Publications

Work evaluating spent nuclear fuel (SNF) dry storage canister surface environments and canister corrosion progressed significantly in FY23, with the goal of developing a scientific understanding of the processes controlling initiation and growth of stress corrosion cracking (SCC) cracks in stainless steel canisters in relevant storage environments. The results of the work performed at Sandia National Laboratories (SNL) will guide future work and will contribute to the development of better tools for predicting potential canister penetration by SCC.

In this study, we demonstrate the ability of polarity inversion of sputtered aluminum scandium nitride thin films through post-fabrication processes with domain widths as small as 220 nm at a periodicity of 440 nm. An approach using photo- and electron-beam lithography to generate sub-quarter micrometer feature size with adjustable duty cycle through a lift-off process is presented. The film with a coercive field Ec+ of 5.35 MV/cm was exercised first with a 1 kHz triangular double bipolar wave and ultimately poled with a 0.5 kHz double monopolar wave using a Radiant Precision Premier II tester. The metal polar (M-polar) and nitrogen polar (N-polar) domains were identified and characterized through potassium hydroxide wet etching as well as piezoresponse force microscopy (PFM). Well-distinguished boundaries between the oppositely polarized domain regions were confirmed through the phase diagram of the PFM results. The relationship between the electrode width, poling voltage, and domain growth was experimentally studied and statistically analyzed, where 7.96 nm/V domain width broadening vs escalating poling voltage was observed. This method produces extremely high domain spatial resolution in III-nitride materials via poling and is transferable to a CMOS-compatible photolithography process. The spatial resolution of the periodically poled Al0.68Sc0.32N is suitable for second-harmonic generation of deep ultraviolet through quasi-phase-matching and RF MEMS operating in the X-Band spectrum.

The US Department of Energy (DOE) Nuclear Energy Research Initiative funded the design and construction of the Seven Percent Critical Experiment (7uPCX) at Sandia National Laboratories. The start-up of the experiment facility and the execution of the experiments described here were funded by the DOE Nuclear Criticality Safety Program. The 7uPCX is designed to investigate critical systems with fuel for light water reactors in the enrichment range above 5 % ²³⁵U. The 7uPCX assembly is a water-moderated and -reflected array of aluminum-clad U(6.90 %)O₂ fuel rods. Other critical experiments performed in the 7uPCX assembly are documented in LEU-COMP-THERM-078, LEU-COMP-THERM-080, LEU-COMP-THERM-096, LEUCOMP-THERM-097, LEU-COMP-THERM-101, and LEU-COMP-THERM-102. The purpose of these experiments was to measure the effects of molybdenum in nearly-critical systems. The molybdenum was introduced into the fuel arrays as tubular sleeves that surrounded some of the fuel rods in the fuel arrays measured. Four hundred molybdenum tubes nominally 12.7 mm outside diameter, 498 mm long, with 0.762 mm wall thickness were provided for the experiments by the Institut de Radioprotection et de Sûreté Nucléaire (IRSN). Small polyethylene adapters at each end of the tubes were used to center each tube on a fuel rod in the assembly. The critical experiments were done using a set of triangular-pitched grid plates fabricated for these experiments. The grid plate set accommodated a fuel array of a total of 1261 fuel rod positions on a pitch of 0.610 in (1.5494 cm) in a series of 20 hexagonal rings surrounding the central fuel rod. The fuel used in these experiments was fabricated using unirradiated 6.90 % enriched UO₂ fuel pellets from fuel elements designed to be used in the internal nuclear superheater section of the Pathfinder boiling water reactor operated in South Dakota by the Northern States Power Company in the 1960s. The fuel elements were obtained from The Pennsylvania State University where they had been stored for many years. The fuel pellets in those fuel elements were removed from the original Incoloy cladding and reclad in 3003 aluminum tubes and end caps for use in the experiments reported here. The five critical experiments in this series were performed in August through December 2022, in the Sandia Critical Experiments (SCX) at the Sandia Pulsed Reactor Facility. Case 1 had no molybdenum sleeves, Case 2 had 208 molybdenum sleeves clustered at the center of the array, Case 3 had 397 molybdenum sleeves clustered at the center of the array, Case 4 had 175 molybdenum sleeves in the central position and in five alternating hexagonal rings, and Case 5 had 331 molybdenum sleeves in the central position and in seven alternating hexagonal rings. All five critical experiments are judged to be acceptable as benchmark experiments.

Accurate prediction of ductile failure is critical to Sandia’s NW mission, but the models are computationally heavy. The costs of including high-fidelity physics and mechanics that are germane to the failure mechanisms are often too burdensome for analysts either because of the person-hours it requires to input them or because of the additional computational time, or both. In an effort to deliver analysts a tool for representing these phenomena with minimal impact to their existing workflow, our project sought to develop modern data-driven methods that would add microstructural information to business-as-usual calculations and expedite failure predictions. The goal is a tool that receives as input a structural model with stress and strain fields, as well as a machine-learned model, and output predictions of structural response in time, including failure. As such, our project spent substantial time performing high-fidelity, three-dimensional experiments to elucidate materials mechanisms of void nucleation and evolution. We developed crystal-plasticity finite-element models from the experimental observations to enrich the findings with fields not readily measured. We developed engineering length-scale simulations of replicated test specimens to understand how the engineering fields evolve in the presence of fine-scale defects. Finally, we developed deep learning convolutional neural networks, and graph-based neural networks to encode the findings of the experiments and simulations and make forward predictions in time for structural performance. This project demonstrated the power of data-driven methods for model development, which have the potential to vastly increase both the accuracy and speed of failure predictions. These benefits and the methods necessary to develop them are highlighted in this report. However, many challenges remain to implementing these in real applications, and these are discussed along with potential methods for overcoming them.

This memo summarizes the simulation of ductile failure propagation work conducted under the ASC project “V&V of Ductile Failure” conducted during FY 23. Physically, the failure propagation consists of crack propagation in the material. In the numerical setting—specifically in a finite element model—propagation can be accomplished through element death when critical conditions occur locally at an element that is then deleted from the simulation. The validation of the finite element models is evaluated by direct comparison between the experimental and simulation results regarding the rate of crack growth and its influence on the load-deflection response of the specimens tested. This work considers two geometries that display stable crack propagation under displacement-controlled conditions. The first geometry consists of hat specimens loaded in compression with nominally identical geometries but made with three different materials: Steel A286, Al 7075-T651 and 304L stainless steel. The three materials represent a range of ductility values that affect the response and crack propagation within the specimen. The crack induced propagates under an essentially mode-II type of deformation. The second geometry consists of a pre-cracked 304L stainless steel compact tension test specimen loaded so as to induce a mode-I deformation at the crack.

Accurate fuel oxidation mechanisms can enable predictive capabilities that aid in advancing combustion technologies. High-level computational kinetics can yield reasonable rate coefficients with uncertainties, in some cases, below a factor of 2. Computed rate coefficients can be constrained further by optimizing against experimental data. Here, we explore the application of genetic algorithm (GA) optimization to constrain computed rate coefficients in complex fuel oxidation mechanisms in conjunction with temperature-dependent species mole fractions from jet-stirred reactor (JSR) measurements. Cyclohexane is a model candidate for understanding the reactivity of cyclic fuels. In this work, we optimize the rate coefficients of the most recent literature cyclohexane mechanism, which incorporates theoretically computed rate coefficients for the reaction networks stemming from the first and second O2 addition pathways, against the experimental results of two separate literature JSR studies. Optimization consistency is evaluated by carrying out three GA optimizations: fitting to the temperature-dependent species mole fractions in each JSR experiment separately and simultaneously fitting the species mole fractions in both experiments. Local sensitivity analyses are used to identify five influential low-temperature oxidation reactions for optimization. Although the three optimizations do not yield identical rate coefficients, the direction of change in all five rate coefficients is consistent among the three optimizations. Performance of the models from the three optimizations is assessed against literature ignition delay times with differences in the level of agreement observed among the different optimizations. Comparisons are made with our recent optimization work of a cyclopentane oxidation master-equation model against time-resolved species concentrations, and insights and improvements of the strategy for constraining rate coefficients using GA optimization are discussed.

Tritium permeability in zirconium-based tritium getter critically impacts tritium storage and environmental safety during operation of tritium-producing burnable absorber rods (TPBARs). Previous experiments indicated that during irradiation operation, the hydrogen equilibrium pressured is increased. Further experimental and modeling studies suggested that the enhanced tritium release observed for reactor scale assemblies might be related to a thermal diffusion known as the Soret effect. A direct measurement of the Soret factor, however, has not been performed. To improve TPBAR and other nuclear applications, here we have applied two non-equilibrium molecular dynamics methods to study thermal diffusion of hydrogen isotopes in low-concentration zirconium hydrides. One of the methods produces sufficiently converged results to distinguish crystal orientation, isotope type, and concentration effects. In conclusion, with this method, crystal orientation, isotope type, and concentration effects are discussed.

Local crystallographic features negatively affect quantum spin defects by changing the local electrostatic environment, often resulting in degraded or varied qubit optical and coherence properties. Few tools exist that enable the deterministic synthesis and study of such intricate systems on the nano-scale, making defect-to-defect strain environment quantification difficult. In this paper, we highlight state-of-the-art capabilities from the U.S. Department of Energy’s Nanoscale Science Research Centers that directly address these shortcomings. Specifically, we demonstrate how complementary capabilities of nano-implantation and nano-diffraction can be used to demonstrate the quantum relevant, spatially deterministic creation of neutral divacancy centers in 4H silicon carbide, while investigating and characterizing these systems on the ≤ 25 nm scale with strain sensitivities on the order of 1 × 10 − 6 , relevant to defect formation dynamics. This work lays the foundation for ongoing studies into the dynamics and deterministic formation of low strain homogeneous quantum relevant spin defects in the solid state.

This report describes specific activities in the Fiscal Year (FY) 2023 associated with the Geologic Disposal Safety Assessment (GDSA) Repository Systems Analysis (RSA) work package funded by the Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy Office of Nuclear Energy (DOE-NE), Office of Spent Fuel and Waste Disposition (SFWD). The overall objective of the GDSA RSA work package is to develop generic deep geologic repository concepts and system performance assessment (PA) models in several potential host-rock environments, and to simulate and analyze these generic repository concepts and models using the GDSA Framework toolkit and other tools as needed. The specific GDSA goal addressed in this report is reference case development, simulation, and analysis for the unsaturated alluvium (UZ), one of the four potential host-rocks considered by the GDSA. Further, we aim to exercise the simulation tools and methodologies under development by GDSA for PA modelling.

Microneedle sensors could enable minimally-invasive, continuous molecular monitoring – informing on disease status and treatment in real-time. Wearable sensors for pharmaceuticals, for example, would create opportunities for treatments personalized to individual pharmacokinetics. Here, we demonstrate a commercial-off-the-shelf (COTS) approach for microneedle sensing using an electrochemical aptamer-based sensor that detects the high-toxicity antibiotic, vancomycin. Wearable monitoring of vancomycin could improve patient care by allowing targeted drug dosing within its narrow clinical window of safety and efficacy. To produce sensors, we miniaturize the electrochemical aptamer-based sensors to a microelectrode format, and embed them within stainless steel microneedles (sourced from commercial insulin pen needles). The microneedle sensors achieve quantitative measurements in body-temperature undiluted blood. Further, the sensors effectively maintain electrochemical signal within porcine skin. This COTS approach requires no cleanroom fabrication or specialized equipment, and produces individually-addressable, sterilizable microneedle sensors capable of easily penetrating the skin. In the future, this approach could be adapted for multiplexed detection, enabling real-time monitoring of a range of biomarkers.

Aging of silicon photovoltaic (PV) module packaging is one of the greatest limiters of PV module service lifetimes. Module characterization typically focuses on power degradation metrics, which do not convey the complexities of often simultaneous degradation mechanisms. In this work, PV modules with pristine references and known fielding histories were investigated by non-destructive and destructive methods. Modules from Canadian Solar, Mission Solar, and Hanwha Q-Cells were fielded for up to three years; select modules were removed from fielding each year for coring to allow for characterization of the encapsulant. Modules are commonly encapsulated with two protective layers of partially-crystalline ethylene vinyl acetate (EVA) polymer that must undergo a crosslinking reaction to achieve desired properties. The extent of crystallinity of the encapsulants as studied by differential scanning calorimetry showed differences between manufacturers and over time. Some encapsulants showed different magnitudes of crystal sizes which changed after fielding; encapsulants with the monodisperse crystal sizes did not change with fielding. This is due to differences in thermal history. These results have implications for stress development during module aging, since EVA crystal melting and crosslinking reactions can result in encapsulant density changes.

The Sibling Pin test campaign is a Department of Energy (DOE) research activity within the Spent Fuel and Waste Science and Technology (SFWST) program that is tasked with characterization of high burnup (HBU) fuel in support of the High Burnup Spent Fuel Data Project. Of the 25 fuel rods in the Sibling Pin inventory, approximately 9 rod lengths have been consumed during the first phase (Phase I) of the test campaign leaving approximately 16 rod lengths for the second phase (Phase II) of testing. This plan outlines the Phase II testing and the motivations for performing these tests. Priorities for Phase II testing are based on previously identified knowledge gaps, lessons-learned from Phase I work, the original objectives of the High Burnup Spent Fuel Data Project and the Sibling Pin test campaign, and input from external stakeholders. The priorities for Phase II testing are to obtain data to characterize the effects of annealing on cladding mechanical properties and fuel rod performance, to quantify the creep behavior of cladding materials and fuel rods and the effects of creep deformations on the performance of cladding and fuel rods, and to gather data to support the final closure of the hydride reorientation and radial hydride induced embrittlement gap for HBU fuel rods.

Translating the surging interest in neuromorphic electronic components, such as those based on nonlinearities near Mott transitions, into large-scale commercial deployment faces steep challenges in the current lack of means to identify and design key material parameters. These issues are exemplified by the difficulties in connecting measurable material properties to device behavior via circuit element models. Here, the principle of local activity is used to build a model of VO2/SiN Mott threshold switches by sequentially accounting for constraints from a minimal set of quasistatic and dynamic electrical and high-spatial-resolution thermal data obtained via in situ thermoreflectance mapping. By combining independent data sets for devices with varying dimensions, the model is distilled to measurable material properties, and device scaling laws are established. The model can accurately predict electrical and thermal conductivities and capacitances and locally active dynamics (especially persistent spiking self-oscillations). The systematic procedure by which this model is developed has been a missing link in predictively connecting neuromorphic device behavior with their underlying material properties, and should enable rapid screening of material candidates before employing expensive manufacturing processes and testing procedures.

Motivated by recent experimental results we calculate from first-principles the lifetime of low-energy quasiparticles in bilayer graphene (BLG). Here, we take into account the scattering rate arising from electron-electron interactions within the GW approximation for the electron self-energy and consider several p-type doping levels ranging from 0 to ρ ≈ 2.4 × 10¹² holes/cm². In the undoped case we find that the average inverse lifetime scales linearly with energy away from the charge neutrality point, with values in good agreement with experiments. The decay rate is approximately three times larger than in monolayer graphene, a consequence of the enhanced screening in BLG. In the doped case, the dependence of the inverse lifetime on quasiparticle energy acquires a non-linear component due to the opening of an additional decay channel mediated by acoustic plasmons.

The Spent Fuel Waste Disposition (SFWD) program is planning to conduct a full-scale seismic shake table test on the dry storage systems of spent nuclear fuel (SNF) to close the gap related to seismic loads on fuel assemblies in dry storage systems. This test will allow for quantifying the strains and accelerations on surrogate fuel assembly hardware and cladding during earthquakes of different magnitudes and frequency content. Full-scale testing is needed because a dry storage system is a complex and highly nonlinear system making it hard to predict (model) the responses to seismic excitations. The non-linearity arises from the multiple spatial gaps in the system – between fuel rods and the basket, between the basket and dry storage canister, between the dry storage canister and the storage cask (overpack), and ventilation gaps. The non-linearities pose significant limitations on the value of tests with scaled systems.

Here, we study the impact of deep-level defects on trap-assisted Auger–Meitner recombination in c-plane InGaN/GaN LEDs using a small-signal electroluminescence (SSEL) method and deep-level optical spectroscopy (DLOS). Carrier dynamics information, including carrier lifetime, recombination rate, and carrier density, is obtained from SSEL, while DLOS is used to obtain the deep-level defect density. Through fitting the nonradiative recombination rates of wafers with different deep-level defect densities, we obtain the Shockley–Read–Hall (SRH) and trap-assisted Auger–Meitner recombination (TAAR) coefficients. We show that defect-related nonradiative recombination, including both SRH and TAAR, accounts for a relatively small fraction of the total nonradiative recombination, which is dominated by intrinsic Auger–Meitner recombination. The interplay between carrier localization and Coulomb enhancement has a different impact on radiative and intrinsic Auger–Meitner recombination. Evidence is presented that the imbalance between the change of radiative and intrinsic Auger–Meitner recombination is the primary cause of the efficiency droop at high carrier densities in the samples studied.

Accurate and efficient constitutive modeling remains a cornerstone issue for solid mechanics analysis. Over the years, the LAMÉ advanced material model library has grown to address this challenge by implementing models capable of describing material systems spanning soft polymers to stiff ceramics including both isotropic and anisotropic responses. Inelastic behaviors including (visco)plasticity, damage, and fracture have all incorporated for use in various analyses. This multitude of options and flexibility, however, comes at the cost of many capabilities, features, and responses and the ensuing complexity in the resulting implementation. Therefore, to enhance confidence and enable the utilization of the LAMÉ library in application, this effort seeks to document and verify the various models in the LAMÉ library. Specifically, the broader strategy, organization, and interface of the library itself is first presented. The physical theory, numerical implementation, and user guide for a large set of models is then discussed. Importantly, a number of verification tests are performed with each model to not only have confidence in the model itself but also highlight some important response characteristics and features that may be of interest to end-users. Finally, in looking ahead to the future, approaches to add material models to this library and further expand the capabilities are presented.

Cast Monel alloys are used in applications requiring a combination of good mechanical properties and excellent resistance to corrosion. Despite prevalent industrial use, relatively few studies have been conducted to investigate the relationships between composition, solidification behavior, and microstructure. Given that these alloys are used in the cast and welded conditions, these factors have a significant influence over the material properties. Here, in this work, microstructural characterization, electron probe microanalysis, X-ray diffraction, and differential scanning calorimetry were used to study how changes in Si and Nb concentrations affected the solidification path and microstructure of Monel alloys. It was found that increasing Nb concentration stabilized higher amounts of MC carbides and suppressed graphite formation during solidification. It was also found that the high nominal concentration and segregation of Si to the liquid led to the formation of Ni₃₁Si₁₂ and other silicides via terminal eutectic reactions at the end of solidification. A pseudo-binary solidification diagram was constructed using experimental data and was applied to predict the mass fraction of solidified eutectic as a function of composition. The modeled microstructures were found to be in good agreement with experimentally measured phase fractions.

We study the impact of deep-level defects on trap-assisted Auger-Meitner recombination in c-plane InGaN/GaN LEDs using a small-signal electroluminescence (SSEL) method and deep-level optical spectroscopy (DLOS). Carrier dynamics information, including carrier lifetime, recombination rate, and carrier density, is obtained from SSEL, while DLOS is used to obtain the deep-level defect density. Through fitting the nonradiative recombination rates of wafers with different deep-level defect densities, we obtain the Shockley-Read-Hall (SRH) and trap-assisted Auger-Meitner recombination (TAAR) coefficients. We show that defect-related nonradiative recombination, including both SRH and TAAR, accounts for a relatively small fraction of the total nonradiative recombination, which is dominated by intrinsic Auger-Meitner recombination. The interplay between carrier localization and Coulomb enhancement has a different impact on radiative and intrinsic Auger-Meitner recombination. Evidence is presented that the imbalance between the change of radiative and intrinsic Auger-Meitner recombination is the primary cause of the efficiency droop at high carrier densities in the samples studied.

Heterogenous materials under shock compression can be expected to reach different shock states throughout the material according to local differences in microstructure and the history of wave propagation. Here, a compact, multiple-beam focusing optic assembly is used with high-speed velocimetry to interrogate the shock response of porous tantalum films prepared through thermal-spray deposition. The distribution of particle velocities across a shocked interface is compared to results obtained using a set of defocused interferometric beams that sampled the shock response over larger areas. The two methods produced velocity distributions along the shock plateau with the same mean, while a larger variance was measured with narrower beams. The finding was replicated using three-dimensional, mesoscopically resolved hydrodynamics simulations of solid tantalum with a pore structure mimicking statistical attributes of the material and accounting for radial divergence of the beams, with agreement across several impact velocities. Accounting for pore morphology in the simulations was found to be necessary for replicating the rise time of the shock plateau. The validated simulations were then used to show that while the average velocity along the shock plateau could be determined accurately with only a few interferometric beams, accurately determining the width of the velocity distribution, which here was approximately Gaussian, required a beam dimension much smaller than the spatial correlation lengthscale of the velocity field, here by a factor of ∼30×, with implications for the study of other porous materials.

Objective. Our objective is to formulate the problem of the magnetoencephalographic (MEG) sensor array design as a well-posed engineering problem of accurately measuring the neuronal magnetic fields. This is in contrast to the traditional approach that formulates the sensor array design problem in terms of neurobiological interpretability the sensor array measurements. Approach. We use the vector spherical harmonics (VSH) formalism to define a figure-of-merit for an MEG sensor array. We start with an observation that, under certain reasonable assumptions, any array of m perfectly noiseless sensors will attain exactly the same performance, regardless of the sensors' locations and orientations (with the exception of a negligible set of singularly bad sensor configurations). We proceed to the conclusion that under the aforementioned assumptions, the only difference between different array configurations is the effect of (sensor) noise on their performance. We then propose a figure-of-merit that quantifies, with a single number, how much the sensor array in question amplifies the sensor noise. Main results. We derive a formula for intuitively meaningful, yet mathematically rigorous figure-of-merit that summarizes how desirable a particular sensor array design is. We demonstrate that this figure-of-merit is well-behaved enough to be used as a cost function for a general-purpose nonlinear optimization methods such as simulated annealing. We also show that sensor array configurations obtained by such optimizations exhibit properties that are typically expected of 'high-quality' MEG sensor arrays, e.g. high channel information capacity. Significance. Our work paves the way toward designing better MEG sensor arrays by isolating the engineering problem of measuring the neuromagnetic fields out of the bigger problem of studying brain function through neuromagnetic measurements.

Here, we perform all-atom molecular dynamics simulations of lithium triflate in 1,2-dimethoxyethane using six different literature force fields. This system is representative of many experimental studies of lithium salts in solvents and polymers. We show that multiple historically common force fields for lithium ions give qualitatively incorrect results when compared with those from experiments and quantum chemistry calculations. We illustrate the importance of correctly selecting force field parameters and give recommendations on the force field choice for lithium electrolyte applications.

Alkene ozonolysis generates short-lived Criegee intermediates that are a significant source of hydroxyl (OH) radicals. This study demonstrates that roaming of the separating OH radicals can yield alternate hydroxycarbonyl products, thereby reducing the OH yield. Specifically, hydroxybutanone has been detected as a stable product arising from roaming in the unimolecular decay of the methyl-ethyl-substituted Criegee intermediate (MECI) under thermal flow cell conditions. The dynamical features of this novel multistage dissociation plus a roaming unimolecular decay process have also been examined with ab initio kinetics calculations. Experimentally, hydroxybutanone isomers are distinguished from the isomeric MECI by their higher ionization threshold and distinctive photoionization spectra. Moreover, the exponential rise of the hydroxybutanone kinetic time profile matches that for the unimolecular decay of MECI. A weaker methyl vinyl ketone (MVK) photoionization signal is also attributed to OH roaming. Complementary multireference electronic structure calculations have been utilized to map the unimolecular decay pathways for MECI, starting with 1,4 H atom transfer from a methyl or methylene group to the terminal oxygen, followed by roaming of the separating OH and butanonyl radicals in the long-range region of the potential. Roaming via reorientation and the addition of OH to the vinyl group of butanonyl is shown to yield hydroxybutanone, and subsequent C-O elongation and H-transfer can lead to MVK. A comprehensive theoretical kinetic analysis has been conducted to evaluate rate constants and branching yields (ca. 10-11%) for thermal unimolecular decay of MECI to conventional and roaming products under laboratory and atmospheric conditions, consistent with the estimated experimental yield (ca. 7%).

As a part of NASA's efforts in space, options are being examined for an Artemis moon base project to be deployed. This project requires a system of interconnected, but separate, DC microgrids for habitation, mining, and fuel processing. This in-place use of power resources is called in-situ resource utilization (ISRU). These microgrids are to be separated by 9-12 km and each contains a photovoltaic (PV) source, energy storage systems (ESS), and a variety of loads, separated by level of criticality in operation. The separate microgrids need to be able to transfer power between themselves in cases where there are generation shortfall, faults, or other failures in order to keep more critical loads running and ensure safety of personnel and the success of mission goals. In this work, a 2 grid microgrid system is analyzed involving a habitation unit and a mining unit separated by a tie line. A set of optimal controls that has been developed, including power flow controls on the tie line, dispatch of PV generation, and dispatch of non-critical loads, is analyzed, and validated in hardware on the Secure Scalable Microgrid Testbed (SSMTB). This testbed includes hardware emulators for a variety of energy sources, energy storage devices, pulsed loads, and other loads.

A wind rotor is disclosed that produces energy optimally for a given thrust overturning moment. By designing rotors with suboptimal aerodynamic efficiency, they can have optimal thrust performance, which will reduce the substructure cost and/or enable greater energy capture for a given substructure.

This report describes specific activities in the Fiscal Year (FY) 2023 associated with the Geologic Disposal Safety Assessment (GDSA) Repository Systems Analysis (RSA) work package funded by the Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy Office of Nuclear Energy (DOE-NE), Office of Spent Fuel and Waste Disposition (SFWD).

This project pursued a novel strategy for incorporating multiple qubits per ion into ion-trap based quantum computing (ITQC) involving Ca⁺ and Ba⁺. By forming molecular complexes of these cations with molecular-scale cages, we hypothesized that molecular energy levels could be incorporated into quantum computing while retaining key properties of the atomic ions intact. We experimented with a variety of molecular cages and found that Na⁺, K⁺, Rb⁺, Ca²⁺, Sr²⁺, and Ba²⁺ could be captured and brought into the gas phase efficiently by imbedding them inside [2.2.2]-benzocryptand. IR and UV spectra of these cage complexes are sensitive to the size and charge state of the ion, reporting on the structures and binding properties of the cage complexes. UV photofragmentation of the Ba²⁺-Acetate^-1-BzCrypt complex produces Ba⁺-BzCrypt, the complex targeted for exploration in the original hypothesis. Follow-on funding is needed to pursue the spectroscopy of this complex as a target for ITQC.

Abstract not provided.

Abstract not provided.

Deep learning (DL) models have enjoyed increased attention in recent years because of their powerful predictive capabilities. While many successes have been achieved, standard deep learning methods suffer from a lack of uncertainty quantification (UQ). While the development of methods for producing UQ from DL models is an active area of current research, little attention has been given to the quality of the UQ produced by such methods. In order to deploy DL models to high-consequence applications, high-quality UQ is necessary. This report details the research and development conducted as part of a Laboratory Directed Research and Development (LDRD) project at Sandia National Laboratories. The focus of this project is to develop a framework of methods and metrics for the principled assessment of UQ quality in DL models. This report presents an overview of UQ quality assessment in traditional statistical modeling and describes why this approach is difficult to apply in DL contexts. An assessment on relatively simple simulated data is presented to demonstrate that UQ quality can differ greatly between DL models trained on the same data. A method for simulating image data that can then be used for UQ quality assessment is described. A general method for simulating realistic data for the purpose of assessing a model’s UQ quality is also presented. A Bayesian uncertainty framework for understanding uncertainty and existing metrics is described. Research that came out of collaborations with two university partners are discussed along with a software toolkit that is currently being developed to implement the UQ quality assessment framework as well as serve as a general guide to incorporating UQ into DL applications.

The understanding of power flow plasmas is important as we look towards next generation pulsed power (NGPP) as current losses could prohibit the goals of that facility. Therefore, it is important to have accurate diagnostics of the plasma parameters on the current machines, which can be used to help inform and improve simulations. Having these plasma parameters will help validate models and simulations to provide confidence when they are expanded to conditions relevant to NGPP. One important plasma parameter that can be measured is the electron density, which can be measured by photonic Doppler velocimetry (PDV). A PDV system has several key advantages over other interferometers by measuring relatively low densities (> 1 × 10¹⁵ cm^-2) with both spatial and temporal resolution. Experiments were performed on the Mykonos pulsed power machine, which is a 1 MA sub scale machine in which recent platforms have been developed to explore current densities relevant to the inner magnetically insulated transmission line (MITL) on the Z machine. Experiments were performed on two different platforms, the thin foil platform and the Mykonos parallel plate platform (MP3). In addition, a combination of both single-point and multi-point measurements were used. The single-point measurements proved to be very promising, providing a clear increase in density at about 70 ns into the current rise on thin foil experiments up to about 5 × 10¹⁷ cm^-3 before the probe stopped providing signal. While we did also see returns from multi-point measurements on both platforms, the signals were not as easy to interpret due to strong background effects. However, they do show initial promise for this diagnostic to measure density at several points across a 1 mm gap. These measurements provide insights in how to improve the diagnostic so that it can provide useful information on power flow relevant experiments.

The wave energy control competition established a benchmark problem which was offered as an open challenge to the wave energy system control community. The competition had two stages: In the first stage, competitors used a standard wave energy simulation platform (WEC-Sim) to evaluate their controllers while, in the second stage, competitors were invited to test their controllers in a real-time implementation on a prototype system in a wave tank. The performance function used was based on converted energy across a range of standard sea states, but also included aspects related to economic performance, such as peak/average power, peak force, etc. This paper compares simulated and experimental results and, in particular, examines if the results obtained in a linear system simulation are borne out in reality. Overall, within the scope of the device tested, the range of sea states employed, and the performance metric used, the conclusion is that high-performance WEC controllers work well in practice, with good carry-over from simulation to experimentation. However, the availability of a good WEC mathematical model is deemed to be crucial.

In this project, we experimented the focused ion beam (FIB) based fabrications of semiconductor quantum dots (QDs) by using metal nano particles (NPs) (e.g., Al) on semiconductor as a template and by means of the FIB induced direct metal-to-QD conversion. We have examined effect of the experimental conditions, including Ga⁺ ion energy and dose as well as substrate temperature. The results of experiments have shown AlGaSb QD formation on GaSb substrate can be achieved under certain conditions but there are many challenges about the techniques, including compositional nonuniformity of the QDs formed, partial conversion of the metal NP to QD, and high defect concentration in the QDs.

Abstract not provided.

Using DTW for HPC / LDMS data clustering

This is the presentation, that goes with a previously-approved, Information-Released abstract of the same title,

This project applies methods in Bayesian inference and modern statistical methods to quantify the value of new experimental data, in the form of new or modified diagnostic configurations and/or experiment designs. We demonstrate experiment design methods that can be used to identify the highest priority diagnostic improvements or experimental data to obtain in order to reduce uncertainties on critical inferred experimental quantities and select the best course of action to distinguish between competing physical models. Bayesian statistics and information theory provide the foundation for developing the necessary metrics, using two high impact experimental platforms on Z as exemplars to develop and illustrate the technique. We emphasize that the general methodology is extensible to new diagnostics (provided synthetic models are available), as well as additional platforms. We also discuss initial scoping of additional applications that began development in the last year of this LDRD.

Abstract not provided.

Abstract not provided.

Distributed acoustic sensing (DAS) has a demonstrated potential for wide-scale and continuous in situ monitoring of near-surface environmental and anthropogenic processes. DAS is attractive for development as a multi-geophysical observatory due to the prevalence of existing fiber infrastructure in regions with environmental, cultural, or strategic significance. To evaluate the efficacy of this technology for monitoring of polar environmental processes, we collected DAS data from a 37-km long section of seafloor telecommunications fiber located on the continental shelf of the Beaufort Sea, Alaska. This experiment spanned eight, one-week, seasonally-distributed periods across two years. This was the first ever deployment of seafloor DAS beneath sea ice, and the first deployment in any marine environment to span multiple seasons. We recorded a variety of environmental and anthropogenic signals with demonstrable utility for the study of sea ice dynamics and tracking of ocean vessels and ice-traversing vehicles.

Code verification plays an important role in establishing the credibility of computational simulations by assessing the correctness of the implementation of the underlying numerical methods. In computational electromagnetics, the numerical solution to integral equations incurs multiple interacting sources of numerical error, as well as other challenges, which render traditional code-verification approaches ineffective. In this paper, we provide approaches to separately measure the numerical errors arising from these different error sources for the method-of-moments implementation of the combined-field integral equation. We demonstrate the effectiveness of these approaches for cases with and without coding errors.

Abstract not provided.

Abstract not provided.

A discussion of systems like ChatGPT, what their legal issues may be, how they are affecting society, and what the ethical considerations of their existence and use are.

This study presents a deep learning based methodology for both remote sensing and design of acoustic scatterers. The ability to determine the shape of a scatterer, either in the context of material design or sensing, plays a critical role in many practical engineering problems. This class of inverse problems is extremely challenging due to their high-dimensional, nonlinear, and ill-posed nature. To overcome these technical hurdles, we introduce a geometric regularization approach for deep neural networks (DNN) based on non-uniform rational B-splines (NURBS) and capable of predicting complex 2D scatterer geometries in a parsimonious dimensional representation. Then, this geometric regularization is combined with physics-embedded learning and integrated within a robust convolutional autoencoder (CAE) architecture to accurately predict the shape of 2D scatterers in the context of identification and inverse design problems. An extensive numerical study is presented in order to showcase the remarkable ability of this approach to handle complex scatterer geometries while generating physically-consistent acoustic fields. The study also assesses and contrasts the role played by the (weakly) embedded physics in the convergence of the DNN predictions to a physically consistent inverse design.

A critical mission need exists to develop new materials that can withstand extreme environments and multiple sequential threats. High entropy materials, those containing 5 or more metals, exhibit many exciting properties which would potentially be useful in such situations. However, a particularly hard challenge in developing new high entropy materials is determining a priori which compositions will form the desired single phase material. The project outlined here combined several modeling and experimental techniques to explore several structure-property-relationships of high entropy ceramics in an effort to better understand the connection between their compositional components, their observed properties, and stability. We have developed novel machine learning algorithms which rapidly predict stable high entropy ceramic compositions, identified the stability interplay between configurational entropy and cation defects, and tested the mechanical stability of high entropy oxides using the unique capabilities at the Dynamic Compression Sector facility and the Saturn accelerator.

Achieving brain-like efficiency in computing requires a co-design between the development of neural algorithms, brain-inspired circuit design, and careful consideration of how to use emerging devices. The recognition that leveraging device-level noise as a source of controlled stochasticity represents an exciting prospect of achieving brain-like capabilities in probabilistic neural algorithms, but the reality of integrating stochastic devices with deterministic devices in an already-challenging neuromorphic circuit design process is formidable. Here, we explore how the brain combines different signaling modalities into its neural circuits as well as consider the implications of more tightly integrated stochastic, analog, and digital circuits. Further, by acknowledging that a fully CMOS implementation is the appropriate baseline, we conclude that if mixing modalities is going to be successful for neuromorphic computing, it will be critical that device choices consider strengths and limitations at the overall circuit level.

In polymer-filled granular composites, damage may develop in mechanical loading prior to material failure. Damage mechanisms such as microcracking or plastic deformation in the binder phase can substantially alter the material's mesostructure. For energetic materials, such as solid propellants and plastic bonded explosives, these mesostructural changes can have far reaching effects including degraded mechanical properties, potentially increased sensitivity to further insults, and changes in expected performance. Unfortunately, predicting damage is nontrivial due to the complex nature of these composites and the entangled interactions between inelastic mechanisms. In this work, we assess the current literature of experimental knowledge, focusing on the pressure-dependent shear response, and propose a simple simulation framework of bonded particles to study four limiting-case material formulations at both meso- and macro-scales. To construct the four cases, we systematically vary the relative interfacial strength between the polymer binder and granular filler phase and also vary the polymer's glass transition temperature relative to operating temperature which determines how much the binder can plastically deform. These simulations identify key trends in global mechanical response, such as the emergence of strain hardening or softening regimes with increasing pressure which qualitatively resemble experimental results. By quantifying the activation of different inelastic mechanisms, such as bonds breaking and plastically straining, we identify when each mechanism becomes relevant and provide insight into potential origins for changes in mechanical responses. The locations of broken bonds are also used to define larger, mesoscopic cracks to test various metrics of damage. We primarily focus on triaxial compression, but also test the opposite case of triaxial extension to highlight the impact of Lode angle on mechanical behavior.

Abstract not provided.

The ion velocity distribution functions of thermonuclear plasmas generated by spherical laser direct drive implosions are studied using deuterium-tritium (DT) and deuterium-deuterium (DD) fusion neutron energy spectrum measurements. A hydrodynamic Maxwellian plasma model accurately describes measurements made from lower temperature (<10 keV), hydrodynamiclike plasmas, but is insufficient to describe measurements made from higher temperature more kineticlike plasmas. The high temperature measurements are more consistent with Vlasov-Fokker-Planck (VFP) simulation results which predict the presence of a bimodal plasma ion velocity distribution near peak neutron production. These measurements provide direct experimental evidence of non-Maxwellian ion velocity distributions in spherical shock driven implosions and provide useful data for benchmarking kinetic VFP simulations.