinent phenomenologies.We developed the DCAT-eOS-AP metadata schema extending the Data Catalog Vocabulary version 2 (DCATv2) standard using an application profile (AP), to fit the needs of multi-disciplinary NA-22 projects. The DCAT-eOS-AP metadata schema describes data at different levels of granularity ranging from general descriptions to more domain-specific granular metadata. Its implementation and serialization is flexible with the ability to include new file or data types. Thus, it will scale with the ever-increasing data management needs of government research. Due to the multitude of phenomenologies represented in the DCAT-eOS-AP schema, we anticipate that it will be easily extensible to various projects across many DOE mission areas. This document describes data management challenges faced within the DNN R&D portfolio and provides insight on how metadata and data standards/guidelines combined with a comprehensive metadata schema can add value to programs throughout the Department of Energy (DOE). It reviews the importance of metadata standards, FAIR (Findability, Accessibility, Interoperability, and Reusability) data principles, and metadata schemas. Additionally, it summarizes input from subject matter experts (SME) at SNL and other National Laboratories that resulted in metadata and data standards/guidelines encompassing domains relevant to NA-22 projects. Finally, we discuss the DCAT-eOS-AP metadata development. Implementation recommendations and future development directions are included for those keen on adopting the DCAT-eOS-AP metadata schema.
Presented in this document are the theoretical aspects of capabilities contained in the Sierra/SM code. This manuscript serves as an ideal starting point for understanding the theoretical foundations of the code. For a comprehensive study of these capabilities, the reader is encouraged to explore the many references to scientific articles and textbooks contained in this manual. It is important to point out that some capabilities are still in development and may not be presented in this document. Further updates to this manuscript will be made as these capabilities come closer to production level.
Graphene plasmons provide a compelling avenue toward chip-scale dynamic tuning of infrared light. Dynamic tunability emerges through controlled alterations in the optical properties of the system defining graphene’s plasmonic dispersion. Typically, electrostatic induced alterations of the carrier concentration in graphene working in conjunction with mobility have been considered the primary factors dictating plasmonic tunability. We find here that the surrounding dielectric environment also plays a primary role, dictating not just the energy of the graphene plasmon but so too the magnitude of its tuning and spectral width. To arrive at this conclusion, poles in the imaginary component of the reflection coefficient are used to efficiently survey the effect of the surrounding dielectric on the tuning of the graphene plasmon. By investigating several common polar materials, optical phonons (i.e., the Reststrahlen band) of the dielectric substrate are shown to appreciably affect not only the plasmon’s spectral location but its tunability, and its resonance shape as well. In particular, tunability is maximized when the resonances are spectrally distant from the Reststrahlen band, whereas sharp resonances (i.e., high-Q) are achievable at the band’s edge. Overall, these observations both underscore the necessity of viewing the dielectric environment in aggregate when considering the plasmonic response derived from two-dimensional materials and provide heuristics to design dynamically tunable graphene-based infrared devices.