Quantifying the sensitivity - how a quantity of interest (QoI) varies with respect to a parameter – and response – the representation of a QoI as a function of a parameter - of a computer model of a parametric dynamical system is an important and challenging problem. Traditional methods fail in this context since sensitive dependence on initial conditions implies that the sensitivity and response of a QoI may be ill-conditioned or not well-defined. If a chaotic model has an ergodic attractor, then ergodic averages of QoIs are well-defined quantities and their sensitivity can be used to characterize model sensitivity. The response theorem gives sufficient conditions such that the local forward sensitivity – the derivative with respect to a given parameter - of an ergodic average of a QoI is well-defined. We describe a method based on ergodic and response theory for computing the sensitivity and response of a given QoI with respect to a given parameter in a chaotic model with an ergodic and hyperbolic attractor. This method does not require computation of ensembles of the model with perturbed parameter values. The method is demonstrated and some of the computations are validated on the Lorenz 63 and Lorenz 96 models.
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.
Sandia National Laboratories (SNL) is designing and developing an Artificial Intelligence (AI)-enabled smart digital assistant (SDA), Inspecta (International Nuclear Safeguards Personal Examination and Containment Tracking Assistant). The goal is to provide inspectors an in-field digital assistant that can perform tasks identified as tedious, challenging, or prone to human error. During 2021, we defined the requirements for Inspecta based on reviews of International Atomic Energy Agency (IAEA) publications and interviews with former IAEA inspectors. We then mapped the requirements to current commercial or open-source technical capabilities to provide a development path for an initial Inspecta prototype while highlighting potential research and development tasks. We selected a highimpact inspection task that could be performed by an early Inspecta prototype and are developing the initial architecture, including hardware platform. This paper describes the methodology for selecting an initial task scenario, the first set of Inspecta skills needed to assist with that task scenario and finally the design and development of Inspecta’s architecture and platform.
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.
Accurate modeling of subsurface flow and transport processes is vital as the prevalence of subsurface activities such as carbon sequestration, geothermal recovery, and nuclear waste disposal increases. Computational modeling of these problems leverages poroelasticity theory, which describes coupled fluid flow and mechanical deformation. Although fully coupled monolithic schemes are accurate for coupled problems, they can demand significant computational resources for large problems. In this work, a fixed stress scheme is implemented into the Sandia Sierra Multiphysics toolkit. Two implementation methods, along with the fully coupled method, are verified with one-dimensional (1D) Terzaghi, 2D Mandel, and 3D Cryer sphere benchmark problems. The impact of a range of material parameters and convergence tolerances on numerical accuracy and efficiency was evaluated. Overall the fixed stress schemes achieved acceptable numerical accuracy and efficiency compared to the fully coupled scheme. However, the accuracy of the fixed stress scheme tends to decrease with low permeable cases, requiring the finer tolerance to achieve a desired numerical accuracy. For the fully coupled scheme, high numerical accuracy was observed in most of cases except a low permeability case where an order of magnitude finer tolerance was required for accurate results. Finally, a two-layer Terzaghi problem and an injection–production well system were used to demonstrate the applicability of findings from the benchmark problems for more realistic conditions over a range of permeability. Simulation results suggest that the fixed stress scheme provides accurate solutions for all cases considered with the proper adjustment of the tolerance. This work clearly demonstrates the robustness of the fixed stress scheme for coupled poroelastic problems, while a cautious selection of numerical tolerance may be required under certain conditions with low permeable materials.
This report details a new method for propagating parameter uncertainty (forward uncertainty quantification) in partial differential equations (PDE) based computational mechanics applications. The method provides full-field quantities of interest by solving for the joint probability density function (PDF) equations which are implied by the PDEs with uncertain parameters. Full-field uncertainty quantification enables the design of complex systems where quantities of interest, such as failure points, are not known apriori. The method, motivated by the well-known probability density function (PDF) propagation method of turbulence modeling, uses an ensemble of solutions to provide the joint PDF of desired quantities at every point in the domain. A small subset of the ensemble is computed exactly, and the remainder of the samples are computed with approximation of the driving (dynamics) term of the PDEs based on those exact solutions. Although the proposed method has commonalities with traditional interpolatory stochastic collocation methods applied directly to quantities of interest, it is distinct and exploits the parameter dependence and smoothness of the dynamics term of the governing PDEs. The efficacy of the method is demonstrated by applying it to two target problems: solid mechanics explicit dynamics with uncertain material model parameters, and reacting hypersonic fluid mechanics with uncertain chemical kinetic rate parameters. A minimally invasive implementation of the method for representative codes SPARC (reacting hypersonics) and NimbleSM (finite- element solid mechanics) and associated software details are described. For solid mechanics demonstration problems the method shows order of magnitudes improvement in accuracy over traditional stochastic collocation. For the reacting hypersonics problem, the method is implemented as a streamline integration and results show very good accuracy for the approximate sample solutions of re-entry flow past the Apollo capsule geometry at Mach 30.
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 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.
Researchers have recently estimated that Arctic submarine permafrost currently traps 60 billion tons of methane and contains 560 billion tons of organic carbon in seafloor sediments and soil, a giant pool of carbon with potentially large feedbacks on the climate system. Unlike terrestrial permafrost, the submarine permafrost system has remained a “known unknown” because of the difficulty in acquiring samples and measurements. Consequently, this potentially large carbon stock never yet considered in global climate models or policy discussions, represents a real wildcard in our understanding of Earth’s climate. This report summarizes our group’s effort at developing a numerical modeling framework designed to produce a first-of-its-kind estimate of Arctic methane gas releases from the marine sediments to the water column, and potentially to the atmosphere, where positive climate feedback may occur. Newly developed modeling capability supported by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories now gives us the ability to probabilistically map gas distribution and quantity in the seabed by using a hybrid approach of geospatial machine learning, and predictive numerical thermodynamic ensemble modeling. The novelty in this approach is its ability to produce maps of useful data in regions that are only sparsely sampled, a common challenge in the Arctic, and a major obstacle to progress in the past. By applying this model to the circum-Arctic continental shelves and integrating the flux of free gas from in situ methanogenesis and dissociating gas hydrates from the sediment column under climate forcing, we can provide the most reliable estimate of a spatially and temporally varying source term for greenhouse gas flux that can be used by global oceanographic circulation and Earth system models (such as DOE’s E3SM). The result will allow us to finally tackle the wildcard of the submarine permafrost carbon system, and better inform us about the severity of future national security threats that sustained climate change poses.
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 Hong-Ou-Mandel (HOM) effect is a fascinating quantum phenomenon that defies classical explanation. Traditionally, remote nonlinear sources have been used to achieve coincident photons at the HOM beam splitter. Here, we suggest that the coincident emission source required for HOM interference can be created locally using superradiant near field coupled emitters positioned across the beam splitter gap. We show that sensitivity to permittivity changes in the beam splitter gap, and corresponding Fisher information can be substantially enhanced with HOM photon detection. Subsequently, we outline several strategies for integration of superradiant emitters with practical sensor systems. Taken together, these findings should pave a way for a wide array of near field HOM quantum sensors and novel quantum devices.
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 MWe up to 100 MWe with many viable designs having costs < 0.06 $/kWh and local minimums occurring between ~25–35 MWe.
Exterior solar glaze was added to a 3 foot x 3 foot x 3 foot aluminum solar collector that had six triangular dimpled fins for enhanced heat transfer. The interior vertical wall on the south side was also dimpled. The solar glaze was added to compare its solar collection performance with unglazed solar collector experiments conducted at Sandia in 2021. The east, west, front, and top sides of the solar collector were encased with solar glaze glass. Because the solar incident heat on the north and bottom sides was minimal, they were insulated to retain the heat that was collected by the other four sides. The advantages of the solar glaze include the entrapment of more solar heat, as well as insulation from the wind. The disadvantages are that it increases the cost of the solar collector and has fragile structural properties when compared to the aluminum walls. Nevertheless, prior to conducting experiments with the glazed solar collector, it was not clear if the benefits outweighed the disadvantages. These issues are addressed herein, with the conclusion that the additional amount of heat collected by the glaze justifies the additional cost. The solar collector glaze design, experimental data, and costs and benefits are documented in this report.
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.
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.
Solar Thermal Ammonia Production has the potential to synthesize ammonia in a green, renewable process that can greatly reduce the carbon footprint left by conventional Haber-Bosch reaction. Ternary nitrides in the family A3BxN (A=Co, Ni, Fe; B=Mo; x=2,3) have been identified as a potential candidate for NH3 production. Experiments with Co3Mo3N in Ammonia Synthesis Reactor demonstrate cyclable NH3 production from bulk nitride under pure H2. Production rates were fairly flat in all the reduction steps with no evident dependence on the consumed solid-state nitrogen, as would be expected from catalytic Mars-van Krevelen mechanism. Material can be re-nitridized under pure N2. Bulk nitrogen per reduction step average between 25 – 40% of the total solid-state nitrogen. Selectivity to NH3 stabilized at 55 – 60% per cycle. Production rates (NH3 and N2) become apparent above 600 °C at P(H2) = 0.5 – 2 bar. Optimal point of operation to keep selectivity high without compromising NH3 rates currently estimated at 650 °C and 1.5 - 2 bar. The next steps are to optimize production rates, examine effect of N2 addition in NH3 synthesis reaction, and test additional ternary nitrides.
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.
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.
In this work we present a novel method for improving the high-temperature performance of silicon photomultipliers (SiPMs) via focused ion beam (FIB) modification of individual microcells. The literature suggests that most of the dark count rate (DCR) in a SiPM is contributed by a small percentage (<5%) of microcells. By using a FIB to electrically deactivate this relatively small number of microcells, we believe we can greatly reduce the overall DCR of the SiPM at the expense of a small reduction in overall photodetection efficiency, thereby improving its high temperature performance. In this report we describe our methods for characterizing the SiPM to determine which individual microcells contribute the most to the DCR, preparing the SiPM for FIB, and modifying the SiPM using the FIB to deactivate the identified microcells.
Representation of soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon climate feedbacks. The magnitude of this uncertainty can be reduced by accurate representation of environmental controllers of SOC stocks in ESMs. In this study, we used data of environmental factors, field SOC observations, ESM projections and machine learning approaches to identify dominant environmental controllers of SOC stocks and derive functional relationships between environmental factors and SOC stocks. Our derived functional relationships predicted SOC stocks with similar accuracy as the machine learning approach. We used the derived relationships to benchmark the coupled model intercomparison project phase six ESM representation of SOC stocks. We found divergent environmental control representation in ESMs in comparison to field observations. Representation of SOC in ESMs can be improved by including additional environmental factors and representing their functional relationships with SOC consistent with observations.
This paper describes a new non-charge-based data storing technique in NAND flash memory called watermark that encodes read-only data in the form of physical properties of flash memory cells. Unlike traditional charge-based data storing method in flash memory, the proposed technique is resistant to total ionizing dose (TID) effects. To evaluate its resistance to irradiation effects, we analyze data stored in several commercial single-level-cell (SLC) flash memory chips from different vendors and technology nodes. These chips are irradiated using a Co-60 gamma-ray source array for up to 100 krad(Si) at Sandia National Laboratories. Experimental evaluation performed on a flash chip from Samsung shows that the intrinsic bit error rate (BER) of watermark increases from mathbf {sim }0.8 % for TID = 0 krad(Si) to mathbf {mathrm {sim }}1 % for TID = 100 krad(Si). Conversely, the BER of charge-based data stored on the same chip increases from 0% at TID = 0 krad(Si) to 1.5% at TID = 100 krad(Si). The results imply that the proposed technique may potentially offer significant improvements in data integrity relative to traditional charge-based data storage for very high radiation (TID mathbf { > } 100 krad(Si)) environments. These gains in data integrity relative to the charge-based data storage are useful in radiation-prone environments, but they come at the cost of increased write times and higher BERs before irradiation.
This project created and demonstrated a framework for the efficient and accurate prediction of complex systems with only a limited amount of highly trusted data. These next generation computational multi-fidelity tools fuse multiple information sources of varying cost and accuracy to reduce the computational and experimental resources needed for designing and assessing complex multi-physics/scale/component systems. These tools have already been used to substantially improve the computational efficiency of simulation aided modeling activities from assessing thermal battery performance to predicting material deformation. This report summarizes the work carried out during a two year LDRD project. Specifically we present our technical accomplishments; project outputs such as publications, presentations and professional leadership activities; and the project’s legacy.
Widespread integration of social media into daily life has fundamentally changed the way society communicates, and, as a result, how individuals develop attitudes, personal philosophies, and worldviews. The excess spread of disinformation and misinformation due to this increased connectedness and streamlined communication has been extensively studied, simulated, and modeled. Less studied is the interaction of many pieces of misinformation, and the resulting formation of attitudes. We develop a framework for the simulation of attitude formation based on exposure to multiple cognitions. We allow a set of cognitions with some implicit relational topology to spread on a social network, which is defined with separate layers to specify online and offline relationships. An individual’s opinion on each cognition is determined by a process inspired by the Ising model for ferromagnetism. We conduct experimentation using this framework to test the effect of topology, connectedness, and social media adoption on the ultimate prevalence of and exposure to certain attitudes.
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".
The methyl radical plays a central role in plasma-assisted hydrocarbon chemistry but is challenging to detect due to its high reactivity and strongly pre-dissociative electronically excited states. We report the development of a photo-fragmentation laser-induced fluorescence (PF-LIF) diagnostic for quantitative 2D imaging of methyl profiles in a plasma. This technique provides temporally and spatially resolved measurements of local methyl distributions, including in near-surface regions that are important for plasma-surface interactions such as plasma-assisted catalysis. The technique relies on photo-dissociation of methyl by the fifth harmonic of a Nd:YAG laser at 212.8 nm to produce CH fragments. These photofragments are then detected with LIF imaging by exciting a transition in the B-X(0, 0) band of CH with a second laser at 390 nm. Fluorescence from the overlapping A-X(0, 0), A-X(1, 1), and B-X(0, 1) bands of CH is detected near 430 nm with the A-state populated by collisional B-A electronic energy transfer. This non-resonant detection scheme enables interrogation close to a surface. The PF-LIF diagnostic is calibrated by producing a known amount of methyl through photo-dissociation of acetone vapor in a calibration gas mixture. We demonstrate PF-LIF imaging of methyl production in methane-containing nanosecond pulsed plasmas impinging on dielectric surfaces. Absolute calibration of the diagnostic is demonstrated in a diffuse, plane-to-plane discharge. Measured profiles show a relatively uniform distribution of up to 30 ppm of methyl. Relative methyl measurements in a filamentary plane-to-plane discharge and a plasma jet reveal highly localized intense production of methyl. The utility of the PF-LIF technique is further demonstrated by combining methyl measurements with formaldehyde LIF imaging to capture spatiotemporal correlations between methyl and formaldehyde, which is an important intermediate species in plasma-assisted oxidative coupling of methane.
Performance of geologic radioactive waste repositories depends on near-field and far-field processes, including km-scale flow and transport in engineered and natural barriers, that may require simulations of up to 1 M years of regulatory period. For a relatively short time span (less than 1000 years), the thermohydro-mechanical-chemical (THMC) coupled processes caused by heat from the waste package will influence near-field multiphase flow, chemical/reactive transport, and mechanical behaviors in the repository system. This study integrates the heat-driven perturbations in thermo-hydro-mechanical characteristics into thermo-hydro-chemical simulations using PFLOTRAN to reduce dimensionality and improve computational efficiency by implementing functions of stress-dependent permeability and saturation-temperature-dependent thermal conductivity. These process couplings are developed for spent nuclear fuel in dual-purpose canisters in two different hypothetical repositories: a shale repository and a salt repository.
The How To Manual supplements the User’s Manual and the Theory Manual. The goal of the How To Manual is to reduce learning time for complex end to end analyses. These documents are intended to be used together. See the User’s Manual for a complete list of the options for a solution case. All the examples are part of the Sierra/SD test suite. Each runs as is. The organization is similar to the other documents: How to run, Commands, Solution cases, Materials, Elements, Boundary conditions, and then Contact. The table of contents and index are indispensable. The Geometric Rigid Body Modes section is shared with the Users Manual.
Michelsen, Hope A.; Campbell, Matthew F.; Johansson, K.O.; Tran, Ich C.; Schrader, Paul; Bambha, Ray; Cenker, Emre; Hammons, Joshua A.; Zhu, Chenhui; Schaible, Eric; Van Buuren, Anthony
We have characterized soot particles measured in situ in a laminar co-flow ethylene-air diffusion flame using small-angle X-ray scattering (SAXS). The analysis includes temperature measurements made with coherent anti-Stokes Raman spectroscopy (CARS) and complements soot volume-fraction and maturity measurements made with laser-induced incandescence (LII). We compared the results of fits to the SAXS measurements using a unified model and a fractal core-shell model. Power-law parameters yielded by the unified model indicate that aggregates of primary particles are in the mass-fractal regime, whereas the primary particles are in the surface-fractal regime in the middle of the flame. Higher and lower in the flame, the primary-particle power-law parameter approaches 4, suggesting smooth primary particles. These trends are consistent with fits using the fractal core-shell model, which indicate that particles have an established core-shell structure in the middle of the flame and are internally homogeneous at higher and lower heights in the flame. Primary-particle size distributions derived using the fractal core-shell model demonstrate excellent agreement with distributions inferred from transmission electron microscopy (TEM) images in the middle of the flame. Higher in the flame, a second small mode appears in the size distributions, suggesting particle fragmentation during oxidation. Surface oxidation would explain (1) aggregate fragmentation and (2) loss of core-shell structure leading to smoother primary-particle surfaces by removal of carbon overlayers. SAXS measurements are much more sensitive to incipient and young soot particles than LII and demonstrate significant volume fraction from particles low in the flame where the LII signal is negligible.
Particle-in-cell simulations are used to study how neutral pressure influences plasma properties at the sheath edge. The high rate of ion–neutral collisions at pressures above several mTorr are found to cause a decrease in the ion velocity at the sheath edge (collisional Bohm criterion), a decrease in the edge-to-center density ratio (hl factor), and an increase in the sheath width and sheath potential drop. A comparison with existing analytic models generally indicates favorable agreement, but with some distinctions. One is that models for the hl factor need to be made consistent with the collisional Bohm criterion. With this and similar corrections, a comprehensive fluid-based model of the plasma boundary transition is constructed that compares well with the simulation results.
Nitric Oxide (NO) can significantly influence the autoignition reactivity and this can affect knock limits in conventional stoichiometric SI engines. Previous studies also revealed that the role of NO changes with fuel type. Fuels with high RON (Research Octane Number) and high Octane Sensitivity (S = RON - MON (Motor Octane Number)) exhibited monotonically retarding knock-limited combustion phasing (KL-CA50) with increasing NO. In contrast, for a high-RON, low-S fuel, the addition of NO initially resulted in a strongly retarded KL-CA50 but beyond the certain amount of NO, KL-CA50 advanced again. The current study focuses on same high-RON, low-S Alkylate fuel to better understand the mechanisms responsible for the reversal in the effect of NO on KL-CA50 beyond a certain amount of NO. Experiments were conducted to measure the responses of KL-CA50 and trace-autoignition CA50, the latter being indicative of CA50 at which end-gas autoignition starts to become measurable from the apparent heat-release rate. Chemical-kinetics simulations were conducted to reveal the role of NO for end-gas autoignition, with a specific focus on sequential autoignition in a thermally stratified end-gas. The simulation results reveal that the magnitude of low-temperature heat release (LTHR) generally increases with NO. However, the relative importance of NO for enhancing LTHR diminishes when the LTHR inherent to a fuel's chemistry is strong, such as at lower temperatures in a thermal boundary layer. This rendered more uniform LTHR within a hypothetical thermal boundary and led to a more sequential (i.e. slower) autoignition event. It was also revealed that a change in compression ratio influences the importance of intermediate-temperature heat release (ITHR) due to changes of the temperature-pressure history of the end-gas. Together with the condition where end-gas autoignition occurs more sequentially, the shorter time spent in LTHR and ITHR regime can counter the increase in autoignition reactivity at high NO levels and allow KL-CA50 to advance.
Molybdenum disulfide (MoS2) is a lamellar solid lubricant often used in aerospace applications because of its extremely low friction coefficient (∼0.01) in inert environments. The lubrication performance of MoS2 is significantly impaired by exposure to even small amounts of water and oxygen, and the mechanisms behind this remain poorly understood. We use density functional theory calculations to study the binding of water on MoS2 sheets with and without defects. In general, we find that pristine MoS2 is slightly hydrophilic but that defects greatly increase the binding affinity for water. Intercalated water disrupts the crystal structure of bulk MoS2 due to the limited space between lamellae (∼3.4 Å), and this leads to generally unfavorable adsorption, except in the cases where water molecules are located on the sites of sulfur vacancies. We also find that water adsorption is more favorable directly below a surface layer of MoS2 compared to in the bulk.
Herein the dynamic deformation response of two quenching and partitioning (Q&P) steels was investigated using a high strain rate tension pressure bar and in-situ synchrotron radiography and diffraction. This allowed for concurrent measurements of the martensitic transformation, the elastic strains/stresses on the martensite and ferrite, and the bulk mechanical behavior. The steel with the greater fraction of ferrite exhibited greater ductility and lower strength, suggesting that dislocation slip in ferrite enhanced the deformability. Meanwhile, the kinetics of the martensitic transformation appeared similar for both steels, although the steel with a greater ferrite fraction retained more austenite in the neck after fracture.
This document presents tests from the Sierra Structural Mechanics verification test suite. Each of these tests is run nightly with the Sierra/SD code suite and the results of the test checked versus the correct analytic result. For each of the tests presented in this document the test setup, derivation of the analytic solution, and comparison of the Sierra/SD code results to the analytic solution is provided. This document can be used to confirm that a given code capability is verified or referenced as a compilation of example problems.
Childhood body mass index (BMI) is a widely used measure of adiposity in children (<18 years of age). Children grow with individual tempo and individuals of the same age, or of the same BMI, might be in different phases in their individual growth curves. Variability between different childhood BMI curves can be separated in two components: phase variability (x-axis; time) and amplitude variability (y-axis; BMI). Phase variability can be thought of arising from differences in maturational age between individuals. This is related to the timing of peaks and valleys in a child’s BMI curve.
Three-dimensional effects play a crucial role during the hot-spot formation in inertial confinement fusion (ICF) implosions. A data analysis technique for 3D hot-spot reconstruction from experimental observables has been developed to characterize the effects of low modes on 3D hot-spot formations. In nuclear measurements, the effective flow direction, governed by the maximum eigenvalue in the velocity variance of apparent ion temperatures, has been found to agree with the measured hot-spot flows for implosions dominated by mode ℓ = 1. Asymmetries in areal-density (ρR) measurements were found to be characterized by a unique cosine variation along the hot-spot flow axis. In x-ray images, a 3D hot-spot x-ray emission tomography method was developed to reconstruct the 3D hot-spot plasma emissivity using a generalized spherical-harmonic Gaussian function. The gradient-descent algorithm was used to optimize the mapping between the projections from the 3D hot-spot emission model and the measured x-ray images along multiple views. Furthermore, this work establishes a platform to analyze 3D low-mode core asymmetries in ICF.
This article illustrates the use of unsupervised probabilistic learning techniques for the analysis of planetary reentry trajectories. A three-degree-of-freedom model was employed to generate optimal trajectories that comprise the training datasets. The algorithm first extracts the intrinsic structure in the data via a diffusion map approach. We find that data resides on manifolds of much lower dimensionality compared to the high-dimensional state space that describes each trajectory. Using the diffusion coordinates on the graph of training samples, the probabilistic framework subsequently augments the original data with samples that are statistically consistent with the original set. The augmented samples are then used to construct conditional statistics that are ultimately assembled in a path planning algorithm. In this framework, the controls are determined stage by stage during the flight to adapt to changing mission objectives in real-Time.
Traditionally electric grid planning strives to maintain safe, reliable, efficient, and affordable service for current and future customers. As policies, social preferences, and the threat landscape evolve, additional considerations for power system planners are emerging, including decarbonization, resilience, and energy equity and justice. The MOD-Plan framework leverages and extends prior work to provide a framework for integrating incorporating resilience, equity, and decarbonization into integrated distribution system planning.
Spherical implosions in Inertial Confinement Fusion (ICF) are inherently sensitive to perturbations that may arise from experimental constraints and errors. Control and mitigation of low-mode (long wavelengths) perturbations is a key milestone to improving implosion performances. Here, we present the first 3-D radiation-hydrodynamic simulations of directly driven ICF implosions with an inline package for polarized Crossed-Beam Energy Transfer (CBET). Simulations match bang times, yields (separately accounting for laser-induced high modes and fuel age), hot spot flow velocities and direction, for which polarized CBET contributes to the systematic flow orientation evident in the OMEGA implosion database. Current levels of beam mispointing, imbalance, target offset and asymmetry from polarized CBET degrade yields by more than 40%. The effectiveness of two mitigation strategies for low-modes is explored.
Rare-earth polynuclear metal-organic frameworks (RE-MOFs) have demonstrated high durability for caustic acid gas adsorption and separation based on gas adsorption to the metal clusters. The metal clusters in the RE-MOFs traditionally contain RE metals bound by μ3-OH groups connected via organic linkers. Recent studies have suggested that these hydroxyl groups could be replaced by fluorine atoms during synthesis that includes a fluorine-containing modulator. Here, a combined modeling and experimental study was undertaken to elucidate the role of metal cluster fluorination on the thermodynamic stability, structure, and gas adsorption properties of RE-MOFs. Through systematic density-functional theory calculations, fluorinated clusters were found to be thermodynamically more stable than hydroxylated clusters by up to 8-16 kJ/mol per atom for 100% fluorination. The extent of fluorination in the metal clusters was validated through a 19F NMR characterization of 2,5-dihydroxyterepthalic acid (Y-DOBDC) MOF synthesized with a fluorine-containing modulator. 19F magic-angle spinning NMR identified two primary peaks in the isotropic chemical shift (δiso) spectra located at -64.2 and -69.6 ppm, matching calculated 19F NMR δiso peaks at -63.0 and -70.0 ppm for fluorinated systems. Calculations also indicate that fluorination of the Y-DOBDC MOF had negligible effects on the acid gas (SO2, NO2, H2O) binding energies, which decreased by only ∼4 kJ/mol for the 100% fluorinated structure relative to the hydroxylated structure. Additionally, fluorination did not change the relative gas binding strengths (SO2 > H2O > NO2). Therefore, for the first time the presence of fluorine in the metal clusters was found to significantly stabilize RE-MOFs without changing their acid-gas adsorption properties.
Pfeifer, Thomas W.; Tomko, John A.; Hoglund, Eric; Scott, Ethan; Hattar, Khalid M.; Huynh, Kenny; Liao, Michael; Goorsky, Mark; Hopkins, Patrick E.
The thermal properties of semiconductors following exposure to ion irradiation are of great interest for the cooling of electronic devices; however, gradients in composition and structure due to irradiation often make the measurement difficult. Furthermore, the nature of spatial variations in thermal resistances due to spatially varying ion irradiation damage is not well understood. In this work, we develop an advancement in the analysis of time-domain thermoreflectance to account for spatially varying thermal conductivity in a material resulting from a spatial distribution of defects. We then use this method to measure the near-surface (≤1 μm) thermal conductivity of silicon wafers irradiated with Kr+ ions, which has an approximate Gaussian distribution centered 260 nm into the sample. Our numerical analysis presented here allows for the spatial gradient of thermal conductivity to be extracted via what is fundamentally a volumetric measurement technique. We validate our findings via transmission electron microscopy, which is able to confirm the spatial variation of the sub-surface silicon structure, and provide additional insight into the local structure resulting from the effects of ion bombardment. Thermal measurements found the ion stopping region to have a nearly 50 × reduction in thermal conductivity as compared to pristine silicon, while TEM showed the region was not fully amorphized. Our results suggest this drastic reduction in silicon thermal conductivity is primarily driven by structural defects in crystalline regions along with boundary scattering between amorphous and crystalline regions, with a negligible contribution being due to implanted krypton ions themselves.
The formation of a stress corrosion crack (SCC) in the canister wall of a dry cask storage system (DCSS) has been identified as a potential issue for the long-term storage of spent nuclear fuel. The presence of an SCC in a storage system could represent a through-wall flow path from the canister interior to the environment. Modern, vertical DCSSs are of particular interest due to the commercial practice of using more significant backfill pressures in the canister, up to approximately 800 kPa. This pressure differential offers a relatively high driving potential for blowdown of any particulates that might be present in the canister. In this study, the rates of gas flow and aerosol transmission of a spent fuel surrogate through an engineered microchannel with dimensions representative of an SCC were evaluated experimentally using coupled mass flow and aerosol analyzers. The microchannel was formed by mating two gage blocks with a linearly tapering slot orifice nominally 13 μm (0.005 in.) tall on the upstream side and 25 μm (0.0010 in.) tall on the downstream side. The orifice is 12.7 mm (0.500 in.) wide by 8.86 mm (0.349 in.) long (flow length). Surrogate aerosols of cerium oxide, CeO2, were seeded and mixed with either helium or air inside a pressurized tank. The aerosol characteristics were measured immediately upstream and downstream of the simulated SCC at elevated and ambient pressures, respectively. These data sets are intended to add to previous testing that characterized SCCs under well-controlled boundary conditions through the inclusion of testing improvements that establish initial conditions in a more consistent way. These ongoing testing efforts are focused on understanding the evolution in both size and quantity of a hypothetical release of aerosolized spent fuel particles from failed fuel to the canister interior and ultimately through an SCC.
On Thursday August 11, 2022, a series of explosive demonstration tests were conducted at the Sandia National Laboratories 9920 Test Complex for the 2022 DOE Packaging Certification Program Explosives Engineering class. Class participants included both SNL engineering student interns as well as SNL and LANL staff members. The test series was designed by the class instructor, W. Gary Rivera Org. 6626, and 9920 site test engineer, Marissa Martinez Org. 6648, with help from Michelle Chatter Org 6514 and Luke Gilbert Org 6815.
The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Fuel Cycle Technology (FCT) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). Two high priorities for SFWST disposal R&D are design concept development and disposal system modeling. These priorities are directly addressed in the SFWST Geologic Disposal Safety Assessment (GDSA) control account, which is charged with developing a geologic repository system modeling and analysis capability, and the associated software, GDSA Framework, for evaluating disposal system performance for nuclear waste in geologic media. GDSA Framework is supported by SFWST Campaign and its predecessor the Used Fuel Disposition (UFD) campaign.
In this study, a complex constitutive relation is identified using inverse modeling with the nominal mechanical response as sole experimental input. The methodology is illustrated for a semicrystalline thermoplastic in the presence of strain localization at finite deformations. The experimental database includes cylindrical tensile bars, compression pins and round notched bars loaded at strain rates spanning up to five decades and temperatures below and above Tg. The data is organized into a calibration set and a validation set. The response of tensile specimens is determined using finite element analyses and a two-phase constitutive relation for semicrystalline polymers that accounts for temperature- and rate-sensitive plastic flow, pressure-sensitivity, small-strain softening and large-strain orientational hardening of the amorphous phase, along with the evolution of crystallinity. The large number of constitutive parameters is identified using an optimization tool coupled with the finite element solver and the calibration set from experiments. The methodology is shown to be successful in predicting the response of round notched bars and replicating the effects of temperature and strain rate on the severity of necking in tensile bars. The proposed model identification strategy is both simple and effective in comparison with other elaborate methods that attempt to access intrinsic behavior directly from high-fidelity experimental measurements.
Gomez, Diego T.; Pratt, Lawrence R.; Asthagiri, Dilipkumar N.; Rempe, Susan
ConspectusThe interactions of hydrated ions with molecular and macromolecular solution and interface partners are strong on a chemical energy scale. Here, we recount the foremost ab initio theory for the evaluation of the hydration free energies of ions, namely, quasi-chemical theory (QCT). We focus on anions, particularly halides but also the hydroxide anion, because they have been outstanding challenges for all theories. For example, this work supports understanding the high selectivity for F-over Cl-in fluoride-selective ion channels despite the identical charge and the size similarity of these ions. QCT is built by the identification of inner-shell clusters, separate treatment of those clusters, and then the integration of those results into the broader-scale solution environment. Recent work has focused on a close comparison with mass-spectrometric measurements of ion-hydration equilibria. We delineate how ab initio molecular dynamics (AIMD) calculations on ion-hydration clusters, elementary statistical thermodynamics, and electronic structure calculations on cluster structures sampled from the AIMD calculations obtain just the free energies extracted from the cluster experiments. That theory-experiment comparison has not been attempted before the work discussed here, but the agreement is excellent with moderate computational effort. This agreement reinforces both theory and experiment and provides a numerically accurate inner-shell contribution to QCT. The inner-shell complexes involving heavier halides display strikingly asymmetric hydration clusters. Asymmetric hydration structures can be problematic for the evaluation of the QCT outer-shell contribution with the polarizable continuum model (PCM). Nevertheless, QCT provides a favorable setting for the exploitation of PCM when the inner-shell material shields the ion from the outer solution environment. For the more asymmetrically hydrated, and thus less effectively shielded, heavier halide ions clustered with waters, the PCM is less satisfactory. We therefore investigate an inverse procedure in which the inner-shell structures are sampled from readily available AIMD calculations on the bulk solutions. This inverse procedure is a remarkable improvement; our final results are in close agreement with a standard tabulation of hydration free energies, and the final composite results are independent of the coordination number on the chemical energy scale of relevance, as they should be. Finally, a comparison of anion hydration structure in clusters and bulk solutions from AIMD simulations emphasize some differences: the asymmetries of bulk solution inner-shell structures are moderated compared with clusters but are still present, and inner hydration shells fill to slightly higher average coordination numbers in bulk solution than in clusters.
Advection of trace species, or tracers, also called tracer transport, in models of the atmosphere and other physical domains is an important and potentially computationally expensive part of a model's dynamical core. Semi-Lagrangian (SL) advection methods are efficient because they permit a time step much larger than the advective stability limit for explicit Eulerian methods without requiring the solution of a globally coupled system of equations as implicit Eulerian methods do. Thus, to reduce the computational expense of tracer transport, dynamical cores often use SL methods to advect tracers. The class of interpolation semi-Lagrangian (ISL) methods contains potentially extremely efficient SL methods. We describe a finite-element ISL transport method that we call the interpolation semi-Lagrangian element-based transport (Islet) method, such as for use with atmosphere models discretized using the spectral element method. The Islet method uses three grids that share an element grid: a dynamics grid supporting, for example, the Gauss-Legendre-Lobatto basis of degree three; a physics parameterizations grid with a configurable number of finite-volume subcells per element; and a tracer grid supporting use of Islet bases with particular basis again configurable. This method provides extremely accurate tracer transport and excellent diagnostic values in a number of verification problems.
Layered material α-RuCl3 has caught wide attention due to its possible realization of Kitaev's spin liquid and its electronic structure that involves the interplay of electron-electron correlations and spin-orbit effects. Several DFT+U studies have suggested that both electron-electron correlations and spin-orbit effects are crucial for accurately describing the band gap. This work studies the importance of these two effects using fixed-node and fixed-phase diffusion Monte Carlo calculations both in spin-averaged and explicit spin-orbit formalisms. In the latter, the Slater-Jastrow trial function is constructed from two-component spin orbitals using our recent quantum Monte Carlo (QMC) developments and thoroughly tested effective core potentials. Our results show that the gap in the ideal crystal is already accurately described by the spin-averaged case, with the dominant role being played by the magnetic ground state with significant exchange and electron correlation effects. We find qualitative agreement between hybrid DFT, DFT+U, and QMC. In addition, QMC results agree very well with available experiments, and we identify the values of exact Fock exchange mixing that provide comparable gaps. Explicit spin-orbit QMC calculations reveal that the effect of spin-orbit coupling on the gap is minor, of the order of 0.2 eV, which corresponds to the strength of the spin orbit of the Ru atom.
Getto, E.; Johnson, M.; Maughan, M.; Nathan, N.; Mcmahan, J.; Baker, B.; Knipling, K.; Briggs, S.; Hattar, Khalid M.; Swenson, M.J.
The joining process for oxide dispersion strengthened (ODS) alloys remains a key challenge facing the nuclear community. The microstructure and mechanical properties were characterized in the base material and friction stir welded ODS MA956 irradiated with 5 MeV Fe2+ ions from 400 to 500°C up to 25 dpa. Nanoindentation was performed to assess changes in hardness and yield stress, and the dispersed barrier hardening (DBH) model was applied to described results. A combination of scanning transmission electron microscopy (STEM) and atom probe tomography (APT) were used to assess evolution of the microstructure including dispersoids, network dislocations and dislocation loops, nanoclusters, and solid solution concentrations. Overall, softening was observed as a result of increased dose, which was exacerbated at 500°C. The formation and coarsening of new dispersoids was noted while nanoclusters tended to dissolve in the base material, and were not observed in the stir zone. Solute nanocluster evolution was identified as a primary driver of the changes in mechanical properties.
Energy-dispersive x-ray diffraction of thermographic phosphors has been explored as a complementary temperature diagnostic to visible phosphor thermometry in environments where the temperature-dependent optical luminescence of the phosphors is occluded. Powder phosphor samples were heated from ambient to 300 °C in incremental steps and probed with polychromatic synchrotron x rays; scattered photons were collected at a fixed diffraction angle of 3.9 °. Crystal structure, lattice parameters, and coefficients of thermal expansion were calculated from the diffraction data. Of the several phosphors surveyed, YAG:Dy, ZnO:Ga, and GOS:Tb were found to be excellent candidates for diffraction thermometry due to their strong, distinct diffraction peaks that shift in a repeatable and linear manner with temperature.
Plate puncture simulations are challenging computational tasks that require advanced material models including high strain rate and thermal-mechanical effects on both deformation and failure, plus finite element techniques capable of representing large deformations and material failure. The focus of this work is on the material issues, which require large sets of experiments, flexible material models and challenging calibration procedures. In this study, we consider the puncture of 12.7 mm thick, 7075-T651 aluminum alloy plates by a cylindrical punch with a hemispherical nose and diameter of 12.7 mm. The plasticity and ductile failure models were isotropic with calibration data obtained from uniaxial tension tests at different temperatures and strain rates plus quasi-static notched tension tests and shear-dominated tests described here. Sixteen puncture experiments were conducted to identify the threshold penetration energy, mode of puncture and punch acceleration during impact, The punch was mounted on a 139 kg mass and dropped on the plates with different impact speeds. Since the mass was the same in all tests, the quantity of interest was the impact speed. The axis and velocity of the punch were perpendicular to the plate surface. The mean threshold punch speed was 3.05 m/s, and the mode of failure was plugging by thermal-mechanical shear banding accompanied by scabbing fragments. Application of the material models in simulations of the tests yielded accurate estimates of the threshold puncture speed and of the mode of failure. Time histories of the punch acceleration compared well between simulation and test. Remarkably, the success of the simulations occurred in spite of even the smallest element used being larger than the width of the shear bands.
The Tier 1 HHS/USDA Select Agent Burkholderia pseudomallei is a bacterial pathogen that is highly virulent when introduced into the respiratory tract and intrinsically resistant to many antibiotics. Transcriptomic- and proteomic-based methodologies have been used to investigate mechanisms of virulence employed by B. pseudomallei and Burkholderia thailandensis, a convenient surrogate; however, analysis of the pathogen and host metabolomes during infection is lacking. Changes in the metabolites produced can be a result of altered gene expression and/or post-transcriptional processes. Thus, metabolomics complements transcriptomics and proteomics by providing a chemical readout of a biological phenotype, which serves as a snapshot of an organism's physiological state. However, the poor signal from bacterial metabolites in the context of infection poses a challenge in their detection and robust annotation. In this study, we coupled mammalian cell culture-based metabolomics with feature-based molecular networking of mono- and co-cultures to annotate the pathogen's secondary metabolome during infection of mammalian cells. These methods enabled us to identify several key secondary metabolites produced by B. thailandensis during infection of airway epithelial and macrophage cell lines. Additionally, the use of in silico approaches provided insights into shifts in host biochemical pathways relevant to defense against infection. Using chemical class enrichment analysis, for example, we identified changes in a number of host-derived compounds including immune lipids such as prostaglandins, which were detected exclusively upon pathogen challenge. Taken together, our findings indicate that co-culture of B. thailandensis with mammalian cells alters the metabolome of both pathogen and host and provides a new dimension of information for in-depth analysis of the host-pathogen interactions underlying Burkholderia infection.
This report summarizes the activities performed by Sandia National Laboratories in FY22 to identify and test coating materials for the prevention, mitigation, and/or repair of potential chloride-induced stress corrosion cracking in spent nuclear fuel dry storage canisters. This work continues efforts by Sandia National Laboratories that are summarized in previous reports in FY20 and FY21 on the same topic. The previous work detailed the specific coating properties desired for application and implementation to spent nuclear fuel canisters (FY20) and identified several potential coatings for evaluation (FY21). In FY22, Sandia National Laboratories, in collaboration with four industry partners through a Memorandum of Understanding, started evaluating the physical, mechanical, and corrosion-resistance properties of 6 different coating systems (11 total coating variants) to develop a baseline understanding of the viability of each coating type for use to prevent, mitigate, and/or repair potential stress corrosion on cracking on spent nuclear fuel canisters. This collaborative R&D program leverages the analytical and laboratory capabilities at Sandia National Laboratories and the material design and synthesis capabilities of the industry collaborators. The coating systems include organic (polyetherketoneketone, modified polyimide/polyurea, modified phenolic resin), organic/inorganic ceramic hybrids (silane-based polyurethane hybrid and a quasi-ceramic sol-gel polyurethane hybrid), and hybrid systems in conjuncture with a Zn-rich primer. These coatings were applied to stainless steel coupons (the same coupons were supplied to all vendors by SNL for direct comparison) and have undergone several physical, mechanical, and electrochemical tests. The results and implications of these tests are summarized in this report. These analyses will be used to identify the most effective coatings for potential use on spent nuclear fuel dry storage canisters, and also to identify specific needs for further optimization of coating technologies for their application on spent nuclear fuel canisters. In FY22, Sandia National Laboratories performed baseline testing and atmospheric exposure tests of the coating samples supplied by the vendors in accordance with the scope of work defined in the Memorandum of Understanding. In FY23, Sandia National Laboratories will continue evaluating coating performance with a focus on thermal and radiolytic stability.
Using two-dimensional simulations of sheared, brittle solids, we characterize the resulting fragmentation and explore its underlying critical nature. Under quasistatic loading, a power-law distribution of fragment masses emerges after fracture which grows with increasing strain. With increasing strain rate, the maximum size of a grain decreases and a shallower distribution is produced. We propose a scaling theory for distributions based on a fractal scaling of the largest mass with system size in the quasistatic limit or with a correlation length that diverges as a power of rate in the finite-rate limit. Critical exponents are measured using finite-size scaling techniques.
We will always want to speed up simulation. While architects are able to pick between levels of detail when designing simulation, we will find further speed-up if we can adjust the level of detail during simulation, depending on the behavior of the simulated components. We create a simple fixed-latency model for each phase. On the first execution of a phase, we record the average latency of accesses. On subsequent executions, we skip DRAMSin3 and send the response back using the average latency.
Hatefipour, Mehdi; Pour; Cuozzo, Joseph J.; Kanter, Jesse; Strickland, William M.; Allemang, Christopher R.; Lu, Tzu M.; Rossi, Enrico; Shabani, Javad
Indium arsenide (InAs) near surface quantum wells (QWs) are promising for the fabrication of semiconductor-superconductor heterostructures given that they allow for a strong hybridization between the two-dimensional states in the quantum well and the ones in the superconductor. In this work, we present results for InAs QWs in the quantum Hall regime placed in proximity of superconducting NbTiN. We observe a negative downstream resistance with a corresponding reduction of Hall (upstream) resistance, consistent with a very high Andreev conversion. We analyze the experimental data using the Landauer-Büttiker formalism, generalized to allow for Andreev reflection processes. We attribute the high efficiency of Andreev conversion in our devices to the large transparency of the InAs/NbTiN interface and the consequent strong hybridization of the QH edge modes with the states in the superconductor.
Virtual machine emulation environments provide ideal testbeds for cybersecurity evaluations because they run real software binaries in a scalable, offline test setting that is suitable for assessing the impacts of software security flaws on the system. Verification of such emulations determines whether the environment is working as intended. Verification can focus on various aspects such as timing realism, traffic realism, and resource realism. In this paper, we study resource realism and issues associated with virtual machine resource utilization. We examine telemetry metrics gathered from a series of structured experiments which involve large numbers of parallel emulations meant to oversubscribe resources at some point. We present an approach to use telemetry metrics for emulation verification, and we demonstrate this approach on two cyber scenarios. Descriptions of the experimental configurations are provided along with a detailed discussion of statistical tests used to compare telemetry metrics. Results demonstrate the potential for a structured experimental framework, combined with statistical analysis of telemetry metrics, to support emulation verification. We conclude with comments on generalizability and potential future work.
Researchers are in constant need of reliable data to develop and evaluate AI/ML methods for networks and cybersecurity. While Internet measurements can provide realistic data, such datasets lack ground truth about application flows. We present a ~750GB dataset that includes ~2000 systematically conducted experiments and the resulting packet captures with video streaming, video teleconferencing, and cloud-based document editing applications. This curated and labeled dataset has bidirectional and encrypted traffic with complete ground truth that can be widely used for assessments and evaluation of AI/ML algorithms.
Electrons in a semiconductor occupy states within certain energy ranges, called energy bands. The position of the Fermi level with respect to these energy bands determines the charge carrier type of the semiconductor. Molybdenum disulfide (MoS2) is a two-dimensional, n-type semiconductor with potential applications in flexible electronics, transparent electronics, and optoelectronics. Electronic devices containing MoS2 could be used in environments where radiation affects device performance. Thus, it is important to determine the impact of radiation on MoS2. A one-molecule-thick layer of MoS2 (monolayer) and a two-molecule-thick layer of MoS2 (bilayer) were placed onto different areas of a gold (Au) substrate containing 1.2-µm-deep holes. The MoS2 was suspended over these holes but supported by the Au elsewhere on the substrate. This sample configuration was used to determine the effect of He+ radiation on the electronic properties of the suspended MoS2 and the Au-supported MoS2. The MoS2 was irradiated by He+ ions in two stages. The energy bands of the MoS2 were measured with respect to the Fermi level via photoelectron emission microscopy before irradiation and after each irradiation stage. From each measurement, the charge carrier type of the MoS2 after the corresponding irradiation stage was determined. The Fermi levels of the suspended monolayer and bilayer decreased by ≈0.15 eV with respect to the bands during the first irradiation stage During the second irradiation stage, however, the Fermi levels didn’t change significantly. This lack of change supports the existence of a radiation threshold, above which the electronic properties of suspended MoS2 remain the same. The Fermi levels of the supported monolayer and bilayer increased over the cumulative irradiation and didn’t show evidence of a threshold. Thus, suspended MoS2 becomes less n-type as it is irradiated. Supported MoS2, however, becomes more n-type as it is irradiated. These results could inform the development of radiation tolerance standards for MoS2, and thus, radiation-tolerant MoS2-based electronics.
Machine learning-based data-driven modeling can allow computationally efficient time-dependent solutions of PDEs, such as those that describe subsurface multiphysical problems. In this work, our previous approach (Kadeethum et al., 2021d) of conditional generative adversarial networks (cGAN) developed for the solution of steady-state problems involving highly heterogeneous material properties is extended to time-dependent problems by adopting the concept of continuous cGAN (CcGAN). The CcGAN that can condition continuous variables is developed to incorporate the time domain through either element-wise addition or conditional batch normalization. Moreover, this framework can handle training data that contain different timestamps and then predict timestamps that do not exist in the training data. As a numerical example, the transient response of the coupled poroelastic process is studied in two different permeability fields: Zinn & Harvey transformation and a bimodal transformation. The proposed CcGAN uses heterogeneous permeability fields as input parameters while pressure and displacement fields over time are model output. Our results show that the model provides sufficient accuracy with computational speed-up. This robust framework will enable us to perform real-time reservoir management and robust uncertainty quantification in poroelastic problems.
PyApprox is a Python-based one-stop-shop for probabilistic analysis of scientific numerical models. Easy to use and extendable tools are provided for constructing surrogates, sensitivity analysis, Bayesian inference, experimental design, and forward uncertainty quantification. The algorithms implemented represent the most popular methods for model analysis developed over the past two decades, including recent advances in multi-fidelity approaches that use multiple model discretizations and/or simplified physics to significantly reduce the computational cost of various types of analyses. Simple interfaces are provided for the most commonly-used algorithms to limit a user’s need to tune the various hyper-parameters of each algorithm. However, more advanced work flows that require customization of hyper-parameters is also supported. An extensive set of Benchmarks from the literature is also provided to facilitate the easy comparison of different algorithms for a wide range of model analyses. This paper introduces PyApprox and its various features, and presents results demonstrating the utility of PyApprox on a benchmark problem modeling the advection of a tracer in ground water.
Write-optimized data structures (WODS), offer the potential to keep up with cyberstream event rates and give sub-second query response for key items like IP addresses. These data structures organize logs as the events are observed. To work in a real-world environment and not fill up the disk, WODS must efficiently expire older events. As the basis for our research into organizing security monitoring data, we implemented a tool, called Diventi, to index IP addresses in connection logs using RocksDB (a write-optimized LSM tree). We extended Diventi to automatically expire data as part of the data structures’ normal operations. We guarantee that Diventi always tracks the N most recent events and tracks no more than N+ k events for a parameter k< N, while ensuring the index is opportunistically pruned. To test Diventi at scale in a controlled environment, we used anonymized traces of IP communications collected at SuperComputing 2019. We synthetically extended the 2.4 billion connection events to 100 billion events. We tested Diventi vs. Elasticsearch, a common log indexing tool. In our test environment, Elasticsearch saw an ingestion rate of at best 37,000 events/s while Diventi sustained ingestion rates greater than 171,000 events/s. Our query response times were as much as 100 times faster, typically answering queries in under 80 ms. Furthermore, we saw no noticeable degradation in Diventi from expiration. We have deployed Diventi for many months where it has performed well and supported new security analysis capabilities.
Lead–acid batteries are important to modern society because of their wide usage and low cost. The primary source for production of new lead–acid batteries is from recycling spent lead–acid batteries. In spent lead–acid batteries, lead is primarily present as lead pastes. In lead pastes, the dominant component is lead sulfate (PbSO4, mineral name anglesite) and lead oxide sulfate (PbO•PbSO4, mineral name lanarkite), which accounts for more than 60% of lead pastes. In the recycling process for lead–acid batteries, the desulphurization of lead sulfate is the key part to the overall process. In this work, the thermodynamic constraints for desulphurization via the hydrometallurgical route for recycling lead pastes are presented. The thermodynamic constraints are established according to the thermodynamic model that is applicable and important to recycling of lead pastes via hydrometallurgical routes in high ionic strength solutions that are expected to be in industrial processes. The thermodynamic database is based on the Pitzer equations for calculations of activity coefficients of aqueous species. The desulphurization of lead sulfates represented by PbSO4 can be achieved through the following routes. (1) conversion to lead oxalate in oxalate-bearing solutions; (2) conversion to lead monoxide in alkaline solutions; and (3) conversion to lead carbonate in carbonate solutions. Among the above three routes, the conversion to lead oxalate is environmentally friendly and has a strong thermodynamic driving force. Oxalate-bearing solutions such as oxalic acid and potassium oxalate solutions will provide high activities of oxalate that are many orders of magnitude higher than those required for conversion of anglesite or lanarkite to lead oxalate, in accordance with the thermodynamic model established for the oxalate system. An additional advantage of the oxalate conversion route is that no additional reductant is needed to reduce lead dioxide to lead oxide or lead sulfate, as there is a strong thermodynamic force to convert lead dioxide directly to lead oxalate. As lanarkite is an important sulfate-bearing phase in lead pastes, this study evaluates the solubility constant for lanarkite regarding the following reaction, based on the solubility data, PbO•PbSO4 + 2H+ ⇌ 2Pb2+ + SO42− + H2O(l).
This document is aimed at providing guidance to the National Nuclear Security Administration’s (NNSA) Office of International Nuclear Security’s (INS) country and regional teams for implementing effective physical protection systems (PPSs) for nuclear power plants (NPPs) to prevent the radiological consequences of sabotage. This recommendation document includes input from the Physical Protection Functional Team (PPFT), the Response Functional Team (RFT), and the Sabotage Functional Team (SFT) under INS. Specifically, this document provides insights into increasing and sustaining physical protection capabilities at INS partner countries’ NPP sites. Nuclear power plants should consider that the intent of this document is to provide a historical context as well as technologies and methodologies that may be applied to improve physical protection capabilities. It also refers to relevant guidance from the International Atomic Energy Agency (IAEA) and the U.S. Nuclear Regulatory Commission (NRC).
ERMA is leveraging Sandia’s Microgrid Design Toolkit (MDT) [1] and adding significant new features to it. Development of the MDT was primarily funded by the Department of Energy, Office of Electricity Microgrid Program with some significant support coming from the U.S. Marine Corps. The MDT is a software program that runs on a Microsoft Windows PC. It is an amalgamation of several other software capabilities developed at Sandia and subsequently specialized for the purpose of microgrid design. The software capabilities include the Technology Management Optimization (TMO) application for optimal trade-space exploration, the Microgrid Performance and Reliability Model (PRM) for simulation of microgrid operations, and the Microgrid Sizing Capability (MSC) for preliminary sizing studies of distributed energy resources in a microgrid.
Ferroelectric HfO2-based materials hold great potential for the widespread integration of ferroelectricity into modern electronics due to their compatibility with existing Si technology. Earlier work indicated that a nanometre grain size was crucial for the stabilization of the ferroelectric phase. This constraint, associated with a high density of structural defects, obscures an insight into the intrinsic ferroelectricity of HfO2-based materials. Here we demonstrate that stable and enhanced polarization can be achieved in epitaxial HfO2 films with a high degree of structural order (crystallinity). An out-of-plane polarization value of 50 μC cm–2 has been observed at room temperature in Y-doped HfO2(111) epitaxial thin films, with an estimated full value of intrinsic polarization of 64 μC cm–2, which is in close agreement with density functional theory calculations. The crystal structure of films reveals the Pca21 orthorhombic phase with small rhombohedral distortion, underlining the role of the structural constraint in stabilizing the ferroelectric phase. Our results suggest that it could be possible to exploit the intrinsic ferroelectricity of HfO2-based materials, optimizing their performance in device applications.
The COVID-19 pandemic has forced many organizations—from national laboratories to private companies—to change their workforce model to incorporate remote work. This study and the summarized results sought to understand the experiences of remote workers and the ways that remote work can impact recruitment and retention, employee engagement, and career development. Sandia, like many companies, has committed to establishing a hybrid work model that will persist postpandemic, and more Sandia employees than ever before have initiated remote work agreements. This parallels the nationwide increase in remote employment and motivates this study on remote work as an enduring part of workforce models.
Villa, Daniel L.; Schostek, Tyler; Bianchi, Carlo; Macmillan, Madeline; Carvallo, Juan P.
The Multi-scenario extreme weather simulator (MEWS) is a stochastic weather generation tool. The MEWS algorithm uses 50 or more years of National Oceanic and Atmospheric Association (NOAA) daily summaries [1] for maximum and minimum temperature and NOAA climate norms [2] to calculate historical heat wave and cold snap statistics. The algorithm takes these statistics and shifts them according to multiplication factors provided in the Intergovernmental Panel on Climate Change (IPCC) physical basis technical summary [3] for heat waves.
The D-value or dangerous quantity system was designed by the International Commission for Radiological Protection for the determination of source protection categories that can be used to reduce the likelihood of accidents, the consequences of which could result in harm to individuals or costly or expensive cleanup. The process includes multiple scenarios for exposure and two different approaches to the evaluation of detriment. This document provides an example calculation using 137Cs to walk through the complex process of determining its D-value in the hopes of making the process easily understandable.