In this study, we report on the thermal conductivity of amorphous carbon generated in diamond via nitrogen ion implantation (N 3 + at 16.5 MeV). Transmission electron microscopy techniques demonstrate amorphous band formation about the longitudinal projected range, localized approximately 7 μm beneath the sample surface. While high-frequency time-domain thermoreflectance measurements provide insight into the thermal properties of the near-surface preceding the longitudinal projected range depth, a complimentary technique, steady-state thermoreflectance, is used to probe the thermal conductivity at depths which could not otherwise be resolved. Through measurements with a series of focusing objective lenses for the laser spot size, we find the thermal conductivity of the amorphous region to be approximately 1.4 W m-1 K-1, which is comparable to that measured for amorphous carbon films fabricated through other techniques.
The outer core of the Earth is composed primarily of liquid iron, and the inner core boundary is governed by the intersection of the melt line and the geotherm. While there are many studies on the thermodynamic equation of state for solid iron, the equation of state of liquid iron is relatively unexplored. We use dynamic compression to diagnose the high‐pressure liquid equation of state of iron by utilizing the shock‐ramp capability at Sandia National Laboratories’ Z‐Machine. This technique enables measurements of material states off the Hugoniot by initially shocking samples and subsequently driving a further, shockless compression. Planetary studies benefit greatly from isentropic, off‐Hugoniot experiments since they can cover pressure‐temperature (P‐T) conditions that are close to adiabatic profiles found in planetary interiors. We used this method to drive iron to P‐T conditions similar to those of the Earth’s outer‐inner core boundary, along an elevated‐temperature isentrope in the liquid from 275 GPa to 400 GPa. We derive the equation of state using a hybrid backward integration – forward Lagrangian technique on particle velocity traces to determine the pressure‐density history of the sample. Our results are in excellent agreement with SESAME 92141, a previously published equation of state table. With our data and previous experimental data on liquid iron we provide new information on the iron melting line and derive new parameters for a Vinet‐based equation of state. The table and our parameterized equation of state are applied to provide an updated means of modeling the pressure, mass, and density of liquid iron cores in exoplanetary interiors.
The solid-electrolyte interphase (SEI) that forms on lithium ion battery (LIB) anodes prevents degradation-causing transfer of electrons to the electrolyte. Grain boundaries (GBs) between different SEI components, like LiF, have been suggested to accelerate Li+transport. However, using the non-equilibrium Green's function technique with density functional theory (NEGF-DFT), we find that GBs enhance electron tunneling in thin LiF films by 1-2 orders of magnitude, depending on the bias. Extrapolating to thicker films using the Wentzel-Kramers-Brillouin (WKB) method emphasizes that safer batteries require passivation of GBs in the SEI.
Wang, Dixiong; Musavigharavi, Pariasadat; Zheng, Jeffrey; Esteves, Giovanni E.; Liu, Xiwen; Stach, Eric A.; Jariwala, Deep; Olsson III, Roy H.
In this work, the frequency-dependent ferroelectric properties of 45 nm (Al,Sc)N films sputter deposited on complementary metal–oxide–semiconductor (CMOS)-compatible Al metal electrodes are measured and compared. Low in-plane compressive stress (-10 ± 20 MPa) is observed in (Al,Sc)N thin films deposited on Al electrodes. The (Al,Sc)N films exhibit an imprint in the measured coercive fields (Ec) of -4.3/+5.3 MV cm-1 at 10 kHz. Using positive-up negative-down (PUND) measurements, ferroelectric switching is observed within ≈200 ns of an applied voltage pulse, which demonstrates the ability of ferroelectric (Al,Sc)N to achieve the fast read/write speeds desired in memory devices.
Each year there are millions of dollars spent on the research and production of high-resolution detectors. This research indicates that the pursuit of higher resolution detectors is not always necessary. The terminal resolution of a NaI detector, or highest detector resolution, at which identification algorithms fail to identify highly enriched uranium (HEU) was evaluated using GADRAS, Genie, and GammaVision. GADRAS employs a template matching algorithm, while Genie and GammaVision utilize a mathematical approach for peak search and identification. The NaI spectra utilized for evaluation were generated using the GADRAS Inject tab and source modeling functions. Each spectrum included terrestrial and cosmic background from Dallas, TX. The resolutions for each spectrum were increased from a default 8.92% to a point where each algorithm would fail to identify 235U from a HEU source. Six different source configurations were used in this research: bare HEU, 50% shielded HEU, 90% shielded HEU, bare HEU with an interference source of 99mTc, bare HEU with 99mTc both shielded 50%, and bare HEU with 99mTc both shielded 90%. T
MLIR (Multi-Level Intermediate Representation), is an extensible compiler framework that supports high-level data structures and operation constructs. These higher-level code representations are particularly applicable to the artificial intelligence and machine learning (AI/ML) domain, allowing developers to more easily support upcoming heterogeneous AI/ML accelerators and develop flexible domain specific compilers/frameworks with higher-level intermediate representations (IRs) and advanced compiler optimizations. The result of using MLIR within the LLVM compiler framework is expected to yield significant improvement in the quality of generated machine code, which in turn will result in improved performance and hardware efficiency
Karve, Vaibhav; Laros, James H.; Abolhelm, Marzieh; Work, Daniel B.; Sowers, Richard B.
This article proposes several advances to sparse nonnegative matrix factorization (SNMF) as a way to identify large-scale patterns in urban traffic data. The input to our model is traffic counts organized by time and location. Nonnegative matrix factorization additively decomposes this information, organized as a matrix, into a linear sum of temporal signatures. Penalty terms encourage this factorization to concentrate on only a few temporal signatures, with weights which are not too large. Our interest here is to quantify and compare the regularity of traffic behavior, particularly across different broad temporal windows. In addition to the rank and error, we adapt a measure introduced by Hoyer to quantify sparsity in the representation. Combining these, we construct several curves which quantify error as a function of rank (the number of possible signatures) and sparsity; as rank goes up and sparsity goes down, the approximation can be better and the error should decreases. Plots of several such curves corresponding to different time windows leads to a way to compare disorder/order at different time scalewindows. In this paper, we apply our algorithms and procedures to study a taxi traffic dataset from New York City. In this dataset, we find weekly periodicity in the signatures, which allows us an extra framework for identifying outliers as significant deviations from weekly medians. We then apply our seasonal disorder analysis to the New York City traffic data and seasonal (spring, summer, winter, fall) time windows. We do find seasonal differences in traffic order.
The role of local molecular structure on calculated 13C and 19F NMR chemical shifts for graphite fluoride materials was explored by using gauge-including projector augmented wave (GIPAW) computational methods for different periodic crystal polymorphs and density functional theory (DFT) gauge-including atomic orbital (GIAO) computational methods for individual graphite fluoride platelets, i.e., fluorinated graphene (FG). The impact of stacking sequences, d-spacing, and ring conformations on fully fluorinated graphite fluoride structures was investigated. A range of different defects including Stone-Wales, F and C vacancies, void formation, and F inversion were also evaluated using FG structures. These calculations show that distinct chemical shift signatures exist for many of these polymorphs and defects, therefore providing a basis for spectral assignment and development of models describing the mean local CF structure in disordered graphite fluoride materials.
Vemuganti, Shreya; Stormont, John C.; Pyrak-Nolte, Laura J.; Dewers, Thomas D.; Taha, M.M.R.
Cement is widely used in wellbores to stabilize the steel casing used in wellbore operations for oil and gas production, enhanced geothermal systems and carbon sequestration, and to limit fluid movement between sub-surface strata. Flaws such as microcracks in wellbore cement can lead to leakage along the wellbore compromising wellbore integrity. There is an increasing need for methods to monitor cement crack propagation in wellbore environments. In this study, we develop and report the first cementitious sensors capable of exhibiting high frequency acoustic bandgaps (ABGs) using carbon nanotubes (CNTs). Computational simulations of a sensor unit cell are used to design cement-multi walled carbon nanotubes (MWCNTs) sensors that show a wide bandgap. When the cement-MWCNTs sensors is embedded in cement specimens, bandgaps were measured experimentally under 300 kHz and under 600 kHz, consistent with the computationally predicted bandgaps in the range of 290–360 kHz, 410–460 kHz and 515–585 kHz. These bandgap features were absent in homogeneous cement specimens. X-ray tomographic reconstructions showed microscopic debonding at cement-MWCNTs sensor interface. Frequency response analysis of a three-dimensional computational model indicated a shift of frequency of minimum transmission due to the interface debonding, but no perturbation of bandgap response was observed. Here, the cement-MWCNTs sensors developed in this study show the potential of a packed CNT inclusion material in cementitious matrix to create ABGs in a cement matrix.
Hamm, Susan G.; X, Arlene A.; Blankenship, Douglas A.; Boyd, Lauren W.; X, Elizabeth B.; Frone, Zachary; X, Ian H.; X, Hannah H.; X, Matthew K.; X, Alethia M.; Mckittrick, Alexis M.W.; X, Lindsey M.; X, Elisabet M.; X, Angel N.; X, Jon P.; Porse, Sean L.; X, Alexandra P.; X, George S.; X, Coryne T.; X, William V.; X, Gerry W.; X, Michael W.; X, Jeffrey W.
Geothermal energy can provide answers to many of America’s essential energy questions. The United States has tremendous geothermal resources, as illustrated by the results of the DOE GeoVision analysis, but technical and non-technical barriers have historically stood in the way of widespread deployment of geothermal energy. The U.S. Department of Energy’s Geothermal Technologies Office within the Office of Energy Efficiency and Renewable Energy has invested more than $470 million in research and development (R&D) since 2015 to meet its three strategic goals: (1) unlock the potential of enhanced geothermal systems, (2) advance technologies to increase geothermal energy on the U.S. electricity grid, and (3) support R&D to expand geothermal energy opportunities throughout the United States. Here, we describe many of those R&D initiatives and outlines future directions in geothermal research.
Pyruvic acid, a representative alpha-keto carboxylic acid, is one of the few organic molecules destroyed in the troposphere by solar radiation rather than by reactions with free radicals. To date, only its stable final products were identified, often with contribution from secondary chemistry, making it difficult to elucidate photodissociation mechanisms following excitation to the lowest singlet excited-state (S1) and the role of the internal hydrogen bond in the most-stable Tc conformer. Using multiplexed photoionization mass spectrometry we report the first direct experimental evidence, via the observation of singlet methylhydroxycarbene (MHC) following 351 nm excitation, supporting the decarboxylation mechanism previously proposed. Decarboxylation to MHC + CO2 represents 97–100% of product branching at 351 nm. We observe vinyl alcohol and acetaldehyde, which we attribute to isomerization of MHC. We also observe a 3 ± 2% yield of the Norrish Type I photoproducts CH3CO + DOCO, but only from d1-pyruvic acid. At 4 Torr pressure, we measure a photodissociation quantum yield of $1.0^{+0}_{–0.4}$, consistent with IUPAC recommendations. However, our measured product branching fractions disagree with IUPAC. In light of previous calculations, these results support a mechanism in which hydrogen transfer on the S1 excited state occurs at least partially by tunneling, in competition with intersystem crossing to the T1 state. Here, we present the first evidence of a bimolecular reaction of MHC in the gas phase, where MHC reacts with pyruvic acid to produce a C4H8O2 product. This observation implies that some MHC produced from pyruvic acid in Earth's troposphere will be stabilized and participate in chemical reactions with O2 and H2O, and should be considered in atmospheric modeling.
Theocharides, Spyros; Theristis, Marios; Makrides, George; Kynigos, Marios; Spanias, Chrysovalantis; Georghiou, George E.
A main challenge for integrating the intermittent photovoltaic (PV) power generation remains the accuracy of day-ahead forecasts and the establishment of robust performing methods. The purpose of this work is to address these technological challenges by evaluating the day-ahead PV production forecasting performance of different machine learning models under different supervised learning regimes and minimal input features. Specifically, the day-ahead forecasting capability of Bayesian neural network (BNN), support vector regression (SVR), and regression tree (RT) models was investigated by employing the same dataset for training and performance verification, thus enabling a valid comparison. The training regime analysis demonstrated that the performance of the investigated models was strongly dependent on the timeframe of the train set, training data sequence, and application of irradiance condition filters. Furthermore, accurate results were obtained utilizing only the measured power output and other calculated parameters for training. Consequently, useful information is provided for establishing a robust day-ahead forecasting methodology that utilizes calculated input parameters and an optimal supervised learning approach. Finally, the obtained results demonstrated that the optimally constructed BNN outperformed all other machine learning models achieving forecasting accuracies lower than 5%.
Rempe, Susan R.; Cetuk, Hannah; Anishkin, Andriy; Scott, Alison J.; Ernst, Robert K.
The outer membrane (OM) of Gram-negative (G-) bacteria presents a barrier for many classes of antibacterial agents. Lipopolysaccharide (LPS), present in the outer leaflet of the OM, is stabilized by divalent cations and is considered to be the major impediment for antibacterial agent permeation. However, the actual affinities of major antibiotic classes toward LPS have not yet been determined. In the present work, we use Langmuir monolayers formed from E. coli Re and Rd types of LPS to record pressure-area isotherms in the presence of antimicrobial agents. Our observations suggest three general types of interactions. First, some antimicrobials demonstrated no measurable interactions with LPS. This lack of interaction in the case of cefsulodin, a third-generation cephalosporin antibiotic, correlates with its low efficacy against G-bacteria. Ampicillin and ciprofloxacin also show no interactions with LPS, but in contrast to cefsulodin, both exhibit good efficacy against G-bacteria, indicating permeation through common porins. Second, we observe substantial intercalation of the more hydrophobic antibiotics, novobiocin, rifampicin, azithromycin, and telithromycin, into relaxed LPS monolayers. These largely repartition back to the subphase with monolayer compression. We find that the hydrophobic area, charge, and dipole all show correlations with both the mole fraction of antibiotic retained in the monolayer at the monolayer-bilayer equivalence pressure and the efficacies of these antibiotics against G-bacteria. Third, amine-rich gentamicin and the cationic antimicrobial peptides polymyxin B and colistin show no hydrophobic insertion but are instead strongly driven into the polar LPS layer by electrostatic interactions in a pressure-independent manner. Their intercalation stably increases the area per molecule (by up to 20%), which indicates massive formation of defects in the LPS layer. These defects support a self-promoted permeation mechanism of these antibiotics through the OM, which explains the high efficacy and specificity of these antimicrobials against G-bacteria.
Pike, Christopher; Whitney, Erin; Wilber, Michelle; Stein, Joshua S.
This paper presents the first systematic comparison between south-facing monofacial and bifacial photovoltaic (PV) modules, as well as between south-facing bifacial and vertical east-west facing bifacial PV modules in Alaska. The state’s solar industry, driven by the high price of energy and dropping equipment costs, is quickly growing. The challenges posed by extreme sun angles in Alaska’s northern regions also present opportunities for unique system designs. Annual bifacial gains of 21% were observed between side by side south-facing monofacial and bifacial modules. Vertical east-west bifacial modules had virtually the same annual production as south-facing latitude tilt bifacial modules, but with different energy production profiles.
Su, Yang D.; Preger, Yuliya P.; Burroughs, Hannah; Sun, Chenhu; Ohodnicki, Paul R.
Applications of fiber optic sensors to battery monitoring have been increasing due to the growing need of enhanced battery management systems with accurate state estimations. The goal of this review is to discuss the advancements enabling the practical implementation of battery internal parameter measurements including local temperature, strain, pressure, and refractive index for general operation, as well as the external measurements such as temperature gradients and vent gas sensing for thermal runaway imminent detection. A reasonable matching is discussed between fiber optic sensors of different range capabilities with battery systems of three levels of scales, namely electric vehicle and heavy-duty electric truck battery packs, and grid-scale battery systems. The advantages of fiber optic sensors over electrical sensors are discussed, while electrochemical stability issues of fiber-implanted batteries are critically assessed. This review also includes the estimated sensing system costs for typical fiber optic sensors and identifies the high interrogation cost as one of the limitations in their practical deployment into batteries. Finally, future perspectives are considered in the implementation of fiber optics into high-value battery applications such as grid-scale energy storage fault detection and prediction systems.
This report details results of a one-year LDRD to understand the dynamics, figures of merit, and fabrication possibilities for levitating a micro-scale, disk-shaped dielectric in an optical field. Important metrics are the stability, positional uncertainty, and required optical power to maintain levitation. Much of the results are contained in a publication written by our academic alliance collaborators. Initial structures were grown at Sandia labs and a test fabrication flow was executed. Owing to our strength in VCSEL lasers, we were particularly interested in calculations and fabrication flows that could be compatible with a VCSEL light source.
For digital twins (DTs) to become a central fixture in mission critical systems, a better understanding is required of potential modes of failure, quantification of uncertainty, and the ability to explain a model’s behavior. These aspects are particularly important as the performance of a digital twin will evolve during model development and deployment for real-world operations.
Herein we report the progress towards an organic glass scintillator with fast and thermal neutron sensitivity providing “triple” pulse shape discrimination (PSD) through the inclusion of a boron-incorporated aromatic molecule. The commercially available molecule 2-(p-tolyl)-1,3,2-dioxaborinane (TDB) can be readily synthesized in one step using inexpensive materials and incorporated into the organic glass scintillator at 20% by weight or 0.25% 10B by mass. In addition, we demonstrate that TDB can be easily scaled up and formulated into organic glass scintillator samples to produce a thermal neutron capture signal with a light yield equivalent to 120.4 ± 3.7 keVee, which is the highest value reported in the literature to date.
Sandia National Laboratories was asked to provide a technical feasibility assessment on whether programmability is a viable means to address the radiation levels in the Export Administration Regulations (EAR). Most (if not all) modern Complementary Metal Oxide Semiconductor (CMOS) technologies will exceed at least one of the EAR radiation criteria inherently, there is no longer anything “special” about domestic parts or technologies that exceed EAR radiation criteria. A modern part today is not “designed” to exceed EAR criteria and in fact a part would need to be “specially designed” to fail to meet all of these criteria. And although it is possible to design a part to fail to meet the EAR radiation criteria in an unprogrammed state, such a part would be difficult to design in some technologies, may not achieve the ultimate radiation levels desired, would fail to meet core reliability principles, and may be unsuitable for high consequence applications.
This report summarizes a virtual workshop on early lessons from the COVID-19 pandemic as they pertain to proactively addressing future biological threats. Co-hosted by Sandia National Laboratory (Sandia) and the Council on Strategic Risks (CSR) in August 2020, the discussion involved experts who at that time were leading innovative efforts in various U.S. government agencies, industry, and academia sharing observations from their ongoing pandemic response efforts. Based on the input by these expert participants, it is clear that even though the pandemic response is ongoing, the following recommendations will be important to consider for more successfully addressing biological threats in the future: Continue building on the cross-sector collaboration and agility shown in the COVID-19 response; Expand capabilities for detecting biological threats early; Prioritize ways to create and disseminate medical countermeasures even faster; Create the U.S. bio industrial base needed for rapid response to biological threats, and keep it healthy; and, Major government reorganization may not be needed if there is effective work to form coalitions, improve coordination, and expand steady-state and surge capacities.
Recent trends in the growth of distributed energy resources (DER) in the electric grid and newfound malware frameworks that target internet of things (IoT) devices is driving an urgent need for more reliable and effective methods for intrusion detection and prevention. Cybersecurity intrusion detection systems (IDSs) are responsible for detecting threats by monitoring and analyzing network data, which can originate either from networking equipment or end-devices. Creating intrusion detection systems for PV/DER networks is a challenging undertaking because of the diversity of the attack types and intermittency and variability in the data. Distinguishing malicious events from other sources of anomalies or system faults is particularly difficult. New approaches are needed that not only sense anomalies in the power system but also determine causational factors for the detected events. In this report, a range of IDS approaches were summarized along with their pros and cons. Using the review of IDS approaches and subsequent gap analysis for application to DER systems, a preliminary hybrid IDS approach to protect PV/DER communications is formed in the conclusion of this report to inform ongoing and future research regarding the cybersecurity and resilience enhancement of DER systems.
Numerical simulations of metallic structures undergoing rapid loading into the plastic range require material models that accurately represent the response. In general, the material response can be seen as having four interrelated parts: the baseline response under slow loading, the effect of strain rate, the conversion of plastic work into heat and the effect of temperature. In essence, the material behaves in a thermal-mechanical manner if the loading is fast enough so when heat is generated by plastic deformation it raises the temperature and therefore influences the mechanical response. In these cases, appropriate models that can capture the aspects listed above are necessary. The material of interest here is 304L stainless steel, and the objective of this work is to calibrate thermal-mechanical models: one for the constitutive behavior and another for failure. The work was accomplished by first designing and conducting a material test program to provide data for the calibration of the models. The test program included uniaxial tension tests conducted at room temperature, 150 and 300 C and at strain rates between 10–4 and 103 1/s. It also included notched tension and shear-dominated compression hat tests specifically designed to calibrate the failure model. All test specimens were extracted from a single piece of plate to maintain consistency. The constitutive model adopted was a modular $J_2$ plasticity model with isotropic hardening that included rate and temperature dependence. A criterion for failure initiation based on a critical value of equivalent plastic strain fitted the failure data appropriately and was adopted. Possible ranges of the values of the parameters of the models were determined partially on historical data from calibrations of the same alloy from other lots and are given here. The calibration of the parameters of the models were based on finite element simulations of the various material tests using relatively ne meshes and hexahedral elements. When using the model in structural finite element calculations, however, element formulations and sizes different from those in the calibration are likely to be used. A brief investigation demonstrated that the failure initiation predictions can be particularly sensitive to the element selection and provided an initial guide to compensate for the effect of element size in a specific example.
Producing a complete and accurate set of signal detections is essential for automatically building and characterizing seismic events of interest for nuclear explosion monitoring. Signal detection algorithms have been an area of research for decades, but still produce large quantities of false detections and misidentify real signals that must be detected to produce a complete global catalog of events of interest. Deep learning methods have shown promising capabilities in effectively characterizing seismic signals for complex tasks such as identifying phase arrival times. We use the PhaseNet model, a UNet-based Neural Network, trained on local distance data from northern California to predict seismic arrivals on data from the International Monitoring System (IMS) global network. We use an analyst-curated bulletin generated from this data set to compare the performance of PhaseNet to that of the Short-Term Average/Long-Term Average (STA/LTA) algorithm. We find that PhaseNet has the potential of outperforming traditional processing methods and recommend the training of a new model with the IMS data to achieve optimal performance.
There are an estimated 48,745 wells producing oil or gas in New Mexico as of August 8, 2020 and with advances in drilling and oil recovery technology the use of hydraulic fracturing has become more commonplace. With a typical well requiring 1.5 to 16 million gallons of water, there is an increased demand for water in the Permian Basin and concern over the regions ability to meet this demand. This report is an addendum to the 2018 report Water Resource Assessment in the New Mexico Permian Basin (SAND2018-12018) to monitor baseline water level and chemistry data established in the original report. Results from this addendum can be used to further understand regional water supply and demands and aid in the BLMs mission of sustainably meeting the needs of water users while protecting human and environmental health.
The Manipulate Process Input/Output (IO) (ManiPIO) program allows users to develop custom scripts to execute Industrial Control System (ICS) manipulations. The driving development principles of ManiPIO are modularity and ease of use. Currently the program can utilize the Modbus TCP communication protocol, but its modular programming structure allows other protocols to be quickly and easily implemented. Additional functionality can be added to fit specific user needs, due to the usage of Python classes. The input configuration instructions are human readable and allow the user to create a complex series of control system manipulations.
Over the past decade as Moore's Law has slowed, the need for new forms of computation that can provide sustainable performance improvements has risen. A new method, called in situ computing, has shown great potential to accelerate matrix vector multiplication (MVM), an important kernel for a diverse range of applications from neural networks to scientific computing. Existing in situ accelerators for scientific computing, however, have a significant limitation: These accelerators provide no acceleration for preconditioning-A key bottleneck in linear solvers and in scientific computing workflows. This paper enables in situ acceleration for state-of-The-Art linear solvers by demonstrating how to use a new in situ matrix inversion accelerator for analog preconditioning. As existing techniques that enable high precision and scalability for in situ MVM are inapplicable to in situ matrix inversion, new techniques to compensate for circuit non-idealities are proposed. Additionally, a new approach to bit slicing that enables splitting operands across multiple devices without external digital logic is proposed. For scalability, this paper demonstrates how in situ matrix inversion kernels can work in tandem with existing domain decomposition techniques to accelerate the solutions of arbitrarily large linear systems. The analog kernel can be directly integrated into existing preconditioning workflows, leveraging several well-optimized numerical linear algebra tools to improve the behavior of the circuit. The result is an analog preconditioner that is more effective (up to 50% fewer iterations) than the widely used incomplete LU factorization preconditioner, ILU(0), while also reducing the energy and execution time of each approximate solve operation by 1025x and 105x respectively.
The purpose of this document is to provide evidence for assessing the adequacy of parameterized material models for a collection of materials used in a finite element analyses setting. “Adequacy” is relative to the intended use of the material in particular analyses. The intended application of the material models covered within this document is for system level abnormal mechanical solid mechanics analyses. Generally, material model parameterizations should be valid from temperatures of approximately -50 to 70° C, across a range of strain rates, and (depending on details of the parts involved) large deformations. Each material covered in this document is presented in its own chapter with a common format across materials. Model assumptions, limitations, existing validation results, readiness for use with uncertainty quantification, general usage guidance, and failure considerations are all provided along with specific parameterization inputs suitable for the finite element analysis code Sierra/Solid Mechanics.
Data from four TGA experiments conducted at Sandia National Laboratories was used for determination of a pyrolysis model using a commercial thermokinetics program developed by Netzsch Instruments (Kinetics NEO, version 2.1). The data measured at 1 K/min and the average of three measurements at 50 K/min were used as input into Kinetics NEO. The model was developed using data in the range 373 to 773 K. An initial estimate of the energy of activation (E) and pre-exponential constant (A) were determined from the model-free Friedman approach.
A site-specific Stormwater Pollution Prevention Plan (SWPPP) is needed for most construction activities/projects that disturb one (1) acre or more of land to meet the requirements of the National Pollutant Discharge Elimination System (NPDES) General Permit for Storm Water Discharges Associated With Construction and Land Disturbance Activities (General Permit) issued by the State Water Resources Control Board (State Board). This Order No. 2009-0009- DWQ was adopted by the State Board on September 2, 2009 and became effective July 1, 2010
Engineers at Sandia National Laboratories (SNL) have used quartz - based clock oscillators in various missions since the 1980s. As such, the design of these frequency control devices requires a high degree of reliability and producibility. The quartz clock oscillators designed at SNL have evolved over many years and are currently developed in house and fabricated using SNLs internal CMOS foundry. They are designed to operate in harsh environments, including temperature, radiation, shock and vibration. This report documents the methodology behind the design of quartz clock oscillators developed at SNL and includes an overview of quartz resonator technology and usage guidelines and a detailed overview of oscillator circuits.
Digital engineering strategies typically assume that digital engineering models interoperate seamlessly across the multiple different engineering modeling software applications involved, such as model- based systems engineering (MBSE), mechanical computer-aided design (MCAD), electrical computer-aided design (ECAD), and other engineering modeling applications. The presumption is that the data schema in these modeling software applications are structured in the familiar flat- tabular schema like any other software application. Engineering domain-specific applications (e.g., systems, mechanical, electrical, simulation) are typically designed to solve domain-specific problems, necessarily excluding explicit representations of non-domain information to help the engineer focus on the domain problems (system definition, design, simulation). Such exclusions become problematic in inter-domain information exchange. The obvious assumptions of one domain might not be so obvious to experts in another domain. Ambiguity in domain-specific language can erode the ability to enable different domain modeling applications to interoperate, unless the underlying language is understood and used as the basis for translation from one application to another. The engineering modeling software application industry has struggled for decades to enable these applications to interoperate. Industry standards have been developed, but they have not unified the industry. Why is this? The authors assert that the industry has relied on traditional database integration methods. The basic issue prohibiting successful application integration then is that traditional database-driven integration does not consider the distinct languages of each domain. An engineering models meaning is expressed through the underlying language of that engineering domain. In essence, traditional integration methods do not retain the semantic context (meaning) of the model. The basis of this research stems from the widely held assumption that systems engineering models are (or can be) structured according to the underlying semantic ontology of the model. This assumption can be imagined from two thoughts. 1) Digital systems engineering models are often represented using graph theory (the graph of a complex systems model can contain millions of nodes and edges). When examining the nodes one at a time and following the outbound edges of each node one by one, one can end up with rudimentary statements about the model (i.e., node A relates to node B), as in a semantic graph. 2) Likewise, from the study of natural languages, a sentence can be structured into unambiguous triples of subject-predicate-object within formal and highly expressive semantic ontologies. The rudimentary statements about a systems model discerned with graph theory closely mimic the triples used in the ontologies that try to structure natural languages. In other words, a systems models semantic graph can be (or is) structured into an ontology. Additionally, it is well established in industry that through natural language processing (NLP), which provides the means to create language structures, that computers can interpret ontological graphs. Therefore, the authors hypothesized that if the integrity of the underlying semantic structure of a systems model is retained, the contextual meaning of the model is retained. By structuring system models into the triples of the underlying ontology during the transformation from one MBSE application to another, the authors have provided a proof of the concept that the meaning of a system model can be retained during transformation. The authors assert that this is the missing ingredient in effective systems model-to-model interoperability. ACKNOWLEDGEMENTS The authors would like to thank the FY19 Model Interoperability team members who provided a solid foundation for the FY20 team to leverage: John McCloud, for the work he did to guide us toward the right use of technology that will appropriately discover and manipulate ontologies. Carlos Tafoya, for the work he did to develop an application programming interface (API)/Adapter that would export ontology-based data from GENESYS. Peter Chandler, for the work he did to architect our overall integration solution, with an eye toward the future that would influence a large-scale federated production-level systems engineering digital model ecosystem.
Brigner, Wesley H.; Hassan, Naimul; Bennett, Christopher H.; Hu, Xuan; Velasquez, Alvaro X.; Garcia-Sanchez, Felipe; Marinella, Matthew J.; Incorvia, Jean A.C.; Friedman, Joseph S.