Publications

Neuromorphic computing systems have been used for the processing of spatiotemporal video-like data, requiring the use of recurrent networks, while attempting to minimize power consumption by utilizing binary activation functions. However, previous work on binary activation networks has primarily focused on training of feed-forward networks due to difficulties in training recurrent binary networks. Spiking neural networks however have been successfully trained in recurrent networks, despite the fact that they operate with binary communication. Intrigued by this discrepancy, we design a generalized leaky-integrate and fire neuron which can be deconstructed to a binary activation unit, allowing us to investigate the minimal dynamics from a spiking network that are required to allow binary activation networks to be trained. We find that a subthreshold integrative membrane potential is the only requirement to allow an otherwise standard binary activation unit to be trained in a recurrent network. Investigating further the trained networks, we find that these stateful binary networks learn a soft reset mechanism by recurrent weights, allowing them to approximate the explicit reset of spiking networks.

Spatial navigation involves the formation of coherent representations of a map-like space, while simultaneously tracking current location in a primarily unsupervised manner. Despite a plethora of neurophysiological experiments revealing spatially-tuned neurons across the mammalian neocortex and subcortical structures, it remains unclear how such representations are acquired in the absence of explicit allocentric targets. Drawing upon the concept of predictive learning, we utilize a biologically plausible learning rule which utilizes sensory-driven observations with internally-driven expectations and learns through a contrastive manner to better predict sensory information. The local and online nature of this approach is ideal for deployment to neuromorphic hardware for edge-applications. We implement this learning rule in a network with the feedforward and feedback pathways known to be necessary for spatial navigation. After training, we find that the receptive fields of the modeled units resemble experimental findings, with allocentric and egocentric representations in the expected order along processing streams. These findings illustrate how a local and self-supervised learning method for predicting sensory information can extract latent structure from the environment.

The U.S. Department of Energy is funding research into studying the consequences of postclosure criticality on the performance of a generic repository by (1) identifying the features, events, and processes (FEPs) that need to be considered in such an analysis, (2) developing the tools needed to model the relevant FEPs in a postclosure performance assessment, and (3) conducting analyses both with and without the occurrence of a postclosure criticality and comparing the results. This paper describes progress in this area of research and presents the results to date of analyzing the consequences of a postulated steady-state criticality in a hypothetical saturated shale repository. Preliminary results indicate that postclosure criticality would not affect repository performance.

This paper provides a summary of planning work for experiments that will be necessary to address the long-term model validation needs required to meet offshore wind energy deployment goals. Conceptual experiments are identified and laid out in a validation hierarchy for both wind turbine and wind plant applications. Instrumentation needs that will be required for the offshore validation experiments to be impactful are then listed. The document concludes with a nominal vision for how these experiments can be accomplished.

This article aims at discovering the unknown variables in the system through data analysis. The main idea is to use the time of data collection as a surrogate variable and try to identify the unknown variables by modeling gradual and sudden changes in the data. We use Gaussian process modeling and a sparse representation of the sudden changes to efficiently estimate the large number of parameters in the proposed statistical model. The method is tested on a realistic dataset generated using a one-dimensional implementation of a Magnetized Liner Inertial Fusion (MagLIF) simulation model, and encouraging results are obtained.

Modal characterization of a structure is necessary to inform predictive simulation models. Unfortunately, cost and schedule limitations tend to prioritize other dynamic tests, which can lead to inadequate or nonexistent modal testing. To utilize the dynamic test data that is acquired, analysts can extract operational deflection shapes (ODS) which can then be used as a substitute for modal data in model updating and structure characterization. However, extremely high levels of excitation during vibration testing may introduce nonlinear behavior that distorts the ODS prediction. This chapter investigates the reliability of using ODS as a replacement for traditional modal testing on an academic structure designed to respond with intermittent impact. This chapter calculates ODS from responses at several input excitation levels, and the influence of nonlinear impact on the resulting operating modes is discussed.

Interim dry storage of spent nuclear fuel involves storing the fuel in welded stainless-steel canisters. Under certain conditions, the canisters could be subjected to environments that may promote stress corrosion cracking leading to a risk of breach and release of aerosol-sized particulate from the interior of the canister to the external environment through the crack. Research is currently under way by several laboratories to better understand the formation and propagation of stress corrosion cracks, however little work has been done to quantitatively assess the potential aerosol release. The purpose of the present work is to introduce a reliable generic numerical model for prediction of aerosol transport, deposition, and plugging in leak paths similar to stress corrosion cracks, while accounting for potential plugging from particle deposition. The model is dynamic (changing leak path geometry due to plugging) and it relies on the numerical solution of the aerosol transport equation in one dimension using finite differences. The model’s capabilities were also incorporated into a Graphical User Interface (GUI) that was developed to enhance user accessibility. Model validation efforts presented in this paper compare the model’s predictions with recent experimental data from Sandia National Laboratories (SNL) and results available in literature. We expect this model to improve the accuracy of consequence assessments and reduce the uncertainty of radiological consequence estimations in the remote event of a through-wall breach in dry cask storage systems.

We demonstrate high-efficiency emission at wavelengths longer than 540 nm from InGaN quantum wells regrown on periodic arrays of GaN nanostructures and explore their incorporation into nanophotonic resonators for semiconductor laser development.

Long-term stable sealing elements are a basic component in the safety concept for a possible repository for heat-emitting radioactive waste in rock salt. The sealing elements will be part of the closure concept for drifts and shafts. They will be made from a welldefinied crushed salt in employ a specific manufacturing process. The use of crushed salt as geotechnical barrier as required by the German Site Selection Act from 2017 /STA 17/ represents a paradigm change in the safety function of crushed salt, since this material was formerly only considered as stabilizing backfill for the host rock. The demonstration of the long-term stability and impermeability of crushed salt is crucial for its use as a geotechnical barrier. The KOMPASS-II project, is a follow-up of the KOMPASS-I project and continues the work with focus on improving the understanding of the thermal-hydraulic-mechanical (THM) coupled processes in crushed salt compaction with the objective to enhance the scientific competence for using crushed salt for the long-term isolation of high-level nuclear waste within rock salt repositories. The project strives for an adequate characterization of the compaction process and the essential influencing parameters, as well as a robust and reliable long-term prognosis using validated constitutive models. For this purpose, experimental studies on long-term compaction tests are combined with microstructural investigations and numerical modeling. The long-term compaction tests in this project focused on the effect of mean stress, deviatoric stress and temperature on the compaction behavior of crushed salt. A laboratory benchmark was performed identifying a variability in compaction behavior. Microstructural investigations were executed with the objective to characterize the influence of pre-compaction procedure, humidity content and grain size/grain size distribution on the overall compaction process of crushed salt with respect to the deformation mechanisms. The created database was used for benchmark calculations aiming for improvement and optimization of a large number of constitutive models available for crushed salt. The models were calibrated, and the improvement process was made visible applying the virtual demonstrator.

Non-stoichiometric perovskite oxides have been studied as a new family of redox oxides for solar thermochemical hydrogen (STCH) production owing to their favourable thermodynamic properties. However, conventional perovskite oxides suffer from limited phase stability and kinetic properties, and poor cyclability. Here, we report a strategy of introducing A-site multi-principal-component mixing to develop a high-entropy perovskite oxide, (La1/6Pr1/6Nd1/6Gd1/6Sr1/6Ba1/6)MnO3 (LPNGSB_Mn), which shows desirable thermodynamic and kinetics properties as well as excellent phase stability and cycling durability. LPNGSB_Mn exhibits enhanced hydrogen production (?77.5 mmol moloxide?1) compared to (La2/3Sr1/3)MnO3 (?53.5 mmol moloxide?1) in a short 1 hour redox duration and high STCH and phase stability for 50 cycles. LPNGSB_Mn possesses a moderate enthalpy of reduction (252.51-296.32 kJ (mol O)?1), a high entropy of reduction (126.95-168.85 J (mol O)?1 K?1), and fast surface oxygen exchange kinetics. All A-site cations do not show observable valence changes during the reduction and oxidation processes. This research preliminarily explores the use of one A-site high-entropy perovskite oxide for STCH.

Li metal anodes are highly sought after for high energy density applications in both primary commercial batteries and next-generation rechargeable batteries. In this research, Li metal electrodes are aged in coin cells for a year with electrolytes relevant to both types of batteries. The aging response is monitored via electrochemical impedance spectroscopy, and Li electrodes are characterized post-mortem. It was found that the carbonate-based electrolytes exhibit the most severe aging effects, despite the use of LiBF₄-based carbonate electrolytes in Li/CF_x Li primary batteries. Highly concentrated LiFSI electrolytes exhibit the most minimal aging effects, with only a small impedance increase with time. This is likely due to the concentrated nature of the electrolyte causing fewer solvent molecules available to react with the electrode surface. LiI-based electrolytes also show improved aging behavior both on their own and as an additive, with a similar impedance response with time as the concentrated LiFSI electrolytes. Since I^— is in its most reduced state, it likely prevents further reaction and may help protect the Li electrode surface with a primarily organic solid electrolyte interphase.

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability improvement of ML models empowered by UQ has the potential to significantly facilitate the broad adoption of ML solutions in high-stakes decision settings, such as healthcare, manufacturing, and aviation, to name a few. In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems. Towards this goal, we start with a comprehensive classification of uncertainty types, sources, and causes pertaining to UQ of ML models. Next, we provide a tutorial-style description of several state-of-the-art UQ methods: Gaussian process regression, Bayesian neural network, neural network ensemble, and deterministic UQ methods focusing on spectral-normalized neural Gaussian process. Established upon the mathematical formulations, we subsequently examine the soundness of these UQ methods quantitatively and qualitatively (by a toy regression example) to examine their strengths and shortcomings from different dimensions. Then, we review quantitative metrics commonly used to assess the quality of predictive uncertainty in classification and regression problems. Afterward, we discuss the increasingly important role of UQ of ML models in solving challenging problems in engineering design and health prognostics. Two case studies with source codes available on GitHub are used to demonstrate these UQ methods and compare their performance in the life prediction of lithium-ion batteries at the early stage (case study 1) and the remaining useful life prediction of turbofan engines (case study 2).

This article characterises the effects of cathode photoemission leading to electrical discharges in an argon gas. We perform breakdown experiments under pulsed laser illumination of a flat cathode and observe Townsend to glow discharge transitions. The breakdown process is recorded by high-speed imaging, and time-dependent voltage and current across the electrode gap are measured for different reduced electric fields and laser intensities. We employ a 0D transient discharge model to interpret the experimental measurements. The fitted values of transferred photoelectron charge are compared with calculations from a quantum model of photoemission. The breakdown voltage is found to be lower with photoemission than without. When the applied voltage is insufficient for ion-induced secondary electron emission to sustain the plasma, laser driven photoemission can still create a breakdown where a sheath (i.e. a region near the electrode surfaces consisting of positive ions and neutrals) is formed. This photoemission induced plasma persists and decays on a much longer time scale ( ∼ 10 s μ s) than the laser pulse length ( 30 ps). The effects of different applied voltages and laser energies on the breakdown voltage and current waveforms are investigated. The discharge model can accurately predict the measured breakdown voltage curves, despite the existence of discrepancy in quantitatively describing the transient discharge current and voltage waveforms.

Redox flow batteries (RFBs) that incorporate solid energy-storing materials are attractive for high-capacity grid-scale energy storage due to their markedly higher theoretical energy densities compared to their fully liquid counterparts. However, this promise of higher energy density comes at the expense of rate capability. In this work we exploit a ZnO nanorod-decorated Ni foam scaffold to create a high surface area Li metal anode capable of rates up to 10 mA cm−2, a 10× improvement over traditional planar designs. The ZnO nanorods enhance Li metal wettability and promote uniform Li nucleation, allowing the RFB to be initially operated with a prelithiated (charged) anode, or with a safety-conscious, Li-less, fully discharged anode. 5 mgS cm−1 were cycled using a mediated S cathode, whereby redox mediators help oxidize and reduce solid S particles. At 2.4 mgS cm−2 and 10 mA cm−2, the RFB becomes limited by the mediation of solid S. Nevertheless, a respectable energy density of 20.3 Wh L−1 is demonstrated, allowing considerable increase if the S mediation rate can be further improved. Lessons learned here may be broadly applied to RFBs with alkali metal anodes, offering an avenue for safe, dense, grid-scale energy storage.

This report represents completion of milestone deliverable M2SF-24SN010309082 Annual Status Update for OWL due on November 30, 2023. It contains the status of fiscal year 2023 (FY2023) updates for the Online Waste Library (OWL).

Sandia National Laboratories (SNL) and the Institut de Radioprotection et de Sûreté Nucléaire (IRSN) have collaborated on the design and execution of a set of critical experiments that explore the effects of molybdenum in water moderated fuel-rod arrays. The molybdenum is included as sleeves (tubes) on some of the fuel rods in the arrays. The fuel used in the experiments is known at Sandia as the Seven Percent Critical Experiment (7uPCX) fuel. This fuel has been used is several published benchmark evaluations in including LEU-COMP-THERM-78 and LEU-COMP THERM-080.

Marine aerosol injections are a key component in further understanding of both the potentials of deliberate injection for marine cloud brightening (MCB), a potential climate intervention (CI) strategy, and key aerosol-cloud interaction behaviors that currently form the largest uncertainty in global climate model (GCM) predictions of our climate. Since the rate of spread of aerosols in a marine environment directly translates to the effectiveness and ability of aerosol injections in impacting cloud radiative forcing, it is crucial to understand the spatial and temporal extent of injected-aerosol effects following direct injection into marine environments. The ubiquity of ship-injected aerosol tracks from satellite imagery renders observational validation of new parameterizations possible in 2D, however, 3D compatible data is more scarce, and necessary for the development of subgrid scale parameterizations of aerosol-cloud interactions in GCMs. This report introduces two novel parameterizations of atmospheric aerosol injection behavior suitable for both 3D (GCM-compatible) and 2D (observation-related) modeling. Their applicability is highlighted using a wealth of different observational data: small and larger scale salt-aerosol injection experiments conducted at SNL, 3D large eddy simulations of ship-injected aerosol tracks and 2D satellite images of ship tracks. The power of experimental data in enhancing knowledge of aerosol-cloud interactions is in particular emphasized by studying key aerosol microphysical and optical properties as observed through their mixing in cloud-like environments.

Abstract not provided.

We report a spontaneous and hierarchical self-assembly mechanism of carbon dots prepared from citric acid and urea into nanowire structures with large aspect ratios (>50). Scattering-type scanning near-field optical microscopy (s-SNOM) with broadly tunable mid-IR excitation was used to interrogate details of the self-assembly process by generating nanoscopic chemical maps of local wire morphology and composition. s-SNOM images capture the evolution of wire formation and the complex interplay between different chemical constituents directing assembly over the nano- to microscopic length scales. We propose that residual citrate promotes tautomerization of melamine surface functionalities to produce supramolecular shape synthons comprised of melamine-cyanurate adducts capable of forming long-range and highly directional hydrogen-bonding networks. This intrinsic, heterogeneity-driven self-assembly mechanism reflects synergistic combinations of high chemical specificity and long-range cooperativity that may be harnessed to reproducibly fabricate functional structures on arbitrary surfaces.

A series of MD and DFT simulations were performed to investigate hydrogen self-clustering and retention in tungsten. Using a newly develop machine learned interatomic potential, spontaneous formation of hydrogen platelets was observed after implanting low-energy hydrogen into tungsten at high fluxes and temperatures. The platelets formed along low miller index orientations and neighboring tetrahedral and octahedral sites and could grow to over 50 atoms in size. High temperatures above 600 K and high hydrogen concentrations were needed to observe significant platelet formation. A critical platelet size of six hydrogen atoms was needed for long term stability. Platelets smaller than this were found to be thermally unstable within a few nanoseconds. To verify these observations, characteristic platelets from the MD simulations were simulated using large-scale DFT. DFT corroborated the MD results in that large platelets were also found to be dynamically stable for five or more hydrogen atoms. The LDOS from the DFT simulated platelets indicated that hydrogen atoms, particularly at the periphery of the platelet, were found to be at least as stable as hydrogen atoms in bulk tungsten. In addition, electrons were found to be localized around hydrogen atoms in the platelet itself and that hydrogen atoms up to 4.2 Å away within the platelet were found to share charge suggesting that the hydrogen atoms are interacting across longer distances than previously suggested. These results reveal a self-clustering mechanisms for hydrogen within tungsten in the absence of radiation induced or microstructural defects that could be a precursor to blistering and potentially explain the experimentally observed high hydrogen retention particularly in the near surface region.

Sandia National Laboratories and the Institut de Radioprotection et de Sûreté Nucléaire have collaborated on the design and execution of a set of critical experiments that explore the effects of molybdenum in water-moderated fuel-rod arrays. The molybdenum was included as sleeves on some of the fuel rods in the critical experiment fuel arrays. Approach-to-critical experiments were performed on five configurations of fuel and molybdenum sleeves using the 7uPCX fuel in core hardware that set the triangular fuel rod pitch at 15.494 mm. The experiments are evaluated as benchmark critical experiments for the 2023 edition of the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook as LEU-COMP-THERM-111.

The purpose of this proposal is to design a new integral critical experiment to investigate the effects of beryllium oxide and high assay low-enriched uranium fuels. this proposal considers using several existing resources at Sandia: (1) the Critical Experiments (SCX) facility and water tank, (2) spare UO₂-BeO fuel for the Annular Core Research Reactor (ACRR), and 7uPCX fuel rods from previous benchmark experiments.

The Integrated Tiger Series (ITS) generates a database containing energy deposition data. This data, when stored on an Exodus file, is not typically suitable for analysis within Sierra Mechanics for finite element analysis. The its2sierra tool maps data from the ITS database to the Sierra database. This document provides information on the usage of its2sierra.

NasGen provides a path for migration of structural models from Nastran bulk data format (BDF) into both an Exodus mesh file and an ASCII input file for Sierra Structural Dynamics (Salinas) and Solid Mechanics (Adagio). Many tools at Sandia National Labs (SNL) use the Exodus format. This document describes capabilities and limitations of the NasGen translation software.

Tritium exhibits unique environmental behavior because of its potential interactions with water and organic substances. Modeling the environmental consequences of tritium releases can be relatively complex and thus an evaluation of MACCS is needed to understand what updates, if any, are needed in MACCS to account for the behavior of tritium. We examine documented tritium releases and previous benchmarking assessments to perform a model intercomparison between MACCS and state-of-practice tritium-specific codes UFOTRI and ETMOD to quantify the difference between MACCS and state of practice models for assessing tritium consequences. Additionally, information to assist an analyst in judging whether a postulated tritium release is likely to lead to significant doses is provided.