Publications

Abstract not provided.

The vast chemical space of high entropy alloys (HEAs) makes trial-and-error experimental approaches for materials discovery intractable and often necessitates data-driven and/or first principles computational insights to successfully target materials with desired properties. In the context of materials discovery for hydrogen storage applications, a theoretical prediction-experimental validation approach can vastly accelerate the search for substitution strategies to destabilize high-capacity hydrides based on benchmark HEAs, e.g. TiVNbCr alloys. Here, machine learning predictions, corroborated by density functional theory calculations, predict substantial hydride destabilization with increasing substitution of earth-abundant Fe content in the (TiVNb)75Cr25-xFex system. The as-prepared alloys crystallize in a single-phase bcc lattice for limited Fe content x < 7, while larger Fe content favors the formation of a secondary C14 Laves phase intermetallic. Short range order for alloys with x < 7 can be well described by a random distribution of atoms within the bcc lattice without lattice distortion. Hydrogen absorption experiments performed on selected alloys validate the predicted thermodynamic destabilization of the corresponding fcc hydrides and demonstrate promising lifecycle performance through reversible absorption/desorption. This demonstrates the potential of computationally expedited hydride discovery and points to further opportunities for optimizing bcc alloy ↔ fcc hydrides for practical hydrogen storage applications.

Abstract not provided.

This project helped address the growing need for efficient and scalable models to support geological carbon and energy storage, which are crucial for achieving net-zero emissions. Traditionally accurate high-fidelity numerical models have been used to simulate relevant storage processes under a handful of processes, however such models are computationally demanding, making uncertainty quantification impractical. Consequently, we first developed a machine learning framework, based on Graph Neural Operators (GNOs), to improving the accuracy of model predictions for a fixed computational budget. We then developed an Ensemble of Improved Neural Operators (ENO), which uses bagging and Monte Carlo dropout techniques, to further improve prediction accuracy. Lastly, we developed the way to explain progressive transfer learning methods to reduce the amount of training data and computational cost of training (i.e., reduce trainable parameters) when using our models for multiple storage sites. Our numerical investigation, which used real-world case studies, demonstrated that our framework can significantly improve the safety and efficiency of geological storage operations, with potential applications in other domains such as geothermal reservoirs and climate modeling.

For acceptable implementations of technologies like wireless communications, remote monitoring, etc., strong mitigations must be developed and evaluated to ensure that new attack pathways do not increase risk for Advanced Reactors. Secure Elements can be adopted and adapted for this purpose based on tamper resistance and cryptographic abilities, but research must be done to properly integrate into critical components such as FPGA-based Important to Safety systems in conjunction with current and future regulations on cyber security features in Advanced Reactors. Typically, the integration of a Secure Element happens during the POST and UEFI boot of a computing platform, performed by the Operating System, which is not possible with FPGAs because they do not include these firmware components. Work must be done to identify a reliable and secure method for integration in FPGA-based systems which lack Operating Systems and therefore complex boot procedures, system calls, etc.

Abstract not provided.

In this study, we present a method for acquiring and characterizing novel microbial consortia that regulates methanogens and methanotrophs through selective cultivation and metagenomic analysis of indigenous microorganisms in the environment. In addition, we present the work performed as part of this project to model the pathways that act as limiting factors in microbial methane metabolism based on a carbon cycle model. In this report, we describe the methods for selective cultivation of methane-metabolism-related microorganisms from environmental samples, the method for monitoring their methane consumption performance, and the method and results for verifying their functions using quantitative PCR and metagenomics techniques. The microbial consortia containing methanotrophs were obtained through selective cultivation and molecular biological verification, and their methane consumption performance was evaluated. In addition, the potential of the existence of bacteriophages interacting with methane metabolism-related microorganisms was identified through metagenomic sequencing.

Numerical linear algebra (NLA) underpins huge swaths of computational science and engineering. For scientists and engineers to make the most of the DOE’s computing resources, it is essential that they have access to high-performance implementations of algorithms with best-in-class scalability and reliability. Despite this, prevailing NLA libraries have little to no support for breakthrough algorithms from the field of randomized numerical linear algebra (RandNLA) that have been developed over the past twenty years. The goal of this LDRD was to break a log-jam that had prevented broad adoption of RandNLA. Our work had two thrusts. The first was to develop RandBLAS: a trustworthy and high-performance C++ library for randomized dimension reduction (an operation widely known as sketching). The second was the development of a novel randomized algorithm for computing a challenging type of matrix decomposition known as Householder QR with column pivoting (Householder QRCP). In this one-year late-start LDRD we successfully delivered RandBLAS 1.0 and new CPU and GPU codes for Householder QRCP. RandBLAS has extensive documentation at https://randblas.readthedocs.io/en/stable/. Papers on RandBLAS and and our high-performance QRCP codes are forthcoming.

Abstract not provided.

Abstract not provided.

Research on infrastructure resilience has produced promising methods to simulate and optimize complex networks to improve performance. However, restrictions on sharing infrastructure models and the steep cost of developing and maintaining infrastructure models presents a roadblock to adoption. To overcome this limitation, this research focuses on methods to create data-driven infrastructure models that will help improve infrastructure resilience and security. The analysis couples incomplete utility data, geospatial data, machine learning, and synthetic network generation methods to rapidly develop and update infrastructure models. The methods are validated using realistic utility models and site-specific data, with a focus on Puerto Rico due to its unique infrastructure challenges and available data. This research highlights promising opportunities for the use of synthetic network generation and machine learning to create infrastructure models when very little data is available. Results demonstrate that hybrid methods, which combine sparse utility data with synthetic models, can enhance model accuracy, and machine learning can predict model attributes using training data from other models. However, the complexity of infrastructure systems means that even minor changes in network connectivity can significantly impact simulation results. Resilience analysis using synthetic infrastructure models shows that while some system behaviors are preserved, the magnitude of disruptions may not be accurately represented, indicating the need for more research and validation before using synthetic models for critical infrastructure investment decisions. The framework outlined in this report represents a significant advance to infrastructure model development and could be applied to additional domains and sites. Future research will continue to streamline and validate methods to help reduce roadblocks to resilience analysis.

Increasing the fluence of z-pinch x-ray radiation sources above ∼ 10 keV has been a long-standing goal for scientists at Sandia National Laboratories’ Z Machine. Optimizing sources for non-thermal “cold Kα” emission in higher atomic-number materials appears to be a promising path to increase warm x-ray yield. However, this emission is generated by supra-thermal electrons, which are not treated in the magnetohydrodynamic (MHD) codes that are typically used in z-pinch source development. MHD codes do not allow for charge separation or space-charge-generated electric fields, and constrain particle kinematics to Maxwellian distributions. The kinetic codes which do accommodate discrete, non-thermal energy distributions are computationally prohibitive when modeling plasmas near solid density and when modeling/tracking higher ionization states. Thus, modeling non-thermal z-pinch sources requires a new simulation tool. In this report, we present a new hybrid modeling capability that uses the fast features of MHD-type particles to the greatest extent possible, then transitions to the slower but more complete kinetic particle treatment to correctly capture the particle energy spectra that generate non-thermal emission. This capability is founded on the fully-relativistic particle-in-cell code Chicago, which already includes fluid particle treatments. The governing equations and hybrid methodology presented here are applied in simulations of an argon gas-puff and a molybdenum wire-array to provide preliminary code validation. The argon simulation is compared to measured implosion times and yields from Jones et al., Phys. Plasmas 22, 020706 (2015). The simulated x-ray yield is within 25% of measurements and the implosion times agree within a few percent. The molybdenum wire array simulation captures the implosion timing reported in Hansen et al., Phys. Plasmas 21, 031202 (2014), but work is needed to verify the available EOS table. These exemplar simulations represents the type of non-thermal sources that will be developed using the hybrid code capability going forward.

For multi-scale multi-physics applications e.g., the turbulent combustion code Pele, robust and accurate dimensionality reduction is crucial to solving problems at exascale and beyond. A recently developed technique, Co-Kurtosis based Principal Component Analysis (CoK-PCA) which leverages principal vectors of co-kurtosis, is a promising alternative to traditional PCA for complex chemical systems. To improve the effectiveness of this approach, we employ Artificial Neural Networks for reconstructing thermo-chemical scalars, species production rates, and overall heat release rates corresponding to the full state space. Our focus is on bolstering confidence in this deep learning based non-linear reconstruction through Uncertainty Quantification (UQ) and Sensitivity Analysis (SA). UQ involves quantifying uncertainties in inputs and outputs, while SA identifies influential inputs. One of the noteworthy challenges is the computational expense inherent in both endeavors. To address this, we employ the Monte Carlo methods to effectively quantify and propagate uncertainties in our reduced spaces while managing computational demands. Our research carries profound implications not only for the realm of combustion modeling but also for a broader audience in UQ. By showcasing the reliability and robustness of CoK-PCA in dimensionality reduction and deep learning predictions, we empower researchers and decision-makers to navigate complex combustion systems with greater confidence.

Abstract not provided.

Poster presentation submitted to the applied superconductivity conference 2024 to be held in Salt Lake City, Utah.

This report summarizes the work performed under the author's two-year John von Neumann LDRD project, which involves the non-intrusive surrogate modeling of dynamical systems with remarkable structural properties. After a brief introduction to the topic, technical accomplishments and project metrics are reviewed including peer-reviewed publications, software releases, external presentations and colloquia, as well as organized conference sessions and minisymposia. The report concludes with a summary of ongoing projects and collaborations which utilize the results of this work.

Achieving maximum performance in a Z-pinch Magnetized Liner Inertial Fusion (MagLIF) implosion requires high levels of symmetry to reach high pressures, high areal densities, and high efficiency energy coupling between the compressed fuel and the imploding shell. In this work, we present the development of a nonlinear model to describe an asymmetric MagLIF liner undergoing magneto-Rayleigh-Taylor (MRT) instabilities in the z = 0 plane. We investigate and present results from our model that show the degradation of the pressure at stagnation under various kinds of asymmetries that can arise during MagLIF experiments.

Plenary lightning talk for DOE ASCR BRN workshop on analog computing

Piezoelectric materials are used as a power source in stockpile components for safety and reliability assurances. The objective of this project is to gain insights into the fundamental pressure-induced behavior and electromechanical response of lead zirconate titanate (PZT). Specifically, to establish the basis for an accurate, physics-based material model for robust model-based design to rapidly optimize PZT-based materials and components for performance studies. Established ab-initio methods are used to interrogate and understand the dynamic behavior of PZT as a function of composition (50/50, 65/35, 80/20, 95/5) and dopants (La, Nb) to overcome the costly and time-consuming experimental methodologies. New cold curves for pure and doped single-crystal PZT are obtained as the reference equation-of-state (EOS). A negligible change in the pressure responses was observed for the systems and strains studied. Dielectric and piezoelectric responses of pure and doped single-crystal PZT were also calculated as a function of pressure. For undoped PZT, there is a clear and orderly pressure and composition dependence. For doped PZT, there is a significant increase in the responses, but the behavior is very disordered and inconclusive.

Abstract not provided.

Radial basis functions (RBFs) are a powerful tool for constructing high-order accurate reduced representations of scattered data in arbitrary dimension and on manifolds. We present a method of constructing data approximations in which we utilize a functional tail to capture a global background profile and a RBF neural network (NN) to capture the smaller-scale features. In the RBF NN the RBF centers, matrix shape parameters were selected adaptively for each RBF. We also utilized a geodesic notion of distance on the manifold on which the data lies, e.g., the spherical geodesic for data on the sphere. Although each of these ideas have been been investigated separately in previous works, their combination into a single algorithm is novel. We defined a machine learning problem in which these properties are learned to minimize the data reduction error. We demonstrate the algorithm for applications of scattered data reduction in the plane and on the sphere.

Abstract not provided.

This project focused on developing topology optimization software to design advanced metal thermal insulators. Initially, solid designs were created that matched the thermal performance of current baseline designs but were significantly heavier. To address this, cellular materials were incorporated, specifically the octet structure which is known for its high strength-to-weight ratio and thermal properties. By leveraging these cellular designs at various densities, superior thermal and mechanical performance was achieved without added weight. This novel approach enhances thermal management and structural integrity under extreme conditions, offering promising advancements for thermal protection systems.

Understanding the age of semiconductor parts being built into devices and systems is of interest for manufacturing quality control. Power spectrum analysis (PSA) is a fast, non-destructive, sensitive method for examining semiconductor parts. This talk will cover the use of multivariate analysis on both PSA data and conventional current-voltage data generated prior to PSA analysis to create algorithms that can be automated to screen semiconductor parts for aging.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Certain regulatory actions under 10 CFR Parts 50, 52, or the proposed part 53 require the assessment of the potential off-site consequence risks to public safety and the environment from a hypothetical severe accident. As an important part of these analyses, atmospheric transport and dispersion (ATD) modeling relies heavily on the prevailing weather patterns of a site. When considering future deployment of new reactor designs in areas where historical onsite meteorological data is not available, there exists a need to assess the potential range of severe accident consequences using synthetic weather data. MACCS is designed to estimate consequence measures such as air concentrations, ground depositions, areas under deposition isopleths, radiological doses, and health and economic impacts on an annual-averaged basis by using inputs such as population and site-specific meteorological data. The objective of this work is to investigate the feasibility of supplementing observed site specific weather files with synthetic weather data to calculate these consequence measures. Understanding the probabilistic consequences estimated using synthetic data compared to site-specific observational meteorological data may lead to more agility and flexibility in performing calculation for future situations. This report details the results of research tasks associated with this goal. First, a review was performed to compile a list of potential site-specific meteorological data sources and evaluate their compatibility with MACCS. After selecting a subset of meteorological data, observed and synthetic MACCS weather files were developed for four regions with distinct weather and terrain conditions (Alaskan coastal, large river valley, flat terrain, and gulf coast). Consequence parameters, such as peak time-integrated air and ground concentrations and area under deposition isopleths as a function of distance, were determined for each site and meteorological data set using two distinct source terms. The results demonstrate that use of synthetic meteorological datasets result in consequence values with differences of less than a factor of two from site-specific observational data. The comparisons provided herein can provide decision-makers with additional insight to help evaluate the potential benefit of using national modeled weather data, especially in instances where site specific data over multiple previous years may not be available. The results of these analyses are provided and discussed in this report.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

This report introduces partitioned communication, a new MPI 4.0 interface that enables early bird communication by overlapping communication and computation. By partitioning messages into smaller sub-messages, MPI can start partial data transfers early. Performance studies show that the RMA implementation outperforms the Persistent implementation, despite some constraints. This report details a new opt-in RMA implementation, offering a high-performance option for partitioned communication that imposes some additional limitations.

Current small modular reactor (SMR) deployment use cases consider both rural and urban deployments, depending on the operational in-country needs for clean and reliable sources of energy. Many studies have been conducted analyzing security in rural and remote deployment locations, but this study looks at the physical security implications of an SMR placed in an urban environment and its uses for electricity production, district heating, and process heating. SMRs used for electricity production, district heating, and process heating may be key sources of both energy infrastructure and commercial infrastructure within a city and a State. As a result, long-term shutdowns could have a serious impact on a State’s overall energy or commercial production. Therefore, operators may consider further security applications to protect an SMR plant from physical attacks against both radiological sabotage and sabotage acts that could result in the SMR facility being offline for a significant amount of time. In this study, the team designed and analyzed a physical protection system (PPS) for securing an urban SMR facility against acts of radiological sabotage and sabotage acts that could disrupt the facility’s long-term operation. Additionally, this work analyzed the nuanced security issues related to siting an SMR near an urban environment (versus in a rural environment). The result of these analyses includes recommendations for PPSs for urban SMR facilities used for energy production, district heating, and process heating.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Afterglow in x-ray imaging for high-speed radiography is a constraint that limits imaging systems to low-light/fast decay screens which create poor data. Current approaches focus purely on using low-light yield screens with fast decay to avoid multiple exposure pileup due to afterglow. The goal of this work is to develop a statistical estimation approach to leverage afterglow to improve image quality thus allowing for higher quality imaging components to be used. This will allow for bright screens will slow decay to be used, and then a post-processing step applies the statistical estimation to separate each frame with superior signal compared to low-light/fast decay screens.

Abstract not provided.

The Reynolds-averaged Navier–Stokes (RANS) equations remain a workhorse technology for simulating compressible fluid flows of practical interest. Due to model-form errors, however, RANS models can yield erroneous predictions that preclude their use on mission-critical problems. This report summarizes work performed from FY22-FY24 focused on improving RANS models for hypersonic flows using data-driven modeling and scientific machine learning. In this work we: 1. Investigate the current capabilities of RANS models in Sandia’s parallel aerodynamics and re-entry code (SPARC) for hypersonic flows with a focus on shock boundary layer interactions (SBLIs), 2. Assess several established corrections that exist in the literature aimed at improving predictions for SBLIs, 3. Develop improved models for the Reynolds stress tensor using tensor-basis neural networks, 4. Develop a neural-network-based variable turbulent Prandtl number model to reduce errors in wall heating in SBLIs. 5. Begin future investigations including employing the LIFE framework to improve wall heating predictions in SBLIs as well as the ensemble Kalman filter. We find that current RANS models in SPARC are deficient for complex SBLI flows. In particular, no current model jointly predicts wall heat flux, wall shear stress, and wall pressure with reasonable accuracy. Existing corrections help, but do not alleviate this issue altogether. The development of improved models for the Reynolds stress tensor via tensor-basis neural networks results in more predictive RANS models across a suite of low-speed and high-speed cases. For hypersonic boundary layers, the inclusion of the wall-normal Reynolds stress via TBNNs has an appreciable impact on the wall-normal momentum balance and wall quantities. However, we find that improvements to the Reynolds stress tensor do not address the over-prediction in wall heat flux in SBLIs. We find that a neural-network-based variable turbulent Prandtl number model systematically and substantially improves wall heating predictions for a range of SBLI cases.

Review of how we use particle beams to support our radiation effects science mission

The Gamma Detector Response and Analysis Software (GADRAS) package includes an inverse modeling tool that is helpful in identifying characteristics of unknown radioactive materials. Traditionally, uncertainties in this analysis were derived solely from measurement data quality and the fit of synthetic spectra. This paper aims to rigorously quantify additional sources of uncertainty, focusing on uncertainties arising from measurements being analyzed, Detector Response Function (DRF) characterization, and DRF extrapolation. Applying these findings to the BeRPBall benchmark data set, we demonstrated the impact of these uncertainties on plutonium and polyethylene estimates. The results underscore the importance of incorporating diverse uncertainty sources to enhance the accuracy and reliability of GADRAS’s inverse modeling capabilities.

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

Abstract not provided.

The DAHCS (pronounced “Dax”) MC is a 7-year, $\$$45 million research portfolio within Sandia’s Laboratory Directed Research and Development program. The DAHCS MC arose in response to a great need: to ensure that the use of digital technologies does not weaken our nation’s high consequence systems. Digital technologies offer many benefits in speed, cost, and flexibility, and we seek to reap those benefits without introducing new system failures. However, digital technologies cannot be evaluated the same way as analog technologies. Initiatives across the nation highlight the capability gap that prevents efficient, effective digital assurance. The Challenge Today’s digital assurance tools, techniques, and methods are inadequate to confidently characterize, assess, and manage digital risk; they are ad hoc, slow, costly, and rarely scalable to increasingly complex digital technologies. The rapidly evolving cyber threat landscape exacerbates this problem because digital assurance now must secure against digital risks now and in the future, including those introduced by rapidly evolving technologies, adversaries, and systems.

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

The talk covers: * Quick orientation around AI * Quick review of relevant domains of Cybersecurity * The promise of AI in cybersecurity – applications * Discussion of the state of technology today, referencing Gartner Hype Cycle diagram * The peril of AI for cybersecurity – threats and risks * A roadmap for where to go from here, emphasizing a trained workforce, referencing published (ISC)^2 survey results

Presenting project which is under development jointly with LBL

Abstract not provided.

Abstract not provided.

This report summarizes the work performed under the author's two-year John von Neumann LDRD project, which involves the non-intrusive surrogate modeling of dynamical systems with remarkable structural properties. After a brief introduction to the topic, technical accomplishments and project metrics are reviewed including peer-reviewed publications, software releases, external presentations and colloquia, as well as organized conference sessions and minisymposia. The report concludes with a summary of ongoing projects and collaborations which utilize the results of this work.

Helium-4-based scintillation detector technology is emerging as a strong alternative to pulse-shape discrimination-capable organic scintillators for fast neutron detection and spectroscopy, particularly in extreme gamma-ray environments. The 4He detector is intrinsically insensitive to gamma radiation, as it has a relatively low cross-section for gamma-ray interactions, and the stopping power of electrons in the 4He medium is low compared to that of 4He recoil nuclei. Consequently, gamma rays can be discriminated by simple energy deposition thresholding instead of the more complex pulse shape analysis. The energy resolution of 4He scintillation detectors has not yet been well-characterized over a broad range of energy depositions, which limits the ability to deconvolve the source spectra. In this work, an experiment was performed to characterize the response of an Arktis S670 4He detector to nuclear recoils up to 9 MeV. The 4He detector was positioned in the center of a semicircular array of organic scintillation detectors operated in coincidence. Deuterium–deuterium and deuterium–tritium neutron generators provided monoenergetic neutrons, yielding geometrically constrained nuclear recoils ranging from 0.0925 to 8.87 MeV. The detector response provides evidence for scintillation linearity beyond the previously reported energy range. The measured response was used to develop an energy resolution function applicable to this energy range for use in high-fidelity detector simulations needed by future applications.

BeyondFingerprinting was a 2021-2024 Sandia Grand Challenge LDRD exploring the potential to develop new resilient materials and manufacturing processes by taking an artificial-intelligence (AI)-guided approach that integrates human-subject-matter expertise with algorithms enriched with physics-based constraints to unearth process-structure-property correlations. Such algorithms, trained on high-throughput experiments and simulations, are shown to serve as surrogate models that efficiently detect key “fingerprints” in materials data, prognose material performance, and guide effective process improvements. To accelerate broader adoption across mission areas, this AI-guided approach was demonstrated with three complex process-centric exemplars: electroplating, physical vapor deposition, and laser powder bed fusion. Together, these exemplars impact nearly every hardware component relevant to DOE and NNSA national security missions.

The current work sought to electrodeposit tungsten from water-based solutions. The work’s initial hypothesis was that methoxide reducing agents could be used to generate urea anions that could enable tungsten electrodeposition. Unfortunately, this hypothesis was found to be incorrect. However, the work led to the understanding that chemical reducing agents not only enable the electrodeposition of refractory metals like rhenium from water-based solutions, but that the key to plating tungsten in the future involves chemical reduction followed by stabilization with proper ligands. The work resulted in a manuscript under review at Inorganic Chemistry Communications on the discovered of L-histidine as a suitable reducing agent from rhenium electrodeposition. Rhenium-tungsten alloys with 4% tungsten were deposited. A technical advance was filed for the rhenium chemistry and parts were delivered to an internal Sandia customer that used the chemistry.

Abstract not provided.

The reaction products for the proposed chemistry for phosgene treatment in the Explosive Destruction System (EDS) are viscous. This will affect the time required to drain the EDS vessel. Viscosity measurements were made on two samples and the results for one sample have been fit to curves.

Abstract not provided.

Abstract not provided.

Abstract not provided.

In several mission contexts, it is desirable to estimate the performance of large language models (LLMs) on tasks that we cannot run directly. In light of published “scaling laws” our hypothesis is that some tasks should be consistently more challenging than others based on characteristics of the task. The goal of this project was to begin quantifying how much information about LLM performance can be gained from the features of a model and a task. Two of our statistical models struggled to converge. Pass/fail test results may provide limited information for inference beyond model quality and task difficulty, but we see no evidence at this time for significant feature interaction effect sizes, arguing for simple models. Future work extending the models to capitalize on perplexity of ground truth answers is suggested. This project also introduces “Depth of Knowledge Variant Testing” as a strategy for more finely assessing language models on open domain question and answer tasks. We developed sets of questions that ask a language model to produce similar information while demonstrating increasing depth of knowledge, and also relabeled existing Q&A test questions with their depth of knowledge. Our results suggest further consideration of Bloom’s taxonomy and further refinement of prompts to properly elicit information at varying depths. In the course of this work, we set up a basic infrastructure for standardizing tasks and testing many language models on these tasks. In addition to testing the predictive quality of model features and performance across test suites, with this project we have introduced two new task features to contextualize each test question: the Dewey Classification main category of information covered, and the Bloom’s taxonomy level that corresponds to the depth of knowledge probed by the question. Splits across these and other features produced over five hundred task subtypes with distinct feature vectors, which we tested on half a dozen models.

Surface ion traps are a promising platform for quantum computing due to their potential to store large numbers of ions that can be addressed by electrical and optical control signals in order to implement quantum algorithms. Increasing the power of the quantum computer requires increasing the number of ions, but this poses a significant challenge in that it leads to a non-linear increase in on-chip power dissipation. The primary contributor to this power scaling in current devices is the capacitance between the radio frequency (RF) electrode and the metal plane that shields the silicon substrate from the RF signals applied to it. Silicon has traditionally been chosen for the substrate material for compatibility with the processing required for multi-metal-level traps. In this work, we address these capacitance and fabrication challenges by replacing the commonly used silicon substrate with an insulating sapphire substrate to fabricate a multi-metal-level ion trap, while still employing common semiconductor manufacturing techniques. This change in substrate allows the design to remove the metal shielding from the device design, reducing the capacitance of the RF electrode. The electrical characteristics of these traps were measured, specifically trap impedance, capacitance, and voltage breakdown, and compared to nearly identical silicon trap devices. Finally, we used laser cutting techniques to shape a sapphire wafer into bowtie shapes matching silicon traps previously fabricated at Sandia National Labs to explore solutions for integrating sapphire substrates into non-rectangular ion trap designs.

Abstract not provided.

Abstract not provided.

In this study, different approaches in performance assessment (PA) of the long-term safety of a repository for radioactive waste were examined. This investigation was carried out as part of the DECOVALEX-2023 project, an international collaborative effort for research and model comparison. One specific task of the DECOVALEX-2023 project was the Salt Performance Assessment Modelling task (Salt PA), which aimed at comparing various models and methods employed in the performance assessment of deep geological repositories in salt. In the context of the Salt PA task, three distinct teams from SNL (United States), Quintessa Ltd (United Kingdom), and GRS (Germany) examined the consequences of employing different levels of abstractions when modelling the repository's geometry and implementing various features and processes, using the example of a simple hypothetical repository structure in domal salt. Each team applied their own tools: PFLOTRAN (SNL), QPAC (Quintessa) and LOPOS (GRS). These differ essentially regarding numerical concept and degree of detail in the representation of the underlying physical processes. The discussion focused on when simplifications can be appropriately applied and what consequences result from them. Furthermore, it was explored when and if a higher level of fidelity in geometry or physical processes is required.

For computational physics simulations, code verification plays a major role in establishing the credibility of the results by assessing the correctness of the implementation of the underlying numerical methods. In computational electromagnetics, surface integral equations, such as the method-of-moments implementations of the electric-, magnetic-, and combinedfield integral equations, are frequently used to solve Maxwell’s equations on the surfaces of electromagnetic scatterers. These electromagnetic surface integral equations yield many code-verification challenges due to the various sources of numerical error and their possible interactions. In this report, we provide approaches to separately measure the numerical errors arising from these different error sources. We demonstrate the effectiveness of these approaches in Gemma.

Abstract not provided.

The Sea Change Project, managed and financed by SWITCH Maritime, produced the first zero-emission 100% hydrogen fuel-cell powered commercial ferry in the world. The MV Sea Change is a 70-foot catamaran equipped with a 360-kW fuel cell powertrain and a 250-bar 246 kg capacity hydrogen storage system. The 75-passenger vessel began public passenger service in July 2024 as part of the San Francisco Bay Ferry system. Summarized here are aspects of the design of the vessel, its construction, and vessel bunkering with high-pressure hydrogen. Results from a fuel cell performance analysis are also presented, along with estimates of the greenhouse gas (GHG) and criterial pollutant emissions assuming different sourcings of hydrogen. Lessons learned from the project will be briefly reviewed.

Total Variation optimization penalizes the gradient of a control variable or state. While this work focuses on image processing in particular, it has also found applications in inverse problems and topology optimization. In image processing, the goal is to maintain faithfulness to the original image while denoising and/or deblurring. Additionally, bilevel optimization over the spatially varying regularization weights can illuminate interfaces such as damage regions and other anomalies. We will address two fundamental challenges with TV-optimization: (i) the typical slow convergence of existing TV-optimization methods, and (ii) the selection of spatially varying TV parameters to promote interface detection. Additionally, we will apply such techniques to image data collected in additive manufacturing. In said context, stochasticity in build events induces flaws in the manufactured piece, compromising the integrity of said part. There is a critical need for in-situ monitoring to spot anomalies once they form, and in this setting we apply our total variation and hyperparameter solvers. We will develop a customized algorithm based on for extreme-scale TV-optimization that achieves super-linear or quadratic-convergence, a critical property for real-time, image-by-image analysis. A worst-case outcome is a preprocessing step that enhances image quality in-situ, specifically for out-of-focus and noisy images.

Abstract not provided.

Abstract not provided.

Abstract not provided.

FLEXO (Flux-Limited Extended-MHD Ohm's Law) is a production-line multiphysics code developed at Sandia to enable more predictive modeling of target physics on pulsed-power devices. FLEXO uses an extended magnetohydrodynamics (XMHD) model which includes a generalized Ohm's law (GOL), an electron inertia term, and Hall physics. This report describes the code's numerical methods, its computational performance, and test problems of interest.

Two complementary approximations for describing aspects of continuum electromagnetics in moving media are discussed: electroquasistatic and magnetoquasistatic. Each has been implemented in the finite element shock code ALEGRA for modeling dynamic electromechanical phenomena on typical engineering time scales, with fully integrated circuit coupling (Niederhaus et al. 2023). The approximations can be obtained by consistent asymptotic balancing of Maxwell's equations relative to timescales associated with magnetic diffusion, charge relaxation, and electromagnetic wave propagation. In ALEGRA, the electroquasistatic approximation is used for ferroelectric (FE) modeling, while the magnetoquasistatic approximation is used for magnetohydrodynamic (MHD) modeling. In this paper we introduce for the first time a detailed derivation of a useful quasi-steady “low-Rm” variant of the MHD approximation applicable for cases, such as with detonators, where the thermodynamic pressure arising from Joule heating dominates over magnetic forces. An additional purpose of this paper is to present a coupling mode using Multiple Program-Multiple Data (MPMD) message passing communication that allows the user to run 3D FE problems together with 2D and/or 3D MHD problems with the respective simulation domains coupled through a common circuit equation. The MPMD coupling capability is used here to model the dynamic coupling of a notional ferroelectric generator with an RP-87 exploding bridgewire detonator. The simulated bridgewire heats up and bursts under current generated by simulated depoling of the ferroelectric generator, as a demonstration of the MPMD capability.

Bugs in digital logic have led to some significant security vulnerabilities. Hardware bugs are particularly troublesome since they cannot be easily patched. Additionally, if the bug is in the root of trust, all trust built upon it can be vulnerable. Traditional testing either require a deep knowledge of the system, creative attack vectors and lots of human interaction. This is not scalable as there are very few engineers that can wear the hat of a designer, a verification engineer, and a cybersecurity expert. Hardware fuzzing is a relatively new research area in dynamic hardware testing. It has proven to be an effective method for discovering bugs, unexpected behaviors, and security vulnerabilities in software. While hardware fuzzing is new to the hardware domain, it has a strong track record in software testing. Fuzzing is a testing technique that randomly mutates the input data to uncover bugs or vulnerabilities in the design. It is especially good at finding corner cases that test engineers can not envision. Another advantage over other dynamic testing techniques is that, if done well, deep knowledge of the design is not required. Additionally, fuzzing scales well. If the system is set up correctly, it can run unsupervised for weeks if necessary. In this work, we propose using hardware fuzzing to improve the input vector generation for an information flow tracking tool. To get reasonable throughput of test vectors, an emulator is targeted as the execution platform. Efficient emulator execution has some specific requirements.

While quantum computing offers the promise of exponential advantages, limited quantum speedups are known, especially for practical applications. To open new avenues for quantum advantages, we propose Unconventional Quantum Advantages for Computation (U-QuACs), with respect to unconventional resources such as space (number of bits or quantum bits of memory required to solve a problem), accuracy of solution, communication, or energy consumption. We focus on space-efficient quantum algorithms, where we seek to design algorithms that solve a problem using much less space than the total size of the input. A natural setting in which space is critical is the streaming model of computation, where the input data arrives sequentially in pieces that must each be processed individually. Streaming is motivated by a variety of problems including analysis of internet traffic or social networks. We design the first exponential quantum space advantage for a natural streaming problem, which also constitutes the first quantum advantage for approximating a discrete optimization problem, albeit with respect to space.

This poster describes progress in training a variety of neural network architectures on a typical detection task. The poster will be presented at the Sandia MLDL Workshop

Abstract not provided.

Nitrogen-containing organic molecules play essential roles in nearly every aspect of chemistry. The importance of these species within gas-phase chemistry has increased in recent years, due to their emissions from wildfires, crude biofuels, ammonia combustion, and CO₂ capture facilities. However, there is a lack of detailed relevant mechanistic and kinetic studies of N-compounds, resulting in poor representation of these pathways within chemical models. In this report, we detail our exploration of the gas-phase oxidation mechanisms of pyrrole, imidazole, pyrrolidine, methylamine, and dimethylamine, done by employing multiplexed VUV photoionization mass spectrometry coupled with KinBot, a computational tool that automatically explores multi-well potential energy surfaces. Overall, we vastly expanded the known gas-phase oxidation mechanisms and chemical kinetics of these species. This work will improve the accuracy and completeness of atmospheric and combustion chemistry models, ultimately leading to better-informed decision-makers in the energy and environmental sectors.

Abstract not provided.

This project develops a Bayesian optimization approach to extracting insights from Z Machine experimental data to determine if and how these insights can be used to extrapolate to a larger facility. The primary goal is to address the scientific challenge of informing how confidently experimental conditions can be predicted on a next generation facility, the design of which requires the reliable extrapolation of current high energy density technologies to regimes yet unobserved, except by costly high-fidelity computational models. Maximizing the use of presently available data and understanding how it informs future endeavors is critically important to enable transformative pulsed power and the science of extreme conditions. We explore a Bayesian optimization approach to experimental design which combines information theory, experimental data, and computational modeling to explore how information gain can be maximized.

Presentation on content from this publication: https://www.sciencedirect.com/science/article/pii/S0952197623017992

Abstract not provided.

Helium-4-based scintillation detector technology is emerging as a strong alternative to pulse-shape discrimination-capable organic scintillators for fast neutron detection and spectroscopy, particularly in extreme gamma-ray environments. The 4He detector is intrinsically insensitive to gamma radiation, as it has a relatively low cross-section for gamma-ray interactions, and the stopping power of electrons in the 4He medium is low compared to that of 4He recoil nuclei. Consequently, gamma rays can be discriminated by simple energy deposition thresholding instead of the more complex pulse shape analysis. The energy resolution of 4He scintillation detectors has not yet been well-characterized over a broad range of energy depositions, which limits the ability to deconvolve the source spectra. In this work, an experiment was performed to characterize the response of an Arktis S670 4He detector to nuclear recoils up to 9 MeV. The 4He detector was positioned in the center of a semicircular array of organic scintillation detectors operated in coincidence. Deuterium–deuterium and deuterium–tritium neutron generators provided monoenergetic neutrons, yielding geometrically constrained nuclear recoils ranging from 0.0925 to 8.87 MeV. The detector response provides evidence for scintillation linearity beyond the previously reported energy range. The measured response was used to develop an energy resolution function applicable to this energy range for use in high-fidelity detector simulations needed by future applications.

Synchronous condensers have been used in power systems for decades, but they are now attracting interest as a potential means for solving certain issues associated with inverter-based resources in islanded power systems. They have been extensively studied in applications to large wind farms and weak grids, but there has been very little study of synchronous condensers in intentionally-islanded systems, and especially self-healing ones. This report documents the results of an LDRD project intended to create modeling tools for use in the study of synchronous condensers in these smaller off-grid systems, and to explore synchronous condensers in this application. The results indicate that synchronous condensers have the potential to provide many benefits, but there are also several unanswered questions and technical challenges requiring further study.

Abstract not provided.

This paper describes Secure MQTT, a scheme proposed by Sandia National Laboratories that employs Identity-Based Encryption (IBE) for securing MQTT communications. Secure MQTT assumes and is conformant with an MQTT version 5 environment.

This report outlines the activities conducted in FY24 focused on updating the liquid salt test loop (LSTL) model through the integration of a new test section featuring 16 radial tubes coupled to a filter section. Some comparative analyses of the updated model with existing experimental data for the LSTL was made and benchmarked against other computational tools, such as the ORNL code, SAM. These actions are part of a comprehensive validation effort and promote collaboration among laboratories participating in the Molten Salt Reactor (MSR) campaign.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

The differences between molten salt reactors (MSRs) and light water reactors (LWRs) has required modification of previous approaches to model reactor accident progression. Part of this is related to the different chemical phenomenology of these reactors as their fuel is not as contained and can react with their surroundings. MELCOR is a reactor simulation tool that has implemented methods and models to model MSRs. However, additional development of MELCOR for modeling MSRs is required. Although understanding the chemistry surrounding MSRs is important to general MSR licensing and operation, only a subset of reactions and phenomenology are required to model beyond design basis incidents and therefore to be captured by MELCOR. This report is intended to guide the chemical phenomenology to improve MELCOR MSR accident modeling and discusses phenomenology models that are in development.

The January 15, 2022 Tonga Hunga volcanic eruption was the largest event to be recorded across the International Monitoring System infrasound network. Signals from the eruption were identified at all 53 operational stations by International Data Centre analysts. This report contains descriptors for signal detection bulletins produced by infrasound researchers at Sandia National Laboratories and Los Alamos National Laboratory. Manual detection bulletins were produced by laboratory staff. Automated detection bulletins were produced using automated infrasound processing tools developed at both laboratories. Initial results are provided to evaluate the utility of extending tools developed for regional infrasound event analysis to global-scale events.

Abstract not provided.

Abstract not provided.

The Gamma Detector Response and Analysis Software (GADRAS) package includes an inverse modeling tool that is helpful in identifying characteristics of unknown radioactive materials. Traditionally, uncertainties in this analysis were derived solely from measurement data quality and the fit of synthetic spectra. This paper aims to rigorously quantify additional sources of uncertainty, focusing on uncertainties arising from measurements being analyzed, Detector Response Function (DRF) characterization, and DRF extrapolation. Applying these findings to the BeRPBall benchmark data set, we demonstrated the impact of these uncertainties on plutonium and polyethylene estimates. The results underscore the importance of incorporating diverse uncertainty sources to enhance the accuracy and reliability of GADRAS’s inverse modeling capabilities.

The motivation behind this research is the growing challenge of handling the massive amounts of data generated by modern imaging systems. Conventional digital image processing techniques are struggling to keep pace with the demands of high-resolution and high-speed imaging systems for remote sensing due to their high-power consumption and data storage requirements. We present a novel approach based on analog photonics to address this challenge. The proposed system utilizes a silicon-photonics-based image encoder positioned after image formation and initial optical-to-electrical conversion. The photonic encoder compresses image data using a passive disordered photonic structure to perform kernel-type random projections of the raw data. The compressed data is then processed by a back-end neural network, which reconstructs the original image with high fidelity (structural similarity exceeding 90%). Our proposed approach has the potential to compress images with ~ 1000X lower power consumption compared to digital approaches with data rates exceeding 1 terapixel/second.

Presentation given by academic subcontractor for work performed under LDRD contract

Abstract not provided.

Abstract not provided.

Abstract not provided.

The verification of warhead dismantlement is expected to be an important component in future arms reduction treaties. Historic approaches developed with future arms control treaty verification in mind often involve intrusive measurements, process monitoring, and/or inspector presence to provide confidence that an authentic warhead has been dismantled. This work explores the possibility of reducing the negative impacts of these invasive approaches while also delivering a method that is more likely to provide non-sensitive data that can be shared with not only other nuclear weapons states but also non-nuclear weapons states partners. This work explores a novel approach for verifying dispositioned non-nuclear weapon components, providing confidence post-dismantlement that a treaty accountable item that was dismantled was in fact a treaty-relevant nuclear weapon system as declared. This method provides an alternative to intrusive inspection processes in nuclear weapons production environments, which would require significant changes to the host’s operational behaviors. It achieves this by identifying intrinsic neutron-induced signatures of non-nuclear components to determine their authenticity and estimate the duration they were exposed within a nuclear weapons system using technologies that are already in use for other national security applications. Intrinsic radiation effects studies are already a part of the stockpile aging and surveillance evaluations. However, none of these technologies and approaches have been previously considered for verification applications of non-nuclear component disposition. In this report, we introduce modeling studies that have been used to identify the most promising candidate parts and materials with signatures that are measurable and actionable. These models have been validated with laboratory measurements of signatures induced by the exposure of candidate materials to neutrons over a range of times. Predictive modeling then demonstrates the methodology for estimating exposure times and/or limits. Laboratory measurements of authentic non-nuclear parts from a dismantled warhead demonstrate the feasibility of employing these signature measurements. And finally, a concept of operations (CONOPS) for the potential use of this methodology is presented.

Presentation on modifying neural network architectures to function for space domain awareness imagery.

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

Seismic imaging methods are critical for Global Security and Energy & Homeland Security missions and activities that rely on subsurface characterization, but traditional methods remain computationally expensive and require significant labor hours and expertise to execute. Within the past few years, machine learning (ML), namely deep learning (DL), has been used to develop data-driven end-to-end full waveform inversion (FWI) methods to estimate 2D P-wave velocity (Vp) models in a fraction of the time as conventional FWI. These methods, however, are trained on simplistic acoustic wave seismic data and Vp models that are not realistic nor representative of real-world observations, leaving a large gap between the state-of-the-art and deployable, feasible, and practical DL FWI methods. Here, we generate a synthetic active-source, 3D, elastic wave seismic data set and a variety of Vp models with realistic geologic structure for training DL FWI methods. We evaluate six different methods that have performed well for acoustic DL FWI or medical imaging tasks using our more realistic dataset. We find that these six trained models do not match the performance of published acoustic end-to-end DL FWI methods, indicating more training data may be needed, physics may need to be incorporated to achieve good accuracy at the sacrifice of the end-to-end advantage, and/or novel methods need to be developed to enable end-to-end DL FWI methods to perform well for real-world seismic data.

his report details the findings from the research and investigation of Geometric Measures of Trustworthiness for Machine Learning Predictions. We explored the trustworthiness of machine learning (ML) models’ predictions using geometric measures to quantify the similarity of a query point with the training data. Predictive uncertainty in ML can originate from at least three sources: (1) Model uncertainty, which represents the uncertainty in model form (e.g. decision tree, vs neural network) and estimating the model parameters from the training data, (2) Data uncertainty, which represents the natural complexities of the data such as class overlap and inherent noise, and (3) Distributional uncertainty, which represents the mismatch between the training and operational distributions. The proposed measures focus on measuring and explaining the data and distributional uncertainties by measuring the relationships of operational data with the training data.

Abstract not provided.

Laser-based nonlinear optical spectroscopy approaches have enabled the direct sensing of important chemistry in nearly every fundamental or applied field of science. Yet in many applications, increased detectivity is needed to unravel fundamental mechanisms. One possibility is the use of quantum entanglement to increase detection cross-sections, but it is unproven that enhancement from quantum light extends to practical intensities for chemical sensing. In this report, we investigated the creation and use of entangled photons in nonlinear optical mixing for second harmonic generation and infrared imaging. We observe that the linear scaling of nonlinear mixing from entangled photon sources extends to the mW laser power regime, and enhances direct infrared imaging on Si-based charge coupled device cameras. These results motivate future experimentation on practical uses of entangled photons for nonlinear optical sensing applications.

Hydrogen is known to embrittle austenitic stainless steels, which are widely used in high-pressure hydrogen storage and delivery systems, but the mechanisms that lead to such material degradation are still being elucidated. The current work investigates the deformation behavior of single crystal austenitic stainless steel 316L through combined uniaxial tensile testing, characterization and atomistic simulations. Thermally precharged hydrogen is shown to increase the critical resolved shear stress (CRSS) without previously reported deviations from Schmid's law. Molecular dynamics simulations further expose the statistical nature of the hydrogen and vacancy contributions to the CRSS in the presence of alloying. Slip distribution quantification over large in-plane distances (>1 mm), achieved via atomic force microscopy (AFM), highlights the role of hydrogen increasing the degree of slip localization in both single and multiple slip configurations. The most active slip bands accumulate significantly more deformation in hydrogen precharged specimens, with potential implications for damage nucleation. For 〈110〉 tensile loading, slip localization further enhances the activity of secondary slip, increases the density of geometrically necessary dislocations and leads to a distinct lattice rotation behavior compared to hydrogen-free specimens, as evidenced by electron backscatter diffraction (EBSD) maps. The results of this study provide a more comprehensive picture of the deformation aspect of hydrogen embrittlement in austenitic stainless steels.

The effects of raw water leaching at the U.S. SPR were modeled using SANSMIC. In addition to 18 caverns identified in previous leaching reports, six caverns have been identified for further monitoring based on the results of this report. SANSMIC continues to serve as a useful tool for monitoring changes in cavern shape. However, some caverns where string cuts were made in the middle of drawdowns are showing overprediction in the SANSMIC model when compared with recent sonars.

Corrosion challenges persist throughout SNL’s mission areas. The primary difficulty lies in the fact that corrosion typically manifests as isolated, rare events, making preemptive identification exceedingly difficult. Our current strategy for addressing corrosion issues, such as anomalies and SFIs, is similarly isolated and reactive. This method is costly, time-consuming, heavily dependent on a limited number of experts, and offers minimal understanding of the overall damage distribution within the stockpile. This technical challenge is not unique to corrosion but is also prevalent in other material aging phenomena, such as tin-whisker growth in lead-free solder and fatigue failure of springs.

Abstract not provided.

Remote observation of infrasound resonant signals emanating from underground tunnel structures could potentially allow the remote quantification of the geometry of the underground structures in which the signals were generated. However, the sensitivity of these observations to tunnel geometry and changes in that geometry are unknown. In this report we outline a numerical simulation study with the following three objectives: 1. Can we model infrasound resonance using numerical simulations? 2. What is the sensitivity of the modeled observations to changes in tunnel geometry or boundary conditions? And 3. Can we accurately model resonant observations of explosions occurring in Redmond Salt Mine in Central Utah. In this report we outline affirmative answers to the first two objectives, but we were unable to accurately model the Redmond Explosions due to numerical instability in the model of the complex structure of the mine. Recommendations for future work emphasize the need to acquire additional datasets and to explore a more data-based approach in which changes in data signatures are detected as a first step towards developing a method which inverts resonant infrasound signals for tunnel geometry.

This report summarizes fiscal year 2024 (FY24) activities centered around a series of field tests in bedded salt at the Waste Isolation Pilot Plant (WIPP) funded by the Office of Spent Fuel and Waste Science and Technology in the Spent Fuel and Waste Disposition (SFWD) program of the US Department of Energy’s Office of Nuclear Energy (DOE-NE). High-level Purpose of Experiments: The Brine Availability Test in Salt (BATS) field tests are revealing both brine occurrence (i.e., where, and how much) and brine migration (i.e., how easily it moves) in the excavation damaged zone (EDZ). This understanding is foundational to develop a safety case for a future heat-generating waste repository in salt, and to starting up a generic repository program in salt to buy down risk. BATS seeks to predict how much brine can flow into both ambient and heated excavations (e.g., boreholes or rooms) in salt. This work is educating and empowering new repository scientists on two fronts: “design and execution of field tests” and “prediction and modeling of coupled processes.” DOE-NE capabilities in salt have grown and been tested through international modeling and benchmarking exercises (e.g., DECOVALEX, RANGERS, KOMPASS, and MEASURES; see Mills et al., 2024). The hands-on expertise we are building is a necessary step towards large-scale disposal demonstrations and eventual implementation.

Sputter-deposited Pt-Au thin films have been reported to develop a hard, stable, nanocrystalline structure, yet little is known about how these characteristics vary with Pt_xAu_1-x composition and process conditions. Toward this end, this document describes an extensive, combinatorial Pt-Au thin film library including characterized film compositions, structure, and properties. Complemented by kinematic Monte Carlo simulations of codeposition, a broad range of Pt_xAu_1-x compositions (from x ~ 0.02 to 0.93) was first established by sputtering with varied magnetron powers and gun tilt angles. Further, the produced films were subsequently interrogated using automated nanoindentation, x-ray reflectivity, x-ray diffraction, atomic force microscopy, surface profilometry, four-point probe sheet resistance techniques, and wavelength dispersive spectroscopy in order to determine how hardness, modulus, density, surface roughness, structure, and resistivity vary with film stoichiometry and process parameters. Combinatorial films displayed an assortment of properties with the hardness of some films exceeding values reported previously for this material system. High hardness, high modulus, and low resistivity were generally attained when using increased deposition energy and reduced angle-of-incidence processes. Overall, the research identified promising, new Pt_xAu_1-x compositions for future study and pinpointed strategies for improved deposition.

We measured the austenitic FeCr18Ni12.5 stainless steel Hugoniot as a function of crystallographic direction to approximately 60 GPa. We shock-compressed FeCr18Ni12.5 samples oriented along ⟨ 100 ⟩ , ⟨ 110 ⟩ , and ⟨ 111 ⟩ to mean stresses ranging 30.5-58.1 GPa via Ta plate impact in a large-bore powder gun and measured the free-surface velocities with laser interferometry. We unambiguously observed the largest post-shock free-surface velocity along ⟨ 100 ⟩ in each experiment, which consequently produced the lowest shock velocity along that orientation. However, the propagation of experimental uncertainties through the impedance matching scheme used to compute the shock velocity produced sufficient uncertainty overlap to preclude definitive conclusion of Hugoniot anisotropy.

Shock invariant relationship, which was conceived for inert shock waves to derive the 4th power relationship between shock pressure and maximum strain rate, is generalized for reactive shock waves such as Chapman-Jouget detonation and shock-induced vaporization. The generalization, based on the first-order reaction models, is a power function relationship between overall dissipated energy ( Δ e d i s ) and reaction time Δ τ such that Δ e d i s Δ τ 1 / α = constant , where the power coefficient α is found to be in the range of 2/3-4. Experimental data, though scarce, are consistent with the generalization. Implication of the generalization for inert shocks is also considered and suggests a broad range of the 4th power coefficient including an inequality equation that constrains the shock and particle velocity relationship.

Here, this work focuses on the representation of model-form uncertainties in molecular dynamics simulations in various statistical ensembles. In prior contributions, the modeling of such uncertainties was formalized and applied to quantify the impact of, and the error generated by, pair-potential selection in the microcanonical ensemble (NVE). In this work, we extend this formulation and present a linear-subspace reduced-order model for the canonical (NVT) and isobaric (NPT) ensembles. The symplectic reduced-order basis is randomized on the tangent space of the Stiefel manifold to provide topological relationships and capture model-form uncertainty. Using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), we assess the relevance of these stochastic reduced-order atomistic models on canonical problems involving a Lennard-Jones fluid and an argon crystal melt.

The valorization of lignin, a currently underutilized component of lignocellulosic biomass, has attracted attention to promote a stable and circular bioeconomy. Successful approaches including thermochemical, biological, and catalytic lignin depolymerization have been demonstrated, enabling opportunities for lignino-refineries and lignocellulosic biorefineries. Although significant progress in lignin valorization has been made, this review describes unexplored opportunities in chemical and biological routes for lignin depolymerization and thereby contributes to economically and environmentally sustainable lignin-utilizing biorefineries. This review also highlights the integration of chemical and biological lignin depolymerization and identifies research gaps while also recommending future directions for scaling processes to establish a lignino-chemical industry.

Laser powder bed fusion (LPBF) Ti-6Al-4V is widely studied for use in structural applications in aerospace and medical industries, but mechanical anisotropy and microstructural inhomogeneity prohibits its wider adoption. Although successful microstructure prediction models have been developed, a remaining challenge is their limited integration across length/time scales and validation by experimental studies. This work proposes a physics-augmented machine learning surrogate model to unite predictions of LPBF temperature, β phase morphology and texture, and α/α’ formation into a single framework that is calibrated and validated with experiments. First, a phase field (PF) model of the martensitic β→α’ transformation is developed and calibrated using data from in-situ synchrotron cyclic heating/cooling studies quantifying the variation of α phase fraction with time. In parallel, an established finite difference-Monte Carlo (FDMC) model predicts the part-scale temperature profile and β grain formation during solidification. A dataset is developed using LPBF cyclic temperature descriptors from the FDMC model as inputs and corresponding α/α’ phase fraction and width from the PF model as outputs. Five machine learning (ML) regression models are tested and optimized, having mean absolute error in testing ≤ 4 %, and the k-nearest neighbors (KNN) model is selected as the best performing. The KNN model is called at the nodal level during post-processing of the FDMC model to replace and downscale the response of the PF model. The combined agility and accuracy of the hybrid FDMC-ML model enables part-scale microstructure predictions that can be further used for property predictions to accelerate AM process optimization.

In this article, We present results from a recent exercise where participating organizations were asked to provide model-based blind predictions of damage evolution in 3D-printed geomaterial analogue test articles. Participants were provided with a range of data characterizing both the undamaged state (e.g., ultrasonic measurements) and damage evolution (e.g., 3-point bending, unconfined compression, and Brazilian testing) of the material. In this paper, we focus on comparisons between the participants’ predictions and the previously secret challenge problem experimental observations. We present valuable lessons learned for the application of numerical methods to deformation and failure in brittle-ductile materials. The exercise also enables us to identify which specific types of calibration data were of most utility to the participants in developing their predictions. Further, we identify additional data that would have been useful for participants to improve the confidence of their predictions. Consequently, this work improves our understanding of how to better characterize a material to enable more accurate prediction of damage and failure propagation in natural and engineered brittle-ductile materials.

In this article, We present results from a recent exercise where participating organizations were asked to provide model-based blind predictions of damage evolution in 3D-printed geomaterial analogue test articles. Participants were provided with a range of data characterizing both the undamaged state (e.g., ultrasonic measurements) and damage evolution (e.g., 3-point bending, unconfined compression, and Brazilian testing) of the material. In this paper, we focus on comparisons between the participants’ predictions and the previously secret challenge problem experimental observations. We present valuable lessons learned for the application of numerical methods to deformation and failure in brittle-ductile materials. The exercise also enables us to identify which specific types of calibration data were of most utility to the participants in developing their predictions. Further, we identify additional data that would have been useful for participants to improve the confidence of their predictions. Consequently, this work improves our understanding of how to better characterize a material to enable more accurate prediction of damage and failure propagation in natural and engineered brittle-ductile materials.

This research article presents a robust approach to optimizing the layout of pressure sensors around an airfoil. A genetic algorithm and a sequential quadratic programming algorithm are employed to derive a sensor layout best suited to represent the expected pressure distribution and, thus, the lift force. The fact that both optimization routines converge to almost identical sensor layouts suggests that an optimum exists and is reached. By comparing against a cosine-spaced sensor layout, it is demonstrated that the underlying pressure distribution can be captured more accurately with the presented layout optimization approach. Conversely, a 39 %-55 % reduction in the number of sensors compared to cosine spacing is achievable without loss in lift prediction accuracy. Given these benefits, an optimized sensor layout improves the data quality, reduces unnecessary equipment and saves cost in experimental setups. While the optimization routine is demonstrated based on the generic example of the IEA 15 MW reference wind turbine, it is suitable for a wide range of applications requiring pressure measurements around airfoils.

On Wednesday, March 8^th and Thursday, March 9^th, 2023, the University of Texas at Austin hosted Sandia National Laboratories (Sandia) for “Sandia Day 2023 at UT Austin” with the intention of reviewing, planning and shaping ongoing and future collaborations in key areas that reflect each organization’s priorities and strengths. The event brought together nearly 100 UT and Sandia participants including executive leadership, researchers, faculty, staff, and students. The primary sessions of Sandia Day consisted of a half-day tour of select J.J. Pickle Research Campus facilities, a networking happy hour, leadership meetings, presentations by both Sandia and UT Austin representatives in areas of research strategic priorities: Grid Resiliency, Examining Climate Change, and Microelectronics, and a research poster session with lunch. The group also discussed growth opportunities in the following research areas: nuclear and radiation engineering, pulsed power and fusion physics, and digital engineering, specifically as it related to materials discovery and advanced manufacturing. Appendix A contains the full Sandia Day agenda.

The list of standards, best practice, and regulations below are intended to give insight into what resources are available for developing a chemical control regime as well as information on what regulations other countries have used to implement such a regime. This list is not intended to be all inclusive and other regulations and standards related to controlling hazardous chemicals exist and should be consulted.

Remote radioactive source applications require frequent transportation of sources from storage locations to remote sites. This introduces risk of theft of a source during the transportation process, with the level of risk proportional to the radioactivity of the source. To that end, theft of smaller sources, such as microcurie-level moisture density gauges, are of minor concern, but larger sources, such as those used for radiography and well logging, present more risk. Radiography sources include ¹⁹²Ir, ⁷⁵Se, or ⁶⁰Co radionuclides with radioactivity amounts at or exceeding IAEA Category 2. Well-logging sources, primarily ²⁴¹Am/Be, are used for their neutron-emission properties. ¹³⁷Cs is also used in well-logging at lower activities than in radiography but at levels that still present some risk. The vulnerability for malicious use of such sources to cause contamination and associated economic effects is dependent on the elemental chemical and physical properties, especially melting point and bulk modulus. Theft of radiography sources is somewhat common, well-logging sources less so. Theft of sources commonly occurs in concert with theft of the vehicle, with the source subsequently abandoned. There have been some instances where a source appears to have been specifically targeted. There are a variety of security measures and protocols, available and under development, to mitigate the risk of theft and assist in source recovery.

Employing quantum mechanical resources in computing and networking opens the door to new computation and communication models and potential disruptive advantages over classical counterparts. However, quantifying and realizing such advantages face extensive scientific and engineering challenges. Investments by the Department of Energy (DOE) have driven progress toward addressing such challenges. Quantum algorithms have been recently developed, in some cases offering asymptotic exponential advantages in speed or accuracy, for fundamental scientific problems such as simulating physical systems, solving systems of linear equations, or solving differential equations. Empirical demonstrations on nascent quantum hardware suggest better performance than classical analogs on specialized computational tasks favorable to the quantum computing systems. However, demonstration of an end-to-end, substantial and rigorously quantifiable quantum performance advantage over classical analogs remains a grand challenge, especially for problems of practical value. The definition of requirements for quantum technologies to exhibit scalable, rigorous, and transformative performance advantages for practical applications also remains an outstanding open question, namely, what will be required to ultimately demonstrate practical quantum advantage?

The project objective is to develop high-magnetization, low-loss iron nitride based soft magnetic composites for electrical machines. These new SMCs will enable low eddy current losses and therefore highly efficient motor operation at rotational speeds up to 20,000 rpm. Additionally, iron nitride and epoxy composites will be capable of operating at temperatures of 150 °C or greater over a lifetime of 300,000 miles or 15 years.

Concentrating Solar Power (CSP) requires precision mirrors, and these in turn require metrology systems to measure their optical slope. In this project we studied a color-based approach to the correspondence problem, which is the association of points on an optical target with their corresponding points seen in a reflection. This is a core problem in deflectometry-based metrology, and a color solution would enable important new capabilities. We modeled color as a vector in the [R,G,B] space measured by a digital camera, and explored a dual-image approach to compensate for inevitable changes in illumination color. Through a series of experiments including color target design and dual-image setups both indoors and outdoors, we collected reference/measurement image pairs for a variety of configurations and light conditions. We then analyzed the resulting image pairs by selecting example [R,G,B] pixels in the reference image, and seeking matching [R,G,B] pixels in the measurement image. Modulating a tolerance threshold enabled us to assess both match reliability and match ambiguity, and for some configurations, orthorectification enabled us to assess match accuracy. Using direct-direct imaging, we demonstrated color correspondence achieving average match accuracy values of 0.004 h, where h is the height of the color pattern. We found that wide-area two-dimensional and linear one-dimensional color targets outperformed hybrid linear/lateral gradient targets in the cases studied. Introducing a mirror degraded performance under our current techniques, and we did not have time to evaluate whether matches could be reliably achieved despite varying light conditions. Nonetheless, our results thus far are promising.

The goal of this Exploratory Express project was to explore the possibility of tunable ferromagnetism in Mn or Cr incorporated epitaxial Ga₂O₃ films. Tunability of magnetic properties can enable novel applications in spintronics, quantum computing, and magnetism-based logics by allowing control of magnetism down to the nanoscale. Carriers (electrons or holes) mediated ferromagnetic ordering in semiconductor can lead to tunable ferromagnetism by leveraging the tunability of carrier density with doping level, gate electric field, or optical pumping of the carriers. The magnetic ions (Cr or Mn) in Ga₂O₃ act as localized spin centers which can potentially be magnetically coupled through conduction electrons to enable ferromagnetic ordering. Here we investigated tunable ferromagnetism in beta Ga₂O₃ semiconductor host with various n-doping levels by incorporating 2.4 atomic percent Mn or Cr. The R&D approach involved growth of epitaxial Ga₂O₃ film on sapphire or Ga₂O₃ substrate, implantation of Mn or Cr ions, annealing of the samples post implantation, and magnetic measurements. We studied magnetic behavior of Mn:Ga₂O₃ as a function of different n-doping levels and various annealing temperatures. The vibrating sample magnetometry (VSM) measurement exhibited strong ferromagnetic signals from the annealed Mn:Ga₂O₃ sample with n-doping level of 5E19 cm^-3. This ferromagnetic behavior disappears from Mn:Ga₂O₃ when the n-doping level is reduced to 5E16 cm^-3. Although these results are to be further verified by other measurement schemes due to the observation of background ferromagnetism from the growth substrate, these results indicate the possibility of tunable ferromagnetism in Mn:Ga₂O₃ mediated by conduction electrons.

Low-dimensional materials show great promise for enhanced computing and sensing performance in mission-relevant environments. However, integrating low-dimensional materials into conventional electronics remains a challenge. Here, we demonstrate a novel transfer method by which low-dimensional materials and their heterostructures can be transferred onto any arbitrary substrate. Our method relies on a water soluble GeO₂ substrate from which lowdimensional materials are transferred without significant perturbation. We apply the method to transfer a working electronic device based on a low-dimensional material. Process developments are achieved to enable the fabrication and transfer of a working electronic device, including the growth of high-k dielectric on GeO₂ by atomic layer deposition and inserting an indium diffusion barrier into the device gate stack. This work supports Sandia’s heterogeneous integration strategy to broaden the implementation of low-dimensional films and their devices.

Copper is a challenging material to process using laser-based additive manufacturing due to its high reflectivity and high thermal conductivity. Sintering-based processes can produce solid copper parts without the processing challenges and defects associated with laser melting; however, sintering can also cause distortion in copper parts, especially those with thin walls. In this study, we use physics-informed Gaussian process regression to predict and compensate for sintering distortion in thin-walled copper parts produced using a Markforged Metal X bound powder extrusion (BPE) additive manufacturing system. Through experimental characterization and computational simulation of copper’s viscoelastic sintering behavior, we can predict sintering deformation. We can then manufacture, simulate, and test parts with various compensation scaling factors to inform Gaussian process regression and predict a compensated as-printed (pre-sintered) part geometry that produces the desired final (post-sintered) part.

Spontaneous isotope fractionation has been reported under nanoconfinement conditions in naturally occurring systems, but the origin of this phenomena is currently unknown. Two existing hypotheses have been proposed, one based on changes in the solvation environment of the isotopes that reduces the non-mass dependent hydrodynamics contribution to diffusion. The other is that isotopes have mass-dependent surface adsorption, varying their total diffusion through nanoconfined channels. To investigate these hypotheses, benchtop experiments, nuclear magnetic resonance (NMR) spectroscopy, and molecule scale modeling were applied. Classical molecular dynamics simulations identified that the Na⁺ and Cl^- hydration shells across the three different salt solutions (²²Na³⁵Cl, ²³Na³⁵Cl, ²⁴Na³⁵Cl) did not vary as a function of the Na⁺ isotope, but that there was a significant pore size effect, with larger hydration shells at larger pore sizes. Additionally, while total adsorption times did not vary as a function of the Na⁺ isotope or pore size, the free ion concentration, or those adsorbed on the surface for <5% of the simulation time did exhibit isotope dependence. Experimentally, challenges occurred developing a repeatable experiment, but NMR characterization of water diffusion rates through ordered alumina membranes was able to identify the existence of two distinct water environments associated with water inside and outside the pore. Further NMR studies could be used to confirm variation in hydration shells and diffusion rates of dissolved ions in water. Ultimately, mass-dependence adsorption is a primary driver of variations in isotope diffusion rates, rather than variation in hydration shells that occur under nanoconfinement.

The Artificial Intelligence Enhanced Co-Design for Next Generation Microelectronics virtual workshop was held April 4-5, 2023, and attended by subject matter experts from universities, industry, and national laboratories. This was the third in a series of workshops to motivate the research community to identify and address major challenges facing microelectronics research and production. The 2023 workshop focused on a set of topics from materials to computing algorithms, and included discussions on relevant federal legislation and such as the Creating Helpful Incentives to Produce Semiconductors and Science Act (CHIPS Act) which was signed into law in the summer of 2022. Talks at the workshop included edge computing in radiation environments, new materials for neuromorphic computing, advanced packaging for microelectronics, and new AI techniques. We also received project updates from several of the Department of Energy (DOE) microelectronics co-design projects funded in the fall of 2021, and from three of the Energy Frontier Research Centers (EFRCs) that had been funded in the fall of 2022. The workshop also conducted a set of breakout discussions around the five principal research directions (PRDs) from the 2018 Department of Energy workshop report: 1) define innovative material, device, and architecture requirements driven by applications, algorithms, and software; 2) revolutionize memory and data storage; 3) re-imagine information flow unconstrained by interconnects; 4) redefine computing by leveraging unexploited physical phenomena; 5) reinvent the electricity grid through new materials, devices, and architectures. We tasked each breakout group to consider one primary PRD (and other PRDs as relevant topics arose during discussions) and to address questions such as whether the research community has embraced co-design as a methodology and whether new developments at any level of innovation from materials to programming models requires the research community to reevaluate the PRDs developed back in 2018.

Actinide thin-film coatings such as uranium dioxide (UO₂) play an important role in nuclear reactors and other mission-relevant applications, but realization of their potential requires a deep fundamental understanding of the chemical vapor deposition (CVD) processes used for their growth. The slow experimental progress can be attributed, in part, to the standard safety guidelines associated with handling uranium byproducts, which are often corrosive, toxic, and radioactive. Accurate simulation techniques, when used in concert with experiment, can improve laboratory safety, material durability, and deliverable timeframes. However, state-of-the-art computational methods are either insufficiently accurate or intractably expensive. To remedy this situation, in this project we suggested a machine-learning (ML) accelerated workflow for simulating molecular clustering toward deposition. As a benchmark test case, we considered molecular clustering in steam and assessed independent components of our workflow by comparing with measured thermodynamic properties of water. After analyzing each component individually and finding no fundamental barrier to realization of the workflow, we attempted to integrate the ML component, a Sandia-developed tool called FitSNAP. As this was the first application of FitSNAP to atoms and molecules in the gas phase at Sandia, the method required more fitting data than was originally anticipated. Systematic improvements were made by including in the fit data diatomic potentials, molecular single-bond-breaking curves, and symmetry-constrained intermolecular potentials. We concluded that our strategy provides a feasible pathway toward modeling CVD and related processes, but that extensive training data must be generated before it can be of practical use.

Sandia is a federally funded research and development center (FFRDC) focused on developing and applying advanced science and engineering capabilities to mitigate national security threats. This is accomplished through the exceptional staff leading research at the Labs and partnering with universities and companies. Sandia’s LDRD program aims to maintain the scientific and technical vitality of the Labs and to enhance the Labs’ ability to address future national security needs. The program funds foundational, leading-edge discretionary research projects that cultivate and utilize core science, technology, and engineering (ST&E) capabilities. Per Congressional intent (P.L. 101-510) and Department of Energy (DOE) guidance (DOE Order 413.2C, Chg 1), Sandia’s LDRD program is crucial to maintaining the nation’s scientific and technical vitality

A colinear Second-Harmonic Orthogonal Polarized (SHOP) interferometer diagnostic capable of making electron areal density measurements of plasmas formed in Magnetically Insulated Transmission Lines (MITLs) has been developed.

​Protocols play an essential role in Advance Reactor systems. A diverse set of protocols are available to these reactors. Advanced Reactors benefit from technologies that can minimize their resource utilization and costs. Evaluation frameworks are often used when assessing protocols and processes related to cryptographic security systems. The following report discusses the various characteristics associated with these protocol evaluation frameworks, and derives a novel evaluative framework.

Photocatalytic water splitting using suspensions of nanoparticle photocatalysts is a promising route to economically sustainable production of green hydrogen. The principal challenge is to develop photocatalysts with overall solar-to-hydrogen conversion efficiency that exceeds 10 percent. In this project we have developed a new platform for investigating candidate materials for photocatalytic water splitting. Our platform consists of patterned Au electrodes and a Ag/AgCl reference electrode on an insulating substrate onto which we disperse nanoparticle photocatalysts. We then cover the substrate with a thin layer of ionogel containing a protic ionic liquid that dissolves water from the ambient. Using this platform we have demonstrated photoelectrochemical activity mapping for single and small clusters of BiVO₄ nanoparticle photocatalysts and correlated these results to their Raman and photoluminescence spectra. The preliminary results suggest a strong correlation for low efficiency nanoparticles, followed by saturation for those with higher activities, indicating that interface reaction or electrolyte transport become the limiting factor. We anticipate that further application of this platform to investigation of candidate photocatalyst materials will provide useful insights into the mechanisms that limit their performance.

The aviation industry stands at a crossroads, facing the dual challenge of meeting the growing global demand for air travel while mitigating its environmental impact. As concerns over climate change intensify, sustainable aviation fuels (SAFs) have emerged as a promising solution to reduce the carbon footprint of air travel. The aviation sector has long been recognized as a contributor to greenhouse gas emissions, with carbon dioxide (CO₂) being a primary concern. SAFs, derived from renewable feedstocks such as biomass, waste oils, or synthetic processes, offer a promising avenue for reducing the net carbon emissions associated with aviation. While SAFs have shown potential in lowering CO₂ emissions, the combustion process introduces complexities related to soot particle formation and contrail generation that require comprehensive exploration. These aspects are pivotal not only for their environmental implications but also for their influence on atmospheric climate interactions. As the aviation industry increasingly embraces SAFs to meet sustainability goals, it is imperative to assess their combustion characteristics, unravel the mechanisms of soot formation, and scrutinize the factors influencing contrail development.

We present a fast and Bayes-optimal-approximating tensor network decoder for planar quantum LDPC codes based on the tensor renormalization group algorithm, originally proposed by Levin, and Nave. By precomputing the renormalization group flow for the null syndrome, we need only recompute tensor contractions in the causal cone of the measured syndrome at the time of decoding. This allows us to achieve an overall runtime complexity of ($pnχ^6$) where p is the depolarizing noise rate, and χ is the cutoff value used to control singular value decomposition approximations used in the algorithm. We apply our decoder to the surface code in the code capacity noise model and compare its performance to the original matrix product state (MPS) tensor network decoder introduced by Bravyi, Suchara, and Vargo. The MPS decoder has a p-independent runtime complexity of $\mathcal{O}(nχ^3)$ resulting in significantly slower decoding times compared to our algorithm in the low-p regime.

We extend an existing approach for efficient use of shared mapped memory across Chapel and C++ for graph data stored as 1-D arrays to sparse tensor data stored using a combination of 2-D and 1-D arrays. We describe the specific extensions that provide use of shared mapped memory tensor data for a particular C++ tensor decomposition tool called GentenMPI. We then demonstrate our approach on several real-world datasets, providing timing results that illustrate minimal overhead incurred using this approach. Finally, we extend our work to improve memory usage and provide convenient random access to sparse shared mapped memory tensor elements in Chapel, while still being capable of leveraging high performance implementations of tensor algorithms in C++.

Nuclear magnetic resonance spectroscopy (NMR) is a form of spectroscopy that yields detailed mechanistic information about chemical structures, reactions, and processes. Photochemistry has widespread use across many industries and holds excellent utility for additive manufacturing (AM) processes. Here, we use photoNMR to investigate three photochemical processes spanning AM relevant timescales. We first investigate the photodecomposition of a photobase generator on the slow timescale, then the photoactivation of a ruthenium catalyst on the intermediate timescale, and finally the radical polymerization of an acrylate system on the fast timescale. In doing so, we gain fundamental insights to mission relevant photochemistries and develop a new spectroscopic capability at SNL.

The first full 3D calculations of MagLIF including in-flight DT reactions due to fuel magnetization have been performed with HYDRA

Batched sparse linear algebra operations in general, and solvers in particular, have become the major algorithmic development activity and foremost performance engineering effort in the numerical software libraries work on modern hardware with accelerators such as GPUs. Many applications, ECP and non-ECP alike, require simultaneous solutions of many small linear systems of equations that are structurally sparse in one form or another. In order to move towards high hardware utilization levels, it is important to provide these applications with appropriate interface designs to be both functionally efficient and performance portable and give full access to the appropriate batched sparse solvers running on modern hardware accelerators prevalent across DOE supercomputing sites since the inception of ECP. To this end, we present here a summary of recent advances on the interface designs in use by HPC software libraries supporting batched sparse linear algebra and the development of sparse batched kernel codes for solvers and preconditioners. We also address the potential interoperability opportunities to keep the corresponding software portable between the major hardware accelerators from AMD, Intel, and NVIDIA, while maintaining the appropriate disclosure levels conforming to the active NDA agreements. The presented interface specifications include a mix of batched band, sparse iterative, and sparse direct solvers with their accompanying functionality that is already required by the application codes or we anticipated to be needed in the near future. This report summarizes progress in Kokkos Kernels and the xSDK libraries MAGMA, Ginkgo, hypre, PETSc, and SuperLU.

This report summarizes the work towards developing stochastic weighted particle methods (SWPM) for future application in hypersonic flows. Extensive changes to Sandia’s direct simulation Monte Carlo (DSMC) solver, SPARTA (Stochastic Particle Real Time Analyzer), were made to enable the necessary particle splitting and reduction capabilities for SWPM. The results from one-dimensional Couette and Fourier flows suggest that SWPM can reproduce the correct transport for a large range of Knudsen numbers with adequate accuracy. The associated velocity and temperature profiles are in good agreement with DSMC. An issue with particle placement during particle number reduction, is identified, to which, a simple but effective solution based on minimizing the center of mass error is proposed. High Mach wheel flows are simulated using the SWPM and DSMC methods. SWPM is capable of providing nearly an order of magnitude increase in efficiency over DSMC while retaining high accuracy.

Condensation trails, or contrails, are aircraft-induced cirrus clouds. They come from the formation of water droplets, later converting to ice crystals as a result of water vapor condensing on aerosols either emitted by the aircraft engines or already present in the upper atmosphere. While there is ongoing debate about their true impact, contrails are estimated to be a major contributor to climate forcing from aviation. We remind that air transportation currently accounts for about 5 % of the global anthropogenic climate forcing, and that it is anticipated that air traffic will double in the coming decade or two. The expected growth reinforces the urgency of the need to develop a plan to better understand contrail formation and persistence, and deploy means to reduce or avoid contrail formation, or greatly mitigate their impact. It is evident that contrails should be part of the picture when developing a plan to make the aviation sector sustainable.

The tension between accuracy and computational cost is a common thread throughout computational simulation. One such example arises in the modeling of mechanical joints. Joints are typically confined to a physically small domain and yet are computationally expensive to model with a high-resolution finite element representation. A common approach is to substitute reduced-order models that can capture important aspects of the joint response and enable the use of more computationally efficient techniques overall. Unfortunately, such reduced-order models are often difficult to use, error prone, and have a narrow range of application. In contrast, we propose a new type of reduced-order model, leveraging machine learning, that would be both user-friendly and extensible to a wide range of applications.

This work demonstrates that classical shear-flow stability theory can be successfully applied to modify wind turbine wakes and also explains the success of several emerging, empirically-arrived control methods (i.e., dynamic induction and helix control). Linear stability theory predictions are extended to include the effects of non-axisymmetric inflow profiles, such as wind shear, which is shown to not strongly affect the primary forcing frequency. The predictions, as well as idealized large-eddy simulations using actuator-line representation of the turbine blades, agree that the n = 0 and ±1 modes have faster initial growth rates than higher-order modes, suggesting the lower-order modes are more appropriate for wake control. Exciting the lower-order modes with periodic pitching of the blades produces higher entrainment into the wake and consequently faster wake recovery.

98% of the budget is deployed, remaining $325,000 to be assigned by the end of May. Costs plus Commitments total 58% of the deployed budget. 29 projects have been kicked off and are in progress. Five project plans are being finalized and will be kicked off early summer. The February start has contributed to the risk of not costing all of the FY23 budget.

Abstract not provided.

We experimentally and computationally investigate a proposed frequency-domain method for detecting and tracking cislunar spacecraft and near-earth asteroids using heliostat fields at night. Unlike imaging, which detects spacecraft and asteroids by their streak in sidereally-fixed long-exposure photographs, our proposed detection method oscillates the orientation of heliostats concentrating light from the stellar field and measures the light’s photocurrent power spectrum at sub-milliHertz resolution. If heliostat oscillation traces out an ellipse fixed in the galactic coordinate system, spacecraft or asteroids produce a peak in photocurrent power spectrum at a frequency slightly shifted from the starlight peak. The frequency shift is on the scale of milliHertz and proportional to apparent angular rate relative to sidereal. Relative phase corresponds to relative angular position, enabling tracking. A potential advantage of this frequency-domain method over imaging is that detectivity improves with apparent angular rate and number of heliostats. Since heliostats are inexpensive compared to an astronomical observatory and otherwise unused at night, the proposed method may cost-effectively augment observatory systems such as NASA’s Asteroid Terrestrial-impact Last Alert System (ATLAS).

Our research was focused on forecasting the position and shape of the winter stratospheric polar vortex at a subseasonal timescale of 15 days in advance. To achieve this, we employed both statistical and neural network machine learning techniques. The analysis was performed on 42 winter seasons of reanalysis data provided by NASA giving us a total of 6,342 days of data. The state of the polar vortex for determined by using geometric moments to calculate the centroid latitude and the aspect ratio of an ellipse fit onto the vortex. Timeseries for thirty additional precursors were calculated to help improve the predictive capabilities of the algorithm. Feature importance of these precursors was performed using random forest to measure the predictive importance and the ideal number of precursors. Then, using the precursors identified as important, various statistical methods were tested for predictive accuracy with random forest and nearest neighbor performing the best. An echo state network, a type of recurrent neural network that features sparsely connected hidden layer and a reduced number of trainable parameters that allows for rapid training and testing, was also implemented for the forecasting problem. Hyperparameter tuning was performed for each methods using a subset of the training data. The algorithms were trained and tuned on the first 41 years of data, then tested for accuracy on the final year. In general, the centroid latitude of the polar vortex proved easier to predict than the aspect ratio across all algorithms. Random forest outperformed other statistical forecasting algorithms overall but struggled to predict extreme values. Forecasting from echo state network suggested a strong predictive capability past 15 days, but further work is required to fully realize the potential of recurrent neural network approaches.

The generation of synthetic seismograms through simulation is a fundamental tool of seismology required to run quantitative hypothesis tests. A variety of approaches have been developed throughout the seismological community and each has their own specific user interface based on their implementation. This causes a challenge to researchers who will need to learn new interfaces with each new software they wish to use and create substantial challenges when attempting to compare results from different tools. Here we provide a unified interface that facilitates interoperability amongst several simulation tools through a modern containerized Python package. Further, this package includes post-processing analysis modules designed to facilitate end-to-end analysis of synthetic seismograms. In this report we present the conceptual guidance and an example implementation of the new Waveform Simulation Framework.

This Sandia National Laboratories Mission Campaign (MC) seeks to create the technical basis that allows national leaders to efficiently assess and manage the digital assurance of high consequence systems. We will call for transformative research that enables efficient (1) development of provably secure systems and secure integration of untrusted products, (2) intelligent threat mitigation, and (3) digital risk-informed engineering trade-offs. Ultimately, this MC will impact multiple national security missions; it will develop an informed Digital Assurance for High Consequence Systems (DAHCS) community and expand Sandia partnerships to build this national capability.

Imaging methods driven by probes, electrons, and ions have played a dominant role in modern science and engineering. Opportunities for machine vision and AI that focus on consumer problems like driving and feature recognition, are now presenting themselves for automating aspects of the scientific processes. This proposal aims to enable and drive discovery in ultra-low energy implantation by taking advantage of faster processing, flexible control and detection methods, and architecture-agnostic workflows that will result in higher efficiency and shorter scientific development cycles. Custom microscope control, collection and analysis hardware will provide a framework for conducting novel in situ experiments revealing unprecedented insight into surface dynamics at the nanoscale. Ion implantation is a key capability for the semiconductor industry. As devices shrink, novel materials enter the manufacturing line, and quantum technologies transition to being more mainstream. Traditional implantation methods fall short in terms of energy, ion species, and positional precision. Here we demonstrate 1 keV focused ion beam Au implantation into Si and validate the results via atom probe tomography. We show the Au implant depth at 1 keV is 0.8 nm and that identical results for low energy ion implants can be achieved by either lowering the column voltage, or decelerating ions using bias – while maintaining a sub-micron beam focus. We compare our experimental results to static calculations using SRIM and dynamic calculations using binary collision approximation codes TRIDYN and IMSIL. A large discrepancy between the static and dynamic simulation is found that is due to lattice enrichment with high stopping power Au and surface sputtering. Additionally, we demonstrate how model details are particularly important to the simulation of these low-energy heavy-ion implantations. Finally, we discuss how our results pave a way to much lower implantation energies, while maintaining high spatial resolution.

We propose the average spectrum norm to study the minimum number of measurements required to approximate a multidimensional array (i.e., sample complexity) via low-rank tensor recovery. Our focus is on the tensor completion problem, where the aim is to estimate a multiway array using a subset of tensor entries corrupted by noise. Our average spectrum norm-based analysis provides near-optimal sample complexities, exhibiting dependence on the ambient dimensions and rank that do not suffer from exponential scaling as the order increases.

This report summarizes Fiscal Year 2023 accomplishments from Sandia National Laboratories Wind Energy Program. The portfolio consists of funding provided by the DOE EERE Wind Energy Technologies Office (WETO), Advanced Research Projects Agency-Energy (ARPA-E), Advanced Manufacturing Office (AMO), the Sandia Laboratory Directed Research and Development (LDRD) program, and private industry. These accomplishments were made possible through capabilities investments by WETO, internal Sandia investment, and partnerships between Sandia and other national laboratories, universities, and research institutions around the world. Sandia’s Wind Energy Program is primarily built around core capabilities as expressed in the strategic plan thrust areas, with 29 staff members in the Wind Energy Design and Experimentation department and the Wind Energy Computational Sciences department leading and supporting R&D at the time of this report. Staff from other departments at Sandia support the program by leveraging Sandia’s unique capabilities in other disciplines.

This final report summarizes the results of the Laboratory Direct Research and Development (LDRD) Project Number 229740. Wide band gap semiconductors such as gallium nitride (GaN) have features highly desirable for multiple mission electronic applications. Realization of their potential requires atomic-scale understanding of electronic behavior. The principal experimental tools for electronically probing defects in GaN are chemically undifferentiating and lack a practical theoretical counterpart needed to identify and characterize specific defects. This project investigated whether a simple idea for modeling defect excited states and their associated photoluminescence (PL) energies is viable, as a path to accelerate the understanding of defect behavior and gain valuable insights into engineering new electronic materials and devices. The research implemented a non-self-consistent total-energy evaluation of a Koopmans-type estimation of an excited electronic state energy in density functional theory (DFT) calculations, and proceeded to design, implement, and assess a self-consistent method for computing excited states based upon an OCcupation-Constrained-DFT (occ-DFT). The occ-DFT was verified in test calculations of defect excited states and validated against well-characterized PL data for 3d transition metal defects in GaN. The method proved stable and robust in computing excited states and gave accurate predictions compared to experimental PL data. The combined ground state/excited-state capability proved capable of chemically differentiating defect species in GaN. In application to 3d dopants in GaN, we reinterpreted extensive experimental literature, proposed new defects as prospective candidates for use in quantum information applications, and outlined design strategies to create and exploit these potentially useful functional defects in GaN.

To move toward rational design of efficient organic light emitting diodes based on the radical idea of inverted singlet-triplet gap (INVEST) systems, we propose a set of novel quantum chemical approaches, predictive but low-cost, to unveil a set of structural-property relationships. We perform a computational study of a series of substituted molecules based on a small set of known INVEST molecules. Our study demonstrates a high degree of correlation between the intramolecular charge transfer and the singlet-triplet energy gap and hints towards the use of a quantitative estimate of charge transfer to predict and modulate these energy gaps. We aim to create a database of INVEST molecules that includes accurate benchmarks of singlet-triplet energy gaps. Furthermore, we aim to link structural features and molecular properties, enabling a control knob for rational design.

The design of high consequence controllers (in weapons systems, autonomy, etc.) that do what they are supposed to do is a significant challenge. Testing simply does not come close to meeting the requirements for assurance. Today circuit designers at Sandia (and elsewhere) typically capture the core behavior of their components using state models in tools such as STATEFLOW. They then check that their models meet certain requirements (e.g. “The system bus must not deadlock” or “both traffic lights at an intersection must not be green at the same time”) using tools called model checkers. If the model checker returns “yes” then the property is guaranteed to be satisfied by the model. However, there are several drawbacks to this industry practice: (1) there is a lot of detail to get right, this is particularly challenging when there are multiple components requiring complex coordination (2) any errors returned by the model checker have to be traced back through the design and fixed, necessitating rework, (3) there are severe scalability problems with this approach, particularly when dealing with concurrency. All this places high demands on the designers who now face not only an accelerated schedule but also controllers of increasing complexity. This report describes a new and fundamentally different approach to the construction of safety-critical digital controllers. Instead of directly constructing a complete model and then trying to verify it, the designer can start with an initial abstract (think “sketch”) model plus the requirements, from which a correct concrete model is automatically synthesized. There is no need for post-hoc verification of required functional properties. Having tool to carry this out will significantly impact the nation’s ability to ensure the safety of high-consequence digital systems. The approach has been implemented in a prototype tool, along with a suite of examples, including ones that reflect actual problems faced by designers. Our approach operates on a variant of Statecharts developed at Sandia called Qspecs. Statecharts are a widely used formalism for developing concurrent reactive systems, supporting scalability through allowing state models containing composite states, which are the serial or parallel composition of substates which can themselves contain statecharts. Statecharts enable an incremental style of development, in which states are progressively refined to incorporate greater detail in an incremental model of software development. Our approach formulates a set of constraints from the structure of the models and the requirements and propagates these constraints to a fixpoint. The solution to the constraints is an inductive invariant along with guards on the transitions. We also show how our approach extends to implementation refinement, decomposition, composition, and elaboration. We currently handle safety requirements written in LTL (Linear Temporal Logic)

High-quality uncertainty quantification (UQ) is a critical component of enabling trust in deep learning (DL) models and is especially important if DL models are to be deployed in high-consequence applications. Conformal prediction (CP) methods represent an emerging nonparametric approach for producing UQ that is easily interpretable and, under weak assumptions, provides a guarantee regarding UQ quality. This report describes the research outputs of an Exploratory Express Laboratory Directed Research and Development (LDRD) project at Sandia National Laboratories. This project focused on how best to implement CP methods for DL models. This report introduces new methodology for obtaining high-quality UQ from DL models using CP methods, describes a novel system of assessing UQ quality, and provides experimental results that demonstrate the quality of the new methodology and utility of the UQ quality assessment system. Avenues for future research and discussion of potential impacts at Sandia and in the wider research community are also given.

The National Solar Thermal Test Facility (NSTTF) is a DOE Core Capability and Technology Deployment Center located in Albuquerque, NM. It is operated by Sandia National Laboratories (Sandia) for the U.S. Department of Energy (DOE). The NSTTF is the only multi-mission, multi-use, multi-story test facility of its type in the United States. The NSTTF was founded in 1978 and began testing with high heat flux that same year. Over the past 45 years, the NSTTF has been at the forefront of the research, design, fabrication, and testing of many of the critical Concentrating Solar Power (CSP) technologies. These technologies have allowed costs to be dramatically reduced from over $\$ $0.40 /kWh to $\$$0.12 /kWh since the conception of this renewable energy technology. The NSTTF has worked to make the Solar Energy Generating Systems (SEGS) parabolic trough plants successful, while also working with the Solar One and Solar Two facilities for successful implementation. Over the four decades since its founding, the mission of the NSTTF has grown to include new receiver technologies, like our generation 3 falling particle system (G3P3 Tower), optical metrology techniques like SOFAST, molten salt testing, thermal energy storage, solar thermal chemistry, and more. We continue to expand our capabilities in pursuit of the DOE SETO mission and the DOE SunShot 2030 goals: unsubsidized LCOE of $\$$0.05/kWh for CSP that includes 12 or more hours of thermal energy storage. To support both the DOE SETO mission and support the CSP sector as a whole, we are working to develop our operations and maintenance framework to provide a world class testing facility in support of our technological achievements. To accomplish both of these missions, the NSTTF draws on the decades of experience and expertise of our staff along with the world-class facilities at Sandia National Laboratories to further the science of concentrated solar thermal technologies in diverse applications. We remain a trusted partner for high-quality and impactful research in both fundamental and applied arenas. We are able to provide our partners with both one-of-a-kind testing platforms as well as world-class analytics.

Here we develop two-stage stochastic programming models for generator winterization that enhance power grid resilience while incorporating social equity. The first stage in our models captures the investment decisions for generator winterization, and the second stage captures the operation of a degraded power grid, with the objective of minimizing load shed and social inequity. To incorporate equity into our models, we propose a concept called adverse effect probability that captures the disproportionate effects of power outages on communities with varying vulnerability levels. Grid operations are modeled using DC power flow, and equity is captured through mean or maximum adverse effects experienced by communities. We apply our models to a synthetic Texas power grid, using winter storm scenarios created from the generator outage data from the 2021 Texas winter storm. Our extensive numerical experiments show that more equitable outcomes, in the sense of reducing adverse effects experienced by vulnerable communities during power outages, are achievable with no impact on total load shed through investing in winterization of generators in different locations and capacities.

Excitation of iron pentacarbonyl [Fe(CO)5], a prototypical photocatalyst, at 266 nm causes the sequential loss of two CO ligands in the gas phase, creating catalytically active, unsaturated iron carbonyls. Despite numerous studies, major aspects of its ultrafast photochemistry remain unresolved because the early excited-state dynamics have so far eluded spectroscopic observation. This has led to the long-held assumption that ultrafast dissociation of gas-phase Fe(CO)5 proceeds exclusively on the singlet manifold. Herein, we present a combined experimental-theoretical study employing ultrafast extreme ultraviolet transient absorption spectroscopy near the Fe M2,3-edge, which features spectral evolution on 100 fs and 3 ps time scales, alongside high-level electronic structure theory, which enables characterization of the molecular geometries and electronic states involved in the ultrafast photodissociation of Fe(CO)5. We assign the 100 fs evolution to spectroscopic signatures associated with intertwined structural and electronic dynamics on the singlet metal-centered states during the first CO loss and the 3 ps evolution to the competing dissociation of Fe(CO)4 along the lowest singlet and triplet surfaces to form Fe(CO)3. Calculations of transient spectra in both singlet and triplet states as well as spin-orbit coupling constants along key structural pathways provide evidence for intersystem crossing to the triplet ground state of Fe(CO)4. Thus, our work presents the first spectroscopic detection of transient excited states during ultrafast photodissociation of gas-phase Fe(CO)5 and challenges the long-standing assumption that triplet states do not play a role in the ultrafast dynamics.

Excitation of iron pentacarbonyl [Fe(CO)5], a prototypical photocatalyst, at 266 nm causes the sequential loss of two CO ligands in the gas phase, creating catalytically active, unsaturated iron carbonyls. Despite numerous studies, major aspects of its ultrafast photochemistry remain unresolved because the early excited-state dynamics have so far eluded spectroscopic observation. This has led to the long-held assumption that ultrafast dissociation of gas-phase Fe(CO)5 proceeds exclusively on the singlet manifold. Herein, we present a combined experimental-theoretical study employing ultrafast extreme ultraviolet transient absorption spectroscopy near the Fe M2,3-edge, which features spectral evolution on 100 fs and 3 ps time scales, alongside high-level electronic structure theory, which enables characterization of the molecular geometries and electronic states involved in the ultrafast photodissociation of Fe(CO)5. We assign the 100 fs evolution to spectroscopic signatures associated with intertwined structural and electronic dynamics on the singlet metal-centered states during the first CO loss and the 3 ps evolution to the competing dissociation of Fe(CO)4 along the lowest singlet and triplet surfaces to form Fe(CO)3. Calculations of transient spectra in both singlet and triplet states as well as spin-orbit coupling constants along key structural pathways provide evidence for intersystem crossing to the triplet ground state of Fe(CO)4. Thus, our work presents the first spectroscopic detection of transient excited states during ultrafast photodissociation of gas-phase Fe(CO)5 and challenges the long-standing assumption that triplet states do not play a role in the ultrafast dynamics.

A highly parallelizable fluid plasma simulation tool based upon the first-order drift-diffusion equations is discussed. Atmospheric pressure plasmas have densities and gradients that require small element sizes in order to accurately simulate the plasm resulting in computational meshes on the order of millions to tens of millions of elements for realistic size plasma reactors. To enable simulations of this nature, parallel computing is required and must be optimized for the particular problem. Here, a finite-volume, electrostatic drift-diffusion implementation for low-temperature plasma is discussed. The implementation is built upon the Message Passing Interface (MPI) library in C++ using Object Oriented Programming. The underlying numerical method is outlined in detail and benchmarked against simple streamer formation from other streamer codes. Electron densities, electric field, and propagation speeds are compared with the reference case and show good agreement. Convergence studies are also performed showing a minimal space step of approximately 4 μm required to reduce relative error to below 1% during early streamer simulation times and even finer space steps are required for longer times. Additionally, strong and weak scaling of the implementation are studied and demonstrate the excellent performance behavior of the implementation up to 100 million elements on 1024 processors. Lastly, different advection schemes are compared for the simple streamer problem to analyze the influence of numerical diffusion on the resulting quantities of interest.

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. Finally, we demonstrate the effectiveness of these approaches for a variety of cases.

A technique is proposed for reproducing particle size distributions in three-dimensional simulations of the crushing and comminution of solid materials. The method is designed to produce realistic distributions over a wide range of loading conditions, especially for small fragments. In contrast to most existing methods, the new model does not explicitly treat the small-scale process of fracture. Instead, it uses measured fragment distributions from laboratory tests as the basic material property that is incorporated into the algorithm, providing a data-driven approach. The algorithm is implemented within a nonlocal peridynamic solver, which simulates the underlying continuum mechanics and contact interactions between fragments after they are formed. The technique is illustrated in reproducing fragmentation data from drop weight testing on sandstone samples.

We present large-scale atomistic simulations that reveal triple junction (TJ) segregation in Pt-Au nanocrystalline alloys in agreement with experimental observations. While existing studies suggest grain boundary solute segregation as a route to thermally stabilize nanocrystalline materials with respect to grain coarsening, here we quantitatively show that it is specifically the segregation to TJs that dominates the observed stability of these alloys. Our results reveal that doping the TJs renders them immobile, thereby locking the grain boundary network and hindering its evolution. In dilute alloys, it is shown that grain boundary and TJ segregation are not as effective in mitigating grain coarsening, as the solute content is not sufficient to dope and pin all grain boundaries and TJs. Our work highlights the need to account for TJ segregation effects in order to understand and predict the evolution of nanocrystalline alloys under extreme environments.

Granular metals (GMs), consisting of metal nanoparticles separated by an insulating matrix, frequently serve as a platform for fundamental electron transport studies. However, few technologically mature devices incorporating GMs have been realized, in large part because intrinsic defects (e.g., electron trapping sites and metal/insulator interfacial defects) frequently impede electron transport, particularly in GMs that do not contain noble metals. Here, we demonstrate that such defects can be minimized in molybdenum-silicon nitride (Mo-SiNx) GMs via optimization of the sputter deposition atmosphere. For Mo-SiNx GMs deposited in a mixed Ar/N2 environment, x-ray photoemission spectroscopy shows a 40%-60% reduction of interfacial Mo-silicide defects compared to Mo-SiNx GMs sputtered in a pure Ar environment. Electron transport measurements confirm the reduced defect density; the dc conductivity improved (decreased) by 104-105 and the activation energy for variable-range hopping increased 10×. Since GMs are disordered materials, the GM nanostructure should, theoretically, support a universal power law (UPL) response; in practice, that response is generally overwhelmed by resistive (defective) transport. Here, the defect-minimized Mo-SiNx GMs display a superlinear UPL response, which we quantify as the ratio of the conductivity at 1 MHz to that at dc, Δ σ ω . Remarkably, these GMs display a Δ σ ω up to 107, a three-orders-of-magnitude improved response than previously reported for GMs. By enabling high-performance electric transport with a non-noble metal GM, this work represents an important step toward both new fundamental UPL research and scalable, mature GM device applications.

Oakes et al. (2023) published a review article in this journal. In that paper, Oakes et al. (2023) developed thermodynamic models to describe electrolyte solutions for HClO₄–NaClO₄–H₂O and HBr–NaBr–H₂O systems, based on literature data. In their paper, previously published work from researchers in the field was criticized; some of it is ours. Here, in this brief Comment, we first comment on their models, and then we briefly provide a technical response to that criticism.

Frequency-modulated (FM) combs based on active cavities like quantum cascade lasers have recently emerged as promising light sources in many spectral regions. Unlike passive modelocking, which generates amplitude modulation using the field’s amplitude, FM comb formation relies on the generation of phase modulation from the field’s phase. They can therefore be regarded as a phase-domain version of passive modelocking. However, while the ultimate scaling laws of passive modelocking have long been known—Haus showed in 1975 that pulses modelocked by a fast saturable absorber have a bandwidth proportional to effective gain bandwidth—the limits of FM combs have been much less clear. Here, we show that FM combs based on fast gain media are governed by the same fundamental limits, producing combs whose bandwidths are linear in the effective gain bandwidth. Not only do we show theoretically that the diffusive effect of gain curvature limits comb bandwidth, but we also show experimentally how this limit can be increased. By adding carefully designed resonant-loss structures that are evanescently coupled to the cavity of a terahertz laser, we reduce the curvature and increase the effective gain bandwidth of the laser, demonstrating bandwidth enhancement. Our results can better enable the creation of active chip-scale combs and be applied to a wide array of cavity geometries.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

A summary of the issues that have come up in the use of the fulcrum radiation detector and the methods tools that I developed to deal with them.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Short and sweet three-minute presentation on CSP metrology techniques

Abstract not provided.

Abstract not provided.

Abstract not provided.

Study of subcooled pool boiling experiments performed using a dielectric coolant to test effects of variations in heater surface configuration on pool boiling characteristics.