Sierra/SD provides a massively parallel implementation of structural dynamics finite element analysis, required for high-fidelity, validated models used in modal, vibration, static and shock analysis of weapons systems. This document provides a user’s guide to the input for Sierra/SD. Details of input specifications for the different solution types, output options, element types and parameters are included. The appendices contain detailed examples, and instructions for running the software on parallel platforms.
Gao, Xiang; Anbar, Nathaniel; Ermanoski, Ivan; Ambrosini, Andrea A.; Stechel, Ellen B.
We propose to demonstrate the feasibility of a solar thermochemical looping technology to produce and store nitrogen (N2) from air for the subsequent production of ammonia (NH3) via an advanced two-stage process.
We present an efficient self-consistent implementation of the Non-Equilibrium Green Function formalism, based on the Contact Block Reduction method for fast numerical efficiency, and the predictor-corrector approach, together with the Anderson mixing scheme, for the self-consistent solution of the Poisson and Schrödinger equations. Then, we apply this quantum transport framework to investigate 2D horizontal Si:P δ-layer Tunnel Junctions. We find that the potential barrier height varies with the tunnel gap width and the applied bias and that the sign of a single charge impurity in the tunnel gap plays an important role in the electrical current.
This report includes a compilation of several slide presentations: 1) Interatomic Potentials for Materials Science and Beyond–Advances in Machine Learned Spectral Neighborhood Analysis Potentials (Wood); 2) Agile Materials Science and Advanced Manufacturing through AI/ML (de Oca Zapiain); 3) Machine Learning for DFT Calculations (Rajamanickam); 4) Structure-preserving ML discovery of a quantum-to-continuum codesign stack (Trask); and 5) IBM Overview of Accelerated Discovery Technology (Pitera)
CO2-neutral ammonia production with concentrated solar technology is theoretically possible based on advanced solar thermochemical looping technology. The parametric analysis points to the re-oxidation temperature and the H3 yield as the most influential parameters in the energy balance. The cycle time and the nitride cost are the most influential parameters on the CAPEX. The techno-economics analysis shows the potential of the plant to achieve a target price <125 $\$$/tonne.
Solar Thermal Ammonia Production has the potential to synthesize ammonia in a green, renewable process that can greatly reduce the carbon footprint left by the conventional Haber-Bosch reaction. Co3Mo3N has been identified as a potential candidate for ammonia production. It is synthesized via oxide precursor synthesis followed by nitridation under 10% H2/N2. The synthesis method can be extended to other candidate nitrides. The Co3Mo3N → Co6Mo6N reduction is demonstrated on TGA with rapid kinetics. The formation of NH3 is qualitatively observed, but not quantitatively determined. The material retains crystal structure, but no secondary phases are observed in XRD. Partial re-nitridation back to CMN331 of ~35% of max nitridation is observed. Reaction parameters in TGA differ from experimental conditions in the literature. Experiments at Georgia Tech better mimic re-nitridation conditions with more sensitive, quantitative analytical techniques (GC-MS). The ASU NH3 synthesis/re-nitridation reactor is under development and will permit experiments (reduction/re-nitridation) under precisely controlled T, pH2.
The atomic precision advanced manufacturing (APAM) enabled vertical tunneling field effect transistor (TFET) presents a new opportunity in microelectronics thanks to the use of ultra-high doping and atomically abrupt doping profiles. We present modeling and assessment of the APAM TFET using TCAD Charon simulation. First, we show, through a combination of simulation and experiment, that we can achieve good control of the gated channel on top of a phosphorus layer made using APAM, an essential part of the APAM TFET. Then, we present simulation results of a preliminary APAM TFET that predict transistor-like current-voltage response despite low device performance caused by using large geometry dimensions. Future device simulations will be needed to optimize geometry and doping to guide device design for achieving superior device performance.
Laser beam directed energy deposition has become an increasingly popular advanced manufacturing technique for materials discovery as a result of the in situ alloying capability. In this study, we leverage an additive manufacturing enabled high throughput materials discovery approach to explore the composition space of a graded Wx(CoCrFeMnNi)100−x sample spanning 0 ≤ x ≤ 21 at%. In addition to microstructural and mechanical characterization, synchrotron high speed x-ray computer aided tomography was conducted on a W20(CoCrFeMnNi)80 composition to visualize melting dynamics, powder-laser interactions, and remelting effects of previously consolidated material. Results reveal the formation of the Fe7W6 intermetallic phase at W concentrations> 6 at%, despite the high configurational entropy. Unincorporated W particles also occurred at W concentrations> 10 at% accompanied by a dissolution band of Fe7W6 at the W/matrix interface and hardness values greater than 400 HV. The primary strengthening mechanism is attributed to the reinforcement of the Fe7W6 and W phases as a metal matrix composite. The in situ high speed x-ray imaging during remelting showed that an additional laser pass did not promote further mixing of the Fe7W6 or W phases suggesting that, despite the dissolution of the W into the Fe7W6 phase being thermodynamically favored, it is kinetically limited by the thickness/diffusivity of the intermetallic phase, and the rapid solidification of the laser-based process.
Simulations of several of the end-irradiated cylindrical photoelectron driven cavity experiments (also known as B-Dot cavities) that were fielded during the July 1 through 2, 2020 shot series at the National Ignition Facility are presented in this report with comparisons to experimental measurements. All cavity B-Dots fielded on the second, third, fourth, fifth and seventh shots were simulated using coupled Integrated Tiger Series (ITS) Monte Carlo transport codes and the Electromagnetic Plasmas in Realistic Environments (EMPIRE) electromagnetic particle-in-cell code.
This progress report describes work performed during FY21 at Sandia National Laboratories (SNL) to assess the localized corrosion performance of canister materials used in the interim storage of spent nuclear fuel (SNF). Of particular concern is stress corrosion cracking (SCC), by which a through-wall crack could potentially form in a canister outer wall over time intervals that are shorter than possible dry storage times. In FY21, modeling and experimental work was performed that further defined our understanding of the potential chemical and physical environment present on canister surfaces at both marine and inland sites. Research also evaluated the relationship between the environment and the rate, extent, and morphology of corrosion, as well as the corrosion processes that occur. Finally, crack growth rate testing under relevant environmental conditions was initiated.
A new method for computing anharmonic thermophysical properties for adsorbates on metal surfaces is presented. Classical Monte Carlo phase space integration is performed to calculate the partition function for the motion of a hydrogen atom on Cu(111). A minima-preserving neural network potential energy surface is used within the integration routine. Two different sampling schema for generating the training data are presented, and two different density functionals are used. The results are benchmarked against direct state counting results by using discrete variable representation. The phase space integration results are in excellent quantitative agreement with the benchmark results. Additionally, both the discrete variable representation and the phase space integration results confirm that the motion of H on Cu(111) is highly anharmonic. The results were applied to calculate the free energy of dissociative adsorption of H2 and the resulting Langmuir isotherms at 400, 800, and 1200 K in a partial pressure range of 0-1 bar. It shows that the anharmonic effects lead to significantly higher predicted surface site fractions of hydrogen.
Understanding liquid hydrogen tank fluid dynamics is key for modeling liquid hydrogen systems. The tank is the source for nearly all liquid hydrogen systems. Accurate flow modeling out of the tank is needed to predict flows through downstream components. Tank contains liquid and gas that may not be at equilibrium. Questions to be addressed are: Does heat and mass transfer between liquid and vapor affect the flow rate? Is boiling an important consideration? For what conditions is a pressure relief valve (PRV) sufficient to relieve pressure and when is the burst disc needed?
Since the classical molecular dynamics simulator LAMMPS was released as an open source code in 2004, it has become a widely-used tool for particle-based modeling of materials at length scales ranging from atomic to mesoscale to continuum. Reasons for its popularity are that it provides a wide variety of particle interaction models for different materials, that it runs on any platform from a single CPU core to the largest supercomputers with accelerators, and that it gives users control over simulation details, either via the input script or by adding code for new interatomic potentials, constraints, diagnostics, or other features needed for their models. As a result, hundreds of people have contributed new capabilities to LAMMPS and it has grown from fifty thousand lines of code in 2004 to a million lines today. In this paper several of the fundamental algorithms used in LAMMPS are described along with the design strategies which have made it flexible for both users and developers. We also highlight some capabilities recently added to the code which were enabled by this flexibility, including dynamic load balancing, on-the-fly visualization, magnetic spin dynamics models, and quantum-accuracy machine learning interatomic potentials.
This project explored chemical vapor deposition as a technique to synthesis cubic boron nitride (c-BN) for electronics and coating applications. Current c-BN synthesis techniques are greatly limiting due to requiring high pressure or non-equilibrium energy sources, such as ion bombardment.
Brittle materials, such as cement, compose major portions of built infrastructure and are vulnerable to degradation and fracture from chemo-mechanical effects. Currently, methods of modeling infrastructure do not account for the presence of a reactive environment, such as water, on the acceleration of failure. Here, we have developed methodologies and models of concrete and cement fracture that account for varying material properties, such as strength, shrinkage, and fracture toughness due to degradation or hydration. The models have been incorporated into peridynamics, non-local continuum mechanics methodology, that can model intersecting and branching brittle fracture that occurs in multicomponent brittle materials, such as concrete. Through development of new peridynamic capabilities, decalcification of cement and differential shrinkage in clay-cement composites have been evaluated, along with exemplar problems in nuclear waste cannisters and wellbores. We have developed methods to simulate multiphase phenomena in cement and cement-composite materials for energy and infrastructure applications.
Two-step solar thermochemical cycles based on reversible reactions of SrFeO3−δand (Ba,La)0.15Sr0.85FeO3−δperovskites were considered for air separation. The cycle steps encompass (1) the thermal reduction of SrFeO3−δor (Ba,La)0.15Sr0.85FeO3−δperovskites driven by concentrated solar irradiation and (2) oxidation in air to remove O2and produce N2. Rate limiting mechanisms were examined for both reactions using a combination of isothermal and non-isothermal thermogravimetry for temperature-swings between 673 and 1373 K, heating rates of 10, 20, and 50 K min−1, and O2pressure-swings between 20% O2/Ar and 100% Ar at atmospheric pressure. Evolved O2and associated lag due to transport behavior were measured with gas chromatography and used with measured sample temperatures to predict equilibrium compositions from a compound energy formalism thermodynamic model. Measured and predicted chemical equilibrium changes in deviation from stoichiometry were compared. Rapid chemical kinetics were observed as the samples equilibrated rapidly for all conditions, indicative that heat and mass transfer were the rate limiting mechanisms. The effects of bulk diffusion (or gas diffusion through the bed or pellet) were examined using pelletized and loose powdered samples and determined to have no discernable impact.
Compliance with future ultra-low nitrogen oxide regulations with diesel engines requires the fastest possible heating of the exhaust aftertreatment system to its proper operating temperature upon cold starting. Late post injections are commonly integrated into catalyst-heating operating strategies. This experimental study provides insight into the complex interactions between the injection-strategy calibration and the tradeoffs between exhaust heat and pollutant emissions. Experiments are performed with certification diesel fuel and blends of diesel fuel with butylal and hexyl hexanoate. Further analyses of experimental data provide insight into fuel reactivity and oxygen content as potential enablers for improved catalyst-heating operation. A statistical design-of-experiments approach is developed to investigate a wide range of injection strategy calibrations at three different intake dilution levels. Thermodynamic and exhaust emissions measurements are taken using a new medium-duty, single-cylinder research engine. Analysis of the results provides insight into the effects of exhaust gas recirculation, oxygenated fuel blends, and fuel reactivity on exhaust heat and pollutant emissions. Late-cycle heat release is an important factor in determining exhaust temperatures. Intake dilution and fuel properties certainly affect late-cycle heat release, but the methods applied in this work are not sufficient to reproduce or explain the mechanisms by which improved fuel cetane rating promotes operation with hotter exhaust and lower pollutant emissions.
Stainless steel TPBAR components undergo neutron radiation-induced segregation and dislocation loop formation. Comparison experiments with ion beams accelerate the damage, and visualize the damage process with in-situ microscopy. In-situ Au irradiation causes defect formation, but no elemental segregation.
For many decades, lithium metal batteries (LMBs) have been studied for their high energy density, though they suffer from high reactivity, which safety concerns such as explosions and fires. These events can occur as a result of degradation due to redox reactions and lithium morphology that evolve during cycling. Mostly, the failure is caused by the electrolyte degradation leading to the creation of a solid electrolyte interphase (SEI), which tends to induce electron leakage, lithium dendrite growth, and capacity decay. SEI formation is practically inevitable. This work investigated the minimization of degradation events by modifying different battery configurations. Modifications were made by exchanging electrolytes, electrode surfaces, and even annexing innovative components. Our goal is to achieve a configuration where Li-ion transfer is facilitated, and electron transfer is limited which would enable a longer lithium metal battery life cycle. When performing these modifications and testing their electrochemical performance, a current reduction was observed when the electrodes were spontaneously coated with preformed SEI. Herein several classes of artificial SEI layers are explored for their ability to form passivating layers through use of a redox active molecule.
We provide further details for using Lim, et al.'s marked multidimensional Hawkes processes with dissimilar decays. We first describe what makes these different from other Hawkes processes, then describe each model hyperparameter and how to initialize it informed by the input data and any prior biases. We derived tighter bounds than Lim, et al. for faster convergence of rejection sampling. The resulting hyperparameters and bounds have been helpful against both synthetic and real-world datasets.
Ultradoping introduces unprecedented dopant levels into Si, which transforms its electronic behavior and enables its use as a next-generation electronic material. Commercialization of ultradoping is currently limited by gas-phase ultra-high vacuum requirements. Solvothermal chemistry is amenable to scale-up. However, an integral part of ultradoping is a direct chemical bond between dopants and Si, and solvothermal dopant-Si surface reactions are not well-developed. This work provides the first quantified demonstration of achieving ultradoping concentrations of boron (∼1e14 cm2) by using a solvothermal process. Surface characterizations indicate the catalyst cross-reacted, which led to multiple surface products and caused ambiguity in experimental confirmation of direct surface attachment. Density functional theory computations elucidate that the reaction results in direct B−Si surface bonds. This proof-of-principle work lays groundwork for emerging solvothermal ultradoping processes.
This Analysis Report (AR) documents the determination of pH correction factors for the observed pH readings. The correction factor converts the observed pH reading recorded from the brines used in geochemical studies in support of the Waste Isolation Pilot Plant (WIPP) to a corrected pH value. The data analysis in this AR falls under AP-157 Rev. 1 Analysis Plan for Determination of pH Correction Factors in Brines (Kirkes et al, 2021). Measurement of pH in some solutions can be challenging due to numerous factors such as high ionic strength, elevated or lowered temperature, complex matrix composition, etc. (Knauss et al., 1990; and Rai et al., 1995). The measured pH can be corrected by applying the correction factor, empirically obtained from a specific test solution.
Technologies based on water adsorption such as water harvesting from air have tremendous potential in mitigating important global crises such as water scarcity. An important challenge to the deployment of such technologies is finding optimal adsorbent materials. Given the large materials space of available adsorbents, large-scale computational screening can be extremely helpful for this task. This work explores the methods and details associated with such screening procedures and recommends best practices. We also shed light on the limitations of traditionally used and inexpensive to compute prescreening approaches involving geometric and energetic features to predict water adsorption behavior of porous materials. Such approaches can provide general trends to predict adsorption behavior but may lead to the overlook of potentially important structures due to the complex nature of water adsorption. Finally, this study offers insights for future water adsorption simulations to facilitate the development of optimal water adsorbents.
Complex organic-inorganic interfaces are important for device and sensing applications. Charge transfer doping is prevalent in such applications and can affect the interfacial energy level alignments (ELA), which are determined by many-body interactions. We develop an approximateab initiomany-body GW approach that can capture many-body interactions due to interfacial charge transfer. The approach uses significantly less resources than a regular GW calculation but gives excellent agreement with benchmark GW calculations on an F4TCNQ/graphene interface. We find that many-body interactions due to charge transfer screening result in gate-tunable F4TCNQ HOMO-LUMO gaps. We further predict the ELA of a large system of experimental interest—4,4′-bis(dimethylamino)bipyridine (DMAP-OED) on monolayer MoS2, where charge transfer screening results in an ∼1 eV reduction of the molecular HOMO-LUMO gap. Comparison with a two-dimensional electron gas model reveals the importance of explicitly considering the intraband transitions in determining the charge transfer screening in organic-inorganic interface systems.
This memo’s objective is to report a calibration of the J2 plasticity model with the Wilkins ductile failure criterion for 17-4 PH H1150 stainless steel under slow loading at room temperature. The calibration of the hardening function was based on uniaxial tension tests, while that of the failure model included data from tension tests on notched specimens, a butterfly specimen shear test, and a set of interrupted compression tests on shear hat specimens. The procedure was that described in, minus the rate and temperature dependence.
The coupling of inter- and intramolecular vibrations plays a critical role in initiating chemistry during the shock-to-detonation transition in energetic materials. Herein, we report on the subpicosecond to subnanosecond vibrational energy transfer (VET) dynamics of the solid energetic material 1,3,5-trinitroperhydro-1,3,5-triazine (RDX) by using broadband, ultrafast infrared transient absorption spectroscopy. Experiments reveal VET occurring on three distinct time scales: subpicosecond, 5 ps, and 200 ps. The ultrafast appearance of signal at all probed modes in the mid-infrared suggests strong anharmonic coupling of all vibrations in the solid, whereas the long-lived evolution demonstrates that VET is incomplete, and thus thermal equilibrium is not attained, even on the 100 ps time scale. Density functional theory and classical molecular dynamics simulations provide valuable insights into the experimental observations, revealing compression-insensitive time scales for the initial VET dynamics of high-frequency vibrations and drastically extended relaxation times for low-frequency phonon modes under lattice compression. Mode selectivity of the longest dynamics suggests coupling of the N-N and axial NO2stretching modes with the long-lived, excited phonon bath.
Time-resolved spectroscopies using high-energy photons in the vacuum ultraviolet (VUV) to the X-ray region of the electromagnetic spectrum, have proven to be powerful probes of chemical dynamics. These high-energy photons can access valence and core orbitals of molecules and materials, providing key information on molecular and electronic structure and their time evolution. This report details the development of table-top sources of extreme ultraviolet (XUV) and VUV pulses at Sandia National Laboratories for use in studies of gas phase chemical dynamics. Femtosecond duration XUV pulses are produced using laser-driven high harmonic generation and their detected range span ~40-140 eV photon energies. These pulses are used in conjunction with ultraviolet pulses in a pump-probe scheme to study excited state dynamics of gas phase molecules. VUV pulses at 7.75 eV are generated using a four-wave-mixing scheme driven by 800 nm and 266 nm pulses in an argon-filled hollow-core fiber.
We propose a domain decomposition method for the efficient simulation of nonlocal problems. Our approach is based on a multi-domain formulation of a nonlocal diffusion problem where the subdomains share “nonlocal” interfaces of the size of the nonlocal horizon. This system of nonlocal equations is first rewritten in terms of minimization of a nonlocal energy, then discretized with a meshfree approximation and finally solved via a Lagrange multiplier approach in a way that resembles the finite element tearing and interconnect method. Specifically, we propose a distributed projected gradient algorithm for the solution of the Lagrange multiplier system, whose unknowns determine the nonlocal interface conditions between subdomains. Several two-dimensional numerical tests on problems as large as 191 million unknowns illustrate the strong and the weak scalability of our algorithm, which outperforms the standard approach to the distributed numerical solution of the problem. Finally, this work is the first rigorous numerical study in a two-dimensional multi-domain setting for nonlocal operators with finite horizon and, as such, it is a fundamental step towards increasing the use of nonlocal models in large scale simulations.
Denoising contaminated seismic signals for later processing is a fundamental problem in seismic signals analysis. Neural network approaches have shown success denoising local signals when trained on short-time Fourier transform spectrograms. One challenge of this approach is the onerous process of hand-labeling event signals for training. By leveraging the SCALODEEP seismic event detector, we develop an automated set of techniques for labeling event data. Despite region specific challenges, training the neural network denoiser on machine curated events shows comparable performance to the neural network trained on hand curated events. We showcase our technique with two experiments, one using Utah regional data and one using regional data from the Korean peninsula.
Turner, Sean W.D.; Nelson, Kristian; Voisin, Nathalie; Tidwell, Vincent C.; Miara, Ariel; Dyreson, Ana; Mantena, Dan; Jin, Julie; Warnken, Pete; Kao, Shih C.
Thermoelectric power plants often depend on multipurpose reservoirs to supply cooling water. Although reservoirs buffer natural hydrologic variability, severe droughts can deplete storage below critical thresholds, or to levels at which the effluent water temperature exceeds the environmental compliance requirement for cooling. This study explores the effects of projected climate change and drought on water storage at 30 major reservoirs in Texas. These reservoirs collectively provide cooling water for about two thirds of thermoelectric power capacity in the Electric Reliability Council of Texas (ERCOT) power grid. Multi-ensemble runoff projections generated from eleven downscaled hydroclimate simulations are mapped to key watersheds to create spatially correlated multi-reservoir inflow sequences. These data are used to drive reservoir storage simulations, which are linked to a metric of “capacity-at-risk” using critical reservoir thresholds. We find that projected impacts of climate change are mixed, with results indicating an increase in the occurrence of thermal disruption under only half of climate models. A critical threshold of 30% storage volume—applied to all reservoirs—results in disruption to about one fifth of ERCOT thermal generation during the most severe projected droughts. The study highlights an important role for detailed reservoir behavior simulations for capturing the effects of drought and climate change on thermoelectric plant performance.
Sierra/SD provides a massively parallel implementation of structural dynamics finite element analysis, required for high fidelity, validated models used in modal, vibration, static and shock analysis of structural systems. This manual describes the theory behind many of the constructs in Sierra/SD. For a more detailed description of how to use Sierra/SD, we refer the reader to User's Manual. Many of the constructs in Sierra/SD are pulled directly from published material. Where possible, these materials are referenced herein. However, certain functions in Sierra/SD are specific to our implementation. We try to be far more complete in those areas. The theory manual was developed from several sources including general notes, a programmer_notes manual, the user's notes and of course the material in the open literature.
This is a simple model designed to run fast but still maintain the key physics and feedback mechanisms of a heat pipe. First, the capillary pressure is a function of the liquid working fluid volume fraction. Second, the boiling and condensation are based on the saturation temperature that is based on the heat pipe pressure. When the pressure goes up, the saturation temperature goes up and the vapor rains on the wick. When the pressure goes down, the saturation temperature goes down and the liquid in the entire wick boils. This is how the heat pipe adjusts to stay robust under different temperatures and heat fluxes.
Photon sources able to emit single or entangled photon pairs are key components in quantum information systems. Semiconductor quantum dots (QDs) are promising candidates due to their high efficiencies and ease of integration with other photonic or electronic components. State-of-the-art QDs, however, are limited to certain emission wavelengths and specific applications due to material choice constraints and their randomness in shape/size. This project is focused on developing a novel in-situ patterning technique to realize QDs with a broad emission range, shape/size control and the ability to emit single/entangled photons. Our approach has two key elements: (1) In-situ patterning via arsenic-induced nanovoid etching on antimonide surfaces and (2) In-filling of nanovoids to form QDs. By closely controlling the experimental conditions, it is shown that this technique can be used to realize III-V QDs in As2- etched nanovoids on a GaSb surface. The exposure to As2 in terms of substrate temperature, time and flux is found to have a significant impact on the process variables such as nanovoid depth, QD dimensions etc. An in-depth analysis of the etch mechanism reveals that by controlling the As2 exposure, uniform 3-dimensional nanostructures with varying areal densities can be obtained without an in-filling step. Preliminary optical characterization of these nanostructures shows that these QDs may be relevant for realizing emitters in the telecom wavelength range.
First-of-their kind datasets from a high-speed X-ray tomography system were collected, and a novel numerical effort utilizing temporal information to reduce measurement uncertainty was shown. The experimental campaign used three high-speed X-ray imaging systems to collect data at 100 kHz of a scene containing high-velocity objects. The scene was a group of known objects propelled by a 12-gauge shotgun shell reaching speeds of hundreds of meters per second. These data represent a known volume where the individual components are known, with experimental uncertainties that can be used for reconstruction algorithm validation. The numerical effort used synthetic volumes in MATLAB to produce projections along known lines of sight to perform tomographic reconstructions. These projections and reconstructions were performed on a single object at two orientations, representing two timesteps, to increase the reconstruction accuracy.
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.
Accurate and efficient constitutive modeling remains a cornerstone issue for solid mechanics analysis. Over the years, the LAMÉ advanced material model library has grown to address this challenge by implementing models capable of describing material systems spanning soft polymers to stiff ceramics including both isotropic and anisotropic responses. Inelastic behaviors including (visco)plasticity, damage, and fracture have all incorporated for use in various analyses. This multitude of options and flexibility, however, comes at the cost of many capabilities, features, and responses and the ensuing complexity in the resulting implementation. Therefore, to enhance confidence and enable the utilization of the LAMÉ library in application, this effort seeks to document and verify the various models in the LAMÉ library. Specifically, the broader strategy, organization, and interface of the library itself is first presented. The physical theory, numerical implementation, and user guide for a large set of models is then discussed. Importantly, a number of verification tests are performed with each model to not only have confidence in the model itself but also highlight some important response characteristics and features that may be of interest to end-users. Finally, in looking ahead to the future, approaches to add material models to this library and further expand the capabilities are presented.
Polymerization-induced phase separation enables fine control over thermoset network morphologies, yielding heterogeneous structures with domain sizes tunable over 1-100 nm. However, the controlled chain-growth polymerization techniques exclusively employed to regulate the morphology at these length scales are unsuitable for a majority of thermoset materials typically formed through step-growth mechanisms. By varying the composition of a binary curing agent mixture in a classic rubber-toughened epoxy thermoset, where the two curing agents are selected based on disparate compatibility with the rubber, we demonstrate facile tunability over morphology through a single compositional parameter. Indeed, this method yields morphologies spanning the nano-scale to the macro-scale, controlled by the relative reactivities and thermodynamic compatibility of the network components. We further demonstrate a profound connection between chain dynamics and microstructure in these materials, with the tunable morphology enabling exquisite variations in glass transition. In addition, previously unattainable control over tensile mechanical properties is realized, including atypical increase of elongation at failure while maintaining the modulus and ultimate strength.
The How To Manual supplements the User’s Manual and the Theory Manual. The goal of the How To Manual is to reduce learning time for complex end to end analyses. These documents are intended to be used together. See the User’s Manual for a complete list of the options for a solution case. All the examples are part of the Sierra/SD test suite. Each runs as is. The organization is similar to the other documents: How to run, Commands, Solution cases, Materials, Elements, Boundary conditions, and then Contact. The table of contents and index are indispensable. The Geometric Rigid Body Modes section is shared with the Users Manual.
Thin, high-density layers of dopants in semiconductors, known as δ-layer systems, have recently attracted attention as a platform for exploration of the future quantum and classical computing when patterned in plane with atomic precision. However, there are many aspects of the conductive properties of these systems that are still unknown. Here we present an open-system quantum transport treatment to investigate the local density of electron states and the conductive properties of the δ-layer systems. A successful application of this treatment to phosphorous δ-layer in silicon both explains the origin of recently-observed shallow sub-bands and reproduces the sheet resistance values measured by different experimental groups. Further analysis reveals two main quantum-mechanical effects: 1) the existence of spatially distinct layers of free electrons with different average energies; 2) significant dependence of sheet resistance on the δ-layer thickness for a fixed sheet charge density.
Designing next generation thin films, tailor-made for specific applications, relies on the availability of robust processing-structure-property relationships. Traditional structure zone diagrams are limited to low-dimensional mappings, with machine-learning methods only recently attempting to relate multiple processing parameters to the final microstructure. Despite this progress, structure-processing relationships are unknown for processing conditions that vary during thin-film deposition, limiting the range of microstructures and properties achievable. In this project, we employed a phase-field computational model combined with a genetic algorithm (GA) to identify and design time-dependent processing protocols that achieve tailor-made microstructures. We simulate the physical vapor deposition of a binary-alloy thin film by employing a phase-field model, where deposition rates and diffusivities are controlled via the genetic algorithm. Our GA-guided protocols achieve targeted microstructures with lateral and vertical concentration modulations, as well as more complex, hierarchical microstructures previously not described in simple structure zone diagrams. Our algorithm provides insight to experimentalists looking for additional avenues to design novel thin-film microstructures.
Computational simulation is increasingly relied upon for high-consequence engineering decisions, and a foundational element to solid mechanics simulations is a credible material model. Our ultimate vision is to interlace material characterization and model calibration in a real-time feedback loop, where the current model calibration results will drive the experiment to load regimes that add the most useful information to reduce parameter uncertainty. The current work investigated one key step to this Interlaced Characterization and Calibration (ICC) paradigm, using a finite load-path tree to incorporate history/path dependency of nonlinear material models into a network of surrogate models that replace computationally-expensive finite-element analyses. Our reference simulation was an elastoplastic material point subject to biaxial deformation with a Hill anisotropic yield criterion. Training data was generated using either a space-filling or adaptive sampling method, and surrogates were built using either Gaussian process or polynomial chaos expansion methods. Surrogate error was evaluated to be on the order of 10⁻5 and 10⁻3 percent for the space-filling and adaptive sampling training data, respectively. Direct Bayesian inference was performed with the surrogate network and with the reference material point simulator, and results agreed to within 3 significant figures for the mean parameter values, with a reduction in computational cost over 5 orders of magnitude. These results bought down risk regarding the surrogate network and facilitated a successful FY22-24 full LDRD proposal to research and develop the complete ICC paradigm.
This is an addendum to the Sierra/SolidMechanics 5.2 User's Guide that documents additional capabilities available only in alternate versions of the Sierra/SolidMechanics (Sierra/SM) code. These alternate versions are enhanced to provide capabilities that are regulated under the U.S. Department of State's International Traffic in Arms Regulations (ITAR) export control rules. The ITAR regulated codes are only distributed to entities that comply with the ITAR export control requirements. The ITAR enhancements to Sierra/SM include material models with an energy-dependent pressure response (appropriate for very large deformations and strain rates) and capabilities for blast modeling. This document is an addendum only; the standard Sierra/SolidMechanics 5.2 User's Guide should be referenced for most general descriptions of code capability and use.
To date, terahertz quantum-cascade vertical-external-cavity surface-emitting lasers (QC-VECSELs) have tended to oscillate in only one or two lasing modes at a time. This is due to the fact that the interaction between all of the longitudinal external cavity modes and the QC gain material is mediated through a single metasurface resonance, whose spatial overlap changes little with frequency; this suppresses spatial-hole-burning induced multi-mode operation. In this Letter, a VECSEL external cavity is demonstrated using an output coupler based upon a high-resistivity silicon etalon, which presents a periodic reflectance spectrum that is nearly matched with the external cavity mode spectrum. As the cavity length is varied, a systematic transition between a single/double-mode lasing regime and a multi-mode lasing regime is realized due to the Vernier effect. Up to nine modes lasing simultaneously with a free-spectral-range of approximately 21 GHz is demonstrated. This result provides a path toward the multi-mode operation necessary for eventual frequency comb operation.
The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & Waste Disposition (SFWD) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and highlevel nuclear waste (HLW). A high priority for SFWST disposal R&D is disposal system modeling (DOE 2012, Table 6; Sevougian et al. 2019). The SFWST Geologic Disposal Safety Assessment (GDSA) work package is charged with developing a disposal system modeling and analysis capability for evaluating generic disposal system performance for nuclear waste in geologic media.
Sierra/SolidMechanics (Sierra/SM) is a Lagrangian, three-dimensional code for finite element analysis of solids and structures. It provides capabilities for explicit dynamic, implicit quasistatic and dynamic analyses. The explicit dynamics capabilities allow for the efficient and robust solution of models with extensive contact subjected to large, suddenly applied loads. For implicit problems, Sierra/SM uses a multi-level iterative solver, which enables it to effectively solve problems with large deformations, nonlinear material behavior, and contact. Sierra/SM has a versatile library of continuum and structural elements, and a large library of material models. The code is written for parallel computing environments enabling scalable solutions of extremely large problems for both implicit and explicit analyses. It is built on the SIERRA Framework, which facilitates coupling with other SIERRA mechanics codes. This document describes the functionality and input syntax for Sierra/SM.
Relativistic weakly collisional plasmas describe a variety of astrophysical and terrestrial plasmas, ranging from relativistic outflows from active galactic nuclei to high power microwave and magnetically insulated transmission lines. In many such systems, high fidelity kinetic models are computationally infeasible due large dynamical scales and long dynamical times. Conversely, most fluid based models such as magnetohydrodynamics (MHD) miss many relevant aspects of plasma behavior. Between these two models, two fluid methods - where the electrons and ions are evolved as separate, coupled fluids - capture many of the plasma physics of a kinetic code while remaining computational tractable for large systems.
We present a novel technique for numerically modeling relativistic magnetrons. The electrons are represented with a 5-moment relativistic fluid. Typically, the particle in cell method is used for simulated relativistic high-power microwave sources. This study considers the A6 magnetron presented by Palevsky and Bekefi [1].
C22-Gd films were deposited onto 316 stainless steel up to 2.4mm thick using three different cold spray conditions. No differences in visible coating adhesion was observed between grit-blasted and as-received 316 stainless steel substrates. All of the coatings produced had a density greater than 95% of bulk. EDS and backscattered imaging suggest that no secondary phases precipitated during coating fabrication, but potential micro-segregation of phases were likely present in the as-received material. The phase segregation was still observable in the as-deposited coatings. EDS could not resolve the differences in composition of the two phases.
Presented in this document is a small portion of the tests that exist in the Sierra/SolidMechanics (Sierra / SM) verification test suite. Most of these tests are run nightly with the Sierra/SM code suite, and the results of the test are checked versus the correct analytical result. For each of the tests presented in this document, the test setup, a description of the analytic solution, and comparison of the Sierra/SM code results to the analytic solution is provided. Mesh convergence is also checked on a nightly basis for several of these tests. This document can be used to confirm that a given code capability is verified or referenced as a compilation of example problems. Additional example problems are provided in the Sierra/SM Example Problems Manual. Note, many other verification tests exist in the Sierra/SM test suite, but have not yet been included in this manual.
Presented in this document are tests that exist in the Sierra/SolidMechanics example problem suite, which is a subset of the Sierra/SM regression and performance test suite. These examples showcase common and advanced code capabilities. A wide variety of other regression and verification tests exist in the Sierra/SM test suite that are not included in this manual.
A coded aperture is used to demonstrate emission spectroscopy from multiple one-dimensional measurement locations simultaneously with a single camera. The coded aperture mask has several columns of periodic apertures, each with a unique spatial frequency. Light transmitted through all mask columns is detected through an imaging spectrometer. Dispersed light from the various mask columns overlaps on the spectrometer camera but is separated using Fourier-domain filtering using the known spatial frequencies of the mask. As the coded aperture is placed at an image plane, each Fourier-filtered spectrogram comes from a unique one-dimensional measurement location. This technique represents a significant increase in the amount of spatially and spectrally resolved emission data available using a single emission spectrometer and camera at the expense of some spatial resolution due to the Fourier filtering. This instrument is particularly useful for studying transient, non-repeating events. Megahertz-rate emission spectroscopy from five one-dimensional measurement locations is demonstrated with explosive fireballs using a single camera. Optical design parameters and instrument performance characteristics are discussed.
The passivation of polycrystalline nickel surfaces against hydrogen uptake by oxygen is investigated experimentally with low energy ion scattering (LEIS), direct recoil spectroscopy (DRS), and thermal desorption spectroscopy (TDS). These techniques are highly sensitive to surface hydrogen, allowing the change in hydrogen adsorption in response to varying amounts of oxygen exposure to be measured. The chemical composition of a nickel surface during a mixed oxygen and hydrogen exposure was characterized with LEIS and DRS, while the uptake and activation energies of hydrogen on a nickel surface with preadsorbed oxygen were quantified with TDS. By and large, these measurements of how the oxygen and hydrogen surface coverage varied in response to oxygen exposure were found to be consistent with predictions of a simple site-blocking model. This finding suggests that, despite the complexities that arise due to polycrystallinity, the oxygen-induced passivation of a polycrystalline nickel surface against hydrogen uptake can be approximated by a simple site-blocking model.
We present a new geodesic-based method for geometry optimization in a basis set of redundant internal coordinates. Our method updates the molecular geometry by following the geodesic generated by a displacement vector on the internal coordinate manifold, which dramatically reduces the number of steps required to converge to a minimum. Our method can be implemented in any existing optimization code, requiring only implementation of derivatives of the Wilson B-matrix and the ability to numerically solve an ordinary differential equation.
Presented in this document are the theoretical aspects of capabilities contained in the Sierra/SM code. This manuscript serves as an ideal starting point for understanding the theoretical foundations of the code. For a comprehensive study of these capabilities, the reader is encouraged to explore the many references to scientific articles and textbooks contained in this manual. It is important to point out that some capabilities are still in development and may not be presented in this document. Further updates to this manuscript will be made as these capabilities come closer to production level.
Mishra, Lubhani; Jang, Taejin; Uppaluri, Maitri; Shah, Krishna; Roberts, Scott A.; Subramanian, Venkat R.
Electrochemical models at different scales and varying levels of complexity have been used in the literature to study the evolution of the anode surface in lithium metal batteries. This includes continuum, mesoscale (phase-field approaches), and multiscale models. In this paper, using a motivating example of a moving boundary model in one dimension, we show how battery models need proper formulation for mass conservation, especially when simulated over multiple charge and discharge cycles. The article concludes with some thoughts on mass conservation and proper formulation for multiscale models.
The finite element method is a scheme to discretize the infinite number of degrees of freedom in continuum-level problems down to a finite number of degrees of freedom. This discretization is done in conjunction with methods that also reduce the field differential equations to sets of algebraic ones that can be solved by arithmetical operations. Therefore, solutions attained by finite element models are approximations to the exact solutions of the field equations.
This report summarizes the current actives in FY21 related to the effort by Sandia National Laboratories to identify and test coating materials for the prevention, mitigation, and repair of spent nuclear fuel dry storage canisters against potential chloride-induced stress corrosion cracking. This work follows up on the details provided in Sandia National Laboratories FY20 report on the same topic, which provided a detailed description of the specific coating properties desired for application and implementation on spent nuclear fuel canisters, as well as provided detail into several different coatings and their applicability to coat spent nuclear fuel canisters. In FY21, Sandia National Laboratories has engaged with private industry to create a Memorandum of Understanding and established a collaborative R&D program building off the analytical and laboratory capabilities at Sandia National Laboratories and the material design and synthesis capabilities of private industry. The resulting Memorandum of Understanding included four companies to date (Oxford Performance Materials, White Horse R&D, Luna Innovations, and Flora Coating) proposing six different coating technologies (polyetherketoneketone, modified polyimide/polyurea, modified phenolic resin, silane-based polyurethane hybrid with and without a Znrich primer, and a quasi-ceramic sol-gel polyurethane hybrid) to be tested, evaluated, and optimized for their potential use for this application. This report provides a detailed description of each of the coating systems proposed by the participating industry partners. It also provides a description of the planned experimental actives to be performed by Sandia National Laboratories including physical tests, electrochemical tests, and characterization methods. These analyses will be used to identify specific ways to further improve coating technologies toward their application and implementation on spent nuclear fuel canisters. In FY21, Sandia National Laboratories began baseline testing of the base metal material in according with activities of the Memorandum of Understanding. In FY22, Sandia National Laboratories will receive coated coupons from each of the participating industry partners and begin characterization, physical, and electrochemical testing following the test plan described herein.
Understanding the fundamental mechanisms underpinning shock initiation is critical to predicting energetic material (EM) safety and performance. Currently, the timescales and pathways by which shock-excited lattice modes transfer energy into specific chemical bonds remains an open question. Towards understanding these mechanisms, our group has previously measured the vibrational energy transfer (VET) pathways in several energetic thin films using broadband, femtosecond transient absorption spectroscopy. However, new technologies are needed to move beyond these thin film surrogates and measure broadband VET pathways in realistic EM morphologies. Herein, we describe a new broadband, femtosecond, attenuated total reflectance spectroscopy apparatus. Performance of the system is benchmarked against published data and the first VET results from a pressed EM pellet are presented. This technology enables fundamental studies of VET dynamics across sample configurations and environments (pressure, temperature, etc .) and supports the potential use of VET studies in the non-destructive surveillance of EM components.
The propagation of a wave pulse due to low-speed impact on a one-dimensional, heterogeneous bar is studied. Due to the dispersive character of the medium, the pulse attenuates as it propagates. This attenuation is studied over propagation distances that are much longer than the size of the microstructure. A homogenized peridynamic material model can be calibrated to reproduce the attenuation and spreading of the wave. The calibration consists of matching the dispersion curve for the heterogeneous material near the limit of long wavelengths. It is demonstrated that the peridynamic method reproduces the attenuation of wave pulses predicted by an exact microstructural model over large propagation distances.
With the rapid proliferation of additive manufacturing and 3D printing technologies, architected cellular solids including truss-like 3D lattice topologies offer the opportunity to program the effective material response through topological design at the mesoscale. The present report summarizes several of the key findings from a 3-year Laboratory Directed Research and Development Program. The program set out to explore novel lattice topologies that can be designed to control, redirect, or dissipate energy from one or multiple insult environments relevant to Sandia missions, including crush, shock/impact, vibration, thermal, etc. In the first 4 sections, we document four novel lattice topologies stemming from this study: coulombic lattices, multi-morphology lattices, interpenetrating lattices, and pore-modified gyroid cellular solids, each with unique properties that had not been achieved by existing cellular/lattice metamaterials. The fifth section explores how unintentional lattice imperfections stemming from the manufacturing process, primarily sur face roughness in the case of laser powder bed fusion, serve to cause stochastic response but that in some cases such as elastic response the stochastic behavior is homogenized through the adoption of lattices. In the sixth section we explore a novel neural network screening process that allows such stocastic variability to be predicted. In the last three sections, we explore considerations of computational design of lattices. Specifically, in section 7 using a novel generative optimization scheme to design novel pareto-optimal lattices for multi-objective environments. In section 8, we use computational design to optimize a metallic lattice structure to absorb impact energy for a 1000 ft/s impact. And in section 9, we develop a modified micromorphic continuum model to solve wave propagation problems in lattices efficiently.
A six-month research effort has advanced the hybrid kinetic-fluid modeling capability required for developing non-thermal warm x-ray sources on Z. The three particle treatments of quasi-neutral, multi-fluid, and kinetic are demonstrated in 1D simulations of an Ar gas puff. The simulations determine required resolutions for the advanced implicit solution techniques and debug hybrid particle treatments with equation-of-state and radiation transport. The kinetic treatment is used in preliminary analysis of the non-Maxwellian nature of a gas target. It is also demonstrates the sensitivity of the cyclotron and collision frequencies in determining the transition from thermal to non-thermal particle populations. Finally, a 2D Ar gas puff simulation of a Z shot demonstrates the readiness to proceed with realistic target configurations. The results put us on a very firm footing to proceed to a full LDRD which includes continued development transition criteria and x-ray yield calculation.
Virtual prototyping in engineering design rely on modern numerical models of contacting structures with accurate resolution of interface mechanics, which strongly affect the system-level stiffness and energy dissipation due to frictional losses. High-fidelity modeling within the localized interfaces is required to resolve local quantities of interest that may drive design decisions. The high-resolution finite element meshes necessary to resolve inter-component stresses tend to be computationally expensive, particularly when the analyst is interested in response time histories. The Hurty/Craig-Bampton (HCB) transformation is a widely used method in structural dynamics for reducing the interior portion of a finite element model while having the ability to retain all nonlinear contact degrees of freedom (DOF) in physical coordinates. These models may still require many DOF to adequately resolve the kinematics of the interface, leading to inadequate reduction and computational savings. This study proposes a novel interface reduction method to overcome these challenges by means of system-level characteristic constraint (SCC) modes and properly orthogonal interface modal derivatives (POIMDs) for transient dynamic analyses. Both SCC modes and POIMDs are computed using the reduced HCB mass and stiffness matrices, which can be directly computed from many commercial finite element analysis software. Comparison of time history responses to an impulse-type load in a mechanical beam assembly indicate that the interface-reduced model correlates well with the HCB truth model. Localized features like slip and contact area are well-represented in the time domain when the beam assembly is loaded with a broadband excitation. The proposed method also yields reduced-order models with greater critical timestep lengths for explicit integration schemes.
Ongoing progress in synthetic biology, metabolic engineering, and catalysis continues to produce a diverse array of advanced biofuels with complex molecular structure and functional groups. In order to integrate biofuels into existing combustion systems, and to optimize the design of next-generation combustion systems, understanding connections between molecular structure and ignition at low-temperature conditions (< 1000 K) remains a priority that is addressed in part using chemical kinetics modeling. The development of predictive models relies on detailed information, derived from experimental and theoretical studies, on molecular structure and chemical reactivity, both of which influence the balance of chain reactions that occur during combustion – propagation, termination, and branching. In broad context, three main categories of reactions affect ignition behavior: (i) initiation reactions that generate a distribution of organic radicals, R˙; (ii) competing unimolecular decomposition of R˙ and bimolecular reaction of R˙ with O2; (iii) decomposition mechanisms of peroxy radical adducts (ROO˙), including isomerization via ROO˙ ⇌ Q˙OOH. All three categories are influenced by functional groups in different ways, which causes a shift in the balance of chain reactions that unfold over complex temperature- and pressure-dependent mechanisms. The objective of the present review is three-fold: (1) to provide a historical account of research on low-temperature oxidation of biofuels, including initiation reactions, peroxy radical reactions, Q˙OOH-mediated reaction mechanisms, and chain-branching chemistry; (2) to summarize the influence of functional groups on chemical kinetics relevant to chain-branching reactions, which are responsible for the accelerated production of radicals that leads to ignition; (3) to identify areas of research that are needed – experimentally and computationally – to address fundamental questions that remain. Results from experimental, quantum chemical, and chemical kinetics modeling studies are reviewed for several classes of biofuels – alcohols, esters, ketones, acyclic ethers and cyclic ethers – and are compared against analogous results in alkane oxidation. The review is organized into separate sections for each biofuel class, which include studies on thermochemistry and bond dissociation energies, rate coefficients for initiation reactions via H-abstraction and related branching fractions, reaction mechanisms and product formation from reactive intermediates, ignition delay times, and chemical kinetics modeling. Each section is then summarized in order to identify areas for which additional functional group-specific work is required. The review concludes with an outline for research directions for improving the fundamental understanding of biofuel ignition chemistry and related chemical kinetics modeling.
There is a dearth in the literature on how to capture the uncertainty generated by material surface evolution in thermal modeling. This leads to inadequate or highly variable uncertainty representations for material properties, specifically emissivity when minimal information is available. Inaccurate understandings of prediction uncertainties may lead decision makers to incorrect conclusions, so best engineering practices should be developed for this domain. In order to mitigate the aforementioned issues, this study explores different strategies to better capture the thermal uncertainty response of engineered systems exposed to fire environments via defensible emissivity uncertainty characterizations that can be easily adapted to a variety of use cases. Two unique formulations (one physics-informed and one mathematically based) are presented. The formulations and methodologies presented herein are not exhaustive but more so are a starting point and give the reader a basis for how to customize their uncertainty definitions for differing fire scenarios and materials. Finally, the impact of using this approach versus other commonly used strategies and the usefulness of adding rigor to material surface evolution uncertainty is demonstrated.
We present a simple and powerful technique for finding a good error model for a quantum processor. The technique iteratively tests a nested sequence of models against data obtained from the processor, and keeps track of the best-fit model and its wildcard error (a metric of the amount of unmodeled error) at each step. Each best-fit model, along with a quantification of its unmodeled error, constitutes a characterization of the processor. We explain how quantum processor models can be compared with experimental data and to each other. We demonstrate the technique by using it to characterize a simulated noisy two-qubit processor.
In this project, our goal was to develop methods that would allow us to make accurate predictions about individual differences in human cognition. Understanding such differences is important for maximizing human and human-system performance. There is a large body of research on individual differences in the academic literature. Unfortunately, it is often difficult to connect this literature to applied problems, where we must predict how specific people will perform or process information. In an effort to bridge this gap, we set out to answer the question: can we train a model to make predictions about which people understand which languages? We chose language processing as our domain of interest because of the well- characterized differences in neural processing that occur when people are presented with linguistic stimuli that they do or do not understand. Although our original plan to conduct several electroencephalography (EEG) studies was disrupted by the COVID-19 pandemic, we were able to collect data from one EEG study and a series of behavioral experiments in which data were collected online. The results of this project indicate that machine learning tools can make reasonably accurate predictions about an individual?s proficiency in different languages, using EEG data or behavioral data alone.
Probabilistic and Bayesian neural networks have long been proposed as a method to incorporate uncertainty about the world (both in training data and operation) into artificial intelligence applications. One approach to making a neural network probabilistic is to leverage a Monte Carlo sampling approach that samples a trained network while incorporating noise. Such sampling approaches for neural networks have not been extensively studied due to the prohibitive requirement of many computationally expensive samples. While the development of future microelectronics platforms that make this sampling more efficient is an attractive option, it has not been immediately clear how to sample a neural network and what the quality of random number generation should be. This research aimed to start addressing these two fundamental questions by examining basic “off the shelf” neural networks can be sampled through a few different mechanisms (including synapse “dropout” and neuron “dropout”) and examine how these sampling approaches can be evaluated both in terms of evaluating algorithm effectiveness and the required quality of random numbers.
Jawaharram, Gowtham S.; Barr, Christopher M.; Hattar, Khalid M.; Dillon, Shen J.
A series of nanopillar compression tests were performed on tungsten as a function of temperature using in situ transmission electron microscopy with localized laser heating. Surface oxidation was observed to form on the pillars and grow in thickness with increasing temperature. Deformation between 850◦C and 1120◦C is facilitated by long-range diffusional transport from the tungsten pillar onto adjacent regions of the Y2O3-stabilized ZrO2 indenter. The constraint imposed by the surface oxidation is hypothesized to underly this mechanism for localized plasticity, which is generally the so-called whisker growth mechanism. The results are discussed in context of the tungsten fuzz growth mechanism in He plasma-facing environments. The two processes exhibit similar morphological features and the conditions under which fuzz evolves appear to satisfy the conditions necessary to induce whisker growth.
Trujillo, Natasha; Rose-Coss, Dylan; Heath, Jason; Dewers, Thomas D.; Ampomah, William; Mozley, Peter S.; Cather, Martha
Leakage pathways through caprock lithologies for underground storage of CO2 and/or enhanced oil recovery (EOR) include intrusion into nano-pore mudstones, flow within fractures and faults, and larger-scale sedimentary heterogeneity (e.g., stacked channel deposits). To assess multiscale sealing integrity of the caprock system that overlies the Morrow B sandstone reservoir, Farnsworth Unit (FWU), Texas, USA, we combine pore-to-core observations, laboratory testing, well logging results, and noble gas analysis. A cluster analysis combining gamma ray, compressional slowness, and other logs was combined with caliper responses and triaxial rock mechanics testing to define eleven lithologic classes across the upper Morrow shale and Thirteen Finger limestone caprock units, with estimations of dynamic elastic moduli and fracture breakdown pressures (minimum horizontal stress gradients) for each class. Mercury porosimetry determinations of CO2 column heights in sealing formations yield values exceeding reservoir height. Noble gas profiles provide a “geologic time-integrated” assessment of fluid flow across the reservoir-caprock system, with Morrow B reservoir measurements consistent with decades-long EOR water-flooding, and upper Morrow shale and lower Thirteen Finger limestone values being consistent with long-term geohydrologic isolation. Together, these data suggest an excellent sealing capacity for the FWU and provide limits for injection pressure increases accompanying carbon storage activities.
Magnetic microscopy with high spatial resolution helps to solve a variety of technical problems in condensed-matter physics, electrical engineering, biomagnetism, and geomagnetism. In this work we used quantum diamond magnetic microscope (QDMM) setups, which use a dense uniform layer of magnetically-sensitive nitrogen-vacancy (NV) centers in diamond to image an external magnetic field using a fluorescence microscope. We used this technique for imaging few-micron ferromagnetic needles used as a physically unclonable function (PUF) and to passively interrogate electric current paths in a commercial 555 timer integrated circuit (IC). As part of the QDMM development, we also found a way to calculate ion implantation recipes to create diamond samples with dense uniform NV layers at the surface. This work opens the possibility for follow-up experiments with 2D magnetic materials, ion implantation, and electronics characterization and troubleshooting.
Machine-learned models, specifically neural networks, are increasingly used as “closures” or “constitutive models” in engineering simulators to represent fine-scale physical phenomena that are too computationally expensive to resolve explicitly. However, these neural net models of unresolved physical phenomena tend to fail unpredictably and are therefore not used in mission-critical simulations. In this report, we describe new methods to authenticate them, i.e., to determine the (physical) information content of their training datasets, qualify the scenarios where they may be used and to verify that the neural net, as trained, adhere to physics theory. We demonstrate these methods with neural net closure of turbulent phenomena used in Reynolds Averaged Navier-Stokes equations. We show the types of turbulent physics extant in our training datasets, and, using a test flow of an impinging jet, identify the exact locations where the neural network would be extrapolating i.e., where it would be used outside the feature-space where it was trained. Using Generalized Linear Mixed Models, we also generate explanations of the neural net (à la Local Interpretable Model agnostic Explanations) at prototypes placed in the training data and compare them with approximate analytical models from turbulence theory. Finally, we verify our findings by reproducing them using two different methods.
Ultra-low voltage drop tunnel junctions (TJs) were utilized to enable multi-active region blue light emitting diodes (LEDs) with up to three active regions in a single device. The multi-active region blue LEDs were grown monolithically by metal-organic chemical vapor deposition (MOCVD) without growth interruption. This is the first demonstration of a MOCVD grown triple-junction LED. Optimized TJ design enabled near-ideal voltage and EQE scaling close to the number of junctions. This work demonstrates that with proper TJ design, improvements in wall-plug efficiency at high output power operation are possible by cascading multiple III-nitride based LEDs.