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At least 1,279 records · Page 71

Asymptotic preserving methods for fluid electron-fluid models in the large magnetic field limit with mathematically guaranteed properties (Final Report)

The current manuscript is a final report on the activities carried out under the Project LDRD-CIS #226834. In scientific terms, the work reported in this manuscript is a continuation of the efforts started with Project LDRD-express #223796 with final report of activities SAND2021-11481, see [83]. In this section we briefly explain what pre-existing developments motivated the current body of work and provide an overview of the activities developed with the funds provided. The overarching goal of the current project LDRD-CIS #226834 and the previous project LDRD-express #223796 is the development of numerical methods with mathematically guaranteed properties in order to solve the Euler-Maxwell system of plasma physics and generalizations thereof. Even though Project #223796 laid out general foundations of space and time discretization of Euler-Maxwell system, overall, it was focused on the development of numerical schemes for purely electrostatic fluid-plasma models. In particular, the project developed a family of schemes with mathematically guaranteed robustness in order to solve the Euler-Poisson model. This model is an asymptotic limit where only electrostatic response of the plasma is considered. Its primary feature is the presence of a non-local force, the electrostatic force, which introduces effects with infinite speed propagation into the problem. Even though instantaneous propagation of perturbations may be considered nonphysical, there are plenty of physical regimes of technical interest where such an approximation is perfectly valid.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

TRUST Sensors in Environments: Thermocouples (SE-TC), Release FY25

The Delivery Environments Testbeds to Reduce Uncertainty in Simulations and Tests (TRUST) project is a broad project intended to analyze simplified problems experimentally and with modeling and simulation. The purpose of analyzing these simplified problems is to extend solution methods to more complex problems, as well as understand deficiencies and gaps in knowledge of methods currently used in more complex analyses. The TRUST project encompasses several smaller testbeds intended to isolate individual phenomena. The testbed under consideration in this report is the Sensors in Environments: Thermocouples testbed. In previous years, the purpose of this testbed was to quantify uncertainty of thermocouple sensors. To accomplish this, an aluminum plate was placed in a thermal chamber and subject to various types of thermal loading. Thermocouples were placed in various locations on the aluminum plate in various configurations (e.g., embedded in the plate, placed under Kapton tape), and an effort was made to quantify uncertainty in these measurements. Finite element simulations were performed to investigate how sensitive these measurements were to parameters such as the boundary conditions on the plate and material properties. However, a fundamental source of uncertainty in this analysis was the convective heat transfer from the plate. Convective heat transfer is a complex physical phenomenon comprised of a number of interacting sub-processes and is difficult to predict accurately a priori. As such, the main purpose of this testbed in FY25 was to better understand, both experimentally and numerically, the convective heat transfer from the plate. This is a highly applicable problem to several more complex problems, as convective heat transfer occurs in nearly all problems where a body is moving through air. Numerically, this required a two-step approach. First, the air flow in the thermal chamber was in vestigated using computational fluid dynamics. The commercial solver Fluent was used to perform these simulations. From these simulations, a heat transfer coefficient over the surface of the plate was calculated. This heat transfer was then used as boundary conditions for finite element heat transfer simulations within the plate, which were performed using Abaqus. Significant effort was devoted to automating the handoff between these two solvers. Experimentally, previous thermocouple results in the plate were used to validate the time-dependent thermal profiles produced from Abaqus. Further experimental efforts were performed both to help validate the Fluent simulations and to inform its boundary conditions. For example, hot-wire anemometers were used to measure the velocity in the chamber, which would be particularly useful in understanding the chamber inlet velocity. Thermocouple measurements were also taken in the chamber, instead of only on the plate, to serve as validation evidence for the Fluent simulations. Numerical results showed that the Fluent to Abaqus workflow matched previous plate thermocouple measurements well. This type of handoff is useful for more complex experiments, or those that are not able to be examined in as great of detail as this testbed, as it was performed without any experimental input. Experimental results, however, were more mixed. The anemometers proved unreliable, with inconsistent measurements across all anemometers, even at locations that were nearly identical. On the other hand, the thermocouples provided a relatively rich view of the temperature field in the chamber.

42 ENGINEERING↗

Mixed Ionic Electronic Conducting Quaternary Perovskites: Materials by Design for Solar Thermochemical Hydrogen

The innovative research conducted by Arizona State University and Princeton University in the project "Mixed Ionic-Electronic Conducting Quaternary Perovskites: Materials by Design for Solar Thermochemical Hydrogen" marks a significant stride forward in thermochemical water splitting. Through an intricate blend of computational design and experimental validation, the project delved into the promising potential of Mixed Ionic Electronic Conducting (MIEC) perovskites. These complex materials, characterized by their unique redox-active nature and adaptability in stoichiometry, present a promising frontier for efficient solar thermochemical hydrogen production. Firstly, the research enhanced the science by utilizing state-of-the-art computational methodologies to unravel the nuanced chemical potentials of MIEC perovskites. By simulating various off-stoichiometric scenarios and redox conditions, the team was able to predict material behaviors under diverse environmental conditions, a feat unachievable through conventional experimental methodologies alone. This approach not only fast-tracks the material screening process, significantly reducing the time from laboratory re-search to practical application, but also uncovers trends and correlations that are pivotal for future materials innovation. Regarding technical effectiveness, the project stands out in its economic feasibility. Traditional methods of materials discovery are often marred by high costs and extensive timeframes, owing to the iterative nature of experimental processes. However, by employing theoretical computations and validating these findings with targeted experiments, the project introduced a cost-effective paradigm for materials discovery and the first ever prediction, synthesis, and preliminary validation of a material solely from computational and theoretical considerations. This synergy between computation and experimentation expedites the discovery of optimal materials conducive to high-efficiency solar-to-hydrogen conversion processes. Furthermore, the public stands to benefit substantially from this research. The success of MIEC perovskites in solar thermochemical applications heralds a shift towards lower cost and lower electricity input for clean hydrogen production, hence potentially impacting climate and energy resilience. By improving the efficiency of solar-to-hydrogen conversions, the research paves the way for reduced dependency on fossil fuels, addressing the urgent global need for accessible and renewable energy sources. Moreover, the project's advancements contribute to scientific literacy in renewable energy technologies, empowering society through knowledge and spurring future innovations. In essence, this research project demonstrates significant progress in the realm of advanced water splitting through solar thermochemistry. Through its groundbreaking approaches in computational materials science and its implications for real-world applications, it holds the promise of a cleaner, more energy-resilient future.

08 HYDROGEN↗

Climate Benchmark Missions: CLARREO

CLARREO (Climate Absolute Radiance and Refractivity Observatory) is one of the four Tier 1 missions recommended by the recent NRC decadal survey report on Earth Science and Applications from Space (NRC, 2007). The CLARREO mission addresses the need to rigorously observe climate change on decade time scales and to use decadal change observations as the most critical method to determine the accuracy of climate change projections such as those used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). A rigorously known accuracy of both decadal change observations as well as climate projections is critical in order to enable sound policy decisions. The CLARREO mission accomplishes this critical objective through highly accurate and SI traceable decadal change observations sensitive to many of the key uncertainties in climate radiative forcings, responses, and feedbacks that in turn drive uncertainty in current climate model projections. The same uncertainties also lead to uncertainty in attribution of climate change to anthropogenic forcing. The CLARREO breakthrough in decadal climate change observations is to achieve the required levels of accuracy and traceability to SI standards for a set of observations sensitive to a wide range of key decadal change variables. These accuracy levels are determined both by the projected decadal changes as well as by the background natural variability that such signals must be detected against. The accuracy for decadal change traceability to SI standards includes uncertainties of calibration, sampling, and analysis methods. Unlike most other missions, all of the CLARREO requirements are judged not by instantaneous accuracy, but instead by accuracy in large time/space scale average decadal changes. Given the focus on decadal climate change, the NRC Decadal Survey concluded that the single most critical issue for decadal change observations was their lack of accuracy and low confidence in observing the small but critical climate change signals. CLARREO is the recommended attack on this challenge, and builds on the last decade of climate observation advances in the Earth Observing System as well as metrological advances at NIST (National Institute of Standards and Technology) and other standards laboratories.

Wielicki, Bruce A.↗

Methods for microbiological and immunological studies of space flight crews

Systematic laboratory procedures compiled as an outgrowth of a joint U.S./U.S.S.R. microbiological-immunological experiment performed during the Apollo-Soyuz Test Project space flight are presented. Included are mutually compatible methods for the identification of aerobic and microaerophilic bacteria, yeast and yeastlike microorganisms, and filamentous fungi; methods for the bacteriophage typing of Staphylococcus aureus; and methods for determining the sensitivity of S. aureus to antibiotics. Immunological methods using blood and immunological and biochemical methods using salivary parotid fluid are also described. Formulas for media and laboratory reagents used are listed.

Taylor, G. R.↗

Three approaches to the classification of inland wetlands

In the Dismal Swamp project, seasonal, color-infrared aerial photographs and LANDSAT digital data were interpreted for a detailed analysis of the vegetative communities in a large, highly altered wetland. In Western Tennessee, seasonal high altitude color-infrared aerial photographs provided the hydrologic and vegetative information needed to map inland wetlands, using a classification system developed for the Tennessee Valley Region. In Florida, color-infrared aerial photographs were analyzed to produce wetland maps using three existing classification systems to evaluate the information content and mappability of each system. The methods used in each of the three projects can be extended or modified for use in the mapping of inland wetlands in other parts of the United States.

Gammon, P. T.↗

New York City Panel on Climate Change 2019 Report Chapter 2: New Methods for Assessing Extreme Temperatures, Heavy Downpours, and Drought

This New York City Panel on Climate Change (NPCC3) chapter builds on the projections developed by the second New York City Panel on Climate Change (NPCC2) (Horton et al., 2015). It confirms NPCC2 projections as those of record for the City of New York, presents new methodology related to climate extremes, and describes new methods for developing the next generation of climate projections for the New York metropolitan region. These may be used by the City of New York as it continues to develop flexible adaptation pathways to cope with climate change. The main topics of the climate science chapter are: (1) Comparison of observed temperature and precipitation trends to NPCC2 2015 projections. (2) New methodology for analysis of historical and future projections of heatwaves, humidity, and cold snaps. (3) Improved characterization of observed heavy downpours. (4) Characterization of observed drought using paleoclimate data. (5) Suggested methods for next generation climate risk information.

Gonzalez, Jorge E.↗

Non-Spherical Microcapsules for Increased Core Content Volume Delivery

The goal of this project was to advance microencapsulation from the standard spherical microcapsule to a non-spherical, high-aspect ratio (HAR), elongated microcapsule. This was to be accomplished by developing reproducible methods of synthesizing or fabricating robust, non-spherical, HAR microcapsules. An additional goal of this project was to develop the techniques to the point where scale-up of these methods could be examined. Additionally, this project investigated ways to apply the microencapsulation techniques developed as part of this project to self-healing formulations.

Technology Portfolio System↗

Bayesian inference of Stochastic reaction networks using Multifidelity Sequential Tempered Markov Chain Monte Carlo

Stochastic reaction network models are often used to explain and predict the dynamics of gene regulation in single cells.These models usually involve several parameters, such as the kinetic rates of chemical reactions, that are not directly measurable and must be inferred from experimental data. Bayesian inference provides a rigorous probabilistic frame-work for identifying these parameters by finding a posterior parameter distribution that captures their uncertainty.Traditional computational methods for solving inference problems such as Markov Chain Monte Carlo methods based on classical Metropolis-Hastings algorithm involve numerous serial evaluations of the likelihood function, which in turn requires expensive forward solutions of the chemical master equation (CME). We propose an alternate approach based on a multifidelity extension of the Sequential Tempered Markov Chain Monte Carlo (ST-MCMC) sampler. This algorithm is built upon Sequential Monte Carlo and solves the Bayesian inference problem by decomposing it into a sequence of efficiently solved subproblems that gradually increase both model fidelity and the influence of the observed data. We reformulate the finite state projection (FSP) algorithm, a well-known method for solving the CME, to produce a hierarchy of surrogate master equations to be used in this multifidelity scheme. To determine the appropriate fidelity, we introduce a novel information-theoretic criteria that seeks to extract the most information about the ultimate Bayesian posterior from each model in the hierarchy without inducing significant bias. This novel sampling scheme is tested with high performance computing resources using biologically relevant problems.

97 MATHEMATICS AND COMPUTING↗

Real-Time Neuromorphic Processing of Spatiotemporal Data for Scientific Discovery

Spiking Neural Networks (SNNs) are brain-inspired computing models incorporating unique temporal dynamics and event-driven processing. Rich dynamics in both space and time offer great challenges and opportunities for efficient processing of sparse spatiotemporal data compared with conventional artificial neural networks (ANNs). Under this context, the goal of this project is to develop spiking neural network based neuromorphic computing to enable energy-efficient real-time learning and processing of spatiotemporal data. This report summarizes the key results on network architecture design, training methods, and SNN hardware acceleration achieved under this project, demonstrating the promise of spiking neural networks.

97 MATHEMATICS AND COMPUTING↗

Deep quantum circuit simulations of low-energy nuclear states

Numerical simulation is an important method for verifying the quantum circuits used to simulate low-energy nuclear states. However, real-world applications of quantum computing for nuclear theory often generate deep quantum circuits that place demanding memory and processing requirements on conventional simulation methods. Here, we present advances in high-performance numerical simulations of deep quantum circuits to efficiently verify the accuracy of low-energy nuclear physics applications. Our approach employs novel methods for accelerating the numerical simulation including management of simulated mid-circuit measurements to verify projection based state preparation circuits. In this study, we test these methods across a variety of high-performance computing systems and our results show that circuits up to 21 qubits and more than 115,000,000 gates can be efficiently simulated.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Comparison Of Downscaled CMIP5 Precipitation Datasets For Projecting Changes In Extreme Precipitation In The San Francisco Bay Area.

Water resource managers planning for the adaptation to future events of extreme precipitation now have access to high resolution downscaled daily projections derived from statistical bias correction and constructed analogs. We also show that along the Pacific Coast the Northern Oscillation Index (NOI) is a reliable predictor of storm likelihood, and therefore a predictor of seasonal precipitation totals and likelihood of extremely intense precipitation. Such time series can be used to project intensity duration curves into the future or input into stormwater models. However, few climate projection studies have explored the impact of the type of downscaling method used on the range and uncertainty of predictions for local flood protection studies. Here we present a study of the future climate flood risk at NASA Ames Research Center, located in South Bay Area, by comparing the range of predictions in extreme precipitation events calculated from three sets of time series downscaled from CMIP5 data: 1) the Bias Correction Constructed Analogs method dataset downscaled to a 1/8 degree grid (12km); 2) the Bias Correction Spatial Disaggregation method downscaled to a 1km grid; 3) a statistical model of extreme daily precipitation events and projected NOI from CMIP5 models. In addition, predicted years of extreme precipitation are used to estimate the risk of overtopping of the retention pond located on the site through simulations of the EPA SWMM hydrologic model. Preliminary results indicate that the intensity of extreme precipitation events is expected to increase and flood the NASA Ames retention pond. The results from these estimations will assist flood protection managers in planning for infrastructure adaptations.

Storm↗

ICME and In-Situ Process Monitoring for Rapid Qualification of Components Made by Laser-based Powder Bed Additive Manufacturing Processes for Nuclear Structural Applications

Additive manufacturing (AM) through selective powder bed melting of successive layers is being considered as disruptive technology for rapid production of low-cost nuclear reactor internal components with complex geometries. However, there is a potential for non-uniform distribution of physical features, such as porosity or microstructural differences, due to variations of temperature across a component build. These heterogeneities make the deployment of traditional material qualification and non-destructive evaluation of AM components difficult. This research project explored the feasibility of using in-situ process monitoring methods and integrated computational materials engineering (ICME) principles as an alternate qualification methodology and approach. This project included six individual tasks: (i) design of artifacts relevant to the nuclear power industry, (ii) evaluation of laser processing and in-situ measurements, (iii) computational modeling, (iv) ex-situ microstructural characterization, (v) evaluation of scaling the methodology for large-scale structures, and (vi) development of a data package to codes and standards organizations. As a part of this research, laser powder bed fusion of metals (316L, Alloy 718, and Ti6Al4V) was explored. This document provides an overview of the research performed over three years and serves as the final U.S. Department of Energy report for this project. In the third year, the project focused on the following activities: (a) in-situ infrared and optical imaging of each layer during processing of representative stainless steel geometries; (b) development of methodologies to analyze the data; and (c) application of ICME methodologies and other experiments to estimate the relevance of defects and microstructure to tensile properties.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

ICME and In-Situ Process Monitoring for Rapid Qualification of Components Made by Laser-based Powder Bed Additive Manufacturing Processes for Nuclear Structural Applications

Additive manufacturing (AM) through selective powder bed melting of successive layers is being considered as disruptive technology for rapid production of low-cost nuclear reactor internal components with complex geometries. However, there is a potential for non-uniform distribution of physical features, such as porosity or microstructural differences, due to variations of temperature across a component build. These heterogeneities make the deployment of traditional material qualification and non-destructive evaluation of AM components difficult. This research project explored the feasibility of using in-situ process monitoring methods and integrated computational materials engineering (ICME) principles as an alternate qualification methodology and approach. This project included six individual tasks: (i) design of artifacts relevant to the nuclear power industry, (ii) evaluation of laser processing and in-situ measurements, (iii) computational modeling, (iv) ex-situ microstructural characterization, (v) evaluation of scaling the methodology for large-scale structures, and (vi) development of a data package to codes and standards organizations. As a part of this research, laser powder bed fusion of metals (316L, Alloy 718, and Ti6Al4V) was explored. This document provides an overview of the research performed over three years and serves as the final U.S. Department of Energy report for this project. In the third year, the project focused on the following activities: (a) in-situ infrared and optical imaging of each layer during processing of representative stainless steel geometries; (b) development of methodologies to analyze the data; and (c) application of ICME methodologies and other experiments to estimate the relevance of defects and microstructure to tensile properties.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

ICME and In-Situ Process Monitoring for Rapid Qualification of Components Made by Laser-based Powder Bed Additive Manufacturing Processes for Nuclear Structural Applications

Additive manufacturing (AM) through selective powder bed melting of successive layers is being considered as disruptive technology for rapid production of low-cost nuclear reactor internal components with complex geometries. However, there is a potential for non-uniform distribution of physical features, such as porosity or microstructural differences, due to variations of temperature across a component build. These heterogeneities make the deployment of traditional material qualification and non-destructive evaluation of AM components difficult. This research project explored the feasibility of using in-situ process monitoring methods and integrated computational materials engineering (ICME) principles as an alternate qualification methodology and approach. This project included six individual tasks: (i) design of artifacts relevant to the nuclear power industry, (ii) evaluation of laser processing and in-situ measurements, (iii) computational modeling, (iv) ex-situ microstructural characterization, (v) evaluation of scaling the methodology for large-scale structures, and (vi) development of a data package to codes and standards organizations. As a part of this research, laser powder bed fusion of metals (316L, Alloy 718, and Ti6Al4V) was explored. This document provides an overview of the research performed over three years and serves as the final U.S. Department of Energy report for this project. In the third year, the project focused on the following activities: (a) in-situ infrared and optical imaging of each layer during processing of representative stainless steel geometries; (b) development of methodologies to analyze the data; and (c) application of ICME methodologies and other experiments to estimate the relevance of defects and microstructure to tensile properties.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

ICME and In-Situ Process Monitoring for Rapid Qualification of Components Made by Laser-based Powder Bed Additive Manufacturing Processes for Nuclear Structural Applications

Additive manufacturing (AM) through selective powder bed melting of successive layers is being considered as disruptive technology for rapid production of low-cost nuclear reactor internal components with complex geometries. However, there is a potential for non-uniform distribution of physical features, such as porosity or microstructural differences, due to variations of temperature across a component build. These heterogenities make the deployment of traditional material qualification and non-destructive evaluation of AM components difficult. This research project explored the feasibility of using in-situ process monitoring methods and integrated computational materials engineering (ICME) principles as an alternate qualification methodology and approach. This project included six individual tasks: (i) design of artifacts relevant to the nuclear power industry, (ii) evaluation of laser processing and in-situ measurements, (iii) computational modeling, (iv) ex-situ microstructural characterization, (v) evaluation of scaling the methodology for large-scale structures, and (vi) development of a data package to codes and standards organizations. As a part of this research, laser powder bed fusion of metals (316L, Alloy 718, and Ti6Al4V) was explored. This document provides an overview of the research performed over three years and serves as the final U.S. Department of Energy report for this project. In the third year, the project focused on the following activities: (a) in-situ infrared and optical imaging of each layer during processing of representative stainless steel geometries; (b) development of methodologies to analyze the data; and (c) application of ICME methodologies and other experiments to estimate the relevance of defects and microstructure to tensile properties.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

ICME and In-Situ Process Monitoring for Rapid Qualification of Components Made by Laser-based Powder Bed Additive Manufacturing Processes for Nuclear Structural Applications

Additive manufacturing (AM) through selective powder bed melting of successive layers is being considered as disruptive technology for rapid production of low-cost nuclear reactor internal components with complex geometries. However, there is a potential for non-uniform distribution of physical features, such as porosity or microstructural differences, due to variations of temperature across a component build. These heterogenities make the deployment of traditional material qualification and non-destructive evaluation of AM components difficult. This research project explored the feasibility of using in-situ process monitoring methods and integrated computational materials engineering (ICME) principles as an alternate qualification methodology and approach. This project included six individual tasks: (i) design of artifacts relevant to the nuclear power industry, (ii) evaluation of laser processing and in-situ measurements, (iii) computational modeling, (iv) ex-situ microstructural characterization, (v) evaluation of scaling the methodology for large-scale structures, and (vi) development of a data package to codes and standards organizations. As a part of this research, laser powder bed fusion of metals (316L, Alloy 718, and Ti6Al4V) was explored. This document provides an overview of the research performed over three years and serves as the final U.S. Department of Energy report for this project. In the third year, the project focused on the following activities: (a) in-situ infrared and optical imaging of each layer during processing of representative stainless steel geometries; (b) development of methodologies to analyze the data; and (c) application of ICME methodologies and other experiments to estimate the relevance of defects and microstructure to tensile properties.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

New Measurements on the Neutrino Mass: KATRIN and Project 8 (Final Technical Report for DE-SC0014130)

The overarching goal of the funded research is to build experiments and develop techniques towards the major goal of measuring the mass of the neutrino, particularly by the method of high-resolution spectroscopy of the endpoint of tritium. The goals of this project were to make hardware and intellectual contributions towards the KATRIN experiment, an international project under construction in Germany, and towards the "Project 8" R&D program to develop new electron spectroscopy methods and apply them to tritium. A minor goal of the project was to support simulation work and R&D on future neutrino and dark matter detectors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗