Publications

A nanostructured oxide-dispersion-strengthened (ODS) CoCrFeMnNi high-entropy alloy (HEA) is synthesized by a powder metallurgy process. The thermal stability, including the grain size and crystal structure of the HEA matrix and oxide dispersions, is carefully investigated by X-ray diffraction (XRD) and electron microscopy characterizations after annealing at 900 °C. The limited grain growth may be attributed to Zener pinning of yttria dispersions that impede the grain boundary mobility and diffusivity. The high hardness is caused by both the fine grain size and yttria dispersions, which are also retained after annealing at 900 °C. Herein, it is implied that the combination of ODS and HEA concepts may provide a new design strategy for the development of thermally stable nanostructured alloys for extreme environments.

Whether calibrating equipment or inspecting products on the factory floor, metrology requires many complicated statistical calculations to achieve a full understanding and evaluation of measurement uncertainty and quality. In order to assist its workforce in performing these calculations in a consistent and rigorous way, the Primary Standards Lab at Sandia National Laboratories (SNL) has developed a free and open-source software package for computing various metrology calculations from uncertainty propagation to risk analysis. In addition to propagating uncertainty through a measurement model using the well-known Guide to Expression of Uncertainty in Measurement or Monte Carlo approaches, evaluating the individual Type A and Type B uncertainty components that go into the measurement model often requires other statistical methods such as analysis of variance or determining uncertainty in a fitted curve. Once the uncertainty in a measurement has been calculated, it is usually evaluated from a risk perspective to ensure the measurement is suitable for making a particular conformance decision. Finally, SNL’s software can perform all these calculations in a single application via an easy-to-use graphical interface, where the different functions are integrated so the results of one calculation can be used as inputs to another calculation.

Battery cells with metal casings are commonly considered incompatible with nuclear magnetic resonance (NMR) spectroscopy because the oscillating radio-frequency magnetic fields ("rf fields") responsible for excitation and detection of NMR active nuclei do not penetrate metals. Here, we show that rf fields can still efficiently penetrate nonmetallic layers of coin cells with metal casings provided "B1 damming"configurations are avoided. With this understanding, we demonstrate noninvasive high-field in situ 7Li and 19F NMR of coin cells with metal casings using a traditional external NMR coil. This includes the first NMR measurements of an unmodified commercial off-the-shelf rechargeable battery in operando, from which we detect, resolve, and separate 7Li NMR signals from elemental Li, anodic β-LiAl, and cathodic LixMnO2 compounds. Real-time changes of β-LiAl lithium diffusion rates and variable β-LiAl 7Li NMR Knight shifts are observed and tied to electrochemically driven changes of the β-LiAl defect structure.

Soldered joints can be made with a wide range of base materials and filler metals that allow the assembly to meet its performance and reliability requirements. Structural solder joints have, as their foremost requirement, to provide mechanical attachment between base material structures. The joint is typically subjected to one, or a combination of, three loading configurations: (a) tensile or compressive force, (b) shear force, or (c) peel force. Solder filler metals and in particular, the so-called “soft solders” based on tin (Sn), lead (Pb), and indium (In), generally have a bulk strength that is less than that of the base materials. Finally, deformation occurs largely in the solder when the joint is subjected to an applied force.

High-performance radiation detection materials are an integral part of national security, medical imaging, and nuclear physics applications. Those that offer compositional and manufacturing versatility are of particular interest. Here, we report a new family of radiological particle-discriminating scintillators containing bis(9,9-dimethyl-9H-fluoren-2-yl)diphe-nylsilane (compound 'P2') and in situ polymerized vinyltoluene (PVT) that is phase stable and mechanically robust at any blend ratio. The gamma-ray light yield increases nearly linearly across the composition range, to 16 400 photons/MeV at 75 wt.% P2. These materials are also capable of performing γ/n pulse shape discrimination (PSD), and between 20% and 50% P2 loading is competitive with the PSD quality of commercially available plastic scintillators. The 137Cs scintillation rise and decay times are sensitive to P2 loading and approach the values for 'pure' P2. Additionally, the radiation detection performance of P2-PVT blends can be made stable in 60 °C air for at least 1.5 months with the application of a thin film of poly(vinylalcohol) to the scintillator surfaces.

Fuel magnetization in magneto-inertial fusion (MIF) experiments improves charged burn product confinement, reducing requirements on fuel areal density and pressure to achieve self-heating. By elongating the path length of 1.01 MeV tritons produced in a pure deuterium fusion plasma, magnetization enhances the probability for deuterium-tritium reactions producing 11.8−17.1 MeV neutrons. Nuclear diagnostics thus enable a sensitive probe of magnetization. Characterization of magnetization, including uncertainty quantification, is crucial for understanding the physics governing target performance in MIF platforms, such as magnetized liner inertial fusion (MagLIF) experiments conducted at Sandia National Laboratories, Z-facility. We demonstrate a deep-learned surrogate of a physics-based model of nuclear measurements. A single model evaluation is reduced from CPU hours on a high-performance computing cluster down to ms on a laptop. This enables a Bayesian inference of magnetization, rigorously accounting for uncertainties from surrogate modeling and noisy nuclear measurements. The approach is validated by testing on synthetic data and comparing with a previous study. We analyze a series of MagLIF experiments systematically varying preheat, resulting in the first ever systematic experimental study of magnetic confinement properties of the fuel plasma as a function of fundamental inputs on any neutron-producing MIF platform. We demonstrate that magnetization decreases from B ∼0.5 to B MG cm as laser preheat energy deposited increases from preheat ∼460 J to E preheat ∼1.4 kJ. This trend is consistent with 2D LASNEX simulations showing Nernst advection of the magnetic field out of the hot fuel and diffusion into the target liner.

A nonmagnetized collisional plasma parameter estimator from two frequency signal interrogation attenuation is developed. The plasma parameters that are estimated are the plasma frequency, electron neutral momentum collision frequency, and the plasma thickness. The plasma frequency and electron neutral momentum collision frequency are considered uniform across the plasma thickness. The relative permittivity is defined, and the complex index of refraction is developed. Using this definition and applying the plasma frequency, electron neutral momentum collision frequency, the radial propagation frequency, and plasma thickness, an attenuation is determined for known cases. The development of the estimator is discussed. The estimator uses a performance index where the minimum difference between the plasma frequencies and electron neutral momentum collision frequencies is determined for the two signal interrogation frequencies under the constraint of the same plasma thickness. The estimator was developed in three stages which include iterative, sequential, and adaptive. The setups of the iterative, sequential, and adaptive approaches are discussed. The impact of the interrogation frequency and the estimator setup is investigated. The estimator in the three development stages is compared with known cases and the plasma parameter estimator performance is quantified.

By generalizing a geometric argument for frictionless spheres, a model is proposed for the jamming density φJ of mechanically stable packings of bidisperse, frictional spheres. The monodisperse, μs-dependent jamming density φJmono(μs) is the only input required in the model, where μs is the coefficient of friction. The predictions of the model are validated by robust estimates of φJ obtained from computer simulations of up to 107 particles for a wide range of μs, and size ratios up to 40:1. Although φJ varies nonmonotonically with the volume fraction of small spheres fs for all μs, its maximum value φJ,max at an optimal fmaxs are both μs dependent. The optimal fmaxs is characterized by a sharp transition in the fraction of small rattler particles.

Typical approaches to classify scenes from light convert the light field to electrons to perform the computation in the digital electronic domain. This conversion and downstream computational analysis require significant power and time. Diffractive neural networks have recently emerged as unique systems to classify optical fields at lower energy and high speeds. Previous work has shown that a single layer of diffractive metamaterial can achieve high performance on classification tasks. In analogy with electronic neural networks, it is anticipated that multilayer diffractive systems would provide better performance, but the fundamental reasons for the potential improvement have not been established. In this work, we present extensive computational simulations of two - layer diffractive neural networks and show that they can achieve high performance with fewer diffractive features than single layer systems.

The ability to engineer the genome of a bacterial strain, not as an isolate, but while present among other microbes in a microbiome, would open new technological possibilities in the areas of medicine, energy and biomanufacturing. Our approach is to develop sets of phages (bacterial viruses) active on the target strain and themselves engineered to act not as killers but as vectors for gene delivery. This approach is rooted in our bioinformatic tools that map prophages accurately within bacterial genomes. We present new bioinformatic results in cross-contig search, design of phage genome assemblies, satellites that embed within prophages, alignment of large numbers of biological sequences, and improvement of reference databases for prophage discovery. We targeted a Pseudomonas putida strain within a lignin-degrading microbiome, but were unable to obtain active phages, and turned toward a defined microbiome of the mouse gut.

Although many software teams across the laboratories comply with yearly software quality engineering (SQE) assessments, the practice of introducing quality into each phase of the software lifecycle, or the team processes, may vary substantially. Even with the support of a quality engineer, many teams struggle to adapt and right-size software engineering best practices in quality to fit their context, and these activities aren’t framed in a way that motivates teams to take action. In short, software quality is often a “check the box for compliance” activity instead of a cultural practice that both values software quality and knows how to achieve it. In this report, we present the results of our 6600 VISTA Innovation Tournament project, "Incentivizing and Motivating High Confidence and Research Software Teams to Adopt the Practice of Quality." We present our findings and roadmap for future work based on 1) a rapid review of relevant literature, 2) lessons learned from an internal design thinking workshop, and 3) an external Collegeville 2021 workshop. These activities provided an opportunity for team ideation and community engagement/feedback. Based on our findings, we believe a coordinated effort (e.g. strategic communication campaign) aimed at diffusing the innovation of the practice of quality across Sandia National Laboratories could over time effect meaningful organizational change. As such, our roadmap addresses strategies for motivating and incentivizing individuals ranging from early career to seasoned software developers/scientists.

This paper presents a practical methodology for propagating and processing uncertainties associated with random measurement and estimation errors (that vary from test-to-test) and systematic measurement and estimation errors (uncertain but similar from test-to-test) in inputs and outputs of replicate tests to characterize response variability of stochastically varying test units. Also treated are test condition control variability from test-to-test and sampling uncertainty due to limited numbers of replicate tests. These aleatory variabilities and epistemic uncertainties result in uncertainty on computed statistics of output response quantities. The methodology was developed in the context of processing experimental data for “real-space” (RS) model validation comparisons against model-predicted statistics and uncertainty thereof. The methodology is flexible and sufficient for many types of experimental and data uncertainty, offering the most extensive data uncertainty quantification (UQ) treatment of any model validation method the authors are aware of. It handles both interval and probabilistic uncertainty descriptions and can be performed with relatively little computational cost through use of simple and effective dimension- and order-adaptive polynomial response surfaces in a Monte Carlo (MC) uncertainty propagation approach. A key feature of the progressively upgraded response surfaces is that they enable estimation of propagation error contributed by the surrogate model. Sensitivity analysis of the relative contributions of the various uncertainty sources to the total uncertainty of statistical estimates is also presented. The methodologies are demonstrated on real experimental validation data involving all the mentioned sources and types of error and uncertainty in five replicate tests of pressure vessels heated and pressurized to failure. Simple spreadsheet procedures are used for all processing operations.

There is a dearth in the literature on how to capture the uncertainty generated by material surface evolution in thermal modeling. This leads to inadequate or highly variable uncertainty representations for material properties, specifically emissivity when minimal information is available. Inaccurate understandings of prediction uncertainties may lead decision makers to incorrect conclusions, so best engineering practices should be developed for this domain. In order to mitigate the aforementioned issues, this study explores different strategies to better capture the thermal uncertainty response of engineered systems exposed to fire environments via defensible emissivity uncertainty characterizations that can be easily adapted to a variety of use cases. Two unique formulations (one physics-informed and one mathematically based) are presented. The formulations and methodologies presented herein are not exhaustive but more so are a starting point and give the reader a basis for how to customize their uncertainty definitions for differing fire scenarios and materials. Finally, the impact of using this approach versus other commonly used strategies and the usefulness of adding rigor to material surface evolution uncertainty is demonstrated.

State-of-the-art engineering and science codes have grown in complexity dramatically over the last two decades. Application teams have adopted more sophisticated development strategies, leveraging third party libraries, deploying comprehensive testing, and using advanced debugging and profiling tools. In today's environment of diverse hardware platforms, these applications also desire performance portability-avoiding the need to duplicate work for various platforms. The Kokkos EcoSystem provides that portable software stack. Based on the Kokkos Core Programming Model, the EcoSystem provides math libraries, interoperability capabilities with Python and Fortran, and Tools for analyzing, debugging, and optimizing applications. In this article, we overview the components, discuss some specific use cases, and highlight how codesigning these components enables a more developer friendly experience.

Recent developments in the renewable energy sector have seen an unprecedented growth in residential photovoltaic (PV) installations. However, high PV penetration levels often lead to overvoltage problems in low-voltage (LV) distribution feeders. Smart inverter control such as active power curtailment (APC)-based overvoltage control can be implemented to overcome these challenges. The APC technique utilizes a constant droop-based approach which curtails power rigidly, which can lead to significant energy curtailment in the LV distribution feeders. In this paper, different variations of the APC technique with linear, quadratic, and exponential droops have been analyzed from the point-of-view of energy curtailment for a LV distribution network in North America. Further, a combinatorial approach using various droop-based APC methods in conjunction with adaptive dynamic programming (ADP) as a supplementary control scheme has also been proposed. The proposed approach minimizes energy curtailment in the LV distribution network by adjusting the droop gains. Simulation results depict that ADP in conjunction with exponential droop reduces the energy curtailment to approximately 50% compared to using the standard linear droop.

Current methods for stochastic media transport are either computationally expensive or, by nature, approximate. Moreover, none of the well-developed, benchmarked approximate methods can compute the variance caused by the stochastic mixing, a quantity especially important to safety calculations. Therefore, we derive and apply a new conditional probability function (CPF) for use in the recently developed stochastic media transport algorithm Conditional Point Sampling (CoPS), which 1) leverages the full intra-particle memory of CoPS to yield errorless computation of stochastic media outputs in 1D, binary, Markovian-mixed media, and 2) leverages the full inter-particle memory of CoPS and the recently developed Embedded Variance Deconvolution method to yield computation of the variance in transport outputs caused by stochastic material mixing. Numerical results demonstrate errorless stochastic media transport as compared to reference benchmark solutions with the new CPF for this class of stochastic mixing as well as the ability to compute the variance caused by the stochastic mixing via CoPS. Using previously derived, non-errorless CPFs, CoPS is further found to be more accurate than the atomic mix approximation, Chord Length Sampling (CLS), and most of memory-enhanced versions of CLS surveyed. In addition, we study the compounding behavior of CPF error as a function of cohort size (where a cohort is a group of histories that share intra-particle memory) and recommend that small cohorts be used when computing the variance in transport outputs caused by stochastic mixing.

The properties of materials can change dramatically at the nanoscale new and useful properties can emerge. An example is found in the paramagnetism in iron oxide magnetic nanoparticles. Using magnetically sensitive nitrogen-vacancy centers in diamond, we developed a platform to study electron spin resonance of nanoscale materials. To implement the platform, diamond substrates were prepared with nitrogen vacancy centers near the surface. Nanoparticles were placed on the surface using a drop casting technique. Using optical and microwave pulsing techniques, we demonstrated T1 relaxometry and double electron-electron resonance techniques for measuring the local electron spin resonance. The diamond NV platform developed in this project provides a combination of good magnetic field sensitivity and high spatial resolution and will be used for future investigations of nanomaterials and quantum materials.

Achieving efficient learning for AI systems was identified as a major challenge in the DOE's recently released, AI for Science, report. The human brain is capable of efficient and low-powered learning. It is likely that implementing brain-like principles will lead to more efficient AI systems. In this LDRD, I aim to contribute to this goal by creating a foundation for implementing and studying a brain phenomenon termed short term plasticity (STP) in spiking artificial neural networks within Sandia. First, data collected by the Allen Institute for Brain Science (AIBS) was analyzed to see if STP could be classified into types using the data collected. Although the data was inadequate at the time, AIBS has updated their database and created models that could be utilized in the future. Second, I began creating a software package to assess the ability of a Boltzmann machine utilizing STP to sample from national security data.

The effect of extreme waves on the coastal community includes inundation, loss of habitats, increasing shoreline erosion, and increasing risks to coastal infrastructures (e.g., ports, breakwaters, oil and gas platforms), important for supporting coastal resilience. The coastal communities along the US Gulf of Mexico are very low-lying, which makes the region particularly vulnerable to impacts of extreme waves generated by storm events. We propose assessing and mapping the risks from extreme waves for the Gulf of Mexico coast to support coastal resiliency planning. The risks will be assessed by computing n-year recurring wave height (e.g., 1, 5, 50, 100-year) using 32-year wave hindcast data and various extreme value analysis techniques including Peak- Over-Threshold and Annual Maxima method. The characteristics of the extreme waves, e.g., relations between the mean and extreme wave climates, directions associated with extreme waves, will be investigated. Hazard maps associated with extreme wave heights at different return periods will be generated to help planners identify potential risks and envision places that are less susceptible to future storm damage.

Abstract not provided.

Abstract not provided.

We looked to build a new type of 100 kV pulse generator that we hope will avoid many of the problems seen with other commercially available pulse generators. We looked to exploit recent improvements in both lasers and solid-state pulse charge units along with an alternative triggering layout to build something new. For the pulse charging unit, we collaborated with Texas Tech to leverage their knowledge and expertise in pulse transformers and solid-state switching. The final result of the LDRD looks promising. Texas Tech was able to build a pulse charge unit that meets our specifications regarding output, and we were able to integrate it with a switch and a laser to create a device that meets the goals of the LDRD. There is still much work to do to advance the design further and improve reliability, but the proto-type design satisfies the goals we set for this LDRD.

This report summarizes the 2021 fiscal year (FY21) status of ongoing borehole heater tests in salt funded by the disposal research and development (R&D) program of the Office of Spent Fuel & Waste Science and Technology (SFWST) of the US Department of Energy’s Office of Nuclear Energy’s (DOE-NE) Office of Spent Fuel and Waste Disposition (SFWD). This report satisfies SFWST milestone M2SF- 21SN010303052 by summarizing test activities and data collected during FY21. The Brine Availability Test in Salt (BATS) is fielded in a pair of similar arrays of horizontal boreholes in an experimental area at the Waste Isolation Pilot Plant (WIPP). One array is heated, the other unheated. Each array consists of 14 boreholes, including a central borehole with gas circulation to measure water production, a cement seal exposure test, thermocouples to measure temperature, electrodes to infer resistivity, a packer-isolated borehole to add tracers, fiber optics to measure temperature and strain, and piezoelectric transducers to measure acoustic emissions. The key new data collected during FY21 include a series of gas tracer tests (BATS phase 1b), a pair of liquid tracer tests (BATS phase 1c), and data collected under ambient conditions (including a period with limited access due to the ongoing pandemic) since BATS phase 1a in 2020. A comparison of heated and unheated gas tracer test results clearly shows a decrease in permeability of the salt upon heating (i.e., thermal expansion closes fractures, which reduces permeability).

University partnerships play an essential role in sustaining Sandia’s vitality as a national laboratory. The SAA is an element of Sandia’s broader University Partnerships program, which facilitates recruiting and research collaborations with dozens of universities annually. The SAA program has two three-year goals. SAA aims to realize a step increase in hiring results, by growing the total annual inexperienced hires from each out-of-state SAA university. SAA also strives to establish and sustain strategic research partnerships by establishing several federally sponsored collaborations and multi-institutional consortiums in science & technology (S&T) priorities such as autonomy, advanced computing, hypersonics, quantum information science, and data science. The SAA program facilitates access to talent, ideas, and Research & Development facilities through strong university partnerships. Earlier this year, the SAA program and campus executives hosted John Myers, Sandia’s former Senior Director of Human Resources (HR) and Communications, and senior-level staff at Georgia Tech, U of Illinois, Purdue, UNM, and UT Austin. These campus visits provided an opportunity to share the history of the partnerships from the university leadership, tours of research facilities, and discussions of ongoing technical work and potential recruiting opportunities. These visits also provided valuable feedback to HR management that will help Sandia realize a step increase in hiring from SAA schools. The 2020-2021 Collaboration Report is a compilation of accomplishments in 2020 and 2021 from SAA and Sandia’s valued SAA university partners.

Develop, verify, and document model capabilities sufficient for comparing field wake measurements from SWiFT with synthetic lidar wake measurements from Nalu-Wind (hereafter referred to as `Nalu').

This report summarizes the FY21 Activities for EBS International Collaborations Work Package. The international collaborations work packages aim to leverage knowledge, expertise, and tools from the international nuclear waste community, as deemed relevant according to SFWST “roadmap” priorities. This report describes research and development (R&D) activities conducted during fiscal year 2021(FY21) specifically related to the Engineered Barrier System (EBS) R&D Work Package in the Spent Fuel and Waste Science and Technology (SFWST) Campaign supported by the United States (U.S.) Department of Energy (DOE). It fulfills the SFWST Campaign deliverable M4SF- 21SN010308062. The R&D activities described in this report focus on understanding EBS component evolution and interactions within the EBS, as well as interactions between the host media and the EBS. A primary goal is to advance the development of process models that can be implemented directly within the Generic Disposal System Analysis (GDSA) platform or that can contribute to the safety case in some manner such as building confidence, providing further insight into the processes being modeled, establishing better constraints on barrier performance, etc. Sandia National Laboratories is participating in THM modeling in the international projects EBS Task Force and DECOVALEX 2023. EBS Task Force, Task 11 is on modeling of laboratory-scale High Temperature Column Test conducted at Lawrence Berkeley National Laboratory. DECOVALEX 2023, Task C is on THM modeling of the full-scale emplacement experiment (FE experiment) at the Mont Terri Underground Rock Laboratory, Switzerland. This report summarizes Sandia’s progress in the modeling studies of DECOVALEX 2023, Task C. Modeling studies related to the High Temperature Column Test will be documented in future reports.

Layered van der Waals heterostructures provide extraordinary opportunities for applications such as thermoelectrics and allow for tunability of optical and electronic properties. The performance of devices made from these heterostructures will depend on their properties, which are sensitive to the nanoarchitecture (constituent layer thicknesses, layer sequence, etc.). However, performance will also be impacted by defects, which will vary in concentration and identity with the nanoarchitecture and preparation conditions. Here, we identify several types of defects and propose mechanisms for their formation, focusing on compounds in the ([SnSe]1+δ)m(TiSe2)n system prepared using the modulated elemental reactants method. The defects were observed by atomic resolution high-angle annular dark-field scanning transmission electron microscopy and can be broadly categorized into those that form domain boundaries as a result of rotational disorder from the self-assembly process and those that are layer-thickness-related and result from local or global deviations in the amount of material deposited. Defect type and density were found to depend on the nanoarchitecture of the heterostructure. Categorizing the defects provides insights into defect formation in these van der Waals layered heterostructures and suggests strategies for controlling their concentrations. Strategies for controlling defect type and concentration are proposed, which would have implications for transport properties for applications in thermoelectrics.

The final review for the FY21 Advanced Simulation and Computing (ASC) Computational Systems and Software Environments (CSSE) L2 Milestone #7840 was conducted on August 25th, 2021 at Sandia National Laboratories in Albuquerque, New Mexico. The review committee/panel unanimously agreed that the milestone has been successfully completed, exceeding expectations on several of the key deliverables.

Steam cracking of ethane, a non-catalytic thermochemical process, remains the dominant means of ethylene production. The severe reaction conditions and energy expenditure involved in this process incentivize the search for alternative reaction pathways and reactor designs which maximize ethylene yield while minimizing cost and energy input. Herein, we report a comparison of catalytic and non-catalytic non-oxidative dehydrogenation of ethane. We achieve ethylene yields as high as 67 % with an open tube quartz reactor without the use of a catalyst at residence times ∼4 s. The open tube reactor design promotes simplicity, low cost, and negligible coke formation. Pristine quartz tubes were most effective, since coke formation was detected when defects were introduced by scratching the surface of the quartz. Surprisingly, the addition of solids to the quartz tube, such as quartz sand, alumina powder, or even Pt-based intermetallic catalysts, led to lower ethylene yield. Pt alloy catalysts are effective at lower temperatures, such as at 575 °C, but conversion is limited due to thermodynamic constraints. When operated at industrially relevant temperatures, such as 700 °C and above, these catalysts were not stable in our tests, causing ethylene yield to drop below that of the open tube. These results suggest that future research on non-oxidative dehydrogenation should be directed at optimizing reactor designs to improve the conversion of ethane to ethylene, since this approach shows promise for decentralized production of ethylene from natural gas deposits.

The synthesis and electrical properties of a new misfit compound containing BiSe, Bi2Se3, and MoSe2 constituent layers are reported. The reaction pathway involves competition between the formation of (BiSe)1+x(Bi2Se3)1+y(BiSe)1+x(MoSe2) and [(Bi2Se3)1+y]2(MoSe2). Excess Bi and Se are required in the precursor to synthesize (BiSe)1+x(Bi2Se3)1+y(BiSe)1+x(MoSe2). High-angle annular dark field-scanning transmission electron microscopy (HAADF-STEM) confirm the stacking sequence of the heterostructure. Small grains of both 2H-and 1T-MoSe2 are observed in the MoSe2 layers. X-ray photoelectron spectroscopy (XPS) indicates that there is a significantly higher percentage of 1T-MoSe2 in (BiSe)1+x(Bi2Se3)1+y(BiSe)1+x(MoSe2) than in (BiSe)0.97(MoSe2), suggesting that more charge transfer to MoSe2 occurs due to the additional BiSe layer. The additional charge transfer results in (BiSe)1+x(Bi2Se3)1+y(BiSe)1+x(MoSe2) having a low resistivity (14-19 μω m) with metallic temperature dependence. The heterogeneous mix of MoSe2 polytypes observed in the XPS complicates the interpretation of the Hall data as two bands contribute to the electrical continuity.

Atomic precision advanced manufacturing (APAM) leverages the highly reactive nature of Si dangling bonds relative to H- or Cl-passivated Si to selectively adsorb precursor molecules into lithographically defined areas with sub-nanometer resolution. Due to the high reactivity of dangling bonds, this process is confined to ultra-high vacuum (UHV) environments, which currently limits its commercialization and broad-based appeal. In this work, we explore the use of halogen adatoms to preserve APAM-derived lithographic patterns outside of UHV to enable facile transfer into real-world commercial processes. Specifically, we examine the stability of H-, Cl-, Br-, and I-passivated Si(100) in inert N2 and ambient environments. Characterization with scanning tunneling microscopy and x-ray photoelectron spectroscopy (XPS) confirmed that each of the fully passivated surfaces were resistant to oxidation in 1 atm of N2 for up to 44 h. Varying levels of surface degradation and contamination were observed upon exposure to the laboratory ambient environment. Characterization by ex situ XPS after ambient exposures ranging from 15 min to 8 h indicated the Br– and I–passivated Si surfaces were highly resistant to degradation, while Cl–passivated Si showed signs of oxidation within minutes of ambient exposure. As a proof-of-principle demonstration of pattern preservation, a H–passivated Si sample patterned and passivated with independent Cl, Br, I, and bare Si regions was shown to maintain its integrity in all but the bare Si region post-exposure to an N2 environment. The successful demonstration of the preservation of APAM patterns outside of UHV environments opens new possibilities for transporting atomically-precise devices outside of UHV for integrating with non-UHV processes, such as other chemistries and commercial semiconductor device processes.

Lithium metal is considered the "holy grail"material to replace typical Li-ion anodes due to the absence of a host structure coupled with a high theoretical capacity. The absence of a host structure results in large volumetric changes when lithium is electrodeposited/dissolved, making the lithium prone to stranding and parasitic reactions with the electrolyte. Lithium research is focused on enabling highly reversible lithium electrodeposition/dissolution, which is important to achieving long cycle life. Understanding the various mechanisms of self-discharge is also critical for realizing practical lithium metal batteries but is often overlooked. In contrast to previous work, it is shown here that self-discharge via galvanic corrosion is negligible, particularly when lithium is cycled to relevant capacities. Rather, the continued electrochemical cycling of lithium metal results in self-discharge when periodic rest is applied during cycling. The extent of self-discharge can be controlled by increasing the capacity of plated lithium, tuning electrolyte chemistry, incorporating regular rest, or introducing lithiophilic materials. The Coulombic losses that occur during periodic rest are largely reversible, suggesting that the dominant self-discharge mechanism in this work is not an irreversible chemical process but rather a morphological process.

Over the last year, the ECP xSDK-multiprecision effort has made tremendous progress in developing and deploying new mixed precision technology and customizing the algorithms for the hardware deployed in the ECP flagship supercomputers. The effort also has succeeded in creating a cross-laboratory community of scientists interested in mixed precision technology and now working together in deploying this technology for ECP applications. In this report, we highlight some of the most promising and impactful achievements of the last year. Among the highlights we present are: Mixed precision IR using a dense LU factorization and achieving a 1.8× speedup on Spock; results and strategies for mixed precision IR using a sparse LU factorization; a mixed precision eigenvalue solver; Mixed Precision GMRES-IR being deployed in Trilinos, and achieving a speedup of 1.4× over standard GMRES; compressed Basis (CB) GMRES being deployed in Ginkgo and achieving an average 1.4× speedup over standard GMRES; preparing hypre for mixed precision execution; mixed precision sparse approximate inverse preconditioners achieving an average speedup of 1.2×; and detailed description of the memory accessor separating the arithmetic precision from the memory precision, and enabling memory-bound low precision BLAS 1/2 operations to increase the accuracy by using high precision in the computations without degrading the performance. We emphasize that many of the highlights presented here have also been submitted to peer-reviewed journals or established conferences, and are under peer-review or have already been published.

Porous nanoscale carbonaceous materials are widely employed for catalysis, separations, and electrochemical devices where device performance often relies upon specific and well-defined regular feature sizes. The use of block polymers as templates has enabled affordable and scalable production of diverse porous carbons. However, popular carbon preparations use equilibrating micelles which can change dimensions in response to the processing environment. Thus, polymer methods have not yet demonstrated carbon nanomaterials with constant average template diameter and tailored wall thickness. In contrast, persistent micelle templates (PMTs) use kinetic control to preserve constant micelle template diameters, and thus PMT has enabled constant pore diameter metrics. With PMT, the wall thickness is independently adjustable via the amount of material precursor added to the micelle templates. Previous PMT demonstrations relied upon thermodynamic barriers to inhibit chain exchange while in solution, followed by rapid evaporation and cross-linking of material precursors to mitigate micelle reorganization once the solvent evaporated. It is shown here that this approach, however, fails to deliver kinetic micelle control when used with slowly cross-linking material precursors such as those for porous carbons. A new modality for kinetic control over micelle templates, glassy-PMTs, is shown using an immobilized glassy micelle core composed of polystyrene (PS). Although PS based polymers have been used to template carbon materials before, all prior reports included plasticizers that prevented kinetic micelle control. Here the key synthetic conditions for carbon materials with glassy-PMT control are enumerated, including dependencies upon polymer block selection, block molecular mass, solvent selection, and micelle processing timeline. The use of glassy-PMTs also enables the direct observation of micelle cores by TEM which are shown to be commensurate with template dimensions. Glassy-PMTs are thus robust and insensitive to material processing kinetics, broadly enabling tailored nanomaterials with diverse chemistries.

The DOE R&D program under the Spent Fuel Waste Science Technology (SFWST) campaign has made key progress in modeling and experimental approaches towards the characterization of chemical and physical phenomena that could impact the long-term safety assessment of heatgenerating nuclear waste disposition in deep-seated clay/shale/argillaceous rock. International collaboration activities such as heater tests, continuous field data monitoring, and postmortem analysis of samples recovered from these have elucidated key information regarding changes in the engineered barrier system (EBS) material exposed to years of thermal loads. Chemical and structural analyses of sampled bentonite material from such tests as well as experiments conducted on these are key to the characterization of thermal effects affecting bentonite clay barrier performance and the extent of sacrificial zones in the EBS during the thermal period. Thermal, hydrologic, and chemical data collected from heater tests and laboratory experiments has been used in the development, validation, and calibration of THMC simulators to model near-field coupled processes. This information leads to the development of simulation approaches (e.g., continuum and discrete) to tackle issues related to flow and transport at various scales of the host-rock, its interactions with barrier materials, and EBS design concept.

In this study, model derivations are carried out of a dynamical system under base excitations with a piezoelectric energy harvesting absorber as the tuned-mass-damper. Additionally, amplitude stoppers are included to the absorber in order to create a broadband resonant response, increasing the window of operational use for energy harvesting and system's control. This study is unique in the fact that the energy harvester is coupled to the source of its excitation. A nonlinear reduced-order model is developed using Euler–Lagrange principle and the Galerkin method to accurately estimate the energy harvesting absorber's displacement, harvested power, and the oscillating response of the primary structure. The nonlinear interaction of the energy harvesting absorber and the amplitude stoppers are the focus of this study, where an in-depth investigation of bifurcation points of the primary structure and energy harvesting absorber responses is performed. Due to a transfer of energy between the primary structure and the absorber, it is shown that a soft stopper with stiffness $5 \times {10^3}\,{\text{N}}\;{{\text{m}}^{ - 1}}\,$ has great control of the primary structure with 60% of the uncontrolled amplitude being reduced, as well as an increase of the harvested energy. Medium stoppers with small initial gaps size and hard stoppers do not control the primary structure and show a decrease in the energy harvesting capabilities due to the activation of the nonlinear contact-impact interactions. Finally, these stoppers also generate aperiodic regions due to the possible presence of grazing bifurcations.

As a general-purpose force field for molecular simulations of layered materials and their fluid interfaces, Clayff continues to see broad usage in atomistic computational modeling for numerous geoscience and materials science applications due to its (1) success in predicting properties of bulk nanoporous materials and their interfaces, (2) transferability to a range of layered and nanoporous materials, and (3) simple functional form which facilitates incorporation into a variety of simulation codes. Here, we review applications of Clayff to model bulk phases and interfaces not included in the original parameter set and recent modifications for modeling surface terminations such as hydroxylated nanoparticle edges. We conclude with a discussion of expectations for future developments.

This report is a functional review of the radionuclide containment strategies of fluoride-salt-cooled high temperature reactor (FHR), molten salt reactor (MSR) and high temperature gas reactor (HTGR) systems. This analysis serves as a starting point for further, more in-depth analyses geared towards identifying phenomenological gaps that still exist, hindering the creation of a mechanistic source term for these reactor types. As background information to this review, an overview of how a mechanistic source term is created and used for consequence assessment necessary for licensing is provided. How a mechanistic source term is used within the Licensing Modernization Project (LMP) is also provided. Lastly, the characteristics of non-LWR mechanistic source terms are examined. This report does not assess the viability of any software system for use with advanced reactor designs, but instead covers system function requirements. Future work within the Nuclear Energy Advanced Modeling and Simulations (NEAMS) program will address such gaps. This document is an update of SAND 2020-6730. An additional chapter is included as well as edits to original content.

Sandia will provide technical assistance to New Mexico Department of Health to provide analysis of SafeGraph mobility data (for which Sandia already has the data and a Data Use Agreement in place with the data provider). Sandia will produce analysis to determine the contribution of travel to SARS-CoV-2 spread within New Mexico.

This report summarizes initial results from a series of gun experiments which were conducted at the DICE facility. The target of these experiments was a modified metal slug composed of a tantalum/tungsten alloy (Ta-10W). The general geometry of the slug was a right circular cylinder with a through-hole cut normal to the cylinder's axis. In all experiments, hardened steel impactors were used, the desired impact velocity was 200 m/s, the slug was preheated to a target temperature of 175° C, photon doppler velocimetry (PDV) was used to measure the projectile velocity before and after impact, and the impact event was recorded with high-speed video. In two of the impacts the slug was oriented perpendicular to the projectile, while in the remaining two it was tilted 8° from normal. Initial high-speed speed video results showed slug failure in the tilted impact case, while the slug survived normal impacts. Recovery fixtures were used to preserve impacted slugs for future postmortem analysis. Discussions are included regarding improvements to potential future experiments involving these slugs.

MELCOR is a fully integrated, engineering-level computer code for modeling the progression of severe accidents in light water reactors (LWR) at nuclear power plants and nuclear fuel cycle facilities. Originally developed to assess severe accidents following Three Mile Island, MELCOR’s flexible modeling framework has enabled it to be applied to safety assessments of a much broader range of nuclear power reactor designs and other types of nuclear facilities processing radioactive material. Further, MELCOR can model a broad spectrum of severe accident phenomena such as thermal-hydraulic response in a reactor coolant system; core heat-up, degradation, and relocation; and transport behavior in both boiling water and pressurized water reactors.

Information from 2015 annual report highlighting several tasks, including: Task 7: Research of microspectrophotometry for inspection and validation of laser color markings. Task 8: Investigate new laser fabrication techniques that produce color markings with improved corrosion resistance. Task 9: Research new methods for laser marking curved surfaces (and large areas). Task 10: Complete model simulations of laser-induced ripple formation-involves an ElectroMagnetic field solver.

The goal of this work is to develop a Bayesian framework to characterize the uncertainty of material response when using a nonlocal, homogenized model to describe wave propagation through heterogeneous, disordered materials. Our approach is based on an operator regression technique combined with Bayesian optimization, through which the nonlocal kernel for a specific disordered microstructure is investigated.

The following trade study was done to answer the following task from the Sandia JPL Collaboration for Europa Lander Statement of Work: Survey facility infrastructure SNL may have for performing aseptic assembly and integration of S/C and assess its suitability for PP applications.

Currently, traditional methods such as short-term average/long-term average (STA/LTA) are used to detect arrivals in three-component seismic waveform data. Accurately establishing the identity and arrival of these waves is helpful in detecting and locating seismic events. Convolutional Neural Networks (CNNs) have been shown to significantly improve performance at local distances. This work will expand the use of CNNs to more remote distances and lower magnitudes. Sandia National Labs (SNL) will explore the advantages and limits of a particular approach and investigate requirements for expanding this technique to different types, distances, and magnitudes of events in the future. The team will describe detailed performance results of this method tuned on a curated dataset from Utah with its expert-defined arrival picks.

Sandia will provide technical assistance to Helpful Engineering to develop and test the Universal Citizen Protection Device (UCPD) which is a UV-based filter-less PPE concept that aims to keep the Sars-CoV-2 virus out of eyes, nose and mouth with a 99%+ reliability. The heart of the device would be a concealed UV Chamber that decontaminates all air going in and out of the PPE. Helpful Engineering’s goal is to build this device to be reusable and cost less than $100 to construct and can be worn for 8 hours. The UCPD is an open source project, and once developed, prototyped, tested and approved, it will be shared with interested manufacturers globally.

The advanced materials team investigated the use of additively manufactured metallic lattice structures for mitigating impact response in a Davis gun earth penetrator impact experiment. High-fidelity finite element models were developed and validated with quasistatic experiments. These models were then used to simulate the response of such lattices when subjected to the acceleration loads expected in the Davis gun experiment. Results reveal how the impact mitigation performance of lattices can change drastically at a certain relative density. Based on these observations, an experiment deck was designed to probe the response of lattices with different relative densities during the Davis gun phase 2 shots. The expected performance of these lattices is predicted before testing based on simulation results. The results of the Davis gun phase 2 shots are expected to provide data which will be used to assess the predictive capability of the finite element simulations in such a complex impact environment.

Historically, nuclear component manufacturing vendors, from small businesses through large conglomerates, have felt compelled to obtain an American Society for Mechanical Engineers (ASME) Nuclear Certification, known colloquially as an "N-stamp", to assure supply chain quality standards that will be acceptable to regulators and safety concerns. Since the N-stamp quality standard is a U.S.-origin code, combined with the apparent decline in the U.S. nuclear industry alongside the growth of the Asian nuclear industry, there is the question of whether the rest of the world, including new entrants to the nuclear industry, also regard N-stamp as a needed certification. This study addresses this question through analysis of the entire N-stamp database of holders, and former holders, of N-stamp certificates of all types and for all regions worldwide from 1989-2020 (the dates available in the database). From this 30 years of data, we find that actually U.S.-based vendors still consistently obtain the largest number of N-stamps worldwide over all time periods, but also find that the countries participating in the N-stamp certification process has broadly expanded beyond just North America, Japan, S. Korea and Western Europe (the primary N-stamp recipients before the mid-2000's). We produced global heats maps and bar charts to illustrate our findings, as well as further investigation into why the data shows changes over time and region. We note that nuclear entities involved with Soviet-type reactors do not participate in the N-stamp process, but instead pursue the Russian version PNAE, which is substantially similar to the ASME code. We conclude that at least from the N-stamp database, the United States nuclear component manufacturing industry is alive and well, although there have been some consolidations, and that the ASME N-stamp appears to still be a valued certificate worldwide, including in China which now ranks second only to the United States in obtaining N-stamp certificates in recent years. We further note that the vendors of new reactor types, in particular High Temperature Gas-Cooled Reactors (HTGRs) and Small Modular Reactors (SMRs), are actively engaged with ASME (and other U.S.-based nuclear standards bodies such as the American Nuclear Society and Nuclear Energy Institute) to coordinate updates to the ASME N-Stamp criteria to ensure applicability of the code for these new designs. Implications of these findings include the following: The global use of the U.S.-origin N-stamp certification supports the view that, despite the decline of the U.S. nuclear industry, the United States remains an esteemed global leader in the area of nuclear safety. As the U.S. Government works to revitalize the U.S. nuclear industry, especially in the area of exports, it may be beneficial to leverage the global standing of the N-stamp certification. The findings indicate that the N-stamp database would be a useful tool for the U.S. Government to use to track the growth of the civil nuclear industry in foreign countries, under certain circumstances. The Excel-format N-stamp database produced as part of this study may be a useful tool for this purpose. N-stamp data may be a useful tool for foreign governments to use to identify nuclear manufacturers within their own country, especially to identify "targets" for outreach on nuclear export control compliance. The U.S. Government could carry this message to foreign partners during bilateral engagements or as part of Nuclear Suppliers Group (NSG) discussions on industry outreach.

We present an approach for constructing a surrogate from ensembles of information sources of varying cost and accuracy. The multifidelity surrogate encodes connections between information sources as a directed acyclic graph, and is trained via gradient-based minimization of a nonlinear least squares objective. While the vast majority of state-of-the-art assumes hierarchical connections between information sources, our approach works with flexibly structured information sources that may not admit a strict hierarchy. The formulation has two advantages: (1) increased data efficiency due to parsimonious multifidelity networks that can be tailored to the application; and (2) no constraints on the training data—we can combine noisy, non-nested evaluations of the information sources. Finally, numerical examples ranging from synthetic to physics-based computational mechanics simulations indicate the error in our approach can be orders-of-magnitude smaller, particularly in the low-data regime, than single-fidelity and hierarchical multifidelity approaches.

This Storm Water Pollution Prevention Plan (SWPPP) has been prepared for the Sandia National Laboratories Water Line Project – Northern Portion, in Livermore, CA. The project, located at 7011 East Avenue, and will entail the portion of the site north of the Arroyo Section. The project is comprised of 19,584 linear feet of water line improvements totaling approximately 9.0 acres. The property is owned by the U.S. Department of Energy, and managed and operated by National Technology & Engineering Solutions of Sandia, LLC with this project being developed by NTESS for Sandia National Laboratories.

As the demand for higher-performance batteries has increased, so has the body of research on theoretical high-capacity anode materials. However, the research has been hindered because the high-capacity anode material properties and interactions are not well understood, largely due to the difficulty of observing cycling in situ. Using electrochemical scanning transmission electron microscopy (ec-STEM), we report the real-time observation and electrochemical analysis of pristine tin (Sn) and titanium dioxide-coated Sn (TiO₂@Sn) electrodes during lithiation/delithiation. As expected, we observed a volume expansion of the pristine Sn electrodes during lithiation, but we further observed that the expansion was followed by Sn detachment from the current collector. Remarkably, although the TiO₂@Sn electrodes also exhibited similar volume expansion during lithiation, they showed no evidence of Sn detachment. We found that the TiO₂ surface layer acted as an electrochemically activated artificial solid-electrolyte interphase that serves to conduct Li ions. As a physical coating, it mechanically prevented Sn detachment following volume changes during cycling, providing significant degradation resistance and 80% Coulombic efficiency for a complete lithiation/delithiation cycle. Interestingly, upon delithiation, TiO₂@Sn electrode displayed a self-healing mechanism of small pore formation in the Sn particle followed by agglomeration into several larger pores as delithiation continued.

Automatic mechanism generation is used to determine mechanisms for the CO₂ hydrogenation on Ni(111) in a two-stage process while considering the correlated uncertainty in DFT-based energetic parameters systematically. In a coarse stage, all the possible chemistry is explored with gas-phase products down to the ppb level, while a refined stage discovers the core methanation submechanism. Five thousand unique mechanisms were generated, which contain minor perturbations in all parameters. Global uncertainty assessment, global sensitivity analysis, and degree of rate control analysis are performed to study the effect of this parametric uncertainty on the microkinetic model predictions. Comparison of the model predictions with experimental data on a Ni/SiO₂ catalyst find a feasible set of microkinetic mechanisms within the correlated uncertainty space that are in quantitative agreement with the measured data, without relying on explicit parameter optimization. Global uncertainty and sensitivity analyses provide tools to determine the pathways and key factors that control the methanation activity within the parameter space. Together, these methods reveal that the degree of rate control approach can be misleading if parametric uncertainty is not considered. The procedure of considering uncertainties in the automated mechanism generation is not unique to CO₂ methanation and can be easily extended to other challenging heterogeneously catalyzed reactions.

Nanostructures with a high density of interfaces, such as in nanoporous materials and nanowires, resist radiation damage by promoting the annihilation and migration of defects. This study details the size effect and origins of the radiation damage mechanisms in nanowires and nanoporous structures in model face-centered (gold) and body-centered (niobium) cubic nanostructures using accelerated multi-cascade atomistic simulations and in-situ ion irradiation experiments. Our results reveal three different size-dependent mechanisms of damage accumulation in irradiated nanowires and nanoporous structures: sputtering for very small nanowires and ligaments, the formation and accumulation of point defects and dislocation loops in larger nanowires, and a face-centered-cubic to hexagonal-close-packed phase transformation for a narrow range of wire diameters in the case of gold nanowires. Smaller nanowires and ligaments have a net effect of lowering the radiation damage as compared to larger wires that can be traced back to the fact that smaller nanowires transition from a rapid accumulation of defects to a saturation and annihilation mechanism at a lower dose than larger nanowires. These irradiation damage mechanisms are accompanied with radiation-induced surface roughening resulting from defect-surface interactions. Comparisons between nanowires and nanoporous structures show that the various mechanisms seen in nanowires provide adequate bounds for the defect accumulation mechanisms in nanoporous structures with the difference attributed to the role of nodes connecting ligaments in nanoporous structures. Taken together, our results shed light on the compounded, size-dependent mechanisms leading to the radiation resistance of nanowires and nanoporous structures.

We describe a method to automatically generate an ion implantation recipe, a set of energies and fluences, to produce a desired defect density profile in a solid using the fewest required energies. We simulate defect density profiles for a range of ion energies, fit them with an appropriate function, and interpolate to yield defect density profiles at arbitrary ion energies. Given N energies, we then optimize a set of N energy-fluence pairs to match a given target defect density profile. Finally, we find the minimum N such that the error between the target defect density profile and the defect density profile generated by the N energy-fluence pairs is less than a given threshold. Inspired by quantum sensing applications with nitrogen-vacancy centers in diamond, we apply our technique to calculate optimal ion implantation recipes to create uniform-density 1 μm surface layers of 15N or vacancies (using 4He).

The trapping of paraffins is beneficial compared to selective olefin adsorption for adsorptive olefin purification from a process engineering point of view. Here we demonstrate the use of a series of Zn2(X-bdc)2(dabco) (where X-bdc2−is bdc2−= 1,4-benzenedicarboxylate with substituting groups X, DM-bdc2−= 2,5-dimethyl-1,4-benzenedicarboxylate or TM-bdc2−= 2,3,5,6-tetramethyl-1,4-benzenedicarboxylate and dabco = diazabicyclo[2.2.2.]octane) metal-organic frameworks (MOFs) for the adsorptive removal of ethane from ethylene streams. The best performing material from this series is Zn2(TM-bdc)2(dabco) (DMOF-TM), which shows a high ethane uptake of 5.31 mmol g−1at 110 kPa, with a good IAST selectivity of 1.88 towards ethane over ethylene. Through breakthrough measurements a high productivity of 13.1 L kg−1per breakthrough is revealed with good reproducibility over five consecutive cycles. Molecular simulations show that the methyl groups of DMOF-TM are forming a van der Waals trap with the methylene groups from dabco, snuggly fitting the ethane. Further, rarely used high pressure coadsorption measurements, in pressure regimes that most scientific studies on hydrocarbon separation on MOFs ignore, reveal an increase in ethane capacity and selectivity for binary mixtures with increased pressures. The coadsorption measurements reveal good selectivity of 1.96 at 1000 kPa, which is verified also through IAST calculations up to 3000 kPa. This study overall showcases the opportunities that pore engineering by alkyl group incorporation and pressure increase offer to improve hydrocarbon separation in reticular materials.

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Fuel Cycle Technology (FCT) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). Two high priorities for SFWST disposal R&D are design concept development and disposal system modeling. These priorities are directly addressed in the SFWST Geologic Disposal Safety Assessment (GDSA) control account, which is charged with developing a geologic repository system modeling and analysis capability, and the associated software, GDSA Framework, for evaluating disposal system performance for nuclear waste in geologic media. GDSA Framework is supported by SFWST Campaign and its predecessor the Used Fuel Disposition (UFD) campaign. This report fulfills the GDSA Uncertainty and Sensitivity Analysis Methods work package (SF-21SN01030404) level 3 milestone, Uncertainty and Sensitivity Analysis Methods and Applications in GDSA Framework (FY2021) (M3SF-21SN010304042). It presents high level objectives and strategy for development of uncertainty and sensitivity analysis tools, demonstrates uncertainty quantification (UQ) and sensitivity analysis (SA) tools in GDSA Framework in FY21, and describes additional UQ/SA tools whose future implementation would enhance the UQ/SA capability of GDSA Framework. This work was closely coordinated with the other Sandia National Laboratory GDSA work packages: the GDSA Framework Development work package (SF-21SN01030405), the GDSA Repository Systems Analysis work package (SF-21SN01030406), and the GDSA PFLOTRAN Development work package (SF-21SN01030407). This report builds on developments reported in previous GDSA Framework milestones, particularly M3SF 20SN010304032.

Organic materials are an attractive choice for structural components due to their light weight and versatility. However, because they decompose at low temperatures relative to tradiational materials they pose a safety risk due to fire and loss of structural integrity. To quantify this risk, analysts use chemical kinetics models to describe the material pyrolysis and oxidation using thermogravimetric analysis. This process requires the calibration of many model parameters to closely match experimental data. Previous efforts in this field have largely been limited to finding a single best-fit set of parameters even though the experimental data may be very noisy. Furthermore the chemical kinetics models are often simplified representations of the true de- composition process. The simplification induces model-form errors that the fitting process cannot capture. In this work we propose a methodology for calibrating decomposition models to thermogravimetric analysis data that accounts for uncertainty in the model-form and experimental data simultaneously. The methodology is applied to the decomposition of a carbon fiber epoxy composite with a three-stage reaction network and Arrhenius kinetics. The results show a good overlap between the model predictions and thermogravimetric analysis data. Uncertainty bounds capture devia- tions of the model from the data. The calibrated parameter distributions are also presented. In conclusion, the distributions may be used in forward propagation of uncertainty in models that leverage this material.

Designing polymers with controlled nanoscale morphologies and scalable synthesis is of great interest in the development of fluorine-free materials for proton-exchange membranes in fuel cells. This study focuses on a precision polyethylene with phenylsulfonic acid branches at every fifth carbon, p5PhSA, with a high ion-exchange capacity (4.2 mmol/g). The polymers self-assemble into hydrophilic and hydrophobic co-continuous nanoscale domains. In the hydrated state, the hydrophilic domain, composed of polar sulfonic acid moieties and water, serves as a pathway for efficient mesoscopic proton conductivity. The morphology and proton transport of p5PhSA are evaluated under hydrated conditions using in situ X-ray scattering and electrochemical impedance spectroscopy techniques. At 40 °C and 95% relative humidity, the proton conductivity of p5PhSA is 0.28 S/cm, which is four times greater than Nafion 117 under the same conditions. Atomistic molecular dynamics (MD) simulations are also used to elucidate the interplay between the structure and the water dynamics. The MD simulations show strong nanophase separation between the percolated hydrophilic and hydrophobic domains over a wide range of water contents. The percolated hydrophilic nanoscale domain facilitates the rapid proton transport in p5PhSA and demonstrates the potential of precise hydrocarbon-based polymers as processible and effective proton-exchange membranes.

Continued creation of harmful emissions such as NO_x and soot from compression-ignition engines utilizing mixing-controlled combustion systems (i.e., diesel engines) remains a problem and is the subject of on-going research. The inherently high efficiency, relatively low cost, and numerous other desirable attributes of such engines, coupled with a widely supported infrastructure, motivates their continued advancement. Recently, a scientifically distinct and mechanically simple technology called ducted fuel injection (DFI) has shown a robust ability to allow such engines to operate with simultaneously low engine-out soot and NO_x emissions when it is employed with simulated exhaust-gas recirculation. To better understand the property ranges of sustainable, oxygenated-fuel blending stocks that will most improve engine performance, two oxygenated blendstocks were separately blended with a commercial diesel base fuel and tested within a heavy-duty diesel optical engine equipped with a four-duct DFI configuration. Conventional and crank-angle-resolved optical diagnostics were used to elucidate the effects of fuel ignition quality, oxygenate molecular structure, and overall oxygen content on engine performance.

The representation of material heterogeneity (also referred to as "spatial variation") plays a key role in the material failure simulation method used in ALEGRA. ALEGRA is an arbitrary Lagrangian-Eulerian shock and multiphysics code developed at Sandia National Laboratories and contains several methods for incorporating spatial variation into simulations. A desirable property of a spatial variation method is that it should produce consistent stochastic behavior regardless of the mesh used (a property referred to as "mesh independence"). However, mesh dependence has been reported using the Weibull distribution with ALEGRA's spatial variation method. This report describes efforts towards providing additional insight into both the theory and numerical experiments investigating such mesh dependence. In particular, we have implemented a discrete minimum order statistic model with properties that are theoretically mesh independent.

Metallic enclosures are commonly used to protect electronic circuits against unwanted electromagnetic (EM) interactions. However, these enclosures may be sealed with imperfect mechanical seams or joints. These joints form narrow slots that allow external EM energy to couple into the cavity and then to the internal circuits. This coupled EM energy can severely affect circuit operations, particularly at the cavity resonance frequencies when the cavity has a high Q factor. To model these slots and the corresponding EM coupling, a thin-slot sub-cell model [1] , developed for slots in infinite ground plane and extended to numerical modeling of cavity-backed apertures, was successfully implemented in Sandia's electromagnetic code EIGER [2] and its next-generation counterpart Gemma [3]. However, this thin-slot model only considers resonances along the length of the slot. At sufficiently high frequencies, the resonances due to the slot depth must also be considered. Currently, slots must be explicitly meshed to capture these depth resonances, which can lead to low-frequency instability (due to electrically small mesh elements). Therefore, a slot sub-cell model that considers resonances in both length and depth is needed to efficiently and accurately capture the slot coupling.

This paper describe our team's experience using minimega, a network emulation system using node and network virtualization, to support evaluation of a set of networked and distributed systems for topology discovery, traffic classification and engineering in the DARPA Searchlight program [18]. We present the methodology we developed to encode network and traffic definitions into an experiment description model, and how our tools compile this model onto the underlying minimega API. We then present three cases studies which demonstrate the ability of our EDM to support experiments with diverse network topologies, diverse traffic mixes, and networks with specialized layer-2 connectivity requirements. We conclude with the overall takeaways from using minimega to support our evaluation process.

Heteroepitaxy of β-phase gallium oxide (β-Ga₂O₃) thin films on foreign substrates shows promise for the development of next-generation deep ultraviolet solar blind photodetectors and power electronic devices. In this work, the influences of the film thickness and crystallinity on the thermal conductivity of ($\bar{2}01$)-oriented β-Ga₂O₃ heteroepitaxial thin films were investigated. Unintentionally doped β-Ga₂O₃ thin films were grown on c-plane sapphire substrates with off-axis angles of 0° and 6° toward $\langle$$11\bar{2}0$$\rangle$ via metal–organic vapor phase epitaxy (MOVPE) and low-pressure chemical vapor deposition. The surface morphology and crystal quality of the β-Ga₂O₃ thin films were characterized using scanning electron microscopy, X-ray diffraction, and Raman spectroscopy. The thermal conductivities of the β-Ga₂O₃ films were measured via time-domain thermoreflectance. The interface quality was studied using scanning transmission electron microscopy. The measured thermal conductivities of the submicron-thick β-Ga₂O₃ thin films were relatively low as compared to the intrinsic bulk value. The measured thin film thermal conductivities were compared with the Debye–Callaway model incorporating phononic parameters derived from first-principles calculations. The comparison suggests that the reduction in the thin film thermal conductivity can be partially attributed to the enhanced phonon-boundary scattering when the film thickness decreases. They were found to be a strong function of not only the layer thickness but also the film quality, resulting from growth on substrates with different offcut angles. Growth of β-Ga₂O₃ films on 6° offcut sapphire substrates was found to result in higher crystallinity and thermal conductivity than films grown on on-axis c-plane sapphire. However, the β-Ga₂O₃ films grown on 6° offcut sapphire exhibit a lower thermal boundary conductance at the β-Ga₂O₃/sapphire heterointerface. In addition, the thermal conductivity of MOVPE-grown ($\bar{2}01$)-oriented β-(Al_xGa_1–x)₂O₃ thin films with Al compositions ranging from 2% to 43% was characterized. Because of phonon-alloy disorder scattering, the β-(Al_xGa_1–x)₂O₃ films exhibit lower thermal conductivities (2.8–4.7 W/m∙K) than the β-Ga₂O₃ thin films. The dominance of the alloy disorder scattering in β-(Al_xGa_1–x)₂O₃ is further evidenced by the weak temperature dependence of the thermal conductivity. This work provides fundamental insight into the physical interactions that govern phonon transport within heteroepitaxially grown β-phase Ga₂O₃ and (Al_xGa_1–x)₂O₃ thin films and lays the groundwork for the thermal modeling and design of β-Ga₂O₃ electronic and optoelectronic devices.

Polymerization induced phase separation (PIPS) in a three component thermoset is studied using molecular dynamics simulations of a new coarse-grained thermoset model. The system includes two crosslinker molecules, which differ in their glass transition temperatures (Tg) and chain length and thus have the potential for phase separation. One crosslinker has a high Tg corresponding to a rubbery behavior, and simulations were performed for a short length (4 beads) and a long length (33 beads). The resin and other crosslinker have low Tg. A coarse-grained model is developed with these features and with interaction parameters determined so that for either rubbery crosslinker length, the system is in the liquid state at the cure temperature. For sufficiently slow reaction rates, the long rubbery molecule exhibits PIPS into a bicontinuous array of nanoscale domains, but the short one does not, reproducing recent experimental results. The simulations demonstrate that the reaction rates must be slow enough to allow diffusion to yield phase separation. Particularly, the reaction rate corresponding to the secondary amine must be very slow, else the structure of crosslinked clusters and the substantially increased diffusion time will prevent PIPS.

We study the deformation of tantalum under extreme loading conditions. Experimental velocity data are drawn from both ramp loading experiments on Sandia's Z-machine and gas gun compression experiments. The drive conditions enable the study of materials under pressures greater than 100 GPa. We provide a detailed forward model of the experiments including a model of the magnetic drive for the Z-machine. Utilizing these experiments, we simultaneously infer several different types of physically motivated parameters describing equation of state, plasticity, and anelasticity via the computational device of Bayesian model calibration. Characteristics of the resulting calculated posterior distributions illustrate relationships among the parameters of interest via the degree of cross correlation. The calibrated velocity traces display good agreement with the experiments up to experimental uncertainty as well as improvement over previous calibrations. Examining the Z-shots and gun-shots together and separately reveals a trade-off between accuracy and transferability across different experimental conditions. Implications for model calibration, limitations from model form, and suggestions for improvements are discussed.

Electric vehicles (EVs) represent an important socio-economic development opportunity for islands and remote locations because they can lead to reduced fuel imports, electricity storage, grid services, and environmental and health benefits. This paper presents an overview of opportunities, challenges, and examples of EVs in islands and remote power systems, and is meant to provide background to researchers, utilities, energy offices, and other stakeholders interested in the impacts of electrification of transportation. The impact of uncontrolled EV charging on the electric grid operation is discussed, as well as several mitigation strategies. Of particular importance in many islands and remote systems is taking advantage of local resources by combining renewable energy and EV charging. Policy and economic issues are presented, with emphasis on the need for an overarching energy policy to guide the strategies for EVs growth. The key conclusion of this paper is that an orderly transition to EVs, one that maximizes benefits while addressing the challenges, requires careful analysis and comprehensive planning.

Ionogels are hybrid materials formed by impregnating the pore space of a solid matrix with a conducting ionic liquid. By combining the properties of both component materials, ionogels can act as self-supporting electrolytes in Li batteries. In this study, molecular dynamics simulations are used to investigate the dependence of mechanical properties of silica ionogels on solid fraction, temperature, and pore width. Comparisons are made with corresponding aerogels. We find that the solid matrix fraction increases the moduli and strength of the ionogel. This varies nonlinearly with temperature and strain rate, according to the contribution of the viscous ionic liquid to resisting deformation. Owing to the temperature and strain sensitivity of the ionic liquid viscosity, the mechanical properties approach a linear mixing law at high temperature and low strain rates. The median pore width of the solid matrix plays a complex role, with its influence varying qualitatively with deformation mode. Narrower pores increase the relevant elastic modulus under shear and uniaxial compression but reduce the modulus obtained under uniaxial tension. Conversely, shear and tensile strength are increased by narrowing the pore width. All of these pore size effects become more pronounced as the silica fraction increases. Pore size effects, similar to the effects of temperature and strain rate, are linked to the ease of fluid redistribution within the pore space during deformation-induced changes in the geometry of the pores.

Superionic phases of bulk anhydrous salts based on large cluster-like polyhedral (carba)borate anions are generally stable only well above room temperature, rendering them unsuitable as solid-state electrolytes in energy-storage devices that typically operate at close to room temperature. To unlock their technological potential, strategies are needed to stabilize these superionic properties down to subambient temperatures. One such strategy involves altering the bulk properties by confinement within nanoporous insulators. In the current study, the unique structural and ion dynamical properties of an exemplary salt, NaCB11H12, nanodispersed within porous, high-surface-area silica via salt-solution infiltration were studied by differential scanning calorimetry, X-ray powder diffraction, neutron vibrational spectroscopy, nuclear magnetic resonance, quasielastic neutron scattering, and impedance spectroscopy. Combined results hint at the formation of a nanoconfined phase that is reminiscent of the high-temperature superionic phase of bulk NaCB11H12, with dynamically disordered CB11H12- anions exhibiting liquid-like reorientational mobilities. However, in contrast to this high-temperature bulk phase, the nanoconfined NaCB11H12 phase with rotationally fluid anions persists down to cryogenic temperatures. Moreover, the high anion mobilities promoted fast-cation diffusion, yielding Na+ superionic conductivities of ∼0.3 mS/cm at room temperature, with higher values likely attainable via future optimization. It is expected that this successful strategy for conductivity enhancement could be applied as well to other related polyhedral (carba)borate-based salts. Thus, these results present a new route to effectively utilize these types of superionic salts as solid-state electrolytes in future battery applications.

Poly(carbon monofluoride), or (CF)n, is a layered fluorinated graphite material consisting of nanosized platelets. Here, we present experimental multidimensional solid-state NMR spectra of (CF)n, supported by density functional theory (DFT) calculations of NMR parameters, which overhauls our understanding of structure and bonding in the material by elucidating many ways in which disorder manifests. We observe strong 19F NMR signals conventionally assigned to elongated or "semi-ionic"C-F bonds and find that these signals are in fact due to domains where the framework locally adopts boat-like cyclohexane conformations. We calculate that C-F bonds are weakened but are not elongated by this conformational disorder. Exchange NMR suggests that conformational disorder avoids platelet edges. We also use a new J-resolved NMR method for disordered solids, which provides molecular-level resolution of highly fluorinated edge states. The strings of consecutive difluoromethylene groups at edges are relatively mobile. Topologically distinct edge features, including zigzag edges, crenellated edges, and coves, are resolved in our samples by solid-state NMR. Disorder should be controllable in a manner dependent on synthesis, affording new opportunities for tuning the properties of graphite fluorides.

Finding dense regions of graphs is fundamental in graph mining. We focus on the computation of dense hierarchies and regions with graph nuclei - -a generalization of k-cores and trusses. Static computation of nuclei, namely through variants of 'peeling', are easy to understand and implement. However, many practically important graphs undergo continuous change. Dynamic algorithms, maintaining nucleus computations on dynamic graph streams, are nuanced and require significant effort to port between nuclei, e.g., from k-cores to trusses. We propose a unifying framework to maintain nuclei in dynamic graph streams. First, we show no dynamic algorithm can asymptotically beat re-computation, highlighting the need to experimentally understand variability. Next, we prove equivalence between k-cores on a special hypergraph and nuclei. Our algorithm splits the problem into maintaining the special hypergraph and maintaining k-cores on it. We implement our algorithm and experimentally demonstrate improvements up to 108 x over re-computation. We show algorithmic improvements on k-cores apply to trusses and outperform truss-specific implementations.

Costs to permit Marine Energy projects are poorly understood. In this paper we examine environmental compliance and permitting costs for 19 projects in the U.S., covering the last 2 decades. Guided discussions were conducted with developers over a 3-year period to obtain historical and ongoing project cost data relative to environmental studies (e.g., baseline or pre-project site characterization as well as post-installation effects monitoring), stakeholder outreach, and mitigation, as well as qualitative experience of the permitting process. Data are organized in categories of technology type, permitted capacity, pre-and post-installation, geographic location, and funding types. We also compare our findings with earlier logic models created for the Department of Energy (i.e., Reference Models). Environmental studies most commonly performed were for Fish and Fisheries, Noise, Marine Habitat/Benthic Studies and Marine Mammals. Studies for tidal projects were more expensive than those performed for wave projects and the range of reported project costs tended to be wider than ranges predicted by logic models. For eight projects reporting full project costs, from project start to FERC or USACE permit, the average amount for environmental permitting compliance was 14.6%.

Recently, lithium nitride (Li3N) has been proposed as a chemical warfare agent (CWA) neutralization reagent for its ability to produce nucleophilic ammonia molecules and hydroxide ions in aqueous solution. Quantum chemical calculations can provide insight into the Li3N neutralization process that has been studied experimentally. Here, we calculate reaction-free energies associated with the Li3N-based neutralization of the CWA VX using quantum chemical density functional theory and ab initio methods. We find that alkaline hydrolysis is more favorable to either ammonolysis or neutral hydrolysis for initial P-S and P-O bond cleavages. Reaction-free energies of subsequent reactions are calculated to determine the full reaction pathway. Notably, products predicted from favorable reactions have been identified in previous experiments.

This document is a guide to setting the system and processing configuration for the Geophysical Monitoring System (GMS) Station State-of-Health (SOH) Monitoring and Interactive Analysis (IAN) applications.

This document is a user’s manual for the Geophysical Monitoring System (GMS) Station State of Health (SOH) Monitoring User Interface.

This document is a user’s manual for the Geophysical Monitoring System (GMS) Interactive Analysis (IAN) User Interface.

High-throughput sequencing technology has enabled researchers to profile microbial communities from a variety of environments, but analysis of multivariate taxon count data remains challenging. We develop a Bayesian nonparametric (BNP) regression model with zero inflation to analyse multivariate count data from microbiome studies. A BNP approach flexibly models microbial associations with covariates, such as environmental factors and clinical characteristics. The model produces estimates for probability distributions which relate microbial diversity and differential abundance to covariates, and facilitates community comparisons beyond those provided by simple statistical tests. We compare the model to simpler models and popular alternatives in simulation studies, showing, in addition to these additional community-level insights, it yields superior parameter estimates and model fit in various settings. The model's utility is demonstrated by applying it to a chronic wound microbiome data set and a Human Microbiome Project data set, where it is used to compare microbial communities present in different environments.

Coupled Mode Theory (CMT) is a classic model that addresses many diverse problems in physics and electrical engineering. Although CMT is well-established in areas such as directional coupler design, its significance in antenna engineering is less well-recognized. Recently, Characteristic Mode Analysis (CMA) has shown how CMT quantitatively models a wide variety of multi-mode patch structures. In this paper, we extend these ideas to a double-tuned slot antenna utilizing some unique features of the method of moments code FEKO. The modeled slot antenna has a stable radiation pattern throughout its 50% impedance bandwidth. CMA decomposition indicates the antenna consists of two resonators: the slot and a lumped LC resonator that represents the feed probe. The two are electromagnetically coupled to produce broad impedance bandwidth. An equivalent circuit whose values are derived directly from the CMA results is given.

Degradation and partial shading impact the long-term reliability and power production of photovoltaic (PV) modules and power plants. Time-series power (Pmp) and current–voltage (I-V) curve datastreams from PV modules enable a remote diagnostic approach to quantify active degradation mechanisms and identify partial shading. We study three to nine years of these datastreams, including 3.6 million I-V curves and 36 million Pmp values, from eight PV modules, four each of double-glass and glass-backsheet module architectures, located in three distinctly different Köppen-Geiger climate zones, to determine the module's performance loss rates (PLR), identify active degradation mechanisms and power loss modes, along with partial shading by local objects. Considering both module architectures, PLR results indicate that the BSh climate zone is the most aggressive for module degradation, while the Alpine ET zone is the mildest climate. PLR of double-glass modules located in BWh and BSh climate zones are different due to the significantly greater uniform current loss (ΔPIsc) for double-glass modules in BSh, at a 5% significance level. Power loss for four out of five modules located in the BWh and BSh climates are dominated by uniform current degradation. Statistical analysis of multistep I-V curves detects partial shading experienced by three studied modules with details of the shading profile, the shading Poynting vector diagram for the obstacle's relative position, shading scenarios, and duration. This work demonstrates how remote monitoring and diagnosis of Pmp & I-V time-series of modules can provide quantitative operations and maintenance insights into system performance, degradation mechanisms, and shading.

Zirconium-based metal-organic frameworks, including UiO-66 and related frameworks, have become the focus of considerable research in the area of chemical warfare agent (CWA) decontamination. However, little work has been reported exploring these metal-organic frameworks (MOFs) for CWA sensing applications. For many sensing approaches, the growth of high-quality thin films of the active material is required, and thin film growth methods must be compatible with complex device architectures. Several approaches to synthesize thin films of UiO-66 have been described but many of these existing methods are complex or time consuming. We describe the development of a simple and rapid microwave assisted synthesis of oriented UiO-66 thin films on unmodified silicon (Si) and gold (Au) substrates. Thin films of UiO-66 and UiO-66-NH2 can be grown in as little as 2 min on gold substrates and 30 min on Si substrates. The film morphology and orientation are characterized and the effects of reaction time and temperature on thin film growth on Au are investigated. Both reaction time and temperature impact the overgrowth of protruding discrete crystallites in the thin film layer but, surprisingly, no strong correlation is observed between film thickness and reaction time or temperature. We also briefly describe the synthesis of Zr/Ce solid solution thin films of UiO-66 on Au and report the first synthesis of a solid solution thin film MOF. Finally, we demonstrate the utility of the microwave method for the facile functionalization of two sensor architectures, plasmonic nanohole arrays and microresonators, with UiO-66 thin films.

Understanding the role of physical processes contributing to breakdown is critical for many applications in which breakdown is undesirable, such as capacitors, and applications in which controlled breakdown is intended, such as plasma medicine, lightning protection, and materials processing. The electron emission from the cathode is a critical source of electrons which then undergo impact ionization to produce electrical breakdown. In this study, the role of secondary electron yields due to photons (γ ph) and ions (γ i) in direct current breakdown is investigated using a particle-in-cell direct simulation Monte Carlo model. The plasma studied is a one-dimensional discharge in 50 Torr of pure helium with a platinum cathode, gap size of 1.15 cm, and voltages of 1.2-1.8 kV. The current traces are compared with experimental measurements. Larger values of γ ph generally result in a faster breakdown, while larger values of γ i result in a larger maximum current. The 58.4 nm photons emitted from He(21P) are the primary source of electrons at the cathode before the cathode fall is developed. Of the values of γ ph and γ i investigated, those which provide the best agreement with the experimental current measurements are γ ph = 0.005 and γ i = 0.01. These values are significantly lower than those in the literature for pristine platinum or for a graphitic carbon film which we speculate may cover the platinum. This difference is in part due to the limitations of a one-dimensional model but may also indicate surface conditions and exposure to a plasma can have a significant effect on the secondary electron yields. The effects of applied voltage and the current produced by a UV diode which was used to initiate the discharge, are also discussed.

Concerns about cyber threats to space systems are increasing. Researchers are developing intrusion detection and protection systems to mitigate these threats, but sparsity of cyber threat data poses a significant challenge to these efforts. Development of credible threat data sets are needed to overcome this challenge. This paper describes the extension/development of three data generation algorithms (generative adversarial networks, variational auto-encoders, and generative algorithm for multi-variate timeseries) to generate cyber threat data for space systems. The algorithms are applied to a use case that leverages the NASA Operational Simulation for Small Satellites (NOS$^{3})$ platform. Qualitative and quantitative measures are applied to evaluate the generated data. Strengths and weaknesses of each algorithm are presented, and suggested improvements are provided. For this use case, generative algorithm for multi-variate timeseries performed best according to both qualitative and quantitative measures.

This paper presents an extended/generalized finite element method for bridging scales in linear elastodynamics in the absence of scale separation. More precisely, the GFEMgl framework is expanded to enable the numerical solution of multiscale problems through the automated construction of specially-tailored shape functions, thereby enabling high-fidelity finite element modeling on simple, fixed finite element meshes. This introduces time-dependencies in the shape functions in that they are subject to continuous adaptation with time. The temporal aspects of the formulation are investigated by considering the Newmark-β time integration scheme, and the efficacy of mass lumping strategies is explored in an explicit time-stepping scheme. This method is demonstrated on representative wave propagation examples as well as a dynamic fracture problem to assess its accuracy and flexibility.

Concerns about cyber threats to space systems are increasing. Researchers are developing intrusion detection and protection systems to mitigate these threats, but sparsity of cyber threat data poses a significant challenge to these efforts. Development of credible threat data sets are needed to overcome this challenge. This paper describes the extension/development of three data generation algorithms (generative adversarial networks, variational auto-encoders, and generative algorithm for multi-variate timeseries) to generate cyber threat data for space systems. The algorithms are applied to a use case that leverages the NASA Operational Simulation for Small Satellites (NOS$^{3})$ platform. Qualitative and quantitative measures are applied to evaluate the generated data. Strengths and weaknesses of each algorithm are presented, and suggested improvements are provided. For this use case, generative algorithm for multi-variate timeseries performed best according to both qualitative and quantitative measures.

In each of our brains, 86 billion neurons work in parallel, processing inputs from senses and memories to produce the many feats of human cognition. The brains of other creatures are less broadly capable, but those animals often exhibit innate aptitudes for particular tasks, abilities honed by millions of years of evolution.

A novel phase-field model for ductile fracture is presented. The model is developed within a consistent variational framework in the context of finite-deformation kinematics. A novel coalescence dissipation introduces a new coupling mechanism between plasticity and fracture by degrading the fracture toughness as the equivalent plastic strain increases. The proposed model is compared with a recent alternative where plasticity and fracture are strongly coupled. Several representative numerical examples motivate specific modeling choices. In particular, a linear crack geometric function provides an “unperturbed” ductile response prior to crack initiation, and Lorentz-type degradation functions ensure that the critical fracture strength remains independent of the phase-field regularization length. In addition, the response of the model is demonstrated to converge with a vanishing phase-field regularization length. The model is then applied to calibrate and simulate a three-point bending experiment of an aluminum alloy specimen with a complex geometry. The effect of the proposed coalescence dissipation coupling on simulations of the experiment is first investigated in a two-dimensional plane strain setting. The calibrated model is then applied to a three-dimensional calculation, where the calculated load-deflection curves and the crack trajectory show excellent agreement with experimental observations. Finally, the model is applied to simulate crack nucleation and growth in a specimen from a recent Sandia Fracture Challenge.

Abstract: Uptake of noble gases into heterogeneous geologic core samples was measured using a piezometric methodology. In addition to measuring accessible porosity—as with gas pycnometry—by monitoring the rate of pressure decay, this method can also be used to estimate the gas effective diffusivity in the sample. In contrast to previous applications of this method, where milligram quantities of fractured grains are characterized, here approximately kilogram core samples were left intact when tested. In doing so, a more representative sample of the heterogeneous field geology is provided. Additionally, alteration of the pore structure and connectivity during sample preparation is avoided. To scale the piezometric method from milligrams to kilograms, the system was designed to operate at medium vacuum (1 to 100 Pa) to restrict transport in pores less than approximately 60 µm to large Knudsen numbers. To test the system performance, two samples of interest were selected: a rhyolitic welded tuff from Blue Canyon Dome at the Energetic Materials Research and Testing Center and a zeolitized non-welded rhyolitic tuff from the Nevada National Security Site. Three noble gases were utilized in this test series; Argon and xenon as they are of direct interest to nuclear monitoring efforts and helium as it is a weakly adsorbing reference standard. Additionally, mercury intrusion porosimetry measurements were made on subsamples of the core to compare the observed porosity by the two methods and to discuss gas transport rates in the context of the measured pore distribution. Article Highlights: The piezometric method was extended to measure transport in intact geologic core samples between 800 and 1400 g.Transport in the pores spaces was restricted to Knudsen flow using medium vacuum, enabling a closed-form solution.Argon and xenon in a zeolitized tuff core exhibited significant adsorption and enhanced transport relative to helium.

In situ TEM/SEM microscopy to investigate the structural evolution that occurs due to various extreme environments

A low-Mach, unstructured, large-eddy-simulation-based, unsteady flamelet approach with a generalized heat loss combustion methodology (including soot generation and consumption mechanisms) is deployed to support a large-scale, quiescent, 5-m JP-8 pool fire validation study. The quiescent pool fire validation study deploys solution sensitivity procedures, i.e., the effect of mesh and time step refinement on capturing key fire dynamics such as fingering and puffing, as mesh resolutions approach O(1) cm. A novel design-order, discrete-ordinate-method discretization methodology is established by use of an analytical thermal/participating media radiation solution on both low-order hexahedral and tetrahedral mesh topologies in addition to quadratic hexahedral elements. The coupling between heat losses and the flamelet thermochemical state is achieved by augmenting the unsteady flamelet equation set with a heat loss source term. Soot and radiation source terms are determined using flamelet approaches for the full range of heat losses experienced in fire applications including radiative extinction. The proposed modeling and simulation paradigm are validated using pool surface radiative heat flux, maximum centerline temperature location, and puffing frequency data, all of which are predicted within 10% accuracy. Simulations demonstrate that under-resolved meshes predict an overly conservative radiative heat flux magnitude with improved comparisons as compared to a previously deployed hybrid Reynolds-averaged Navier-Stokes/eddy dissipation concept-based methodology.

We discuss thinned InAsSb resonant infrared detectors that are designed to enable high quantum efficiency by using interleaved nanoantennas to read out two wavelengths from each pixel simultaneously.

In previous studies using the University of Nevada, Reno's (UNR's) high-impedance Zebra Marx generator (1.9 ω, 1.7 MA, 100 ns), Double Planar Wire Arrays (DPWAs) proved to be excellent radiators, and Double Planar Foil Liners (DPFLs) proved useful for future inertial confinement fusion applications. This article presents the results of joint UNR/UM (University of Michigan) experiments with aluminum (Al) DPWAs, Al DPFLs, and tungsten (W) DPWAs using UM's Michigan Accelerator for Inductive Z-Pinch Experiments (MAIZE) generator, a low-impedance Linear Transformer Driver (LTD) (0.1 ω, 0.5-1 MA, and 100-250 ns). The main goals of this study were twofold: the first was a pioneering effort to test whether a relatively heavy Al DPFL could successfully be imploded on a low-impedance university-scale LTD like the MAIZE generator, and, if so, to analyze the results and make comparisons to the optimized, lighter DPWA configurations that have been previously studied. The DPWAs consisted of two planes of micrometer-scale diameter Al or W wires, while the DPFLs consisted of two planes of micrometer-scale thickness Al foils. Diagnostics include filtered Si-diodes, an absolutely calibrated filtered PCD, x-ray pinhole cameras, spectrometers, and gated optical self-emission imaging. The implosion dynamics and radiative properties of Al DPWAs and DPFLs and W DPWAs on the MAIZE LTD are discussed and compared. Time-dependent load inductance calculations derived from measurements of the load current and a MAIZE circuit model provide a relative measurement of pinch strength. In experiments on MAIZE, W planar wire arrays exhibited a higher peak load inductance throughout the pinch than Al DPWAs and DPFLs, while x-ray pulses from Al DPFLs had the longest emission duration.

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Nonlocal models, including peridynamics, often use integral operators that embed lengthscales in their definition. However, the integrands in these operators are difficult to define from the data that are typically available for a given physical system, such as laboratory mechanical property tests. In contrast, molecular dynamics (MD) does not require these integrands, but it suffers from computational limitations in the length and time scales it can address. To combine the strengths of both methods and to obtain a coarse-grained, homogenized continuum model that efficiently and accurately captures materials’ behavior, we propose a learning framework to extract, from MD data, an optimal Linear Peridynamic Solid (LPS) model as a surrogate for MD displacements. To maximize the accuracy of the learnt model we allow the peridynamic influence function to be partially negative, while preserving the well-posedness of the resulting model. To achieve this, we provide sufficient well-posedness conditions for discretized LPS models with sign-changing influence functions and develop a constrained optimization algorithm that minimizes the equation residual while enforcing such solvability conditions. This framework guarantees that the resulting model is mathematically well-posed, physically consistent, and that it generalizes well to settings that are different from the ones used during training. We illustrate the efficacy of the proposed approach with several numerical tests for single layer graphene. Our two-dimensional tests show the robustness of the proposed algorithm on validation data sets that include thermal noise, different domain shapes and external loadings, and discretizations substantially different from the ones used for training.

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This report outlines the mathematical formulation for the atomic environment vector (AEV) construction used in the aevmod software package. The AEV provides a summary of the geometry of a molecule or atomic configuration. We also present the formulation for the analytical Jacobian of the AEV with respect to the atomic Cartesian coordinates. The software provides functionality for both the AEV and AEV-Jacobian, as well as the AEV-Hessian which is available via reliance on the third party library Sacado.

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We present a dynamic laboratory spontaneous imbibition test and interpretation method, demonstrated on volcanic tuff samples from the Nevada National Security Site. The method includes numerical inverse modeling to quantify uncertainty of estimated two-phase fluid flow properties. As opposed to other approaches requiring multiple different laboratory instruments, the dynamic imbibition method simultaneously estimates capillary pressure and relative permeability from one test apparatus.

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