Hypervelocity impact-driven vaporization is characteristic of late-stage planet formation. Yet the behavior and properties of liquid-vapor mixtures of planetary materials of interest are typically unknown. Multiphase equations of state used in hydrodynamic simulations of planet impacts therefore lack reliable data for this important phenomenon. Here, we present the first constraints on the liquid-vapor critical point and coexistence phase boundary of Mg2SiO4 computed from ab initio molecular dynamics simulations. We found that the vapor is depleted in magnesium and enriched in silica and oxygen, while the coexisting liquid is enriched in magnesium and depleted in oxygen, from which we infer vaporization is incongruent. The critical point was estimated from an equation of state fit to the data. The results are in line with recent calculations of MgSiO3 and together confirm that extant multiphase equation of state (EOS) models used in planetary accretion modeling significantly underestimate the amount of supercritical material postimpact.
This final report on Laboratory Directed Research and Development (LDRD) project 209234 presents background material for electrokinetics at the pore and porous media scales. We present some theoretical developments related to uncoupling electrokinetic flow solutions, from a manuscript recently accepted into Mathematical Geosciences for publication. We present a summary of two pore-scale modeling efforts undertaken as part of the academic alliance with University of Illinois, resulting in one already submitted journal publication to Transport in Porous Media and another in preparation for submission to a journal. We finally show the laboratory apparatus built in Laboratory B59 in Building 823 and discuss some of the issues that occurred with it.
Intuition tells us that a rolling or spinning sphere will eventually stop due to the presence of friction and other dissipative interactions. The resistance to rolling and spinning or twisting torque that stops a sphere also changes the microstructure of a granular packing of frictional spheres by increasing the number of constraints on the degrees of freedom of motion. We perform discrete element modeling simulations to construct sphere packings implementing a range of frictional constraints under a pressure-controlled protocol. Mechanically stable packings are achievable at volume fractions and average coordination numbers as low as 0.53 and 2.5, respectively, when the particles experience high resistance to sliding, rolling, and twisting. Only when the particle model includes rolling and twisting friction were experimental volume fractions reproduced.
TLA is a popular temporal logic for writing stuttering-invariant specifications of digital systems. However, TLA lacks higher-order features useful for specifying modern software written in higherorder programming languages.We use categorical techniques to recast a real-time semantics for TLA in terms of the actions of a group of time dilations, or "stutters, "and an extension by a monoid incorporating delays, or "falters."Via the geometric morphism of the associated presheaf topoi induced by the inclusion of stutters into falters, we construct the first model of a higher-order TLA.
The layered Ruddlesden–Popper crystal structure can host a broad range of functionally important behaviors. In this work, we establish extraordinary configurational disorder in a layered Ruddlesden–Popper (RP) structure using entropy stabilization assisted synthesis. A protype A2CuO4 RP cuprate oxide with five cations on the A-site sublattice is designed and fabricated into epitaxial single crystal films using pulsed laser deposition. When grown on a near lattice matched substrate, the (La0.2Pr0.2Nd0.2Sm0.2Eu0.2)2CuO4 film features a T'-type RP structure with uniform A-site cation mixing and square-planar CuO4 units. These observations are made with a range of combined characterizations using X-ray diffraction, atomic-resolution scanning transmission electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray absorption spectroscopy measurements. It is further found that heteroepitaxial strain plays an important role in crystal phase formation during synthesis. Compressive strain over ~1.5% results in the formation of a non-RP cubic phase consistent with a CuX2O4 spinel structure. The ability to manipulate configurational complexity and move between 2D layered RP and 3D cubic crystal structures in cuprate and related materials promises to enable flexible design strategies for a range of functionalities, such as magnetoresistance, unconventional superconductivity, ferroelectricity, catalysis, and ion transport.
Austenitic stainless steel microstructures produced by directed energy deposition (DED)are analogous to those developed during welding, particularly high energy density welding. To better understand microstructural development during DED, theories of microstructural evolution,which have been established to contextualize weld microstructures, are applied in this study to microstructural development in DED austenitic stainless steels. Phenomenological welding models that describe the development of oxide inclusions, compositional microsegregation, ferrite,matrix austenite grains, and dislocation substructures are utilized to clarify microstructural evolution during deposition of austenitic stainless steels. Two different alloys, 304L and 316L, arecompared to demonstrate the broad applicability of this framework for understanding microstmctural development during the DED process. Despite differences in grain morphology and solidification mode for these two alloys (which can be attributed to compositional differences),similar tensile properties are achieved. It is the fine-scale compositional segregation and dislocation structures that ultimately determine the strength of these materials. The evolution of microsegregation and dislocation structures is shown to be dependent on the rapid solidification and thermomechanical history of the DED processing method and not the composition of the starting material.
Detection and capture of toxic nitrogen oxides (NO x ) is important for emissions control of exhaust gases and general public health. The ability to directly electrically detect trace (0.5–5 ppm) NO 2 by a metal–organic framework (MOF)‐74‐based sensor at relatively low temperatures (50 °C) is demonstrated via changes in electrical properties of M‐MOF‐74, M = Co, Mg, Ni. The magnitude of the change is ordered Ni > Co > Mg and explained by each variant's NO 2 adsorption capacity and specific chemical interaction. Ni‐MOF‐74 provides the highest sensitivity to NO 2 ; a 725× decrease in resistance at 5 ppm NO 2 and detection limit <0.5 ppm, levels relevant for industry and public health. Furthermore, the Ni‐MOF‐74‐based sensor is selective to NO 2 over N 2 , SO 2 , and air. Linking this fundamental research with future technologies, the high impedance of MOF‐74 enables applications requiring a near‐zero power sensor or dosimeter, with the active material drawing <15 pW for a macroscale device 35 mm 2 with 0.8 mg MOF‐74. This represents a 10 4 –10 6 × decrease in power consumption compared to other MOF sensors and demonstrates the potential for MOFs as active components for long‐lived, near‐zero power chemical sensors in smart industrial systems and the internet of things.
We experimentally demonstrate that electrically neutral particles, neutrons, can be used to directly visualize the electrostatic field inside a target volume that can be physically isolated or occupied. Electric field images are obtained using a spin-polarized neutron beam with a recently developed polarimetry method for polychromatic beams that permits detection of a small angular change in spin orientation. This Letter may enable a new diagnostic technique sensitive to the structure of electric potential, electric polarization, charge distribution, and dielectric constant by imaging spatially dependent electric fields in objects that cannot be accessed by other probes.
Although chemically inert, Xe and other noble gases have been shown to have functional effects on biological systems. For example, Xe is a powerful anesthetic with neuroprotective properties. Recent reports have claimed that Xe inhibits the activity of tissue plasminogen activator (tPA) and urate oxidase (UOX), indicating that the use of Xe as an anesthetic may have undesirable side effects. Here, we revisited the methods used to demonstrate Xe inhibition of UOX and tPA, testing both indirect and direct gas delivery methods with variable bubble sizes and gas flowrates. Our results indicate that Xe or Kr do not affect the activity of UOX or tPA and that the previously reported inhibition is due to protein damage attendant to directly bubbling gases into protein solutions. The lack of evidence to support Xe inhibition of UOX or tPA alleviates concerns regarding possible side effects for the clinical application of Xe as an anesthetic. Furthermore, this study illustrates the importance of using indirect methods of gas dissolution for studying gas-protein interactions in aqueous solution.
We consider the problem of recovering program structure from compiled binary code. We first extract the call graph and layout of functions in memory from the compiled code and represent this information in a graphical format. We then employ Louvain's modularity algorithm to identify clusters of functions that are considered to be related. We find that the quality and properties of clusters extracted by our technique are greatly impacted by the relative importance we assign to the call graph and the ordering of functions in memory.
Background: The quality of Digital Volume Correlation (DVC) full-field displacement measurements depends directly on the characteristics of the X-ray Computed Tomography (XCT) equipment, and scan procedures used to acquire the tomographic images. Objective: In this work, we seek to experimentally study the effects of XCT equipment and tomographic scan procedures on the quality of these images for DVC analysis, and to survey the level of DVC error that may be achieved using standard XCT operating procedures. Methods: Six participants in an interlaboratory study acquired high-quality XCT scans of a syntactic foam before and after rigid body motion. The resulting images were correlated using commercial DVC software to quantify error sources due to random image noise, reconstruction artifacts, as well as systematic spatial or temporal distortion. Results: In the absence of rigid body motion, the standard deviation of the displacement measurements ranged from 0.012 to 0.043 voxels using a moderate subvolume size, indicating that subvoxel measurement resolution could readily be achieved with a variety of XCT equipment and scan recipes. Comparison of consecutive scans without rigid body motion showed transient dilatational displacement gradients due to self-heating of the X-ray source and/or thermal expansion of the foam. Evaluation of the scans after rigid body motion showed significant, machine-specific spatial distortion in the displacement fields of up to 0.5 voxels; new approaches to remove this error need to be developed. Conclusions: Analysis of the scan protocols used in the interlaboratory study, as well as a complementary parametric sensitivity study, showed that the DVC error was strongly influenced by the XCT equipment, but could be mitigated by adjusting the total scan duration.
The goal of this transportation analysis (TA) is to update the 2008 TA in order to evaluate the impacts associated with the transportation of transuranic (TRU) waste from waste generator sites to the Waste Isolation Pilot Plant (WIPP) facility and from waste generator sites to the Idaho National Laboratory (INL).
Electric discharge across an air gap can be self-healing, providing a unique capability for repetitive, fast, high-voltage/current switching applications through arc conduction. Furthermore, incorporating dielectric granules in the air gap stimulates gas ionization, which lowers the breakdown voltage and narrows breakdown voltage distribution, thereby enabling engineered surge protection from multiple lightning strikes on aerospace vehicles and sensitive solid-state electronics in critical systems. This study investigates the effect of the permittivity of dielectric granules, gap filling, surface roughness, and metal work function on fast-rising, high-voltage breakdowns. In addition to the air gap width, these factors play important roles in gas ionization, field concentration, and initiation of electrical discharge and arcing. Therefore, they could potentially be used to control and narrow operational breakdown voltages for practical applications. Additionally, a modified Langevin-Debye model is developed to correlate the breakdown voltage and the permittivity of the dielectric filler. These investigations identify and highlight key underpinning mechanisms governing the gas discharge behavior across a dielectric filled air gap during voltage surge events.
High temperature operation of molten sodium batteries impacts cost, reliability, and lifetime, and has limited the widespread adoption of these grid-scale energy storage technologies. Poor charge transfer and high interfacial resistance between molten sodium and solid-state electrolytes, however, prevents the operation of molten sodium batteries at low temperatures. Here, in situ formation of tin-based chaperone phases on solid state NaSICON ion conductor surfaces is shown in this work to greatly improve charge transfer and lower interfacial resistance in sodium symmetric cells operated at 110 °C at current densities up to an aggressive 50 mA cm-2. It is shown that static wetting testing, as measured by the contact angle of molten sodium on NaSICON, does not accurately predict battery performance due to the dynamic formation of a chaperone NaSn phase during cycling. This work demonstrates the promise of sodium intermetallic-forming coatings for the advancement of low temperature molten sodium batteries by improved mating of sodium-NaSICON surfaces and reduced interfacial resistance.
For systems that require complete metallic enclosures (e.g., containment buildings for nuclear reactors), it is impossible to access interior sensors and equipment using standard electromagnetic techniques. A viable way to communicate and supply power through metallic barriers is the use of elastic waves and ultrasonic transducers, introducing several design challenges that must be addressed. The objective of this work is to investigate the use of piezoelectric transducers for both sending and receiving power and data through a metallic barrier using elastic waves at ultrasonic frequencies above 1 MHz. High-fidelity numerical and simplified analytical models are developed for ultrasonic transmission and novel strategies are explored to eliminate crosstalk between channels.
The Computer Science Research Institute (CSRI) brings university faculty and students to Sandia for focused collaborative research on Department of Energy (DOE) computer and computational science problems. The institute provides an opportunity for university researchers to learn about problems in computer and computational science at DOE laboratories. Participants conduct leading-edge research, interact with scientists and engineers at the laboratories, and help transfer results of their research to programs at the labs. Some specific CSRI research interest areas are: scalable solvers, optimization, adaptivity and mesh refinement, graph-based, discrete, and combinatorial algorithms, uncertainty estimation, mesh generation, dynamic load-balancing, virus and other malicious-code defense, visualization, scalable cluster computers and heterogeneous computers, data-intensive computing, environments for scalable computing, parallel input/output, advanced architectures, and theoretical computer science. The CSRI Summer Program includes the organization of a weekly seminar series and the publication of this summer proceedings.
In this paper, we consider the optimal control of semilinear elliptic PDEs with random inputs. These problems are often nonconvex, infinite-dimensional stochastic optimization problems for which we employ risk measures to quantify the implicit uncertainty in the objective function. In contrast to previous works in uncertainty quantification and stochastic optimization, we provide a rigorous mathematical analysis demonstrating higher solution regularity (in stochastic state space), continuity and differentiability of the control-to-state map, and existence, regularity and continuity properties of the control-to-adjoint map. Our proofs make use of existing techniques from PDE-constrained optimization as well as concepts from the theory of measurable multifunctions. We illustrate our theoretical results with two numerical examples motivated by the optimal doping of semiconductor devices.
Nenoff, T.M.; Walton, Ian; Chen, Carmen; Rimsza, Jessica M.; Walton, Krista S.
Adsorption of corrosive SO2 gas occurs in metal-organic frameworks (MOFs) including UiO-66. Improvements in SO2 capacity is obtained through the incorporation of residual modulators in the UiO-66 framework by introducing new binding sites in the material, through residual modulators. Four residual modulators were investigated (acetic acid, trifluoroacetic acid, 3-DMAP acid, cyanoacetic acid), and the UiO-66 framework modulated with cyanoacetic acid exhibited nearly twice the SO2 uptake for the 18:1 modulator/linker synthesis ratio compared with other modulated UiO-66 structures. Density functional theory investigations confirmed that targeted host-guest interactions were maintained after the modulator was incorporated into the framework. The strongest binding energy was between SO2 and cyanoacetic acid, consistent with dynamic SO2 adsorption data, and identified contributions from both the SO2 reacting with the residual modulator and the coordinating linkers. The successful increase in dynamic SO2 capacity illustrates how often-overlooked non-covalent interactions can be used in targeted adsorption applications. Further investigation into weak electrostatic interactions for adsorption properties is also needed to advance the potential selectivity and capacity in the adsorption sphere.
We provide a comprehensive overview of mixed-integer programming formulations for the unit commitment (UC) problem. UC formulations have been an especially active area of research over the past 12 years due to their practical importance in power grid operations, and this paper serves as a capstone for this line of work. We additionally provide publicly available reference implementations of all formulations examined. We computationally test existing and novel UC formulations on a suite of instances drawn from both academic and real-world data sources. Driven by our computational experience from this and previous work, we contribute some additional formulations for both generator production upper bounds and piecewise linear production costs. By composing new UC formulations using existing components found in the literature and new components introduced in this paper, we demonstrate that performance can be significantly improved—and in the process, we identify a new state-of-the-art UC formulation.
Crystal plasticity theory is often employed to predict the mesoscopic states of polycrystalline metals, and is well-known to be costly to simulate. Using a neural network with convolutional layers encoding correlations in time and space, we were able to predict the evolution of the dominant component of the stress field given only the initial microstructure and external loading. In comparison to our recent work, we were able to predict not only the spatial average of the stress response but the evolution of the field itself. We show that the stress fields and their rates are in good agreement with the two dimensional crystal plasticity data and have no visible artifacts. Furthermore the distribution of stress throughout the elastic to fully plastic transition match the truth provided by held out crystal plasticity data. Lastly we demonstrate the efficacy of the trained model in material characterization and optimization tasks.
Increasing penetrations of interoperable distributed energy resources (DER) in the electric power system are expanding the power system attack surface. Maloperation or malicious control of DER equipment can now cause substantial disturbances to grid operations. Fortunately, many options exist to defend and limit adversary impact on these newly-created DER communication networks, which typically traverse the public internet. However, implementing these security features will increase communication latency, thereby adversely impacting real-time DER grid support service effectiveness. In this work, a collection of software tools called SCEPTRE was used to create a co-simulation environment where SunSpec-compliant photovoltaic inverters were deployed as virtual machines and interconnected to simulated communication network equipment. Network segmentation, encryption, and moving target defence security features were deployed on the control network to evaluate their influence on cybersecurity metrics and power system performance. The results indicated that adding these security features did not impact DER-based grid control systems but improved the cybersecurity posture of the network when implemented appropriately.
This user’s guide documents capabilities in Sierra/SolidMechanics which remain “in-development” and thus are not tested and hardened to the standards of capabilities listed in Sierra/SM 4.58 User’s Guide. Capabilities documented herein are available in Sierra/SM for experimental use only until their official release. These capabilities include, but are not limited to, novel discretization approaches such as peridynamics and the reproducing kernel particle method (RKPM), numerical fracture and failure modeling aids such as the extended finite element method (XFEM) and /-integral, explicit time step control techniques, dynamic mesh rebalancing, as well as a variety of new material models and finite element formulations
Stockpile stewardship requires accurate and predictive models relying on the generation of extreme environments which is both incredibly difficult and profoundly necessary. Next generation pulsed power facilities (NGPPF), where these environments are created, may require a paradigm shift in equipment engineering/manufacture to fulfill this need. Therefore, this research aims to investigate the limitations, capabilities and efficacy of leveraging advancements in the field of additive manufacturing (AM) in order to produce novel power flow components for NGPPFs. This work focused on commercial 3D metal AM equipment producing several prototypes addressing prescient needs/shortcomings, and a technique wherein a lightweight polymer core is metalized. Ultimately, commercial 3D metal AM is considered a viable path forward but would require a sizeable investment and does not currently support the scale and complexity necessary for NGPPFs. Moreover, initial results from our composite technique are promising and is considered a realizable path forward given further investigation.
COVID-19 patient care management would greatly benefit from new tools that enable accurate assessment of disease severity and stage, potentially enabling a personalized medicine approach. Detection of the SARS-CoV-2 virus itself, or even quantitation of viral loads, is not sufficient for accurate assessment of disease state beyond diagnosis of infection [eg, doi:10.1093/cid/ciaa344]. Levels of usual suspect protein biomarkers associated with host response to infection [eg, C-reactive protein (CRP); cytokines like IL-6, TNF-alpha, and IL-10; complement proteins like C3a and C5a], and of individual blood cell types (eg, leukocytes, lymphocytes, and subsets thereof), show limited correlation with disease severity and stage, with high patient-to-patient and study-to-study variability [eg, doi:10.1093/cid/ciaa248]. High-dimensional panels of biomarkers should have greater predictive power and resilience to unavoidable sources of variability; however, their assembly from proteins and cell types is extremely difficult, due to technical limitations in analyte measurement, especially with regard to starting material requirements and detection sensitivity. Host response profiling through Next Generation Sequencing (NGS) of gene expression patterns (ie, RNA-Seq) is a promising approach, but at the time of this project there were only two publicly available datasets of relevance [doi:10.1093/cid/ciaa203, doi:10.1080/22221751.2020.1747363], and close inspection of them revealed that each had at least one major flaw that severely undermined its value in supporting robust analysis of host response to SARS-CoV- 2 infection. However, the first of these studies [doi:10.1093/cid/ciaa203] fortuitously collected NGS data not only from host cells, but also from bacteria present in bronchoalveolar lavage fluid (BALF) recovered from COVID-19 patients; and because the respiratory microbiome (in terms of bacterial species content) is far less complex than the human transcriptome, the NGS data collected were sufficient to provide coverage depth supporting robust analysis. Surprisingly, the authors of the study did not carry out a detailed analysis of these data and their potential for revealing important new information about COVID-19. Therefore, we carried out a meta-analysis of the dataset as a first step in evaluating the potential for profiling of respiratory microbiome dynamics as a means of accurately assessing COVID-19 disease state.
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 United States Department of Energy (DOE) National Nuclear Security Administration. The National Nuclear Security Administration’s Sandia Field Office administers the contract and oversees contractor operations at Sandia National Laboratories, New Mexico. Activities at the site support research and development programs with a wide variety of national security missions, resulting in technologies for nonproliferation, homeland security, energy and infrastructure, and defense systems and assessments. DOE and its management and operating contractor are committed to safeguarding the environment, assessing sustainability practices, and ensuring the validity and accuracy of the monitoring data presented in this Annual Site Environmental Report. This report summarizes the environmental protection and monitoring programs in place at Sandia National Laboratories, New Mexico, during calendar year 2019. Environmental topics include air quality, ecology, environmental restoration, oil storage, site sustainability, terrestrial surveillance, waste management, water quality, and implementation of the National Environmental Policy Act. This report is prepared in accordance with and as required by DOE O 231.1B, Admin Change 1, Environment, Safety, and Health Reporting, and has been approved for public distribution.
This document presents tests from the Sierra Structural Mechanics verification test suite. Each of these tests is run nightly with the Sierra/SD code suite and the results of the test checked versus the correct analytic result. For each of the tests presented in this document the test setup, derivation of the analytic solution, and comparison of the Sierra/SD code results to the analytic solution is provided. This document can be used to confirm that a given code capability is verified or referenced as a compilation of example problems.
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 United States Department of Energy (DOE) National Nuclear Security Administration. The National Nuclear Security Administration’s Sandia Field Office administers the contract and oversees contractor operations at Sandia National Laboratories, Tonopah Test Range (SNL/TTR) in Nevada and Sandia National Laboratories, Kaua‘i Test Facility (SNL/KTF) in Hawai‘i. Activities at SNL/TTR are conducted in support of DOE weapons programs and have operated at the site since 1957. SNL/KTF has operated as a rocket preparation launching and tracking facility since 1962. DOE and its management and operating contractor are committed to safeguarding the environment, assessing sustainability practices, and ensuring the validity and accuracy of the monitoring data presented in this Annual Site Environmental Report. This report summarizes the environmental protection, restoration, and monitoring programs in place at SNL/TTR and SNL/KTF during calendar year 2019. Environmental topics include air quality, ecology, environmental restoration, oil storage, site sustainability, terrestrial surveillance, waste management, water quality, and implementation of the National Environmental Policy Act. This report is prepared in accordance with and as required by DOE O 231.1B, Admin Change 1, Environment, Safety, and Health Reporting, and has been approved for public distribution.
Recent observations of seismic events at the subsurface energy exploration sites show that spatial and temporal correlations sometimes do not match the spatial order of the known or detected fault location from the injection well. This study investigates the coupled flow and geomechanical control on the patterns of induced seismicity along multiple basement faults that show an unusual spatiotemporal relation with induced seismicity occurring in the far field first, followed by the near field. Two possible geological scenarios considered are (1) the presence of conductive hydraulic pathway within the basement connected to the distant fault (hydraulic connectivity) and (2) no hydraulic pathway, but the coexistence of faults with mixed polarity (favorability to slip) as observed at Azle, TX. Based on the Coulomb stability analysis and seismicity rate estimates, simulation results show that direct pore pressure diffusion through a hydraulic pathway to the distant fault can generate a larger number of seismicity than along the fault close to the injection well. Prior to pore pressure diffusion, elastic stress transfer can initiate seismic activity along the favorably oriented fault, even at the longer distance to the well, which may explain the deep 2013–2014 Azle earthquake sequences. This study emphasizes that hydrological and geomechanical features of faults will locally control poroelastic coupling mechanisms, potentially influencing the spatiotemporal pattern of injection-induced seismicity, which can be used to infer subsurface architecture of fault/fracture networks.
We describe here diffusion models apt to construct a multipole-based, cable braid time domain model for conducting wires. Implementation details of both a ladder network valid for time-domain signals with all frequency content and an approximate single-stage circuit valid for low-frequency dominated time signals (such as electromagnetic pulses) are reported. This time domain model can be leveraged to treat system-generated electromagnetic pulse events, as well as used to further confirm the correctness of the multipole-based, cable braid frequency domain model.
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. 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.
GPUs are now a fundamental accelerator for many high-performance computing applications. They are viewed by many as a technology facilitator for the surge in fields like machine learning and Convolutional Neural Networks. To deliver the best performance on a GPU, we need to create monitoring tools to ensure that we optimize the code to get the most performance and efficiency out of a GPU. Since NVIDIA GPUs are currently the most commonly implemented in HPC applications and systems, NVIDIA tools are the solution for performance monitoring. The Light-Weight Distributed Metric System (LDMS) at Sandia is an infrastructure widely adopted for large-scale systems and application monitoring. Sandia has developed CPU application monitoring capability within LDMS. Therefore, we chose to develop a GPU monitoring capability within the same framework. In this report, we discuss the current limitations in the NVIDIA monitoring tools, how we overcame such limitations, and present an overview of the tool we built to monitor GPU performance in LDMS and its capabilities. Also, we discuss our current validation results. Most of the performance counter results are the same in both vendor tools and our tool when using LDMS to collect these results. Furthermore, our tool provides these statistics during the entire runtime of the tool as a time series and not just aggregate statistics at the end of the application run. This allows the user to see the progress of the behavior of the applications during their lifetime.
The well-known vulnerability of Deep Neural Networks to adversarial samples has led to a rapid cycle of increasingly sophisticated attack algorithms and proposed defenses. While most contemporary defenses have been shown to be vulnerable to carefully configured attacks, methods based on gradient regularization and out-of-distribution detection have attracted much interest recently by demonstrating higher resilience to a broad range of attack algorithms. However, no study has yet investigated the effect of combining these techniques. In this paper, we consider the effect of Jacobian matrix regularization on the detectability of adversarial samples on the CIFAR-10 image benchmark dataset. We find that regularization has a significant effect on detectability, and in some cases can make an undetectable attack on a baseline model detectable. In addition, we give evidence that regularization may mitigate the known weaknesses of detectors to high-confidence adversarial samples. The defenses we consider here are highly generalizable, and we believe they will be useful for further investigations to transfer machine learning robustness to other data domains.
Impact loads can induce a series of undesirable physical phenomena including vibration, acoustical shock, perforation, fracture and fragmentation, etc. The energy associated with the impact loads can lead to severe structure damage and human injuries. A design approach which effectively reduces these negative impacts through shock/stress wave diversion is highly needed. In this paper, a computational model which predicts stress wave propagation by considering different beam geometries and configurations is developed. A novel concept of wave guide design which modifies the stress wave propagation path without disturbance is also presented. This design approach is not only useful for material property characterization particularly at intermediate or high strain rates, but also allows stress wave propagation in a desired direction as the shock/impact energy can be redistributed in controllable paths. The numerical results are experimentally verified through a Drop-Hopkinson bar apparatus at Sandia National Laboratories.
Researchers have been extensively studying wide-bandgap (WBG) semiconductor materials such as gallium nitride (GaN) with an aim to accomplish an improvement in size, weight, and power of power electronics beyond current devices based on silicon (Si). However, the increased operating power densities and reduced areal footprints of WBG device technologies result in significant levels of self-heating that can ultimately restrict device operation through performance degradation, reliability issues, and failure. Typically, self-heating in WBG devices is studied using a single measurement technique while operating the device under steady-state direct current measurement conditions. However, for switching applications, this steady-state thermal characterization may lose significance since the high power dissipation occurs during fast transient switching events. Therefore, it can be useful to probe the WBG devices under transient measurement conditions in order to better understand the thermal dynamics of these systems in practical applications. In this work, the transient thermal dynamics of an AlGaN/GaN high electron mobility transistor (HEMT) were studied using thermoreflectance thermal imaging and Raman thermometry. Also, the proper use of iterative pulsed measurement schemes such as thermoreflectance thermal imaging to determine the steady-state operating temperature of devices is discussed. These studies are followed with subsequent transient thermal characterization to accurately probe the self-heating from steady-state down to submicrosecond pulse conditions using both thermoreflectance thermal imaging and Raman thermometry with temporal resolutions down to 15 ns.
Improving predictive models for noble gas transport through natural materials at the field-scale is an essential component of improving US nuclear monitoring capabilities. Several field-scale experiments with a gas transport component have been conducted at the Nevada National Security Site (Non-Proliferation Experiment, Underground Nuclear Explosion Signatures Experiment). However, the models associated with these experiments have not treated zeolite minerals as gas adsorbing phases. This is significant as zeolites are a common alteration mineral with a high abundance at these field sites and are shown here to significantly fractionate noble gases during field-scale transport. This fractionation and associated retardation can complicate gas transport predictions by reducing the signal-to-noise ratio to the detector (e.g. mass spectrometers or radiation detectors) enough to mask the signal or make the data difficult to interpret. Omitting adsorption-related retardation data of noble gases in predictive gas transport models therefore results in systematic errors in model predictions where zeolites are present.Herein is presented noble gas adsorption data collected on zeolitized and non-zeolitized tuff. Experimental results were obtained using a unique piezometric adsorption system designed and built for this study. Data collected were then related to pure-phase mineral analyses conducted on clinoptilolite, mordenite, and quartz. These results quantify the adsorption capacity of materials present in field-scale systems, enabling the modeling of low-permeability rocks as significant sorption reservoirs vital to bulk transport predictions.
Incorporating experimental data is essential for increasing the credibility of simulation-aided decision making and design. This paper presents a method which uses a computational model to guide the optimal acquisition of experimental data to produce data-informed predictions of quantities of interest (QoI). Many strategies for optimal experimental design (OED) select data that maximize some utility that measures the reduction in uncertainty of uncertain model parameters, for example the expected information gain between prior and posterior distributions of these parameters. In this paper, we seek to maximize the expected information gained from the push-forward of an initial (prior) density to the push-forward of the updated (posterior) density through the parameter-to-prediction map. The formulation presented is based upon the solution of a specific class of stochastic inverse problems which seeks a probability density that is consistent with the model and the data in the sense that the push-forward of this density through the parameter-to-observable map matches a target density on the observable data. While this stochastic inverse problem forms the mathematical basis for our approach, we develop a one-step algorithm, focused on push-forward probability measures, that leverages inference-for-prediction to bypass constructing the solution to the stochastic inverse problem. A number of numerical results are presented to demonstrate the utility of this optimal experimental design for prediction and facilitate comparison of our approach with traditional OED.
Analog hardware accelerators, which perform computation within a dense memory array, have the potential to overcome the major bottlenecks faced by digital hardware for data-heavy workloads such as deep learning. Exploiting the intrinsic computational advantages of memory arrays, however, has proven to be challenging principally due to the overhead imposed by the peripheral circuitry and due to the non-ideal properties of memory devices that play the role of the synapse. We review the existing implementations of these accelerators for deep supervised learning, organizing our discussion around the different levels of the accelerator design hierarchy, with an emphasis on circuits and architecture. We explore and consolidate the various approaches that have been proposed to address the critical challenges faced by analog accelerators, for both neural network inference and training, and highlight the key design trade-offs underlying these techniques.
Polymorphism in metal−organic frameworks (MOFs) means that the same chemical building blocks (nodes and linkers) can be used to construct isomeric MOFs with different topological networks. The choice of topology can substantially impact the pore network of the MOF, changing the sizes and shapes of the pores, which has implications for adsorption and separation applications. In this work, we look at the influence of topology in 38 polymorphic MOFs on the separation of linear and branched C4–C6 alkane isomers, a separation of great importance to the petrochemical industry. We find that the MOF Cu2(1,4-benzenedicarboxylate) in nbo topology (nbo-Cu2BDC) has particularly high affinity for linear alkanes due to its small pore size, which excludes the branched isomers. Upon studying this MOF in further detail, we find that it can take either of two conformations: a cubic conformation, which is typical of nbo MOFs, and a unique star conformation that contains 1D triangular and hexagonal channels. The determination of which conformation the MOF will adopt depends on steric effects between the nodes and linkers.
Electrochemical techniques were used to investigate the erosion-corrosion of titanium in simulated acidic mineral leaching slurries. Erosion-corrosion of titanium was caused by solid particle impingement. Electrochemical noise revealed that solid particle impacts resulted in localised fracture of the passive film, and erosion-corrosion of titanium proceeded in the form of current transients. As conditions become more abrasive, erosion-corrosion is an increasing threat to titanium equipment exposed to acidic slurries.
Particle characteristics can drastically influence the process-structure-property-performance aspects of granular materials in compression. We aim to computationally simulate the mechanical processes of stress redistribution in compacts including the kinematics of particle rearrangement during densification and particle deformation leading to fragmentation. Confined compression experiments are conducted with three sets of commercial microcrystalline cellulose particles nearly spherical in shape with different mean particle size. Experimentally measured compression curves from tall powder columns are fitted with the Kenkre et al. (J. of American Chemical Society, Vol. 79, No. 12) model. This model provides a basis to derive several common two-parameter literature models and as a framework to incorporate statistical representations of critical particle behaviors. We focus on the low-stress compression data and the model comparisons typically not discussed in the literature. Additional single particle compressions report fracture strength with particle size for comparison to the apparent particle strength extracted from bulk compression data.
The current COVID-19 pandemic has resulted in globally constrained supplies for face masks and personal protective equipment (PPE). Production capacity is limited in many countries and the future course of the pandemic will likely continue with shortages for high quality masks and PPE in the foreseeable future. Hence, expectations are that mask reuse, extended wear and similar approaches will enhance the availability of personal protective measures. Repeated thermal disinfection could be an important option and likely easier implemented in some situations, at least on the small scale, than UV illumination, irradiation or hydrogen peroxide vapor exposure. An overview on thermal responses and ongoing filtration performance of multiple face mask types is provided. Most masks have adequate material properties to survive a few cycles (i.e. 30 min disinfection steps) of thermal exposure in the 75°C regime. Some are more easily affected, as seen by the fusing of plastic liner or warping, given that preferred conditioning temperatures are near the softening point for some of the plastics and fibers used in these masks. Hence adequate temperature control is equally important. As guidance, disinfectants sprayed via dilute solutions maintain a surface presence over extended time at 25 and 37°C. Some spray-on alcohol-based solutions containing disinfectants were gently applied to the top surface of masks. Neither moderate thermal aging (less than 24 h at 80 and 95°C) nor gentle application of surface disinfectant sprays resulted in measurable loss of mask filter performance. Subject to bio-medical concurrence (additional checks for virus kill efficiency) and the use of low risk non-toxic disinfectants, such strategies, either individually or combined, by offering additional anti-viral properties or short term refreshing, may complement reuse options of professional masks or the now ubiquitous custom-made face masks with their often unknown filtration effectiveness.
Aerosol jet printing offers a versatile, high-resolution digital patterning capability broadly relevant to flexible and printed electronic systems. Despite its promise and numerous demonstrations, the theoretical principles driving process outputs have not been thoroughly explored. Here a custom-built, modular printing system is developed to provide a head-to-head comparison of two print nozzle geometries to better understand the technology. Print resolution data from a range of process parameters are analyzed using a support vector machine framework. The linear deposition rate is identified as a key variable, which can confound careful studies of printing performance. Taking this into account, a clear difference is observed between the printheads, corresponding to a difference in resolution of 57% 11% under typical conditions. Models to understand differences in aerodynamic and mass transport effects identify enhanced drying within the NanoJet printhead as a likely cause of this difference. Overall, this study provides improved understanding of the aerosol jet printing process, including valuable insight to inform process optimization, robust data analysis, ink formulation, and printer geometric design.
The self-interstitial atom (SIA) is one of two fundamental point defects in bulk Si. Isolated Si SIAs are extremely difficult to observe experimentally. Even at very low temperatures, they anneal before typical experiments can be performed. Given the challenges associated with experimental characterization, accurate theoretical calculations provide valuable information necessary to elucidate the properties of these defects. Previous studies have applied Kohn-Sham density functional theory (DFT) to the Si SIA, using either the local density approximation or the generalized gradient approximation to the exchange-correlation (XC) energy. The consensus of these studies indicates that a Si SIA may exist in five charge states ranging from -2 to +2 with the defect structure being dependent on the charge state. This study aims to re-examine the existence of these charge states in light of recently derived "approximate bounds"on the defect levels obtained from finite-size supercell calculations and new DFT calculations using both semi-local and hybrid XC approximations. We conclude that only the neutral and +2 charge states are directly supported by DFT as localized charge states of the Si SIA. Within the current accuracy of DFT, our results indicate that the +1 charge state likely consists of an electron in a conduction-band-like state that is coulombically bound to a +2 SIA. Furthermore, the -1 and -2 charge states likely consist of a neutral SIA with one and two additional electrons in the conduction band, respectively.
In this article, we studied the total ionization dose (TID) effects on the multilevel-cell (MLC) 3-D NAND flash memory using Co-60 gamma radiation. We found a significant page-to-page bit error variation within a physical memory block of the irradiated memory chip. Our analysis showed that the origin of the bit error variation is the unique vertical layer-dependent TID response of the 3-D NAND. We found that the memory pages located at the upper and lower layers of the 3-D stack show higher fails compared to the middle-layer pages of a given memory block. We confirmed our findings by comparing radiation response of four different chips of the same specification. In addition, we compared the TID response of the MLC 3-D NAND with that of the 2-D NAND chip, which showed less page-to-page variation in bit error within a given memory block. We discuss the possible application of our findings for the radiation-tolerant smart memory controller design.
Reducing ion beam damage from the focused ion beam (FIB) during fabrication of cross sections is a well-known challenge for materials characterization, especially cross sectional characterization of nanostructures. To address this, a new method has been developed for cross section fabrication enabling high resolution transmission electron microscopy (TEM) analysis of 3-D nanostructures free of surrounding material and free of damage detectable by TEM analysis. Before FIB processing, nanopillars are encapsulated in a sacrificial oxide which acts as a protective layer during FIB milling. The cross sectional TEM lamella containing the nanopillars is then mounted and thinned with some modifications to conventional FIB sample preparation that provide stability for the lamella during the following wet-chemical dip etch. The wet-chemical etch of the TEM lamella removes the sacrificial oxide layer, freeing the nanopillars from any material that would obscure TEM imaging. Both high resolution TEM and aberration corrected scanning TEM images of Si/SiGe pillars with diameters down to 30 nm demonstrate the successful application of this approach.