Publications

As the length scales of materials decrease, the heterogeneities associated with interfaces become almost as important as the surrounding materials. This has led to extensive studies of emergent electronic and magnetic interface properties in superlattices. However, the interfacial vibrations that affect the phonon-mediated properties, such as thermal conductivity, are measured using macroscopic techniques that lack spatial resolution. Although it is accepted that intrinsic phonons change near boundaries, the physical mechanisms and length scales through which interfacial effects influence materials remain unclear. Here we demonstrate the localized vibrational response of interfaces in strontium titanate–calcium titanate superlattices by combining advanced scanning transmission electron microscopy imaging and spectroscopy, density functional theory calculations and ultrafast optical spectroscopy. Structurally diffuse interfaces that bridge the bounding materials are observed and this local structure creates phonon modes that determine the global response of the superlattice once the spacing of the interfaces approaches the phonon spatial extent. Our results provide direct visualization of the progression of the local atomic structure and interface vibrations as they come to determine the vibrational response of an entire superlattice. Direct observation of such local atomic and vibrational phenomena demonstrates that their spatial extent needs to be quantified to understand macroscopic behaviour. Tailoring interfaces, and knowing their local vibrational response, provides a means of pursuing designer solids with emergent infrared and thermal responses.

Alloying is often employed to stabilize nanocrystalline materials against microstructural coarsening. The stabilization process results from the combined effects of thermodynamically reducing the curvature-dominated driving force of grain-boundary motion via solute segregation and kinetically pinning these same grain boundaries by solute drag and Zener pinning. The competition between these stabilization mechanisms depends not only on the grain-boundary character but can also be affected by imposed compositional and thermal fields that further promote or inhibit grain growth. In this work, we study the origin of the stability of immiscible nanocrystalline alloys in both homogeneous and heterogeneous compositional and thermal fields by using a multi-phase-field formulation for anisotropic grain growth with grain-boundary character-dependent segregation properties. This generalized formulation allows us to model the distribution of mobilities of segregated grain boundaries and the role of grain-boundary heterogeneity on solute-induced stabilization. As an illustration, we compare our model predictions to experimental results of microstructures in platinum-gold nanocrystalline alloys. Our results reveal that increasing the initial concentration of available solute progressively slows the rate of grain growth via both heterogeneous grain-boundary segregation and Zener pinning, while increasing the temperature generally weakens thermodynamic stabilization effects due to entropic contributions. Finally, we demonstrate as a proof-of-concept that spatially-varying compositional and thermal fields can be used to construct dynamically-stable, graded, nanostructured materials. We discuss the implications of using such concepts as alternatives to conventional plastic deformation methods.

We propose a novel statistical inference paradigm for zero-inflated multiway count data that dispenses with the need to distinguish between true and false zero counts. Our approach ignores all zero entries and applies zero-truncated Poisson regression on the positive counts. Inference is accomplished via tensor completion that imposes low-rank structure on the Poisson parameter space. Our main result shows that an $\textit{N}$-way rank-R parametric tensor 𝓜 ϵ (0, ∞)^{$I$Χ∙∙∙Χ$I$} generating Poisson observations can be accurately estimated from approximately $IR^2 \text{log}^2_2(I)$ non-zero counts for a nonnegative canonical polyadic decomposition. Several numerical experiments are presented demonstrating that our zero-truncated paradigm is comparable to the ideal scenario where the locations of false zero counts are known $\textit{a priori}$.

Sandia National Labs has created a noteworthy and effective internship program whose focus is creating a talent pipeline for the laboratory. Our program utilizes industry standard conversion calculations to examine the effectiveness of the program and to compare to our competitors. Sandia defines students eligible for conversion as graduating in the given fiscal year and in their final degree program. Students indicate to SIP upon hire and at certain checkpoints throughout their internship if they are in their final degree program or not. This means that they will not continue to a higher degree program after they graduate. For instance, someone who is graduating with a master’s degree in the current FY and does not plan to pursue a PhD would be considered eligible, while an undergrad student who is graduating in the same year, but plans to pursue a graduate degree, would not be considered eligible for conversion. Conversion data pulled for this report includes all eligible interns for fiscal year 2021. We use a rolling population, which includes anyone who was an intern at some point during FY21. To calculate conversion, we narrow our population down to the students who graduated between October 2020 through September 2021, who have indicated that they are in their final degree program. The conversion data was pulled on 10/29/2021, so any conversions completed after this date will not be included in the calculation. Our conversion data includes students who separated from Sandia and returned as a staff member. Conversions also include FTE, LTE, and postdoc positions. We do not include conversion to contractor positions in our calculations.

The following report summarizes the status update during this quarter for the National Nuclear Security Agency (NNSA) initiated Minority Serving Institution Partnership Plan's (MSIPP) project titled, Partnership for Advanced Manufacturing Education and Research (PAMER). In 2016, the National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) targeting Tribal Colleges and Universities (TCUs) to offer programs that will prepare students for technical careers in NNSA’s laboratories and production plants. The MSIPP consortium’s approach is as follows: 1) align investments at the college and university level to develop a curriculum and workforce needed to support NNSA’s nuclear weapon enterprise mission, and 2) to enhance research and education at under-represented colleges and universities.

The Machine Learning for Correlated Intelligence Laboratory Directed Research & Development (LDRD) Project explored competing a variety of machine learning (ML) classification techniques against a known, open source dataset through the use of a rapid and automated algorithm research & development (RD) infrastructure. This approach relied heavily on creating an infrastructure in which to provide a pipeline for automatic target recognition (ATR) ML algorithm competition. Results are presented for nine ML classifiers against a primary dataset using the pipeline infrastructure developed for this project. New approaches to feature set extraction are presented and discussed as well.

Anomalous behavior is ubiquitous in subsurface solute transport due to the presence of high degrees of heterogeneity at different scales in the media. Although fractional models have been extensively used to describe the anomalous transport in various subsurface applications, their application is hindered by computational challenges. Simpler nonlocal models characterized by integrable kernels and finite interaction length represent a computationally feasible alternative to fractional models; yet, the informed choice of their kernel functions still remains an open problem. We propose a general data-driven framework for the discovery of optimal kernels on the basis of very small and sparse data sets in the context of anomalous subsurface transport. Using spatially sparse breakthrough curves recovered from fine-scale particle-density simulations, we learn the best coarse-scale nonlocal model using a nonlocal operator regression technique. Predictions of the breakthrough curves obtained using the optimal nonlocal model show good agreement with fine-scale simulation results even at locations and time intervals different from the ones used to train the kernel, confirming the excellent generalization properties of the proposed algorithm. A comparison with trained classical models and with black-box deep neural networks confirms the superiority of the predictive capability of the proposed model.

Nuclear spins were among the first physical platforms to be considered for quantum information processing1,2, because of their exceptional quantum coherence3 and atomic-scale footprint. However, their full potential for quantum computing has not yet been realized, owing to the lack of methods with which to link nuclear qubits within a scalable device combined with multi-qubit operations with sufficient fidelity to sustain fault-tolerant quantum computation. Here we demonstrate universal quantum logic operations using a pair of ion-implanted 31P donor nuclei in a silicon nanoelectronic device. A nuclear two-qubit controlled-Z gate is obtained by imparting a geometric phase to a shared electron spin4, and used to prepare entangled Bell states with fidelities up to 94.2(2.7)%. The quantum operations are precisely characterized using gate set tomography (GST)5, yielding one-qubit average gate fidelities up to 99.95(2)%, two-qubit average gate fidelity of 99.37(11)% and two-qubit preparation/measurement fidelities of 98.95(4)%. These three metrics indicate that nuclear spins in silicon are approaching the performance demanded in fault-tolerant quantum processors6. We then demonstrate entanglement between the two nuclei and the shared electron by producing a Greenberger–Horne–Zeilinger three-qubit state with 92.5(1.0)% fidelity. Because electron spin qubits in semiconductors can be further coupled to other electrons7–9 or physically shuttled across different locations10,11, these results establish a viable route for scalable quantum information processing using donor nuclear and electron spins.

Increasing growth in land-based wind turbine blades to enable higher machine capacities and capacity factors is creating challenges in design, manufacturing, logistics, and operation. Enabling further blade growth will require technology innovation. An emerging solution to overcome logistics constraints is to segment the blades spanwise and chordwise, which is effective, but the additional field-assembled joints result in added mass and loads, as well as increased reliability concerns in operation. An alternative to this methodology is to design slender flexible blades that can be shipped on rail lines by flexing during transport. However, the increased flexibility is challenging to accommodate with a typical glass-fiber, upwind design. In a two-part paper series, several design options are evaluated to enable slender flexible blades: downwind machines, optimized carbon fiber, and active aerodynamic controls. Part 1 presents the system-level optimization of the rotor variants as compared to conventional and segmented baselines, with a low-fidelity representation of the blades. The present work, Part 2, supplements the system-level optimization in Part 1 with high-fidelity blade structural optimization to ensure that the designs are at feasible optima with respect to material strength and fatigue limits, as well as global stability and structural dynamics constraints. To accommodate the requirements of the design process, a new version of the Numerical Manufacturing And Design (NuMAD) code has been developed and released. The code now supports laminate-level blade optimization and an interface to the International Energy Agency Wind Task 37 blade ontology. Transporting long, flexible blades via controlled flapwise bending is found to be a viable approach for blades of up to 100m. The results confirm that blade mass can be substantially reduced by going either to a downwind design or to a highly coned and tilted upwind design. A discussion of active and inactive constraints consisting of material rupture, fatigue damage, buckling, deflection, and resonant frequencies is presented. An analysis of driving load cases revealed that the downwind designs are dominated by loads from sudden, abrupt events like gusts rather than fatigue. Finally, an analysis of carbon fiber spar caps for downwind machines finds that, compared to typical carbon fibers, the use of a new heavy-tow carbon fiber in the spar caps is found to yield between 9% and 13% cost savings. Copyright:

High-protein algal biomass is an important bio-commodity that has the potential to provide a new source of sustainable protein products. Herein is a critical review that identifies (1) the most relevant sustainability findings related to the processing of proteinaceous algal biomass to higher value protein products and (2) the potential pathways to improve life cycle assessment (LCA) and techno-economic analysis (TEA) metrics, including life-cycle carbon dioxide equivalent (CO2eq), life cycle energy, and minimum selling price (MSP) of these products. The critical review of the literature revealed a large variation in model input parameters relating to these metrics. Therefore, a Monte Carlo analysis was conducted to assess the risk associated with these input variations. To understand the uncertainties that propagate into high-protein algae to products' systems, we reviewed more than 20 state-of-the-art unit operations for algal biomass processing., including cell disruption, protein solubilization, protein precipitation and purification, and protein concentration. We evaluated displacement of proteinaceous products by algal-bioproducts, including ruminant feed, aquaculture feed, protein tablets, and biopolymers and biopolyesters, with prices in the market ranging from 1.9 to 120 $ kg―1 protein. This review realized that the MSP of ruminant and non-ruminant feed ranges from 0.65 ± 0.56 to 2.9 ± 1.1 $ kg―1 protein, and bioplastics' MSP ranges from 0.97 to 7.0 $ kg―1 protein. Regarding LCA metrics, there is limited research on life cycle energy in proteinaceous biomass concentration and bioproduct systems, reported at 32.7 MJ kgprotein―1, for animal feed displacement. Animal feed emissions in the literature report negative fluxes, representing environmental benefits, as low as ―3.7 kgCO2eq kg―1 protein and positive fluxes, i.e., global warming potential, as high as 12.8 kgCO2eq kg―1 protein. There is limited research on bioplastics life cycle emissions reported at 0.6 kgCO2eq kg―1 protein. In general, the studies to date of algae-derived protein bioproducts showed similar life cycle emissions to soybean meals, nylon, polymers, and polystyrenes. Our risk analysis realized that more than 50% of scenarios can result in negative-net life cycle CO2eq emissions. This review and risk analysis assess and demonstrate the scenarios that improve economic and environmental sustainability metrics in high-protein algal bioproduct systems.

Optical anticounterfeiting tags utilize the photoluminescent properties of materials to encode unique patterns, enabling identification and validation of important items and assets. These tags must combine optical complexity with ease of production and authentication to both prevent counterfeiting and to remain practical for widespread use. Metal-organic frameworks (MOFs) based on polynuclear, rare earth clusters are ideal materials platforms for this purpose, combining fine control over structure and composition, with tunable, complex energy transfer mechanisms via both linker and metal components. Here we report the design and synthesis of a set of heterometallic MOFs based on combinations of Eu, Nd, and Yb with the tetratopic linker 1,3,6,8-tetrakis(4-carboxyphenyl)pyrene. The energetics of this linker facilitate the intentional concealment of the visible emissions from Eu while retaining the infrared emissions of Nd and Yb, creating an optical tag with multiple covert elements. Unique to the materials system reported herein, we document the occurrence of a previously not observed 11-metal cluster correlated with the presence of Yb in the MOFs, coexisting with a commonly encountered 9-metal cluster. We demonstrate the utility of these materials as intricate optical tags with both rapid and in-depth screening techniques, utilizing orthogonal identifiers across composition, emission spectra, and emission decay dynamics. This work highlights the important effect of linker selection in controlling the resulting photoluminescent properties in MOFs and opens an avenue for the targeted design of highly complex, multifunctional optical tags.

We present a framework for calibration of parameters in elastoplastic constitutive models that is based on the use of automatic differentiation (AD). The model calibration problem is posed as a partial differential equation-constrained optimization problem where a finite element (FE) model of the coupled equilibrium equation and constitutive model evolution equations serves as the constraint. The objective function quantifies the mismatch between the displacement predicted by the FE model and full-field digital image correlation data, and the optimization problem is solved using gradient-based optimization algorithms. Forward and adjoint sensitivities are used to compute the gradient at considerably less cost than its calculation from finite difference approximations. Through the use of AD, we need only to write the constraints in terms of AD objects, where all of the derivatives required for the forward and inverse problems are obtained by appropriately seeding and evaluating these quantities. We present three numerical examples that verify the correctness of the gradient, demonstrate the AD approach's parallel computation capabilities via application to a large-scale FE model, and highlight the formulation's ease of extensibility to other classes of constitutive models.

The controlled fabrication of vertical, tapered, and high-aspect ratio GaN nanowires via a two-step top-down process consisting of an inductively coupled plasma reactive ion etch followed by a hot, 85% H3PO4 crystallographic wet etch is explored. The vertical nanowires are oriented in the [0001] direction and are bound by sidewalls comprising of 3362 ¯ } semipolar planes which are at a 12° angle from the [0001] axis. High temperature H3PO4 etching between 60 °C and 95 °C result in smooth semipolar faceting with no visible micro-faceting, whereas a 50 °C etch reveals a micro-faceted etch evolution. High-angle annular dark-field scanning transmission electron microscopy imaging confirms nanowire tip dimensions down to 8–12 nanometers. The activation energy associated with the etch process is 0.90 ± 0.09 eV, which is consistent with a reaction-rate limited dissolution process. The exposure of the 3362 ¯ } type planes is consistent with etching barrier index calculations. The field emission properties of the nanowires were investigated via a nanoprobe in a scanning electron microscope as well as by a vacuum field emission electron microscope. The measurements show a gap size dependent turn-on voltage, with a maximum current of 33 nA and turn-on field of 1.92 V nm−1 for a 50 nm gap, and uniform emission across the array.

Research on renewable and alternative fuels is crucial for improving the energy and environmental efficiency of modern gasoline internal combustion engines. To highlight the influence of fuel rheological and thermodynamic properties on phase change and atomisation processes, three types of gasoline blends were tested. More specifically, the campaign comprised a reference gasoline, an ethanol/gasoline blend (10% v/v) representative of renewable fuels, and an additised gasoline sample treated with viscoelasticity-inducing agents. High-speed imaging of the transient two-phase flow field arising in the internal geometry and the near-nozzle spray region of gasoline injectors was performed employing Diffuse Backlight Illumination. The metallic body of a commercial injector was modified to fit transparent tips realising two nozzle layouts, namely a two-hole real size model resembling the Engine Combustion Network spray G injector and an enraged replica with an offset hole. Experiments were conducted at realistic operating conditions comprising an injection pressure of 100 bar and ambient pressures in the range of 0.1–6.0 bar to cover the entire range of chamber pressures prevailing in Gasoline Direct Injection engines. The action of viscoelastic additives was verified to have a suppressive effect on in-nozzle cavitation (6% reduction in cavitation extent), while also enhancing spray atomisation at flash-boing conditions, in a manner resembling the more volatile gasoline/ethanol blends. Finally, persisting liquid ligaments were found to form after the end of injection for the additised sample, owing to the surfactant nature of the additives.

Ion interactions strongly determine the solvation environments of multivalent electrolytes even at concentrations below that required for practical battery-based energy storage. This statement is particularly true of electrolytes utilizing ethereal solvents due to their low dielectric constants. These solvents are among the most commonly used for multivalent batteries based on reactive metals (Mg, Ca) due to their reductive stability. Recent developments in multivalent electrolyte design have produced a variety of new salts for Mg2+ and Ca2+ that test the limits of weak coordination strength and oxidative stability. Such electrolytes have great potential for enabling full-cell cycling of batteries based on these working ions. However, the ion interactions in these electrolytes exhibit significant and non-intuitive concentration relationships. In this work, we investigate a promising exemplar, calcium tetrakis(hexafluoroisopropoxy)borate (Ca(BHFIP)2), in the ethereal solvents 1,2-dimethoxyethane (DME) and tetrahydrofuran (THF) across a concentration range of several orders of magnitude. Surprisingly, we find that effective salt dissociation is lower at relatively dilute concentrations (e.g. 0.01 M) than at higher concentrations (e.g. 0.2 M). Combined experimental and computational dielectric and X-ray spectroscopic analyses of the changes occurring in the Ca2+ solvation environment across these concentration regimes reveals a progressive transition from well-defined solvent-separated ion pairs to de-correlated free ions. This transition in ion correlation results in improvements in both conductivity and calcium cycling stability with increased salt concentration. Comparison with previous findings involving more strongly associating salts highlights the generality of this phenomenon, leading to important insight into controlling ion interactions in ether-based multivalent battery electrolytes.

Abstract: In this study, dense bulk iron nitrides (FexN) were synthesized for the first time ever using spark plasma sintering (SPS) of FexN powders. The Fe4N phase of iron nitride in particular has significant potential to serve as a new soft magnetic material in both transformer and inductor cores and electrical machines. The density of SPSed FexN increased with SPS temperature and pressure. The microstructure of the consolidated bulk FexN was characterized with X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and superconducting quantum interference device (SQUID) magnetometry. XRD revealed a primary phase of Fe4N with secondary phases of Fe3N and metallic iron. Finite element analysis (FEA) was also applied to investigate and explain localized heating and temperature distribution during SPS. The effects of processing on interface bonding formation and phase evolution were investigated and discussed in detail to provide insight into fundamental phenomena and microstructural evolution in SPSed FexN. Graphic abstract: [Figure not available: see fulltext.]

Haynes 230 nickel alloy is one of the main contenders for salt containment in the design of thermal energy storage systems based on molten salts. A key problem for these systems is understanding the corrosion phenomena at the alloy–salt interface, and, in particular, the role played by chromium in these processes. In this study, thin films of Haynes 230, which is also rich in chromium, were measured with polarized neutron reflectometry and Rutherford backscattering spectrometry as a function of annealing temperature. Migration of chromium to the surface was observed for films annealed at 400 and 600 °C. Combining the two techniques determined that more than 60% of chromium comprising the as-prepared Haynes 230 layer moves to the surface when annealed at 600 °C, where it forms an oxide layer.

Krylov subspace recycling is a powerful tool when solving a long series of large, sparse linear systems that change only slowly over time. In PDE constrained shape optimization, these series appear naturally, as typically hundreds or thousands of optimization steps are needed with only small changes in the geometry. In this setting, however, applying Krylov subspace recycling can be a difficult task. As the geometry evolves, in general, so does the finite element mesh defined on or representing this geometry, including the numbers of nodes and elements and element connectivity. This is especially the case if re-meshing techniques are used. As a result, the number of algebraic degrees of freedom in the system changes, and in general the linear system matrices resulting from the finite element discretization change size from one optimization step to the next. Changes in the mesh connectivity also lead to structural changes in the matrices. In the case of re-meshing, even if the geometry changes only a little, the corresponding mesh might differ substantially from the previous one. Obviously, this prevents any straightforward mapping of the approximate invariant subspace of the linear system matrix (the focus of recycling in this work) from one optimization step to the next; similar problems arise for other selected subspaces. In this paper, we present an algorithm to map an approximate invariant subspace of the linear system matrix for the previous optimization step to an approximate invariant subspace of the linear system matrix for the current optimization step, for general meshes. This is achieved by exploiting the map from coefficient vectors to finite element functions on the mesh, combined with interpolation or approximation of functions on the finite element mesh. We demonstrate the effectiveness of our approach numerically with several proof of concept studies for a specific meshing technique.

Graphite electrodes in the lithium-ion battery exhibit various particle shapes, including spherical and platelet morphologies, which influence structural and electrochemical characteristics. It is well established that porous structures exhibit spatial heterogeneity, and the particle morphology can influence transport properties. The impact of the particle morphology on the heterogeneity and anisotropy of geometric and transport properties has not been previously studied. This study characterizes the spatial heterogeneities of 18 graphite electrodes at multiple length scales by calculating and comparing the structural anisotropy, geometric quantities, and transport properties (pore-scale tortuosity and electrical conductivity). We found that the particle morphology and structural anisotropy play an integral role in determining the spatial heterogeneity of directional tortuosity and its dependency on pore-scale heterogeneity. Our analysis reveals that the magnitude of in-plane and through-plane tortuosity difference influences the multiscale heterogeneity in graphite electrodes.

Liquid crystal elastomers (LCEs) exhibit soft elasticity due to the alignment and reorientation of mesogens upon mechanical loading, which provides additional mechanisms to absorb and dissipate energy. This enhanced response makes LCEs potentially transformative materials for biomedical devices, tissue replacements, and protective equipment. However, there is a critical knowledge gap in understanding the highly rate-dependent dissipative behaviors of LCEs due to the lack of real-time characterization techniques that probe the microscale network structure and link it to the mechanical deformation of LCEs. In this work, we employ in situ optical measurements to evaluate the alignment and reorientation degree of mesogens in LCEs. The data are correlated to the quantitative physical analysis using polarized Fourier-transform infrared spectroscopy. The time scale of mesogen alignment is determined at different strain levels and loading rates. The mesogen reorientation kinetics is characterized to establish its relationship with the macroscale tensile strain, and compared to theoretical predictions. Overall, this work provides the first detailed study on the time-dependent evolution of mesogen alignment and reorientation in deformed LCEs. It also provides an effective and more accessible approach for other researchers to investigate the structural-property relationships of different types of polymers.

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.

The SIERRA Low Mach Module: Fuego, henceforth referred to as Fuego, is the key element of the ASC re 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.

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 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 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.

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/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.

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.

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.

Natural convection in porous media is a highly nonlinear multiphysical problem relevant to many engineering applications (e.g., the process of CO₂ sequestration). Here, we extend and present a non-intrusive reduced order model of natural convection in porous media employing deep convolutional autoencoders for the compression and reconstruction and either radial basis function (RBF) interpolation or artificial neural networks (ANNs) for mapping parameters of partial differential equations (PDEs) on the corresponding nonlinear manifolds. To benchmark our approach, we also describe linear compression and reconstruction processes relying on proper orthogonal decomposition (POD) and ANNs. Further, we present comprehensive comparisons among different models through three benchmark problems. The reduced order models, linear and nonlinear approaches, are much faster than the finite element model, obtaining a maximum speed-up of 7 × 10⁶ because our framework is not bound by the Courant–Friedrichs–Lewy condition; hence, it could deliver quantities of interest at any given time contrary to the finite element model. Our model’s accuracy still lies within a relative error of 7% in the worst-case scenario. We illustrate that, in specific settings, the nonlinear approach outperforms its linear counterpart and vice versa. We hypothesize that a visual comparison between principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) could indicate which method will perform better prior to employing any specific compression strategy.

Nalu-Wind is part of the ExaWind code suite.

Abstract not provided.

This study presents a method that can be used to gain information relevant to determining the corrosion risk for spent nuclear fuel (SNF) canisters during extended dry storage. Currently, it is known that stainless steel canisters are susceptible to chloride-induced stress corrosion cracking (CISCC). However, the rate of CISCC degradation and the likelihood that it could lead to a through-wall crack is unknown. This study uses well-developed computational fluid dynamics and particle-tracking tools and applies them to SNF storage to determine the rate of deposition on canisters. The deposition rate is determined for a vertical canister system and a horizontal canister system, at various decay heat rates with a uniform particle size distribution, ranging from 0.25 to 25 µm, used as an input. In all cases, most of the dust entering the overpack passed through without depositing. Most of what was retained in the overpack was deposited on overpack surfaces (e.g., inlet and outlet vents); only a small fraction was deposited on the canister itself. These results are provided for generalized canister systems with a generalized input; as such, this technical note is intended to demonstrate the technique. This study is a part of an ongoing effort funded by the U.S. Department of Energy, Nuclear Energy Office of Spent Fuel Waste Science and Technology, which is tasked with doing research relevant to developing a sound technical basis for ensuring the safe extended storage and subsequent transport of SNF. This work is being presented to demonstrate a potentially useful technique for SNF canister vendors, utilities, regulators, and stakeholders to utilize and further develop for their own designs and site-specific studies.

Given a graph, finding the distance-2 maximal independent set (MIS-2) of the vertices is a problem that is useful in several contexts such as algebraic multigrid coarsening or multilevel graph partitioning. Such multilevel methods rely on finding the independent vertices so they can be used as seeds for aggregation in a multilevel scheme. We present a parallel MIS-2 algorithm to improve performance on modern accelerator hardware. This algorithm is implemented using the Kokkos programming model to enable performance portability. We demonstrate the portability of the algorithm and the performance on a variety of architectures (x86/ARM CPUs and NVIDIA/AMD GPUs). The resulting algorithm is also deterministic, producing an identical result for a given input across all of these platforms. The new MIS-2 implementation outperforms implementations in state of the art libraries like CUSP and ViennaCL by 3-8x while producing similar quality results. We further demonstrate the benefits of this approach by developing parallel graph coarsening scheme for two different use cases. First, we develop an algebraic multigrid (AMG) aggregation scheme using parallel MIS-2 and demonstrate the benefits as opposed to previous approaches used in the MueLu multigrid package in Trilinos. We also describe an approach for implementing a parallel multicolor 'cluster' Gauss-Seidel preconditioner using this MIS-2 coarsening, and demonstrate better performance with an efficient, parallel, mul-ticolor Gauss-Seidel algorithm.

Femtosecond laser electronic excitation tagging (FLEET) is a powerful unseeded velocimetry technique typically used to measure one component of velocity along a line, or two or three components from a dot. In this Letter, we demonstrate a dotted-line FLEET technique which combines the dense profile capability of a line with the ability to perform two-component velocimetry with a single camera on a dot. Our set-up uses a single beam path to create multiple simultaneous spots, more than previously achieved in other FLEET spot configurations. We perform dotted-line FLEET measurements downstream of a highly turbulent, supersonic nitrogen free jet. Dotted-line FLEET is created by focusing light transmitted by a periodic mask with rectangular slits of 1.6 × 40 mm2 and an edge-to-edge spacing of 0.5 mm, then focusing the imaged light at the measurement region. Up to seven symmetric dots spaced approximately 0.9 mm apart, with mean full-width at half maximum diameters between 150 and 350 µm, are simultaneously imaged. Both streamwise and radial velocities are computed and presented in this Letter.

Rock salt is being considered as a medium for energy storage and radioactive waste disposal. A Disturbed Rock Zone (DRZ) develops in the immediate vicinity of excavations in rock salt, with an increase in permeability, which alters the migration of gases and liquids around the excavation. When creep occurs adjacent to a stiff inclusion such as a concrete plug, it is expected that the stress state near the inclusion will become more hydrostatic and less deviatoric, promoting healing (permeability reduction) of the DRZ. In this scoping study, we measured the permeability of DRZ rock salt with time adjacent to inclusions (plugs) of varying stiffness to determine how the healing of rock salt, as reflected in the permeability changes, is a function of the stress and time. Samples were created with three different inclusion materials in a central hole along the axis of a salt core: (i) very soft silicone sealant, (ii) sorel cement, and (iii) carbon steel. The measured permeabilities are corrected for the gas slippage effect. We observed that the permeability change is a function of the inclusion material. The stiffer the inclusion, the more rapidly the permeability reduces with time.

The penetration of renewable energy resources (RER) and energy storage systems (ESS) into the power grid has been accelerated in recent times due to the aggressive emission and RER penetration targets. The Integrated resource planning (IRP) framework can help in ensuring long-term resource adequacy while satisfying RER integration and emission reduction targets in a cost-effective and reliable manner. In this paper, we present pIRP (probabilistic Integrated Resource Planning), an open-source Python-based software tool designed for optimal portfolio planning for an RER and ESS rich future grid and for addressing the capacity expansion problem. The tool, which is planned to be released publicly, with its ESS and RER modeling capabilities along with enhanced uncertainty handling make it one of the more advanced non-commercial IRP tools available currently. Additionally, the tool is equipped with an intuitive graphical user interface and expansive plotting capabilities. Impacts of uncertainties in the system are captured using Monte Carlo simulations and lets the users analyze hundreds of scenarios with detailed scenario reports. A linear programming based architecture is adopted which ensures sufficiently fast solution time while considering hundreds of scenarios and characterizing profile risks with varying levels of RER and ESS penetration levels. Results for a test case using data from parts of the Eastern Interconnection are provided in this paper to demonstrate the capabilities offered by the tool.

Quantum computers can now run interesting programs, but each processor’s capability—the set of programs that it can run successfully—is limited by hardware errors. These errors can be complicated, making it difficult to accurately predict a processor’s capability. Benchmarks can be used to measure capability directly, but current benchmarks have limited flexibility and scale poorly to many-qubit processors. We show how to construct scalable, efficiently verifiable benchmarks based on any program by using a technique that we call circuit mirroring. With it, we construct two flexible, scalable volumetric benchmarks based on randomized and periodically ordered programs. We use these benchmarks to map out the capabilities of twelve publicly available processors, and to measure the impact of program structure on each one. We find that standard error metrics are poor predictors of whether a program will run successfully on today’s hardware, and that current processors vary widely in their sensitivity to program structure.

The focus of this study is on spectral equivalence results for higher-order tensor product finite elements in the H(curl), H(div), and L2 function spaces. For certain choices of the higher-order shape functions, the resulting mass and stiffness matrices are spectrally equivalent to those for an assembly of lowest-order edge-, face- or interior-based elements on the associated Gauss–Lobatto–Legendre (GLL) mesh.

This paper presents a run-to-run (R2R) controller for mechanical serial sectioning (MSS). MSS is a destructive material analysis process which repeatedly removes a thin layer of material and images the exposed surface. The images are then used to gain insight into the material properties and often to construct a 3-dimensional reconstruction of the material sample. Currently, an experience human operator selects the parameters of the MSS to achieve the desired thickness. The proposed R2R controller will automate this process while improving the precision of the material removal. The proposed R2R controller solves an optimization problem designed to minimize the variance of the material removal subject to achieving the expected target removal. This optimization problem was embedded in an R2R framework to provide iterative feedback for disturbance rejection and convergence to the target removal amount. Since an analytic model of the MSS system is unavailable, we adopted a data-driven approach to synthesize our R2R controller from historical data. The proposed R2R controller is demonstrated through simulations. Future work will empirically demonstrate the proposed R2R through experiments with a real MSS system.

Metasurface lenses are fabricated using membrane projection lithography following a CMOS-compatible process flow. The lenses are 10-mm in diameter and employ 3-dimensional unit cells designed to function in the mid-infrared spectral range.

The detonation of explosives produces luminous fireballs often containing particulates such as carbon soot or remnants of partially reacted explosives. The spatial distribution of these particulates is of great interest for the derivation and validation of models. In this work, three ultra-high-speed imaging techniques: diffuse back-illumination extinction, schlieren, and emission imaging, are utilized to investigate the particulate quantity, spatial distribution, and structure in a small-scale fireball. The measurements show the evolution of the particulate cloud in the fireball, identifying possible emission sources and regions of high optical thickness. Extinction measurements performed at two wavelengths shows that extinction follows the inverse wavelength behavior expected of absorptive particles in the Rayleigh scattering regime. The estimated mass from these extinction measurements shows an average soot yield consistent with previous soot collection experiments. The imaging diagnostics discussed in the current work can provide detailed information on the spatial distribution and concentration of soot, crucial for validation opportunities in the future.

In accident scenarios involving release of tritium during handling and storage, the level of risk to human health is dominated by the extent to which radioactive tritium is oxidized to the water form (T2O or THO). At some facilities, tritium inventories consist of very small quantities stored at sub-atmospheric pressure, which means that tritium release accident scenarios will likely produce concentrations in air that are well below the lower flammability limit. It is known that isotope effects on reaction rates should result in slower oxidation rates for heavier isotopes of hydrogen, but this effect has not previously been quantified for oxidation at concentrations well below the lower flammability limit for hydrogen. This work describes hydrogen isotope oxidation measurements in an atmospheric tube furnace reactor. These measurements consist of five concentration levels between 0.01% and 1% protium or deuterium and two residence times. Oxidation is observed to occur between about 550°C and 800°C, with higher levels of conversion achieved at lower temperatures for protium with respect to deuterium at the same volumetric inlet concentration and residence time. Computational fluid dynamics simulations of the experiments were used to customize reaction orders and Arrhenius parameters in a 1-step oxidation mechanism. The trends in the rates for protium and deuterium are extrapolated based on guidance from literature to produce kinetic rate parameters appropriate for tritium oxidation at low concentrations.

Abstract not provided.

This paper presents the formulation, implementation, and demonstration of a new, largely phenomenological, model for the damage-free (micro-crack-free) thermomechanical behavior of rock salt. Unlike most salt constitutive models, the new model includes both drag stress (isotropic) and back stress (kinematic) hardening. The implementation utilizes a semi-implicit scheme and a fall-back fully-implicit scheme to numerically integrate the model's differential equations. Particular attention was paid to the initial guesses for the fully-implicit scheme. Of the four guesses investigated, an initial guess that interpolated between the previous converged state and the fully saturated hardening state had the best performance. The numerical implementation was then used in simulations that highlighted the difference between drag stress hardening versus combined drag and back stress hardening. Simulations of multi-stage constant stress tests showed that only combined hardening could qualitatively represent reverse (inverse transient) creep, as well as the large transient strains experimentally observed upon switching from axisymmetric compression to axisymmetric extension. Simulations of a gas storage cavern subjected to high and low gas pressure cycles showed that combined hardening led to substantially greater volume loss over time than drag stress hardening alone.

Abstract not provided.

Density fluctuations in compressible turbulent boundary layers cause aero-optical distortions that affect the performance of optical systems such as sensors and lasers. The development of models for predicting the aero-optical distortions relies on theory and reference data that can be obtained from experiments and time-resolved simulations. This paper reports on wall-modeled large-eddy simulations of turbulent boundary layers over a flat plate at Mach 3.5, 7.87, and 13.64. The conditions for the Mach 3.5 case match those for the DNS presented by Miller et al.1 The Mach 7.87 simulation match those inside the Hypersonic Wind Tunnel at Sandia National Laboratories. For the Mach 13.64, the conditions inside the Arnold Engineering Development Complex Hypervelocity Tunnel 9 are matched. Overall, adequate agreement of the velocity and temperature as well as Reynolds stress profiles with reference data from direct numerical simulations is obtained for the different Mach numbers. For all three cases, the normalized root-mean-square optical path difference was computed and compared with data obtained from the reference direct numerical simulations and experiments, as well as predictions obtained with a semi-analytical relationship by Notre Dame University. Above Mach five, the normalized path difference obtained from the simulations is above the model prediction. This provides motivation for future work aimed at evaluating the assumptions behind the Notre Dame model for hypersonic boundary layer flows.

Effects of gamma and proton irradiation, and of forward bias minority carrier injection, on minority carrier diffusion and photoresponse were investigated for long-wave (LW) and mid-wave (MW) infrared detectors with engineered majoritycarrier barriers. The LWIR detector was a type-II GaSb/InAs strained-layer superlattice pBiBn structure. The MWIR detector was a InAsSb/AlAsSb nBp structure without superlattices. Room temperature gamma irradiations degraded the minority carrier diffusion length of the LWIR structure, and minority carrier injections caused dramatic improvements, though there was little effect from either treatment on photoresponse. For the MWIR detector, effects of room temperature gamma irradiation and injection on minority carrier diffusion and photoresponse were negligible. Subsequently, both types of detectors were subjected to gamma irradiation at 77 K. In-situ photoresponse was unchanged for the LWIR detectors, while that for the MWIR ones decreased 19% after cumulative dose of ~500 krad(Si). Minority carrier injection had no effect on photoresponse for either. The LWIR detector was then subjected to 4 Mrad(Si) of 30 MeV proton irradiation at 77 K, and showed a 35% decrease in photoresponse, but again no effect from forward bias injection. These results suggest that photoresponse of the LWIR detectors is not limited by minority carrier diffusion.

It is impossible in practice to comprehensively test even small software programs due to the vastness of the reachable state space; however, modern cyber-physical systems such as aircraft require a high degree of confidence in software safety and reliability. Here we explore methods of generating test sets to effectively and efficiently explore the state space for a module based on the Traffic Collision Avoidance System (TCAS) used on commercial aircraft. A formal model of TCAS in the model-checking language NuSMV provides an output oracle. We compare test sets generated using various methods, including covering arrays, random, and a low-complexity input paradigm applied to 28 versions of the TCAS C program containing seeded errors. Faults are triggered by tests for all 28 programs using a combination of covering arrays and random input generation. Complexity-based inputs perform more efficiently than covering arrays, and can be paired with random input generation to create efficient and effective test sets. A random forest classifier identifies variable values that can be targeted to generate tests even more efficiently in future work, by combining a machine-learned fuzzing algorithm with more complex model oracles developed in model-based systems engineering (MBSE) software.

Creation of streaming video stimuli that allow for strict experimental control while providing ease of scene manipulation is difficult to achieve but desired by researchers seeking to approach ecological validity in contexts that involve processing streaming visual information. To that end, we propose leveraging video game modding tools as a method of creating research quality stimuli. As a pilot effort, we used a video game sandbox tool (Garry’s Mod) to create three steaming video scenarios designed to mimic video feeds that physical security personnel might observe. All scenarios required participants to identify the presences of a threat appearing during the video feed. Each scenario differed in level of complexity, in that one scenario required only location monitoring, one required location and action monitoring, and one required location, action, and conjunction monitoring in that when an action was performed it was only considered a threat when performed by a certain character model. While there was no behavioral effect of scenario in terms of accuracy or response times, in all scenarios we found evidence of a P300 when comparing response to threatening stimuli to that of standard stimuli. Results therefore indicate that sufficient levels of experimental control may be achieved to allow for the precise timing required for ERP analysis. Thus, we demonstrate the feasibility of using existing modding tools to create video scenarios amenable to neuroimaging analysis.