Elastomeric rubbers serve a vital role as sealing materials in the hydrogen storage and transport infrastructure. With applications including O-rings and hose-liners, these components are exposed to pressurized hydrogen at a range of temperatures, cycling rates, and pressure extremes. Cyclic (de)pressurization is known to degrade these materials through the process of cavitation. This readily visible failure mode occurs as a fracture or rupture of the material and is due to the oversaturated gas localizing to form gas bubbles. Computational modeling in the Hydrogen Materials Compatibility Program (H-Mat), co-led by Sandia National Laboratories and Pacific Northwest National Laboratory, employs multi-scale simulation efforts to build a predictive understanding of hydrogen-induced damage in materials. Modeling efforts within the project aim to provide insight into how to formulate materials that are less sensitive to high-pressure hydrogen-induced failure. In this document, we summarize results from atomistic molecular dynamics simulations, which make predictive assessments of the effects of compositional variations in the commonly used elastomer, ethylene propylene diene monomer (EPDM).
Members of the Nuclear Criticality Safety (NCS) Program at Sandia National Laboratories (SNL) have updated the suite of benchmark problems developed to validate MCNP6 Version 2.0 for use in NCS applications. The updated NCS benchmark suite adds approximately 600 new benchmarks and includes peer review of all input files by two different NCS engineers (or one NCS engineer and one candidate NCS engineer). As with the originally released benchmark suite, the updated suite covers a broad range of fissile material types, material forms, moderators, reflectors, and neutron energy spectra. The benchmark suite provides a basis to establish a bias and bias uncertainty for use in NCS analyses at SNL.
Mishra, Umakant; Yeo, Kyongmin; Adhikari, Kabindra; Riley, William J.; Hoffman, Forrest M.; Hudson, Corey
Accurate representation of environmental controllers of soil organic carbon (SOC) stocks in Earth System Model (ESM) land models could reduce uncertainties in future carbon–climate feedback projections. Using empirical relationships between environmental factors and SOC stocks to evaluate land models can help modelers understand prediction biases beyond what can be achieved with the observed SOC stocks alone. In this study, we used 31 observed environmental factors, field SOC observations (n = 6,213) from the continental United States, and two machine learning approaches (random forest [RF] and generalized additive modeling [GAM]) to (a) select important environmental predictors of SOC stocks, (b) derive empirical relationships between environmental factors and SOC stocks, and (c) use the derived relationships to predict SOC stocks and compare the prediction accuracy of simpler model developed with the machine learning predictions. Out of the 31 environmental factors we investigated, 12 were identified as important predictors of SOC stocks by the RF approach. In contrast, the GAM approach identified six (of those 12) environmental factors as important controllers of SOC stocks: potential evapotranspiration, normalized difference vegetation index, soil drainage condition, precipitation, elevation, and net primary productivity. The GAM approach showed minimal SOC predictive importance of the remaining six environmental factors identified by the RF approach. Our derived empirical relations produced comparable prediction accuracy to the GAM and RF approach using only a subset of environmental factors. The empirical relationships we derived using the GAM approach can serve as important benchmarks to evaluate environmental control representations of SOC stocks in ESMs, which could reduce uncertainty in predicting future carbon–climate feedbacks.
Complex challenges across Sandia National Laboratories' (SNL) mission areas underscore the need for systems level thinking, resulting in a better understanding of the organizational work systems and environments in which our hardware and software will be used. SNL researchers have successfully used Activity Theory (AT) as a framework to clarify work systems, informing product design, delivery, acceptance, and use. To increase familiarity with AT, a working group assembled to select key resources on the topic and generate an annotated bibliography. The resources in this bibliography are arranged in six categories: 1) An introduction to AT; 2) Advanced readings in AT; 3) AT and human computer interaction (HCI); 4) Methodological resources for practitioners; 5) Case studies; and 6) Related frameworks that have been used to study work systems. This annotated bibliography is expected to improve the reader's understanding of AT and enable more efficient and effective application of it.
Battery storage systems are increasingly being installed at photovoltaic (PV) sites to address supply-demand balancing needs. Although there is some understanding of costs associated with PV operations and maintenance (O&M), costs associated with emerging technologies such as PV plus storage lack details about the specific systems and/or activities that contribute to the cost values. This study aims to address this gap by exploring the specific factors and drivers contributing to utility-scale PV plus storage systems (UPVS) O&M activities costs, including how technology selection, data collection, and related and ongoing challenges. Specifically, we used semi-structured interviews and questionnaires to collect information and insights from utility-scale owners and operators. Data was collected from 14 semi-structured interviews and questionnaires representing 51.1 MW with 64.1 MWh of installed battery storage capacity within the United States (U.S.). Differences in degradation rate, expected life cycle, and capital costs are observed across different storage technologies. Most O&M activities at UPVS related to correcting under-performance. Fires and venting issues are leading safety concerns, and owner operators have installed additional systems to mitigate these issues. There are ongoing O&M challenges due the lack of storage-specific performance metrics as well as poor vendor reliability and parts availability. Insights from this work will improve our understanding of O&M consideration at PV plus storage sites.
While the use of machine learning (ML) classifiers is widespread, their output is often not part of any follow-on decision-making process. To illustrate, consider the scenario where we have developed and trained an ML classifier to find malicious URL links. In this scenario, network administrators must decide whether to allow a computer user to visit a particular website, or to instead block access because the site is deemed malicious. It would be very beneficial if decisions such as these could be made automatically using a trained ML classifier. Unfortunately, due to a variety of reasons discussed herein, the output from these classifiers can be uncertain, rendering downstream decisions difficult. Herein, we provide a framework for: (1) quantifying and propagating uncertainty in ML classifiers; (2) formally linking ML outputs with the decision-making process; and (3) making optimal decisions for classification under uncertainty with single or multiple objectives.
On October 1, 2022, sound level measurements were taken at various locations throughout Kirtland Air Force Base (KAFB) and Southeastern Albuquerque. The purpose was to support sound propagation modeling predictions and sound regulations for public exposure during the detonation of an approximately 300-pound energetic experiment. Ground Zero was located on Range 7 of Sandia Thunder Range (06647). A total of 8 measurement locations were identified (e.g., 5 on KAFB and 3 in the Southeastern Albuquerque neighborhoods).
Members of the Workforce (MOW) who are exposed to noise levels above 140 dBC, regardless of hearing protection worn, are required to be enrolled into the SNL Hearing Conservation Program which includes audiometric testing, online training (HCP100) and wearing hearing protection. Based on the area impact noise sample results, the attenuation provided by the MFCP was protective for mitigating noise to levels below the ACGIH TLV of 140 dBC. The results also validated the scaled distance equation in an open-air environment as the results at K635 (864 feet) were below 140 dBC.
The recent discovery of bright, room-temperature, single photon emitters in GaN leads to an appealing alternative to diamond best single photon emitters given the widespread use and technological maturity of III-nitrides for optoelectronics (e.g. blue LEDs, lasers) and high-speed, high-power electronics. This discovery opens the door to on-chip and on-demand single photon sources integrated with detectors and electronics. Currently, little is known about the underlying defect structure nor is there a sense of how such an emitter might be controllably created. A detailed understanding of the origin of the SPEs in GaN and a path to deterministically introduce them is required. In this project, we develop new experimental capabilities to then investigate single photon emission from GaN nanowires and both GAN and AlN wafers. We ion implant our wafers with the ion implanted with our focused ion beam nanoimplantation capabilities at Sandia, to go beyond typical broad beam implantation and create single photon emitting defects with nanometer precision. We've created light emitting sources using Li+ and He+, but single photon emission has yet to be demonstrated. In parallel, we calculate the energy levels of defects and transition metal substitutions in GaN to gain a better understanding of the sources of single photon emission in GaN and AlN. The combined experimental and theoretical capabilities developed throughout this project will enable further investigation into the origins of single photon emission from defects in GaN, AlN, and other wide bandgap semiconductors.
The goal of the project was to protect US critical infrastructure and improve energy security through technical analysis of the risk landscape presented by the anticipated massive deployment of interoperable EV chargers.
This manual describes the installation and use of the Xyce™ XDM Netlist Translator. XDM simplifies the translation of netlists generated by commercial circuit simulator tools into Xyce-compatible netlists. XDM currently supports translation from PSpice, HSPICE, and Spectre netlists into Xyce™ netlists.
Cyber security has been difficult to quantify from the perspective of defenders. The effort to develop a cyber-attack with some ability, function, or consequence has not been rigorously investigated in Operational Technologies. This specification defines a testing structure that allows conformal and repeatable cyber testing on equipment. The purpose of the ETE is to provide data necessary to analyze and reconstruct cyber-attack timelines, effects, and observables for training and development of Cyber Security Operation Centers. Standardizing the manner in which cyber security on equipment is investigated will allow a greater understanding of the progression of cyber attacks and potential mitigation and detection strategies in a scientifically rigorous fashion.
Book, Cameron; Hoffman, Matthew J.; Kachuck, Samuel B.; Hillebrand, Trevor R.; Price, Stephen F.; Perego, Mauro P.; Bassis, Jeremy N.
Viscoelastic rebound of the solid Earth upon the removal of ice loads has the potential to inhibit marine ice sheet instability, thereby forestalling ice-sheet retreat and global mean sea-level rise. The timescale over which the solid Earth - ice sheet system responds to changes in ice thickness and bedrock topography places a strong control on the spatiotemporal influence of this negative feedback mechanism. In this study, we assess the impact of solid-earth rheological structure on model projections of the retreat of Thwaites Glacier, West Antarctica, and the concomitant sea-level rise by coupling the dynamic ice sheet model MALI to a regional glacial isostatic adjustment (GIA) model. We test the sensitivity of model projections of ice-sheet retreat and associated sea-level rise across a range of four different solid-earth rheologies, forced by standard ISMIP6 ocean and atmospheric datasets for the RCP8.5 climate scenario. These model parameters are applied to 500-year, coupled ice-sheet - GIA simulations. For the mantle viscosity best supported by observations, the negative GIA feedback leads to a reduction in mass loss that remains above 20% after about a hundred years. Mass-loss reduction peaks at 50% around 2300, which is when a control simulation without GIA experiences its maximum rate of retreat. For a weaker solid-earth rheology that is unlikely but compatible with observational uncertainty, mass loss reduction remains above 50% after 2150. At 2100, mass loss reduction is 10% for the best-fit rheology and 25% for the weakest rheology. At the same time, we estimate that water expulsion from the rebounding solid Earth beneath the ocean near Thwaites Glacier may increase sea-level rise by up to 20% at five centuries. Additionally, the reduction in ice-sheet retreat caused by GIA is substantially reduced under stronger climate forcings, suggesting that the stabilizing feedback of GIA will also be an indirect function of emissions scenario. We hypothesize that feedbacks between the solid Earth - ice sheet system are controlled by a competition between the spatial extent and timescale of bedrock uplift relative to the rate of grounded ice retreat away from the region of most rapid unloading. Although uncertainty in solid-earth rheology leads to large uncertainty in future sea-level rise contribution from Thwaites Glacier, under all plausible parameters the GIA effects are too large to be ignored for future projections of Thwaites Glacier of more than a century.
The HyRAM+ software toolkit provides a basis for conducting quantitative risk assessment and consequence modeling for hydrogen, natural gas, and autogas systems. HyRAM+ is designed to facilitate the use of state-of-the-art models to conduct robust, repeatable assessments of safety, hazards, and risk. HyRAM+ integrates deterministic and probabilistic models for quantifying leak sizes and rates, predicting physical effects, characterizing hazards (thermal effects from jet fires, overpressure effects from delayed ignition), and assessing impacts on people. HyRAM+ is developed at Sandia National Laboratories to support the development and revision of national and international codes and standards, and to provide developed models in a publicly-accessible toolkit usable by all stakeholders. This document provides a description of the methodology and models contained in HyRAM+ version 5.0. The most significant change for HyRAM+ version 5.0 from HyRAM+ version 4.1 is the ability to model blends of different fuels. HyRAM+ was previously only suitable for use with hydrogen, methane, or propane, with users having the ability to use methane as a proxy for natural gas and propane as a proxy for autogas/liquefied petroleum gas. In version 5.0, real natural gas or autogas compositions can be modeled as the fuel, or even blends of natural gas with hydrogen. These blends can be used in the standalone physics models, but not yet in the quantitative risk assessment mode of HyRAM+.
This manual describes the use of the Xyce™ Parallel Electronic Simulator. Xyce™ has been designed as a SPICE-compatible, high-performance analog circuit simulator, and has been written to support the simulation needs of the Sandia National Laboratories electrical designers. This development has focused on improving capability over the current state-of-the-art in the following areas: (1) Capability to solve extremely large circuit problems by supporting large-scale parallel computing platforms (up to thousands of processors). This includes support for most popular parallel and serial computers. (2) A differential-algebraic-equation (DAE) formulation, which better isolates the device model package from solver algorithms. This allows one to develop new types of analysis without requiring the implementation of analysis-specific device models. (3) Device models that are specifically tailored to meet Sandia's needs, including some radiation-aware devices (for Sandia users only). (4) Object-oriented code design and implementation using modern coding practices. Xyce™ is a parallel code in the most general sense of the phrase—a message passing parallel implementation—which allows it to run efficiently a wide range of computing platforms. These include serial, shared-memory and distributed-memory parallel platforms. Attention has been paid to the specific nature of circuit-simulation problems to ensure that optimal parallel eficiency is achieved as the number of processors grows.
This work presents the development of a multistaged stabilized continuation for the three-dimensional unpowered hypersonic trajectory planning problem using indirect optimal control methods. The stabilized continuation method is noniterative and guaranteed to terminate within a finite number of floating point operations, thereby making it well suited for onboard autonomous implementations. We present a multistage formulation of the stabilized continuation scheme that involves starting with a “loose” integration tolerance during the first stage and ramping up toward a “strict” integration tolerance through subsequent stages. An important benefit of this approach is that even when the solution to the underlying optimal control problem is numerically unstable, such as with the hypersonic vehicle footprint generation problem, the stabilized continuation algorithm is shown to be successful in finding a solution while providing some additional insights into the underlying cause of the numerical instability.
Numerical Methods for Partial Differential Equations
D'Elia, Marta D.; Aulisa, Eugenio; Capodaglio, Giacomo; Chierici, Andrea
In this paper, we design efficient quadrature rules for finite element (FE) discretizations of nonlocal diffusion problems with compactly supported kernel functions. Two of the main challenges in nonlocal modeling and simulations are the prohibitive computational cost and the nontrivial implementation of discretization schemes, especially in three-dimensional settings. In this work, we circumvent both challenges by introducing a parametrized mollifying function that improves the regularity of the integrand, utilizing an adaptive integration technique, and exploiting parallelization. We first show that the “mollified” solution converges to the exact one as the mollifying parameter vanishes, then we illustrate the consistency and accuracy of the proposed method on several two- and three-dimensional test cases. Furthermore, we demonstrate the good scaling properties of the parallel implementation of the adaptive algorithm and we compare the proposed method with recently developed techniques for efficient FE assembly.
Metal hydride hydrogen compression utilizes a reversible heat-driven interaction of a hydride-forming metal alloy with hydrogen gas. This paper reports on the development of a laboratory scale two-stage Metal Hydride Compressor (MHC) system with a feed pressure of 150 bar delivering high purity H2 gas at outlet pressures up to 875 bar. Stage 1 and stage 2 AB2 metal hydrides are identified based on experimental characterization of the pressure-composition-temperature (PCT) behavior of candidate materials. The selected metal hydrides are each combined with expanded natural graphite, increasing the thermal conductivity of the composites by an order of magnitude. These composites are integrated in two compressor beds with internal heat exchangers that alternate between hydrogenation and dehydrogenation cycles by thermally cycling between 20 °C and 150 °C. The prototype compressor achieved compression of hydrogen from 150 bar to 700 bar with an average flow rate of 33.6 g/hr.
Sangoleye, Fisayo; Johnson, Jay; Chavez, Adrian R.; Tsiropoulou, Eirini E.; Marton, Nicholas L.; Hentz, Charles R.; Yannarelli, Albert
Microgrids require reliable communication systems for equipment control, power delivery optimization, and operational visibility. To maintain secure communications, Microgrid Operational Technology (OT) networks must be defensible and cyber-resilient. The communication network must be carefully architected with appropriate cyber-hardening technologies to provide security defenders the data, analytics, and response capabilities to quickly mitigate malicious and accidental cyberattacks. In this work, we outline several best practices and technologies that can support microgrid operations (e.g., intrusion detection and monitoring systems, response tools, etc.). Then we apply these recommendations to the New Jersey TRANSITGRID use case to demonstrate how they would be deployed in practice.
Niobium doped lead-tin-zirconate-titanate ceramics near the PZT 95/5 orthorhombic AFE – rhombohedral FE morphotropic phase boundary Pb1-0.5y(Zr0.865-xTixSn0.135)1-yNbyO3 were prepared according to a 22+1 factorial design with x = 0.05, 0.07 and y = 0.0155, 0.0195. The ceramics were prepared by a traditional solid-state synthesis route and sintered to near full density at 1250°C for 6 h. All compositions were ∼98% dense with no detectable secondary phases by X-ray diffraction. The ceramics exhibited equiaxed grains with intergranular porosity, and grain size was ∼5 µm, decreasing with niobium substitution. Compositions exhibited remnant polarization values of ∼32 µC/cm2, increasing with Ti substitution. Depolarization by the hydrostatic pressure induced FE-AFE phase transition was drastically affected by variation of the Ti and Nb substitution, increasing at a rate of 113 MPa /1% Ti and 21 MPa/1% Nb. Total depolarization output was insensitive to the change in Ti and Nb substitution, ∼32.8 µC/cm2 for the PSZT ceramics. The R3c-R3m and R3m-Pm3m phase transition temperatures on heating ranged from 90 to 105°C and 183 to 191°C, respectively. Ti substitution stabilized the R3c and R3m phases to higher temperatures, while Nb substitution stabilized the Pm3m phase to lower temperatures. Thermal hysteresis of the phase transitions was also observed in the ceramics, with transition temperature on cooling being as much as 10°C lower.
Geologic Disposal Safety Assessment Framework is a state-of-the-art simulation software toolkit for probabilistic post-closure performance assessment of systems for deep geologic disposal of nuclear waste developed by the United States Department of Energy. This paper presents a generic reference case and shows how it is being used to develop and demonstrate performance assessment methods within the Geologic Disposal Safety Assessment Framework that mitigate some of the challenges posed by high uncertainty and limited computational resources. Variance-based global sensitivity analysis is applied to assess the effects of spatial heterogeneity using graph-based summary measures for scalar and time-varying quantities of interest. Behavior of the system with respect to spatial heterogeneity is further investigated using ratios of water fluxes. This analysis shows that spatial heterogeneity is a dominant uncertainty in predictions of repository performance which can be identified in global sensitivity analysis using proxy variables derived from graph descriptions of discrete fracture networks. New quantities of interest defined using water fluxes proved useful for better understanding overall system behavior.
Self-determination has been an on-going effort for Native American people and gained much traction with the passing of The Energy Policy Act of 2005, which included the Indian Tribal Energy Development and Self-Determination Act. Congress passed this act to assist Native American tribes and Alaska Native villages with planning, development, and assistance to achieve their energy goals. The Ute Mountain Ute Tribe (UMUT) has relied on oil and natural gas for economic support the last 70 years. Burning fossil fuels, along with oil and gas development, decreases the quality of air and leads to increased greenhouse gas emissions. Subsequently, the burning of fossil fuels to produce energy is now more costly than many renewable energy sources, including solar photovoltaic (PV) systems. Environmental stewardship, along with the need to maintain revenue generation, has led UMUT’s efforts to achieve energy self-determinism employing PV and exploring other technology. In the past, the tribe completed a 1 megawatt PV project near Towaoc, Colorado, which serves as a case study on the tribe’s energy goals: a future where renewables will dominate their energy landscape. This paper explores UMUT’s past and on-going efforts toward energy independence and how it relates to the broader landscape of Native American energy sovereignty.