Publications

Physical Review Letters

Salt formations are one of the potential host rocks for the final disposal of high-level radioactive waste (HLW) in deep geological repositories, both in Germany and the United States. The safe isolation of radioactive waste in these repositories relies on a multi-barrier system, combining engineered and natural barriers. The natural barrier is provided by the salt rock itself, known for its self-sealing properties and long-term stability. The engineered barrier, on the other hand, comprises sealing components strategically placed within the repository to enhance its containment capabilities. In both Germany and the United States, long-term safety assessments require demonstrating the integrity of the natural barrier for a period of up to 1 million years. Concurrently, the engineered barrier system (EBS) must maintain its structural and functional integrity until the long-term sealing, such as the granular salt backfill material, has re-consolidated to its final low porosity and permeability. Based on extensive expertise and experience with engineered barriers in salt formations, BGE TECHNOLOGY GmbH and Sandia National Laboratories have partnered to develop a robust methodology for the integrity and performance assessment of EBS in HLW repositories through the RANGERS project. This collaborative effort aims to establish a unified approach to geotechnical engineering, repository design, integrity and performance evaluation of EBS in salt repositories.

Sandia National Labs will provide Technical Assistance and consulting to Requester on a series of Requester’s test structures and devices to characterize and aid in the determination of optimal fabrication parameters via fabrication of functional test structures and cells. Working with previously established processes and test die modules, Sandia will construct a series of test chips with varying materials properties to allow the electrical characterization and component testing of Requester’s test structures and devices. Material variations will be selected for optimization of yield and desired target operation parameters. A deliverable in the form of a report will be written. Sandia intends to deliver testable die to the Requester.

The Human Readiness Level (HRL) scale is a simple nine-level scale that brings structure and consistency to the real-world application of user-centered design. It enables multidisciplinary consideration of human-focused elements during the system development process. Use of the standardized set of questions comprising the HRL scale results in a single human readiness number that communicates system readiness for human use. The Human Views (HVs) are part of an architecture framework that provides a repository for human-focused system information that can be used during system development to support the evaluation of HRL levels. This paper illustrates how HRLs and HVs can be used in combination to support user-centered design processes. A real-world example for a U.S. Army software modernization program is described to demonstrate application of HRLs and HVs in the context of user-centered design.

Analyzing X-ray radiographs is crucial for understanding target behavior in Inertial Confinement Fusion (ICF) and High Energy Density (HED) platforms. However, the density of Magneto Raleigh Taylor (MRT) bands and limitations of target materials often obscure relevant spike growth and density information. To address this issue, machine learning postprocessing techniques can be applied to remove darkened regions in radiography images. In this study, a novel method is presented for removing MRT darkened regions from z-target radiographs using a convolutional neural network (CNN). The CNN, consisting of six layers, treats the darkened regions as noise and employs a mixed loss function and end-to-end frameworks to suppress them while preserving sharpness. The six-layer architecture is designed to effectively learn features when provided with a larger volume of learning space. Each layer is optimized using a mixed loss function that combines a standard loss pixel approach with a multi-scaled structural similarity index loss, which considers luminance, contrast, and structure in local neighborhoods. This approach is particularly beneficial for capturing the stochastic structure of MRT limbs. Due to the limited availability of experimental data, training is conducted using synthetic target radiography from 3D Alegra simulations.

The rapid expansion of photovoltaic (PV) systems, particularly inverters, has introduced new cybersecurity challenges that threaten both local operations as well as the broader electrical grid’s stability. PV inverters, integrated into critical energy infrastructure are potential targets for cyber attacks due to vulnerabilities in firmware, remote access systems, and communication protocols. The Coordinated Vulnerability Disclosure (CVD) process, as defined by the Cybersecurity and Infrastructure Security Agency (CISA), provides a framework for identifying, reporting, and addressing these vulnerabilities in a transparent and collaborative manner. This report outlines the CVD process as it applies to PV systems, detailing the roles of key stakeholders, such as manufacturers, grid operators, and security researchers. The report also highlights specific challenges in managing vulnerabilities for new and legacy PV systems, which includes those introduced by insecure communications and third-party supply chain components. By adhering to the CVD process, the PV industry can mitigate cybersecurity risks, ensure regulatory compliance, and maintain consumer trust, while safeguarding the operational resilience of the energy grid. Ultimately, the effective coordination of vulnerability management is crucial for securing the future of PV systems within the critical electric grid infrastructure landscape.

The use of differential scanning calorimetry (DSC) to measure the thermal behavior of individual components and electrolyte/electrode combinations is common. However, here we focus on DSC tests on an anode, cathode, and electrolyte (ACE) component combination over a temperature range that includes many of the phase transitions and key reactions (i.e., to 500 °C) that contribute to thermal runaway. This method can help quantify the complex reaction network in a full cell, thereby informing potential safety issues. Here, we used DSC heat flow data from a solid-state Li0.43CoO2+C+PVDF | LLZO | Li metal ACE sample and its components to quantify key factors affecting results. We focused on three areas: (1) ACE sample preparation and assembly in DSC pans, (2) DSC measurement parameters, and (3) heat flow analysis. Key points include the choice of component ratios (e.g., commercially relevant N:P capacity ratio), the importance of conductive carbon and binder, type of pan used, DSC ramp rate, and integration method used when dealing with broad and overlapping exothermic peaks. This work deepens the scientific basis and best practices for obtaining heat flow data from ACE samples for early-stage evaluation of solid-state and beyond battery safety.

The valorization and dissolution of lignin using ionic liquids (ILs) is critical for developing sustainable biorefineries and a circular bioeconomy. This review aims to critically assess the current state of computational and machine learning methods for understanding and optimizing IL-based lignin dissolution and valorization processes reported since 2022. The paper examines various computational approaches, from quantum chemistry to machine learning, highlighting their strengths, limitations, and recent advances in predicting and optimizing lignin-IL interactions. Key themes include the challenges in accurately modeling lignin’s complex structure, the development of efficient screening methodologies for ionic liquids to enhance lignin dissolution and valorization processes, and the integration of machine learning with quantum calculations. These computational advances will drive progress in IL-based lignin valorization by providing deeper molecular-level insights and facilitating the rapid screening of novel IL-lignin systems.

Abstract not provided.

The H3D was characterized using GADRAS 19.3.5 using data sent by the IAEA. Everything related to the measurement setup was provided by the IAEA. The sources measured were detailed and the distance from the detector was also provided. The data sheets of the sources were used to determine the source activity at the time of calibration, as needed for characterization.

This paper presents a probabilistic model for various machine learning (ML) applications. While deep learning (DL) has produced state-of-the-art results in many domains, DL models are complex and over-parameterized, which leads to high uncertainty about what the model has learned, as well as its decision process. Further, DL models are not probabilistic, making reasoning about their output challenging. In contrast, the proposed model, referred to as Yet Another Discriminate Analysis(YADA), is less complex than other methods, is based on a mathematically rigorous foundation, and can be utilized for a wide variety of ML tasks including classification, explainability, and uncertainty quantification. YADA is thus competitive in most cases with many state-of-the-art DL models. Ideally, a probabilistic model would represent the full joint probability distribution of its features, but doing so is often computationally expensive and intractable. Hence, many probabilistic models assume that the features are either normally distributed, mutually independent, or both, which can severely limit their performance. YADA is an intermediate model that (1) captures the marginal distributions of each variable and the pairwise correlations between variables and (2) explicitly maps features to the space of multivariate Gaussian variables. Numerous mathematical properties of the YADA model can be derived, thereby improving the theoretic underpinnings of ML. Validation of the model can be statistically verified on new or held-out data using native properties of YADA. However, there are some engineering and practical challenges that we enumerate to make YADA more useful.

Machine-learning function representations such as neural networks have proven to be excellent constructs for constitutive modeling due to their flexibility to represent highly nonlinear data and their ability to incorporate constitutive constraints, which also allows them to generalize well to unseen data. In this work, we extend a polyconvex hyperelastic neural network framework to (isotropic) thermo-hyperelasticity by specifying the thermodynamic and material theoretic requirements for an expansion of the Helmholtz free energy expressed in terms of deformation invariants and temperature. Different formulations which a priori ensure polyconvexity with respect to deformation and concavity with respect to temperature are proposed and discussed. The physics-augmented neural networks are furthermore calibrated with a recently proposed sparsification algorithm that not only aims to fit the training data but also penalizes the number of active parameters, which prevents overfitting in the low data regime and promotes generalization. The performance of the proposed framework is demonstrated on synthetic data, which illustrate the expected thermomechanical phenomena, and existing temperature-dependent uniaxial tension and tension-torsion experimental datasets.

Abstract not provided.

Working together, Sandia National Laboratories, Southern California Edison (SCE) - an Investor-Owned Utility (IOU) - and the California Public Utilities Commission (CPUC) are studying how electric utilities can use equity and resilience metrics to help inform the prioritization and sequencing of resilience-driven infrastructure investments. To this end, this project evaluated “Social Burden,” an equitable resilience metric which measures the potential impact of disruptions in access to non-electric critical services on people and estimates community resilience to these disruptions. The Social Burden was expanded to incorporate SCE’s existing equity metric and applied to evaluate the potential impacts from a range of climate-informed hypothetical outage scenarios developed under SCE’s 2022 Climate Adaptation Vulnerability Assessment. One baseline (“blue-sky”) state and eight different outage scenarios were evaluated to measure the potential impacts of the outages on non-electric infrastructure, critical services, and people. Key findings include: 1) the Social Burden framework is flexible enough to adapt to and build upon existing utility equity and/or resilience metrics, 2) Social Burden results highlight the high degree of non-electric service redundancy within the SCE service area with most (6/8) hypothetical outage scenarios predicted to increase people’s Social Burden by less than 10%; however, 3) access to critical services and people’s ability to obtain them is unequal and spatially clustered, meaning that there are some hypothetical outage scenarios (2/8) that will exert a higher toll on communities directly experiencing the outage as well as some nearby communities with pre-existing vulnerabilities. The report concludes with recommendations for potential use cases of the expanded Social Burden metric and identifies priority follow-on work. Potential use cases may include incorporating equity into IOU’s prioritization of climate resilience investments. Additionally, Social Burden analysis may provide additional data and insights to augment grid planning, potentially by identifying additional needs and/or prioritizing previously identified needs.

Next-generation space remote sensing systems may be equipped with imaging arrays that sense data at a rate that outstrips the processing capability of any computing hardware that can operate within a satellite’s power budget. This project developed novel convolutional and recurrent neural networks to detect and estimate point-like events amid clutter, and investigated their efficient and accurate implementation on analog in-memory computing systems that are 10-1000× more energy-efficient than digital processors. This project leveraged two memory devices at different levels of technological maturity: a large-scale analog computing prototype using commercial SONOS charge-trap memory, and electrochemical memory (ECRAM) with intrinsic radiation hardness. We experimentally demonstrated end-to-end analog processing of our neural networks on SONOS and characterized the radiation response of both SONOS and ECRAM. We advanced the state-of-the-art in ECRAM precision and reliability, and developed co-design methods to enable accurate long-term operation of SONOS analog accelerators in space radiation environments.

Abstract not provided.

Hydrogen-powered locomotives may present an alternative to diesel for achieving transportation-related decarbonization, energy security, and resilience goals. As an emerging technology, hydrogen locomotives may not have adequate representation in current safety codes and standards. NFPA 2 and other appropriate codes and standards were reviewed to identify technical gaps in the code for hydrogen rail defueling, refueling, maintenance, and storage. Several technical gaps pertaining to setback distances, allowable quantities, ventilation rates, and grounding have been identified. Fueling requirements can be informed by road vehicle standards but may need to be revised for larger quantity hydrogen locomotive systems. Since there are no hydrogen-specific locomotive design and safety standards, some aspects of diesel-based locomotive standards may apply. CGA G- 5.5 currently provides the most guidance on height, placement, orientation, and design of vent systems, while other standards emphasize the importance of shielding vent stacks and provide requirements for orientation of their discharge.

Pozzolans rich in silica and alumina react with lime to form cementing compounds and are incorporated into portland cement as supplementary cementitious materials (SCMs). However, pozzolanic reactions progress slower than portland cement hydration, limiting their use in modern construction due to insufficient early-age strength. Hence, alternative SCMs that enable faster pozzolanic reactions are necessary including synthetic zeolites, which have high surface areas and compositional purity that indicate the possibility of rapid pozzolanic reactivity. Synthetic zeolites with varying cation composition (Na-zeolite, H-zeolite), SiO2/Al2O3 ratio, and framework type were evaluated for pozzolanic reactivity via Ca(OH)2 consumption using ion exchange and in-situ X-ray diffraction experiments. Na-zeolites exhibited limited exchange reactions with KOH and Ca(OH)2 due to the occupancy of acid sites by Na+ and hydroxyl groups. Meanwhile, H-zeolites readily adsorbed K+ and Ca2+ from a hydroxide solution by exchanging cations with H+ at Brønsted acid sites or cation adsorption at vacant acid sites. By adsorbing cations, the H-zeolite reduced the pH and increased Ca2+ solubility to promote pozzolanic reactions in a system where Ca(OH)2 dissolution/diffusion was a rate limiting factor. High H-zeolite reactivity resulted in 0.8 g of Ca(OH)2 consumed per 1 g of zeolites after 16 h of reaction versus 0.4 g of Ca(OH)2 consumed per 1 g of Na-zeolite. The H-zeolite modulated the pore fluid alkalinity and created a low-density amorphous silicate phase via mechanisms analogous to two-step C-S-H nucleation experiments. Controlling these reaction mechanisms is key to developing next generation pozzolanic cementitious systems with comparable hydration rates to portland cement.

This document serves as a comprehensive final report for the Single-Volume Scatter Camera (SVSC) project. Most of the work over the course of this project has been documented in journal articles, conference papers, and other reports. We therefore reference those materials for detailed presentation of our technical results. The most recent efforts on the project have not been published and are presented in detail here. We present characterizations of two neutron scatter camera prototypes; one using a monolithic geometry, and one using an optically segmented geometry. Both detectors employ plastic scintillator with SiPM-based readout. For the monolithic prototype, we present calibrations of several detector parameters made using a set of dark counts. In particular, we employ a coincidence analysis to characterize the distribution external optical crosstalk, along with the relative timing of the different SiPMs. For the optically segmented prototype we present the results of calibrations made using a tagged ²²Na source, as well as the results of an imaging measurement made using a tagged AmBe source.

The accurate modeling of electromagnetic penetration is an important topic in computational electromagnetics. Electromagnetic penetration occurs through intentional or inadvertent openings in an otherwise closed electromagnetic scatterer, which prevent the contents from being fully shielded from external fields. To efficiently model electromagnetic penetration, aperture or slot models can be used with surface integral equations to solve Maxwell's equations. A necessary step towards establishing the credibility of these models is to assess the correctness of the implementation of the underlying numerical methods through code verification. Surface integral equations and slot models yield multiple interacting sources of numerical error and 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 electric-field integral equation with a slot model. We demonstrate the effectiveness of these approaches for a variety of cases.

A new tool has been developed to perform variance-based global sensitivity analysis (VBGSA) on data from a set-based concurrent engineering software called Success Assured (SA). The tool is part of a digital component design app, which is currently in production as an Accelerated Digital Engineering Pathfinder at Sandia National Laboratories. When working with complex digital models, it is important to understand relationships between inputs and outputs, i.e., how “sensitive” model outputs are to changes in model inputs. After extensive research and trials of various sensitivity analysis methods, it was determined that estimation of Sobol’ indices for VBGSA with Monte Carlo simulation, paired with simple surrogate models, produces the best results for SA data. This tool increases understanding of SA models and streamlines the creation of SA datasets. This report details the methodology and implementation of this sensitivity analysis tool so others can understand it and implement it.

Biaxial stress is identified to play an important role in the polar orthorhombic phase stability in hafnium oxide-based ferroelectric thin films. However, the stress state during various stages of wake-up has not yet been quantified. In this work, the stress evolution with field cycling in hafnium zirconium oxide capacitors is evaluated. The remanent polarization of a 20 nm thick hafnium zirconium oxide thin film increases from 9.80 to 15.0 µC cm−2 following 106 field cycles. This increase in remanent polarization is accompanied by a decrease in relative permittivity that indicates that a phase transformation has occurred. The presence of a phase transformation is supported by nano-Fourier transform infrared spectroscopy measurements and scanning transmission electron microscopy that show an increase in ferroelectric phase content following wake-up. The stress of individual devices field cycled between pristine and 106 cycles is quantified using the sin2(ψ) technique, and the biaxial stress is observed to decrease from 4.3 ± 0.2 to 3.2 ± 0.3 GPa. The decrease in stress is attributed, in part, to a phase transformation from the antipolar Pbca phase to the ferroelectric Pca21 phase. This work provides new insight into the mechanisms controlling and/or accompanying polarization wake-up in hafnium oxide-based ferroelectrics.

Abstract not provided.

Distributed Acoustic Sensing (DAS) can record acoustic wavefields at high sampling rates and with dense spatial resolution difficult to achieve with seismometers. Using optical scattering induced by cable deformation, DAS can record strain fields with spatial resolution of a few meters. However, many experiments utilizing DAS have relied on unused, dark telecommunication fibers. As a result, the geophysical community has not fully explored DAS survey parameters to characterize the ideal array design. This limits our understanding of guiding principles in array design to deploy DAS effectively and efficiently in the field. A better quantitative understanding of DAS array behavior can improve the quality of the data recorded by guiding the DAS array design. Here we use steered response functions, which account for DAS fiber’s directional sensitivity, as well as beamforming and back-projection results from forward modelling calculations to assess the performance of varying DAS array geometries to record regional and local sources. A regular heptagon DAS array demonstrated improved capabilities for recording regional sources over other polygonal arrays, with potential improvements in recording and locating local sources. These results help reveal DAS array performance as a function of geometry and can guide future DAS deployments.

Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In this paper, we introduce UM-Bridge (the UQ and Modeling Bridge), a high-level abstraction and software protocol that facilitates universal interoperability of UQ software with simulation codes. It breaks down the technical complexity of advanced UQ applications and enables separation of concerns between experts. UM-Bridge democratizes UQ by allowing effective interdisciplinary collaboration, accelerating the development of advanced UQ methods, and making it easy to perform UQ analyses from prototype to High Performance Computing (HPC) scale. In addition, we present a library of ready-to-run UQ benchmark problems, all easily accessible through UM-Bridge. These benchmarks support UQ methodology research, enabling reproducible performance comparisons. We demonstrate UM-Bridge with several scientific applications, harnessing HPC resources even using UQ codes not designed with HPC support.