The Unstructured Time-Domain ElectroMagnetics (UTDEM) portion of the EMPHASIS suite solves Maxwell's equations using finite-element techniques on unstructured meshes. This document provides user-specific information to facilitate the use of the code for applications of interest.
NNSA policy requires that personnel working in the Nuclear Security Enterprise (NSE) receive comprehensive training to be fully capable before they begin their work and that personnel receive ongoing training to maintain their capability and competence. However, the loss of experienced NSE staff through retirements and the influx of personnel who are inexperienced and untrained in nuclear weapons technology make these training requirements difficult to achieve. This paper presents an approach for developing and delivering a nuclear deterrent (ND) fundamentals curriculum to respond to these challenges. The training approach leverages existing material already used by Sandia National Laboratories in the ND training arena and proposes to deliver the material in an e-learning format as much as possible. The approach also seeks to maximize the amount of material to be delivered in an unclassified setting.
The supercritical CO2 (sCO2) Brayton Economics Tool (sBET) was developed to evaluate and perform sensitivity studies on recompression closed Brayton cycles (RCBCs). This integrated techno-economic tool calculates key system performance and levelized cost of energy (LCOE) based on user-defined input on key variables such as system size, recuperator effectiveness, turbine inlet temperature, etc. The goal of this integrated tool is to allow system designers to understand the tradeoffs associated with various key design decisions, such as recuperator effectiveness and overall system cost. This work includes a description of LCOE calculation methodology, component system cost models for turbomachinery and heat exchangers based on vendor quotes and published literature, and the results of several parameter studies to identify desirable system parameters.
Sandia has a history of testing supercritical CO2 Brayton Cycles to explore operation fundamentals and provide validation data for computer models. These systems have always had data acquisition and controls features. The Development Platform (DP) has been a flagship system for loop testing and operation but has been limited to manual control of many systems. Manual operation has increased operating complexity and reduced stability and repeatability. This work documents automated control development by linearizing otherwise non-linear valves, addition of closed-loop proportional-integral control software that has been tuned to the Sandia DP. It also describes testing of control methods that have improved test quality and reliability as shown in actual test data.
Four Hellma visible grade glass samples were irradiated at the following radiation doses: 0 rads Baseline; 100 — 200 Krads; 300 — 400 Krads; 1.0 — 1.1 Mrads; 3.0 — 3.1 Mrads; and, 10 — 10.1 Mrads. Note that exact irradiation values are still be calculated based on the TLD measured values but the range is expected to be close to what is listed above . A fifth sample was utilized as a control sample where it's transmission was measured with the other samples but this sample was never exposed to radiation.
This report was developed to help the U.S. Defense Threat Reduction Agency understand the radionuclide detection requirements necessary to establish air monitoring systems that can detect airborne radionuclide activity at levels that could warrant protective actions.. The report provides representative integrated air activity derived response levels that correspond to the U.S. Environmental Protection Agency's protective action guidelines for the Early Phase (0-96 h) following a release to the environment. Environmental releases from nuclear fallout, nuclear power plants, and radiological dispersal devices are considered.
A parallel, adaptive overlay grid procedure is proposed for use in generating all-hex meshes for stochastic (SVE) and representative (RVE) volume elements in computational materials modeling. The mesh generation process is outlined including several new advancements such as data filtering to improve mesh quality from voxelated and 3D image sources, improvements to the primal contouring method for constructing material interfaces and pillowing to improve mesh quality at boundaries. We show specific examples in crystal plasticity and syntactic foam modeling that have benefitted from the proposed mesh generation procedure and illustrate results of the procedure with several practical mesh examples.
The transport properties of porous geological media are of fundamental importance when modeling the migration of chemical and radiological species in subterranean systems. Due to their relatively high mobility, short-lived noble gas species are of particular interest as detection of these species at the surface is a tell-tale indicator of recent nuclear activity. However, determining the diffusivity of these species is challenging due to their inert and transparent nature, requiring chemically insensitive techniques, such as mass spectroscopy, to quantify noble gas concentrations. The work described herein details recent advances in the methodology for determining diffusivity on porous media and results obtained on samples relevant to the UNESE project.
Hydrogen Risk Assessment Models (HyRAM) is a software toolkit that provides a basis for quantitative risk assessment and consequence modeling for hydrogen infrastructure and transportation systems. HyRAM integrates validated, analytical models of hydrogen behavior, statistics, and a standardized QRA approach to generate useful, repeatable data for the safety analysis of various hydrogen systems. HyRAM is a software developed by Sandia National Laboratories for the U.S. Department of Energy. This document demonstrates how to use HyRAM to recreate a hydrogen system and obtain relevant data regarding potential risk. Specific examples are utilized throughout this document, providing detailed tutorials of HyRAM features with respect to hydrogen system safety analysis and risk assessment.
This Sandia National Laboratories, New Mexico Environmental Restoration Operations (ER) Consolidated Quarterly Report (ER Quarterly Report) fulfills all quarterly reporting requirements set forth in the Compliance Order on Consent. Table I-1 lists the six sites remaining in the corrective action process. This ER Quarterly Report presents activities and data as follows: SECTION I: Environmental Restoration Operations Consolidated Quarterly Report, January — March 2019 SECTION II: Because there is no perchlorate sampling collection to report this quarter, this edition of the ER Quarterly Report does not include any analysis of data in Section II "Perchlorate Screening Quarterly Groundwater Monitoring Report." SECTION III: Technical Area-V In-Situ Bioremediation Treatability Study Full-Scale Operation, January — March 2019.
Discharge Permit (DP)-1845 was issued by the New Mexico Environment (NMED) Ground Water Quality Bureau (GWQB) for discharges via up to three injection wells in a phased Treatability Study of in-situ bioremediation of groundwater at the Sandia National Laboratories, New Mexico, Technical Area-V Groundwater Area of Concern. This report fulfills the quarterly reporting requirements set forth in DP-1845, Section IV.B, Monitoring and Reporting. The reporting period is January 1 through March 31, 2019. All applicable terms and conditions were met for this reporting period. The report is due to NMED GWQB by August 1, 2019.
This report presents results of a study of the dielectric properties of several high explosive materials, including Composition B, C-4, Detasheet, Semtex 1A, Semtex 1H, and Semtex 10. The capacitance and dispersion of samples up to ten mm thick were measured at 24 and 65°C using a parallel plate test fixture. In this configuration, capacitance is proportional to the real component of the complex dielectric constant (or permittivity) of the material, while the dispersion is equal to the imaginary component of the dielectric constant divided by the real component. Measurements were performed at two temperatures to determine whether these dielectric properties might be used to monitor moderate temperature changes within these materials using an external fringing field capacitor. It was found that the real dielectric constant of the high explosives generally changed by only about 1% on heating from 24 to 65°C, and that the externally measured capacitance is therefore not a reliable parameter for monitoring temperature changes in these explosives. Temperature- based changes in the dispersion were often 20% or greater, and for several materials (e.g., Composition B, Detasheet) the temperature and dispersion showed good correlation as the temperature of the explosive was cycled up and down. It is thus possible that for some high explosives the externally measured dispersion is a useful parameter for monitoring temperature changes. However, other factors may limit the usefulness of this technique, including the need to have the plates of a fringing field capacitor in direct contact with the high explosive to obtain measurements, and the limited penetration depth of the fringing field into the explosive material.
In a recent paper, the authors proposed a general methodology for probabilistic learning on manifolds. The method was used to generate numerical samples that are statistically consistent with an existing dataset construed as a realization from a non-Gaussian random vector. The manifold structure is learned using diffusion manifolds and the statistical sample generation is accomplished using a projected Itô stochastic differential equation. This probabilistic learning approach has been extended to polynomial chaos representation of databases on manifolds and to probabilistic nonconvex constrained optimization with a fixed budget of function evaluations. The methodology introduces an isotropic-diffusion kernel with hyperparameter ε. Currently, ε is more or less arbitrarily chosen. In this paper, we propose a selection criterion for identifying an optimal value of ε, based on a maximum entropy argument. The result is a comprehensive, closed, probabilistic model for characterizing data sets with hidden constraints. This entropy argument ensures that out of all possible models, this is the one that is the most uncertain beyond any specified constraints, which is selected. Applications are presented for several databases.
Over the past few years, interest has rocketed in the use of wide bandgap devices for energy-efficiency applications such as the electric grid, vehicle electrification, and more-electric aircraft. Deployed in these situations, devices must have a high reliability. In fact, this attribute is so crucial that it is a primary gatingfactor, determining the rate at which these wide bandgap devices are being inserted into these system applications.
Genedy, Moneeb; Matteo, Edward N.; Stenko, Michael; Stormont, John C.; Taha, Mahmoud R.
Microscale defects (microannuli) at the steel-cement and rock-cement interfaces are a major cause of failure in the integrity of wellbore systems. Microscale defects/microcracks as small as 30 μm are sufficient to create a significant leakage pathway for fluids. In this paper, the authors propose the use of nanomodified methyl methacrylate (NM-MMA) polymer as a seal material for 30-μm microcracks. Four materials were evaluated for their ability to serve as an effective seal material to seal 30-μm microcracks: microfine cement, epoxy, methyl methacrylate (MMA), and NM-MMA incorporating 0.5% by weight aluminum nanoparticles (ANPs). The seal materials' bond strengths with shale were investigated using push-out tests. In addition, the ability to flow fluid through the microcracks was investigated using sagittal microscopic images. Viscosity, surface tension, and contact angle measurements explain the superior ability of MMA seal materials to flow into very thin microcracks compared with other materials. Post-test analysis shows MMA repair materials are capable of completely filling the microcracks. In addition, incorporating ANPs in MMA resulted in significant improvement in seal material ductility. Dynamic mechanical analysis (DMA) showed that incorporating ANPs in MMA reduced the creep compliance and improved creep recovery of NM-MMA. X-ray diffraction (XRD) analysis shows that incorporating ANPs in MMA resin increases the degree of polymer crystallization, resulting in significant improvement in seal material ductility.
Koski, Jason P.; Krook, Nadia M.; Ford, Jamie; Yahata, Yoshikazu; Ohno, Kohji; Murray, Christopher B.; Frischknecht, Amalie L.; Composto, Russell J.; Riggleman, Robert A.
There are limited theoretically predicted phase diagrams for polymer nanocomposites (PNCs) because conventional modeling techniques are largely unable to predict the macroscale phase behavior of PNCs. Here, we show that recent field-based methods, including PNC field theory (PNC-FT) and theoretically informed Langevin dynamics, can be used to calculate phase diagrams for polymer-grafted nanoparticles (gNPs) incorporated into a polymer matrix. We calculate binodals for the transition from the miscible, dispersed phase to the macrophase separated state as functions of important experimental parameters, including the ratio of the matrix chain degree of polymerization (P) to the grafted chain degree of polymerization (N), the enthalpic repulsion between the matrix and grafted chains, the grafting density (σ), the size of the NPs, and the NP volume fraction. We demonstrate that thermal and polymer conformational fluctuations enhance the degree of phase separation in gNP-PNCs, a result of depletion interaction effects. We support this conclusion by experimentally investigating the phase separation of poly(methyl methacrylate)-grafted silica NPs in a polystyrene matrix as a function of P/N. The simulations only agree with experiments when fluctuations are included because fluctuations are needed to properly capture the depletion interactions between the gNPs. We clarify the role of conformational entropy in driving depletion interactions in PNCs and suggest that inconsistencies in the literature may be due to polymer chain length effects since conformational entropy increases with increasing chain length.
The third Sandia Fracture Challenge (SFC3) was a benchmark problem for comparing experimental and simulated ductile deformation and failure in an additively manufactured (AM) 316L stainless steel structure. One surprising observation from the SFC3 was the Challenge-geometry specimens had low variability in global load versus displacement behavior, attributed to the large stress-concentrating geometric features dominating the global behavior, rather than the AM voids that tend to significantly influence geometries with uniform cross-sections. This current study reinvestigates the damage and failure evolution of the Challenge-geometry specimens, utilizing interrupted tensile testing with micro-computed tomography (micro-CT) scans to monitor AM void and crack growth from a virgin state through complete failure. This study did not find a correlation between global load versus displacement behavior and AM void attributes, such as void volume, location, quantity, and relative size, which incidentally corroborates the observation from the SFC3. However, this study does show that the voids affect the local behavior of damage and failure. Surface defects (i.e. large voids located on the surface, far exceeding the nominal surface roughness) that were near the primary stress concentration affected the location of crack initiation in some cases, but they did not noticeably affect the global response. The fracture surfaces were a combination of classic ductile dimples and crack deviation from a more direct path favoring intersection with AM voids. Even though the AM voids promoted crack deviation, pre-test micro-CT scan statistics of the voids did not allow for conclusive predictions of preferred crack paths. This study is a first step towards investigating the importance of voids on the ductile failure of AM structures with stress concentrations.
An outline of a Bayesian source location framework for using seismic and acoustic observations is developed and tested on synthetic and real data. Seismic and acoustic phenomena are both commonly used in detection and location of a variety of natural or man-made events, such as volcanic eruptions, quarry blasts, and military exercises. Typically, seismic and acoustic observations have been utilized independently of each other. Here, we outline a Bayesian formulation for combining the two observations in a single estimate of the location and origin time. Using realistic estimates of uncertainty, we subsequently explore how combining the different observation types can benefit event location at local to near-regional distances. We apply the method to synthetic data and to real observations from a mining blast in Bingham Mine in Utah. Our findings suggest that, for relatively sparse or azimuthally limited observations, the relative strengths of the two different phenomenologies enable more precise joint-event localization than either seismic or infrasonic measurements alone.
The mounting reliance on computational simulations to predict all aspects of the lifecycle of a mechanical system, from fabrication to failure, has prompted the mechanics community to selfassess its abilities to perform those predictions. Benchmark problems in mechanics that compare simulations that use different computational approaches with experiments have sprung up lately, including the NIST AM-Bench looking at additively manufactured (AM) materials (https://www.nist.gov/ambench),the Contact-Mechanics Challenge (Miiser, 2017) considering adhesion between two nominally flat surfaces, Numisheet providing semiannual benchmarking activities in sheet metal forming (http://numisheet2018.org),and the Sandia Fracture Challenge (SFC) (Boyce, 2014 and Boyce, 2016) investigating ductile failure. The previous SFCs have shown that progress has been made in computations of ductile failure, but improvements still can be made, hence the third Sandia Fracture Challenge (SFC3), the subject of this Special Volume. The most recent installment of SFC is building on previous successes and tackling the difficult problem of fracture in an AM 316L stainless steel structure.
Scientific data collections grow ever larger, both in terms of the size of individual data items and of the number and complexity of items. To use and manage them, it is important to directly address issues of robust and actionable provenance. We identify three key drivers as our focus: managing the size and complexity of metadata, lack of a priori information to match usage intents between publishers and consumers of data, and support for campaigns over collections of data driven by multi-disciplinary, collaborating teams. We introduce the Hoarde abstraction as an attempt to formalize a way of looking at collections of data to make them more tractable for later use. Hoarde leverages middleware and systems infrastructures for scientific and technical data management. Through the lens of a select group of challenging data usage scenarios, we discuss some of the aspects of implementation, usage, and forward portability of this new view on data management.
Sinuous antennas are capable of producing ultrawideband (UWB) radiation with polarization diversity. This capability makes the sinuous antenna an attractive candidate for UWB polarimetric radar applications. Additionally, the ability of the sinuous antenna to be implemented as a planar structure makes it a good fit for close-in sensing applications such as ground penetrating radar. However, recent literature has shown the sinuous antenna to suffer from resonances, which degrade performance. Such resonances produce late time ringing, which is particularly troubling for pulsed close-in sensing applications. The resonances occur in two forms: log-periodic resonances on the arms, and a resonance due to the sharp ends left by the outer truncation. A detailed investigation as to the correlation between the log-periodic resonances and the sinuous antenna design parameters indicates the resonances may be mitigated by selecting appropriate design parameters. In addition, a novel truncation method is proposed to remove the sharp end resonance. Both simulation and measured results are provided to support the developed sinuous antenna design guidance.
Many applications have growing demands for memory, particularly in the HPC space, making the memory system a potential bottleneck of next-generation computing systems. Sharing the memory system across processor sockets and nodes becomes a compelling argument given that memory technology is scaling at a slower rate than processor technology. Moreover, as many applications rely on shared data, e.g., graph applications and database workloads, having a large number of nodes accessing shared memory allows for efficient use of resources and avoids duplicating huge files, which can be infeasible for large graphs or scientific data. As new memory technologies come on the market, the flexibility of upgrading memory and system updates become major a concern, disaggregated memory systems where memory is shared across different computing nodes, e.g., System-on-Chip (SoC), is expected to become the most common design/architecture on memory-centric systems, e.g., The Machine project from HP Labs. However, due to the nature of such systems, different users and applications compete for the available memory bandwidth, which can lead to severe contention due to memory traffic from different SoCs. In this paper, we discuss the contention problem in disaggregated memory systems and suggest mechanisms to ensure memory fairness and enforce QoS. Our simulation results show that employing our proposed QoS techniques can speed up memory response time by up to 55%.
The present study introduces the coupled multiphysics model as part of the structural health monitoring (SHM) system. In particular, the Ultrasonic Guided Waves (UGWs) propagation is tracked in order to identify the damage to the structure. For this purpose, a multiphysics mathematical model is proposed. The model constitutes a monolithically coupled system of acoustic and elastic wave equations (WpFSI problem) where the wave signal displacement measurements are analyzed as the UGWs propagates in the solid, fluid and their interface. The ultimate goal of this paper is to explore and develop the efficient numerical solution of the WpFSI problem using the finite element method. A detailed description of the modeling framework and conditions that facilitate the coupling is provided. To couple the displacement-based acoustic wave equations for the isothermal fluid with elastic wave equations for the Saint Venant-Kirchhoff material model, the present study uses a monolithic solution algorithm. In particular, at each time step, wave equations are transformed to a fixed reference configuration via the arbitrary Lagrangian-Eulerian (ALE) mapping and automatically adopt the boundary conditions from the previous time step. The implementation is accomplished via the finite element library deal.II (Goll et al., 2017) based software toolbox DOPELIB (Bangerth et al., 2007) which provides modularized higher level algorithms. Beyond SHM systems, the model is relevant for problems from biomechanics and biomedicine, vibromechanics, poroelasticity as well as subsurface and porous media flow. The model developed here serves as a first step towards the on-line SHM system modeling, where structural dynamics are accounted for.
FUSION 2019 - 22nd International Conference on Information Fusion
Reynen, Olivia; Vadrevu, Samhita; Nagi, Rakesh; Legrand, Keith
In this paper, we present alternate integer programming formulations for the multi-dimensional assignment problem, which is typically employed for multi-sensor fusion, multi-target tracking (MTT) or data association in general. The first formulation is the Axial Multidimensional Assignment Problem with Decomposable Costs (MDADC). The decomposable costs comes from the fact that there are only pairwise costs between stages or scans of a target tracking problem or corpuses of a data association context. The difficulty with this formulation is the large number of transitivity or triangularity constraints that ensure if entity A is associated to entity B and entity B is associated with entity C, then it must also be that entity A is associated to entity C. The second formulation uses both pairs and triplets of observations, which offer more accurate representation for kinematic tracking of targets. This formulation avoids the large number of transitivity constraints but significantly increases the number of variables due to triples. Solution to large-scale problems has alluded researchers and practitioners alike. We present solution methods based on Lagrangian Relaxation and massively parallel algorithms that are implemented on Graphics Processing Units (GPUs). We test the problem formulations and solution algorithms on MTT problems. The triples formulation tends to be more accurate for tracking measures and the MDADC solver can solve much larger problems in reasonable computational time.
A new potential-based TDIE formulation for dielectric regions is proposed that directly uses magnetic current densities as unknowns. This new formulation avoids the use of inconvenient integral operators that complicate the discretization, while also providing simpler computation of far-field results due to direct access to the magnetic current density. Appropriate marching-on-in-time discretization schemes are discussed so that stable results can be achieved at middle frequencies. Overall, this results in the improved performance of these new equations compared to previous formulations. The accuracy and stability of this new formulation is demonstrated through numerical results.
The ability to rapidly drill through diverse, layered materials can greatly enhance future mine-rescue operations, energy exploration, and underground operations. Pneumatic-percussive drilling holds great promise in this area due to its ability to penetrate very hard materials and potential for portability. Currently such systems require expert operators who require extensive training. We envision future applications where first responders who lack such training can still respond rapidly and safely perform operations. Automated techniques can reduce the dependence on expert operators while increasing efficiency and safety. However, current progress in this area is restricted by the difficulty controlling such systems and the complexity of modeling percussive rock-bit interactions. In this work we develop and experimentally validate a novel intelligent percussive drilling architecture that is tailored to autonomously operate in diverse, layered materials. Our approach combines low-level feedback control, machine learning-based material classification, and on-line optimization. Our experimental results demonstrate the effectiveness of this approach and illustrate the performance benefits over conventional methods.
Electromagnetic shields are usually employed to protect cables and other devices; however, these are generally not perfect, and may permit external magnetic and electric fields to penetrate into the interior regions of the cable, inducing unwanted current and voltages. The aim of this paper is to verify a circuit model tool with our previously proposed analytical model [1] for evaluating currents and voltages induced in the inner conductor of braided-shield cables. This circuit model will enable coupling between electromagnetic and circuit simulations.
This study examines the single-event upset and single-event latch-up susceptibility of the Xilinx 16nm FinFET Zynq UltraScale+ RFSoC FPGA in proton irradiation. Results for SEU in configuration memory, BlockRAM memory, and device SEL are given.
This study examines the single-event upset and single-event latch-up susceptibility of the Xilinx 16nm FinFET Zynq UltraScale+ RFSoC FPGA in proton irradiation. Results for SEU in configuration memory, BlockRAM memory, and device SEL are given.
Ryder, Kaitlyn L.; Ryder, Landen D.; Sternberg, Andrew L.; Kozub, John A.; Zhang, Enxia; Khachatrian, Ani; Buchner, Steven P.; Mcmorrow, Dale P.; Hales, Joel M.; Zhao, Yuanfu; Wang, Liang; Wang, Chuanmin; Weller, Robert A.; Schrimpf, Ronald D.; Weiss, Sharon M.; Reed, Robert A.; Black, Dolores; King, Michael P.
The insect brain is a great model system for low power electronics: Insects carry out multisensory integration and are able to change the way the process information, learn, and adapt to changes in their environment with a very limited power budget. This context-dependent processing allows them to implement multiple functionalities within the same network, as well as to minimize power consumption by having context-dependent gains in their first layers of input processing. The combination of low power consumption, adaptability and online learning, and robustness makes them particularly appealing for a number of space applications, from rovers and probes to satellites, all having to deal with the progressive degradation of their capabilities in remote environments. In this work, we explore architectures inspired in the insect brain capable of context-dependent processing and learning. Starting from algorithms, we have explored three different implementations: A spiking implementation in a neuromorphic chip, a custom implementation in an FPGA, and finally hybrid analog/digital implementations based on cross-bar arrays. For the latter, we found that the development of novel resistive materials is crucial in order to enhance the energy efficiency of analog devices while maintaining an adequate footprint. Metal-oxide nanocomposite materials, fabricated using ALD with processes compatible with semiconductor processing, are promising candidates to fill in that role.
Energy storage systems are flexible and controllable resources that can provide a number of services for the electric power grid. Many technologies are available, and corresponding models vary greatly in level of detail and tractability. In this work, we propose an adaptive optimal control and estimation approach for real-time dispatch of energy storage systems that neither requires accurate state-of-energy measurements nor knowledge of an accurate state-of-energy model. Specifically, we formulate an online optimization problem that simultaneously solves moving horizon estimation and model predictive control problems, which results in estimates of the state-of-energy, estimates of the charging and discharging efficiencies, and future dispatch signals. We present a numerical example in which the plant is a nonlinear, time-varying Lithium-ion battery model and show that our approach effectively estimates the state-of-energy and dispatches the system without accurate knowledge of the dynamics and in the presence of significant measurement noise.