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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

3D hydrogeophysical characterization of managed aquifer recharge basins

Aquifers are increasingly stressed. Managed aquifer recharge provides a potential solution to mitigate this stress and provide sustainable groundwater resources. Subsurface properties are known to have a strong control on the infiltration rates that can be achieved. However, these properties are often highly heterogeneous and difficult to assess with conventional probing techniques. Here, we show the application of 3D geophysical imaging to assess the recharge potential and its variation across several basins used for managed aquifer recharge. We link in-situ measurements of saturated hydraulic conductivity with the electrical resistivity of the subsurface to establish petrophysical relationships and use those relationships to estimate the distribution of hydraulic conductivity throughout the five recharge basins. Our results show a considerable variability in the hydraulic properties, i.e., soil texture and saturated hydraulic conductivity, that have a direct impact on potential infiltration rates. We use the 3D hydraulic property distributions to model groundwater recharge and provide estimates for infiltration rates and volumes, and use this approach to assess the impact of management activities on groundwater recharge performance. Having such data not only enables us to predict infiltration rates, but also provides means for optimizing such water infrastructure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Prescreening-Based Subset Selection for Improving Predictions of Earth System Models With Application to Regional Prediction of Red Tide

We present the ensemble method of prescreening-based subset selection to improve ensemble predictions of Earth system models (ESMs). In the prescreening step, the independent ensemble members are categorized based on their ability to reproduce physically-interpretable features of interest that are regional and problem-specific. The ensemble size is then updated by selecting the subsets that improve the performance of the ensemble prediction using decision relevant metrics. We apply the method to improve the prediction of red tide along the West Florida Shelf in the Gulf of Mexico, which affects coastal water quality and has substantial environmental and socioeconomic impacts on the State of Florida. Red tide is a common name for harmful algal blooms that occur worldwide, which result from large concentrations of aquatic microorganisms, such as dinoflagellate Karenia brevis, a toxic single celled protist. We present ensemble method for improving red tide prediction using the high resolution ESMs of the Coupled Model Intercomparison Project Phase 6 (CMIP6) and reanalysis data. The study results highlight the importance of prescreening-based subset selection with decision relevant metrics in identifying non-representative models, understanding their impact on ensemble prediction, and improving the ensemble prediction. These findings are pertinent to other regional environmental management applications and climate services. Additionally, our analysis follows the FAIR Guiding Principles for scientific data management and stewardship such that data and analysis tools are findable, accessible, interoperable, and reusable. As such, the interactive Colab notebooks developed for data analysis are annotated in the paper. This allows for efficient and transparent testing of the results’ sensitivity to different modeling assumptions. Moreover, this research serves as a starting point to build upon for red tide management, using the publicly available CMIP, Coordinated Regional Downscaling Experiment (CORDEX), and reanalysis data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Fission In R-Process Elements

The goal of the FIRE topical collaboration in nuclear theory is to determine the astrophysical conditions of the rapid neutron capture process (r-process), which is responsible for the formation of heavy elements. This will be achieved by including in r-process simulations the most advanced models of fission (spontaneous, neutron-induced, β-delayed) that have been developed at LLNL and LANL. The collaboration is composed of LLNL (lead) and LANL for fission work, BNL for nuclear data management, and the university of Notre Dame and North Carolina State University for r -process simulations. Under DOE/NNSA agreement, both universities receive funds from DOE Office of Science, while national laboratories receive funds directly from NA221.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Best Practices for Nuclear Experiment Data Preservation at Idaho National Laboratory: A Guide for Researchers and Reactor Operators

Preserving experimental data is essential for supporting advancements in nuclear science and ensuring the longevity of Idaho National Laboratory's contributions to reactor technology and safety. This report provides a comprehensive guide to best practices for experimental data management and preservation, focusing on standardized data formats, redundancy in storage, metadata documentation, and alignment with international standards. By following these recommendations, experimentalists and reactor operators can enhance the accessibility, reproducibility, and utility of critical datasets for regulatory review, validation computational methods, and future research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

IRIS-HEP Strategic Plan for the Next Phase of Software Upgrades for HL-LHC Physics

The quest to understand the fundamental building blocks of nature and their interactions is one of the oldest and most ambitious of human scientific endeavors. CERN's Large Hadron Collider (LHC) represents a huge step forward in this quest. The discovery of the Higgs boson, the observation of exceedingly rare decays of $B$ mesons, and stringent constraints on many viable theories of physics beyond the Standard Model (SM) demonstrate the great scientific value of the LHC physics program. The next phase of this global scientific project will be the High-Luminosity LHC (HL-LHC) which will collect data starting circa 2029 and continue through the 2030s. The primary science goal is to search for physics beyond the SM and, should it be discovered, to study its implications. In the HL-LHC era, the ATLAS and CMS experiments will record around 100 times as many collisions as were used to discover the Higgs boson (and at twice the energy). Both NSF and DOE are making large detector upgrade investments so the HL-LHC can operate in this high-rate environment. Similar investment in software R&D for acquiring, managing, processing and analyzing HL-LHC data is critical to maximize the return-on-investment in the upgraded accelerator and detectors. This report presents a strategic plan for a possible second 5-year funded phase (2023 through 2028) for the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) which will close remaining software and computing gaps to deliver HL-LHC science.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Building 438 Drainage Channel Soil Sampling and Analysis Plan (February 2023)

This Soil Sampling and Analysis Plan (SAP) was prepared by the Environmental Function Area (EFA)/Technical Services Department (TSD) of the Environment, Safety & Health (ES&H) Directorate for the Project Management Office (PMO) for the proposed Building 438 Drainage Channel Project (project) located near Building 438 (B438) in the southern portion, Building 433 (B433) in the middle portion, and Building 543 (B543) in the northern portion (project area as shown on Figure 1). The purpose of the SAP was to describe the procedures for collection and analysis of environmental samples and evaluation of analytical data (chemical and radiological) to determine management options of excavated soil during project construction in accordance with Lawrence Livermore National Laboratory’s (LLNL) Soils Screening and Management Plan (SSMP) (LLNL 2022), which is consistent with U.S. Environmental Protection Agency (EPA) guidance for developing Data Quality Objectives for environmental data (EPA 2006). The SAP was developed in accordance with the SSMP and based on preliminary design information provided to EFA by PMO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Technical Characterization and Benefit Evaluation of 5G-Enabled Grid Data Transport and Applications

This report summarizes the Year 1 work of Pacific Northwest National Laboratory’s (PNNL’s) 5G Fabricated Resource and Asset Management Encompassment for energy infrastructure (Energy FRAME) project funded by the Department of Energy Office of Science’s Advanced Scientific Computing Research Program. 5G is a breakthrough technology that enables a fully mobile and connected society, and a 5G-enabled digital continuum will be one of the critical foundations for a clean energy economy and grid modernization. In collaboration with PNNL’s Advanced Wireless Communication team and Center for Advanced Technology Evaluation team, the project team has been evaluating the system performance of 5G testbeds in the PNNL 5G Innovation Studio, and has formulated a co-simulation test case of power system transmission, distribution, and communication (T&D&C) networks considering 5G technology and high penetration of distributed energy resources. The methodology developed in the 5G Energy FRAME project can be customized to fit different future grid scenarios to evaluate multiple (dynamic) configurations (computing, sensing, communication, environment) for different stakeholders. In summary, our main technical highlights in project Year 1 are as follows: 1) Technical characterization of 5G standalone architectures, 2) Formulation of co-simulation test case of T&D&C networks embedded with 5G, 3) Initial benefit evaluation of 5G communication platform for grid use cases, and 4) Additional extended discussions on edge computing, artificial intelligence and machine learning, and high-performance computing and cloud computing adoptions. In addition, a collection of system performance data is shared through the publicly available weblink, https://www.pnnl.gov/projects/5g-energy-frame/publications

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Fission In R-process Elements (FY20 Q2 Quarterly Report)

The goal of the FIRE topical collaboration in nuclear theory is to determine the astrophysical conditions of the rapid neutron capture process (r-process), which is responsible for the formation of heavy elements. This will be achieved by including in r-process simulations the most advanced models of fission (spontaneous, neutron-induced, β-delayed) that have been developed at LLNL and LANL. The collaboration is composed of LLNL (lead) and LANL for fission work, BNL for nuclear data management, and the university of Notre Dame and North Carolina State University for r-process simulations. Under DOE/NNSA agreement, both universities receive funds from DOE Office of Science, while national laboratories receive funds directly from NA221.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

NSDS Nuclear Science Data Solutions [Poster]

NSDS is the adept coordination of Los Alamos National Laboratory's nuclear science data, including the collection, integration, storage and access. The poster highlights historical milestones.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Fission In R-process Elements (Q4 FY2020 Quarterly Report)

The goal of the FIRE topical collaboration in nuclear theory is to determine the astrophysical conditions of the rapid neutron capture process (r-process), which is responsible for the formation of heavy elements. This will be achieved by including in r-process simulations the most advanced models of fission (spontaneous, neutron-induced, β-delayed) that have been developed at LLNL and LANL. The collaboration is composed of LLNL (lead) and LANL for fission work, BNL for nuclear data management, and the university of Notre Dame and North Carolina State University for r-process simulations. Under DOE/NNSA agreement, both universities receive funds from DOE Office of Science, while national laboratories receive funds directly from NA221.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Fission In R-process Elements (Q1 FY2021)

The goal of the FIRE topical collaboration in nuclear theory is to determine the astrophysical conditions of the rapid neutron capture process (r-process), which is responsible for the formation of heavy elements. This will be achieved by including in r-process simulations the most advanced models of fission (spontaneous, neutron-induced, β-delayed) that have been developed at LLNL and LANL. The collaboration is composed of LLNL (lead) and LANL for fission work, BNL for nuclear data management, and the university of Notre Dame and North Carolina State University for r-process simulations. Under DOE/NNSA agreement, both universities receive funds from DOE Office of Science, while national laboratories receive funds directly from NA221

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Fission In R-Process Elements

The goal of the FIRE topical collaboration in nuclear theory is to determine the astrophysical conditions of the rapid neutron capture process (r-process), which is responsible for the formation of heavy elements. This will be achieved by including in r-process simulations the most advanced models of fission (spontaneous, neutron-induced, β-delayed) that have been developed at LLNL and LANL. The collaboration is composed of LLNL (lead) and LANL for fission work, BNL for nuclear data management, and the university of Notre Dame and North Carolina State University for r-process simulations. Under DOE/NNSA agreement, both universities receive funds from DOE Office of Science, while national laboratories receive funds directly from NA221.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Fission In R-Process Elements

The goal of the FIRE topical collaboration in nuclear theory was to determine the astrophysical conditions of the rapid neutron capture process (r-process), which is responsible for the formation of heavy elements. This was be achieved by including in r-process simulations the most advanced models of fission (spontaneous, neutron-induced, β-delayed) that have been developed at LLNL and LANL. The collaboration was composed of LLNL (lead) and LANL for fission work, BNL for nuclear data management, and the university of Notre Dame and North Carolina State University for r-process simulations. Under DOE/NNSA agreement, both universities received funds from DOE Office of Science, while national laboratories receive funds directly from NA221.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Fission In R-process Elements (FIRE) - Quarterly Report (Q2FY22)

The goal of the FIRE topical collaboration in nuclear theory was to determine the astrophysical conditions of the rapid neutron capture process (r-process), which is responsible for the formation of heavy elements. This was be achieved by including in r-process simulations the most advanced models of fission (spontaneous, neutron-induced, β-delayed) that have been developed at LLNL and LANL. The collaboration was composed of LLNL (lead) and LANL for fission work, BNL for nuclear data management, and the university of Notre Dame and North Carolina State University for r-process simulations. Under DOE/NNSA agreement, both universities received funds from DOE Office of Science, while national laboratories receive funds directly from NA221.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Fission In R-process Elements (FIRE)

The goal of the FIRE topical collaboration in nuclear theory was to determine the astrophysical conditions of the rapid neutron capture process (r-process), which is responsible for the formation of heavy elements. This was be achieved by including in r-process simulations the most advanced models of fission (spontaneous, neutron-induced, β-delayed) that have been developed at LLNL and LANL. The collaboration was composed of LLNL (lead) and LANL for fission work, BNL for nuclear data management, and the university of Notre Dame and North Carolina State University for r-process simulations. Under DOE/NNSA agreement, both universities received funds from DOE Office of Science, while national laboratories receive funds directly from NA221.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗