Publications

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Quantum Information Science (QIS) is an emerging technology being pursued by fundamental science research groups worldwide, as well as commercial companies and government programs. There are a variety of QIS disciplines, including quantum computing, quantum sensing and quantum encryption. Some of the commodities needed for a robust quantum laboratory are particular to quantum phenomenon, but in general the equipment needed is similar to that needed for a typical high - technology lab (e.g. oscilloscopes, lasers, vacuum chambers, etc.). This study focuses on identifying commodities manufactured worldwide that would be needed for a robust quantum lab. The authors' own knowledge of needed equipment and primary vendors was used as a starting point, follow ed by extensive internet searching and utilization of buyer's guides to create a large spreadsheet of most of the components needed, the company offering the components, and country of manufacture. With this extensive spreadsheet, stakeholders can identify commodities that would be needed for a quantum lab oratory and potentially identify market choke points.

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The National Solar Thermal Test Facility (NSTTF) at Sandia National Laboratories New Mexico (SNL/NM) developed this Life Cycle Management Plan (LCMP) to document its process for executing, monitoring, controlling and closing-out Phase 3 of the Gen 3 Particle Pilot Plant (G3P3). This plan serves as a resource for stakeholders who wish to be knowledgeable of project objectives and how they will be accomplished.

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In this work we infer the underlying distribution on pore radius in human cortical bone samples using ultrasonic attenuation data. We first discuss how to formulate polydisperse attenuation models using a probabilistic approach and the Waterman Truell model for scattering attenuation. We then compare the Independent Scattering Approximation and the higher-order Waterman Truell models’ forward predictions for total attenuation in polydisperse samples. Following this, we formulate an inverse problem under the Prohorov Metric Framework coupled with variational regularization to stabilize this inverse problem. We then use experimental attenuation data taken from human cadaver samples and solve inverse problems resulting in nonparametric estimates of the probability density function on pore radius. We compare these estimates to the “true” microstructure of the bone samples determined via microCT imaging. We find that our methodology allows us to reliably estimate the underlying microstructure of the bone from attenuation data.

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Almost all studies of specific ion binding by carboxylates (-COO−) have considered only a single cation, but clustering of ions and ligands is a common phenomenon. We apply density functional theory to investigate how variations in the number of acetate ligands in binding to two monovalent cations affects ion binding preferences. We study a series of monovalent (Li+, Na+, K+, Cs+) ions relevant to experimental work on many topics, including ion channels, battery storage, water purification and solar cells. We find that the preferred optimal structure has 3 acetates except for Cs+, which has 2 acetates. The optimal coordination of the cation by the carboxylate O atoms is 4 for both Na+ and K+, and 3 for Li+ and Cs+. There is a 4-fold coordination minimum just a few kcal mol−1 higher than the optimal 3-fold structure for Li+. For two cations, multiple minima occur in the vicinity of the lowest free energy state. We find that, for Li, Na and K, the preferred optimal structure with two cations is favored over a mixture of single cation complexes, providing a basis for understanding ionic cluster formation that is relevant for engineering proteins and other materials for rapid, selective ion transport.

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This report summarizes the needs, challenges, and opportunities associated with carbon-free energy and energy storage for manufacturing and industrial decarbonization. Energy needs and challenges for different manufacturing and industrial sectors (e.g., cement/steel production, chemicals, materials synthesis) are identified. Key issues for industry include the need for large, continuous on-site capacity (tens to hundreds of megawatts), compatibility with existing infrastructure, cost, and safety. Energy storage technologies that can potentially address these needs, which include electrochemical, thermal, and chemical energy storage, are presented along with key challenges, gaps, and integration issues. Analysis tools to value energy storage technologies in the context of manufacturing and industrial decarbonizations are also presented. Material is drawn from the Energy Storage for Manufacturing and Industrial Decarbonization (Energy StorM) Workshop, held February 8 - 9, 2022. The objective was to identify research opportunities and needs for the U.S. Department of Energy as part of its Energy Storage Grand Challenge program.

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The goal of this workshop is to role play and walk through various UAS incursion scenarios to: 1. Recognize the complex interactions between physical protection, response, and UAS technologies in a nuclear security event; 2. Identify potential regulatory and legal complications dealing with UAS as aircraft; 3. Identify communication/coordination touch points with facility security and law enforcement; 4. Identify possible physical security and response strategies to help mitigate UAS impact.

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Due to significant computational expense, discrete element method simulations of jammed packings of size-dispersed spheres with size ratios greater than 1:10 have remained elusive, limiting the correspondence between simulations and real-world granular materials with large size dispersity. Invoking a recently developed neighbor binning algorithm, we generate mechanically stable jammed packings of frictionless spheres with power-law size distributions containing up to nearly 4 000 000 particles with size ratios up to 1:100. By systematically varying the width and exponent of the underlying power laws, we analyze the role of particle size distributions on the structure of jammed packings. The densest packings are obtained for size distributions that balance the relative abundance of large-large and small-small particle contacts. Although the proportion of rattler particles and mean coordination number strongly depend on the size distribution, the mean coordination of nonrattler particles attains the frictionless isostatic value of six in all cases. The size distribution of nonrattler particles that participate in the load-bearing network exhibits no dependence on the width of the total particle size distribution beyond a critical particle size for low-magnitude exponent power laws. This signifies that only particles with sizes greater than the critical particle size contribute to the mechanical stability. However, for high-magnitude exponent power laws, all particle sizes participate in the mechanical stability of the packing.

The Lip-field approach was introduced in Moës and Chevaugeon (2021) as a new way to regularize softening material models. It was tested in 1D quasistatic in Moës and Chevaugeon (2021) and 2D quasistatic in Chevaugeon and Moës (2021): this paper extends it to 1D dynamics, on the challenging problem of dynamic fragmentation. The Lip-field approach formulates the mechanical problem to be solved as an optimization problem, where the incremental potential to be minimized is the non-regularized one. Spurious localization is prevented by imposing a Lipschitz constraint on the damage field. The displacement and damage field at each time step are obtained by a staggered algorithm, that is the displacement field is computed for a fixed damage field, then the damage field is computed for a fixed displacement field. Indeed, these two problems are convex, which is not the case of the global problem where the displacement and damage fields are sought at the same time. The incremental potential is obtained by equivalence with a cohesive zone model, which makes material parameters calibration simple. A non-regularized local damage equivalent to a cohesive zone model is also proposed. It is used as a reference for the Lip-field approach, without the need to implement displacement jumps. These approaches are applied to the brittle fragmentation of a 1D bar with randomly perturbed material properties to accelerate spatial convergence. Both explicit and implicit dynamic implementations are compared. Favorable comparison to several analytical, numerical and experimental references serves to validate the modeling approach.

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This SAND report documents CIS Late Start LDRD Project 22-0311, "Differential geometric approaches to momentum-based formulations for fluids". The project primarily developed geometric mechanics formulations for momentum-based descriptions of nonrelativistic fluids, utilizing a differential geometry/exterior calculus treatment of momentum and a space+time splitting. Specifically, the full suite of geometric mechanics formulations (variational/Lagrangian, Lie-Poisson Hamiltonian and Curl-Form Hamiltonian) were developed in terms of exterior calculus using vector-bundle valued differential forms. This was done for a fairly general version of semi-direct product theory sufficient to cover a wide range of both neutral and charged fluid models, including compressible Euler, magnetohydrodynamics and Euler-Maxwell. As a secondary goal, this project also explored the connection between geometric mechanics formulations and the more traditional Godunov form (a hyperbolic system of conservation laws). Unfortunately, this stage did not produce anything particularly interesting, due to unforeseen technical difficulties. There are two publications related to this work currently in preparation, and this work will be presented at SIAM CSE 23, at which the PI is organizing a mini-symposium on geometric mechanics formulations and structure-preserving discretizations for fluids. The logical next step is to utilize the exterior calculus based understanding of momentum coupled with geometric mechanics formulations to develop (novel) structure-preserving discretizations of momentum. This is the main subject of a successful FY23 CIS LDRD "Structure-preserving discretizations for momentum-based formulations of fluids".

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Time-resolved X-ray thermometry is an enabling technology for measuring temperature and phase change of components. However, current diagnostic methods are limited in their ability due to the invasive nature of probes or the requirement of coatings and optical access to the component. Our proposed developments overcome these challenges by utilizing X-rays to directly measure the objects temperature. Variable-Temperature X-ray Diffraction (VT-XRD) was performed over a wide range of temperatures and diffraction angles and was performed on several materials to analyze the patterns of the bulk materials for sensitivity. "High-speed" VT-XRD was then performed for a single material over a small range of diffraction angles to see how fast the experiments could be performed, whilst still maintaining peaks sufficiently large enough for analysis.

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This report details work that was completed to address the Fiscal Year 2022 Advanced Science and Technology (AS&T) Laboratory Directed Research and Development (LDRD) call for “AI-enhanced Co-Design of Next Generation Microelectronics.” This project required concurrent contributions from the fields of 1) materials science, 2) devices and circuits, 3) physics of computing, and 4) algorithms and system architectures. During this project, we developed AI-enhanced circuit design methods that relied on reinforcement learning and evolutionary algorithms. The AI-enhanced design methods were tested on neuromorphic circuit design problems that have real-world applications related to Sandia’s mission needs. The developed methods enable the design of circuits, including circuits that are built from emerging devices, and they were also extended to enable novel device discovery. We expect that these AI-enhanced design methods will accelerate progress towards developing next-generation, high-performance neuromorphic computing systems.

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Tritium has a unique physical and chemical behavior which causes it to be highly mobile in the environment. As it behaves similarly to hydrogen in the environment, it may also be readily incorporated into the water cycle and other biological processes. These factors and other environmental transformations may also cause the oxidation of an elemental tritium release, resulting in a multiple order of magnitude increase in dose coefficient and radiotoxicity. While source term development and understanding for advanced reactors are still underway, tritium may be a radionuclide of interest. It is thus important to understand how tritium moves through the environment and how the MACCS accident consequence code handles acute tritium releases in an accident scenario. Additionally, existing tritium models may have functionalities that could inform updates to MACCS to handle tritium. In this report tritium transport is reviewed and existing tritium models are summarized in view of potential updates to MACCS.

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Computational design-based optimization is a well-used tool in science and engineering. Our report documents the successful use of a particle sensitivity analysis for design-based optimization within Monte Carlo sampling-based particle simulation—a currently unavailable capability. Such a capability enables the particle simulation communities to go beyond forward simulation and promises to reduce the burden on overworked analysts by getting more done with less computation.

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The focus of this project is to accelerate and transform the workflow of multiscale materials modeling by developing an integrated toolchain seamlessly combining DFT, SNAP, LAMMPS, (shown in Figure 1-1) and a machine-learning (ML) model that will more efficiently extract information from a smaller set of first-principles calculations. Our ML model enables us to accelerate first-principles data generation by interpolating existing high fidelity data, and extend the simulation scale by extrapolating high fidelity data (10² atoms) to the mesoscale (10⁴ atoms). It encodes the underlying physics of atomic interactions on the microscopic scale by adapting a variety of ML techniques such as deep neural networks (DNNs), and graph neural networks (GNNs). We developed a new surrogate model for density functional theory using deep neural networks. The developed ML surrogate is demonstrated in a workflow to generate accurate band energies, total energies, and density of the 298K and 933K Aluminum systems. Furthermore, the models can be used to predict the quantities of interest for systems with more number of atoms than the training data set. We have demonstrated that the ML model can be used to compute the quantities of interest for systems with 100,000 Al atoms. When compared with 2000 Al system the new surrogate model is as accurate as DFT, but three orders of magnitude faster. We also explored optimal experimental design techniques to choose the training data and novel Graph Neural Networks to train on smaller data sets. These are promising methods that need to be explored in the future.

The goal of this project was test how different representations of state uncertainty impact human decision making. Across a series of experiments, we sought to answer fundamental questions about human cognitive biases and how they are impacted by visual and numerical information. The results of these experiments identify problems and pitfalls to avoid when for presenting algorithmic outputs that include state uncertainty to human decision makers. Our findings also point to important areas for future research that will enable system designers to minimize biases in human interpretation for the outputs of artificial intelligence, machine learning, and other advanced analytic systems.

This report is a summary of a 3-year LDRD project that developed novel methods to detect faults in the electric power grid dramatically faster than today’s protection systems. Accurately detecting and quickly removing electrical faults is imperative for power system resilience and national security to minimize impacts to defense critical infrastructure. The new protection schemes will improve grid stability during disturbances and allow additional integration of renewable energy technologies with low inertia and low fault currents. Signal-based fast tripping schemes were developed that use the physics of the grid and do not rely on communication to reduce cyber risks for safely removing faults.

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Predictive design of REHEDS experiments with radiation-hydrodynamic simulations requires knowledge of material properties (e.g. equations of state (EOS), transport coefficients, and radiation physics). Interpreting experimental results requires accurate models of diagnostic observables (e.g. detailed emission, absorption, and scattering spectra). In conditions of Local Thermodynamic Equilibrium (LTE), these material properties and observables can be pre-computed with relatively high accuracy and subsequently tabulated on simple temperature-density grids for fast look-up by simulations. When radiation and electron temperatures fall out of equilibrium, however, non-LTE effects can profoundly change material properties and diagnostic signatures. Accurately and efficiently incorporating these non-LTE effects has been a longstanding challenge for simulations. At present, most simulations include non-LTE effects by invoking highly simplified inline models. These inline non-LTE models are both much slower than table look-up and significantly less accurate than the detailed models used to populate LTE tables and diagnose experimental data through post-processing or inversion. Because inline non-LTE models are slow, designers avoid them whenever possible, which leads to known inaccuracies from using tabular LTE. Because inline models are simple, they are inconsistent with tabular data from detailed models, leading to ill-known inaccuracies, and they cannot generate detailed synthetic diagnostics suitable for direct comparisons with experimental data. This project addresses the challenge of generating and utilizing efficient, accurate, and consistent non-equilibrium material data along three complementary but relatively independent research lines. First, we have developed a relatively fast and accurate non-LTE average-atom model based on density functional theory (DFT) that provides a complete set of EOS, transport, and radiative data, and have rigorously tested it against more sophisticated first-principles multi-atom DFT models, including time-dependent DFT. Next, we have developed a tabular scheme and interpolation methods that compactly capture non-LTE effects for use in simulations and have implemented these tables in the GORGON magneto-hydrodynamic (MHD) code. Finally, we have developed post-processing tools that use detailed tabulated non-LTE data to directly predict experimental observables from simulation output.

In the context of experimental vibration data, strain gauges can obtain linear and nonlinear dynamic measurements. However, measuring strain can be disincentivizing and expensive due to the complexity of data acquisition systems, lack of portability, and high costs. This research introduces the use of a low-cost efficient wireless intelligent sensor for strain (LEWIS-S) that is based on a portable-sensor-design platform that streamlines strain sensing. The softening behavior of a cantilever beam with geometric and inertial nonlinearities is characterized by the LEWIS-S based on high force level inputs. Two experiments were performed on a nonlinear cantilever beam with measurements obtained by the LEWIS-S sensor and an accelerometer. First, a sine sweep test was performed through the fundamental resonance of the system, then a ring-down test was performed from a large initial static deformation. Good agreement was revealed in quantities of interest such as frequency response functions, the continuous wavelet transforms, and softening behavior in the backbone curves.

This report documents the results of an FY22 ASC V&V level 2 milestone demonstrating new algorithms for multifidelity uncertainty quantification. Part I of the report describes the algorithms, studies their performance on a simple model problem, and then deploys the methods to a thermal battery example from the open literature. Part II (restricted distribution) applies the multifidelity UQ methods to specific thermal batteries of interest to the NNSA/ASC program.

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This progress report describes work performed during FY22 at Sandia National Laboratories (SNL) to assess the corrosion performance of cold spray coatings to enable optimization of cold spray for the purposes of mitigation and/or repair of potentially susceptible regions, corrosion, or stress corrosion cracking (SCC) in austenitic stainless steel for spent nuclear fuel (SNF) storage. Of particular concern is SCC, by which a through-wall crack could potentially form in a canister outer wall over time intervals that may be shorter than possible dry storage times. In FY21, initial corrosion explorations of cold spray coating were evaluated and in FY22, an expanded set of cold spray coatings with in-depth analysis of post-exposure accelerated testing was explored. Additionally, relevant atmospheric exposure testing was carried out and initial results are presented herein. The corrosion attack from the accelerated testing and more realistic atmospheric exposures environments were compared to identify potentially deleterious factors for corrosion as well as help to understand the applicability of accelerated testing for cold spray optimization. This initial analysis will help to enable optimization of the corrosion resistance cold spray, one of the more promising coating and repair techniques, for potential application in an SNF environment. Learnings from both are summarized, and implications and future work are presented in this report.

Liquid hydrogen (LH₂) used as a fuel onboard a heavy-duty vehicle can result in increased storage capacity and faster refueling relative to compressed gas. However, there are concerns about hydrogen losses from boil-off, potential safety issues, gaps in codes and standards for cryogenic hydrogen fuel, and technical challenges with LH₂ systems for widespread transportation applications. A failure modes and effects analysis (FMEA), a safety codes and standards review, and a design review of the onboard liquid hydrogen system for a heavy-duty vehicle identified some of these potential safety issues and gaps in the codes and standards. The FMEA identified some medium and low risk failure points of the conceptual design, and the design review identified how carefully pressure relief needs to be considered for LH₂ systems. In addition, a conceptual design for a LH₂ refueling station was developed. Rough capital costs for the refueling station design were $\$1 million$ and the layout occupied approximately 13,000 ft². These results can be used to inform future designs and analyses for LH₂ heavy-duty vehicles.

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Advanced, superscalar microprocessors ((Formula presented.)) are highly susceptible to wear-out failures because of their highly complex, densely packed circuit structure and extreme operational frequencies. Although many types of fault detection and mitigation strategies have been proposed, none have addressed the specific problem of detecting faults that lead to information leakage events on I/O channels of the (Formula presented.). Information leakage can be defined very generally as any type of output that the executing program did not intend to produce. In this work, we restrict this definition to output that represents a security concern, and in particular, to the leakage of plaintext or encryption keys, and propose a counter-based countermeasure to detect faults that cause this type of leakage event. Fault injection (FI) experiments are carried out on two RISC-V microprocessors emulated as soft cores on a Xilinx multi-processor System-on-chip (MPSoC) FPGA. The (Formula presented.) designs are instrumented with a set of counters that records the number of transitions that occur on internal nodes. The transition counts are collected from all internal nodes under both fault-free and faulty conditions, and are analyzed to determine which counters provide the highest fault coverage and lowest latency for detecting leakage faults. We show that complete coverage of all leakage faults is possible using only a single counter strategically placed within the branch compare logic of the (Formula presented.).

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When modeling complex physical systems with advanced dynamics, such as shocks and singularities, many classic methods for solving partial differential equations can return inaccurate or unusable results. One way to resolve these complex dynamics is through r-adaptive refinement methods, in which a fixed number of mesh points are shifted to areas of high interest. The mesh refinement map can be found through the solution of the Monge-Ampére equation, a highly nonlinear partial differential equation. Due to its nonlinearity, the numerical solution of the Monge-Ampére equation is nontrivial and has previously required computationally expensive methods. In this report, we detail our novel optimization-based, multigrid-enabled solver for a low-order finite element approximation of the Monge-Ampére equation. This fast and scalable solver makes r-adaptive meshing more readily available for problems related to large-scale optimal design. Beyond mesh adaptivity, our report discusses additional applications where our fast solver for the Monge-Ampére equation could be easily applied.

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Classification of features in a scene typically requires conversion of the incoming photonic field int the electronic domain. Recently, an alternative approach has emerged whereby passive structured materials can perform classification tasks by directly using free-space propagation and diffraction of light. In this manuscript, we present a theoretical and computational study of such systems and establish the basic features that govern their performance. We show that system architecture, material structure, and input light field are intertwined and need to be co-designed to maximize classification accuracy. Our simulations show that a single layer metasurface can achieve classification accuracy better than conventional linear classifiers, with an order of magnitude fewer diffractive features than previously reported. For a wavelength λ, single layer metasurfaces of size 100λ x 100λ with aperture density λ^-2 achieve ~96% testing accuracy on the MNIST dataset, for an optimized distance ~100λ to the output plane. This is enabled by an intrinsic nonlinearity in photodetection, despite the use of linear optical metamaterials. Furthermore, we find that once the system is optimized, the number of diffractive features is the main determinant of classification performance. The slow asymptotic scaling with the number of apertures suggests a reason why such systems may benefit from multiple layer designs. Finally, we show a trade-off between the number of apertures and fabrication noise.

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Multiple physical and chemical forms of a given radionuclide may be released in the event of a nuclear accident. Given that variable forms of an isotope may elicit changes in how that isotope moves through the environment and ultimately impacts human receptors, it is pertinent to understand how nuclear accident consequence models, such as MACCS, account for variable forms. This report documents a review of MACCS modeling capabilities for variability in radionuclide chemical and physical forms. This review centers on the current state-of-practice for dosimetry and deposition modeling of varying radionuclide forms to understand how consistent existing MACCS capabilities are with state of practice. This analysis is also used to inform potential MACCS model upgrades. MACCS conceptual models along with dosimetry and deposition related practices are discussed. Recommendations and suggestions for model improvements are posited.

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The composition and phase fraction of the intergranular phase of 94ND10 ceramic is determined and fabricated ex situ. The fraction of each phase is 85.96 vol% Al₂O₃ bulk phase, 9.46 vol% Mg-rich intergranular phase, 4.36 vol% Ca/Si-rich intergranular phase, and 0.22 vol% voids. The Ca/Si-rich phase consists of 0.628 at% Mg, 12.59 at% Si, 10.24 at% Ca, 17.23 at% Al, and balance O. The Mgrich phase consists of 14.17 at% Mg, 0.066 at% Si, 0.047 at% Ca, 28.69 at% Al, and balance O. XRD of the ex situ intergranular material made by mixed oxides consisting of the above phase and element fractions yielded 92 vol% MgAl₂O₄ phase and 8 vol% CaAl₂Si₂O₈ phase. The formation of MgAl₂O₄ phase is consistent with prior XRD of 94ND10, while the CaAl₂Si₂O₈ phase may exist in 94ND10 but at a concentration not readily detected with XRD. The MgAl₂O₄ and CaAl₂Si₂O₈ phases determined from XRD are expected to have the elemental compositions for the Mg-rich and Ca/Si-rich phases above by cation substitutions (e.g., some Mg substituted for by Ca in the Mg-rich phase) and impurity phases not detectable with XRD.

Estimation of two-phase fluid flow properties is important to understand and predict water and gas movement through the vadose zone for agricultural, hydrogeological, and engineering applications, such as for vapor-phase contaminant transport and/or containment of noble gases in the subsurface. In this second progress report of FY22, we present two ongoing activities related to imbibition testing on volcanic rock samples. We present the development of a new analytical solution predicting the temperature response observed during imbibition into dry samples, as discussed in our previous first progress report for FY22. We also illustrate the use of a multi-modal capillary pressure distribution to simulate both early- and late-time imbibition data collected on tuff core that can exhibit multiple pore types. These FY22 imbibition tests were conducted for an extended period (i.e., far beyond the time required for the wetting front to reach the top of the sample), which is necessary for parameter estimation and characterization of two different pore types within the samples.

Cathode-directed streamer evolution in near atmospheric air is modeled in 3D pin-to-plane geometries using a 3D kinetic Particle-In-Cell (PIC) code that simulates particle-particle collisions via the Direct Simulation Monte Carlo (DSMC) method. Due to the computational challenges associated with a complete 360° volumetric domain, a practical alternative was achieved using a wedge domain and a range of azimuthal angles was explored (5°, 15°, 30°, and 45°) to study possible effects on the streamer growth and propagation due to the finite wedge angle. A DC voltage of 6 kV is administered to a hemispherical anode of radius 100 μm, with a planar cathode held at ground potential, generating an over-volted state with an electric field of 4 MV/m across a 1500 μm gap. The domain is seeded with an initial ion and electron density of 10¹⁸ m^-3 at 1 eV temperature confined to a spherical region of radius 100 μm centered at the tip of the anode. The air chemistry model [1] includes standard Townsend breakdown mechanisms (electron-neutral elastic, excitation, ionization, attachment, and detachment collision chemistry and secondary electron emission) as well as streamer mechanisms (photoionization and ion-neutral collisions) via tracking excited state neutrals which can then either quench via collisions or spontaneously emit a photon based on specific Einstein-A coefficients [2, 3]. In this work, positive streamer dynamics are formally quantified for each wedge angle in terms of electron velocity and density as temporal functions of coordinates r, Φ, and z. Applying a random plasma seed for each simulation, particles of interest are tracked with near femtosecond temporal resolution out to 1.4 ns and spatially binned. This process is repeated six times and results are averaged. Prior 2D studies have shown that the reduced electric field, E/n, can significantly impact streamer evolution [4]. We extend the analysis to 3D wedge geometries, to limit computational costs, and examine the wedge angle’s effect on streamer branching, propagation, and velocity. Results indicate that the smallest wedge angle that produced an acceptably converged solution is 30°. The potential effects that a mesh, when under-resolved with respect to the Debye length, can impart on streamer dynamics and numerical heating were not investigated, and we explicitly state here that the smallest cell size was approximately 10 times the minimum λ_D in the streamer channel at late times. This constraint on cell size was the result of computational limitations on total mesh count.

The purpose of our report is to discuss the notion of entropy and its relationship with statistics. Our goal is to provide a manner in which you can think about entropy, its central role within information theory and relationship with statistics. We review various relationships between information theory and statistics—nearly all are well-known but unfortunately are often not recognized. Entropy quantities the "average amount of surprise" in a random variable and lies at the heart of information theory, which studies the transmission, processing, extraction, and utilization of information. For us, data is information. What is the distinction between information theory and statistics? Information theorists work with probability distributions. Instead, statisticians work with samples. In so many words, information theory using samples is the practice of statistics.

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Prediction of flow, transport, and deformation in fractured and porous media is critical to improving our scientific understanding of coupled thermal-hydrological-mechanical processes related to subsurface energy storage and recovery, nonproliferation, and nuclear waste storage. Especially, earth rock response to changes in pressure and stress has remained a critically challenging task. In this work, we advance computational capabilities for coupled processes in fractured and porous media using Sandia Sierra Multiphysics software through verification and validation problems such as poro-elasticity, elasto-plasticity and thermo-poroelasticity. We apply Sierra software for geologic carbon storage, fluid injection/extraction, and enhanced geothermal systems. We also significantly improve machine learning approaches through latent space and self-supervised learning. Additionally, we develop new experimental technique for evaluating dynamics of compacted soils at an intermediate scale. Overall, this project will enable us to systematically measure and control the earth system response to changes in stress and pressure due to subsurface energy activities.

Modeling real-world phenomena to any degree of accuracy is a challenge that the scientific research community has navigated since its foundation. Lack of information and limited computational and observational resources necessitate modeling assumptions which, when invalid, lead to model-form error (MFE). The work reported herein explored a novel method to represent model-form uncertainty (MFU) that combines Bayesian statistics with the emerging field of universal differential equations (UDEs). The fundamental principle behind UDEs is simple: use known equational forms that govern a dynamical system when you have them; then incorporate data-driven approaches – in this case neural networks (NNs) – embedded within the governing equations to learn the interacting terms that were underrepresented. Utilizing epidemiology as our motivating exemplar, this report will highlight the challenges of modeling novel infectious diseases while introducing ways to incorporate NN approximations to MFE. Prior to embarking on a Bayesian calibration, we first explored methods to augment the standard (non-Bayesian) UDE training procedure to account for uncertainty and increase robustness of training. In addition, it is often the case that uncertainty in observations is significant; this may be due to randomness or lack of precision in the measurement process. This uncertainty typically manifests as “noisy” observations which deviate from a true underlying signal. To account for such variability, the NN approximation to MFE is endowed with a probabilistic representation and is updated using available observational data in a Bayesian framework. By representing the MFU explicitly and deploying an embedded, data-driven model, this approach enables an agile, expressive, and interpretable method for representing MFU. In this report we will provide evidence that Bayesian UDEs show promise as a novel framework for any science-based, data-driven MFU representation; while emphasizing that significant advances must be made in the calibration of Bayesian NNs to ensure a robust calibration procedure.

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Writing software is difficult. However, writing complex, well tested and designed, and functionally correct software is incredibly difficult. An entire field of study is devoted to the validation and verification of software to address this problem, and in this paper we analyze the landscape of currently available third party software. We have divided our analyses into three separate subsections with regards to software validation: formal methods, static analysis, and test generation. Formal verification is the most complex method in which to validate software correctness, but also the most thorough as it truly validates the mathematical validity of the source code. Static analysis generally is relegated to abstract syntax tree traversal techniques to find errors related to faulty software such as memory leaks or stack overflow issues. Automatic test generation is similar in implementation to static analysis, but pushes a bit further in verifying the boundedness of function inputs and outputs with regards to annotated or parsed criteria. The crux of this report is to analyze and describe the software tools that implement these techniques to validate and verify software. Pros and cons related to installation, utilization, and capabilities of the frameworks are described, and reproducible examples are provided with a focus on usability. The initial survey concluded that the most interesting tools of note are Z3, Isabelle/HOL, and TLA+ with regards to formal verification; and Infer, Frama-C, and SonarQube with regards to static analysis. With these tools in mind, a final conjecture is provided that describes future avenues of utilizing these tools for developing a verification framework to assist in validating existing software at Sandia National Laboratories.

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A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.

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The increasing use of machine learning (ML) models to support high-consequence decision making drives a need to increase the rigor of ML-based decision making. Critical problems ranging from climate change to nonproliferation monitoring rely on machine learning for aspects of their analyses. Likewise, future technologies, such as incorporation of data-driven methods into the stockpile surveillance and predictive failure analysis for weapons components, will all rely on decision-making that incorporates the output of machine learning models. In this project, our main focus was the development of decision scientific methods that combine uncertainty estimates for machine learning predictions, with a domain-specific model of error costs. Other focus areas include uncertainty measurement in ML predictions, designing decision rules using multiobjecive optimization, the value of uncertainty reduction, and decision-tailored uncertainty quantification for probability estimates. By laying foundations for rigorous decision making based on the predictions of machine learning models, these approaches are directly relevant to every national security mission that applies, or will apply, machine learning to data, most of which entail some decision context.

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This report documents the progress made in simulating the HERMES-III Magnetically Insulated Transmission Line (MITL) and courtyard with EMPIRE and ITS. This study focuses on the shots that were taken during the months of June and July of 2019 performed with the new MITL extension. There were a few shots where there was dose mapping of the courtyard, 11132, 11133, 11134, 11135, 11136, and 11146. This report focuses on these shots because there was full data return from the MITL electrical diagnostics and the radiation dose sensors in the courtyard. The comparison starts with improving the processing of the incoming voltage into the EMPIRE simulation from the experiment. The currents are then compared at several location along the MITL. The simulation results of the electrons impacting the anode are shown. The electron impact energy and angle is then handed off to ITS which calculates the dose on the faceplate and locations in the courtyard and they are compared to experimental measurements. ITS also calculates the photons and electrons that are injected into the courtyard, these quantities are then used by EMPIRE to calculated the photon and electron transport in the courtyard. The details for the algorithms used to perform the courtyard simulations are presented as well as qualitative comparisons of the electric field, magnetic field, and the conductivity in the courtyard. Because of the computational burden of these calculations the pressure was reduce in the courtyard to reduce the computational load. The computation performance is presented along with suggestion on how to improve both the computational performance as well as the algorithmic performance. Some of the algorithmic changed would reduce the accuracy of the models and detail comparison of these changes are left for a future study. As well as, list of code improvements there is also a list of suggested experimental improvements to improve the quality of the data return.

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This project was broadly motivated by the need for new hardware that can process information such as images and sounds right at the point of where the information is sensed (e.g. edge computing). The project was further motivated by recent discoveries by group demonstrating that while certain organic polymer blends can be used to fabricate elements of such hardware, the need to mix ionic and electronic conducting phases imposed limits on performance, dimensional scalability and the degree of fundamental understanding of how such devices operated. As an alternative to blended polymers containing distinct ionic and electronic conducting phases, in this LDRD project we have discovered that a family of mixed valence coordination compounds called Prussian blue analogue (PBAs), with an open framework structure and ability to conduct both ionic and electronic charge, can be used for inkjet-printed flexible artificial synapses that reversibly switch conductance by more than four orders of magnitude based on electrochemically tunable oxidation state. Retention of programmed states is improved by nearly two orders of magnitude compared to the extensively studied organic polymers, thus enabling in-memory compute and avoiding energy costly off-chip access during training. We demonstrate dopamine detection using PBA synapses and biocompatibility with living neurons, evoking prospective application for brain - computer interfacing. By application of electron transfer theory to in-situ spectroscopic probing of intervalence charge transfer, we elucidate a switching mechanism whereby the degree of mixed valency between N-coordinated Ru sites controls the carrier concentration and mobility, as supported by density functional theory (DFT) .

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A semianalytic fluid model has been developed for characterizing relativistic electron emission across a warm diode gap. We demonstrate the use of this model in: 1) verifying multifluid codes in modeling compressible relativistic electron flows (the EMPIRE-Fluid code is used as an example); 2) elucidating key physics mechanisms characterizing the influence of compressibility and relativistic injection speed of the electron flow; and 3) characterizing the regimes over which a fluid model recovers physically reasonable solutions.

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Presented in this document are the theoretical aspects of capabilities contained in the Sierra/SM code. This manuscript serves as an ideal starting point for understanding the theoretical foundations of the code. For a comprehensive study of these capabilities, the reader is encouraged to explore the many references to scientific articles and textbooks contained in this manual. It is important to point out that some capabilities are still in development and may not be presented in this document. Further updates to this manuscript will be made as these capabilities come closer to production level.

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This report documents the results and conclusions of a recent project to understand the technoeconomics of utility-scale, particle-based concentrating solar power (CSP) facilities leveraging unique operational strategies. This project included two primary objectives. The first project objective was to build confidence in the modeling approaches applied to falling particle receivers (FPRs) including the effect s of wind. The second project objective was to create the necessary modeling capability to adequately predict and maximize the annual performance of utility-scale, particle-based CSP plants under anticipated conditions with and without active heliostat control. Results of an extensive model validation study provided the strongest evidence to date for the modeling strategies typically applied to FPRs, albeit at smaller receiver scales. This modeling strategy was then applied in a parametric study of candidate utility-scale FPRs, including both free-falling and multistage FPR concepts, to develop reduced order models for predicting the receiver thermal efficiency under anticipated environmental and operating conditions. Multistage FPRs were found to significantly improve receiver performance at utility-scales. These reduced order models were then leveraged in a sophisticated technoeconomic analysis to optimize utility-scale , particle-based CSP plants considering the potential of active heliostat control. In summary, active heliostat control did not show significant performance benefits to future utility-scale CSP systems though some benefit may still be realized in FPR designs with wide acceptance angles and/or with lower concentration ratios. Using the latest FPR technologies available, the levelized-cost of electricity was quantified for particle-based CSP facilities with nominal powers ranging from 5 MW_e up to 100 MW_e with many viable designs having costs < 0.06 $/kWh and local minimums occurring between ~25–35 MW_e.

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Power spectrum analysis (PSA) is a fast, non-destructive, sensitive method for examining commercial off-the-shelf ( COTS ) electronic components. These features make PSA attractive for both component screening and surveillance in support of component reliability efforts. Current analysis methods limit the utility of PSA due to the need to manually examine the results of analysis to identify anomalous parts. This study demonstrates the development and application of a workflow to automate the screening of COTS electronic components. Further, this study demonstrates the use of multivariate algorithms to assess aging of Zener diodes. These workflows can be readily extended to other components, combining the benefits of PSA and multivariate analysis to screen and evaluate COTS electronic components.

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This study investigates the octane requirements of a hybrid flame propagation and controlled autoignition mode referred to as mixed-mode combustion (MMC), which allows for strong control over combustion parameters via a spark-initiated deflagration phase. Due to the throughput limitations associated with both experiments and 3-D computational fluid dynamics calculations, a hybrid 0-D and 1-D modeling methodology was developed, supported by experimental validation data. This modeling approach relied on 1-D, two-zone engine simulations to predict bulk in-cylinder thermodynamic conditions over a range of engine speeds, compression ratios, intake pressures, trapped residual levels, fueling rates, and spark timings. Those predictions were then transferred to a 0-D chemical kinetic model, which was used to evaluate the autoignition behavior of fuels when subjected to temperature-pressure trajectories of interest. Finally, the predicted autoignition phasings were screened relative to the progress of the modeled deflagration-based combustion in order to determine if an operating condition was feasible or infeasible due to knock or stability limits. The combined modeling and experimental results reveal that MMC has an octane requirement similar to modern stoichiometric spark-ignition engines in that fuels with high research octane number (RON) and high octane sensitivity (S) enable higher loads. Experimental trends with varying RON and S were well predicted by the model for 1000 and 1400 rpm, confirming its utility in identifying the compatibility of a fuel's autoignition behavior with an engine configuration and operating strategy. However, the model was not effective in predicting (nor designed to predict) operability limits due to cycle-to-cycle variations, which experimentally inhibited operation of some fuels at 2000 rpm. Putting the operable limits and efficiency from MMC in the context of a state-of-the-art engine, the MMC showed superior efficiencies over the range investigated, demonstrating the potential to further improve fuel economy.

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This report documents a method for the quantitative identification of radionuclides of potential interest for accident consequence analysis involving advanced nuclear reactors. Based on previous qualitative assessments of radionuclide inventories for advanced reactors coupled with the review of a radiological inventory developed for a heat pipe reactor, a 1 Ci activity airborne release was calculated for 137 radionuclides using the MACCS 4.1 code suite. Several assumptions regarding release conditions were made and discussed herein. The potential release of a heat pipe reactor inventory was also modeled following the same assumptions. Results provide an estimation of the relative EARLY and CHRONC phase dose contribution from advanced reactor radionuclides and are normalized to doses from equivalent releases of I-131 and Cs-137, respectively. Ultimately, a list of 69 radionuclides with EARLY or CHRONC dose contributions at least 1/100th that of I-131 or Cs-137, respectively – 48 of which are currently considered for LWR consequence analyses – was identified of being of potential importance for analyses involving a heat pipe reactor.

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Brittle material failure in high consequence systems can appear random and unpredictable at subcritical stresses. Gaps in our understanding of how structural flaws and environmental factors (humidity, temperature) impact fracture propagation need to be addressed to circumvent this issue. A combined experimental and computational approach composed of molecular dynamics (MD) simulations, numerical modeling, and atomic force microscopy (AFM) has been undertaken to identify mechanisms of slow crack growth in silicate glasses. AFM characterization of crack growth as slow as 10^-13 m/s was observed, with some stepwise crack growth. MD simulations have identified the critical role of inelastic relaxation in crack propagation, including evolution of the structure during relaxation. A numerical model for the existence of a stress intensity threshold, a stress intensity below which a fracture will not propagate, was developed. This transferrable model for predicting slow crack growth is being incorporated into mission-based programs.

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Simulating molecules and atomic systems at quantum accuracy is a grand challenge for science in the 21^st century. Quantum-accurate simulations would enable the design of new medicines and the discovery of new materials. The defining problem in this challenge is that quantum calculations on large molecules, like proteins or DNA, are fundamentally impossible with current algorithms. In this work, we explore a range of different methods that aim to make large, quantum-accurate simulations possible. We show that using advanced classical models, we can accurately simulate ion channels, an important biomolecular system. We show how advanced classical models can be implemented in an exascale-ready software package. Lastly, we show how machine learning can learn the laws of quantum mechanics from data and enable quantum electronic structure calculations on thousands of atoms, a feat that is impossible for current algorithms. Altogether, this work shows that combining advances in physics models, computing, and machine learning, we are moving closer to the reality of accurately simulating our molecular world.

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Chemical engineering systems often involve a functional porous medium, such as in catalyzed reactive flows, fluid purifiers, and chromatographic separations. Ideally, the flow rates throughout the porous medium are uniform, and all portions of the medium contribute efficiently to its function. The permeability is a property of a porous medium that depends on pore geometry and relates flow rate to pressure drop. Additive manufacturing techniques raise the possibilities that permeability can be arbitrarily specified in three dimensions, and that a broader range of permeabilities can be achieved than by traditional manufacturing methods. Using numerical optimization methods, we show that designs with spatially varying permeability can achieve greater flow uniformity than designs with uniform permeability. We consider geometries involving hemispherical regions that distribute flow, as in many glass chromatography columns. By several measures, significant improvements in flow uniformity can be obtained by modifying permeability only near the inlet and outlet.

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Particle heat exchangers are a critical enabling technology for next generation concentrating solar power (CSP) plants that use supercritical carbon dioxide (sCO₂) as a working fluid. This report covers the design, manufacturing and testing of a prototype particle-to-sCO₂ heat exchanger targeting thermal performance levels required to meet commercial scale cost targets. In addition, the the design and assembly of integrated particle and sCO₂ flow loops for heat exchanger performance testing are detailed. The prototype heat exchanger was tested to particle inlet temperatures of 500 °C at 17 MPa which resulted in overall heat transfer coefficients of approximately 300 W/m²-K at the design point and cases using high approach temperature with peak values as high as 400 W/m²-K

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