Publications

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Cooperation between network nodes is critical for supporting services in ad hoc networks. Cooperation, however, is an idealized assumption that may not always be present. This assumption can fail because of moral hazard, a scenario in part caused by misaligned incentives between the requesting node and supporting node. In this paper, we characterize a moral hazard that perversely incentivizes nodes to increase their routing payments by transmitting interference into the multi-hop network. We refer to this as the interference moral hazard (IMH) problem which is inherent to strategyproof mechanisms with low overpayments. We investigate IMH as a non-cooperative game played by network nodes on a random graph. For large networks, we show that IMH can be solved in the network design space. Finally, we provide sufficient conditions on the network distribution that guarantee an equilibrium path with interference-free play. This is achieved by 1) lower-bounding the number of nodes and 2) bounding the network density slightly above the 2-connectedness threshold and below a proposed upper-bound. Simulations suggest that density plays a fundamental role in IMH.

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Probability of loss of assured safety (PLOAS) is modeled for weak link (WL)/strong link (SL) systems in which one or more WLs or SLs could potentially degrade into a precursor condition to link failure that will be followed by an actual link failure after some amount of elapsed time. The descriptor loss of assured safety (LOAS) is used because failure of the WL system places the entire system in an inoperable configuration while failure of the SL system before failure of the WL system, although undesirable, does not necessarily result in an unintended operation of the entire system. Thus, safety is “assured” by failure of the WL system before failure of the SL system. The following topics are considered: (i) Definition of precursor occurrence time cumulative distribution functions (CDFs) for individual WLs and SLs, (ii) Formal representation, approximation and illustration of PLOAS with (a) constant delay times, (b) aleatory uncertainty in delay times, and (c) delay times defined by functions of link properties at occurrence times for link failure precursors, and (iii) Procedures for the verification of PLOAS calculations for the three indicated definitions of delayed link failure.

Ducted fuel injection (DFI) is a novel combustion strategy that has been shown to significantly attenuate soot formation in diesel engines. While previous studies have used optical diagnostics and optical filter smoke number methods to show that DFI reduces in-cylinder soot formation and engine-out soot emissions, respectively, this is the first study to measure solid particle number (PN) emissions in addition to particle mass (PM). Furthermore, this study quantitatively evaluates the use of transient particle instruments for measuring particles from skip-fired operation in an optical single cylinder research engine (SCRE). Engine-out PN was measured using an engine exhaust particle sizer following a catalytic stripper, and PM was measured using a photoacoustic analyzer. The study improves on earlier preliminary emissions studies by clearly showing that DFI reduces overall PM by 76%–79% and PN for particles larger than 23 nm by 77% relative to conventional diesel combustion at a 1200-rpm, 13.3-bar gross indicated mean effective pressure operating condition. The degree of engine-out PM reduction with DFI was similar across both particulate measurement instruments used in the work. Through the use of bimodal distribution fitting, DFI was also shown to reduce the geometric mean diameter of accumulation mode particles by 26%, similar to the effects of increased injection pressure in conventional diesel combustion systems. This work clearly shows the significant solid particulate matter reductions enabled by DFI while also demonstrating that engine-out PN can be accurately measured from an optical SCRE operating in a skip-fired mode. Based on these results, it is believed that DFI has the potential to enable fuel savings when implemented in multi-cylinder engines, both by lowering the required frequency of active diesel particulate filter regeneration, and by reducing the backpressure imposed by exhaust filtration systems.

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Absolute double differential cross sections (DDCS) of electrons emitted from uracil and 5-bromouracil (BrU) in collisions with protons of energy 200 keV have been measured for various forward and backward emission angles over wide range of electron energies. The measured DDCS are compared with the continuum distorted wave-eikonal initial state (CDW-EIS) calculations. The optimized structure of the BrU was estimated along with the population analysis of all the occupied orbitals using a self-consistent field density. A comparison between the measured DDCS data for the two molecules show that the cross section of low energy electrons emitted from BrU is substantially larger than that for uracil. The BrU-to-uracil DDCS ratios obtained from the present measurements indicate an enhancement of the electron emission by a factor which is as large as 2.0 to 2.5. These electrons being the major agent for damaging the DNA/RNA of the malignant tissues, the present results are expected to provide an important input for the radiosensitization effect in hadron therapy. It is noteworthy to mention that the CDW-EIS calculations for Coulomb ionization cannot predict such enhancement. A large angular asymmetry is observed for uracil with a broad structure, which is absent in case of BrU.

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The microstructural-scale mechanisms that produce cracks in metals during deformation at elevated temperatures are relevant to applications that involve thermal exposure. Prior studies of cavitation during high-temperature deformation, for example, creep, suffered from an inability to directly observe the microstructural evolution that occurs during deformation and leads to void nucleation. The current study takes advantage of modern high-speed electron backscatter diffraction (EBSD) detectors to observe cavitation in oxygen-free, high-conductivity copper in situ during deformation at 300°C. Most voids formed at the triple junction between a twin boundary and a high-angle grain boundary (HAGB). This finding does not contradict previous studies that suggested that twins are resistant to cracking—it reveals that cracks in HAGBs originate at twin/HAGB triple junctions and that cracks preferentially grow along HAGBs rather than the accompanying twins. Atomistic simulations explored the origins of this observation and suggest that twin/HAGB triple junctions are microstructural weak points.

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The objective of this milestone was to finish integrating GenTen tensor software with combustion application Pele using the Ascent in situ analysis software, partnering with the ALPINE and Pele teams. Also, to demonstrate the usage of the tensor analysis as part of a combustion simulation.

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An analytical expression for turbulent kinematic viscosity (vt), based solely on the hydraulic Reynolds number (Re), was derived and evaluated. The analytical expression is valid for the fast estimation of vt for internal, isotropic, fully developed flows. The expression was compared with experimental and simulation data for air, water, and liquid sodium, and was shown to provide reasonable values for 2100 ≤ Re ≤ 3.6 × 106 and Prandtl number (Pr) range of 0.0107 ≤ Pr ≤ 5.65. In addition, new expressions suitable for the central portion of internal flows, away from the wall, were derived for the turbulent Reynolds number (ReT), showing its relationship to Re, as well as to the ratio of vt and the molecular kinematic viscosity (v).

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In the near future, grid operators are expected to regularly use advanced distributed energy resource (DER) functions, defined in IEEE 1547-2018, to perform a range of grid-support operations. Many of these functions adjust the active and reactive power of the device through commanded or autonomous operating modes which induce new stresses on the power electronics components. In this work, an experimental and theoretical framework is introduced which couples laboratory-measured component stress with advanced inverter functionality and derives a reduction in useful lifetime based on an applicable reliability model. Multiple DER devices were instrumented to calculate the additional component stress under multiple reactive power setpoints to estimate associated DER lifetime reductions. A clear increase in switch loss was demonstrated as a function of irradiance level and power factor. This is replicated in the system-level efficiency measurements, although magnitudes were different—suggesting other loss mechanisms exist. Using an approximate Arrhenius thermal model for the switches, the experimental data indicate a lifetime reduction of 1.5% when operating the inverter at 0.85 PF—compared to unity PF—assuming the DER failure mechanism thermally driven within the H-bridge. If other failure mechanisms are discovered for a set of power electronics devices, this testing and calculation framework can easily be tailored to those failure mechanisms.

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Incipient melting is a phenomenon that can occur in aluminum alloys where solute rich areas, such as grain boundaries, can melt before the rest of the material; incipient melting can degrade mechanical and corrosion properties and is irreversible, resulting in material scrapping. After detecting indications of incipient melting as the cause of failure in 7075 aluminum alloy parts (AA7075), a study was launched to determine threshold temperature for incipient melting. Samples of AA7075 were solution annealed using temperatures ranging from 870-1090F. A hardness profile was developed to demonstrate the loss of mechanical properties through the progression of incipient melting. Additionally, Zeiss software Zen Core Intellesis was utilized to more accurately quantify the changes in microstructural properties as AA7075 surpassed the onset of incipient melting. The results from this study were compared with previous AA7075 material that demonstrated incipient melting.

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Time-resolved soot and PAH formation from gasoline and diesel spray pyrolysis are visualized and quantified using diffuse back illumination (DBI) and laser induced fluorescence (LIF) at 355 nm, respectively, in a constant-volume vessel at 60 bar from 1400 to 1700 K for up to 30 ms. The delay, maximum formation rate, and yield of soot and PAHs are compared across fuels and temperatures and correlated with the yield sooting indices on either the mass or mole basis. The delays generally decrease with increasing temperature, and the formation rates of both PAHs and soot generally increase with temperature. The apparent PAH-LIF yield may decrease with temperature due to PAH growth and conversion into larger species, signal trapping, and thermal quneching. Soot yield generally increases with temperature. The mass-based YSI correlates reasonably well with soot delay, but YSI does not correlate well with soot yield. The mass-based YSI is a more appropriate predictor of sooting propensity than the mole-based YSI.

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The Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC (NTESS), a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration (DOE/NNSA) under contract DE-NA0003525. In 2008, a Notice of Intent (NOI) was filed for the Sandia National Laboratories, California (SNL/CA) facility to be covered under the State Water Resources Control Board (SWRCB) Order No. 2006-0003-DWQ Statewide General Waste Discharge Requirements (WDR) for Sanitary Sewer Systems (General Permit) and was issued the WDID No. 2SSO11605. The General Permit requires a proactive approach to reduce the number and frequency of sanitary sewer overflows (SSOs) within the State. Provision D.11 of the General Permit requires the development and implementation of a written Sewer System Management Plan (SSMP). This SSMP is prepared according to the mandatory elements required by Provision D.13 and D.14, as well as the schedule for a population less than 2,500 as outlined in Provision D.15.

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As the U.S. electrifies the transportation sector, cyberattacks targeting vehicle charging could impact several critical infrastructure sectors including power systems, manufacturing, medical services, and agriculture. This is a growing area of concern as charging stations increase power delivery capabilities and must communicate to authorize charging, sequence the charging process, and manage load (grid operators, vehicles, OEM vendors, charging network operators, etc.). The research challenges are numerous and complicated because there are many end users, stakeholders, and software and equipment vendors interests involved. Poorly implemented electric vehicle supply equipment (EVSE), electric vehicle (EV), or grid operator communication systems could be a significant risk to EV adoption because the political, social, and financial impact of cyberattacks — or public perception of such — would ripple across the industry and produce lasting effects. Unfortunately, there is currently no comprehensive EVSE cybersecurity approach and limited best practices have been adopted by the EV/EVSE industry. There is an incomplete industry understanding of the attack surface, interconnected assets, and unsecured inter faces. Comprehensive cybersecurity recommendations founded on sound research are necessary to secure EV charging infrastructure. This project provided the power, security, and automotive industry with a strong technical basis for securing this infrastructure by developing threat models, determining technology gaps, and identifying or developing effective countermeasures. Specifically, the team created a cybersecurity threat model and performed a technical risk assessment of EVSE assets across multiple manufacturers and vendors, so that automotive, charging, and utility stakeholders could better protect customers, vehicles, and power systems in the face of new cyber threats.

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The elemental equation governing heat transfer in aerodynamic flows is the internal energy equation. For a boundary layer flow, a double integration of the Reynolds-averaged form of this equation provides an expression of the wall heat flux in terms of the integrated effects, over the boundary layer, of various physical processes: turbulent dissipation, mean dissipation, turbulent heat flux, etc. Recently available direct numerical simulation data for a Mach 11 cold-wall turbulent boundary layer allows a comparison of the exact contributions of these terms in the energy equation to the wall heat flux with their counterparts modeled in the Reynolds-averaged Navier-Stokes (RANS) framework. Various approximations involved in RANS, both closure models as well as approximations involved in adapting incompressible RANS models to a compressible form, are assessed through examination of the internal energy balance. There are a number of potentially problematic assumptions and terms identified through this analysis. The effect of compressibility corrections of the dilatational dissipation type is explored, as is the role of the modeled turbulent dissipation, in the context of wall heat flux predictions. The results indicate several potential avenues for RANS model improvement for hypersonic cold-wall boundary-layer flows.

A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.

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Hydrides based on magnesium and intermetallic compounds provide a viable solution to the challenge of energy storage from renewable sources, thanks to their ability to absorb and desorb hydrogen in a reversible way with a proper tuning of pressure and temperature conditions. Therefore, they are expected to play an important role in the clean energy transition and in the deployment of hydrogen as an efficient energy vector. This review, by experts of Task 40 'Energy Storage and Conversion based on Hydrogen' of the Hydrogen Technology Collaboration Programme of the International Energy Agency, reports on the latest activities of the working group 'Magnesium- and Intermetallic alloys-based Hydrides for Energy Storage'. The following topics are covered by the review: multiscale modelling of hydrides and hydrogen sorption mechanisms; synthesis and processing techniques; catalysts for hydrogen sorption in Mg; Mg-based nanostructures and new compounds; hydrides based on intermetallic TiFe alloys, high entropy alloys, Laves phases, and Pd-containing alloys. Finally, an outlook is presented on current worldwide investments and future research directions for hydrogen-based energy storage.

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The phase-field method is a popular modeling technique used to describe the dynamics of microstructures and their physical properties at the mesoscale. However, because in these simulations the microstructure is described by a system of continuous variables evolving both in space and time, phase-field models are computationally expensive. They require refined spatio-temporal discretization and a parallel computing approach to achieve a useful degree of accuracy. As an alternative, we present and discuss an accelerated phase-field approach which uses a recurrent neural network (RNN) to learn the microstructure evolution in latent space. We perform a comprehensive analysis of different dimensionality-reduction methods and types of recurrent units in RNNs. Specifically, we compare statistical functions combined with linear and nonlinear embedding techniques to represent the microstructure evolution in latent space. We also evaluate several RNN models that implement a gating mechanism, including the long short-term memory (LSTM) unit and the gated recurrent unit (GRU) as the microstructure-learning engine. We analyze the different combinations of these methods on the spinodal decomposition of a two-phase system. Our comparison reveals that describing the microstructure evolution in latent space using an autocorrelation-based principal component analysis (PCA) method is the most efficient. We find that the LSTM and GRU RNN implementations provide comparable accuracy with respect to the high-fidelity phase-field predictions, but with a considerable computational speedup relative to the full simulation. This study not only enhances our understanding of the performance of dimensionality reduction on the microstructure evolution, but it also provides insights on strategies for accelerating phase-field modeling via machine learning techniques.

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Achieving practical, high-energy-density calcium batteries requires controlling the stability of Ca2+electrolytes during calcium metal cycling. Because of the highly reactive nature of calcium, most typical electrolyte constituents are unstable, leading to electrode passivation and low Coulombic efficiency. Among various commercially available salts, calcium bis(trifluoromethylsulfonyl)imide (Ca(TFSI)2) is attractive because of its oxidative stability and high solubility in a variety of solvents. However, this salt does not allow for calcium metal plating, and it has been proposed that TFSI-instability induced by Ca2+coordination is to blame. In this work, we test the ability of strongly coordinating Ca2+cosalts such as halides and borohydrides to displace TFSI-from the first coordination shell of Ca2+and thereby stabilize TFSI-based electrolytes to enable calcium plating. Through spectroscopic analysis, we find that the effectiveness of these cosalts at displacing the TFSI-anion is dependent on the solvent's coordination strength toward Ca2+. Surprisingly, electrochemical calcium deposition behavior is not correlated to the population of bound or free TFSI-. Instead, the nature of the coordination interaction between Ca2+and the cosalt anion is more important for determining stability. Our findings indicate that TFSI-anions are inherently unstable during calcium deposition even in the nominally free state. Therefore, strategies aimed at eliminating the interactions of these anions with the electrode surface via interface/interphase design are required.

This report represents the milestone deliverable M4SF-22SN010309092 “Modeling Activities Related to Waste Form Degradation: Progress Report” that describes the progress of R&D activities of ongoing modeling investigations specifically on nuclear waste glass degradation, Density Functional Theory (DFT) studies on clarkeite structure and stability, and electrochemical model development of spent nuclear fuel (SNF). These activities are part of the Waste Form Testing, Modeling, and Performance work package at Sandia National Laboratories (SNL). This work package is part of the “Inventory and Waste Form Characteristics and Performance” control account that includes various experimental and modeling activities on nuclear waste degradation conducted at Oak Ridge National Laboratory (ORNL), SNL, Argonne National Laboratory (ANL), and Pacific Northwest National Laboratory (PNNL).

We present an adaptive algorithm for constructing surrogate models of multi-disciplinary systems composed of a set of coupled components. With this goal we introduce “coupling” variables with a priori unknown distributions that allow surrogates of each component to be built independently. Once built, the surrogates of the components are combined to form an integrated-surrogate that can be used to predict system-level quantities of interest at a fraction of the cost of the original model. The error in the integrated-surrogate is greedily minimized using an experimental design procedure that allocates the amount of training data, used to construct each component-surrogate, based on the contribution of those surrogates to the error of the integrated-surrogate. The multi-fidelity procedure presented is a generalization of multi-index stochastic collocation that can leverage ensembles of models of varying cost and accuracy, for one or more components, to reduce the computational cost of constructing the integrated-surrogate. Extensive numerical results demonstrate that, for a fixed computational budget, our algorithm is able to produce surrogates that are orders of magnitude more accurate than methods that treat the integrated system as a black-box.

Climate change is an existential threat to the vast global permafrost domain. The diverse human cultures, ecological communities, and biogeochemical cycles of this tenth of the planet depend on the persistence of frozen conditions. The complexity, immensity, and remoteness of permafrost ecosystems make it difficult to grasp how quickly things are changing and what can be done about it. Here, we summarize terrestrial and marine changes in the permafrost domain with an eye toward global policy. While many questions remain, we know that continued fossil fuel burning is incompatible with the continued existence of the permafrost domain as we know it. If we fail to protect permafrost ecosystems, the consequences for human rights, biosphere integrity, and global climate will be severe. The policy implications are clear: the faster we reduce human emissions and draw down atmospheric CO2, the more of the permafrost domain we can save. Emissions reduction targets must be strengthened and accompanied by support for local peoples to protect intact ecological communities and natural carbon sinks within the permafrost domain. Some proposed geoengineering interventions such as solar shading, surface albedo modification, and vegetation manipulations are unproven and may exacerbate environmental injustice without providing lasting protection. Conversely, astounding advances in renewable energy have reopened viable pathways to halve human greenhouse gas emissions by 2030 and effectively stop them well before 2050. We call on leaders, corporations, researchers, and citizens everywhere to acknowledge the global importance of the permafrost domain and work towards climate restoration and empowerment of Indigenous and immigrant communities in these regions.

A recently discovered, enhanced Ge diffusion mechanism along the oxidizing interface of Si/SiGe nanostructures has enabled the formation of single-crystal Si nanowires and quantum dots embedded in a defect-free, single-crystal SiGe matrix. Here, we report oxidation studies of Si/SiGe nanofins aimed at gaining a better understanding of this novel diffusion mechanism. A superlattice of alternating Si/Si0.7Ge0.3layers was grown and patterned into fins. After oxidation of the fins, the rate of Ge diffusion down the Si/SiO2interface was measured through the analysis of HAADF-STEM images. The activation energy for the diffusion of Ge down the sidewall was found to be 1.1 eV, which is less than one-quarter of the activation energy previously reported for Ge diffusion in bulk Si. Through a combination of experiments and DFT calculations, we propose that the redistribution of Ge occurs by diffusion along the Si/SiO2interface followed by a reintroduction into substitutional positions in the crystalline Si.

A series of experiments will be performed to test the integral effects of molybdenum on the reactivity of a critical system. These experiments will use the 7uPCX assembly with the 1.55 cm triangular pitch grid plates. Molybdenum sleeves, consisting of 19.6 inch long 0.5-inch nominal outside diameter molybdenum tubes with 0.031-inch nominal wall thickness and centering hardware, will be placed on some of the fuel rods in the array. The purpose of this analysis is to examine two configurations of the 7uPCX using the 1.55 cm triangular pitch grid plates in fully-reflected approach-to-critical experiments with the number of fuel rods in the array as the approach parameter. This document presents the results of the analysis that was done to allow completion of the 7uPCX Configuration Checklist from Appendix A of SPRF-AP-005 [SNL 2020] for the cores noted above. The checklists for these cores are shown in Appendix A.

The inverse methods team provides a set of tools for solving inverse problems in structural dynamics and thermal physics, and also sensor placement optimization via Optimal Experimental Design (OED). These methods are used for designing experiments, model calibration, and verification/validation analysis of weapons systems. This document provides a user’s guide to the input for the three apps that are supported for these methods. Details of input specifications, output options, and optimization parameters are included.

Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the objective is to discover part of the hidden physics from the available data, and PIML has been shown to be particularly effective for such problems for which conventional methods may fail. Unlike commercial machine learning where training of deep neural networks requires big data, in PIML big data are not available. Instead, we can train such networks from additional information obtained by employing the physical laws and evaluating them at random points in the space-time domain. Such PIML integrates multimodality and multifidelity data with mathematical models, and implements them using neural networks or graph networks. Here, we review some of the prevailing trends in embedding physics into machine learning, using physics-informed neural networks (PINNs) based primarily on feed-forward neural networks and automatic differentiation. For more complex systems or systems of systems and unstructured data, graph neural networks (GNNs) present some distinct advantages, and here we review how physics-informed learning can be accomplished with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs). We present representative examples for both forward and inverse problems and discuss what advances are needed to scale up PINNs, PIGNs and more broadly GNNs for large-scale engineering problems.

A method is developed to calculate the length into a sample to which a Frequency Domain Thermoreflectance (FDTR) measurement is sensitive. Sensing depth and sensing radius are defined as limiting cases for the spherically spreading FDTR measurement. A finite element model for FDTR measurements is developed in COMSOL multiphysics and used to calculate sensing depth and sensing radius for silicon and silicon dioxide samples for a variety of frequencies and laser spot sizes. The model is compared to experimental FDTR measurements. Design recommendations for sample thickness are made for experiments where semi-infinite sample depth is desirable. For measurements using a metal transducer layer, the recommended sample thickness is three thermal penetration depths, as calculated from the lowest measurement frequency.

The SIERRA Low Mach Module: Fuego, henceforth referred to as Fuego, is the key element of the ASC fire environment simulation project. The fire environment simulation project is directed at characterizing both open large-scale pool fires and building enclosure fires. Fuego represents the turbulent, buoyantly-driven incompressible flow, heat transfer, mass transfer, combustion, soot, and absorption coefficient model portion of the simulation software. Using MPMD coupling, Scefire and Nalu handle the participating-media thermal radiation mechanics. This project is an integral part of the SIERRA multi-mechanics software development project. Fuego depends heavily upon the core architecture developments provided by SIERRA for massively parallel computing, solution adaptivity, and mechanics coupling on unstructured grids.

The SNL Sierra Mechanics code suite is designed to enable simulation of complex multiphysics scenarios. The code suite is composed of several specialized applications which can operate either in standalone mode or coupled with each other. Arpeggio is a supported utility that enables loose coupling of the various Sierra Mechanics applications by providing access to Framework services that facilitate the coupling. More importantly Arpeggio orchestrates the execution of applications that participate in the coupling. This document describes the various components of Arpeggio and their operability. The intent of the document is to provide a fast path for analysts interested in coupled applications via simple examples of its usage.

As part of the development process, scaled testing of wave energy converter devices are necessary to prove a concept, study hydrodynamics, and validate control system approaches. Creating a low-cost, small, lightweight data acquisition system suitable for scaled testing is often a barrier for wave energy converter developers’ ability to test such devices. This paper outlines an open-source solution to these issues, which can be customized based on specific needs. Furthermore, this will help developers with limited resources along a path toward commercialization.

Presented in this document is a portion of the tests that exist in the Sierra Thermal/Fluids verification test suite. Each of these tests is run nightly with the Sierra/TF code suite and the results of the test checked under mesh refinement against 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 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.

SIERRA/Aero is a compressible fluid dynamics program intended to solve a wide variety compressible fluid flows including transonic and hypersonic problems. This document describes the commands for assembling a fluid model for analysis with this module, henceforth referred to simply as Aero for brevity. Aero is an application developed using the SIERRA Toolkit (STK). The intent of STK is to provide a set of tools for handling common tasks that programmers encounter when developing a code for numerical simulation. For example, components of STK provide field allocation and management, and parallel input/output of field and mesh data. These services also allow the development of coupled mechanics analysis software for a massively parallel computing environment.

The present study has used a variety of characterization techniques to determine the products and reaction pathways involved in the rechargeable Li-FeS2 system. We revisit both the initial lithiation and subsequent cycling of FeS2 employing an ionic liquid electrolyte to investigate the intermediate and final charge products formed under varying thermal conditions (room temperature to 100 °C). The detection of Li2S and hexagonal FeS as the intermediate phases in the initial lithiation and the electrochemical formation of greigite, Fe3S4, as a charge product in the rechargeable reaction differ significantly from previous reports. The conditions for Fe3S4 formation are shown to be dependent on both the temperature (∼60 °C) and the availability of sulfur to drive a FeS to Fe3S4 transformation. Upon further cycling, Fe3S4 transforms to a lower sulfur content iron sulfide phase, a process which coincides with the loss of sulfur based on the new reaction pathways established in this work. The connection between sulfur loss, capacity fade, and charge product composition highlights the critical need to retain sulfur in the active material upon cycling.

The SIERRA Low Mach Module: Fuego, henceforth referred to as Fuego, is the key element of the ASC fire environment simulation project. The fire environment simulation project is directed at characterizing both open large-scale pool fires and building enclosure fires. Fuego represents the turbulent, buoyantly-driven incompressible flow, heat transfer, mass transfer, combustion, soot, and absorption coefficient model portion of the simulation software. Using MPMD coupling, Scefire and Nalu handle the participating-media thermal radiation mechanics. This project is an integral part of the SIERRA multi-mechanics software development project. Fuego depends heavily upon the core architecture developments provided by SIERRA for massively parallel computing, solution adaptivity, and mechanics coupling on unstructured grids.

Aria is a Galerkin finite element based program for solving coupled-physics problems described by systems of PDEs and is capable of solving nonlinear, implicit, transient and direct-to-steady state problems in two and three dimensions on parallel architectures. The suite of physics currently supported by Aria includes thermal energy transport, species transport, and electrostatics as well as generalized scalar, vector and tensor transport equations. Additionally, Aria includes support for manufacturing process ows via the incompressible Navier-Stokes equations specialized to a low Reynolds number (Re < 1) regime. Enhanced modeling support of manufacturing processing is made possible through use of either arbitrary Lagrangian-Eulerian (ALE) and level set based free and moving boundary tracking in conjunction with quasi-static nonlinear elastic solid mechanics for mesh control. Coupled physics problems are solved in several ways including fully-coupled Newton’s method with analytic or numerical sensitivities, fully-coupled Newton-Krylov methods and a loosely-coupled nonlinear iteration about subsets of the system that are solved using combinations of the aforementioned methods. Error estimation, uniform and dynamic ℎ-adaptivity and dynamic load balancing are some of Aria’s more advanced capabilities.

Aria is a Galerkin finite element based program for solving coupled-physics problems described by systems of PDEs and is capable of solving nonlinear, implicit, transient and direct-to-steady state problems in two and three dimensions on parallel architectures. The suite of physics currently supported by Aria includes thermal energy transport, species transport, and electrostatics as well as generalized scalar, vector and tensor transport equations. Additionally, Aria includes support for manufacturing process flows via the incompressible Navier-Stokes equations specialized to a low Reynolds number (Re < 1) regime. Enhanced modeling support of manufacturing processing is made possible through use of either arbitrary Lagrangian-Eulerian (ALE) and level set based free and moving boundary tracking in conjunction with quasi-static nonlinear elastic solid mechanics for mesh control. Coupled physics problems are solved in several ways including fully-coupled Newton’s method with analytic or numerical sensitivities, fully-coupled Newton-Krylov methods and a loosely-coupled nonlinear iteration about subsets of the system that are solved using combinations of the aforementioned methods. Error estimation, uniform and dynamic ℎ-adaptivity and dynamic load balancing are some of Aria’s more advanced capabilities.

The SIERRA Low Mach Module: Fuego, henceforth referred to as Fuego, is the key element of the ASC fire environment simulation project. The fire environment simulation project is directed at characterizing both open large-scale pool fires and building enclosure fires. Fuego represents the turbulent, buoyantly-driven incompressible flow, heat transfer, mass transfer, combustion, soot, and absorption coefficient model portion of the simulation software. Using MPMD coupling, Scefire and Nalu handle the participating-media thermal radiation mechanics. This project is an integral part of the SIERRA multi-mechanics software development project. Fuego depends heavily upon the core architecture developments provided by SIERRA for massively parallel computing, solution adaptivity, and mechanics coupling on unstructured grids.

The decomposition of higher-order joint cumulant tensors of spatio-temporal data sets is useful in analyzing multi-variate non-Gaussian statistics with a wide variety of applications (e.g. anomaly detection, independent component analysis, dimensionality reduction). Computing the cumulant tensor often requires computing the joint moment tensor of the input data first, which is very expensive using a naïve algorithm. The current state-of-the-art algorithm takes advantage of the symmetric nature of a moment tensor by dividing it into smaller cubic tensor blocks and only computing the blocks with unique values and thus reducing computation. We propose a refactoring of this algorithm by posing its computation as matrix operations, specifically Khatri-Rao products and standard matrix multiplications. An analysis of the computational and cache complexity indicates significant performance savings due to the refactoring. Implementations of our refactored algorithm in Julia show speedups up to 10x over the reference algorithm in single processor experiments. We describe multiple levels of hierarchical parallelism inherent in the refactored algorithm, and present an implementation using an advanced programming model that shows similar speedups in experiments run on a GPU.

New and novel HPC platforms provide interesting challenges and opportunities. Analysis of these systems can provide a better understanding of both the specific platform being studied as well as large-scale systems in general. Arm is one such architecture that has been explored in HPC for several years, however little is still known about its viability for supporting large-scale production workloads in terms of system reliability. The Astra system at Sandia National Laboratories was the first public peta-FLOPS Arm-based system on the Top500 and has been successfully running production HPC applications for a couple of years. In this paper, we analyze memory failure data collected from Astra while the system was in production running unclassified applications. This analysis revealed several interesting contributions related to both the Arm platform and to HPC systems in general. First, we outline the number of components replaced due to reliability issues in standing-up this first-of-its-kind, large-scale HPC system. We show the distribution differences between correctable DRAM faults and errors on Astra, showing that, not properly accounting for faults can lead to erroneous conclusions. Additionally, we characterize DRAM faults on the system and show contrary to existing work that memory faults are uniformly distributed across CPU socket, DRAM column, bank and rack region, but are not uniform across node, DIMM rank, DIMM slot on the motherboard, and system rack: some racks, ranks and DIMM slots experience more faults than others. Similarly, we show the impact of temperature and power on DRAM correctable errors. Finally, we make a detailed comparison of results presented here with the positional affects found in several previous large-scale reliability studies. The results of this analysis provide valuable guidance to organizations standing-up first-in- class platforms in HPC, organizations using Arm in HPC, and the entire large-scale HPC community in general.

There is a need to understand materials exposed to overlapping extreme environments such as high temperature, radiation, or mechanical stress. When these stressors are combined there may be synergistic effects that enable unique microstructural evolution mechanisms to activate. Understanding of these mechanisms is necessary for the input and refinement of predictive models and critical for engineering of next generation materials. The basic physics and underlying mechanisms require advanced tools to be investigated. The in situ ion irradiation transmission electron microscope (I³TEM) is designed to explore these principles. To quantitatively probe the complex dynamic interactions in materials, careful preparation of samples and consideration of experimental design is required. Particular handling or preparation of samples can easily introduce damage or features that obfuscate the measurements. There is no one correct way to prepare a sample; however, many mistakes can be made. The most common errors and things to consider are highlighted within. The I³TEM has many adjustable variables and a large potential experimental space, therefore it is best to design experiments with a specific scientific question or questions in mind. Experiments have been performed on large number of sample geometries, material classes, and with many irradiation conditions. The following are a subset of examples that demonstrate unique in situ capabilities utilizing the I3TEM. Au nanoparticles prepared by drop casting have been used to investigate the effects of single ion strikes. Au thin films have been used in studies on the effects of multibeam irradiation on microstructure evolution. Zr films have been exposed to irradiation and mechanical tension to examine creep. Ag nanopillars were subjected to simultaneous high temperature, mechanical compression, and ion irradiation to study irradiation induced creep as well. These results impact fields including: structural materials, nuclear energy, energy storage, catalysis, and microelectronics in space environments.

Power Spectrum Analysis (PSA) is a Sandia-developed, non-intrusive, electrical technique that captures distinct frequency-domain signatures of microelectronics devices using an innovative, unconventional biasing scheme (off-normal biasing). PSA can identify subtle differences in devices and is applicable in various areas such as device screening, counterfeit identification, reliability assurance, and trust authentication. From October 2020 to April 2021, Sandia worked with entrepreneurs from a new start-up company, Chiplytics, to commercialize PSA technology through NNSA-sponsored FedTech Program. In September 2021, Sandia received funding through Covid-19 Technical Assistance Program (CTAP) to provide technical assistance to Chiplytics for commercialization. Under the CTAP Statement of Work, Sandia was tasked with providing technical assistance to Chiplytics in PSA pilot testing for Naval Surface Warfare Center (NSWC) at Crane and other pilot participants. Sandia was also tasked with assisting Chiplytics in hardware development and evaluation of Chiplytics prototype system.

The magneto-Rayleigh-Taylor instability (MRTI) plays an essential role in astrophysical systems and in magneto-inertial fusion, where it is known to be an important degradation mechanism of confinement and target performance. In this Letter, we show for the first time experimental evidence of mode mixing and the onset of an inverse-cascade process resulting from the nonlinear coupling of two discrete preseeded axial modes (400- and 550-μm wavelengths) on an Al liner that is magnetically imploded using the 20-MA, 100-ns rise-time Z Machine at Sandia National Laboratories. Four radiographs captured the temporal evolution of the MRTI. We introduce a novel unfold technique to analyze the experimental radiographs and compare the results to simulations and to a weakly nonlinear model. We find good quantitative agreement with simulations using the radiation magnetohydrodynamics code hydra. Spectral analysis of the MRTI time evolution obtained from the simulations shows evidence of harmonic generation, mode coupling, and the onset of an inverse-cascade process. The experiments provide a benchmark for future work on the MRTI and motivate the development of new analytical theories to better understand this instability.

Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for individual components in binary fluid mixtures. The ANNs were tested on an experimental database of 4328 self-diffusion constants from 131 mixtures containing 75 unique compounds. The presence of strong hydrogen bonding molecules may lead to clustering or dimerization resulting in non-linear diffusive behavior. To address this, self- and binary association energies were calculated for each molecule and mixture to provide information on intermolecular interaction strength and were used as input features to the ANN. An accurate, generalized ANN model was developed with an overall average absolute deviation of 4.1%. Forward input feature selection reveals the importance of critical properties and self-association energies along with other fluid properties. Additional ANNs were developed with subsets of the full input feature set to further investigate the impact of various properties on model performance. The results from two specific mixtures are discussed in additional detail: one providing an example of strong hydrogen bonding and the other an example of extreme pressure changes, with the ANN models predicting self-diffusion well in both cases.

The E3 transition in irradiated GaAs observed in deep level transient spectroscopy (DLTS) was recently discovered in Laplace-DLTS to encompass three distinct components. The component designated E3c was found to be metastable, reversibly bleached under minority carrier (hole) injection, with an introduction rate dependent upon Si doping density. It is shown through first-principles modeling that the E3c must be the intimate Si-vacancy pair, best described as a Si sitting in a divacancy Si_vv. The bleached metastable state is enabled by a doubly site-shifting mechanism: Upon recharging, the defect undergoes a second site shift rather returning to its original E3c-active configuration via reversing the first site shift. Identification of this defect offers insights into the short-time annealing kinetics in irradiated GaAs.

A direct comparison between electron transparent transmission electron microscope (TEM) samples prepared with gallium (Ga) and xenon (Xe) focused ion beams (FIBs) is performed to determine if equivalent quality samples can be prepared with both ion species. We prepared samples using Ga FIB and Xe plasma focused ion beam (PFIB) while altering a variety of different deposition and milling parameters. The samples' final thicknesses were evaluated using STEM-EELS t/λ data. Using the Ga FIB sample as a standard, we compared the Xe PFIB samples to the standard and to each other. We show that although the Xe PFIB sample preparation technique is quite different from the Ga FIB technique, it is possible to produce high-quality, large area TEM samples with Xe PFIB. We also describe best practices for a Xe PFIB TEM sample preparation workflow to enable consistent success for any thoughtful FIB operator. For Xe PFIB, we show that a decision must be made between the ultimate sample thickness and the size of the electron transparent region.

In this work we present the concept of g'clipping', scheduling receive events for wireless transmissions only on receivers within some distance of the transmitter. Combined with spatial indexing, this technique enables faster simulation of large-scale wireless networks containing tens of thousands or even hundreds of thousands of wireless nodes. We detail our additions and changes to ns-3 to implement this feature, demonstrate how it yields a 2 × speedup for a complex 5G scenario with minimal impact on simulation fidelity, and show how under special circumstances a speedup of over 40 × is achievable while producing identical results.

This is an addendum to the Sierra/SolidMechanics 5.8 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 5.8 User’s Guide should be referenced for most general descriptions of code capability and use.

The effect of psychosocial factors after a disaster is well acknowledged. They can manifest in many ways including policy decisions, increased medical problems due to stress, avoidance behavior, and lessened trust. These can have direct economic effects, including increased medical costs, decreased tourism, de facto boycotts of product, and delated remedial actions due to delayed decision making.

Civil infrastructure is made primarily of concrete structures or components and therefore understanding durability and fracture behavior of concrete is of utmost importance. Concrete contains an interfacial transition zone (ITZ), a porous region surrounding the aggregates, that is often considered to be the weakest region in the concrete. The ITZ is poorly characterized and property estimates for the ITZ differ considerably. In this simulation study, representative concrete mesostructures are produced by packing coarse aggregates with realistic geometries into a mortar matrix. A meshless numerical method, peridynamics, is utilized to simulate the mechanical response including fracture under uniaxial compression and tension. The sensitivity of the stiffness and fracture toughness of the samples to the ITZ properties is computed, showing strong relationships between the ITZ properties and the effective modulus and effective yield strength of the concrete. These results provides insight into the influence of the poorly characterized ITZ on the stiffness and strength of concrete. This work showcases the applicability of peridynamics to concrete systems, matching experimental strength and modulus values. Additionally, relationships between the ITZ's mechanical properties and the overall concrete strength and stiffness are presented to enable future design decisions.

X-ray tomography is capable of imaging the interior of objects in three dimensions non-invasively, with applications in biomedical imaging, materials science, electronic inspection, and other fields. The reconstruction process can be an ill-conditioned inverse problem, requiring regularization to obtain satisfactory results. Recently, deep learning has been adopted for tomographic reconstruction. Unlike iterative algorithms which require a distribution that is known a priori, deep reconstruction networks can learn a prior distribution through sampling the training distributions. In this work, we develop a Physics-assisted Generative Adversarial Network (PGAN), a two-step algorithm for tomographic reconstruction. In contrast to previous efforts, our PGAN utilizes maximum-likelihood estimates derived from the measurements to regularize the reconstruction with both known physics and the learned prior. Compared with methods with less physics assisting in training, PGAN can reduce the photon requirement with limited projection angles to achieve a given error rate. The advantages of using a physics-assisted learned prior in X-ray tomography may further enable low-photon nanoscale imaging.

This report updates the high-level test plan for evaluating surface deposition on three commercial 32PTH2 spent nuclear fuel (SNF) canisters inside NUTECH Horizontal Modular Storage (NUHOMS) Advanced Horizontal Storage Modules (AHSMs) from Orano (formerly Transnuclear Inc.) and provides a description of the surface characterization activities that have been conducted to date. The details contained in this report represent the best designs and approaches explored for testing as of this publication. Given the rapidly developing nature of this test program, some of these plans may change to accommodate new objectives or requirements. The goal of the testing is to collect highly defensible and detailed dust deposition measurements from the surface of dry storage canisters in a marine coastal environment to guide chloride-induced stress corrosion crack (CISCC) research. To facilitate surface sampling, the otherwise highly prototypic dry storage systems will not contain SNF but rather will be electrically heated to mimic the decay heat and thermal hydraulic environment. Test and heater design is supported by detailed computational fluid dynamics modeling. Instrumentation throughout the canister, storage module, and environment will provide extensive information about thermal-hydraulic behavior. Manual sampling over a comprehensive portion of the canister surface at regular time intervals will offer a high-fidelity quantification of the conditions experienced in a harsh yet realistic environment. Functional testing of the finalized heater assemblies and test apparatus is set to begin in December 2022. The proposed delivery of the canisters to the host test site is June/July 2023, which is well ahead of when the AHSM installations would be completed.

Recoil nuclei produce high ionization and excitation densities in organic scintillators leading to reduced light yield via ionization quenching. To improve understanding of the relationship between organic scintillator specific luminescence and the characteristics of the recoil particle, this work evaluates proton and carbon light yield data using ionization quenching models over an energy range of tens of keV to several MeV for protons and 1-5 MeV for carbon ions. Previously-measured proton and carbon light yield data were examined for a variety of commercial and novel organic scintillating media: EJ-309, a liquid with pulse shape discrimination (PSD) properties; EJ-204, a fast plastic; EJ-276, a PSD-capable plastic; and a custom organic glass scintillator developed by Sandia National Laboratories. The canonical model of Birks did not adequately describe the ionization quenching behavior. Models proposed by Yoshida et al. and Voltz et al. provided a reasonable description of the proton light yield of a variety of organic scintillators over a broad energy range, but additional work is needed to extend the models to carbon ions. The impact of stopping power data was also investigated by comparing model predictions using SRIM and PSTAR/MSTAR libraries, and the results show a significant discrepancy for carbon ions. This work enhances understanding of ionization quenching and facilitates the accurate modeling of scintillator-based neutron detection systems relevant for medical physics, nuclear security and nonproliferation, and basic science studies.

Abstract not provided.

Nonlocal operators of fractional type are a popular modeling choice for applications that do not adhere to classical diffusive behavior; however, one major challenge in nonlocal simulations is the selection of model parameters. In this work we propose an optimization-based approach to parameter identification for fractional models with an optional truncation radius. We formulate the inference problem as an optimal control problem where the objective is to minimize the discrepancy between observed data and an approximate solution of the model, and the control variables are the fractional order and the truncation length. For the numerical solution of the minimization problem we propose a gradient-based approach, where we enhance the numerical performance by an approximation of the bilinear form of the state equation and its derivative with respect to the fractional order. Several numerical tests in one and two dimensions illustrate the theoretical results and show the robustness and applicability of our method.

Accelerating the deep decarbonization of the world's electric grids requires the coordination of complex energy systems and infrastructures across timescales from seconds to decades. In this paper, we present a new multiscale simulation framework that integrates process- and grid-centric modeling paradigms to better design, operate, and control integrated energy systems (IESs), which combine multiple technologies, in wholesale energy markets. Traditionally, IESs are analyzed with a process-centric paradigm such as levelized cost of electricity (LCOE) or annualized net revenue, ignoring important interactions with electricity markets. This framework explicitly models the complex interactions between an IES's bidding, scheduling, and control decisions and the energy market's clearing and settlement processes, while incorporating operational uncertainties. Through two case studies, we show the importance of understanding and quantifying complex resource-grid interactions. In case study 1, we demonstrate that optimized bidding from one resource shifts the profit distribution for all energy systems in the market. This result suggests new and more flexible IES technologies can disrupt the economics of all market participants, possibly leading to accelerated retirements of less flexible resources. Interestingly, the optimized bidding has little impact on grid-level aggregate statistics, such as total generation costs and renewable penetration rate. While aggregate modeling strategies may remain valid under some IES adoption scenarios for analysis focused on regional outcomes, direct comparisons of IES technologies at specific locations without considering these interactions may lead to misleading or incorrect conclusions. In case study 2, we consider the design and flexible operation of IESs that hybridize conventional generators with energy storage. Through a sensitivity analysis, we find that as the size of the storage system increases, the total number of start-ups for coal- and natural gas-based IESs reduced by 25% and 33.6%, and the total thermal generator ramping (i.e., mileage) reduced by 86.5% and 62.5%, respectively. This shows the primary benefit of storage may not be reduced operational costs (which do not change significantly) but fewer start-ups and less ramping, which may greatly simplify the design, operation, and control of carbon capture systems. The new modeling and optimization capabilities from this work enable the coupling of rigorous, dynamic process models with grid-level production cost models to quantitatively identify the nuanced interdependencies across these vast timescales that must be addressed to realize clean, safe, and secure energy production. Moreover, the proposed general multiscale simulation framework is applicable to all IES technologies and can be easily extended to consider other energy carriers (e.g., hydrogen, ammonia) and energy infrastructures (e.g., natural gas pipelines).

Conversion cathodes represent a viable route to improve rechargeable Li+battery energy densities, but their poor electrochemical stability and power density have impeded their practical implementation. Here, we explore the impact cell fabrication, electrolyte interaction, and current density have on the electrochemical performance of FeS2/Li cells by deconvoluting the contributions of the various conversion and intercalation reactions to the overall capacity. By varying the slurry composition and applied pressure, we determine that the capacity loss is primarily due to the large volume changes during (de)lithiation, leading to a degradation of the conductive matrix. Through the application of an external pressure, the loss is minimized by maintaining the conductive matrix. We further determine that polysulfide loss can be minimized by increasing the current density (>C/10), thus reducing the sulfur formation period. Analysis of the kinetics determines that the conversion reactions are rate-limiting, specifically the formation of metallic iron at rates above C/8. While focused on FeS2, our findings on the influence of pressure, electrolyte interaction, and kinetics are broadly applicable to other conversion cathode systems.

The characterization of the three-dimensional arrangement of dislocations is important for many analyses in materials science. Dislocation tomography in transmission electron microscopy is conventionally accomplished through intensity-based reconstruction algorithms. Although such methods work successfully, a disadvantage is that they require many images to be collected over a large tilt range. Here, we present an alternative, semi-automated object-based approach that reduces the data collection requirements by drawing on the prior knowledge that dislocations are line objects. Our approach consists of three steps: (1) initial extraction of dislocation line objects from the individual frames, (2) alignment and matching of these objects across the frames in the tilt series, and (3) tomographic reconstruction to determine the full three-dimensional configuration of the dislocations. Drawing on innovations in graph theory, we employ a node-line segment representation for the dislocation lines and a novel arc-length mapping scheme to relate the dislocations to each other across the images in the tilt series. We demonstrate the method for a dataset collected from a dislocation network imaged by diffraction-contrast scanning transmission electron microscopy. Based on these results and a detailed uncertainty analysis for the algorithm, we discuss opportunities for optimizing data collection and further automating the method.

This quick note outlines what we found after our conversion with you and your team. As suggested, we loaded 1547-2003 source requirements document (SRD) and then went back and loaded 1547-2018 SRD. This did result in implementing the new 1547-2018 settings. This short report focuses on the frequency-watt function and shows a couple of screen shots of the parameter settings via the Mojave HMI interface and plots of the results of the inverter with FW function enabled in both default and most aggressive settings response to frequency events. The first screen shot shows the 1547-2018 selected after selecting 1547-2003.

Rechargeable Zn metal batteries (RZMBs) may provide a more sustainable and lower-cost alternative to established battery technologies in meeting energy storage applications of the future. However, the most promising electrolytes for RZMBs are generally aqueous and require high concentrations of salt(s) to bring efficiencies toward commercially viable levels and mitigate water-originated parasitic reactions including hydrogen evolution and corrosion. Electrolytes based on nonaqueous solvents are promising for avoiding these issues, but full cell performance demonstrations with solvents other than water have been very limited. To address these challenges, we investigated MeOH as an alternative electrolyte solvent. These MeOH-based electrolytes exhibited exceptional Zn reversibility over a wide temperature range, with a Coulombic efficiency > 99.5% at 50% Zn utilization without cell short-circuit behavior for > 1,800 h. More important, this remarkable performance translates well to Zn jj metal-free organic cathode full cells, supporting < 6% capacity decay after > 800 cycles at 240 °C.

We propose a new scheme for simulation of collisions with multiple possible outcomes in variable-weight DSMC computations. The scheme is applied to a 0-D ionization rate coefficient computation, and 1-D electrical breakdown simulation. We show that the scheme offers a significant (up to an order of magnitude) improvement in the level of stochastic noise over the usual acceptance-rejection algorithm, even when controlling for the slight additional computational costs. Furthermore, the benefits and performance of the scheme are analyzed in detail, and possible extensions are proposed.

Here, we report the first nonjellium, systematic, density functional theory (DFT) study of intrinsic and extrinsic defects and defect levels in zinc-blende (cubic) gallium nitride. We use the local moment counter charge (LMCC) method, the standard Perdew-Becke-Ernzerhoff (PBE) exchange-correlation potential, and two pseudopotentials, where the Ga 3$\textit{d}$ orbitals are either in the core ($d^0$) or explicitly in the valence set ($d^{10}$). We studied 64, 216, 512, and 1000 atom supercells, and demonstrated convergence to the infinite limit, crucial for delineating deep from shallow states near band edges, and for demonstrating the elimination of finite cell-size errors. Contrary to common claims, we find that exact exchange is not required to obtain defect levels across the experimental band gap. As was true in silicon, silicon carbide, and gallium arsenide, the extremal LMCC defect levels of the aggregate of defects yield an effective LMCC defect band gap that is within 10% of the experimental gap (3.3 eV) for both pseudopotentials. We demonstrate that the gallium vacancy is more complicated than previously reported. There is dramatic metastability–a nearest-neighbor nitrogen atom shifts into the gallium site, forming an antisite, nitrogen vacancy pair, which is more stable than the simple vacancy for positive charge states. Our assessment of the $d^0$ and $d^{10}$ pseudopotentials yields minimal differences in defect structures and defect levels. The better agreement of the $d^0$ lattice constant with experiment suggests that the more computationally economical $d^0$ pseudopotentials are sufficient to achieve the fidelity possible within the physical accuracy of DFT, and thereby enable calculations in larger supercells necessary to demonstrate convergence with respect to finite size supercell errors.

Thermodynamic modeling has been used to predict chemical compositions of brines formed by the deliquescence of sea salt aerosols. Representative brines have been mixed, and physical and chemical properties have been measured over a range of temperatures. Brine properties are discussed in terms of atmospheric corrosion of austenitic stainless steel, using spent nuclear fuel dry storage canisters as an example. After initial loading with spent fuel, during dry storage, the canisters cool over time, leading to increased surface relative humidities and evolving brine chemistries and properties. These parameters affect corrosion kinetics and damage distributions, and may offer important constraints on the expected timing, rate, and long-term impacts of canister corrosion.

Lithium-ion batteries can last many years but sometimes exhibit rapid, nonlinear degradation that severely limits battery lifetime. In this work, we review prior work on “knees” in lithium-ion battery aging trajectories. We first review definitions for knees and three classes of “internal state trajectories” (termed snowball, hidden, and threshold trajectories) that can cause a knee. We then discuss six knee “pathways”, including lithium plating, electrode saturation, resistance growth, electrolyte and additive depletion, percolation-limited connectivity, and mechanical deformation—some of which have internal state trajectories with signals that are electrochemically undetectable. Additionally, we also identify key design and usage sensitivities for knees. Finally, we discuss challenges and opportunities for knee modeling and prediction. Our findings illustrate the complexity and subtlety of lithium-ion battery degradation and can aid both academic and industrial efforts to improve battery lifetime.

Chemomechanical processes such as water weakening can control the permeability and deformation of rocks and manmade materials. Here, atomistic modeling and nanomechanical experiments were used to identify molecular origins of chemomechanical effects in calcium oxide (CaO) and its effect on observed elastic, plastic, and brittle deformation. Classical molecular dynamics simulations with the bond order-based reactive force-field ReaxFF were used to assess brittle fracture. In the presence of water, CaO fractured earlier and more often during quasi-static loading, with a calculated reduction in fracture toughness of ∼80% associated with changes in the stress distribution around the crack tip. Experimentally, elastic and plastic deformation of CaO surfaces exposed to water was assessed experimentally using in situ liquid nanoindentation. Nanoindentation showed that following exposure to water, the contact hardness decreased by 1-2 orders of magnitude and decreased the modulus by 2-3 orders of magnitude due to surface hydroxylation. The strong chemomechanical effects on the mechanical processes in CaO suggests that minerals with similar structures may exhibit comparable effects, influencing the stability of cements and geomaterials.

Indium (In) and other low melting point metals are used as interconnects in a variety of hybridized circuits and a full understanding of the metallurgy of these interconnects is important to the reliability and performance of the devices. This paper shows that room temperature focused ion beam (FIB) preparation of cross-sections, using Ga+ or Xe+ can result in artifacts that obscure the true In microbump structure. The use of modified milling strategies to minimize the increased local sample temperature are shown to produce cross-sections that are representative of the In bump microstructure in some sample configurations. Furthermore, cooling of the sample to cryogenic temperatures is shown to reliably eliminate artifacts in FIB prepared cross-sections of In bumps allowing the true bump microstructure to be observed.

This study examines the flame evolution of autoigniting H2 jets with high-speed schlieren and OH∗ chemiluminescence optical methods in a constant-volume combustion chamber over a wide range of simulated compression-ignition engine conditions. Parametric variations include the injector nozzle orifice diameter (0.31–0.83 mm), injection reservoir pressure (100–200 bar), ambient temperature (1000–1140 K), density (12.5–24 kg/m3) and O2 concentration (10–21 vol.%). The jet ignition delay was found to be highly sensitive to changes in ambient temperature while all other parameter variations resulted in minor ignition delay changes. Optical imaging reveals that in most cases, the reaction front of the H2 jet initiates from a localised kernel, before engulfing the entire jet volume downstream and recessing towards the nozzle. The flames attach to the nozzle, except at the lowest ambient oxygen condition of 10 vol.% O2 for which a lifted flame is observed. The H2 diffusion flame length shows a dependence on both the mass flow rate and the level of O2 entrainment that follows the same correlations as previously established for atmospheric H2 jet flames.

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.

Multiferroic materials are an interesting functional material family combining two ferroic orderings, e.g., ferroelectric and ferromagnetic orderings, or ferroelectric and antiferromagnetic orderings, and find various device applications, such as spintronics, multiferroic tunnel junctions, etc. Coupling multiferroic materials with plasmonic nanostructures offers great potential for optical-based switching in these devices. Here, we report a novel nanocomposite system consisting of layered Bi1.25AlMnO3.25 (BAMO) as a multiferroic matrix and well dispersed plasmonic Au nanoparticles (NPs) and demonstrate that the Au nanoparticle morphology and the nanocomposite properties can be effectively tuned. Specifically, the Au particle size can be tuned from 6.82 nm to 31.59 nm and the 6.82 nm one presents the optimum ferroelectric and ferromagnetic properties and plasmonic properties. Besides the room temperature multiferroic properties, the BAMO-Au nanocomposite system presents other unique functionalities including localized surface plasmon resonance (LSPR), hyperbolicity in the visible region, and magneto-optical coupling, which can all be effectively tailored through morphology tuning. This study demonstrates the feasibility of coupling single phase multiferroic oxides with plasmonic metals for complex nanocomposite designs towards optically switchable spintronics and other memory devices.

Once limited to chain-growth polymerizations, fine control over polymerization-induced phase separation (PIPS) has recently been demonstrated in rubber-toughened thermoset materials formed through step-growth polymerizations. The domain length scales of these thermoset materials can be elegantly tuned by utilizing a binary mixture of curing agents (CAs) that individually yield disparate morphologies. Importantly, varying the composition of the binary mixture affects characteristics of the materials such as glass transition temperature and tensile behavior. Here, we establish a full phase diagram of PIPS in a rubber-toughened epoxy system tuned by a binary CA mixture to provide a robust framework of phase behaviour. X-Ray scattering in situ and post-PIPS is employed to elucidate the PIPS mechanism whereby an initial polymerization-induced compositional fluctuation causes nanoscale phase separation of rubber and epoxy components prior to local chain crosslinking and potential macrophase separation. We further demonstrate the universality of this approach by alternatively employing binary epoxy or binary rubber mixtures to achieve broad variations in morphology and glass transitions.

Three dimensional polarization-dependent CBET plus beam balance and pointing are required to model nonuniformity in direct-drive implosions on OMEGA. Uniform laser energy absorption is essential for successful laser-direct-drive inertial confinement fusion but a growing body of evidence suggests OMEGA implosions are more asymmetric than predictions. By measuring the intensity and polarization of light scattered from individual beams, we have identified OMEGA’s polarization smoothing via distributed polarization rotators (DPR’s) as one previously unrealized source of nonuniformity. Polarization-dependent CBET along with beam energy balance, and beam pointing require three-dimensional modeling. Laser absorption mode 1 predictions from a fully three-dimensional CBET model correlate well with the observed direction of the core flow.